Prevalence of depression in Uganda: A systematic review and meta-analysis

Mark Mohan Kaggwa; Sarah Maria Najjuka; Felix Bongomin; Mohammed A Mamun; Mark D Griffiths

doi:10.1371/journal.pone.0276552

. 2022 Oct 20;17(10):e0276552. doi: 10.1371/journal.pone.0276552

Prevalence of depression in Uganda: A systematic review and meta-analysis

Mark Mohan Kaggwa ^1,^2,^3,^*, Sarah Maria Najjuka ⁴, Felix Bongomin ⁵, Mohammed A Mamun ^6,⁷, Mark D Griffiths ⁸

Editor: Muhammed Elhadi⁹

PMCID: PMC9584512 PMID: 36264962

Abstract

Background

Depression is one of the most studied mental health disorders, with varying prevalence rates reported across study populations in Uganda. A systematic review and meta-analysis was carried out to determine the pooled prevalence of depression and the prevalence of depression across different study populations in the country.

Methods

Papers for the review were retrieved from PubMed, Scopus, PsycINFO, African Journal OnLine, and Google Scholar databases. All included papers were observational studies regarding depression prevalence in Uganda, published before September 2021. The Joanna Briggs Institute Checklist for Prevalence Studies was used to evaluate the risk of bias and quality of the included papers, and depression pooled prevalence was determined using a random-effects meta-analysis.

Results

A total of 127 studies comprising 123,859 individuals were identified. Most studies were conducted among individuals living with HIV (n = 43; 33.9%), and the most frequently used instrument for assessing depression was the Depression sub-section of the Hopkins Symptom Checklist (n = 34). The pooled prevalence of depression was 30.2% (95% confidence interval [CI]: 26.7–34.1, I² = 99.80, p<0.001). The prevalence of depression was higher during the COVID-19 pandemic than during the pre-pandemic period (48.1% vs. 29.3%, p = 0.021). Refugees had the highest prevalence of depression (67.6%; eight studies), followed by war victims (36.0%; 12 studies), individuals living with HIV (28.2%; 43 studies), postpartum or pregnant mothers (26.9%; seven studies), university students (26.9%; four studies), children and adolescents (23.6%; 10 studies), and caregivers of patients (18.5%; six studies).

Limitation

Significantly high levels of heterogeneity among the studies included.

Conclusion

Almost one in three individuals in Uganda has depression, with the refugee population being disproportionately affected. Targeted models for depression screening and management across various populations across the country are recommended.

Trial registration

Protocol registered with PROSPERO (CRD42022310122).

Introduction

The global prevalence rates of depression and other mental disorders have been increasing since 1990 [1], and approximately 3.8% of individuals worldwide have depression [2]. The prevalence of depression increased by an additional 27.6% globally during the coronavirus disease 2019 (COVID-19) pandemic (2020 to 2021) and by 23% in sub-Saharan Africa [3]. Despite the lower increase in the burden of depression in sub-Saharan Africa, most individuals with depression go undiagnosed and untreated in the region [4]. This has led to some individuals having extreme complications of depression, such as suicide [5–11]. The effect is even worse in low-income countries such as Uganda, with a high prevalence of depression (i.e., approximately one-fifth of the population between 2010 and 2017, and 27% of outpatients being depressed based on previous systematic review and meta-analyses [12, 13]).

In Uganda, due to the high burden of depression, various studies have been conducted among different populations (e.g., infected with human immunodeficiency virus [HIV], women, cancer patients, caregivers of patients, students, etc.) to understand its effects and design possible interventions [14–17]. Ugandan clinicians and researchers have employed various psychometrically validated tools to screen and diagnose depression among Ugandans, including the Patient Health Questionnaire (PHQ), Beck’s Depression Inventory (BDI), Hamilton Rating Scale for Depression, Symptom Checklist-20, Center for Epidemiologic Studies—Depression Scale, Akena Visual Depression Inventory, and the Mini-International Neuropsychiatric Interview (MINI) [18–24].

Uganda is a landlocked low-income country that has been affected by multiple adverse events, including civil wars, extreme poverty, high rates of HIV, various epidemics (e.g., Ebola), and poor mental health services, as well as being one of the largest refugee-hosting countries in the world [25–30]. These adverse events put many Ugandans at risk of developing depression due to the multiple physical, psychological, emotional, and social difficulties they are associated with. The effects of these difficulties are evidenced by the high levels of depression within various study groups in the country, such as women, children, students, individuals living with HIV, refugees, and members of the general public, with many groups reporting depression prevalence rates of over 70% [14, 31–37].

Due to the high burden of depression and a large amount of literature concerning mental health in Uganda, various systematic reviews have been conducted, especially among individuals living with HIV [12, 15, 38]. However, a comprehensive systematic synthesis of all published literature on depression in Uganda is lacking. Therefore, the present systematic review and meta-analysis aimed to determine the pooled prevalence of depression in Uganda and determine the prevalence of depression among various study populations in the country.

Methods

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [39] and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines for systematic reviews and meta-analysis of observational studies [40]. The study protocol was prospectively registered with PROSPERO (CRD42022310122). The review question was formed according to the Joanna Briggs Institute (JBI) Checklist for Prevalence Studies and the CoCoPop (Condition, Context, and Population) [41]. The condition was depression, the context was Uganda, and the population was all studied groups. Therefore, the review’s research questions were: (i) “What is the prevalence of depression in Uganda?” and (ii) “What is the prevalence of depression among various study populations in Uganda?”

Search strategy

With the help of the university Librarian at Mbarara University of Science and Technology, relevant databases were used for the literature search, including PubMed, Scopus, PsychInfo, and Google Scholar (in the present review, eligible papers from the first 100 pages based on relevance were included), and African Journal OnLine (AJOL). The study included articles (both peer-reviewed and preprints) in all languages from 1972 (the first published paper about depression in Uganda [42]) to September 2021. The following key words were used in the literature search from different databases: (i) ‘depression,’ (ii) ‘Uganda,’ and (iv) ‘prevalence’ or ‘incidence.’ All systematic reviews concerning depression in Uganda, East Africa, and Africa were reviewed for eligible studies. The PRISMA 2020 flow chart shows the details of the search hits retrieved, included, or excluded papers [43] (Fig 1).

Inclusion criteria and exclusion criteria

The literature search included all observational studies (cross-sectional, case-control, and cohort studies) published in all languages regarding depression prevalence (based on different assessment tools or subjective reports) in Uganda, based on various assessment tools and cut-offs; and excluded case reports, case series, qualitative research, letters to the editor, commentaries, conference proceedings or abstracts, policy papers, protocols, reviews, and meta-analyses.

Study and data management

All identified papers were entered into EndNote 9 to ascertain duplicates. After removing duplicates and review articles, the titles and abstracts of the articles were screened for inclusion independently by two team members. MAM settled any discrepancies after a discussion with the two team members for the reason of exclusion. The final selected papers were read for the full review, and this was done in pairs after dividing the results into two. Two members of the research team reviewed the first half, and another two members of the research team reviewed the second half. For papers whose full texts were not fully accessible, the corresponding authors were contacted by email. The papers were assessed for a quality check using the JBI Checklist for Prevalence Studies [44], as used in other systematic reviews [45]. Finally, a research team member screened for eligible papers from the systematic reviews regarding depression in the region.

Data extraction

A pre-piloted and self-designed Google Forms document with the following information was used to collate the data. The data extracted included: the first author, year of data collection, study design, paper quality assessment questions based on the JBI Checklist, study group, sample size, age of participants, number of male and female participants, tools/questions used to assess for depression, and prevalence of depression.

Bias evaluation and quality assessment of the included papers

The nine-item JBI Checklist was used to evaluate the risk of bias and the quality of the included papers [44]. The JBI Checklist uses a four-point response system: “no,” “yes,” “unclear,” and “not applicable” for the following study characteristics: (i) appropriateness of the sample frame; (ii) recruitment procedure; (iii) adequacy of the sample size; (iv) description of participants and setting; (v) description of the identified sample; (vi) validity of the methods used to screen for depression; (vii) reliability of the methods used to screen for depression; (viii) adequacy of statistical analyses; and (ix) response rate. Articles were assigned one point for each ‘yes’ response, and the remaining responses were assigned zero points. Therefore, the total score ranged from 0 to 9. Studies with a score of 4 or above were considered good quality. One article was excluded due to scoring poorly on most parameters [42]. The scores of the papers are presented in Table 1.

Table 1. Participants’ characteristics of all studies included in the meta-analysis.

First author, year of publication	Study design (JBI Checklist score)	Year of data collection	Districts	Study group	Sample size	Female (male)	Age (in years)	Tools used to access depression (cutoff)	Depression n (%)
Bolton 2004 [55]	CS (9)	2000	Masaka and Rakai	General population	587	364 (223)	39.3±2.9	Depression section of the Hopkins Symptom Checklist (DHSCL)	123 (21)
Ovuga 2006 [56]	CS (7)	2000 and 2002	Kampala	Non-medical undergraduate students in 2000 and undergraduate medical students in 2002	253 and 101	92 (161) and 31 (70)	21.3± 2.4 and 23.5± 5.0	Beck Depression Inventory (BDI)–(10)	37 (16.2) and 4 (4)
Nakasujja 2007 [57]	CS (9)	2001	Kampala	Elderly patients in non-psychiatric wards	127	64 (63)		Self-Reporting Questionnaire (SRQ 25)–(>5)	23 (18)
Nalugya-Sserunjogi 2016 [58]	CS (9)	2003	Mukono	school-going adolescents	519	218 (301)	16±2.18	Children Depression Inventory (CDI)–(19) MINI-KID	109 (21) and 8 (2.9)
Nakku 2006 [59]	CS (9)	2002–2003	Kampala	Women 6 weeks postpartum	523	523	23.4±4.76	MINI and SRQ-25 –(>5)	32 (6.1) and 38 (7.3)
Kaharuza 2006 [60]	RCT (9)	2003–2004	Bugiri, Busia, Mbale, and Tororo	Individuals living with HIV	1017	781 (236)	31–40 = 452; 31–40 = 452; 41–50 = 291; and 50+ = 94	Center for Epidemiological Studies Depression scale (CES-D)	476 (47)
Kinyanda 2011b [61]	CS (9)	2003–2004	Adjumani, Apac, Arua, Bugiri, Bushenyi, Kaberamaido, Kapchorwa, Katakwi, Lira, Moyo, Mubende, Nebbi, Soroti, and Yumbe	General populations	4,660			DHSCL–(>1.75)	1366 (29.3)
Ovuga 2005 [62]	CS (9)		Adjumani, Bugiri	General populations	524			BDI	91 (17.4)
Lundberg 2011 [63]	CS (9)	2004–2005	Kampala, Mbarara	Residents from urban and semi-urban Kampala and Mbarara	630	312 (334)	18–24 = 337; 25–30 = 437	DHSCL–(>1.75)	96 (9.84)
Nakimuli-Mpungu 2013a [64]	CS (8)	2004–2005	Kiruhura	Individuals living with HIV	244	156 (88)	36.2±8.9; Range = 18–60	DSMIV	97 (40)
Agardh 2012 [65]	CS (8)	2005	Mbarara	Undergraduate university students at MUST	976	362 (614)	Median age = 23. Younger ≤23: 628. Older>23: 329 (34.4), missing 23	DHSCL-25—(31)	146 (15)
Vinck 2007 [66]	CS (9)	2005	Gulu, Kitgum, Lira, and Soroti	War victims in displacement camps	2585	1293 (1292)	36.7±13.8	DHSCL–(40)	1151 (44.5)
Pfeiffer 2011 [67]	CS (7)	2005	Gulu	War victims	72	31 (41)	Median = 23.7	DHSCL–(>1.75)	51 (71)
Martinez 2008 [68]	CS (9)	2005	Mbarara	Individuals living with HIV	421	266 (155)	Median = 36, IQR = 11	DHSCL–(>1.75)	79 (18.8)
Muhwezi 2007 [69]	CS (9)		Kampala, Mpigi, and Mubende	Individuals attending primary health care facilities	199	119 (80)		MINI	74 (31.6)
Petrushkin 2005 [70]	CS (7)		Kampala	Individuals living with HIV	46	24 (22)	36.6	MINI	25 (54.35)
Psaros 2015 [71]	Cohort (9)	2005–2010	Mbarara	Uganda AIDS Rural Treatment Outcomes (UARTO) cohort study	453	314 (139)	34.9±8.3	DHSCL–(>1.75)	172 (37.97)
Nakasujja 2010 [72]	CS (8)	2005–2007	Kampala	HIV-positive patients are at risk for cognitive impairment, and HIV-negative patients	127	84 (43)		CES-D	62 (48.82)
Kaida 2014 [73]	Cohort (9)	2005–2012	Mbarara	Pregnant and postpartum HIV-positive women	447	447 (0)	Median = 32; IQR = 10	DHSCL–(>1.75)	173 (38.9)
Roberts 2008 [74]	CS (9)	2006	Amuru, Gulu	War victims	1210	727 (483)	35.4	DHSCL–(>1.75)	811 (67)
Klasen 2013 [75]	CS (9)	2006	Gulu	Child soldiers (children and adolescents of war abducted victims who became soldiers)	330	160 (170)	14.44±1.57	MINI-KID	120 (36.4)
Klasen 2010a [76]	CS (9)	2006	Gulu	Child soldiers (children and adolescents of war abducted victims who became soldiers)	330	160 (170)	14.44±1.57	MINI-KID	120 (36.4)
Klasen 2010b [77]	CS (9)	2006	Gulu	Child soldiers (children and adolescents of war abducted victims who became soldiers)	330	160 (170)	14.44±1.57	MINI-KID	120 (36.4)
Abbo 2009 [78]	CS (9)	2007	Iganga and Jinja	Clients of traditional healers	387	208 (178)	34.8±13.55	MINI	21 (5.4)
Hatcher 2012 [79]	Secondary data analysis (9)	2007	Mbarara	HIV-infected women	270	270 (0)	Median = 34. IQR = 10	DHSCL–(>1.75)	64 (6.7)
Pham 2009 [80]	CS (9)	2007	Amuria, Amuru, Gulu, Kitgum, Lira, Oyam, and Pader	War victims	2875	1417 (1458)	35.4±14.35	DHSCL–(42)	1150 (40)
Tsai 2012 [81]	Cohort (9)	2007–2010	Mbarara	Individuals living with HIV	456	324 (132)		DHSCL–(>1.75)	71 (15.57)
Tsai 2016 [82]	Cohort (9)	2007–2011	Mbarara	Individuals living with HIV	173	173 (0)	Median = 32; IQR = 11	DHSCL–(>1.75)	57 (33)
Nakimuli-Mpungu 2013b [83]	A prospective study (9)	2007–2012	Gulu, Kitgum, Soroti, Tororo	War victim with post-trauma disorder	2868			SRQ-20	1297 (45.22)
Winkler 2015 [84]	CS (9)	2008	Gulu, Kitgum, and Lira	child soldiers and war-affected victims	843	355 (488)	19.0±2.7.	DHSCL–(>2.65)	64 (7.6)
Musisi 2009 [85]	CS (8)		Kampala	HIV positive adolescents	82	46 (36)	13.4±1.8	SRQ-25	34 (40.8)
Nsereko 2018 [51]–Thesis	CS (9)	2018	Kampala	School adolescents	549	317 (228)		The Youth Self-Report (YSR)	115 (21.1)
Wagner 2012a [86]	Cohort (9)	2008–2009	Jinja, Kampala	Individuals living with HIV	602	409 (193)	35.7	Patient Health Questionnaire– 9 (PHQ-9)–(10)	78 (13)
Shumba 2013 [87]	CS (7)	2008–2009		Individuals living with HIV	732	504 (228)	19–39 = 294; 40–50 = 290; 50+ = 91	Developed own tool	432 (59)
Okeke 2013 [88]	Cohort (9)	2008–2010	Kampala	Individuals living with HIV	482		34.60±8.51	PHQ-9 –(10)	40 (8.3)
Wagner 2014a [89]	Cohort (9)	2008–2011	Jinja, Kampala, Mityana, and Mukono	3 study cohorts of Individuals living with HIV	750	435 (315)	34.5	PHQ-9 –(10)	45 (6)
Wagner 2017a [90]	Cohort (9)	2008–2011	Kampala, Mityana, Mukono, and Wakiso	Individuals living with HIV	1021	653 (368)	36	PHQ-9 –(10)	92 (9)
Wang 2018 [91]	CS (6)	2009	Wakiso	Individuals living with HIV	981			Self-report to a question, “During the last 12 months, have you had a period lasting several days when you felt sad, empty, or depressed?”	221 (22.5)
Ager 2012 [92]	CS (9)	2009	Amuru, Gulu	National Humanitarian Aid Workers	376	134 (238)	30.88±6.60	DHSCL–(>1.75)	256 (68)
Wagner 2011 [93]	CS (9)		Jinja, Kampala	New HIV clients attending the clinics	602	410 (192)	36; Range = 20–62	PHQ-9 –(10)	78 (13)
Ngo 2015 [34]	Cohort (9)	2009–2011	Mityana, Mukono, and Wakiso	Individuals living with HIV	1903	1492 (411)	36±9	PHQ-9–10 and MINI	1604 (84.3)
Kinyanda 2011a [94]	CS (9)	2010	Wakiso	Individuals living with HIV	618	449 (169)	18-24yrs = 58; 25–34 = 238; 35–44 = 217; and >44 = 103	MINI	50 (8.1)
Kinyanda 2012 [95]	CS (9)	2010	Wakiso	Individuals living with HIV	618	449 (169)	18-24yrs = 58; 25–34 = 238; 35–44 = 217; and >44 = 103	MINI	50 (8.1)
Morof 2014 [35]	CS (9)	2010	Kampala	female urban refugees	117	117 (0)	31.6±4.7	DHSCL–(>1.75) and DHSCL–(>2.65)	112 (92) and 70 (54)
Nakku 2013 [96]	CS (9)	2010	Wakiso	Individuals living with HIV	618	449 (169)	18-24yrs = 58; 25–34 = 238; 35–44 = 217; and >44 = 103	MINI	50 (8.1)
Kakyo 2012 [97]	CS (9)		Kabarole	Postpartum mothers	202	202 (0)	24±4.33	Edinburgh postpartum depression scale (EPDS)–(10)	87 (43)
Wagner 2012b [98]	Cohort (9)	2010–2011	Kampala, Mityana, and Mukono	Individuals living with HIV	798	530 (268)	36.1±9.5	MINI and PHQ-9 –(10)	111 (13.9) and 187 (.23.43)
Wagner 2014b [99]	Cohort (9)	2010–2011	Jinja, Kampala	Individuals living with HIV	1731	1131 (600)	36	PHQ-9 –(10)	156 (9)
Kiene 2018 [100]	Cohort (9)	2010–2011	Butambala	HIV positive and HIV negative clients	244	122 (122)	HIV positive = 34.60±9. 03; HIV negative = 33.28±10.12	DHSCL–(>1.75)	117 (48.0)
Okello 2015 [101]	Cohort (9)	2010–2011	Kampala	Individuals living with HIV	798	530 (268)	36.1± 9.53	PHQ-9 –(10)	100 (12.5)
Musisi 2014 [102]	Cohort (9)	2010–2011	Mukono, Wakiso, Kampala, and Mityana	Individuals living with HIV	386	225 (161)	35.7±8.7	PHQ-9 –(10)	116 (0.3)
Akena 2012 [103]	CS (9)	2011	Kampala	Individuals living with HIV	368	265 (103)	38.8±9.81; range = 18–71.	MINI	64 (17.4)
Akena 2013 [104]	CS (9)	2011	Kampala	Individuals living with HIV	735	525 (210)	38±10.08, range = 18–71	MINI	72 (9.8)
Nakimuli-Mpungu 2011a [105]	CS (9)	2011	Mubende	Individuals living with HIV	500	349 (151)	40±10.7, range: = 18–80	MINI	230 (46)
Katende 2017 [106]	CS (7)		Kampala	Caregivers of cancer patients	119	79 (40)	33±10.69	Hospital Anxiety and Depression Scale (HADS) standardized tool
Kinyanda 2013 [107]	CS (9)		Gulu, Kaberamaido, Lira, and Tororo	Children and adolescent	1587	853 (734)	≤5 = 286; 6–9 = 416; 10–13 = 550; 14–19 = 335	MINI-KID	136 (8.6)
Spittal 2018 [108]	Cohort (9)	2011–2012	Amuru, Gulu, Nwoya	War victims—The Cango Lyec (Healing the Elephant) Project members 13 to 49 years who were sexually active	2008	1189 (819)	Range 13–49	DHSCL–(>1.75)	337 (16.78)
Whyte 2015 [109]	CS (5)	2011–2012	Agago, Amuru, Gulu, and Nwoya	OPD patients in several districts in northern Uganda	11325			Clinician diagnosis	40 (0.4)
Perkins 2018 [110]	CS (9)	2011–2012	Mbarara	General populations	1499	822 (677)	below 30 = 640	DHSCL–(>1.75)	268 (17.88)
Malamba 2016 [111]	Cohort (9)	2011–2012	Gulu, Nwoya, and Amuru	War victims—The Cango Lyec (Healing the Elephant) Project members 13 to 49 years	2388	1397 (991)	Range 13–49	DHSCL–(>1.75)	360 (14.9)
Hakim 2019 [112]	CS (9)	2011–2013	Kampala, Wakiso	All clients aged 13 or older attending Mildmay for client-initiated HIV testing and counseling	counseling	6998 (5234)	13–19 = 95; 20–24 = 2607; 25–34 = 5151; 35–49 = 3010; 50+ = 510	PHQ-2 –(4)	1884 (15.4)
Familiar 2019 [113]	Cohort (9)	2011–2014	Soroti	HIV-positive women attending a clinic	288	288 (0)	33.5; Range = 18–54	DHSCL–(>1.75)	139 (61)
Cavazos‑Rehg 2020 [114]	RCT (9)	2012–2017	Mbarara	HIV positive adolescents	592	335 (257)	12.13±0.65	CDI	298 (52.29)
Akena 2015 [115]	CS (9)	2013	Gulu, Kampala, and Mbarara	Patients with diabetes Mellitus	437	283 (154)	51±14.06, Range = 18–90	MINI	154 (34.8)
Familiar 2021 [116]	CS (9)	2013	Kampala	Self-settled Democratic Republic of Congo female refugees in Kampala	580	580 (0)	Mean = 33.7. 18–24 = 121; 25–34 = 210; 35+ = 249	PHQ-2	331 (57)
Akena 2016 [117]	Cohort (9)	2013	Luweero, Mityana, and Mpigi, Wakiso	Individuals living with HIV	1252	961 (291)	40±11. Range = 18–85	PHQ-9 –(10)	200 (67)
Rathod 2018 [118]	CS (9)	2013	Kamuli	General populations	1893	1500 (393)	Median = 28; IQR = 15	PHQ-9 –(10)	80 (4.2)
Mugisha 2016 [28]	CS (9)	2013	Amuru, Gulu, and Nwoya	Individuals in post-conflict northern Uganda	2361	1475 (886)	49% of respondents were aged above 34 years	MINI	599 (24.9)
Mugisha 2015 [29]	CS (9)	2013	Amuru, Gulu, and Nwoya	Community in the post-conflict northern Uganda	2361	1475 (886)	49% of respondents were aged above 34 years	MINI	599 (24.7)
Mwesiga 2015 [119]	CS (9)	2013	Kampala	Individuals living with HIV	345	245 (100)	Median = 35; (IQR = 12)	MINI	17 (5)
Nakku 2019 [120]	CS (9)	2013	Kamuli	General populations	1290	867 (423)	16–30 = 494; 31–49 = 555; ≥ 50 = 240	PHQ-9 –(10)	325 (25.4)
Nalwadda 2018 [121]	CS (9)	2013	Kamuli	The male population in the region	1129	0 (1129)	33% of participants were below 30 years	PHQ-9 –(10)	292 (25.9)
Jones 2017 [122]	Cohort (7)	2013	Kampala	Post-tuberculosis lung disease patients	29	14 (15)	45±13; Range = 17–69	PHQ-9 –(5)	7 (24)
Wagner 2017b [123]	Cohort (9)	2013	Luweero, Mityana, Mpigi, Wakiso	Individuals living with HIV	1252	976 (276)	39.8±11.2	PHQ-9 –(10)	375 (30)
Rukundo 2013 [124]	CS (9)		Mbarara	Physically ill patients	258	120 (138)	18–24 = 50, 25–40 = 130, 41–60 = 52	MINI	87 (33.7)
Olema 2014 [125]	CS (9)		Gulu	Adolescents and their parents in GULU	300			DHSCL–(>1.75)	120 (40)
Huang 2017 [126]	CS (9)	2013–2014		Parents of children in primary school	303	248 (55)	35.92±9.80; Range = 18–79	PHQ-9 –(10)	85 (28)
Wagner 2016a [127]	Cohort (9)	2013–2014	Luweero, Mityana, Mpigi, and Wakiso	Individuals living with HIV	1252	962 (290)	40±11.2	PHQ-9 –(10)	375 (30)
Wagner 2016b [128]	Cohort (9)	2013–2014	Luweero, Mityana, Mpigi, and Wakiso	Individuals living with HIV	1252	962 (290)	40±11.2	PHQ-9 –(10)	375 (30)
Henry 2019 [129]	CS (7)	2013–2015	Kampala	Adolescents attending an adolescent health clinic	514	372 (142)	16; Range = 10–19	Investigator designed tool (not standard)	174 (33.9)
Meffert 2019 [15]	Cohort (9)	2013–2017		Individuals living with HIV	475	282 (157)	18–33 = 146; 34–48 = 227; 49–63 = 69; 65+ = 5	CES-D	104 (22)
Swahn 2021 [130]	Cohort (9)	2014	Kampala	Individuals living with HIV—comparing HIV positive and non-HIV positive youth (12–18 years)	1096	614 (481)	12–14 = 219; 15–16 = 291; 17–18 = 586	Self-report—“In the past year, did you ever feel so sad or hopeless almost every day for two weeks or more in a row that you stopped doing your usual activities?”	673 (62)
Gyagenda 2015 [131]	CS (9)	2014	Kampala	Post-stroke patients	73	43 (30)	20–39 = 6; 40–59 = 25; 60–79 = 35; and 80–99 = 7	PHQ-9 –(10)	23 (31.5)
Fisher 2019 [31]	CS (6)	2014	Kisiro	Women attending OPD	115	115		SRQ-20	87 (75.65)
Kinyanda 2020 [132]	CS (9)	2014	Kampala, Masaka	caregivers of patients with HIV	1336			Child and Adolescent Symptom Inventory-5 (CASI-5)	Baseline = 66 (5.0); 6month = 47 (4.2); 12months = 43 (4.4)
Muhammad 2018 [133]	CS (6)	2014	Kabarole	Individuals living with HIV	150	84 (66)	16.53±5.24	-	37 (47)
Nakku 2016 [21]	CS (9)	2014	Kamuli	Patients attending a health facility	1415	1017 (398)	33±13	PHQ-9 –(8)	140 (10)
Ashaba 2015 [134]	Case-control (9)	2014	Mbarara	Mother of malnourished children	172	172 (0)	25±4.43; Range = 18–40	MINI	46 (26.7)
Kinyanda 2016a [135]	CS (9)		Amuria, Katakwi	Post-conflict communities	1110	631 (479)	56% were aged between 18 to 44 years.	DHSCL–(>1.75)	462 (41.6)
Manne-Goehler 2019 [136]	Cohort (9)		Mbarara	Older individuals (above 40 years) living with HIV and HIV uninfected individuals of similar sex	296	141 (155)	52	DHSCL–(>1.75)	81 (27.36)
Kinyanda 2016b [137]	CS (9)			Caregivers of patients with mental illness	468	292 (176)	All above 50	MINI	43 (9.19)
Smith 2019 [138]	CS (9)	2014–2015	Mbarara	General populations	1620	869 (751)		DHSCL–(>1.75)	460 (28.39)
Akimana 2019 [17]	CS (9)	2015	Kampala`	Children and adolescents with cancer	352	112 (240)	11.5±3.2; Range = 7–17	MINI-KID	91 (26)
Nampijja 2019 [139]	CS (9)	2015	Kampala	Postpartum women in Nsambya hospital	300	300 (0)	28±4.8; Range = 17–44	MINI	4 (1.3)
Cooper-Vince 2018 [140]	CS (9)	2015	Mbarara	Community-based study	1603	898 (705)		DHSCL–(>1.75)	461 (28.76)
Kiene 2017 [141]	CS (8)			Individuals living with HIV	325	165 (160)	Men = 34.93±10.59; Women = 32.21±8.93	DHSCL–(>1.75)	63 (19.4)
Kinyanda 2017b [142]	CS (9)		Masaka, Wakiso	Individuals living with HIV	899	705 (194)		MINI	126 (14.0)
Sohail 2019 [143]	CS (9)	2015–2017	Rakai	Rakai HIV community cohort	333	160 (170)	37± 9	CES-D	28 (8.4)
Raggio 2019 [144]	CS (8)	2016	Mbarara	Pregnant and non-pregnant women	225	225 (0)	Median = 27; IQR = 9	EPDS–(10) and DHSCL–(>1.75)	26 (11.56) and 60 (26.67)
Kinyanda 2017a [145]	Cohort (9)		Masaka, Wakiso	Individuals living with HIV	1099	847 (252)	35.1± 9.3	MINI	155 (14.1)
Akinbo 2016 [146]	CS (5)		Bushenyi	Alcoholic youth	204	71 (133)	30.6% were 15–19 years old	Researchers designed questionnaire	4 (2)
Cooper-Vince 2017 [147]	CS (9)		Mbarara	Female household head	257	257 (0)	33.5±7.9	DHSCL–(>1.75)	133 (44)
Ashaba 2021 [148]	CS (9)	2016–2017	Mbarara	HIV positive adolescents	224	131 (93)	14.8±1.4.	MINI-KID	37 (16)
Ashaba 2018 [149]	CS (9)	2016–2017	Mbarara	HIV positive adolescents	224	131 (93)	14.8±1.4.	MINI-KID	37 (16)
Ortblad 2020 [150]	Prospective studies (9)	2016–2019	Kampala	Individuals living with HIV	960		Median = 28, IQR = 8	PHQ-9	416 (43.2)
Satinsky 2021 [151]	CS (9)	2016–2018	Mbarara	General populations	1626	908 (718)	17–26 = 409; 27–39 = 488; 40+ = 705	DHSCL–(>1.75) and DSM V	331 (20.36) and 159 (9.78)
Alinaitwe 2021 [152]	CS (9)	2017	Kampala	TB patients	308	112 (188)	36±10.8	MINI	73 (23.7)
Bapolisi 2020 [153]	CS (9)	2017	Isingiro	Refugees in Nakivale refugee camp	387	219 (168)	33.01±12.2	MINI	224 (58)
Forry 2019 [154]	CS (9)	2017	Mbarara	Prison inmates in Mbarara Municipality	414	25 (389)	Aged 22–35 years (60%)	MINI	182 (44)
Seffren 2018 [155]	Cohort (9)		Busia, Tororo	caregivers of HIV patients	288	288 (0)	33.5±5.8	DHSCL–(>1.75)	28 (9.7)
Kuteesa 2020 [156]	CS (9)	2017	Mukono	15–24 years individuals in a fishing community	1281	606 (675)	Range = 15–24	PHQ-9 –(10)	29 (2)
Mubangizi 2020 [54]–Preprint	CS (9)	2017–2018	Kampala	Adults with sickle cell disease at Mulago Sickle cell clinic	255	161 (94)	Median = 21, IQR = 6	SRQ-25 –(>5)	174 (68.2)
Nabunya 2020 [157]	Longitudinal cluster randomized study (9)	2017–2022 (baseline)	Five districts in southwestern Uganda	Adolescent girls in southern Uganda	1260	1260 (0)	15.4±; Range = 14–15	BDI–(21)	580 (46.03)
Arach 2020 [158]	CS (9)	2018	Lira	Postpartum women	1789	1789 (0)	25±7; Range = 12–47	EPDS–(14)	377 (21.1)
Logie 2020 [159]	CS (9)	2018	Kampala	Refugee and displaced youth aged 16–24 living in five informal settlements in Kampala	445	333 (112)		PHQ-9 –(10)	297 (66.7)
Ssewanyana 2021 [32]	CS (7)	2018	Kampala	Patients with stomas	15	11 (3)	Range = 18–60	PHQ-9 –(5)	13 (88)
Musinguzi 2018 [160]	CS (9)		Masaka, Wakiso	Peri-urban Individuals living with HIV of Masaka and Entebbe	201	160 (41)	18–29 = 67; 30–34 = 39; 35–39 = 26; 40–49 = 48; 50+ = 21	MINI	62 (30.8)
Muliira 2019 [161]	CS (9)		Kampala	Caregivers of cancer patients	284	208 (76)	36±13.8	HADS–(8)	137 (48.2)
Lukenge 2019 [52]–Thesis	CS (9)	2018–2019	Kampala	Women attending infertility clinic	377	377 (0)		PHQ-9 –(10)	167 (44.3)
Misghinna 2020 [162]	CS (9)	2018–2019	Kampala	Refugees	374	218 (156)	37.1±12.1	MINI	174 (46.52)
Bahati 2021 [36]–Preprint	CS (5)	2019	Mbarara	Urban refugees in Mbarara municipality	343	145 (198)	28.8±11.0	PHQ-9 –(10)	329 (96)
Boduszek 2021 [163]	CS (9)	2019		Primary and secondary school students aged 9–17 years	11518	6035 (5483)	14±1.95	The 14-item Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Short Form–(31)	1224 (10.62)
Kabunga 2020 [37]	CS (6)	2019	Isingiro	Refugees in Nakivale camp	146	76 (70)	18–30 = 26; 31–44 = 42; 44–59 = 45; and 60+ = 27	PHQ-9 –(10)	119 (81)
Kyohangirwe 2020 [164]	CS (9)	2019	Kampala	Adolescents	281	176 (105)	10–13 = 101; 14–17 = 180	MINI-KID	51 (18.2)
Olum 2020 [165]	CS (9)	2019	Kampala	Medical students at Makerere University	331	133 (196)	23.1±3.3	PHQ-9 –(10)	71 (21.5)
Mootz 2019 [166]	CS (9)		Amuria, Katakwi, and Kumi	Girls and women war victims	605	605 (0)		DHSCL–(30)	181 (30)
Ssebunnya 2019 [167]	CS (9)		Kamuli	General populations	1290	848 (442)	<25 = 281; 25–34 = 367; 35–44 = 302; >44 = 337	PHQ-9 –(10)	82 (6.4)
Kabunga 2021a [168]	CS (5)	2020	Isingiro	Refugees in Nakivale camp	146	77 (69)	Majority of respondents 31.5% (n = 46) were aged between 44 and 59	PHQ-9 –(10)	66 (45.2)
Bongomin 2021 [33]	CS (9)	2020	Kampala	Patients with rheumatoid arthritis	48	44 (4)	Median = 52; IQR = 17	EQ-5D-5L anxiety/depression dimension	34 (70.8)
Kabunga 2021b [169]	CS (9)	2020	Lira	Out-of-school adolescents	164	87 (77)	10–13 = 13; 14–16 = 48; 17–19 = 103	PHQ-9 –(10)	55 (34)
Kizito 2020 [53]–Thesis	Cohort (9)	2020	Masaka	Postpartum mothers	167	167 (0)	The majority were between 25–29	EPDS	58 (34.7)
Ouma 2021 [170]	CS (9)	2020	Gulu	Female sex workers	300	300 (0)	26.4±6.0	MINI	143 (47.7)
Najjuka 2020 [14]	CS (9)	2020		University students	321	123 (198)	24.8±5.1	Depression Anxiety and Stress Scale (DASS-21)	259 (80.7)
Mahmud 2021 [171]	Cohort (9)	2020	Kyenjojo	General population	1075			PHQ-9 –(10)	150 (14)
Nyundo 2020 [6]	CS (9)		Iganga, Mayuge	Adolescents (10–19)	598	286 (312)	14.2±2.6	the 6-Item Kutcher Adolescent Depression Scale (KADS-6)	158 (26.5)
Kaggwa 2021 [16]	CS (9)	2021	Isingiro	Married/cohabiting women	153	153 (0)	33.3±6.7.	PHQ-9 –(10)	100 (65.4)

Open in a new tab

Note: Table studies have been sorted according to the year of data collection. In those studies without the year of data collection, the year of submission to the journal was used to estimate the year of data collection. CS = Cross-sectional; DHSCL = Depression sub-section of the Hopkins Symptom Checklist; PHQ = Patient Health Questionnaire (PHQ); MINI = Mini-International Neuropsychiatric Interview; MINI-KID = Mini-International Neuropsychiatric Interview for Children and Adolescents; SRQ = Self-Reporting Questionnaire; CES-D = Center for Epidemiological Studies Depression Scale; EPDS = Edinburgh Postpartum Depression Scale; BDI = Beck Depression Inventory; CDI = Children Depression Inventory; HADS = Hospital Anxiety and Depression Scale; DSM = Diagnostic and Statistical Manual of Mental Disorders; CASI-5 = Child and Adolescent Symptom Inventory-5; DASS-21 = 21-item Depression Anxiety and Stress Scale; EQ-5D-5L = European Quality of Life Five Dimension-Five Level anxiety/depression dimension; KADS-6 = Kutcher Adolescent Depression Scale; PROMIS = Patient-Reported Outcomes Measurement Information System Depression Short Form

Ethical considerations

The present study was a secondary analysis of previously published literature. Therefore, formal clearance by a Research and Ethics Committee was not required.

Data synthesis and analysis

Data for this analysis are available at figshare [46]. Microsoft Office 2016 (Microsoft Inc., Washington, USA) and STATA 16.0 software (Stata Corp LLC, College Station, Texas, USA) were used for data cleaning and statistical analysis. Descriptive statistics and qualitative narrative analysis were used to present individual study and participant characteristics. A random-effects meta-analysis [47] was performed using the meta command to determine the pooled prevalence of depression and prevalence of depression in the different study groups. The results were presented on forest plots. The Higgins Inconsistency index (I²) and univariate random effect meta-regression [48] were used to evaluate the heterogeneity among the selected studies. Publication bias was assessed visually using funnel plots symmetry [49], and fill and trim estimated the number of missing studies [50]. Egger’s test was also used to assess for small study effects. Univariate meta-regression was used to determine the source of heterogeneity based on the following: mean age, number per gender (males or females), data collection period (pre-COVID-19 pandemic vs. during the pandemic), study design, JBI Checklist score, sample size, and depression assessment tool used. Subgroup analysis was also conducted based on study types, study tools used, the diagnostic status of the tool, and the data collection period.

Results

A total of 136 papers met the criteria for inclusion in the review (comprising three theses [51–53], two preprints [36, 54], and 131 peer-reviewed journal papers). Using Microsoft Excel 2016, duplicate papers were automatically identified [including republished datasets in different papers] (n = 9) based on year of data collection, type of study, district of study, sample size, study population, the prevalence of depression, and assessment tool used for depression. The remaining 127 papers, comprising a total of 123,859 individuals, comprised the total study sample (Table 1).

The identified papers were published between 2004 and 2021, and the data included were collected between 2000 and 2021 from 45 districts in Uganda.

Most of the studies were conducted in the capital city, Kampala (n = 43), followed by the districts of Mbarara (n = 23) and Gulu (n = 16). The pooled mean age of the participants was 27.19 years (95% CI: 24.31–30.09 years; I² = 85.38, p<0.001). A total of eight studies were conducted during the COVID-19 pandemic [6, 14, 16, 33, 53, 168–171].

Tools used in assessing depression

Both diagnostic and screening tools were used to assess depression among different populations in Uganda (Table 1). The tools used included: (i) Depression sub-section of the Hopkins Symptom Checklist (DHSCL) (n = 34, 26.8%) [35, 55, 61, 63, 65–68, 71, 73, 74, 79–82, 84, 92, 100, 108, 110, 111, 113, 125, 135, 136, 138, 140, 141, 144, 147, 151, 155, 166], (ii) Patient Health Questionnaire (PHQ) (n = 33, 26.0%) [16, 21, 32, 34, 36, 37, 52, 86, 88–90, 93, 98, 99, 101, 102, 112, 116–118, 120–123, 126–128, 131, 150, 156, 159, 165, 167–169, 171], (iii) Mini-International Neuropsychiatric Interview (MINI) (n = 24, 18.9%) [28, 29, 34, 59, 69, 70, 78, 94–96, 98, 103–105, 115, 119, 124, 134, 137, 139, 142, 145, 152–154, 160, 162, 170], (iv) Mini-International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) (n = 6, 4.7%) [17, 58, 75–77, 107, 148, 149, 164], (v) Self-Reporting Questionnaire (SRQ) (n = 6, 4.7%) [31, 54, 57, 59, 83, 85] (vi) Center for Epidemiological Studies Depression Scale (CES-D) (n = 4, 3.1%) [15, 60, 72, 143], (vii) Edinburgh Postpartum Depression Scale (EPDS) (n = 4, 3.1%) [53, 97, 144, 158], (viii) Beck Depression Inventory (BDI) (n = 3, 2.4%) [56, 62, 157], (ix) Children Depression Inventory (CDI) (n = 2, 1.6%) [58, 114], (x) Hospital Anxiety and Depression Scale (HADS) standardized tool (n = 2, 1.6%) [106, 161], (xi) Diagnostic and Statistical Manual of Mental Disorders (DSM) (n = 2, 1.6%) [64, 151], (xii) Child and Adolescent Symptom Inventory-5 (CASI-5) (n = 1, 0.8%) [132], (xiii) European Quality of Life Five Dimension-Five Level (EQ-5D-5L) anxiety/depression dimension (n = 1, 0.8) [33], (xiv) the six-item Kutcher Adolescent Depression Scale (KADS-6) (n = 1, 0.8%) [6], (xv) the 14-item Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Short Form (n = 1, 0.8%) [163], and (xvi) the Youth Self-Report (YSR) (n = 1) [51]. Two studies used a single self-report question to screen for depression [91, 130], and three studies developed their own tool to assess for depression [87, 129, 146]. One study used a clinician’s diagnosis of depression [109]. The DASS-21 was also used in one study [14]. Six studies used more than one tool to screen and/or diagnose depression [34, 58, 59, 98, 144, 151].

Prevalence of depression

A total of 27,989 (out of 123,859) individuals screened positive for depression. The pooled prevalence of depression was 30.2% (95% confidence interval [CI]: 26.7%-34.1%; I² = 99.80, p<0.001). The funnel plot was asymmetrical consistent with publication bias (S1 Fig). Despite the asymmetry, no studies were missing based on trim and fill analysis. The estimated slope from Egger’s test was 6.12 (standard error [SE] = 0.598, p<0.001), suggesting publication bias due to small study effects. A sensitivity analysis was performed using the four studies within the funnel [56, 109, 139, 146]. The pooled prevalence of depression was 0.9% (95% CI: 0.1%-1.7%; I² = 37.82, p = 0.021) (Fig 2). In univariate meta-regression analysis (to explore potential sources of heterogeneity), depression increased with the use of DASS-21 or SRQ-20 assessment tools and studies conducted during the COVID-19 pandemic. However, depression decreased with increased sample size and the number of females in the study.

Due to the high heterogeneity, a subgroup analysis was performed. There were significant differences between the COVID-19 pandemic period (Q difference [QD] = 5.37, p<0.021), the assessment tool used for screening and/or diagnosing depression (QD = 2205.32, p<0.001), and the type of assessment tool used, i.e., diagnostic (DSM V, DSM VI, MINI, MINI-KID, and clinical diagnosis) or non-diagnostic (QD = 8.03, p = 0.005) (Table 2). The pooled prevalence of depression during the COVID-19 pandemic was higher than before the pandemic (48.1% vs. 29.3%). Fig 3 shows the forest plot of the prevalence of depression during the COVID-19 pandemic.

Table 2. Subgroup analysis of the prevalence of depression in Uganda.

Categories	Subgroups	Number of studies	Pooled prevalence (95% CI)	Q	I²	p-value	Group difference χ² (p-value)
Pandemic	Pre-pandemic	119	29.3 (25.5–33.0)	23453.03	99.81	<0.001	5.37 (0.021)
Pandemic	During the pandemic	8	48.1 (32.6–63.6)	319.21	96.74	<0.001	5.37 (0.021)
Type of Study	Cross-sectional	92	31.3 (26.7–35.9)	17035.98	99.84	<0.001	1.06 (0.588)
	Cohort	32	29.0 (22.5–35.5)	3616.12	99.43	<0.001
	Case-control	1	26.7 (19.0–34.5)	0	NA	NA
Tools used	BDI	3	20.5 (2.9–38.1)	256.48	98.75	<0.001	2205.32 (<0.001)
	CASI-5	1	4.9 (3.7–6.1)	0	NA	NA
	CDI	2	36.6 (5.9–67.3)	83.04	98.80	<0.001
	CES-D	4	31.1 (11.8–50.4)	225.02	98.60	<0.001
	Clinician diagnosis	1	0.4 (0.2–0.5)	0	NA	NA
	DASS-21	1	80.7 (70.9–90.5)	0	NA	NA
	DHSCL	34	33.7 (27.3–45.4)	2022.12	99.25	<0.001
	DSM V	1	9.8 (8.3–11.3)	0	NA	NA
	DSM IV	1	39.8 (31.8–47.7)	0	NA	NA
	EPDS	4	27.1 (13.4–40.8)	49.28	96.62	<0.001
	EQ-5D-5L	1	70.8 (47.0–94.6)	0	NA	NA
	HADS	2	37.3 (15.5–59.0)	12.66	92.10	<0.001
	KADS-6	1	26.4 (22.3–30.5)	0	NA	NA
	MINI	24	24.9 (18.1–31.8)	1296.48	99.16	<0.001
	MINI-KID	6	17.6 (7.7–27.4)	253.96	98.93	<0.001
	Study designed tool	3	31.5 (-0.9–63.9)	450.27	99.47	<0.001
	PHQ-2	2	36.1 (-4.77.7)	174.21	99.43	<0.001
	PHQ-9	31	31.0 (21.9–40.1)	3475.21	99.78	<0.001
	PROMIS	1	10.6 (10.0–11.2)	0	NA	NA
	SRQ-20	2	59.4 (29.6–89.1)	13.75	92.72	<0.001
	SRQ-25	4	33.4 (6.7–60.1)	154.00	98.20	<0.001
	Self-report	2	41.9 (3.8–80.0)	191.34	99.48	<0.001
	YSR	1	20.9 (17.1–24.8)	0	NA	NA
Tool diagnostic	No	94	33.2 (28.6–37.7)	10489.46	99.68	<0.001	8.03 (0.005)
Tool diagnostic	Yes	33	30.5 (26.7–34.2)	3258.96	99.65	<0.001	8.03 (0.005)

Open in a new tab

DHSCL = Depression sub-section of the Hopkins Symptom Checklist; PHQ = Patient Health Questionnaire (PHQ); MINI = Mini-International Neuropsychiatric Interview; MINI-KID = Mini-International Neuropsychiatric Interview for Children and Adolescents; SRQ = Self-Reporting Questionnaire; CES-D = Center for Epidemiological Studies Depression Scale; EPDS = Edinburgh Postpartum Depression Scale; BDI = Beck Depression Inventory; CDI = Children Depression Inventory; HADS = Hospital Anxiety and Depression Scale; DSM = Diagnostic and Statistical Manual of Mental Disorders; CASI-5 = Child and Adolescent Symptom Inventory-5; DASS-21 = 21-item Depression Anxiety and Stress Scale; EQ-5D-5L = European Quality of Life Five Dimension-Five Level anxiety/depression dimension; KADS-6 = Kutcher Adolescent Depression Scale; PROMIS = Patient-Reported Outcomes Measurement Information System Depression Short Form

Prevalence of depression in different study populations in Uganda

Depression was screened or diagnosed in the following study groups in Uganda: (i) individuals living with HIV (n = 43) [15, 34, 60, 64, 68, 70–73, 79, 81, 82, 85–91, 93–96, 98–105, 112–114, 117, 119, 123, 127, 128, 130, 132, 133, 136, 141–143, 145, 148–150, 160], (ii) females only (n = 25) [16, 31, 35, 52, 59, 73, 79, 97, 113, 116, 134, 139, 144, 158, 166, 170], including seven studies among pregnant or postpartum women [53, 59, 73, 97, 139, 144, 158], (iii) general population (n = 19) [55, 61–63, 78, 92, 110, 118, 120, 125, 140, 146, 151, 156, 167, 171], (iv) war victims (n = 12) [28, 29, 66, 67, 74–77, 80, 83, 84, 108, 111, 135, 166], (v) special patient groups (such as elderly, outpatients, diabetes mellitus, post-tuberculosis lung diseases, physically ill, post-stroke, cancer, tuberculosis patients, sickle cell disease, patients with stoma, rheumatoid arthritis) (n = 12) [17, 21, 31–33, 54, 57, 109, 115, 122, 124, 131, 152], (vi) children and adolescents (n = 10) [6, 17, 51, 58, 75–77, 84, 85, 107, 114, 129, 146, 157, 163, 164, 169], (vii) refugees (n = 8) [35–37, 116, 153, 159, 162, 168], (viii) caregivers of patients (n = 6) [106, 132, 134, 137, 155, 161], (ix) university students (n = 4) [14, 56, 65, 165], (x) prisoners (n = 1) [154], and males only (n = 1) [121] (S1 Table). The pooled prevalence of depression across the different study groups was highest among refugees (67.6%) and lowest among caregivers of patients (18.5%) (Fig 4).

Depression among refugees in Uganda

A total of 2,538 refugees were assessed for depression in Uganda, and 1,652 screened positive for depression in eight studies. The prevalence of depression ranged between 45.2% [168] and 96% [36]. The pooled prevalence of depression was 67.6% (95 CI: 53.7%-81.5%; I² = 94.82, p<0.001) (Fig 5). The estimated slope from Egger’s test was 6.67 (SE = 3.037, p<0.0281), suggesting publication bias due to small study effects. The funnel plot showed publication bias on visual inspection. A sensitivity analysis was performed using studies within the funnel [37, 159], and the pooled prevalence of depression was 72.7% (95 CI: 58.5%-87.0%; I² = 67.51, p<0.001). Based on meta-regression, the prevalence decreased with the increase in mean age (β = -0.07, SE = 0.02, p<0.001). Other factors were not significant.

Depression among studies involving only females in Uganda

A total of 4,222 out of 11476 females had depression in 25 female-only studies. The prevalence of depression ranged between 6.1% [59] and 92.0% [35]. The pooled prevalence of depression was 38.2% (95 CI: 29.0%-47.5%; I² = 99.16, p<0.001) (Fig 6). The funnel plot showed asymmetrical distribution, therefore showing publication bias. The estimated slope from Egger’s test was 8.65 (SE = 0.1.428, p<0.001), suggesting publication bias due to small study effects. Following meta-regression, no factor significantly affected the prevalence of depression among females in Uganda.

Depression among postpartum or pregnant women in Uganda

Out of the 6296 postpartum or pregnant women in Uganda, 2028 had depression in seven studies. The prevalence of depression ranged between 1.3% [139] and 45.2% [83]. The pooled prevalence of depression among postpartum or pregnant women was 26.9% (95 CI: 13.6%-40.3%; I² = 99.44, p<0.001) (Fig 7). The estimated slope from Egger’s test was 6.76 (SE = 3.694, p<0.067), suggesting no publication bias due to small study effects. Based on meta-regression, the prevalence of depression among postpartum women statistically significantly decreased with use of MINI (β = -0.35, SE = 0.11, p = 0.002) and increased when the study design was cohort (β = 0.22, SE = 0.11, p = 0.045).

Depression among special patient groups in Uganda

The prevalence of depression ranged from 0.4% among outpatients in northern Uganda [109] and 88% among patients with stomas [32]. The prevalence among those who (i) were elderly was 18% [57], (ii) had tuberculosis was 23.7% [152], (iii) had post-tuberculosis lung diseases was 24% [122], (iv) had cancer was 26% [17], (v) with post-stroke was 31.5% [131], (vi) were physically ill was 33.7% [124], (vii) had diabetes mellitus was 34.8% [115], (viii) had sickle cell disease was 68.2% [54], and (ix) had rheumatoid arthritis was 70.8% [33]. The pooled prevalence of depression from the 14,405 special patient groups (of whom 855 had depression) in 12 studies was 37.1% (95 CI: 22.3%-52.0%; I² = 99.55, p<0.001) (Fig 8). The estimated slope from Egger’s test was 3.82 (SE = 1.124, p<0.001), suggesting publication bias due to small study effects. Due to the significant heterogeneity, a sensitivity analysis was performed with studies within the funnel, and [115, 122, 124, 131], the pooled prevalence of depression in these studies was 33.8% (95 CI: 29.8%-37.9%; I² = 0.03, p<0.001). At meta-regression, the prevalence of depression among special patient groups statistically significantly increased with increase in the mean age (β = 0.01, SE = 0.004, p = 0.009) and use of SRQ-20 to assess depression (β = 0.75, SE = 0.30, p = 0.013).

Depression among war victims in Uganda

A total of 6583 (out of 19255) war victims had depression in 12 studies. The prevalence of depression ranged between 7.6% [84] and 71% [67]. The pooled prevalence of depression was 36.0% (95 CI: 25.5%-46.6%; I² = 99.50, p<0.001) (Fig 9). The estimated slope from Egger’s test was 5.24 (SE = 2.109, p = 0.013), suggesting publication bias due to small study effects. Only one study was within the funnel [75]. At meta-regression, no factor significantly affected the prevalence of depression among war victims in Uganda.

Depression among individuals living with HIV in Uganda

A total of 7704 (out of 26255) individuals living with HIV had depression in 43 studies. The prevalence of depression ranged between 5% [119] and 84% [34]. The pooled prevalence of depression was 28.2% (95 CI: 22.7%-33.7%; I² = 99.16, p<0.001) (Fig 10).

The estimated slope from Egger’s test was 5.72 (SE = 0.1.406, p<0.001), suggesting publication bias due to small study effects. At meta-regression, no factor statistically significantly affected the prevalence of depression among HIV patients in Uganda.

Depression among university students in Uganda

A total of 517 (out of 1982) university students had depression in five studies. The prevalence of depression ranged between 0.4% [56] and 80.7% [14]. The pooled prevalence of depression among university students was 26.9% (95 CI: 0.4%-53.4%; I² = 99.48, p<0.001) (Fig 11). The estimated slope from Egger’s test was 19.85 (SE = 4.566, p<0.001), suggesting publication bias due to small study effects. The pooled prevalence from the studies within the funnel during sensitivity analysis was 14.9% (95 CI: 12.7%-17.0%; I² = 0.04, p<0.001). The prevalence of depression among students was significantly higher when the DASS-21 was used to screen for depression (β = 0.71, SE = 0.10, p<0.001) and when the study was conducted during the COVID-19 pandemic (β = 0.67, SE = 0.09, p<0.001).

Depression among children and adolescents in Uganda

A total of 2535 (out of 17072) children and adolescents in Uganda screened positive for depression in 10 studies. The prevalence of depression among children and adolescents ranged from 2.9% [58] to 46.03% [157]. The pooled prevalence of depression among children and adolescents was 23.6% (95 CI: 14.5%-32.8%; I² = 99.55, p<0.001) (Fig 12).

The estimated slope from Egger’s test was 5.27 (SE = 2.008, p = 0.009), suggesting publication bias due to small study effects. Only two studies were within the funnel [6, 51], and sensitivity analysis based on these studies had a pooled prevalence of 23.6% (95 CI: 18.3%-29.0%; I² = 72.52, p<0.001). The prevalence of depression was significantly lower when the following assessment tools were used: (i) MINI-KID (β = -0.37, SE = 0.09, p<0.001), (ii) PROMIS (β = -0.35, SE = 0.11, p = 0.002), and (iii) YSR (β = -0.25, SE = 0.12, p = 0.032).

Depression among caregivers of patients in Uganda

Different types of caregivers were included in this review and they included caregivers for the following patients: individuals living with (i) HIV (n = 3) [112, 132, 155], cancer (n = 2) [106, 161], and (iii) mental health illness (n = 1) [137]. A total of 2189 (out of 14727) caregivers had depression. The pooled prevalence of depression was 18.5% (95 CI: 5.9%-31.2%; I² = 99.62, p<0.001) (Fig 13). The estimated slope from Egger’s test was 6.88 (SE = 2.710, p<0.011), suggesting publication bias due to small study effects. Only one study was inside the funnel [106]. At meta-regression, no factor statistically significantly affected the prevalence of depression among caregivers of patients in Uganda.

Depression among the general population

A total of 4,250 (out of 21,347) members of the general population screened positive for depression in 19 studies. The prevalence of depression ranged between 2.0% among individuals in a fishing community [156] and 68% among national humanitarian aid workers [92]. The pooled prevalence of depression was 20.8% (95 CI: 13.6%-27.9%; I² = 99.61, p<0.001) (Fig 14). The estimated slope from Egger’s test was 10.91 (SE = 1.889, p<0.001), suggesting publication bias due to small study effects. At meta-regression, no factor statistically significantly affected the prevalence of depression among the general population in Uganda.

Discussion

The present systematic review and meta-analysis pooling data of close to 124,000 Ugandans collected between 2000 and 2021 showed that approximately one in three individuals had depression. This finding is much higher than the global depression rate of 3.8% [2]. This large difference may be because the majority of the studies included in this review involved study populations that are at higher risk of developing depression, such as refugees, war victims, individuals living with HIV, and caregivers of patients, among others [172–175]. However, the prevalence of depression in Uganda was slightly higher than 27% from a previous systematic review and meta-analysis of the prevalence of depression among outpatients [13].

The prevalence of depression was also higher than previously obtained pooled prevalence rates of depression in Uganda (21.2% among adults and 20.2% among children for studies published between 2010 and 2018 [12]). Since all the previous review studies are included in this study, the difference between the pooled prevalence of depression between the present study and the previous reviews may be due to the effect of the COVID-19 pandemic that led to increased levels of depression [176]. This was clearly indicated by the subgroup analysis, which showed a higher difference between the pre-pandemic pooled prevalence of depression and that during the pandemic. The present systematic review had a different prevalence than the former studies because it included more studies which could have resulted in the pooled prevalence rate being closer to “the true value” of the prevalence of depression in Uganda.

The prevalence of depression in the different study groups was highest among refugees (67.6%) compared to other groups. This prevalence was over twice as high as a previously reported prevalence of depression among refugees and asylum seekers (31.5%) [172]. Uganda, the world’s fourth largest refugee hosting country, has been host to refugees from Congo, South Sudan, Rwanda, Burundi, Somalia, and Ethiopia, among other countries [25]. The high prevalence of depression may be due to refugees leaving their countries to come to a low-income country that is also affected by multiple health, social, and financial struggles, leaving many refugees with depression or worsening their psychological states [172]. The higher prevalence of depression among refugees compared to other studied groups may be because these refugees, on top of their struggles and settling into a new environment, are also affected by the challenges of the country to which other groups are used to.

Uganda has also been affected by civil wars, especially in the northern part of the country. The prevalence of depression among the war victims in Uganda (36.0%) was higher than the 27% global estimate from a systematic review and meta-analysis of war victims [173]. The psychological impact of civil war, refugees, and wars in the neighboring countries on the victims and the workers may be the high prevalence of depression among the national humanitarian workers compared to the rest of the general population.

Despite the declining prevalence of HIV among Ugandans [177], many individuals were affected by the mental and psychological impacts of HIV, such as depression [38, 178]. The prevalence of depression among individuals living with HIV in Uganda (28.2%) in the present study was lower than the global prevalence of 31% [179]. The lower prevalence may be attributed to the efforts made by many researchers to understand and reduce the burden of depression, as evidenced by the high number of studies regarding depression in the present review.

The prevalence was also lower than the previous prevalence of depression among Ugandans with HIV (30.88%) that involved studies published before 2018 [38]. This difference may be attributed to a few studies being included in the previous review (n = 10) [38]. In Uganda, depression among individuals living with HIV has been studied widely and has been assessed as various risk factors such as depression among caregivers of individuals living with HIV [112, 132, 155]. Based on the present review, caregivers of patients have less depression compared to the patients and other study groups. However, they play an integral role in patient care. The prevalence of depression among special groups of caregivers, such as cancer patient caregivers, was higher (42.3%) [180] compared to the pooled prevalence in the present review. This difference may be attributed to the Ugandan culture, where the caregiving role is shared among all family members and creates family support for the affected caregivers, helping prevent depression [161, 181, 182].

Being female was highly represented in the review, with a total of 25 studies being carried out among female-only studies compared to only one male-only study. This possibly shows neglect of male gender mental health by researchers. Future research should include more studies among males, so that true estimates of the burden of depression can be determined and evidence-based interventions can be designed. Depression among children and adolescents has also been studied more than studies of male adults. The prevalence of depression among children and adolescents (23.6%) was higher than 20.2% among children in Uganda for papers published between 2010 and 2018 [12].

Despite having no missing studies imputed into the overall prevalence, the heterogeneity was high. Following sensitivity analysis, the prevalence of depression in Uganda was 0.9%—a prevalence lower than the estimated global prevalence of 3.8% [2]. Based on the various analyses, the main sources of heterogeneity were (i) the COVID-19 pandemic, where the prevalence of depression was significantly higher than in the period before the pandemic as reported by various researchers and meta-analyses [176, 183]; and (ii) the tools used in screening/diagnosing depression with the DASS-21 detecting significantly higher prevalence rates of depression compared to other study tools. The significant difference may be due to the tool being used during the early stages of the COVID-19 pandemic [14] when many of the individuals were experiencing severe depression due to various stressors [176, 183]. The difference in the reported prevalence of depression could be due to various studies using different assessment tools with different psychometric properties regarding depression. Also, some tools were diagnostic, such as the DSM criteria, while others were screening tools, such as the DASS-21.

Limitations and recommendations

When interpreting these results, the following limitations need to be considered. First, despite only 16% of the 127 papers not having a total score of nine on the JBI Checklist and the use of random effect models, there were significantly high levels of heterogeneity due to the depression assessment tools and the period of study. Future researchers should conduct reviews of studies with fewer variations, especially in relation to the tools used to assess depression. However, for better quality and to increase reliability in future meta-analyses, future researchers should continue using the commonly used tools such as PHQ-9, DHSCL, and MINI. Also, the classification of the different study groups in the present study may have caused heterogeneity in the included studies, for example, among the general population. Second, some of the included studies were prone to recall biases since all their data were based on self-report. Third, despite data from various regions and districts in Uganda being presented, a large majority of the country was still not represented. This suggests more research regarding depression in other parts of this multicultural and multilingual country should be conducted and/or a nationally representative survey study [18]. Moreover, despite conducting a detailed literature search, some of the common databases (e.g., EMBASE, CINAHL) and journals that publish papers on mental health illness were not included. Therefore, some studies could have been missed. Also, the search strategy did not include some of the common terms associated with depression, such as mental health, psychological disorder/problem, and mood. It is recommended that future studies include sources for unpublished data to generalize the findings better.

While the meta-analysis was comprehensive and provided a broader picture of the prevalence of depression in various populations, it is still difficult to generalize the results because the prevalence of depression in Uganda in many regions was not represented, and different populations’ generalizations or groupings were subjective (e.g., humanitarian workers). Future studies within these populations and across wider regions in the country would be helpful in implementing treatments according to targeted needs (socioeconomic, cultural, refugee-status, etc.).

Conclusion

In the present meta-analysis, the synthesized data showed that approximately one in three individuals in Uganda has depression, which was highest among refugees and other special populations. Interventions for active screening, diagnosis, and management of depression among the general population and special populations and cohorts are recommended.

Supporting information

S1 Fig. Funnel plot for the included studies about depression in Uganda.

(TIF)

Click here for additional data file.^{(338.2KB, tif)}

S2 Fig

(TIF)

Click here for additional data file.^{(313.7KB, tif)}

S1 Table. Prevalence of depression in study populations in Uganda.

(DOCX)

Click here for additional data file.^{(14.9KB, docx)}

Acknowledgments

We acknowledge the support and guidance provided by the Librarians at Mbarara University of Science and technology in developing the search strand strings and literature retrieval for the selected papers.

Data Availability

https://doi.org/10.6084/m9.figshare.19579096.v1.

Funding Statement

The author(s) received no specific funding for this work.

References

1.Patel V, Chisholm D, Parikh R, Charlson FJ, Degenhardt L, Dua T, et al. Addressing the burden of mental, neurological, and substance use disorders: key messages from Disease Control Priorities, 3rd edition. The Lancet. 2016;387(10028):1672–85. doi: 10.1016/s0140-6736(15)00390-6 [DOI] [PubMed] [Google Scholar]
2.World Health Organisation. Depression 2021 [cited 2021 Novemeber 29, 2021]. https://www.who.int/health-topics/depression#tab=tab_1.
3.Santomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet. 2021;398(10312):1700–12. doi: 10.1016/S0140-6736(21)02143-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Sankoh O, Sevalie S, Weston M. Mental health in Africa. The Lancet Global Health. 2018;6(9):e954–e5. doi: 10.1016/S2214-109X(18)30303-6 [DOI] [PubMed] [Google Scholar]
5.Jörns-Presentati A, Napp A-K, Dessauvagie AS, Stein DJ, Jonker D, Breet E, et al. The prevalence of mental health problems in sub-Saharan adolescents: A systematic review. PloS One. 2021;16(5):e0251689. doi: 10.1371/journal.pone.0251689 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Nyundo A, Manu A, Regan M, Ismail A, Chukwu A, Dessie Y, et al. Factors associated with depressive symptoms and suicidal ideation and behaviours amongst sub-Saharan African adolescents aged 10–19 years: Cross-sectional study. Tropical Medicine & International Health. 2020;25(1):54–69. doi: 10.1111/tmi.13336 [DOI] [PubMed] [Google Scholar]
7.Kaggwa MM, Arinaitwe I, Nduhuura E, Muwanguzi M, Kajjimu J, Kule M, et al. Prevalence and factors associated with depression and suicidal ideation during the COVID-19 pandemic among university students in Uganda: A cross-sectional study. Frontiers in Psychiatry. 2022;13:842466. doi: 10.3389/fpsyt.2022.842466 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Kaggwa MM, Muwanguzi M, Nduhuura E, Kajjimu J, Arinaitwe I, Kule M, et al. Suicide among Ugandan university students: Evidence from media reports for 2010–2020. BJPsych International. 2021;18(3):63–7. doi: 10.1192/bji.2021.13 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Kaggwa MM, Rukundo GZ, Wakida EK, Maling S, Sserumaga BM, Atim LM, et al. Suicide and suicide attempts among patients attending primary health care facilities in Uganda: A medical records review. Risk Management and Healthcare Policy. 2022;15:703–11. doi: 10.2147/RMHP.S358187 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Kaggwa MM, Arinaitwe I, Muwanguzi M, Nduhuura E, Kajjimu J, Kule M, et al. Suicidal behaviours among Ugandan university students: A cross-sectional study. BMC Psychiatry. 2022;22(1):234. doi: 10.1186/s12888-022-03858-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Mamun MA, Al Mamun F, Hosen I, Hasan M, Rahman A, Jubayar AM, et al. Suicidality in Bangladeshi young adults during the COVID-19 pandemic: The role of behavioral factors, COVID-19 risk and fear, and mental health problems. Risk Management and Healthcare Policy. 2021;14:4051–61. doi: 10.2147/RMHP.S330282 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Opio JN, Munn Z, Aromataris E. Prevalence of Mental Disorders in Uganda: A Systematic Review and Meta-Analysis. The Psychiatric Quarterly. 2021. doi: 10.1007/s11126-021-09941-8 [DOI] [PubMed] [Google Scholar]
13.Wang J, Wu X, Lai W, Long E, Zhang X, Li W, et al. Prevalence of depression and depressive symptoms among outpatients: A systematic review and meta-analysis. BMJ Open. 2017;7(8):e017173. doi: 10.1136/bmjopen-2017-017173 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Najjuka SM, Checkwech G, Olum R, Ashaba S, Kaggwa MM. Depression, anxiety, and stress among Ugandan university students during the COVID-19 lockdown: An online survey. African Health Sciences. 2021;21(4):1533–43. doi: 10.4314/ahs.v21i4.6 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Meffert SM, Neylan TC, McCulloch CE, Maganga L, Adamu Y, Kiweewa F, et al. East African HIV care: Depression and HIV outcomes. Global Mental Health. 2019;6:e9. doi: 10.1017/gmh.2019.6 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Kaggwa MM, Namatanzi B, Kule M, Nkola R, Najjuka SM, Al Mamun F, et al. Depression in Ugandan rural women involved in a money saving group: The role of spouse’s unemployment, extramarital relationship, and substance use. International Journal of Womens Health. 2021;13:869–78. doi: 10.2147/IJWH.S323636 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Akimana B, Abbo C, Balagadde-Kambugu J, Nakimuli-Mpungu E. Prevalence and factors associated with major depressive disorder in children and adolescents at the Uganda Cancer Institute. BMC Cancer. 2019;19(1):466. doi: 10.1186/s12885-019-5635-z [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kaggwa MM, Najjuka SM, Ashaba S, Mamun MA. The psychometrics of Patient Health Questionnaire (PHQ-9) in Uganda: A systematic review. Frontiers in Psychiatry. 2022:13(781095). doi: 10.3389/fpsyt.2022.781095 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Miller AP, Kintu M, Kiene SM. Challenges in measuring depression among Ugandan fisherfolk: a psychometric assessment of the Luganda version of the Center for Epidemiologic Studies Depression Scale (CES-D). BMC Psychiatry. 2020;20(1):45. doi: 10.1186/s12888-020-2463-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Ashaba S, Cooper-Vince C, Vořechovská D, Maling S, Rukundo GZ, Akena D, et al. Development and validation of a 20-item screening scale to detect major depressive disorder among adolescents with HIV in rural Uganda: A mixed-methods study. SSM—Population Health. 2019;7:100332. doi: 10.1016/j.ssmph.2018.100332 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Nakku JEM, Rathod SD, Kizza D, Breuer E, Mutyaba K, Baron EC, et al. Validity and diagnostic accuracy of the Luganda version of the 9-item and 2-item Patient Health Questionnaire for detecting major depressive disorder in rural Uganda. Global Mental Health.2016;3:e20. doi: 10.1017/gmh.2016.14 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Natamba BK, Achan J, Arbach A, Oyok TO, Ghosh S, Mehta S, et al. Reliability and validity of the center for epidemiologic studies-depression scale in screening for depression among HIV-infected and -uninfected pregnant women attending antenatal services in northern Uganda: A cross-sectional study. BMC Psychiatry. 2014;14:303. doi: 10.1186/s12888-014-0303-y [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Miller AP, Espinosa da Silva C, Ziegel L, Mugamba S, Kyasanku E, Bulamba RM, et al. Construct validity and internal consistency of the Patient Health Questionnaire-9 (PHQ-9) depression screening measure translated into two Ugandan languages. Psychiatry Research Communications. 2021;1(2):100002. doi: 10.1016/j.psycom.2021.100002 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Akena D, Joska J, Stein DJ. Sensitivity and specificity of the Akena Visual Depression Inventory (AViDI-18) in Kampala (Uganda) and Cape Town (South Africa). The British Journal of Psychiatry. 2018;212(5):301–7. doi: 10.1192/bjp.2018.9 [DOI] [PubMed] [Google Scholar]
25.Reliefweb. Uganda resettlement and complementary pathways factsheet—31 October 2021 UN High Commissioner for Refugees 2021 [cited 2022 13 March]. https://reliefweb.int/report/uganda/uganda-resettlement-and-complementary-pathways-factsheet-31-october-2021#:~:text=UNHCR%20estimates%20that%20125%2C403%20refugees,Uganda%20since%201%20January%202021.
26.Musumari PM, Techasrivichien T, Srithanaviboonchai K, Wanyenze RK, Matovu JKB, Poudyal H, et al. HIV epidemic in fishing communities in Uganda: A scoping review. PloS One. 2021;16(4):e0249465. doi: 10.1371/journal.pone.0249465 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Kaggwa MM, Nkola R, Najjuka SM, Bongomin F, Ashaba S, Mamun MA. Extrapyramidal side effects in a patient with alcohol withdrawal symptoms: A reflection of quality of the mental health care system. Risk Management and Healthcare Policy. 2021;14:2789–95. doi: 10.2147/RMHP.S314451 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Mugisha J, Muyinda H, Kagee A, Wandiembe P, Mpugu SK, Vancampfort D, et al. Prevalence of suicidal ideation and attempt: associations with psychiatric disorders and HIV/AIDS in post-conflict Northern Uganda. African Health Sciences. 2016;16(4):1027–35. doi: 10.4314/ahs.v16i4.20 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Mugisha J, Muyinda H, Malamba S, Kinyanda E. Major depressive disorder seven years after the conflict in northern Uganda: Burden, risk factors and impact on outcomes (The Wayo-Nero Study). BMC Psychiatry. 2015;15:48. doi: 10.1186/s12888-015-0423-z [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Okware SI, Omaswa FG, Zaramba S, Opio A, Lutwama JJ, Kamugisha J, et al. An outbreak of Ebola in Uganda. Tropical Medicine & International Health. 2002;7(12):1068–75. doi: 10.1046/j.1365-3156.2002.00944.x [DOI] [PubMed] [Google Scholar]
31.Fischer M, Ramaswamy R, Fischer-Flores L, Mugisha G. Measuring and understanding depression in women in Kisoro, Uganda. Culture, Medicine, and Psychiatry. 2019;43(1):160–80. doi: 10.1007/s11013-018-9604-9 [DOI] [PubMed] [Google Scholar]
32.Ssewanyana Y, Ssekitooleko B, Suuna B, Bua E, Wadeya J, Makumbi TK, et al. Quality of life of adult individuals with intestinal stomas in Uganda: A cross sectional study. African Health Sciences. 2021;21(1):427–36. doi: 10.4314/ahs.v21i1.53 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Bongomin F, Natukunda B, Sekimpi M, Olum R, Baluku JB, Makhoba A, et al. High prevalence of depressive symptoms among Ugandan patients with rheumatoid arthritis. Open access rheumatology: Research and Reviews. 2021;13:93–102. doi: 10.2147/OARRR.S306503 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Ngo VK, Wagner GJ, Nakasujja N, Dickens A, Aunon F, Musisi S. Effectiveness of antidepressants and predictors of treatment response for depressed HIV patients in Uganda. International Journal of STD & AIDS. 2015;26(14):998–1006. doi: 10.1177/0956462414564606 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Morof DF, Sami S, Mangeni M, Blanton C, Cardozo BL, Tomczyk B. A cross-sectional survey on gender-based violence and mental health among female urban refugees and asylum seekers in Kampala, Uganda. International Journal of Gynaecology and Obstetrics. 2014;127(2):138–43. doi: 10.1016/j.ijgo.2014.05.014 [DOI] [PubMed] [Google Scholar]
36.Bahati R, Ainamani HE, Sigmund CD, Rukundo GZ. Association between stigma and depression among urban refugees in Mbarara Municipality, Southwestern Uganda. Research Square (Preprint). 2021. doi: 10.21203/rs.3.rs-17932/v1 [DOI] [Google Scholar]
37.Kabunga A. Depressive disorders in refugees in Uganda: A case of Nakivale Refugee Camp. Journal of Psychology and Neuroscience. 2020;2(3). doi: 10.47363/JNRRR/2021(3)132 [DOI] [Google Scholar]
38.Ayano G, Solomon M, Abraha M. A systematic review and meta-analysis of epidemiology of depression in people living with HIV in East Africa. BMC Psychiatry. 2018;18(1):254. doi: 10.1186/s12888-018-1835-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Moher D, Liberati A, Tetzlaff J, Altman DG, The PG. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PloS Medicine. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of Observational Studies in EpidemiologyA Proposal for Reporting. JAMA. 2000;283(15):2008–12. doi: 10.1001/jama.283.15.2008 [DOI] [PubMed] [Google Scholar]
41.Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. International Journal of Eidence-based Halthcare. 2015;13(3):147–53. doi: 10.1097/XEB.0000000000000054 [DOI] [PubMed] [Google Scholar]
42.Assael MI, Namboze JM, German GA, Bennett FJ. Psychiatric disturbances during pregnancy in a rural group of African women. Social Science & Medicine (1967). 1972;6(3):387–95. doi: 10.1016/0037-7856(72)90109-6 [DOI] [PubMed] [Google Scholar]
43.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Joanna Briggs Institute. The Joanna Briggs Institute Critical Appraisal tools for use in JBI Systematic Reviews. Checklist for Prevalence Studies. 2017 [cited 2022 13 March]. https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Prevalence_Studies2017_0.pd.
45.Kaggwa MM, Kajjimu J, Sserunkuma J, Najjuka SM, Atim LM, Olum R, et al. Prevalence of burnout among university students in low- and middle-income countries: A systematic review and meta-analysis. PloS One. 2021;16(8):e0256402. doi: 10.1371/journal.pone.0256402 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Kaggwa MM. Prevalence of depression in Uganda. A systematic review and meta-analysis; 2022[cited 2022 29 July]. Database: Figshare [internet]. doi: 10.6084/m9.figshare.19579096.v1 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.StataCorp. STATA Meta-analysis reference manual release 17 2021 [cited 2022 18/July/2021]. https://www.stata.com/manuals/meta.pdf.
48.Thompson SG, Higgins JPT. How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine. 2002;21(11):1559–73. doi: 10.1002/sim.1187 [DOI] [PubMed] [Google Scholar]
49.Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002. doi: 10.1136/bmj.d4002 [DOI] [PubMed] [Google Scholar]
50.Shi L, Lin L. The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses. Medicine. 2019;98(23):e15987. doi: 10.1097/MD.0000000000015987 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Nsereko JR. The prevalence and risk factors of symptoms of depression, anxiety and somatic syndromes among secondary school students in Kampala, Uganda [thesis]. South Africa: Stellenbosch: Stellenbosch University; 2018.
52.Lukenge E. Prevalence of depression, associated factors and coping styles among women attending infertility clinic at Mulago Hospital Kampala, Uganda [Dissertation]. School of Medicine Collections: Makerere University.; 2019.
53.Kizito S. Maternal mental illness, infant growth and infant psychomotor functioning in rural Masaka district, Uganda: A longitudinal cohort study [Thesis]. South Africa: Stellenbosch University; 2020.
54.Mubangizi I, Nakimuli-Mpungu E, Kawooya I, Sekaggya-wiltshire C. Prevalence and factors associated with depression among adults with sickle cell disease at Mulago Hospital, Uganda: A cross sectional study. Research Square (Preprint). 2020. doi: 10.21203/rs.3.rs-104883/v1 [DOI] [Google Scholar]
55.Bolton P, Wilk CM, Ndogoni L. Assessment of depression prevalence in rural Uganda using symptom and function criteria. Social Psychiatry and Psychiatric Epidemiology. 2004;39(6):442–7. doi: 10.1007/s00127-004-0763-3 [DOI] [PubMed] [Google Scholar]
56.Ovuga E, Boardman J, Wasserman D. Undergraduate student mental health at Makerere university, Uganda. World Psychiatry. 2006;5(1):51–2. https://pubmed.ncbi.nlm.nih.gov/16757997. [PMC free article] [PubMed] [Google Scholar]
57.Nakasujja N, Musisi S, Walugembe J, Wallace D. Psychiatric disorders among the elderly on non-psychiatric wards in an African setting. International Psychogeriatrics. 2007;19(4):691–704. doi: 10.1017/S1041610207005418 [DOI] [PubMed] [Google Scholar]
58.Nalugya-Sserunjogi J, Rukundo GZ, Ovuga E, Kiwuwa SM, Musisi S, Nakimuli-Mpungu E. Prevalence and factors associated with depression symptoms among school-going adolescents in Central Uganda. Child and Adolescent Psychiatry and Mental Health. 2016;10:39. doi: 10.1186/s13034-016-0133-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Nakku JE, Nakasi G, Mirembe F. Postpartum major depression at six weeks in primary health care: Prevalence and associated factors. African Health Sciences. 2006;6(4):207–14. doi: 10.5555/afhs.2006.6.4.207 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Kaharuza FM, Bunnell R, Moss S, Purcell DW, Bikaako-Kajura W, Wamai N, et al. Depression and CD4 cell count among persons with HIV infection in Uganda. AIDS and Behavior. 2006;10(4 Suppl):S105–11. doi: 10.1007/s10461-006-9142-2 [DOI] [PubMed] [Google Scholar]
61.Kinyanda E, Woodburn P, Tugumisirize J, Kagugube J, Ndyanabangi S, Patel V. Poverty, life events and the risk for depression in Uganda. Social Psychiatry and Psychiatric Epidemiology. 2011;46(1):35–44. doi: 10.1007/s00127-009-0164-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Ovuga E, Boardman J, Wasserman D. The prevalence of depression in two districts of Uganda. Social Psychiatry and Psychiatric Epidemiology. 2005;40(6):439–45. doi: 10.1007/s00127-005-0915-0 [DOI] [PubMed] [Google Scholar]
63.Lundberg P, Rukundo G, Ashaba S, Thorson A, Allebeck P, Östergren P-O, et al. Poor mental health and sexual risk behaviours in Uganda: A cross-sectional population-based study. BMC Public Health. 2011;11(1):125. doi: 10.1186/1471-2458-11-125 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Nakimuli-Mpungu E, Munyaneza GM. Depression, alcohol abuse, and disclosure of HIV serostatus among rural HIV-positive individuals in western Uganda. Neurobehavioral HIV Medicine. 2013;3:3–19. doi: 10.2147/NBHIV.S13277 [DOI] [Google Scholar]
65.Agardh A, Cantor-Graae E, Ostergren PO. Youth, sexual risk-taking behavior, and mental health: a study of university students in Uganda. International Journal of Behavioral Medicine. 2012;19(2):208–16. doi: 10.1007/s12529-011-9159-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Vinck P, Pham PN, Stover E, Weinstein HM. Exposure to war crimes and implications for peace building in northern Uganda. JAMA. 2007;298(5):543–54. doi: 10.1001/jama.298.5.543 [DOI] [PubMed] [Google Scholar]
67.Pfeiffer A, Elbert T. PTSD, depression and anxiety among former abductees in Northern Uganda. Conflict and Health. 2011;5(1):14. doi: 10.1186/1752-1505-5-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Martinez P, Andia I, Emenyonu N, Hahn JA, Hauff E, Pepper L, et al. Alcohol use, depressive symptoms and the receipt of antiretroviral therapy in southwest Uganda. AIDS and Behavior. 2008;12(4):605–12. doi: 10.1007/s10461-007-9312-x [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Muhwezi WW, Ågren H, Musisi S. Detection of major depression in Ugandan primary health care settings using simple questions from a subjective well-being (SWB) subscale. Social Psychiatry and Psychiatric Epidemiology. 2007;42(1):61–9. doi: 10.1007/s00127-006-0132-5 [DOI] [PubMed] [Google Scholar]
70.Petrushkin H, Boardman J, Ovuga E. Psychiatric disorders in HIV-positive individuals in urban Uganda. Psychiatric Bulletin. 2005;29(12):455–8. doi: 10.1192/pb.29.12.455 [DOI] [Google Scholar]
71.Psaros C, Haberer JE, Boum Y 2nd, Tsai AC, Martin JN, Hunt PW, et al. The factor structure and presentation of depression among HIV-positive adults in Uganda. AIDS and Behavior. 2015;19(1):27–33. doi: 10.1007/s10461-014-0796-x [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Nakasujja N, Skolasky RL, Musisi S, Allebeck P, Robertson K, Ronald A, et al. Depression symptoms and cognitive function among individuals with advanced HIV infection initiating HAART in Uganda. BMC Psychiatry. 2010;10:44. doi: 10.1186/1471-244X-10-44 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Kaida A, Matthews LT, Ashaba S, Tsai AC, Kanters S, Robak M, et al. Depression during pregnancy and the postpartum among HIV-infected women on antiretroviral therapy in Uganda. Journal of Acquired Immune Deficiency Syndromes (1999). 2014;67 Suppl 4(Suppl 4):S179–87. doi: 10.1097/QAI.0000000000000370 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Roberts B, Ocaka KF, Browne J, Oyok T, Sondorp E. Factors associated with post-traumatic stress disorder and depression amongst internally displaced persons in northern Uganda. BMC Psychiatry. 2008;8(1):38. doi: 10.1186/1471-244X-8-38 [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Klasen F, Gehrke J, Metzner F, Blotevogel M, Okello J. Complex trauma symptoms in former Ugandan child soldiers. Journal of Aggression, Maltreatment & Trauma. 2013;22(7):698–713. doi: 10.1080/10926771.2013.814741 [DOI] [Google Scholar]
76.Klasen F, Oettingen G, Daniels J, Adam H. Multiple trauma and mental health in former Ugandan child soldiers. Journal of Traumatic Stress. 2010;23(5):573–81. doi: 10.1002/jts.20557 [DOI] [PubMed] [Google Scholar]
77.Klasen F, Oettingen G, Daniels J, Post M, Hoyer C, Adam H. Posttraumatic resilience in former Ugandan child soldiers. Child Development. 2010;81(4):1096–113. doi: 10.1111/j.1467-8624.2010.01456.x [DOI] [PubMed] [Google Scholar]
78.Abbo C, Ekblad S, Waako P, Okello E, Musisi S. The prevalence and severity of mental illnesses handled by traditional healers in two districts in Uganda. African Health Sciences. 2009;9 Suppl 1(Suppl 1):S16–22. [PMC free article] [PubMed] [Google Scholar]
79.Hatcher AM, Tsai AC, Kumbakumba E, Dworkin SL, Hunt PW, Martin JN, et al. Sexual relationship power and depression among HIV-infected women in Rural Uganda. PloS One. 2012;7(12):e49821. doi: 10.1371/journal.pone.0049821 [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Pham PN, Vinck P, Stover E. Returning home: forced conscription, reintegration, and mental health status of former abductees of the Lord’s Resistance Army in northern Uganda. BMC Psychiatry. 2009;9(1):23. doi: 10.1186/1471-244X-9-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Tsai AC, Bangsberg DR, Frongillo EA, Hunt PW, Muzoora C, Martin JN, et al. Food insecurity, depression and the modifying role of social support among people living with HIV/AIDS in rural Uganda. Social Science & Medicine (1982). 2012;74(12):2012–9. doi: 10.1016/j.socscimed.2012.02.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Tsai AC, Wolfe WR, Kumbakumba E, Kawuma A, Hunt PW, Martin JN, et al. Prospective study of the mental health consequences of sexual violence among women living with HIV in rural Uganda. Journal of Interpersonal Violence. 2016;31(8):1531–53. doi: 10.1177/0886260514567966 [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Nakimuli-Mpungu E, Alderman S, Kinyanda E, Allden K, Betancourt TS, Alderman JS, et al. Implementation and scale-up of psycho-trauma centers in a post-conflict area: A case study of a private-public partnership in northern Uganda. PloS Medicine. 2013;10(4):e1001427. doi: 10.1371/journal.pmed.1001427 [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Winkler N, Ruf-Leuschner M, Ertl V, Pfeiffer A, Schalinski I, Ovuga E, et al. From war to classroom: PTSD and depression in formerly abducted youth in Uganda. Frontier in Psychiatry. 2015;6:2. doi: 10.3389/fpsyt.2015.00002 [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Musisi S, Kinyanda E. Emotional and behavioural disorders in HIV seropositive adolescents in urban Uganda. East African Medical Journal. 2009;86(1):16–24. doi: 10.4314/eamj.v86i1.46923 [DOI] [PubMed] [Google Scholar]
86.Wagner GJ, Ghosh-Dastidar B, Garnett J, Kityo C, Mugyenyi P. Impact of HIV antiretroviral therapy on depression and mental health among clients with HIV in Uganda. Psychosomatic Medicine. 2012;74(9):883–90. doi: 10.1097/PSY.0b013e31826629db [DOI] [PubMed] [Google Scholar]
87.Shumba C, Atukunda R, Imakit R, Memiah P. Prevalence of depressive symptoms amongst highly active antiretroviral therapy (HAART) patients in AIDSRelief Uganda. Journal of Public Health in Africa. 2013;4(2):e19. doi: 10.4081/jphia.2013.e19 [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Okeke EN, Wagner GJ. AIDS treatment and mental health: evidence from Uganda. Social Science & Medicine (1982). 2013;92:27–34. doi: 10.1016/j.socscimed.2013.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Wagner GJ, Ghosh-Dastidar B, Slaughter ME, Akena D, Nakasujja N, Musisi S. Changes in condom use during the first year of HIV treatment in Uganda and the relationship to depression. Annals of Behavioral Medicine. 2014;48(2):175–83. doi: 10.1007/s12160-013-9586-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Wagner GJ, Slaughter M, Ghosh-Dastidar B. Depression at treatment initiation predicts HIV antiretroviral adherence in Uganda. Journal of the International Association of Providers of AIDS Care. 2017;16(1):91–7. doi: 10.1177/2325957416677121 [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Wang C, Pu R, Ghose B, Tang S. Chronic musculoskeletal pain, self-reported health and quality of life among older populations in South Africa and Uganda. International Journal of Environmental Research and Public Health. 2018;15(12). doi: 10.3390/ijerph15122806 [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Ager A, Pasha E, Yu G, Duke T, Eriksson C, Cardozo BL. Stress, mental health, and burnout in national humanitarian aid workers in Gulu, northern Uganda. Journal of Traumatic Stress. 2012;25(6):713–20. doi: 10.1002/jts.21764 [DOI] [PubMed] [Google Scholar]
93.Wagner GJ, Holloway I, Ghosh-Dastidar B, Kityo C, Mugyenyi P. Understanding the influence of depression on self-efficacy, work status and condom use among HIV clients in Uganda. Journal of Psychosomatic Research. 2011;70(5):440–8. doi: 10.1016/j.jpsychores.2010.10.003 [DOI] [PubMed] [Google Scholar]
94.Kinyanda E, Hoskins S, Nakku J, Nawaz S, Patel V. Prevalence and risk factors of major depressive disorder in HIV/AIDS as seen in semi-urban Entebbe district, Uganda. BMC Psychiatry. 2011;11:205. doi: 10.1186/1471-244X-11-205 [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Kinyanda E, Hoskins S, Nakku J, Nawaz S, Patel V. The prevalence and characteristics of suicidality in HIV/AIDS as seen in an African population in Entebbe district, Uganda. BMC Psychiatry. 2012;12(1):63. doi: 10.1186/1471-244X-12-63 [DOI] [PMC free article] [PubMed] [Google Scholar]
96.Nakku J, Kinyanda E, Hoskins S. Prevalence and factors associated with probable HIV dementia in an African population: A cross-sectional study of an HIV/AIDS clinic population. BMC Psychiatry. 2013;13(1):126. doi: 10.1186/1471-244X-13-126 [DOI] [PMC free article] [PubMed] [Google Scholar]
97.Kakyo TA, Muliira JK, Mbalinda SN, Kizza IB, Muliira RS. Factors associated with depressive symptoms among postpartum mothers in a rural district in Uganda. Midwifery. 2012;28(3):374–9. doi: 10.1016/j.midw.2011.05.001 [DOI] [PubMed] [Google Scholar]
98.Wagner GJ, Ghosh-Dastidar B, Dickens A, Nakasujja N, Okello E, Luyirika E, et al. Depression and its Relationship to Work Status and Income Among HIV Clients in Uganda. World journal of AIDS. 2012;2(3):126–34. doi: 10.4236/wja.2012.23018 [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Wagner GJ, Ghosh-Dastidar B, Slaughter M, Akena D, Nakasujja N, Okello E, et al. The role of depression in work-related outcomes of HIV treatment in Uganda. International Journal of Behavioral Medicine. 2014;21(6):946–55. doi: 10.1007/s12529-013-9379-x [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Kiene SM, Dove M, Wanyenze RK. Depressive symptoms, disclosure, HIV-related stigma, and coping following HIV testing among outpatients in Uganda: A daily process analysis. AIDS and Behavior. 2018;22(5):1639–51. doi: 10.1007/s10461-017-1953-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Okello ES, Wagner GJ, Ghosh-Dastidar B, Garnett J, Akena D, Nakasujja N, et al. Depression, internalized HIV stigma and HIV disclosure. World Journal of AIDS. 2015;5(01):30. doi: 10.4236/wja.2015.51004 [DOI] [Google Scholar]
102.Musisi S, Wagner GJ, Ghosh-Dastidar B, Nakasujja N, Dickens A, Okello E. Depression and sexual risk behaviour among clients about to start HIV antiretroviral therapy in Uganda. International Journal of STD & AIDS. 2014;25(2):130–7. doi: 10.1177/0956462413495186 [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Akena D, Musisi S, Joska J, Stein DJ. The association between aids related stigma and major depressive disorder among HIV-positive individuals in Uganda. PloS One. 2012;7(11):e48671. doi: 10.1371/journal.pone.0048671 [DOI] [PMC free article] [PubMed] [Google Scholar]
104.Akena D, Stein DJ, Joska J. Does screening HIV-positive individuals in Uganda for major depressive disorder improve case detection rates and antidepressant prescription? AIDS and Behavior. 2013;17(8):2802–7. doi: 10.1007/s10461-012-0383-y [DOI] [PubMed] [Google Scholar]
105.Nakimuli-Mpungu E, Musisi S, Katabira E, Nachega J, Bass J. Prevalence and factors associated with depressive disorders in an HIV+ rural patient population in southern Uganda. Journal of Affective Disorders. 2011;135(1–3):160–7. doi: 10.1016/j.jad.2011.07.009 [DOI] [PubMed] [Google Scholar]
106.Katende G, Nakimera L. Prevalence and correlates of anxiety and depression among family carers of cancer patients in a cancer care and treatment facility in Uganda: A cross-sectional study. African Health Sciences. 2017;17(3):868–76. doi: 10.4314/ahs.v17i3.30 [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Kinyanda E, Kizza R, Abbo C, Ndyanabangi S, Levin J. Prevalence and risk factors of depression in childhood and adolescence as seen in 4 districts of north-eastern Uganda. BMC International Health and Human Rights. 2013;13(1):19. doi: 10.1186/1472-698X-13-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Spittal PM, Malamba SS, Ogwang MD, Musisi S, Ekwaru JP, Sewankambo NK, et al. Cango Lyec (healing the elephant): Gender differences in HIV infection in post-conflict Northern Uganda. Journal of Acquired Immune Deficiency Syndromes (1999). 2018;78(3):257–68. doi: 10.1097/QAI.0000000000001671 [DOI] [PMC free article] [PubMed] [Google Scholar]
109.Whyte SR, Park SJ, Odong G, Ojara M, Lamwaka A. The visibility of non-communicable diseases in Northern Uganda. African Health Sciences. 2015;15(1):82–9. doi: 10.4314/ahs.v15i1.11 [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Perkins JM, Nyakato VN, Kakuhikire B, Tsai AC, Subramanian SV, Bangsberg DR, et al. Food insecurity, social networks and symptoms of depression among men and women in rural Uganda: A cross-sectional, population-based study. Public Health Nutrition. 2018;21(5):838–48. doi: 10.1017/S1368980017002154 [DOI] [PMC free article] [PubMed] [Google Scholar]
111.Malamba SS, Muyinda H, Spittal PM, Ekwaru JP, Kiwanuka N, Ogwang MD, et al. “The Cango Lyec Project—Healing the Elephant”: HIV related vulnerabilities of post-conflict affected populations aged 13–49 years living in three Mid-Northern Uganda districts. BMC infectious diseases. 2016;16(1):690. doi: 10.1186/s12879-016-2030-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
112.Hakim AJ, Mukasa B, Hundley L, Odiit M, Ogwal M, Sendagala S, et al. Correlates of undiagnosed HIV infection and retesting among voluntary HIV testing clients at Mildmay Clinic, Uganda. AIDS and Behavior. 2019;23(4):820–34. doi: 10.1007/s10461-018-2274-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
113.Familiar I, Sikorskii A, Murray S, Ruisenor-Escudero H, Nakasujja N, Korneffel C, et al. Depression symptom trajectories among mothers living with HIV in rural Uganda. AIDS and Behavior. 2019;23(12):3411–8. doi: 10.1007/s10461-019-02465-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
114.Cavazos-Rehg P, Xu C, Kasson E, Byansi W, Bahar OS, Ssewamala FM. Social and economic equity and family cohesion as potential protective factors from depression among adolescents living with HIV in Uganda. AIDS and Behavior. 2020;24(9):2546–54. doi: 10.1007/s10461-020-02812-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
115.Akena D, Kadama P, Ashaba S, Akello C, Kwesiga B, Rejani L, et al. The association between depression, quality of life, and the health care expenditure of patients with diabetes mellitus in Uganda. Journal of Affective Disorders. 2015;174:7–12. doi: 10.1016/j.jad.2014.11.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
116.Familiar I, Muniina PN, Dolan C, Ogwal M, Serwadda D, Kiyingi H, et al. Conflict-related violence and mental health among self-settled Democratic Republic of Congo female refugees in Kampala, Uganda–a respondent driven sampling survey. Conflict and Health. 2021;15(1):42. doi: 10.1186/s13031-021-00377-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
117.Akena D, Ngo V, Guotman P, Glick P, Musisi S, Wagner GJJR, et al. Accuracy of depression evaluations by trained lay and medical health workers in HIV clinics in Uganda. Research and Advances in Psychiatry 2016;3:44–52. [Google Scholar]
118.Rathod SD, Roberts T, Medhin G, Murhar V, Samudre S, Luitel NP, et al. Detection and treatment initiation for depression and alcohol use disorders: Facility-based cross-sectional studies in five low-income and middle-income country districts. BMJ Open. 2018;8(10):e023421. doi: 10.1136/bmjopen-2018-023421 [DOI] [PMC free article] [PubMed] [Google Scholar]
119.Mwesiga EK, Mugenyi L, Nakasujja N, Moore S, Kaddumukasa M, Sajatovic M. Depression with pain co morbidity effect on quality of life among HIV positive patients in Uganda: A cross sectional study. Health and Quality of Life Outcomes. 2015;13(1):206. doi: 10.1186/s12955-015-0403-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
120.Nakku JEM, Rathod SD, Garman EC, Ssebunnya J, Kangere S, De Silva M, et al. Evaluation of the impacts of a district-level mental health care plan on contact coverage, detection and individual outcomes in rural Uganda: A mixed methods approach. International Journal of Mental Health Systems. 2019;13:63. doi: 10.1186/s13033-019-0319-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
121.Nalwadda O, Rathod SD, Nakku J, Lund C, Prince M, Kigozi F. Alcohol use in a rural district in Uganda: findings from community-based and facility-based cross-sectional studies. International Journal of Mental Health Systems. 2018;12:12. doi: 10.1186/s13033-018-0191-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
122.Jones R, Kirenga BJ, Katagira W, Singh SJ, Pooler J, Okwera A, et al. A pre–post intervention study of pulmonary rehabilitation for adults with post-tuberculosis lung disease in Uganda. International Journal of Chronic Obstructive Pulmonary Diseases 2017;12:3533–3539. doi: 10.2147/COPD.S146659 [DOI] [PMC free article] [PubMed] [Google Scholar]
123.Wagner GJ, Ghosh-Dastidar B, Robinson E, Ngo VK, Glick P, Musisi S, et al. Effects of depression alleviation on work productivity and income among HIV patients in Uganda. International Journal of Behavioral Medicine. 2017;24(4):628–33. doi: 10.1007/s12529-017-9642-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
124.Rukundo ZG, Musisi S, Nakasujja N. Psychiatric morbidity among physically ill patients in a Ugandan Regional Referral Hospital. African Health Sciences. 2013;13(1):87–93. doi: 10.4314/ahs.v13i1.13 [DOI] [PMC free article] [PubMed] [Google Scholar]
125.Olema DK, Catani C, Ertl V, Saile R, Neuner F. The hidden effects of child maltreatment in a war region: Correlates of psychopathology in two generations living in Northern Uganda. Journal of Traumatic Stress. 2014;27(1):35–41. doi: 10.1002/jts.21892 [DOI] [PubMed] [Google Scholar]
126.Huang KY, Abura G, Theise R, Nakigudde J. Parental depression and associations with parenting and children’s physical and mental health in a Sub-Saharan African setting. Child Psychiatry and Human Development. 2017;48(4):517–27. doi: 10.1007/s10578-016-0679-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Wagner G, Ghosh-Dastidar B, Ngo V, Robinson E, Musisi S, Glick P, et al. A cluster randomized controlled trial of two task-shifting depression care models on depression alleviation and antidepressant response among HIV clients in Uganda. Research and Advances in Psychiatry. 2016;3(1):12–21. [PMC free article] [PubMed] [Google Scholar]
128.Wagner GJ, Ngo V, Goutam P, Glick P, Musisi S, Akena D. A structured protocol model of depression care versus clinical acumen: A cluster randomized trial of the effects on depression screening, diagnostic evaluation, and treatment uptake in Ugandan HIV clinics. PloS One. 2016;11(5):e0153132. doi: 10.1371/journal.pone.0153132 [DOI] [PMC free article] [PubMed] [Google Scholar]
129.Henry MB, Bakeera-Kitaka S, Lubega K, Snyder SA, LaRussa P, Pfeffer B. Depressive symptoms, sexual activity, and substance use among adolescents in Kampala, Uganda. African Health Sciences. 2019;19(2):1888–96. doi: 10.4314/ahs.v19i2.12 [DOI] [PMC free article] [PubMed] [Google Scholar]
130.Swahn MH, Culbreth R, Masyn KE, Salazar LF, Wagman J, Kasirye R. The intersection of alcohol use, gender based violence and HIV: Empirical findings among disadvantaged service-seeking youth in Kampala, Uganda. AIDS and Behavior. 2021;25(10):3106–14. doi: 10.1007/s10461-021-03301-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
131.Gyagenda JO, Ddumba E, Odokonyero R, Kaddumukasa M, Sajatovic M, Smyth K, et al. Post-stroke depression among stroke survivors attending two hospitals in Kampala Uganda. African Health Sciences. 2015;15(4):1220–31. doi: 10.4314/ahs.v15i4.22 [DOI] [PMC free article] [PubMed] [Google Scholar]
132.Kinyanda E, Salisbury TT, Muyingo SK, Ssembajjwe W, Levin J, Nakasujja N, et al. Major depressive disorder among HIV infected youth in Uganda: Incidence, persistence and their predictors. AIDS and Behavior. 2020;24(9):2588–96. doi: 10.1007/s10461-020-02815-3 [DOI] [PubMed] [Google Scholar]
133.Muhammad Usman K, Okorie P, Umahi E, Paul S. Factors influencing the occurrence of mental illness in persons living with HIV/AIDS in Kabarole District, Uganda. International Journal of HIV/AIDS Prevention, Education and Behavioural Science. 2018;4(2):35–43. doi: 10.4314/njpar.v39i2 [DOI] [Google Scholar]
134.Ashaba S, Rukundo GZ, Beinempaka F, Ntaro M, LeBlanc JC. Maternal depression and malnutrition in children in southwest Uganda: A case control study. BMC Public Health. 2015;15:1303. doi: 10.1186/s12889-015-2644-y [DOI] [PMC free article] [PubMed] [Google Scholar]
135.Kinyanda E, Weiss HA, Mungherera M, Onyango-Mangen P, Ngabirano E, Kajungu R, et al. Intimate partner violence as seen in post-conflict eastern Uganda: Prevalence, risk factors and mental health consequences. BMC International Health and Human Rights. 2016;16(1):5. doi: 10.1186/s12914-016-0079-x [DOI] [PMC free article] [PubMed] [Google Scholar]
136.Manne-Goehler J, Kakuhikire B, Abaasabyoona S, Bärnighausen TW, Okello S, Tsai AC, et al. Depressive symptoms before and after antiretroviral therapy initiation among older-aged individuals in rural Uganda. AIDS and Behavior. 2019;23(3):564–71. doi: 10.1007/s10461-018-2273-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
137.Kinyanda E, Kuteesa M, Scholten F, Mugisha J, Baisley K, Seeley J. Risk of major depressive disorder among older persons living in HIV-endemic central and southwestern Uganda. AIDS Care. 2016;28(12):1516–21. doi: 10.1080/09540121.2016.1191601 [DOI] [PubMed] [Google Scholar]
138.Smith ML, Kakuhikire B, Baguma C, Rasmussen JD, Perkins JM, Cooper-Vince C, et al. Relative wealth, subjective social status, and their associations with depression: Cross-sectional, population-based study in rural Uganda. SSM—Population Health. 2019;8:100448. doi: 10.1016/j.ssmph.2019.100448 [DOI] [PMC free article] [PubMed] [Google Scholar]
139.Nampijja M, Natamba B, Mpango R, Kinyanda E. The burden and risk factors for postnatal depression and depressive symptomatology among women in Kampala. Tropical Doctor. 2019;49(3):170–7. doi: 10.1177/0049475519837107 [DOI] [PubMed] [Google Scholar]
140.Cooper-Vince CE, Arachy H, Kakuhikire B, Vořechovská D, Mushavi RC, Baguma C, et al. Water insecurity and gendered risk for depression in rural Uganda: A hotspot analysis. BMC Public Health. 2018;18(1):1143. doi: 10.1186/s12889-018-6043-z [DOI] [PMC free article] [PubMed] [Google Scholar]
141.Kiene SM, Lule H, Sileo KM, Silmi KP, Wanyenze RK. Depression, alcohol use, and intimate partner violence among outpatients in rural Uganda: Vulnerabilities for HIV, STIs and high risk sexual behavior. BMC Infectious Diseases. 2017;17(1):88. doi: 10.1186/s12879-016-2162-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
142.Kinyanda E, Nakasujja N, Levin J, Birabwa H, Mpango R, Grosskurth H, et al. Major depressive disorder and suicidality in early HIV infection and its association with risk factors and negative outcomes as seen in semi-urban and rural Uganda. Journal of Affective Disorders. 2017;212:117–27. doi: 10.1016/j.jad.2017.01.033 [DOI] [PubMed] [Google Scholar]
143.Sohail S, Nakigozi G, Anok A, Batte J, Kisakye A, Mayanja R, et al. Headache prevalence and its functional impact among HIV-infected adults in rural Rakai District, Uganda. Journal of Neurovirology. 2019;25(2):248–53. doi: 10.1007/s13365-018-0710-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
144.Raggio GA, Psaros C, Fatch R, Goodman G, Matthews LT, Magidson JF, et al. High rates of biomarker-confirmed alcohol use among pregnant women living with HIV in South Africa and Uganda. Journal of Acquired Immune Deficiency Syndromes (1999). 2019;82(5):443–51. doi: 10.1097/QAI.0000000000002156 [DOI] [PMC free article] [PubMed] [Google Scholar]
145.Kinyanda E, Weiss HA, Levin J, Nakasujja N, Birabwa H, Nakku J, et al. Incidence and persistence of major depressive disorder among people living with HIV in Uganda. AIDS and Behavior. 2017;21(6):1641–54. doi: 10.1007/s10461-016-1575-7 [DOI] [PubMed] [Google Scholar]
146.Akinbo E, Agwu E, Okoruwa G, Adedeji A. Prevalence and Magnitude of alcohol-related health problems among youths in disease endemic Bushenyi district, Uganda. Special Pathogens Review Journal. 2016; 1(2):53–9. [Google Scholar]
147.Cooper-Vince CE, Kakuhikire B, Vorechovska D, McDonough AQ, Perkins J, Venkataramani AS, et al. Household water insecurity, missed schooling, and the mediating role of caregiver depression in rural Uganda. Global Mental Health. 2017;4:e15. doi: 10.1017/gmh.2017.14 [DOI] [PMC free article] [PubMed] [Google Scholar]
148.Ashaba S, Cooper-Vince CE, Maling S, Satinsky EN, Baguma C, Akena D, et al. Childhood trauma, major depressive disorder, suicidality, and the modifying role of social support among adolescents living with HIV in rural Uganda. Journal of Affective Disorders Reports. 2021;4:100094. doi: 10.1016/j.jadr.2021.100094 [DOI] [PMC free article] [PubMed] [Google Scholar]
149.Ashaba S, Cooper-Vince C, Maling S, Rukundo GZ, Akena D, Tsai AC. Internalized HIV stigma, bullying, major depressive disorder, and high-risk suicidality among HIV-positive adolescents in rural Uganda. Global Mental Health. 2018;5:e22. doi: 10.1017/gmh.2018.15 [DOI] [PMC free article] [PubMed] [Google Scholar]
150.Ortblad KF, Musoke DK, Chanda MM, Ngabirano T, Velloza J, Haberer JE, et al. Knowledge of HIV status is associated with a decrease in the severity of depressive symptoms among female sex workers in Uganda and Zambia. Journal of Acquired Immune Deficiency Syndromes (1999). 2020;83(1):37–46. doi: 10.1097/QAI.0000000000002224 [DOI] [PMC free article] [PubMed] [Google Scholar]
151.Satinsky EN, Kakuhikire B, Baguma C, Rasmussen JD, Ashaba S, Cooper-Vince CE, et al. Adverse childhood experiences, adult depression, and suicidal ideation in rural Uganda: A cross-sectional, population-based study. PloS Medicine. 2021;18(5):e1003642–e. doi: 10.1371/journal.pmed.1003642 [DOI] [PMC free article] [PubMed] [Google Scholar]
152.Alinaitwe R, Birungi C, Bangirana P, Nakasujja N. Prevalence and factors associated with depressive illness in patients with tuberculosis in Mulago hospital, Kampala- Uganda: A cross sectional study. Journal of Psychosomatic Research. 2021;149:110591. doi: 10.1016/j.jpsychores.2021.110591 [DOI] [PubMed] [Google Scholar]
153.Bapolisi AM, Song SJ, Kesande C, Rukundo GZ, Ashaba S. Post-traumatic stress disorder, psychiatric comorbidities and associated factors among refugees in Nakivale camp in southwestern Uganda. BMC Psychiatry. 2020;20(1):53. doi: 10.1186/s12888-020-2480-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
154.Forry JB, Ashaba S, Rukundo GZ. Prevalence and associated factors of mental disorders among prisoners in Mbarara municipality, southwestern Uganda: A cross-sectional study. BMC Psychiatry. 2019;19(1):178. doi: 10.1186/s12888-019-2167-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
155.Seffren V, Familiar I, Murray SM, Augustinavicius J, Boivin MJ, Nakasujja N, et al. Association between coping strategies, social support, and depression and anxiety symptoms among rural Ugandan women living with HIV/AIDS. AIDS Care. 2018;30(7):888–95. doi: 10.1080/09540121.2018.1441969 [DOI] [PMC free article] [PubMed] [Google Scholar]
156.Kuteesa MO, Weiss HA, Cook S, Seeley J, Ssentongo JN, Kizindo R, et al. Epidemiology of alcohol misuse and illicit drug use among young people aged 15–24 years in fishing communities in Uganda. International Journal of Enviromental Research and Public Health 2020;17(7):2401. doi: 10.3390/ijerph17072401 [DOI] [PMC free article] [PubMed] [Google Scholar]
157.Nabunya P, Damulira C, Byansi W, Muwanga J, Bahar OS, Namuwonge F, et al. Prevalence and correlates of depressive symptoms among high school adolescent girls in southern Uganda. BMC Public Health. 2020;20(1):1792. doi: 10.1186/s12889-020-09937-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
158.Arach AAO, Nakasujja N, Nankabirwa V, Ndeezi G, Kiguli J, Mukunya D, et al. Perinatal death triples the prevalence of postpartum depression among women in Northern Uganda: A community-based cross-sectional study. PloS One. 2020;15(10):e0240409. doi: 10.1371/journal.pone.0240409 [DOI] [PMC free article] [PubMed] [Google Scholar]
159.Logie CH, Okumu M, Mwima S, Hakiza R, Chemutai D, Kyambadde P. Contextual factors associated with depression among urban refugee and displaced youth in Kampala, Uganda: findings from a cross-sectional study. Conflict and Health. 2020;14(1):45. doi: 10.1186/s13031-020-00289-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
160.Musinguzi K, Obuku A, Nakasujja N, Birabwa H, Nakku J, Levin J, et al. Association between major depressive disorder and pro-inflammatory cytokines and acute phase proteins among HIV-1 positive patients in Uganda. BMC Immunology. 2018;19(1):1. doi: 10.1186/s12865-017-0239-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
161.Muliira JK, Kizza IB. The other untold burden of cancer in sub-Saharan Africa: Anxiety and depressive symptoms among family caregivers of adult cancer patients. International Journal of Africa Nursing Sciences. 2019;11:100166. doi: 10.1016/j.ijans.2019.100166 [DOI] [Google Scholar]
162.Misghinna TK, Nakimuli-Mpungu E, Abbo C, Kinyanda E,. Major depressive disorder and associated factors among adult refugees attending a refugee center in Kampala, Uganda. African Journal of Traumatic Stress. 2020;5(1):1–21. [Google Scholar]
163.Boduszek D, Debowska A, Ochen EA, Fray C, Nanfuka EK, Powell-Booth K, et al. Prevalence and correlates of non-suicidal self-injury, suicidal ideation, and suicide attempt among children and adolescents: Findings from Uganda and Jamaica. Journal of Affective Disorders. 2021;283:172–8. doi: 10.1016/j.jad.2021.01.063 [DOI] [PubMed] [Google Scholar]
164.Kyohangirwe L, Okello E, Namuli JD, Ndeezi G, Kinyanda E. Prevalence and factors associated with major depressive disorder among adolescents attending a primary care facility in Kampala, Uganda. Tropical Doctor. 2020;50(1):30–6. doi: 10.1177/0049475519879586 [DOI] [PubMed] [Google Scholar]
165.Olum R, Nakwagala FN, Odokonyero R. Prevalence and factors associated with depression among medical students at Makerere university, Uganda. Advance in Medical Education and Practice. 2020;11:853–60. doi: 10.2147/AMEP.S278841 [DOI] [PMC free article] [PubMed] [Google Scholar]
166.Mootz JJ, Muhanguzi F, Greenfield B, Gill M, Gonzalez MB, Panko P, et al. Armed conflict, intimate partner violence, and mental distress of women in Northeastern Uganda: A mixed methods study. Psychology of Women Quarterly. 2019;43(4):457–71. doi: 10.1177/0361684319864366 [DOI] [PMC free article] [PubMed] [Google Scholar]
167.Ssebunnya J, Medhin G, Kangere S, Kigozi F, Nakku J, Lund C. Prevalence, correlates and help-seeking behaviour for depressive symptoms in rural Uganda: a population-based survey. Global Mental Health. 2019;6:e27. Epub 2019/12/07. doi: 10.1017/gmh.2019.25 [DOI] [PMC free article] [PubMed] [Google Scholar]
168.Kabunga A, Anyayo L. Depression and associated factors among refugees amidst covid-19 in Nakivale refugee camp in Uganda. Journal of Neurology Research Reviews & Reports. 2021;3:1–5. doi: 10.47363/JNRRR/2021(3)132 [DOI] [Google Scholar]
169.Kabunga A, Nambozo S. Prevalence and factors associated with depression among out-of-school adolescents in Ayere village, Northern Uganda. International Journal of Academic Research in Progressive Education and Development. 2021;10. doi: 10.6007/IJARPED/v10-i1/9080 [DOI] [Google Scholar]
170.Ouma S, Tumwesigye NM, Ndejjo R, Abbo C. Prevalence and factors associated with major depression among female sex workers in post-conflict Gulu district: A cross-sectional study. BMC Public Health. 2021;21(1):1134. doi: 10.1186/s12889-021-11207-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
171.Mahmud M, Riley E. Household response to an extreme shock: Evidence on the immediate impact of the Covid-19 lockdown on economic outcomes and well-being in rural Uganda. World Development. 2021;140:105318. doi: 10.1016/j.worlddev.2020.105318 [DOI] [PMC free article] [PubMed] [Google Scholar]
172.Blackmore R, Boyle JA, Fazel M, Ranasinha S, Gray KM, Fitzgerald G, et al. The prevalence of mental illness in refugees and asylum seekers: A systematic review and meta-analysis. PloS Medicine. 2020;17(9):e1003337. doi: 10.1371/journal.pmed.1003337 [DOI] [PMC free article] [PubMed] [Google Scholar]
173.Morina N, Stam K, Pollet TV, Priebe S. Prevalence of depression and posttraumatic stress disorder in adult civilian survivors of war who stay in war-afflicted regions. A systematic review and meta-analysis of epidemiological studies. Journal of Affective Disorders. 2018;239:328–38. doi: 10.1016/j.jad.2018.07.027 [DOI] [PubMed] [Google Scholar]
174.Nanni MG, Caruso R, Mitchell AJ, Meggiolaro E, Grassi L. Depression in HIV infected patients: A review. Current Psychiatry Report. 2015;17(1):530. doi: 10.1007/s11920-014-0530-4 [DOI] [PubMed] [Google Scholar]
175.Del-Pino-Casado R, Rodríguez Cardosa M, López-Martínez C, Orgeta V. The association between subjective caregiver burden and depressive symptoms in carers of older relatives: A systematic review and meta-analysis. Plos One. 2019;14(5):e0217648. doi: 10.1371/journal.pone.0217648 [DOI] [PMC free article] [PubMed] [Google Scholar]
176.Al Mamun F, Hosen I, Misti JM, Kaggwa MM, Mamun MA. Mental disorders of Bangladeshi students during the COVID-19 pandemic: A systematic review. Psychological Research and Behavior Management. 2021;14:645–54. doi: 10.2147/PRBM.S315961 [DOI] [PMC free article] [PubMed] [Google Scholar]
177.Vithalani J, Herreros-Villanueva M. HIV epidemiology in Uganda: Survey based on age, gender, number of sexual partners and frequency of testing. African Health Sciences. 2018;18(3):523–30. doi: 10.4314/ahs.v18i3.8 [DOI] [PMC free article] [PubMed] [Google Scholar]
178.Lundberg P, Nakasujja N, Musisi S, Thorson AE, Cantor-Graae E, Allebeck P. HIV prevalence in persons with severe mental illness in Uganda: a cross-sectional hospital-based study. International Journal of Mental Health Systems. 2013;7(1):20. doi: 10.1186/1752-4458-7-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
179.Rezaei S, Ahmadi S, Rahmati J, Hosseinifard H, Dehnad A, Aryankhesal A, et al. Global prevalence of depression in HIV/AIDS: A systematic review and meta-analysis. BMJ Supportive & Palliative Care. 2019;9(4):404–12. doi: 10.1136/bmjspcare-2019-001952 [DOI] [PubMed] [Google Scholar]
180.Geng HM, Chuang DM, Yang F, Yang Y, Liu WM, Liu LH, et al. Prevalence and determinants of depression in caregivers of cancer patients: A systematic review and meta-analysis. Medicine. 2018;97(39):e11863. doi: 10.1097/MD.0000000000011863 [DOI] [PMC free article] [PubMed] [Google Scholar]
181.Muliira JK, Kizza IB, Nakitende G. Roles of family caregivers and perceived burden when caring for hospitalized adult cancer patients: Perspective from a low-income country. Cancer Nursing. 2019;42(3):208–17. doi: 10.1097/NCC.0000000000000591 [DOI] [PubMed] [Google Scholar]
182.Adejoh SO, Boele F, Akeju D, Dandadzi A, Nabirye E, Namisango E, et al. The role, impact, and support of informal caregivers in the delivery of palliative care for patients with advanced cancer: A multi-country qualitative study. Palliative Medicine. 2020;35(3):552–62. doi: 10.1177/0269216320974925 [DOI] [PMC free article] [PubMed] [Google Scholar]
183.Johns G, Samuel V, Freemantle L, Lewis J, Waddington L. The global prevalence of depression and anxiety among doctors during the covid-19 pandemic: Systematic review and meta-analysis. Journal of Affective Disorders. 2022;298(Pt A):431–41. doi: 10.1016/j.jad.2021.11.026 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0276552.r001

Decision Letter 0

Muhammed Elhadi

19 Jul 2022

PONE-D-22-10716Prevalence of depression in Uganda. A systematic review and meta-analysisPLOS ONE

Dear Dr. Kaggwa,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 02 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Muhammed Elhadi, MBBCh

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

4. We note that Figure 2 in your submission contain Map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

a. You may seek permission from the original copyright holder of Figure 2 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

b. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com

5. Please upload a new copy of Figure 11 as the detail is not clear. Please follow the link for more information: https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/" https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/

Additional Editor Comments :

We request that a point-by-point response letter accompanies your revised manuscript. This letter must provide a detailed response to each reviewer/editorial point raised, describing what amendments have been made to the manuscript text and where these can be found (e.g. Methods section, line 12, page 5). If you disagree with any comments raised, please provide a detailed rebuttal to help explain and justify your decision.

The way of presenting data is needing major and detailed revision , more clarity on the analysis process can be addressed in a revision.

There are many formatting issues that may need to be addressed (many spacing issues). The Figures seem to also need to be higher quality.

The reporting of results and discussion requires revisions to be more succinct and concise. Some reviewers were suggesting rejection or major revision.

Additional comments are described in the feedback to the authors.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Partly

Reviewer #7: Yes

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #10: Yes

Reviewer #11: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: I Don't Know

Reviewer #6: I Don't Know

Reviewer #7: Yes

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #10: Yes

Reviewer #11: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: No

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #10: Yes

Reviewer #11: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #10: Yes

Reviewer #11: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study aimed to determine the pooled prevalence of depression in Uganda and determine the prevalence of depression among various study populations in the country. The strength of this study was to conduct analysis using a rigorous method of systematic reviews. However, there were some concerns in this study.

First, in the Abstract, the authors had better adhere to PRISMA 2020 for Abstract. For example, they had better describe the following information in the abstract: inclusion criteria and exclusion criteria for the review, the date when each database was last searched, the methods used to assess the risk of bias in the included studies, the methods used to present and synthesize results, the number of participants for the prevalence of depression in refugees, war victims, individuals living with HIV, postpartum or pregnant mothers, university students, children and adolescents, caregivers of patients, a brief summary of the limitations of the evidence included in the review (e.g. study risk of bias, inconsistency and imprecision), the primary source of funding for the review, the register name and registration number.

Second, in Figure 1, they had better report the correct number for records. For example, the sum of records for databases is not n=513 whereas they reported the sum of records for databases is n=513. Moreover, they reported that they excluded 103 records from 216 records, then 121 records remained.

Third, they had better describe the results of each nine-item for JBI checklist in results. They had better describe the discussion for the results of each item for the JBI checklist in the Discussion.

Reviewer #2: Dear authors,

Thanks for submitting your manuscript “Prevalence of depression in Uganda. A systematic review and meta-analysis” to be considered for publication in PlosONE.

Very well written and interesting paper, methodologically strong and relevant for the journal and the field of global mental health. There are only minor inaccuracies which I think should be addressed before publication. Please, refer to my detailed comments below.

Regards

Table 1. May I ask why the articles have not been reported in alphabetical order, or what was the sorting rule used? It looks like year (oldest -> newest), but year of data collection is missing for some studies. I am not asking to change it, just to clarify, maybe in Table caption or as a note?

Moreover, I think it would be good to add “(years)” to Age, as you did for the other variable and, maybe, instead of n you could report % Female?

At page 25, L176, you reported p<0.001, is this in relation to heterogeneity? This also applies to page 26, L206 and on all other occasions. As before, maybe it would be good to state it clearly in the text, e.g., by using a semi-colon before I2?

Please, correct “we” to “were” at L176.

There is something weird in the PDF at P26, L182, could you make sure, in your doc version, that all references in relation to “Depression sub-section of 182 the Hopkins Symptom Checklist (DHSCL)..” have been included?

Considering you detected publication bias, would you consider using trim and fill analysis to estimate the number of missing studies? Good that you carried out a sensitivity analysis, at L209.

P28, could you please consider adding a statement of the results presented in Table 2, in relation to Tools used?

Could you please consider improving readability in sentence “The prevalence among the elderly [140], 288 tuberculosis patients [111], post tuberculosis lung diseases [91], cancer [11], post-stroke 289 [86], physically ill [117], diabetes mellitus [119], sickle cell disease [43], and rheumatoid 290 arthritis [27] were 18%, 23.7%, 24%, 26%, 31.5%, 33.7%, 34.8%, 68.2%, and 70.8%, 291 respectively.”? Maybe adding each percentage close to the group label?

Previous systematic review and MA (166) is only cited in the discussion, but I think it would be good to mention, in the introduction, how yours is different from previous SR/MA already published on the topic.

I have particularly appreciated the discussion on factors potentially leading to increased depression rates in Uganda, compared to other countries.

Reviewer #3: Recently, Opio JN et al. performed a meta-analysis to determine the prevalence of mental disorders in Uganda, including major depressive disorder. However, to date, the present work is the first meta-analysis specifically addressing the prevalence of depression in Uganda, with a very large sample size and across several special populations (caregivers, individuals living with HIV or other chronic disease, etc.). The statistical analysis was correctly performed, as well as the risk of bias and the quality of the included studies were correctly assessed.

Reviewer #4: This systematic review reports the prevalence of depression in Uganda. This review is written according to the PRISM guidelines. The authors know the limitations on the judge of depression. It is important to be interpreted.

Reviewer #5: The authors aimed to assess the pooled prevalence of depression and the prevalence of depression among various study populations in Uganda. The study gives updates on the prevalence of depression in the country and the distribution of depression by population and region. The manuscript was well-written. There are some issues on eligibility criteria, discussion of the high prevalence of depression in this study, and limitations that need to be addressed. Below are more specific comments by section:

2.2 Inclusion criteria and exclusion criteria:

- If there were other pre-specified of eligibility criteria, including language (e.g. English, Luganda), definition of depression (e.g. depressed mood, depressive disorder), assessment tools for depression (e.g. type, cutoff point), minimum sample size, study quality (e.g. JBI score of 4 or more), they should be mentioned in this section.

- Having information on prevalence of depression should be mentioned as one of the inclusion criteria.

4. Discussion:

(Line 372-387) The finding that the pooled prevalence of depression in Uganda from the present study is higher than that from the previous study might be due to the difference in assessment tools for depression between the two studies, besides the effects of COVID-19 pandemic. Specifically, the previous review by Opio used more strict criteria for depressive disorder. Most of the included studies in Opio’s review used M.I.N.I., a structural interview, to assess depressive disorder.

4.1 Limitations:

- (Lines 433-435) How “high levels of heterogeneity due to the depression assessment tools and the period of the study” affect the results/implementation of the results. This should be explained.

- (Lines 436-437) “Second, the included studies were all prone to recall biases, since all the data were self-report.” I am not sure if this statement is correct. For example, M.I.N.I. is a structured interview, not a self-report measure.

- (Lines 437-439) How “a large majority of the country was still not represented” affect the results/implementation of the results. This should be explained.

- Some relevant literature in other languages (e.g. Luganda) or from other databases (e.g. EMBASE, CINAHL) might be missed. This should be mentioned as a limitation.

- Some relevant terms for depression, such as mental health, psychological disorder/problem, and mood, were not included in the search strategy. Therefore, some relevant literature might be missed. This should be mentioned as a limitation.

Reviewer #6: The present paper presents a meta-analysis of studies measuring depression among people in Uganda. The pooled prevalence was quite high (around 35%), with even higher rates during the COVID pandemic and in the refugee’s subpopulation.

The manuscript is overall well written (except maybe for the discussion part where more wording errors were found) and is focused on a very important issue. It is great that the authors focused on Uganda because as a low-income country, it could bring results that could apply to and inform treatment in similar countries, when most of medical studies are restrained to high income countries. I think it is also very important that this kind of data is published in generalist journals such as PLOS One and not only in regional journals, in order to bring these issues to the world population.

I do have some methodological issues though that need addressing.

Major points

1. Line 205-206: “The pooled prevalence of depression was 30.2%”

This number is very different from a simple prevalence computation (27989/123859 * 100 = 22.6%, which is already huge actually). Pardon my ignorance but please provide more background for the readers to understand how you computed the pooled prevalence (I guess there is a specific weight attributed to each study, but in what way?). It would be useful to have this information in the "Data synthesis and analysis" section. Maybe the weight was attributed in part due to quality scores of the studies that were given by the authors when reviewing the articles? If this is the case it needs to be specified.

2. Line 206-207: “The funnel plot was asymmetrical”

Very asymmetrical indeed… Are you sure there was no mistake made in plotting the funnel plot? Usually, it is the low powered studies that are lower on the y axis and that are more biased towards the extremes on the x-axis. Here, all studies are at the top of the y-axis, and there are almost all highly biased toward the right. All studies but 4 exceed the triangular region, which is crazy! Overall, the data points on this plot behave in a very strange way and data should be checked again for errors. If the funnel plot is correct, how can we be confident in the pooled valence estimation with such an apparent publication bias? The heterogeneity is also very high. Can the authors identify a subgroup of studies that are maybe less biased and more coherent? If the funnel plot is correct, the publication bias must absolutely be discussed in the discussion section. Is there evidence that publication biases are higher in low-income countries for example? What could be the reason?

3. Line 273: “the estimated slope from Egger’s test was 6.12 (SE=0.598, p<0.001).”

Is it normal for this value to be the same for every subgroup analysis? Is there a mistake? The value was the same for the complete pool of studies as well. Why not report this value once if it's not a mistake ? Please double check these values or provide an explanation for why these numbers are all the same.

Minor points

1. Line 364: “The prevalence of depression ranged between 2.0% among individuals in a fishing community [83] and 68% among national humanitarian aid workers”

Are humanitarian aid workers really part of the general population? It seems to me that it's quite a stressful and special status to be part of the national humanitarian aid but maybe I have a misconception of this status. Are they confronted to stressful events? I ask this question also because the depression prevalence for this subgroup is so high compared to the fishing community.

2. Line 430 and throughout the text : “This difference may be because the present review included more studies than the former.”

I think that this assertion is uninformative. Adding more studies could either lower or increase the prevalence. They could also have no impact on the former prevalence. A more informative assertion could be that adding more studies brought the pooled prevalence closer to the "true" value, the one that could be measured in the complete population, since you are increasing the sample size (and then you are reducing the discrepancy between population and sample). It could also be due to the fact that something happened in the country in 2018 or later that changed how depression affect people in Uganda (the COVID pandemic? Another factor?).

3. Line 436 : “Future researchers should conduct reviews of studies with fewer variations especially in relation to the tools used to assess depression”.

I completely agree with this, but maybe the authors could try to do this work themself, at least in part? Maybe with a subanalysis focused on one tool that was used in the majority of the studies (for example the PHQ or the DHSCL?). It could be very interesting for readers if you could show for instance that when you take only the PHQ tool, heterogeneity is lowered drastically. This could lead to a straightforward advice of systematically use this tool for all future studies in Uganda in order to increase comparability between studies and then allow for higher quality meta-analyses? This is merely a suggestion, but it could really improve the impact of your paper, since you already collected all these data.

Reviewer #7: This is a systematic review and meta-analysis to estimate the prevalence of depression in Uganda. As the authors wrote, it would be difficult to conduct a census or cohort to estimate prevalence in low-income countries, such as Uganda. The strengths of the study include its methodology. The manuscript meets the high standard of systematic review and meta-analysis. I have a superficial comment on wording.

I thought it problematic to state “Prevalence of depression in Uganda” because they included studies with people with specific characteristics, such as HIV positive. “3.3.10. Depression among the general population” would not show the prevalence of depression in the general population given that national humanitarian aid workers had substantially high depression prevalence.

Reviewer #8: Thank you for the opportunity to review the paper entitled “Prevalence of depression in Uganda. A systematic review and meta-analysis”. Across 127 identified studies, the pooled estimate of depression was 30% in Uganda, somewhat larger than previously reported in systematic reviews (27%). Prevalence of depression was highest in people who were refugees, war victims, post-partem, living with HIV, or young in age. Prevalence of depression was also higher after the COVID-19 pandemic (48%) compared to before (29%). This paper is very well written and reemphasizes the lack of mental health needs being addressed in at-risk groups within Uganda. The methods used to conduct the systematic review and meta-analysis were appropriate and contribute to a thorough and objective evaluation of the findings across the literature. I have a couple of comments to address.

For the analyses pre and post COVID-19 pandemic, are there any third factors that could explain higher rates of depression (differences in sample characteristics between studies assessed at either period). How confident are the authors that this effect is due to the COVID-19 pandemic rather than study differences? This should be addressed to help show readers understand how best to trust such differences.

The authors noted significant evidence of publication bias and small sample study bias in many of their analyses. I commend the authors on their close examination of potential bias. I hope the authors could speak more to this in the limitations part of the discussion.

The authors bring up the high level of inconsistency across studies (Higgins I2) in the limitations section. In the limitations section, the authors should highlight the use of random effect meta-analyses which are preferred in such cases (as opposed to fixed effect meta-analyses). Also, did the authors consider meta-regressions to assess study-level predictors of variation (i.e., assessment type, population type, age of sample, gender ratio, etc.)? This could directly test many of these hypothesized contributors to variation. I think if the authors were able, a meta-regression across all the selected studies would be worthwhile.

If the authors are referring to gender (a social construct), please refer to women/men rather than female and male (female/male refers to sex).

Reviewer #9: Comments to the author

I would like to thank the editor for giving me the chance to act as an academic reviewer of this interesting work from Uganda. I am really impressed by this paper starting from its title to the way it is presented. I will have few issues as I have pointed below to be considered by the authors.

The authors should use the PRISM-P instead of the PRISMA guideline for the systematic review and meta-analysis.

The authors reported as they excluded those observational studies which were included in previous systematic review studies in the PRISMA flow diagram. This is against your justification of this study which is to determine the comprehensive pooled prevalence of depression in Uganda. I would like to the author to strongly consider this and explain how the inclusion of these studies will have a problem in their study result.

The authors reported as they have conducted sensitivity analysis since there was heterogeneity between studies. What sources of heterogeneity were identified as a result of your sensitivity analysis? Explain and better to include in the manuscript

The authors added too many figures in this paper; I would like to advise them to minimize the number of tables and figures.

Similarly, the authors used too many references. Better to limit by avoiding the outdated references used.

The discussion section is interesting but it is satisfactory as of using 173 references. Your study can best be supported by more scientific evidences in addition to your presented evidences. I strongly advice to make it stronger.

Reviewer #10: In this systematic review and meta-analysis, authors sought to determine prevalence of depression across various study populations in Uganda.

While the objectives are clearly stated and a clinically relevant question was included, it would be useful to indicate specific and focused questions regarding the subgroups of the populations being examined.

A comprehensive literature search was conducted and information sources are indicated well and search terms used seem reasonable, however it would be useful to indicate any reasonable limitations placed on the search (e.g. English language, journal, etc). Additionally, were there any attempts made at collecting unpublished data?

In terms of data abstraction, as the authors point out, the data was quite heterogenous and thus difficult to standardize—Some more details on how that translates into generalizable results would be useful. This is the biggest limitation in interpreting the results—the assessment tools in the studies may not be combinable and generalizable. However, the authors acknowledge this and have used appropriate methods to combine results and synthesize the highly heterogenous data. It would be useful to indicate if, in addition to self-report tools, there were some standard clinical interviews used to diagnose depression in the populations studied, and if any interview-based diagnosis prevalence can be obtained and compared meaningfully.

While the metanalysis is comprehensive and it provides a broader picture of prevalence of depression in various populations, it is still difficult to generalize the results to affect the clinical and treatment outcomes in a systematic manner. Future studies within these populations, as well as across wider regions in the country would be more useful in implementing treatments according to the needs (socioeconomic, cultural, refugee-status, etc).

Reviewer #11: -Thank you for the invitation to review this paper.

-Overall the article is not interesting for those who might look for data on depression at the county level within some specified population and way of analyzing it. I have a few comments as follows;

-The title needs modification, “prevalence of depression in Uganda”, in what population the review was conducted?

-Background: A systematic review and meta-analysis was carried out to determine the prevalence of depression across study populations in the country, which needs modification. Fix with your study population.

-Why do you include various populations in a single study? It is very difficult to compare unrelated studies in a single paper. Why did not you do it separately? I need sufficient reason. Some of the ideas in the paper seem comparative studies.

-I disagree with this choice of a title since it leaves out a group that is unrelated to this study and has depressive symptoms, which is a very difficult topic to declare "prevalence of depression in Uganda." Moreover, why did you decide to include only a few populations in the study? Given that the pooled prevalence of depression is (P=30%) in several included research, you cannot generalize at the national level.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Masahiro Banno

Reviewer #2: Yes: Alessio Bellato

Reviewer #3: Yes: martina billeci

Reviewer #4: No

Reviewer #5: No

Reviewer #6: Yes: Matias Baltazar

Reviewer #7: No

Reviewer #8: Yes: Tyler Reed Bell

Reviewer #9: No

Reviewer #10: No

Reviewer #11: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: Review report.docx

Click here for additional data file.^{(13KB, docx)}

PLoS One. 2022 Oct 20;17(10):e0276552. doi: 10.1371/journal.pone.0276552.r002

Author response to Decision Letter 0

22 Aug 2022

Reference: PONE-D-22-10716, Title: Prevalence of depression in Uganda. A systematic review and meta-analysis.

Journal: PLOS ONE

Journal requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We have added a figshare DOI (https://doi.org/10.6084/m9.figshare.19579096.v1) for the data used in analysis.

Response: This has been rectified based on the guidelines.

Response: This map was an illustration of the study data. There are no copyright issues in relation to this illustration.

Response: This has been provided as requested.

Additional editors’ comments

Comment: We request that a point-by-point response letter accompanies your revised manuscript. This letter must provide a detailed response to each reviewer/editorial point raised, describing what amendments have been made to the manuscript text and where these can be found (e.g. Methods section, line 12, page 5). If you disagree with any comments raised, please provide a detailed rebuttal to help explain and justify your decision.

Response: Our detailed responses have been provided below as requested.

Comment: The way of presenting data is needing major and detailed revision, more clarity on the analysis process can be addressed in a revision.

Response: We have made significant changes to the analysis section and the data presentation has been improved based on the multiple reviewers’ suggestions.

Comment: There are many formatting issues that may need to be addressed (many spacing issues). The Figures seem to also need to be higher quality.

Response: We have addressed the formatting issues in the revised version and the figures are now in higher quality.

Comment: The reporting of results and discussion requires revisions to be more succinct and concise. Some reviewers were suggesting rejection or major revision.

Response: Thanks to the many reviewers’ comments, we have revised the sections based on their comments and have been more succinct and concise.

Comments from reviewers

Response to the Reviewer 1’s Comments

Comment: First, in the Abstract, the authors had better adhere to PRISMA 2020 for Abstract. For example, they had better describe the following information in the abstract: inclusion criteria and exclusion criteria for the review, the date when each database was last searched, the methods used to assess the risk of bias in the included studies, the methods used to present and synthesize results, the number of participants for the prevalence of depression in refugees, war victims, individuals living with HIV, postpartum or pregnant mothers, university students, children and adolescents, caregivers of patients, a brief summary of the limitations of the evidence included in the review (e.g. study risk of bias, inconsistency and imprecision), the primary source of funding for the review, the register name and registration number.

Response: This has been updated in the revised manuscript as suggested. Abstract section, Page 3, lines 18 to 43.

Comment: Second, in Figure 1, they had better report the correct number for records. For example, the sum of records for databases is not n=513 whereas they reported the sum of records for databases is n=513. Moreover, they reported that they excluded 103 records from 216 records, then 121 records remained.

Response: The most recent version with the correct number for records has now been uploaded. Fig 1.

Comment: Third, they had better describe the results of each nine-item for JBI checklist in results. They had better describe the discussion for the results of each item for the JBI checklist in the Discussion.

Response: We now have a table listing the total scores of the JBI from the different studies. We used a cut-off (4) for studies with possible bias and poor quality. With 16% (21/127) of the studies scoring less than 9, we would prefer not to present a large table of the 127 papers showing their individual scores. However, the scores are interpreted in the revised discussion (see limitation section, page 39, lines 487 to 496).

Response to the Reviewer 2’s Comments

Comment: Table 1. May I ask why the articles have not been reported in alphabetical order, or what was the sorting rule used? It looks like year (oldest -> newest), but year of data collection is missing for some studies. I am not asking to change it, just to clarify, maybe in Table caption or as a note?

Response: Year of data collection was the first criterion considered for the arrangement but for those without a year of data collection, we used the year the paper was first submitted to a journal to estimate the year of data collection based on the period of data collection. Table 1

Comment: Moreover, I think it would be good to add “(years)” to Age, as you did for the other variable and, maybe, instead of n you could report % Female?

Response: In the revised manuscript, ‘Years’ has been added as suggested, but for gender, we used absolute numbers, that is, female (male) numbers. Table 1.

Comment: At page 25, L176, you reported p<0.001, is this in relation to heterogeneity? This also applies to page 26, L206 and on all other occasions. As before, maybe it would be good to state it clearly in the text, e.g., by using a semi-colon before I2?

Response: This has been rectified as suggested in the revised manuscript.

Comment: Please, correct “we” to “were” at L176

Response: This has been rectified in the revised manuscript as suggested.

Comment: There is something weird in the PDF at P26, L182, could you make sure, in your doc version, that all references in relation to “Depression sub-section of 182 the Hopkins Symptom Checklist (DHSCL).” have been included?

Response: This has been rectified in the revised manuscript. Thanks for spotting this

Comment: Considering you detected publication bias, would you consider using trim and fill analysis to estimate the number of missing studies? Good that you carried out a sensitivity analysis, at L209.

Response: We have now added trim and fill analysis findings to the revised Results as suggested. Thanks for the positive feedback concerning the sensitivity analysis.

Comment: P28, could you please consider adding a statement of the results presented in Table 2, in relation to Tools used?

Response: As requested, we have now added information describing Table 2. Results section, Page 29, lines 238 and 245.

Comment: Could you please consider improving readability in sentence “The prevalence among the elderly [140], 288 tuberculosis patients [111], post tuberculosis lung diseases [91], cancer [11], post-stroke 289 [86], physically ill [117], diabetes mellitus [119], sickle cell disease [43], and rheumatoid 290 arthritis [27] were 18%, 23.7%, 24%, 26%, 31.5%, 33.7%, 34.8%, 68.2%, and 70.8%, 291 respectively.”? Maybe adding each percentage close to the group label?

Response: This has now been rewritten as suggested in the revised manuscript.

Comment: Previous systematic review and MA (166) is only cited in the discussion, but I think it would be good to mention, in the introduction, how yours is different from previous SR/MA already published on the topic.

Response: We have now added information relating to this study in the revised Introduction.

Comment: I have particularly appreciated the discussion on factors potentially leading to increased depression rates in Uganda, compared to other countries.

Response: Thank you for the positive feedback.

Response to the Reviewer 3’s Comments

Comment: Recently, Opio JN et al. performed a meta-analysis to determine the prevalence of mental disorders in Uganda, including major depressive disorder. However, to date, the present work is the first meta-analysis specifically addressing the prevalence of depression in Uganda, with a very large sample size and across several special populations (caregivers, individuals living with HIV or other chronic disease, etc.). The statistical analysis was correctly performed, as well as the risk of bias and the quality of the included studies were correctly assessed.

Response: Thank you for the positive feedback

Response to the Reviewer 4’s Comments

Comment: Reviewer #4: This systematic review reports the prevalence of depression in Uganda. This review is written according to the PRISM guidelines. The authors know the limitations on the judge of depression. It is important to be interpreted.

Response: Thank you for the positive feedback.

Response to the Reviewer 5’s Comments

Comment: 2.2 Inclusion criteria and exclusion criteria: - If there were other pre-specified of eligibility criteria, including language (e.g. English, Luganda), definition of depression (e.g. depressed mood, depressive disorder), assessment tools for depression (e.g. type, cutoff point), minimum sample size, study quality (e.g. JBI score of 4 or more), they should be mentioned in this section.

Response: This has now been added in the revised manuscript as suggested. Methods section, Page 6, lines 112 to 118.

Comment: - Having information on prevalence of depression should be mentioned as one of the inclusion criteria.

Response: This has now been added in the revised manuscript as suggested. Methods section, Page 6, line 114.

Comment: 4.0 Discussion: (Line 372-387) The finding that the pooled prevalence of depression in Uganda from the present study is higher than that from the previous study might be due to the difference in assessment tools for depression between the two studies, besides the effects of COVID-19 pandemic. Specifically, the previous review by Opio used more strict criteria for depressive disorder. Most of the included studies in Opio’s review used M.I.N.I., a structural interview, to assess depressive disorder.

Response: We included all the studies present in Opio’s review. We strongly believe (as explained in the manuscript) that the difference could be due to the additional papers added. However, we have now added the discussion of study tools in a new paragraph concerning the causes of heterogeneity. Discussion section, page 39, lines 472 to 485.

Comment: (Lines 433-435) How “high levels of heterogeneity due to the depression assessment tools and the period of the study” affect the results/implementation of the results. This should be explained.

Responses: This has now been expanded in the revised manuscript to make it clearer for readers. Discussion section, page 39, lines 472 to 485.

Comment: (Lines 436-437) “Second, the included studies were all prone to recall biases, since all the data were self-report.” I am not sure if this statement is correct. For example, M.I.N.I. is a structured interview, not a self-report measure.

Response: Thank you for spotting this error. We have now rectified the statement in the revised manuscript. Limitation section, page 40, lines 496 to 497.

Comment: Some relevant literature in other languages (e.g. Luganda) or from other databases (e.g. EMBASE, CINAHL) might be missed. This should be mentioned as a limitation.

Response: This has been added as suggested. However, there were no academic publications written in Luganda. Limitation section, page 40, lines 501 to 504.

Comment: Some relevant terms for depression, such as mental health, psychological disorder/problem, and mood, were not included in the search strategy. Therefore, some relevant literature might be missed. This should be mentioned as a limitation.

Response: This has been added to the limitations in the revised manuscript as suggested. Limitation section, page 40, lines 504 to 505

Response to the Reviewer 6’s Comments

Comment: Line 205-206: “The pooled prevalence of depression was 30.2%”

Response: Thanks for the comment. A pooled prevalence is weighted based on several factors such as sample size of the individual included studies. It will rarely be as close to simple computed prevalence. We have now added a reference with information about the mathematical basis of meta-analysis pooled prevalence in the revised manuscript to guide readers. Reference 47.

Comment: Line 206-207: “The funnel plot was asymmetrical”. Very asymmetrical indeed… Are you sure there was no mistake made in plotting the funnel plot? Usually, it is the low powered studies that are lower on the y axis and that are more biased towards the extremes on the x-axis. Here, all studies are at the top of the y-axis, and there are almost all highly biased toward the right. All studies but 4 exceed the triangular region, which is crazy! Overall, the data points on this plot behave in a very strange way and data should be checked again for errors. If the funnel plot is correct, how can we be confident in the pooled valence estimation with such an apparent publication bias? The heterogeneity is also very high. Can the authors identify a subgroup of studies that are maybe less biased and more coherent? If the funnel plot is correct, the publication bias must absolutely be discussed in the discussion section. Is there evidence that publication biases are higher in low-income countries for example? What could be the reason?

Response: In addition to the sensitivity analysis we performed, we carried out further tests to elaborate on the publication bias of the included studies. The computation of all the images in this study are through a computer program and we believe there was no error in the output (they remained the same following rerunning the commands). The whole paper was based on a subgroup analysis and we also provided a prevalence rate following the sensitivity analysis, Figure 3. We have now added information in the revised Discussion to also explain the possible publication biases.

Comment: Line 273: “the estimated slope from Egger’s test was 6.12 (SE=0.598, p<0.001).”

Is it normal for this value to be the same for every subgroup analysis? Is there a mistake? The value was the same for the complete pool of studies as well. Why not report this value once if it's not a mistake? Please double check these values or provide an explanation for why these numbers are all the same.

Response: This has been double-checked and the value has been recalculated using STATA 17.

Comment: Line 364: “The prevalence of depression ranged between 2.0% among individuals in a fishing community [83] and 68% among national humanitarian aid workers”. Are humanitarian aid workers really part of the general population? It seems to me that it's quite a stressful and special status to be part of the national humanitarian aid but maybe I have a misconception of this status. Are they confronted to stressful events? I ask this question also because the depression prevalence for this subgroup is so high compared to the fishing community.

Response: They are indeed a special group, but we consider them as the general population because these individuals were all workers and return to their normal jobs following the activity. However, for clarity, we have now added a statement concerning our classification's limitations since different reasons may lead to these classifications. Limitation section, page 40, lines 508 to 513.

Comment: Line 430 and throughout the text: “This difference may be because the present review included more studies than the former.” I think that this assertion is uninformative. Adding more studies could either lower or increase the prevalence. They could also have no impact on the former prevalence. A more informative assertion could be that adding more studies brought the pooled prevalence closer to the "true" value, the one that could be measured in the complete population, since you are increasing the sample size (and then you are reducing the discrepancy between population and sample). It could also be due to the fact that something happened in the country in 2018 or later that changed how depression affect people in Uganda (the COVID pandemic? Another factor?).

Response: This has now been amended in the revised manuscript as suggested. Discussion section, page 39, lines 472 to 485.

Comment: Line 436: “Future researchers should conduct reviews of studies with fewer variations especially in relation to the tools used to assess depression”. I completely agree with this, but maybe the authors could try to do this work themself, at least in part? Maybe with a subanalysis focused on one tool that was used in the majority of the studies (for example the PHQ or the DHSCL?). It could be very interesting for readers if you could show for instance that when you take only the PHQ tool, heterogeneity is lowered drastically. This could lead to a straightforward advice of systematically use this tool for all future studies in Uganda in order to increase comparability between studies and then allow for higher quality meta-analyses? This is merely a suggestion, but it could really improve the impact of your paper, since you already collected all these data.

Response: This subgroup analysis was provided in the manuscript (Table 2) but all the most commonly used instruments had high levels of heterogeneity (all with I2 above 99%). We have not gone further to recommend any particular instrument for future studies. However, we have recommended the commonly used tools to be used in future studies to increase comparability between studies and allow for higher quality meta-analyses. Limitation section, page 40, lines 492 to 494.

Response to the Reviewer 7’s Comments

Comment: I thought it problematic to state “Prevalence of depression in Uganda” because they included studies with people with specific characteristics, such as HIV positive. “3.3.10. Depression among the general population” would not show the prevalence of depression in the general population given that national humanitarian aid workers had substantially high depression prevalence.

Response: We used a general description to represent all the study groups and tried to show the differences by showing the prevalence rate in the different special groups. For the case of adding humanitarian workers to the general population, we have now added this as a limitation since different individuals will have different interpretations of this finding. Limitation section, page 40, lines 510 to 512.

Response to the Reviewer 8’s Comments

Comment: For the analyses pre and post COVID-19 pandemic, are there any third factors that could explain higher rates of depression (differences in sample characteristics between studies assessed at either period). How confident are the authors that this effect is due to the COVID-19 pandemic rather than study differences? This should be addressed to help show readers understand how best to trust such differences.

Response: We have now added this perspective to the revised Discussion of the findings. Our systematic review included all the studies included in the previous review.

Comment: The authors noted significant evidence of publication bias and small sample study bias in many of their analyses. I commend the authors on their close examination of potential bias. I hope the authors could speak more to this in the limitations part of the discussion.

Response: We have now provided a more elaborate discussion on the publication bias of the included studies in the revised limitation section. Page 40, lines 487 to 496.

Comment: The authors bring up the high level of inconsistency across studies (Higgins I2) in the limitations section. In the limitations section, the authors should highlight the use of random effect meta-analyses which are preferred in such cases (as opposed to fixed effect meta-analyses). Also, did the authors consider meta-regressions to assess study-level predictors of variation (i.e., assessment type, population type, age of sample, gender ratio, etc.)? This could directly test many of these hypothesized contributors to variation. I think if the authors were able, a meta-regression across all the selected studies would be worthwhile.

Response: Our meta-analysis was based on the preferred random effect model (limitation section, page 40, line 489). Also, a meta-regression has now been added to the revised manuscript as suggested.

Comment: If the authors are referring to gender (a social construct), please refer to women/men rather than female and male (female/male refers to sex).

Response: Different studies were not consistent concerning gender or sex. We did not generalize the terms used but collected the data presented, capturing males and females as shown in Table 1.

Response to the Reviewer 9’s Comments

Comment: The authors should use the PRISM-P instead of the PRISMA guideline for the systematic review and meta-analysis.

Response: Thank you for your comment but our study was not a protocol.

Comment: The authors reported as they excluded those observational studies which were included in previous systematic review studies in the PRISMA flow diagram. This is against your justification of this study which is to determine the comprehensive pooled prevalence of depression in Uganda. I would like to the author to strongly consider this and explain how the inclusion of these studies will have a problem in their study result.

Response: These studies were included in our study. This is clearly shown in the PRISMA flow chart. Fig 1.

Comment: The authors reported as they have conducted sensitivity analysis since there was heterogeneity between studies. What sources of heterogeneity were identified as a result of your sensitivity analysis? Explain and better to include in the manuscript

Response: In the revised manuscript, we added further analysis to assess for publication bias and elaborated on the causes of heterogeneity.

Comment: The authors added too many figures in this paper; I would like to advise them to minimize the number of tables and figures.

Response: Based on the comments from other reviewers, we have retained most of the figures to clearly show the readers the results and to avoid any misinterpretation.

Comment: Similarly, the authors used too many references. Better to limit by avoiding the outdated references used.

Response: We have reviewed the references and made sure we have updated references and some were eliminated. The many references are mainly due to the many studies included in the review.

Comment: The discussion section is interesting but it is satisfactory as of using 173 references. Your study can best be supported by more scientific evidences in addition to your presented evidences. I strongly advice to make it stronger.

Response: We have added more evidence-based information to the revised Discussion.

Response to the Reviewer 10’s Comments

Comment: In this systematic review and meta-analysis, authors sought to determine prevalence of depression across various study populations in Uganda.

Response: Thank you for your kind observations and suggestions to us. These helped us to strengthen the manuscript.

Comment: While the objectives are clearly stated and a clinically relevant question was included, it would be useful to indicate specific and focused questions regarding the subgroups of the populations being examined.

Response: We have now added a specific question about the subgroups.

Comment: A comprehensive literature search was conducted and information sources are indicated well and search terms used seem reasonable, however it would be useful to indicate any reasonable limitations placed on the search (e.g. English language, journal, etc). Additionally, were there any attempts made at collecting unpublished data?

Response: No. We have now added this to the limitations in the revised manuscript.

Comment: In terms of data abstraction, as the authors point out, the data was quite heterogenous and thus difficult to standardize—Some more details on how that translates into generalizable results would be useful. This is the biggest limitation in interpreting the results—the assessment tools in the studies may not be combinable and generalizable. However, the authors acknowledge this and have used appropriate methods to combine results and synthesize the highly heterogenous data. It would be useful to indicate if, in addition to self-report tools, there were some standard clinical interviews used to diagnose depression in the populations studied, and if any interview-based diagnosis prevalence can be obtained and compared meaningfully.

Response: A subgroup classification has now been added based on the diagnostic status of the study tool. See Table 2.

Comment: While the metanalysis is comprehensive and it provides a broader picture of prevalence of depression in various populations, it is still difficult to generalize the results to affect the clinical and treatment outcomes in a systematic manner. Future studies within these populations, as well as across wider regions in the country would be more useful in implementing treatments according to the needs (socioeconomic, cultural, refugee-status, etc).

Response: We have now added this as a recommendation in the revised manuscript.

Response to the Reviewer 11’s Comments

Comment: The title needs modification, “prevalence of depression in Uganda”, in what population the review was conducted?

Response: None of the other 10 reviewers had any problem with the title and we also believe we have followed the examples of many other similar studies in the literature. Therefore, we have retained our original title.

Comment: Background: A systematic review and meta-analysis was carried out to determine the prevalence of depression across study populations in the country, which needs modification. Fix with your study population.

Response: We have specified that we included different study populations in the country as shown in the Results section of the revised Abstract (Abstract section, page 2, lines 21 and 22).

Comment: Why do you include various populations in a single study? It is very difficult to compare unrelated studies in a single paper. Why did not you do it separately? I need sufficient reason. Some of the ideas in the paper seem comparative studies.

Response: The pooled prevalence of depression in Uganda was the focus of our study. For this purpose, all the studies (whether they are unrelated or not in terms of population) have to be included. We have followed standard protocol for estimating pooled prevalence. Despite the different populations, the estimated prevalence rate from this study is helpful for understanding the overall situation of depression in the country. Synthesizing data from such diverse studies is standard practice in these types of meta-analysis

Comment: I disagree with this choice of a title since it leaves out a group that is unrelated to this study and has depressive symptoms, which is a very difficult topic to declare "prevalence of depression in Uganda." Moreover, why did you decide to include only a few populations in the study? Given that the pooled prevalence of depression is (P=30%) in several included research, you cannot generalize at the national level.

Response: We refer you to our previous responses. The title captures what we have done and we have followed the usual procedure for estimating pooled prevalence rates.

Attachment

Submitted filename: Plos One Response to reviewers and editors comments 2.docx

Click here for additional data file.^{(39.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0276552.r003

Decision Letter 1

Muhammed Elhadi

10 Oct 2022

Prevalence of depression in Uganda: A systematic review and meta-analysis

PONE-D-22-10716R1

Dear Dr. Kaggwa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Muhammed Elhadi, MBBCh

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #5: All comments have been addressed

Reviewer #6: All comments have been addressed

Reviewer #7: All comments have been addressed

Reviewer #8: All comments have been addressed

Reviewer #9: All comments have been addressed

Reviewer #11: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #11: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: No

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #11: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #11: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

Reviewer #8: Yes

Reviewer #9: Yes

Reviewer #11: No

**********

6. Review Comments to the Author

Reviewer #1: The authors have revised the manuscript thoroughly. I have no additional comments about the manuscript.

Reviewer #2: Thanks for addressing all comments.

Paper is now suitable for publication, in my opinion.

Interesting and methodologically correct.

Reviewer #5: The authors have addressed all my comments. I am satisfied with the revisions made on the manuscript.

Reviewer #6: The reviewers have responded satisfactorily to all my comments. I am still puzzled by the very asymetrical funnel plot but the authors assure that they rerun the analysis and that all is correct so I guess this is the reflection of a strong publication bias. Fortunately, the authors address this issue in the discussion section. I thank the authors for all their hard work.

Reviewer #7: The authors did their best to respond to the reviewers' comments. I have no concerns on research ethics.

Reviewer #8: Thank you for addressing my comments. I believe the paper is substantially improved. Results highlight an important public health target, i.e., depressive symptoms, in Uganda.

Reviewer #9: I would like to say thank you to the author for addressing my concerns. All is about the improvement of the paper for its better quality as it is a scientific paper.

Reviewer #11: The manuscript has a significant improvement. It has a good scientific contribution. But, some grammatical issues should be resolved.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Masahiro Banno

Reviewer #2: No

Reviewer #5: No

Reviewer #6: Yes: Matias Baltazar

Reviewer #7: No

Reviewer #8: Yes: Tyler Reed Bell

Reviewer #9: No

Reviewer #11: No

**********

PLoS One. doi: 10.1371/journal.pone.0276552.r004

Acceptance letter

Muhammed Elhadi

12 Oct 2022

PONE-D-22-10716R1

Prevalence of depression in Uganda: A systematic review and meta-analysis

Dear Dr. Kaggwa:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Muhammed Elhadi

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Funnel plot for the included studies about depression in Uganda.

(TIF)

Click here for additional data file.^{(338.2KB, tif)}

S2 Fig

(TIF)

Click here for additional data file.^{(313.7KB, tif)}

S1 Table. Prevalence of depression in study populations in Uganda.

(DOCX)

Click here for additional data file.^{(14.9KB, docx)}

Attachment

Submitted filename: Review report.docx

Click here for additional data file.^{(13KB, docx)}

Attachment

Submitted filename: Plos One Response to reviewers and editors comments 2.docx

Click here for additional data file.^{(39.2KB, docx)}

Data Availability Statement

https://doi.org/10.6084/m9.figshare.19579096.v1.

[pone.0276552.ref001] 1.Patel V, Chisholm D, Parikh R, Charlson FJ, Degenhardt L, Dua T, et al. Addressing the burden of mental, neurological, and substance use disorders: key messages from Disease Control Priorities, 3rd edition. The Lancet. 2016;387(10028):1672–85. doi: 10.1016/s0140-6736(15)00390-6 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref002] 2.World Health Organisation. Depression 2021 [cited 2021 Novemeber 29, 2021]. https://www.who.int/health-topics/depression#tab=tab_1.

[pone.0276552.ref003] 3.Santomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet. 2021;398(10312):1700–12. doi: 10.1016/S0140-6736(21)02143-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref004] 4.Sankoh O, Sevalie S, Weston M. Mental health in Africa. The Lancet Global Health. 2018;6(9):e954–e5. doi: 10.1016/S2214-109X(18)30303-6 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref005] 5.Jörns-Presentati A, Napp A-K, Dessauvagie AS, Stein DJ, Jonker D, Breet E, et al. The prevalence of mental health problems in sub-Saharan adolescents: A systematic review. PloS One. 2021;16(5):e0251689. doi: 10.1371/journal.pone.0251689 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref006] 6.Nyundo A, Manu A, Regan M, Ismail A, Chukwu A, Dessie Y, et al. Factors associated with depressive symptoms and suicidal ideation and behaviours amongst sub-Saharan African adolescents aged 10–19 years: Cross-sectional study. Tropical Medicine & International Health. 2020;25(1):54–69. doi: 10.1111/tmi.13336 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref007] 7.Kaggwa MM, Arinaitwe I, Nduhuura E, Muwanguzi M, Kajjimu J, Kule M, et al. Prevalence and factors associated with depression and suicidal ideation during the COVID-19 pandemic among university students in Uganda: A cross-sectional study. Frontiers in Psychiatry. 2022;13:842466. doi: 10.3389/fpsyt.2022.842466 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref008] 8.Kaggwa MM, Muwanguzi M, Nduhuura E, Kajjimu J, Arinaitwe I, Kule M, et al. Suicide among Ugandan university students: Evidence from media reports for 2010–2020. BJPsych International. 2021;18(3):63–7. doi: 10.1192/bji.2021.13 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref009] 9.Kaggwa MM, Rukundo GZ, Wakida EK, Maling S, Sserumaga BM, Atim LM, et al. Suicide and suicide attempts among patients attending primary health care facilities in Uganda: A medical records review. Risk Management and Healthcare Policy. 2022;15:703–11. doi: 10.2147/RMHP.S358187 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref010] 10.Kaggwa MM, Arinaitwe I, Muwanguzi M, Nduhuura E, Kajjimu J, Kule M, et al. Suicidal behaviours among Ugandan university students: A cross-sectional study. BMC Psychiatry. 2022;22(1):234. doi: 10.1186/s12888-022-03858-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref011] 11.Mamun MA, Al Mamun F, Hosen I, Hasan M, Rahman A, Jubayar AM, et al. Suicidality in Bangladeshi young adults during the COVID-19 pandemic: The role of behavioral factors, COVID-19 risk and fear, and mental health problems. Risk Management and Healthcare Policy. 2021;14:4051–61. doi: 10.2147/RMHP.S330282 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref012] 12.Opio JN, Munn Z, Aromataris E. Prevalence of Mental Disorders in Uganda: A Systematic Review and Meta-Analysis. The Psychiatric Quarterly. 2021. doi: 10.1007/s11126-021-09941-8 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref013] 13.Wang J, Wu X, Lai W, Long E, Zhang X, Li W, et al. Prevalence of depression and depressive symptoms among outpatients: A systematic review and meta-analysis. BMJ Open. 2017;7(8):e017173. doi: 10.1136/bmjopen-2017-017173 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref014] 14.Najjuka SM, Checkwech G, Olum R, Ashaba S, Kaggwa MM. Depression, anxiety, and stress among Ugandan university students during the COVID-19 lockdown: An online survey. African Health Sciences. 2021;21(4):1533–43. doi: 10.4314/ahs.v21i4.6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref015] 15.Meffert SM, Neylan TC, McCulloch CE, Maganga L, Adamu Y, Kiweewa F, et al. East African HIV care: Depression and HIV outcomes. Global Mental Health. 2019;6:e9. doi: 10.1017/gmh.2019.6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref016] 16.Kaggwa MM, Namatanzi B, Kule M, Nkola R, Najjuka SM, Al Mamun F, et al. Depression in Ugandan rural women involved in a money saving group: The role of spouse’s unemployment, extramarital relationship, and substance use. International Journal of Womens Health. 2021;13:869–78. doi: 10.2147/IJWH.S323636 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref017] 17.Akimana B, Abbo C, Balagadde-Kambugu J, Nakimuli-Mpungu E. Prevalence and factors associated with major depressive disorder in children and adolescents at the Uganda Cancer Institute. BMC Cancer. 2019;19(1):466. doi: 10.1186/s12885-019-5635-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref018] 18.Kaggwa MM, Najjuka SM, Ashaba S, Mamun MA. The psychometrics of Patient Health Questionnaire (PHQ-9) in Uganda: A systematic review. Frontiers in Psychiatry. 2022:13(781095). doi: 10.3389/fpsyt.2022.781095 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref019] 19.Miller AP, Kintu M, Kiene SM. Challenges in measuring depression among Ugandan fisherfolk: a psychometric assessment of the Luganda version of the Center for Epidemiologic Studies Depression Scale (CES-D). BMC Psychiatry. 2020;20(1):45. doi: 10.1186/s12888-020-2463-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref020] 20.Ashaba S, Cooper-Vince C, Vořechovská D, Maling S, Rukundo GZ, Akena D, et al. Development and validation of a 20-item screening scale to detect major depressive disorder among adolescents with HIV in rural Uganda: A mixed-methods study. SSM—Population Health. 2019;7:100332. doi: 10.1016/j.ssmph.2018.100332 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref021] 21.Nakku JEM, Rathod SD, Kizza D, Breuer E, Mutyaba K, Baron EC, et al. Validity and diagnostic accuracy of the Luganda version of the 9-item and 2-item Patient Health Questionnaire for detecting major depressive disorder in rural Uganda. Global Mental Health.2016;3:e20. doi: 10.1017/gmh.2016.14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref022] 22.Natamba BK, Achan J, Arbach A, Oyok TO, Ghosh S, Mehta S, et al. Reliability and validity of the center for epidemiologic studies-depression scale in screening for depression among HIV-infected and -uninfected pregnant women attending antenatal services in northern Uganda: A cross-sectional study. BMC Psychiatry. 2014;14:303. doi: 10.1186/s12888-014-0303-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref023] 23.Miller AP, Espinosa da Silva C, Ziegel L, Mugamba S, Kyasanku E, Bulamba RM, et al. Construct validity and internal consistency of the Patient Health Questionnaire-9 (PHQ-9) depression screening measure translated into two Ugandan languages. Psychiatry Research Communications. 2021;1(2):100002. doi: 10.1016/j.psycom.2021.100002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref024] 24.Akena D, Joska J, Stein DJ. Sensitivity and specificity of the Akena Visual Depression Inventory (AViDI-18) in Kampala (Uganda) and Cape Town (South Africa). The British Journal of Psychiatry. 2018;212(5):301–7. doi: 10.1192/bjp.2018.9 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref025] 25.Reliefweb. Uganda resettlement and complementary pathways factsheet—31 October 2021 UN High Commissioner for Refugees 2021 [cited 2022 13 March]. https://reliefweb.int/report/uganda/uganda-resettlement-and-complementary-pathways-factsheet-31-october-2021#:~:text=UNHCR%20estimates%20that%20125%2C403%20refugees,Uganda%20since%201%20January%202021.

[pone.0276552.ref026] 26.Musumari PM, Techasrivichien T, Srithanaviboonchai K, Wanyenze RK, Matovu JKB, Poudyal H, et al. HIV epidemic in fishing communities in Uganda: A scoping review. PloS One. 2021;16(4):e0249465. doi: 10.1371/journal.pone.0249465 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref027] 27.Kaggwa MM, Nkola R, Najjuka SM, Bongomin F, Ashaba S, Mamun MA. Extrapyramidal side effects in a patient with alcohol withdrawal symptoms: A reflection of quality of the mental health care system. Risk Management and Healthcare Policy. 2021;14:2789–95. doi: 10.2147/RMHP.S314451 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref028] 28.Mugisha J, Muyinda H, Kagee A, Wandiembe P, Mpugu SK, Vancampfort D, et al. Prevalence of suicidal ideation and attempt: associations with psychiatric disorders and HIV/AIDS in post-conflict Northern Uganda. African Health Sciences. 2016;16(4):1027–35. doi: 10.4314/ahs.v16i4.20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref029] 29.Mugisha J, Muyinda H, Malamba S, Kinyanda E. Major depressive disorder seven years after the conflict in northern Uganda: Burden, risk factors and impact on outcomes (The Wayo-Nero Study). BMC Psychiatry. 2015;15:48. doi: 10.1186/s12888-015-0423-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref030] 30.Okware SI, Omaswa FG, Zaramba S, Opio A, Lutwama JJ, Kamugisha J, et al. An outbreak of Ebola in Uganda. Tropical Medicine & International Health. 2002;7(12):1068–75. doi: 10.1046/j.1365-3156.2002.00944.x [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref031] 31.Fischer M, Ramaswamy R, Fischer-Flores L, Mugisha G. Measuring and understanding depression in women in Kisoro, Uganda. Culture, Medicine, and Psychiatry. 2019;43(1):160–80. doi: 10.1007/s11013-018-9604-9 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref032] 32.Ssewanyana Y, Ssekitooleko B, Suuna B, Bua E, Wadeya J, Makumbi TK, et al. Quality of life of adult individuals with intestinal stomas in Uganda: A cross sectional study. African Health Sciences. 2021;21(1):427–36. doi: 10.4314/ahs.v21i1.53 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref033] 33.Bongomin F, Natukunda B, Sekimpi M, Olum R, Baluku JB, Makhoba A, et al. High prevalence of depressive symptoms among Ugandan patients with rheumatoid arthritis. Open access rheumatology: Research and Reviews. 2021;13:93–102. doi: 10.2147/OARRR.S306503 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref034] 34.Ngo VK, Wagner GJ, Nakasujja N, Dickens A, Aunon F, Musisi S. Effectiveness of antidepressants and predictors of treatment response for depressed HIV patients in Uganda. International Journal of STD & AIDS. 2015;26(14):998–1006. doi: 10.1177/0956462414564606 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref035] 35.Morof DF, Sami S, Mangeni M, Blanton C, Cardozo BL, Tomczyk B. A cross-sectional survey on gender-based violence and mental health among female urban refugees and asylum seekers in Kampala, Uganda. International Journal of Gynaecology and Obstetrics. 2014;127(2):138–43. doi: 10.1016/j.ijgo.2014.05.014 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref036] 36.Bahati R, Ainamani HE, Sigmund CD, Rukundo GZ. Association between stigma and depression among urban refugees in Mbarara Municipality, Southwestern Uganda. Research Square (Preprint). 2021. doi: 10.21203/rs.3.rs-17932/v1 [DOI] [Google Scholar]

[pone.0276552.ref037] 37.Kabunga A. Depressive disorders in refugees in Uganda: A case of Nakivale Refugee Camp. Journal of Psychology and Neuroscience. 2020;2(3). doi: 10.47363/JNRRR/2021(3)132 [DOI] [Google Scholar]

[pone.0276552.ref038] 38.Ayano G, Solomon M, Abraha M. A systematic review and meta-analysis of epidemiology of depression in people living with HIV in East Africa. BMC Psychiatry. 2018;18(1):254. doi: 10.1186/s12888-018-1835-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref039] 39.Moher D, Liberati A, Tetzlaff J, Altman DG, The PG. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PloS Medicine. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref040] 40.Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of Observational Studies in EpidemiologyA Proposal for Reporting. JAMA. 2000;283(15):2008–12. doi: 10.1001/jama.283.15.2008 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref041] 41.Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. International Journal of Eidence-based Halthcare. 2015;13(3):147–53. doi: 10.1097/XEB.0000000000000054 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref042] 42.Assael MI, Namboze JM, German GA, Bennett FJ. Psychiatric disturbances during pregnancy in a rural group of African women. Social Science & Medicine (1967). 1972;6(3):387–95. doi: 10.1016/0037-7856(72)90109-6 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref043] 43.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref044] 44.Joanna Briggs Institute. The Joanna Briggs Institute Critical Appraisal tools for use in JBI Systematic Reviews. Checklist for Prevalence Studies. 2017 [cited 2022 13 March]. https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Prevalence_Studies2017_0.pd.

[pone.0276552.ref045] 45.Kaggwa MM, Kajjimu J, Sserunkuma J, Najjuka SM, Atim LM, Olum R, et al. Prevalence of burnout among university students in low- and middle-income countries: A systematic review and meta-analysis. PloS One. 2021;16(8):e0256402. doi: 10.1371/journal.pone.0256402 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref046] 46.Kaggwa MM. Prevalence of depression in Uganda. A systematic review and meta-analysis; 2022[cited 2022 29 July]. Database: Figshare [internet]. doi: 10.6084/m9.figshare.19579096.v1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref047] 47.StataCorp. STATA Meta-analysis reference manual release 17 2021 [cited 2022 18/July/2021]. https://www.stata.com/manuals/meta.pdf.

[pone.0276552.ref048] 48.Thompson SG, Higgins JPT. How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine. 2002;21(11):1559–73. doi: 10.1002/sim.1187 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref049] 49.Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002. doi: 10.1136/bmj.d4002 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref050] 50.Shi L, Lin L. The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses. Medicine. 2019;98(23):e15987. doi: 10.1097/MD.0000000000015987 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref051] 51.Nsereko JR. The prevalence and risk factors of symptoms of depression, anxiety and somatic syndromes among secondary school students in Kampala, Uganda [thesis]. South Africa: Stellenbosch: Stellenbosch University; 2018.

[pone.0276552.ref052] 52.Lukenge E. Prevalence of depression, associated factors and coping styles among women attending infertility clinic at Mulago Hospital Kampala, Uganda [Dissertation]. School of Medicine Collections: Makerere University.; 2019.

[pone.0276552.ref053] 53.Kizito S. Maternal mental illness, infant growth and infant psychomotor functioning in rural Masaka district, Uganda: A longitudinal cohort study [Thesis]. South Africa: Stellenbosch University; 2020.

[pone.0276552.ref054] 54.Mubangizi I, Nakimuli-Mpungu E, Kawooya I, Sekaggya-wiltshire C. Prevalence and factors associated with depression among adults with sickle cell disease at Mulago Hospital, Uganda: A cross sectional study. Research Square (Preprint). 2020. doi: 10.21203/rs.3.rs-104883/v1 [DOI] [Google Scholar]

[pone.0276552.ref055] 55.Bolton P, Wilk CM, Ndogoni L. Assessment of depression prevalence in rural Uganda using symptom and function criteria. Social Psychiatry and Psychiatric Epidemiology. 2004;39(6):442–7. doi: 10.1007/s00127-004-0763-3 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref056] 56.Ovuga E, Boardman J, Wasserman D. Undergraduate student mental health at Makerere university, Uganda. World Psychiatry. 2006;5(1):51–2. https://pubmed.ncbi.nlm.nih.gov/16757997. [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref057] 57.Nakasujja N, Musisi S, Walugembe J, Wallace D. Psychiatric disorders among the elderly on non-psychiatric wards in an African setting. International Psychogeriatrics. 2007;19(4):691–704. doi: 10.1017/S1041610207005418 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref058] 58.Nalugya-Sserunjogi J, Rukundo GZ, Ovuga E, Kiwuwa SM, Musisi S, Nakimuli-Mpungu E. Prevalence and factors associated with depression symptoms among school-going adolescents in Central Uganda. Child and Adolescent Psychiatry and Mental Health. 2016;10:39. doi: 10.1186/s13034-016-0133-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref059] 59.Nakku JE, Nakasi G, Mirembe F. Postpartum major depression at six weeks in primary health care: Prevalence and associated factors. African Health Sciences. 2006;6(4):207–14. doi: 10.5555/afhs.2006.6.4.207 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref060] 60.Kaharuza FM, Bunnell R, Moss S, Purcell DW, Bikaako-Kajura W, Wamai N, et al. Depression and CD4 cell count among persons with HIV infection in Uganda. AIDS and Behavior. 2006;10(4 Suppl):S105–11. doi: 10.1007/s10461-006-9142-2 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref061] 61.Kinyanda E, Woodburn P, Tugumisirize J, Kagugube J, Ndyanabangi S, Patel V. Poverty, life events and the risk for depression in Uganda. Social Psychiatry and Psychiatric Epidemiology. 2011;46(1):35–44. doi: 10.1007/s00127-009-0164-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref062] 62.Ovuga E, Boardman J, Wasserman D. The prevalence of depression in two districts of Uganda. Social Psychiatry and Psychiatric Epidemiology. 2005;40(6):439–45. doi: 10.1007/s00127-005-0915-0 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref063] 63.Lundberg P, Rukundo G, Ashaba S, Thorson A, Allebeck P, Östergren P-O, et al. Poor mental health and sexual risk behaviours in Uganda: A cross-sectional population-based study. BMC Public Health. 2011;11(1):125. doi: 10.1186/1471-2458-11-125 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref064] 64.Nakimuli-Mpungu E, Munyaneza GM. Depression, alcohol abuse, and disclosure of HIV serostatus among rural HIV-positive individuals in western Uganda. Neurobehavioral HIV Medicine. 2013;3:3–19. doi: 10.2147/NBHIV.S13277 [DOI] [Google Scholar]

[pone.0276552.ref065] 65.Agardh A, Cantor-Graae E, Ostergren PO. Youth, sexual risk-taking behavior, and mental health: a study of university students in Uganda. International Journal of Behavioral Medicine. 2012;19(2):208–16. doi: 10.1007/s12529-011-9159-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref066] 66.Vinck P, Pham PN, Stover E, Weinstein HM. Exposure to war crimes and implications for peace building in northern Uganda. JAMA. 2007;298(5):543–54. doi: 10.1001/jama.298.5.543 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref067] 67.Pfeiffer A, Elbert T. PTSD, depression and anxiety among former abductees in Northern Uganda. Conflict and Health. 2011;5(1):14. doi: 10.1186/1752-1505-5-14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref068] 68.Martinez P, Andia I, Emenyonu N, Hahn JA, Hauff E, Pepper L, et al. Alcohol use, depressive symptoms and the receipt of antiretroviral therapy in southwest Uganda. AIDS and Behavior. 2008;12(4):605–12. doi: 10.1007/s10461-007-9312-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref069] 69.Muhwezi WW, Ågren H, Musisi S. Detection of major depression in Ugandan primary health care settings using simple questions from a subjective well-being (SWB) subscale. Social Psychiatry and Psychiatric Epidemiology. 2007;42(1):61–9. doi: 10.1007/s00127-006-0132-5 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref070] 70.Petrushkin H, Boardman J, Ovuga E. Psychiatric disorders in HIV-positive individuals in urban Uganda. Psychiatric Bulletin. 2005;29(12):455–8. doi: 10.1192/pb.29.12.455 [DOI] [Google Scholar]

[pone.0276552.ref071] 71.Psaros C, Haberer JE, Boum Y 2nd, Tsai AC, Martin JN, Hunt PW, et al. The factor structure and presentation of depression among HIV-positive adults in Uganda. AIDS and Behavior. 2015;19(1):27–33. doi: 10.1007/s10461-014-0796-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref072] 72.Nakasujja N, Skolasky RL, Musisi S, Allebeck P, Robertson K, Ronald A, et al. Depression symptoms and cognitive function among individuals with advanced HIV infection initiating HAART in Uganda. BMC Psychiatry. 2010;10:44. doi: 10.1186/1471-244X-10-44 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref073] 73.Kaida A, Matthews LT, Ashaba S, Tsai AC, Kanters S, Robak M, et al. Depression during pregnancy and the postpartum among HIV-infected women on antiretroviral therapy in Uganda. Journal of Acquired Immune Deficiency Syndromes (1999). 2014;67 Suppl 4(Suppl 4):S179–87. doi: 10.1097/QAI.0000000000000370 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref074] 74.Roberts B, Ocaka KF, Browne J, Oyok T, Sondorp E. Factors associated with post-traumatic stress disorder and depression amongst internally displaced persons in northern Uganda. BMC Psychiatry. 2008;8(1):38. doi: 10.1186/1471-244X-8-38 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref075] 75.Klasen F, Gehrke J, Metzner F, Blotevogel M, Okello J. Complex trauma symptoms in former Ugandan child soldiers. Journal of Aggression, Maltreatment & Trauma. 2013;22(7):698–713. doi: 10.1080/10926771.2013.814741 [DOI] [Google Scholar]

[pone.0276552.ref076] 76.Klasen F, Oettingen G, Daniels J, Adam H. Multiple trauma and mental health in former Ugandan child soldiers. Journal of Traumatic Stress. 2010;23(5):573–81. doi: 10.1002/jts.20557 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref077] 77.Klasen F, Oettingen G, Daniels J, Post M, Hoyer C, Adam H. Posttraumatic resilience in former Ugandan child soldiers. Child Development. 2010;81(4):1096–113. doi: 10.1111/j.1467-8624.2010.01456.x [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref078] 78.Abbo C, Ekblad S, Waako P, Okello E, Musisi S. The prevalence and severity of mental illnesses handled by traditional healers in two districts in Uganda. African Health Sciences. 2009;9 Suppl 1(Suppl 1):S16–22. [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref079] 79.Hatcher AM, Tsai AC, Kumbakumba E, Dworkin SL, Hunt PW, Martin JN, et al. Sexual relationship power and depression among HIV-infected women in Rural Uganda. PloS One. 2012;7(12):e49821. doi: 10.1371/journal.pone.0049821 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref080] 80.Pham PN, Vinck P, Stover E. Returning home: forced conscription, reintegration, and mental health status of former abductees of the Lord’s Resistance Army in northern Uganda. BMC Psychiatry. 2009;9(1):23. doi: 10.1186/1471-244X-9-23 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref081] 81.Tsai AC, Bangsberg DR, Frongillo EA, Hunt PW, Muzoora C, Martin JN, et al. Food insecurity, depression and the modifying role of social support among people living with HIV/AIDS in rural Uganda. Social Science & Medicine (1982). 2012;74(12):2012–9. doi: 10.1016/j.socscimed.2012.02.033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref082] 82.Tsai AC, Wolfe WR, Kumbakumba E, Kawuma A, Hunt PW, Martin JN, et al. Prospective study of the mental health consequences of sexual violence among women living with HIV in rural Uganda. Journal of Interpersonal Violence. 2016;31(8):1531–53. doi: 10.1177/0886260514567966 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref083] 83.Nakimuli-Mpungu E, Alderman S, Kinyanda E, Allden K, Betancourt TS, Alderman JS, et al. Implementation and scale-up of psycho-trauma centers in a post-conflict area: A case study of a private-public partnership in northern Uganda. PloS Medicine. 2013;10(4):e1001427. doi: 10.1371/journal.pmed.1001427 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref084] 84.Winkler N, Ruf-Leuschner M, Ertl V, Pfeiffer A, Schalinski I, Ovuga E, et al. From war to classroom: PTSD and depression in formerly abducted youth in Uganda. Frontier in Psychiatry. 2015;6:2. doi: 10.3389/fpsyt.2015.00002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref085] 85.Musisi S, Kinyanda E. Emotional and behavioural disorders in HIV seropositive adolescents in urban Uganda. East African Medical Journal. 2009;86(1):16–24. doi: 10.4314/eamj.v86i1.46923 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref086] 86.Wagner GJ, Ghosh-Dastidar B, Garnett J, Kityo C, Mugyenyi P. Impact of HIV antiretroviral therapy on depression and mental health among clients with HIV in Uganda. Psychosomatic Medicine. 2012;74(9):883–90. doi: 10.1097/PSY.0b013e31826629db [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref087] 87.Shumba C, Atukunda R, Imakit R, Memiah P. Prevalence of depressive symptoms amongst highly active antiretroviral therapy (HAART) patients in AIDSRelief Uganda. Journal of Public Health in Africa. 2013;4(2):e19. doi: 10.4081/jphia.2013.e19 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref088] 88.Okeke EN, Wagner GJ. AIDS treatment and mental health: evidence from Uganda. Social Science & Medicine (1982). 2013;92:27–34. doi: 10.1016/j.socscimed.2013.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref089] 89.Wagner GJ, Ghosh-Dastidar B, Slaughter ME, Akena D, Nakasujja N, Musisi S. Changes in condom use during the first year of HIV treatment in Uganda and the relationship to depression. Annals of Behavioral Medicine. 2014;48(2):175–83. doi: 10.1007/s12160-013-9586-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref090] 90.Wagner GJ, Slaughter M, Ghosh-Dastidar B. Depression at treatment initiation predicts HIV antiretroviral adherence in Uganda. Journal of the International Association of Providers of AIDS Care. 2017;16(1):91–7. doi: 10.1177/2325957416677121 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref091] 91.Wang C, Pu R, Ghose B, Tang S. Chronic musculoskeletal pain, self-reported health and quality of life among older populations in South Africa and Uganda. International Journal of Environmental Research and Public Health. 2018;15(12). doi: 10.3390/ijerph15122806 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref092] 92.Ager A, Pasha E, Yu G, Duke T, Eriksson C, Cardozo BL. Stress, mental health, and burnout in national humanitarian aid workers in Gulu, northern Uganda. Journal of Traumatic Stress. 2012;25(6):713–20. doi: 10.1002/jts.21764 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref093] 93.Wagner GJ, Holloway I, Ghosh-Dastidar B, Kityo C, Mugyenyi P. Understanding the influence of depression on self-efficacy, work status and condom use among HIV clients in Uganda. Journal of Psychosomatic Research. 2011;70(5):440–8. doi: 10.1016/j.jpsychores.2010.10.003 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref094] 94.Kinyanda E, Hoskins S, Nakku J, Nawaz S, Patel V. Prevalence and risk factors of major depressive disorder in HIV/AIDS as seen in semi-urban Entebbe district, Uganda. BMC Psychiatry. 2011;11:205. doi: 10.1186/1471-244X-11-205 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref095] 95.Kinyanda E, Hoskins S, Nakku J, Nawaz S, Patel V. The prevalence and characteristics of suicidality in HIV/AIDS as seen in an African population in Entebbe district, Uganda. BMC Psychiatry. 2012;12(1):63. doi: 10.1186/1471-244X-12-63 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref096] 96.Nakku J, Kinyanda E, Hoskins S. Prevalence and factors associated with probable HIV dementia in an African population: A cross-sectional study of an HIV/AIDS clinic population. BMC Psychiatry. 2013;13(1):126. doi: 10.1186/1471-244X-13-126 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref097] 97.Kakyo TA, Muliira JK, Mbalinda SN, Kizza IB, Muliira RS. Factors associated with depressive symptoms among postpartum mothers in a rural district in Uganda. Midwifery. 2012;28(3):374–9. doi: 10.1016/j.midw.2011.05.001 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref098] 98.Wagner GJ, Ghosh-Dastidar B, Dickens A, Nakasujja N, Okello E, Luyirika E, et al. Depression and its Relationship to Work Status and Income Among HIV Clients in Uganda. World journal of AIDS. 2012;2(3):126–34. doi: 10.4236/wja.2012.23018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref099] 99.Wagner GJ, Ghosh-Dastidar B, Slaughter M, Akena D, Nakasujja N, Okello E, et al. The role of depression in work-related outcomes of HIV treatment in Uganda. International Journal of Behavioral Medicine. 2014;21(6):946–55. doi: 10.1007/s12529-013-9379-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref100] 100.Kiene SM, Dove M, Wanyenze RK. Depressive symptoms, disclosure, HIV-related stigma, and coping following HIV testing among outpatients in Uganda: A daily process analysis. AIDS and Behavior. 2018;22(5):1639–51. doi: 10.1007/s10461-017-1953-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref101] 101.Okello ES, Wagner GJ, Ghosh-Dastidar B, Garnett J, Akena D, Nakasujja N, et al. Depression, internalized HIV stigma and HIV disclosure. World Journal of AIDS. 2015;5(01):30. doi: 10.4236/wja.2015.51004 [DOI] [Google Scholar]

[pone.0276552.ref102] 102.Musisi S, Wagner GJ, Ghosh-Dastidar B, Nakasujja N, Dickens A, Okello E. Depression and sexual risk behaviour among clients about to start HIV antiretroviral therapy in Uganda. International Journal of STD & AIDS. 2014;25(2):130–7. doi: 10.1177/0956462413495186 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref103] 103.Akena D, Musisi S, Joska J, Stein DJ. The association between aids related stigma and major depressive disorder among HIV-positive individuals in Uganda. PloS One. 2012;7(11):e48671. doi: 10.1371/journal.pone.0048671 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref104] 104.Akena D, Stein DJ, Joska J. Does screening HIV-positive individuals in Uganda for major depressive disorder improve case detection rates and antidepressant prescription? AIDS and Behavior. 2013;17(8):2802–7. doi: 10.1007/s10461-012-0383-y [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref105] 105.Nakimuli-Mpungu E, Musisi S, Katabira E, Nachega J, Bass J. Prevalence and factors associated with depressive disorders in an HIV+ rural patient population in southern Uganda. Journal of Affective Disorders. 2011;135(1–3):160–7. doi: 10.1016/j.jad.2011.07.009 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref106] 106.Katende G, Nakimera L. Prevalence and correlates of anxiety and depression among family carers of cancer patients in a cancer care and treatment facility in Uganda: A cross-sectional study. African Health Sciences. 2017;17(3):868–76. doi: 10.4314/ahs.v17i3.30 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref107] 107.Kinyanda E, Kizza R, Abbo C, Ndyanabangi S, Levin J. Prevalence and risk factors of depression in childhood and adolescence as seen in 4 districts of north-eastern Uganda. BMC International Health and Human Rights. 2013;13(1):19. doi: 10.1186/1472-698X-13-19 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref108] 108.Spittal PM, Malamba SS, Ogwang MD, Musisi S, Ekwaru JP, Sewankambo NK, et al. Cango Lyec (healing the elephant): Gender differences in HIV infection in post-conflict Northern Uganda. Journal of Acquired Immune Deficiency Syndromes (1999). 2018;78(3):257–68. doi: 10.1097/QAI.0000000000001671 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref109] 109.Whyte SR, Park SJ, Odong G, Ojara M, Lamwaka A. The visibility of non-communicable diseases in Northern Uganda. African Health Sciences. 2015;15(1):82–9. doi: 10.4314/ahs.v15i1.11 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref110] 110.Perkins JM, Nyakato VN, Kakuhikire B, Tsai AC, Subramanian SV, Bangsberg DR, et al. Food insecurity, social networks and symptoms of depression among men and women in rural Uganda: A cross-sectional, population-based study. Public Health Nutrition. 2018;21(5):838–48. doi: 10.1017/S1368980017002154 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref111] 111.Malamba SS, Muyinda H, Spittal PM, Ekwaru JP, Kiwanuka N, Ogwang MD, et al. “The Cango Lyec Project—Healing the Elephant”: HIV related vulnerabilities of post-conflict affected populations aged 13–49 years living in three Mid-Northern Uganda districts. BMC infectious diseases. 2016;16(1):690. doi: 10.1186/s12879-016-2030-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref112] 112.Hakim AJ, Mukasa B, Hundley L, Odiit M, Ogwal M, Sendagala S, et al. Correlates of undiagnosed HIV infection and retesting among voluntary HIV testing clients at Mildmay Clinic, Uganda. AIDS and Behavior. 2019;23(4):820–34. doi: 10.1007/s10461-018-2274-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref113] 113.Familiar I, Sikorskii A, Murray S, Ruisenor-Escudero H, Nakasujja N, Korneffel C, et al. Depression symptom trajectories among mothers living with HIV in rural Uganda. AIDS and Behavior. 2019;23(12):3411–8. doi: 10.1007/s10461-019-02465-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref114] 114.Cavazos-Rehg P, Xu C, Kasson E, Byansi W, Bahar OS, Ssewamala FM. Social and economic equity and family cohesion as potential protective factors from depression among adolescents living with HIV in Uganda. AIDS and Behavior. 2020;24(9):2546–54. doi: 10.1007/s10461-020-02812-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref115] 115.Akena D, Kadama P, Ashaba S, Akello C, Kwesiga B, Rejani L, et al. The association between depression, quality of life, and the health care expenditure of patients with diabetes mellitus in Uganda. Journal of Affective Disorders. 2015;174:7–12. doi: 10.1016/j.jad.2014.11.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref116] 116.Familiar I, Muniina PN, Dolan C, Ogwal M, Serwadda D, Kiyingi H, et al. Conflict-related violence and mental health among self-settled Democratic Republic of Congo female refugees in Kampala, Uganda–a respondent driven sampling survey. Conflict and Health. 2021;15(1):42. doi: 10.1186/s13031-021-00377-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref117] 117.Akena D, Ngo V, Guotman P, Glick P, Musisi S, Wagner GJJR, et al. Accuracy of depression evaluations by trained lay and medical health workers in HIV clinics in Uganda. Research and Advances in Psychiatry 2016;3:44–52. [Google Scholar]

[pone.0276552.ref118] 118.Rathod SD, Roberts T, Medhin G, Murhar V, Samudre S, Luitel NP, et al. Detection and treatment initiation for depression and alcohol use disorders: Facility-based cross-sectional studies in five low-income and middle-income country districts. BMJ Open. 2018;8(10):e023421. doi: 10.1136/bmjopen-2018-023421 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref119] 119.Mwesiga EK, Mugenyi L, Nakasujja N, Moore S, Kaddumukasa M, Sajatovic M. Depression with pain co morbidity effect on quality of life among HIV positive patients in Uganda: A cross sectional study. Health and Quality of Life Outcomes. 2015;13(1):206. doi: 10.1186/s12955-015-0403-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref120] 120.Nakku JEM, Rathod SD, Garman EC, Ssebunnya J, Kangere S, De Silva M, et al. Evaluation of the impacts of a district-level mental health care plan on contact coverage, detection and individual outcomes in rural Uganda: A mixed methods approach. International Journal of Mental Health Systems. 2019;13:63. doi: 10.1186/s13033-019-0319-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref121] 121.Nalwadda O, Rathod SD, Nakku J, Lund C, Prince M, Kigozi F. Alcohol use in a rural district in Uganda: findings from community-based and facility-based cross-sectional studies. International Journal of Mental Health Systems. 2018;12:12. doi: 10.1186/s13033-018-0191-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref122] 122.Jones R, Kirenga BJ, Katagira W, Singh SJ, Pooler J, Okwera A, et al. A pre–post intervention study of pulmonary rehabilitation for adults with post-tuberculosis lung disease in Uganda. International Journal of Chronic Obstructive Pulmonary Diseases 2017;12:3533–3539. doi: 10.2147/COPD.S146659 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref123] 123.Wagner GJ, Ghosh-Dastidar B, Robinson E, Ngo VK, Glick P, Musisi S, et al. Effects of depression alleviation on work productivity and income among HIV patients in Uganda. International Journal of Behavioral Medicine. 2017;24(4):628–33. doi: 10.1007/s12529-017-9642-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref124] 124.Rukundo ZG, Musisi S, Nakasujja N. Psychiatric morbidity among physically ill patients in a Ugandan Regional Referral Hospital. African Health Sciences. 2013;13(1):87–93. doi: 10.4314/ahs.v13i1.13 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref125] 125.Olema DK, Catani C, Ertl V, Saile R, Neuner F. The hidden effects of child maltreatment in a war region: Correlates of psychopathology in two generations living in Northern Uganda. Journal of Traumatic Stress. 2014;27(1):35–41. doi: 10.1002/jts.21892 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref126] 126.Huang KY, Abura G, Theise R, Nakigudde J. Parental depression and associations with parenting and children’s physical and mental health in a Sub-Saharan African setting. Child Psychiatry and Human Development. 2017;48(4):517–27. doi: 10.1007/s10578-016-0679-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref127] 127.Wagner G, Ghosh-Dastidar B, Ngo V, Robinson E, Musisi S, Glick P, et al. A cluster randomized controlled trial of two task-shifting depression care models on depression alleviation and antidepressant response among HIV clients in Uganda. Research and Advances in Psychiatry. 2016;3(1):12–21. [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref128] 128.Wagner GJ, Ngo V, Goutam P, Glick P, Musisi S, Akena D. A structured protocol model of depression care versus clinical acumen: A cluster randomized trial of the effects on depression screening, diagnostic evaluation, and treatment uptake in Ugandan HIV clinics. PloS One. 2016;11(5):e0153132. doi: 10.1371/journal.pone.0153132 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref129] 129.Henry MB, Bakeera-Kitaka S, Lubega K, Snyder SA, LaRussa P, Pfeffer B. Depressive symptoms, sexual activity, and substance use among adolescents in Kampala, Uganda. African Health Sciences. 2019;19(2):1888–96. doi: 10.4314/ahs.v19i2.12 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref130] 130.Swahn MH, Culbreth R, Masyn KE, Salazar LF, Wagman J, Kasirye R. The intersection of alcohol use, gender based violence and HIV: Empirical findings among disadvantaged service-seeking youth in Kampala, Uganda. AIDS and Behavior. 2021;25(10):3106–14. doi: 10.1007/s10461-021-03301-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref131] 131.Gyagenda JO, Ddumba E, Odokonyero R, Kaddumukasa M, Sajatovic M, Smyth K, et al. Post-stroke depression among stroke survivors attending two hospitals in Kampala Uganda. African Health Sciences. 2015;15(4):1220–31. doi: 10.4314/ahs.v15i4.22 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref132] 132.Kinyanda E, Salisbury TT, Muyingo SK, Ssembajjwe W, Levin J, Nakasujja N, et al. Major depressive disorder among HIV infected youth in Uganda: Incidence, persistence and their predictors. AIDS and Behavior. 2020;24(9):2588–96. doi: 10.1007/s10461-020-02815-3 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref133] 133.Muhammad Usman K, Okorie P, Umahi E, Paul S. Factors influencing the occurrence of mental illness in persons living with HIV/AIDS in Kabarole District, Uganda. International Journal of HIV/AIDS Prevention, Education and Behavioural Science. 2018;4(2):35–43. doi: 10.4314/njpar.v39i2 [DOI] [Google Scholar]

[pone.0276552.ref134] 134.Ashaba S, Rukundo GZ, Beinempaka F, Ntaro M, LeBlanc JC. Maternal depression and malnutrition in children in southwest Uganda: A case control study. BMC Public Health. 2015;15:1303. doi: 10.1186/s12889-015-2644-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref135] 135.Kinyanda E, Weiss HA, Mungherera M, Onyango-Mangen P, Ngabirano E, Kajungu R, et al. Intimate partner violence as seen in post-conflict eastern Uganda: Prevalence, risk factors and mental health consequences. BMC International Health and Human Rights. 2016;16(1):5. doi: 10.1186/s12914-016-0079-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref136] 136.Manne-Goehler J, Kakuhikire B, Abaasabyoona S, Bärnighausen TW, Okello S, Tsai AC, et al. Depressive symptoms before and after antiretroviral therapy initiation among older-aged individuals in rural Uganda. AIDS and Behavior. 2019;23(3):564–71. doi: 10.1007/s10461-018-2273-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref137] 137.Kinyanda E, Kuteesa M, Scholten F, Mugisha J, Baisley K, Seeley J. Risk of major depressive disorder among older persons living in HIV-endemic central and southwestern Uganda. AIDS Care. 2016;28(12):1516–21. doi: 10.1080/09540121.2016.1191601 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref138] 138.Smith ML, Kakuhikire B, Baguma C, Rasmussen JD, Perkins JM, Cooper-Vince C, et al. Relative wealth, subjective social status, and their associations with depression: Cross-sectional, population-based study in rural Uganda. SSM—Population Health. 2019;8:100448. doi: 10.1016/j.ssmph.2019.100448 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref139] 139.Nampijja M, Natamba B, Mpango R, Kinyanda E. The burden and risk factors for postnatal depression and depressive symptomatology among women in Kampala. Tropical Doctor. 2019;49(3):170–7. doi: 10.1177/0049475519837107 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref140] 140.Cooper-Vince CE, Arachy H, Kakuhikire B, Vořechovská D, Mushavi RC, Baguma C, et al. Water insecurity and gendered risk for depression in rural Uganda: A hotspot analysis. BMC Public Health. 2018;18(1):1143. doi: 10.1186/s12889-018-6043-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref141] 141.Kiene SM, Lule H, Sileo KM, Silmi KP, Wanyenze RK. Depression, alcohol use, and intimate partner violence among outpatients in rural Uganda: Vulnerabilities for HIV, STIs and high risk sexual behavior. BMC Infectious Diseases. 2017;17(1):88. doi: 10.1186/s12879-016-2162-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref142] 142.Kinyanda E, Nakasujja N, Levin J, Birabwa H, Mpango R, Grosskurth H, et al. Major depressive disorder and suicidality in early HIV infection and its association with risk factors and negative outcomes as seen in semi-urban and rural Uganda. Journal of Affective Disorders. 2017;212:117–27. doi: 10.1016/j.jad.2017.01.033 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref143] 143.Sohail S, Nakigozi G, Anok A, Batte J, Kisakye A, Mayanja R, et al. Headache prevalence and its functional impact among HIV-infected adults in rural Rakai District, Uganda. Journal of Neurovirology. 2019;25(2):248–53. doi: 10.1007/s13365-018-0710-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref144] 144.Raggio GA, Psaros C, Fatch R, Goodman G, Matthews LT, Magidson JF, et al. High rates of biomarker-confirmed alcohol use among pregnant women living with HIV in South Africa and Uganda. Journal of Acquired Immune Deficiency Syndromes (1999). 2019;82(5):443–51. doi: 10.1097/QAI.0000000000002156 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref145] 145.Kinyanda E, Weiss HA, Levin J, Nakasujja N, Birabwa H, Nakku J, et al. Incidence and persistence of major depressive disorder among people living with HIV in Uganda. AIDS and Behavior. 2017;21(6):1641–54. doi: 10.1007/s10461-016-1575-7 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref146] 146.Akinbo E, Agwu E, Okoruwa G, Adedeji A. Prevalence and Magnitude of alcohol-related health problems among youths in disease endemic Bushenyi district, Uganda. Special Pathogens Review Journal. 2016; 1(2):53–9. [Google Scholar]

[pone.0276552.ref147] 147.Cooper-Vince CE, Kakuhikire B, Vorechovska D, McDonough AQ, Perkins J, Venkataramani AS, et al. Household water insecurity, missed schooling, and the mediating role of caregiver depression in rural Uganda. Global Mental Health. 2017;4:e15. doi: 10.1017/gmh.2017.14 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref148] 148.Ashaba S, Cooper-Vince CE, Maling S, Satinsky EN, Baguma C, Akena D, et al. Childhood trauma, major depressive disorder, suicidality, and the modifying role of social support among adolescents living with HIV in rural Uganda. Journal of Affective Disorders Reports. 2021;4:100094. doi: 10.1016/j.jadr.2021.100094 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref149] 149.Ashaba S, Cooper-Vince C, Maling S, Rukundo GZ, Akena D, Tsai AC. Internalized HIV stigma, bullying, major depressive disorder, and high-risk suicidality among HIV-positive adolescents in rural Uganda. Global Mental Health. 2018;5:e22. doi: 10.1017/gmh.2018.15 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref150] 150.Ortblad KF, Musoke DK, Chanda MM, Ngabirano T, Velloza J, Haberer JE, et al. Knowledge of HIV status is associated with a decrease in the severity of depressive symptoms among female sex workers in Uganda and Zambia. Journal of Acquired Immune Deficiency Syndromes (1999). 2020;83(1):37–46. doi: 10.1097/QAI.0000000000002224 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref151] 151.Satinsky EN, Kakuhikire B, Baguma C, Rasmussen JD, Ashaba S, Cooper-Vince CE, et al. Adverse childhood experiences, adult depression, and suicidal ideation in rural Uganda: A cross-sectional, population-based study. PloS Medicine. 2021;18(5):e1003642–e. doi: 10.1371/journal.pmed.1003642 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref152] 152.Alinaitwe R, Birungi C, Bangirana P, Nakasujja N. Prevalence and factors associated with depressive illness in patients with tuberculosis in Mulago hospital, Kampala- Uganda: A cross sectional study. Journal of Psychosomatic Research. 2021;149:110591. doi: 10.1016/j.jpsychores.2021.110591 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref153] 153.Bapolisi AM, Song SJ, Kesande C, Rukundo GZ, Ashaba S. Post-traumatic stress disorder, psychiatric comorbidities and associated factors among refugees in Nakivale camp in southwestern Uganda. BMC Psychiatry. 2020;20(1):53. doi: 10.1186/s12888-020-2480-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref154] 154.Forry JB, Ashaba S, Rukundo GZ. Prevalence and associated factors of mental disorders among prisoners in Mbarara municipality, southwestern Uganda: A cross-sectional study. BMC Psychiatry. 2019;19(1):178. doi: 10.1186/s12888-019-2167-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref155] 155.Seffren V, Familiar I, Murray SM, Augustinavicius J, Boivin MJ, Nakasujja N, et al. Association between coping strategies, social support, and depression and anxiety symptoms among rural Ugandan women living with HIV/AIDS. AIDS Care. 2018;30(7):888–95. doi: 10.1080/09540121.2018.1441969 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref156] 156.Kuteesa MO, Weiss HA, Cook S, Seeley J, Ssentongo JN, Kizindo R, et al. Epidemiology of alcohol misuse and illicit drug use among young people aged 15–24 years in fishing communities in Uganda. International Journal of Enviromental Research and Public Health 2020;17(7):2401. doi: 10.3390/ijerph17072401 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref157] 157.Nabunya P, Damulira C, Byansi W, Muwanga J, Bahar OS, Namuwonge F, et al. Prevalence and correlates of depressive symptoms among high school adolescent girls in southern Uganda. BMC Public Health. 2020;20(1):1792. doi: 10.1186/s12889-020-09937-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref158] 158.Arach AAO, Nakasujja N, Nankabirwa V, Ndeezi G, Kiguli J, Mukunya D, et al. Perinatal death triples the prevalence of postpartum depression among women in Northern Uganda: A community-based cross-sectional study. PloS One. 2020;15(10):e0240409. doi: 10.1371/journal.pone.0240409 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref159] 159.Logie CH, Okumu M, Mwima S, Hakiza R, Chemutai D, Kyambadde P. Contextual factors associated with depression among urban refugee and displaced youth in Kampala, Uganda: findings from a cross-sectional study. Conflict and Health. 2020;14(1):45. doi: 10.1186/s13031-020-00289-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref160] 160.Musinguzi K, Obuku A, Nakasujja N, Birabwa H, Nakku J, Levin J, et al. Association between major depressive disorder and pro-inflammatory cytokines and acute phase proteins among HIV-1 positive patients in Uganda. BMC Immunology. 2018;19(1):1. doi: 10.1186/s12865-017-0239-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref161] 161.Muliira JK, Kizza IB. The other untold burden of cancer in sub-Saharan Africa: Anxiety and depressive symptoms among family caregivers of adult cancer patients. International Journal of Africa Nursing Sciences. 2019;11:100166. doi: 10.1016/j.ijans.2019.100166 [DOI] [Google Scholar]

[pone.0276552.ref162] 162.Misghinna TK, Nakimuli-Mpungu E, Abbo C, Kinyanda E,. Major depressive disorder and associated factors among adult refugees attending a refugee center in Kampala, Uganda. African Journal of Traumatic Stress. 2020;5(1):1–21. [Google Scholar]

[pone.0276552.ref163] 163.Boduszek D, Debowska A, Ochen EA, Fray C, Nanfuka EK, Powell-Booth K, et al. Prevalence and correlates of non-suicidal self-injury, suicidal ideation, and suicide attempt among children and adolescents: Findings from Uganda and Jamaica. Journal of Affective Disorders. 2021;283:172–8. doi: 10.1016/j.jad.2021.01.063 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref164] 164.Kyohangirwe L, Okello E, Namuli JD, Ndeezi G, Kinyanda E. Prevalence and factors associated with major depressive disorder among adolescents attending a primary care facility in Kampala, Uganda. Tropical Doctor. 2020;50(1):30–6. doi: 10.1177/0049475519879586 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref165] 165.Olum R, Nakwagala FN, Odokonyero R. Prevalence and factors associated with depression among medical students at Makerere university, Uganda. Advance in Medical Education and Practice. 2020;11:853–60. doi: 10.2147/AMEP.S278841 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref166] 166.Mootz JJ, Muhanguzi F, Greenfield B, Gill M, Gonzalez MB, Panko P, et al. Armed conflict, intimate partner violence, and mental distress of women in Northeastern Uganda: A mixed methods study. Psychology of Women Quarterly. 2019;43(4):457–71. doi: 10.1177/0361684319864366 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref167] 167.Ssebunnya J, Medhin G, Kangere S, Kigozi F, Nakku J, Lund C. Prevalence, correlates and help-seeking behaviour for depressive symptoms in rural Uganda: a population-based survey. Global Mental Health. 2019;6:e27. Epub 2019/12/07. doi: 10.1017/gmh.2019.25 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref168] 168.Kabunga A, Anyayo L. Depression and associated factors among refugees amidst covid-19 in Nakivale refugee camp in Uganda. Journal of Neurology Research Reviews & Reports. 2021;3:1–5. doi: 10.47363/JNRRR/2021(3)132 [DOI] [Google Scholar]

[pone.0276552.ref169] 169.Kabunga A, Nambozo S. Prevalence and factors associated with depression among out-of-school adolescents in Ayere village, Northern Uganda. International Journal of Academic Research in Progressive Education and Development. 2021;10. doi: 10.6007/IJARPED/v10-i1/9080 [DOI] [Google Scholar]

[pone.0276552.ref170] 170.Ouma S, Tumwesigye NM, Ndejjo R, Abbo C. Prevalence and factors associated with major depression among female sex workers in post-conflict Gulu district: A cross-sectional study. BMC Public Health. 2021;21(1):1134. doi: 10.1186/s12889-021-11207-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref171] 171.Mahmud M, Riley E. Household response to an extreme shock: Evidence on the immediate impact of the Covid-19 lockdown on economic outcomes and well-being in rural Uganda. World Development. 2021;140:105318. doi: 10.1016/j.worlddev.2020.105318 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref172] 172.Blackmore R, Boyle JA, Fazel M, Ranasinha S, Gray KM, Fitzgerald G, et al. The prevalence of mental illness in refugees and asylum seekers: A systematic review and meta-analysis. PloS Medicine. 2020;17(9):e1003337. doi: 10.1371/journal.pmed.1003337 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref173] 173.Morina N, Stam K, Pollet TV, Priebe S. Prevalence of depression and posttraumatic stress disorder in adult civilian survivors of war who stay in war-afflicted regions. A systematic review and meta-analysis of epidemiological studies. Journal of Affective Disorders. 2018;239:328–38. doi: 10.1016/j.jad.2018.07.027 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref174] 174.Nanni MG, Caruso R, Mitchell AJ, Meggiolaro E, Grassi L. Depression in HIV infected patients: A review. Current Psychiatry Report. 2015;17(1):530. doi: 10.1007/s11920-014-0530-4 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref175] 175.Del-Pino-Casado R, Rodríguez Cardosa M, López-Martínez C, Orgeta V. The association between subjective caregiver burden and depressive symptoms in carers of older relatives: A systematic review and meta-analysis. Plos One. 2019;14(5):e0217648. doi: 10.1371/journal.pone.0217648 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref176] 176.Al Mamun F, Hosen I, Misti JM, Kaggwa MM, Mamun MA. Mental disorders of Bangladeshi students during the COVID-19 pandemic: A systematic review. Psychological Research and Behavior Management. 2021;14:645–54. doi: 10.2147/PRBM.S315961 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref177] 177.Vithalani J, Herreros-Villanueva M. HIV epidemiology in Uganda: Survey based on age, gender, number of sexual partners and frequency of testing. African Health Sciences. 2018;18(3):523–30. doi: 10.4314/ahs.v18i3.8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref178] 178.Lundberg P, Nakasujja N, Musisi S, Thorson AE, Cantor-Graae E, Allebeck P. HIV prevalence in persons with severe mental illness in Uganda: a cross-sectional hospital-based study. International Journal of Mental Health Systems. 2013;7(1):20. doi: 10.1186/1752-4458-7-20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref179] 179.Rezaei S, Ahmadi S, Rahmati J, Hosseinifard H, Dehnad A, Aryankhesal A, et al. Global prevalence of depression in HIV/AIDS: A systematic review and meta-analysis. BMJ Supportive & Palliative Care. 2019;9(4):404–12. doi: 10.1136/bmjspcare-2019-001952 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref180] 180.Geng HM, Chuang DM, Yang F, Yang Y, Liu WM, Liu LH, et al. Prevalence and determinants of depression in caregivers of cancer patients: A systematic review and meta-analysis. Medicine. 2018;97(39):e11863. doi: 10.1097/MD.0000000000011863 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref181] 181.Muliira JK, Kizza IB, Nakitende G. Roles of family caregivers and perceived burden when caring for hospitalized adult cancer patients: Perspective from a low-income country. Cancer Nursing. 2019;42(3):208–17. doi: 10.1097/NCC.0000000000000591 [DOI] [PubMed] [Google Scholar]

[pone.0276552.ref182] 182.Adejoh SO, Boele F, Akeju D, Dandadzi A, Nabirye E, Namisango E, et al. The role, impact, and support of informal caregivers in the delivery of palliative care for patients with advanced cancer: A multi-country qualitative study. Palliative Medicine. 2020;35(3):552–62. doi: 10.1177/0269216320974925 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0276552.ref183] 183.Johns G, Samuel V, Freemantle L, Lewis J, Waddington L. The global prevalence of depression and anxiety among doctors during the covid-19 pandemic: Systematic review and meta-analysis. Journal of Affective Disorders. 2022;298(Pt A):431–41. doi: 10.1016/j.jad.2021.11.026 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Prevalence of depression in Uganda: A systematic review and meta-analysis

Mark Mohan Kaggwa

Sarah Maria Najjuka

Felix Bongomin

Mohammed A Mamun

Mark D Griffiths

Roles

Abstract

Background

Methods

Results

Limitation

Conclusion

Trial registration

Introduction

Methods

Search strategy

Fig 1. The PRISMA flow chart.

Inclusion criteria and exclusion criteria

Study and data management

Data extraction

Bias evaluation and quality assessment of the included papers

Table 1. Participants’ characteristics of all studies included in the meta-analysis.

Ethical considerations

Data synthesis and analysis

Results

Tools used in assessing depression

Prevalence of depression

Fig 2. Forest plot showing the pooled prevalence of depression in Uganda following sensitivity analysis.

Table 2. Subgroup analysis of the prevalence of depression in Uganda.

Fig 3. Forest plot on the prevalence of depression during the COVID-19 pandemic.

Prevalence of depression in different study populations in Uganda

Fig 4. Prevalence of depression in different study groups in Uganda.

Depression among refugees in Uganda

Fig 5. Forest plot on the prevalence of depression among refugees in Uganda.

Depression among studies involving only females in Uganda

Fig 6. Forest plot on the prevalence of depression among females in Uganda.

Depression among postpartum or pregnant women in Uganda

Fig 7. Forest plot on the prevalence of depression among postpartum or pregnant females in Uganda.

Depression among special patient groups in Uganda

Fig 8. Forest plot on the prevalence of depression among special patient groups in Uganda.

Depression among war victims in Uganda

Fig 9. Forest plot on the prevalence of depression among war victims in Uganda.

Depression among individuals living with HIV in Uganda

Fig 10. Forest plot on the prevalence of depression among individuals living with HIV in Uganda.

Depression among university students in Uganda

Fig 11. Forest plot on the prevalence of depression among university students in Uganda.

Depression among children and adolescents in Uganda

Fig 12. Forest plot on the prevalence of depression among children and adolescents in Uganda.

Depression among caregivers of patients in Uganda

Fig 13. Forest plot on the prevalence of depression among caregivers of selected patient groups in Uganda.

Depression among the general population

Fig 14. Forest plot on the prevalence of depression among the general population in Uganda.

Discussion

Limitations and recommendations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Muhammed Elhadi

Roles

Author response to Decision Letter 0

Decision Letter 1

Muhammed Elhadi

Roles

Acceptance letter

Muhammed Elhadi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles