Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis

Lydia M L Holtgrewe; Ann Johnson; Kate Nyhan; Jody Boffa; Sheela V Shenoi; Aaron S Karat; J Lucian Davis; Salome Charalambous

doi:10.1371/journal.pgph.0003753

. 2024 Oct 3;4(10):e0003753. doi: 10.1371/journal.pgph.0003753

Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis

Lydia M L Holtgrewe ^1,^*, Ann Johnson ^1,², Kate Nyhan ^3,⁴, Jody Boffa ⁵, Sheela V Shenoi ^1,², Aaron S Karat ⁶, J Lucian Davis ^1,^2,^#, Salome Charalambous ^1,^5,^#

Editor: Marguerite Massinga Loembe⁷

PMCID: PMC11449336 PMID: 39361564

Author summary

Identifying case-finding strategies to reduce tuberculosis (TB) incidence in high-burden countries requires better knowledge of the disease burden in key contributing populations and settings. To inform South Africa’s National Tuberculosis Strategic Plan 2023–2028, we conducted a systematic review of active TB disease and latent TB infection (LTBI) prevalence and incidence in underserved populations, defined as those living in informal settlements, townships, or impoverished communities. We identified articles published from January 2010 to December 2023, assessed study quality, and conducted a meta-analysis to estimate pooled TB and LTBI prevalence stratified by HIV status. We calculated prevalence ratios for underserved populations compared to the overall South African population. The search yielded 726 unique citations. We identified 22 studies reporting TB prevalence (n = 12), TB incidence (n = 5), LTBI prevalence (n = 5), and/or LTBI incidence (n = 2) eligible for the review, including six high-quality studies. Meta-analysis demonstrated a high prevalence of TB disease among persons living without HIV (2.7% 95% CI 0.1 to 8.5%) and persons living with HIV (PLWH) (22.7%, 95% CI 15.8 to 30.4%), but heterogeneity was high (I² = 95.5% and 92.3%, p-value<0.00). LTBI prevalence was high among persons living without HIV (44.8%, 95% 42.5 to 47%) with moderate heterogeneity (I² = 14.6%, p-value = 0.31), and lower among PLWH (33%, 95% CI 22.6 to 44.4%) based on one study. Compared to the national average, underserved populations of persons living without HIV had a 4-fold higher TB prevalence and a 3.3-fold higher LTBI prevalence. Underserved PLWH had a 31-fold higher TB prevalence than the national average, but similar LTBI prevalence as measured in one study. Our findings illustrate that underserved populations in South Africa have a substantially higher TB and LTBI prevalence than the general population, making targeted TB interventions potentially beneficial. More research is needed to explore the heterogeneous TB epidemiology in South Africa.

Introduction

The 2023 WHO Global Tuberculosis (TB) Report found a significant global recovery in the number of people diagnosed with and treated for TB, following two years of disruptions to TB services due to the COVID-19 pandemic [1]. In spite of this, TB remains the world’s second leading infectious disease killer after COVID-19 [1]. The 2015 End TB Strategy proposed ambitious targets to reduce TB incidence by 80% and TB deaths by 90% by 2030 [2]. While cumulative global reductions in these indicators fell short of the 2020 interim milestones, South Africa reported a 53% reduction in incidence between 2015 and 2022, from 988 to 468 persons with TB per 100,000 population, although the TB mortality has not fallen as rapidly [1–4]. Novel strategies are needed to reach the WHO targets.

Addressing the diagnostic gap in South Africa and other high-burden countries demands current and reliable TB prevalence and incidence estimates, especially among key populations at highest risk of TB [5]. Such estimates can be obtained using mathematical modelling or population-based studies. The former use a variety of readily available data inputs, including annual notifications of people with TB and previous surveys of TB prevalence and TB risk factors to estimate TB incidence [6]. Although modelling studies are convenient and less costly than population-based surveys, it can be challenging to obtain reliable sub-national estimates of TB incidence and prevalence given local heterogeneity in notifications of people with TB and in prevalence of HIV and other TB risk factors [7–10]. Therefore, the WHO recommends using population-based studies to obtain reliable estimates that can be used by policymakers to prioritise TB service delivery for those at highest risk and project the added impact of targeted interventions [11].

One key population of particular interest is people who are ‘underserved’, a group who faces structural barriers to accessing TB services because of disadvantaged or marginalised socioeconomic positions that require them to live in areas with fewer nearby clinics [12]. In the context of South Africa’s social system, we defined underserved populations as those living in informal settlements, townships, or other impoverished communities, in line with the United Nations’ third Sustainable Development Goal [13, 14]. Underserved populations share individual and environmental risk factors for acquiring TB infection and/or progressing to active TB disease, such as HIV, malnutrition, diabetes, overcrowding, poor ventilation, urban residence, and poor access to health services [15–17]. In a previous scoping review, we found populations living in informal settlements to be the largest contributor to the absolute number of people with TB in South Africa followed by people living with HIV (PLWH) [14]. To inform South Africa’s revised National TB Strategic Plan 2023–2028 [5], we undertook an updated systematic review to determine the prevalence and incidence of active and latent TB disease among underserved populations in South Africa between 2010 and 2023.

Methods

Study protocol and search strategy

We developed a systematic review protocol following the PRISMA-P reporting guidelines [18] (see S1 Checklist) for systematic reviews and prospectively registered it with PROSPERO [19]. Our search strategy identified published literature and pre-prints in the databases Lens.org, EMBASE, Africa Index Medicus and the Clarivate Incidence & Prevalence Database on June 26, 2023. Lens.org aggregates content from multiple sources including PubMed, Microsoft Academic, and Crossref, and helps identify free full text versions of papers. Our search strategy consisted of key words, database-specific subject headings, and title/abstract search terms (see S1 Table). We combined terms related to active TB disease, latent TB infection (LTBI), epidemiologic measures of disease burden, South Africa, and underserved populations. We also searched journal names and full text articles for terms related to South Africa to capture articles whose titles and abstracts may have omitted this information. Finally, we conducted a backward search of the reference list for our prior systematic review on TB incidence and prevalence in informal settlements in South Africa [15].

Study selection

We imported search results into Covidence systematic review software (Melbourne, Australia) and removed all duplicates. Two reviewers (L.H., A.J.) independently conducted title and abstract screening, followed by full-text screening. We included 1) peer-reviewed articles and pre-print manuscripts of prospective and retrospective cohort studies, cross-sectional studies, non-randomized studies and randomized controlled trials that were 2) published or made available between 1 January 2010 and 26 July 2023, regardless of language. We required studies to 3) report prevalence, incidence, or notification data on active TB disease or LTBI, and to include 4) underserved populations in South Africa, including townships, informal settlements, and impoverished populations. To guide screening of articles, we defined townships as tightly regulated, racially segregated residential areas built outside cities during the Apartheid Era. We defined informal settlements as residential areas constructed on land that occupants have no legal claim to occupy, often in the context of rapid population growth and an inadequate housing supply. Both are characterized by substandard living conditions [20, 21]. We defined impoverished communities as those consisting of individuals of low socio-economic status, characterised by low household income [22]. Because studies reported wealth and income in different ways, we classified a study population as impoverished if the study referenced any measure or proxy indicative of low household wealth or income. We resolved discrepancies between reviewers on study inclusion or exclusion by consensus.

Data extraction

One reviewer (L.H.) collected relevant study characteristics using standardised data extraction forms, including details on study design, TB diagnostic tools, and participant demographic and clinical characteristics (see S2 Table). We recorded prevalence and cumulative incidence as the number of people with TB per 100,000 and incidence rates as the number of people with TB per 100,000 person-years, including 95% confidence intervals (CI) if available. If the estimates were not directly reported, we calculated them by dividing TB notifications by the population size (prevalence) or total population at risk (cumulative incidence). We stratified all outcome estimates by HIV status (PLWH and people living without HIV). When study outcomes were not broken down by HIV status, we reported the overall study cohort’s outcomes (people living with and without HIV). A second reviewer (A.J.) checked the extracted data for accuracy.

Risk-of-bias assessment

Two reviewers (L.H., A.J.) independently assessed for risk-of-bias among included studies using all nine items in JBI’s Prevalence Critical Appraisal Tool using standardised forms (see S3 Table). Items 1–5 evaluate for selection bias and generalisability by assessing the sample frame; sampling approach; sample size; participant characteristics and study setting; and sample coverage to determine if the study population is representative of the target population and sufficiently large. Items 6–7 address measurement error, including whether valid methods were used to identify the condition and applied consistently to all participants. Last, items 8–9 examine the statistical methods used, including the appropriateness of the statistical analysis plan and the adequacy of the response rate [23]. We scored each item as ‘Yes’, ‘No’, ‘Unclear’ or ‘Not applicable’, resulting in an overall decision to either label studies as ‘low risk-of-bias’ or ‘high risk-of-bias’. A study’s risk of bias refers to the potential influence of its methods on the observed outcomes. Studies classified as having a high risk-of-bias are more likely to deviate in their estimates of the true effect than those classified as having a low risk-of-bias. Because of the importance of sample frame (item 1), sampling approach (item 2), and diagnostic methods (item 6) for determining prevalence and incidence in a target population, studies receiving a ‘No” response to any of these items were labelled as ‘high risk-of-bias’. Reviewers resolved all discrepancies through discussion.

Data synthesis

We summarised the characteristics and outcomes of all individual studies descriptively, then undertook meta-analyses of the pooled prevalence of active TB disease, prevalence of LTBI, incidence of active TB disease, and incidence of LTBI, reported as standardised proportions, and stratified by HIV status. For the summary estimates of the burden of active TB disease, we only included studies using WHO-endorsed diagnostic tools [19, 24–26], including mycobacterial culture, smear microscopy, loop-mediated isothermal amplification (LAMP)-based assays, automated nucleic acid amplification tests (NAAT) and lateral flow lipoarabinomannan assays (LF-LAM) for TB diagnosis in the entire study cohort [24]. For summary estimates of the burden of LTBI, we included studies using tuberculin skin tests (TST) or interferon-γ-release assays (IGRA) for LTBI diagnosis in the entire study cohort. The criteria for conducting meta-analysis required the presence of two or more studies within each meta-analysis group, provided that those studies reported sufficiently similar summary estimates using the same prevalence or incidence units to ensure comparability.

Primary analyses

We conducted the meta-analysis according to the recommendations of the Cochrane Handbook for Systematic Reviews [26]. Specifically, we conducted a proportion meta-analysis after recasting outcome values using the double arcsine transformation to stabilise variances and ensure interpretable confidence intervals [26, 27]. We fitted DerSimonian and Laird inverse-variance random-effects models given the statistical heterogeneity among studies and their clinical and methodological variability [28]. We explored statistical, methodological, and clinical heterogeneity through visual inspection of forest plots and the I² statistic [29]. We assessed for publication bias using funnel plots and the Egger test at a significance level of α = 0.05.

Sensitivity and subgroup analyses

We repeated the meta-analysis after excluding studies that were statistical or clinical outliers (e.g., different population characteristics or study settings) within each HIV status subgroup (see S1 Data) [26]. We also examined the influence of diagnostic tools, age, sex, ethnicity, study setting, and studies’ risk of bias on outcome estimates.

Other effect estimates

We calculated the prevalence ratio (PR) for active TB disease among underserved populations by dividing pooled summary estimates, obtained through sensitivity analysis, by the most recent national TB prevalence figures from South Africa’s 2018 national TB prevalence survey (see S2 Data) [30]. We calculated the prevalence ratio for LTBI in underserved populations by using modelled LTBI prevalence estimates from a 2014 study of the global burden of LTBI as the denominator (see S2 Data) [31]. We conducted all analyses in R Core Team (2023) using ‘meta’ [32], ‘metafor’[33] and Wang’s R code [34]. The analytic code used to generate meta-analyses can be accessed under https://osf.io/uzj65/?view_only=e562685a34804ebb8a8f0286804ae4ce.

Results

Search process and study selection

The search returned 1426 search results (see Fig 1). After excluding 700 duplicates, we screened 726 titles and abstracts. All screened articles can be found in S3 Data. We selected 74 reports for full-text review, of which 21 studies met our inclusion criteria. One study included in the previous version of the review [14] met out inclusion criteria and was added, resulting in a total of 22 studies in this review [35–56]. The most common reasons for exclusion were ineligible study design and setting, not including underserved populations, and/or ineligible study outcomes like TB morbidity, TB mortality, and TB-related treatment costs.

Study characteristics

Out of the 22 studies included in this study, 17 studies provided prevalence estimates [35–51], while six studies reported incidence figures [49, 52–56] (Table 1). Notably, only three [38, 39, 52] of these 22 studies were also captured in the previously conducted scoping review on TB prevalence and incidence in informal settlements in South Africa [14]. Study participants were recruited from outpatient facilities (n = 13) [35, 37–40, 48, 49, 51–55], homes (n = 9) [41–47, 50, 56] and a hospital (n = 1) [36]. Most studies (n = 16) [35–41, 43, 44, 48–54] were conducted in townships in the Western Cape Province. Other study locations included Gauteng [42, 46, 47, 56], Eastern Cape [45] and KwaZulu-Natal [55] provinces. Bacterial culture was the most frequently used active TB diagnostic tool of active TB disease (n = 6) [35, 37–40, 43], followed by self-report (n = 4) [44–46, 56]. Other studies used multiple methods (n = 7) [36, 41, 42, 52–55], including combinations of bacterial culture, NAAT, sputum smear microscopy, and clinical scoring systems. TST was the most commonly used diagnostic tool for LTBI (n = 6) [47–50, 53], with a positivity threshold of ≥10 mm among people living without HIV and ≥5 mm among PLWH. One study used IGRA [51]. Several studies recruited targeted rather than population-based samples, including eight studies in which the majority of participants were women [35–39, 47, 52, 55] and six studies focused on PLWH only [35–38, 52, 55].

Table 1. Characteristics of included studies.

a. TB PREVALENCE
Data extractors (Date)	Author (Year)	Study design (Study setting)	Sampling strategy	Diagnostic tool	Exposure definition	Geographic location	Sample size	Age group	Female (%)	PWH (%)
L.H.; A.J. (01.03.2023)	Lawn (2011) [35]	Cross-sectional (Outpatient)	Convenience sampling	Bacterial culture	Township population	Gugulethu Township, Cape Town	468	Adults	66	100
L.H.; A.J. (04.03.2023)	Lawn (2017) [36]	Cross-sectional (Hospital)	Convenience sampling	Automated NAAT; Bacterial culture	Township population	G.F. Jooste Hospital, Cape Town	427	Adults	61	100
L.H.; A.J. (07.03.2023)	Lawn (2011) [37]	Cross-sectional (Outpatient)	Convenience sampling	Bacterial culture	Township population	Gugulethu Township, Cape Town	542	Adults	64	100
L.H.; A.J. (21.03.2023)	Dawson (2010) [38]	Cross-sectional (Outpatient)	Convenience sampling	Bacterial culture	Township population	Gugulethu Township, Cape Town	235	Adults	73	100
L.H.; A.J. (21.03.2023)	Kranzer (2012) [39]	Cross-sectional (Outpatient)	Convenience sampling	Bacterial culture	Township population	Peri-urban areas, Cape Town	1,011	Adults	64	47
L.H.; A.J. (01.03.2023)	Cox (2010) [40]	Cross-sectional (Outpatient)	Convenience sampling	Bacterial culture	Township population	Khayelitsha, Cape Town	1,630	Adults	NR	55–71^*
L.H.; A.J. (28.02.2023)	Middelkoop (2010) [41]	Cross-sectional (Home)	Population-based sampling	Sputum smear microscopy Bacterial culture	Township population	Cape Town	1,250	Adults (≥ 15 years)	48	25
L.H.; A.J. (02.03.2023)	Van Rie (2018) [42]	Cross-sectional (Home)	Population-based sampling	Automated NAAT Self-report	Township population	Diepsloot, Johannesburg	1,231	Adults (≥ 15 years)	54	8.4 (New diagnosis)
L.H.; A.J. (07.03.2023)	Yates (2018) [43]	Cross-sectional (Home)	Population-based sampling	Bacterial culture	Low SES^†	8 communities, Western Cape	15,036	Adults	NR	9.9^‡
L.H.; A.J. (01.03.2023)	Govender (2010) [44]	Cross-sectional (Home)	Unclear	Self-report (Household survey)	Informal population	Low-cost housing communities, Cape Town	370	All ages	50	3
L.H.; A.J. (28.02.2023)	Cramm (2011) [45]	Cross-sectional (Home)	Population-based sampling	Self-report (Household survey)	Township population	Grahamstown, Eastern Cape	977	Adults	NR	NR
L.H.; A.J. (03.03.2023)	Booi (2022) [46]	Cross-sectional (Home)	Population-based sampling	Self-report (Household survey)	Township population	Mamelodi, Gauteng	114,348	NR	NR	NR
b. LTBI PREVALENCE
L.H.; A.J. (04.03.2023)	Ncayiyana. (2015) [47]	Cross-sectional (Home)	Population-based sampling	TST (≥5mm in PLWH, ≥10mm in others)	Township population	Diepsloot, Johannesburg	446	All ages	60	18
L.H.; A.J. (01.03.2023)	Wood (2010) [48]	Cross-sectional (Outpatient)	Population-based sampling	TST (≥10mm)	Township population	Cape Town	1,061	5–17 (78); 18–40 years (22)	NR	0
L.H.; A.J. (02.03.2023)	Middelkoop (2014) [49]	Cross-sectional (Outpatient)	Population-based sampling	TST (≥10mm)	Township population	Cape Town	1,100	5–22 years	50	0
L.H.; A.J. (04.03.2023)	Du Preez (2011) [50]	Cross-sectional (Home)	Convenience sampling	TST (≥10mm)	Township population	Uitsig/Ravensmead, Cape Town	196	3 months- 15 years	48	0
L.H.; A.J. (02.03.2023)	Bunyasi (2019) [51]	Cross-sectional (Outpatient)	Convenience sampling	IGRA	Low SES^§	Cape Town	5,929	12–19 years	NR	NR
c. TB INCIDENCE
L.H.; A.J. (03.03.2023)	Gupta (2012) [52]	Prospective cohort (Outpatient)	Convenience sampling	Various diagnostic tools (Incl. bacterial culture)	Township population	Gugulethu Township, Cape Town	1,544	Adults (≥ 16 years)	70	100
L.H.; A.J. (02.03.2023)	Martinez (2017) [53]	Prospective cohort (Outpatient)	Population-based sampling	Various diagnostic tools	Township population	Paarl, Cape Town	915	Children (Birth-5 years)	49	<1
L.H.; A.J. (03.03.2023)	Wood (2010) [54]	Retrospective cohort (Outpatient)	Convenience sampling	Various diagnostic tools (Incl. bacterial culture and sputum smear microscopy)	Township population	Cape Town	14,788	All ages	NR	NR
L.H.; A.J. (09.08.2023)	Naidoo (2014) [55]	Prospective cohort (Outpatient)	Convenience sampling	Various diagnostic tools (Incl. bacterial culture)	Township population	Vulindlela, KwaZulu-Natal	969	Adults	68	100
L.H.; A.J. (07.03.2023)	Ilunga (2020) [56]	Prospective cohort (Home)	Population-based sampling	Self-report	Township population	Mamelodi, Gauteng	184,351	All ages	NR	NR
d. LTBI INCIDENCE
L.H.; A.J. (02.03.2023)	Middelkoop (2014) [49]	Retrospective cohort (Outpatient)	Population-based sampling	TST (≥10mm)	Township population	Cape Town	67	5–22 years	51	0
L.H.; A.J. (02.03.2023)	Martinez (2017) [53]	Prospective cohort (Outpatient)	Population-based sampling	TST (≥10mm)	Township population	Paarl, Cape Town	915	Birth-5 years	49	<1

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Abbreviations: TB = Tuberculosis; PLWH = People Living With HIV; NAAT = Nucleic Acid Amplification Test; NR = Not reported; LTBI = Latent Tuberculosis; TST = Tuberculin Skin Test; IGRA = Interferon-Gamma Release Assay.

*Among newly diagnosed TB cases and previously treated people with TB, respectively.

†Defined as individuals with a very low or low Household Wealth Index.

‡Among the overall population (individuals of all socio-economic statuses).

§Defined as individuals attending low-income state schools.

Risk-of-bias assessment

As shown in Table 2, six studies were labelled as low risk-of-bias, including one study reporting active TB disease prevalence [41], three reporting LTBI prevalence [47–49], one reporting active TB disease incidence [52], and one reporting LTBI incidence [49]. The most common reasons for high risk-of-bias scores were methodological weaknesses in sampling, outcome ascertainment, and statistical analysis. Sampling weaknesses included recruitment from non-representative populations, including six studies enrolling only PLWH and one enrolling only hospitalised individuals; low response rates from men in eight studies, resulting in female-predominant samples; and non-random sampling, such as convenience sampling in eleven studies. In five studies, outcome ascertainment was limited by use of non-standardised or subjective diagnostic tools, such as self-report or clinical scoring systems. Last, statistical analyses lacked sample size calculations in 22 studies, while nine studies presented incidence or prevalence estimates without confidence intervals. Please refer to Tables A and B in S4 Table for a more detailed justification of the overall appraisal of individual studies.

Table 2. Risk-of-bias assessment of included studies.

Author (Year)	1\| Sample frame	2\| Sampling	3\| Sample size	4\| Study subjects and setting	5\| Data analysis	6\| Identification of the condition	7\| Measurement of the condition	8\| Statistical analysis	9\| Response rate	Overall appraisal
a. TB PREVALENCE
Cox (2010) [40]	No	No	Unclear	Yes	Yes	Yes	Yes	No	Yes	High risk of bias
Dawson (2010) [38]	No	No	Unclear	Yes	No	Yes	Yes	Yes	No	High risk of bias
Govender.(2010) [44]	Yes	Unclear	Unclear	Yes	Yes	No	Yes	No	Yes	High risk of bias
Middelkoop (2010) [41]	Yes	Yes	Unclear	Yes	Yes	Yes	Unclear	No	Yes	Low risk of bias
Cramm (2011) [45]	Yes	Yes	Unclear	No	Yes	No	Unclear	No	Yes	High risk of bias
Lawn (2011) [35]	No	No	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	High risk of bias
Lawn (2011) [36]	No	No	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	High risk of bias
Kranzer (2012) [39]	Yes	No	Unclear	Yes	No	Yes	Yes	Yes	No	High risk of bias
Lawn (2017) [37]	No	No	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	High risk of bias
Van Rie (2018) [42]	Yes	Yes	Unclear	Yes	Yes	No	Yes	No	Yes	High risk of bias
Yates (2018) [43]	No	Yes	Unclear	Yes	No	Yes	Unclear	No	No	High risk of bias
Booi (2022) [46]	Yes	Yes	Yes	No	Yes	No	Unclear	No	Unclear	High risk of bias
b. LTBI PREVALENCE
Wood (2010) [48]	Yes	Yes	Unclear	No	NA	Yes	Unclear	Yes	NA	Low risk of bias
Du Preez (2011) [50]	No	No	Unclear	Yes	Unclear	Yes	Yes	No	Unclear	High risk of bias
Middelkoop (2014) [49]	Yes	Yes	Unclear	Yes	Unclear	Yes	Yes	Yes	Yes	Low risk of bias
Ncayiyana (2015) [47]	Yes	Yes	Unclear	Yes	Yes	Yes	Yes	Yes	Unclear	Low risk of bias
Bunyasi (2019) [51]	No	No	Yes	Yes	Unclear	Yes	Yes	Yes	Unclear	Exclude
c. TB INCIDENCE
Wood (2010) [54]	Yes	No	Unclear	Yes	Unclear	Yes	Unclear	Yes	Unclear	High risk of bias
Gupta (2012) [52]	No	No	Unclear	Yes	Unclear	Yes	No	Yes	Yes	High risk of bias
Naidoo (2014) [55]	No	No	Unclear	Yes	No	No	Yes	Yes	Yes	High risk of bias
Martinez (2017) [53]	Yes	Yes	Unclear	Yes	Unclear	Yes	Yes	Yes	Yes	Low risk of bias
Ilunga (2020) [56]	Yes	Yes	Yes	Yes	Yes	No	Unclear	No	Unclear	High risk of bias
d. LTBI PREVALENCE
Middelkoop (2014) [49]	Yes	Yes	Unclear	Yes	Unclear	Yes	Yes	Yes	Yes	Low risk of bias
Martinez (2017) [53]	Yes	Yes	Unclear	Yes	Unclear	Yes	Yes	Yes	Yes	Low risk of bias

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Abbreviations: TB = Tuberculosis; LTBI = Latent Tuberculosis; NA = Not applicable.

*Because of the importance of sample frame (item 1), sampling approach (item 2) and diagnostic methods (item 6) for determining prevalence and incidence in a target population, studies receiving a ‘No” response to any of these items were labelled as ‘high risk-of-bias’.

Results of individual studies

Prevalence of active TB disease and LTBI

Twelve studies reported an active TB disease prevalence estimate in adults, ranging from 0.4% [42] to 34% [40] (Table 3). Overall, studies using self-report (n = 4) [43–45] tended to yield lower prevalence estimates than studies using bacterial culture (n = 8) [35–41, 43]. Furthermore, studies conducted in PLWH (n = 4) [35–38] tended to have higher active TB prevalence estimates than other studies. Lastly, four studies reported TB prevalence stratified by sex, ranging from 3.4% [42] to 34.8% [35] in women and from 6.1% [42] to 29.2% [35] in men.

Table 3. Prevalence of active TB disease and LTBI in included studies.

a. TB PREVALENCE
Author (Year)	HIV status	Sample size	TB cases (% Prevalence)	Diagnostic tool	Data collection unit	Included in meta-analysis? (Reason for exclusion)
Lawn (2011) [35]	Positive	468	81 (17.3)	Bacterial culture	Individual	Yes (Not applicable)
Lawn (2017) [36]	Positive	427	139 (32.6)	NAAT; Bacterial culture	Individual	Yes (Not applicable)
Lawn (2011) [37]	Positive	542	94 (17.3)	Bacterial culture	Individual	Yes (Not applicable)
Dawson (2010) [38]	Positive	235	58 (24.7)	Bacterial culture	Individual	Yes (Not applicable)
Kranzer (2012) [39]	Mixed^†	1 011	56 (5.5)	Bacterial culture	Individual	Yes (Not applicable)
	Positive	520	30 (5.8)
	Negative	491	26 (5.3)
Cox (2010) [40]	Mixed^†	1 575	535 (34)	Bacterial culture	Individual	Yes (Not applicable)
	Positive	NR	300
	Negative	NR	176
	NR	NR	59
Middelkoop (2010) [41]	Mixed^†	1 250	20 (1.6)	Sputum smear-microscopy; Bacterial culture	Individual	Yes (Not applicable)
	Positive	306	11 (3.6)
	Negative	901	9 (1)
	NR	43	NR
Van Rie (2018) [42]	Mixed^†	1 231	NAAT^*: 5 (0.4) Self-report: 57 (4.6)	NAAT: Self-report	Individual	No (Use of self-report)
Yates (2018) [43]	Mixed^†	15 036	371 (2.5)	Bacterial culture	Individual	Yes (Not applicable)
Govender (2010) [44]	Mixed^†	370	14 (3.8)	Self-report	Household	No (Use of self-report)
Cramm (2011) [45]	NR	977	316 (32.5)	Self-report	Household	No (Use of self-report)
Booi (2022) [46]	NR	114 348	1 742 (1.5)	Self-report	Individual	No (Use of self-report)
b. LTBI PREVALENCE
Ncayiyana (2015) [47]	Mixed^†	446	153 (34.3)	TST	Individual	Yes (Not applicable)
	Positive	70	23 (32.9)
	Negative	317	115 (36.3)
	NR	59	15 (25.4)
Wood (2010) [48]	Negative	1 061	477 (45)	TST	Individual	Yes (Not applicable)
Middelkoop (2014) [49]	Negative	1 100	480 (43.6)	TST	Individual	Yes (Not applicable)
Du Preez (2011) [50]	Negative	196	97 (49.5)	TST	Individual	Yes (Not applicable)
Bunyasi (2019) [51]	NR	5 929	3 236 (54.6)	IGRA	Individual	Yes (Not applicable)

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Abbreviations: TB = Tuberculosis; NAAT = Nucleic Acid Amplification Assay; NR = Not reported; LTBI = Latent Tuberculosis; TST = Tuberculin Skin Test; IGRA = Interferon-Gamma Release Assay.

*NAAT only performed in individuals with presumptive TB.

†Including both people living with HIV and people living without HIV (the overall study cohort) when study outcomes were not broken down by HIV status.

‡Sex-stratified outcome estimates are not provided due to the limited number of studies reporting Tb/LTBI prevalence separately for each sex.

Five studies reported on LTBI prevalence [47–51], ranging from 25.4% [47] to 54.6% [51]. Four studies sampled pediatric and adolescent populations [48–51], with LTBI prevalence ranging from 43.6% [49] to 54.6 [51]. The three studies among populations living without HIV [48–50] showed comparable LTBI prevalence estimates as the studies conducted among PLWH. One study reported LTBI prevalence by sex, finding a LTBI prevalence of 32.3% in women and 37.1% in men [47].

Incidence of active TB disease and LTBI

Six studies reported active TB disease incidence estimates [52–56] (Table 4). These ranged from 0.7 [53] to 7.44 [52] people with TB per 100 person-years (n = 4 studies) [52–55]. The cumulative incidence ranged from 0.43% [56] to 31.4% [52] (n = 3 studies) [52, 54, 56]. One study reported sex-stratified TB incidence rates, finding a TB incidence of 2.4 people with TB per 100 person-years in women and 3.5 people with TB per 100 person-years in men [53]. Because of the small number of included studies, the use of different measures and units of incidence, and inconsistent age groupings, we could not assess the influence of age, HIV status, or diagnostic tool on TB incidence estimates. Of the two LTBI incidence studies, one was conducted in children and reported an incidence rate of 11.8 people with TB per 100 person-years overall, 9.4 people with TB per 100 person-years in girls, and 14.3 people with TB per 100 person-years in boys using TST [53]. The other study was conducted in children and adolescents and reported a cumulative incidence of 23.9% [49].

Table 4. Incidence of active TB disease and LTBI in included studies.

a. TB INCIDENCE
Author (Year)	HIV status	Sample size	TB cases (% Incidence)	TB incidence rate [95%CI]	Diagnostic tool	Data collection unit
Gupta (2012) [52]	Positive	1 544	Total: 484 (31.4) Culture-confirmed: 356 (23.1)	7.44 [6.8–8.13] people with TB per 100 PY Culture-confirmed: 23 057 [21 762–24 288] people with TB per 100 000	Various	Individual
Martinez (2017) [53]	Negative^*	915	Total: 81 (8.9) Microbiologically confirmed: 18 (2)	Total: 2.9 [2.4–3.7] per 100 PY Microbiologically confirmed: 0.7 [0.4–1.0] per 100 PY	Various	Individual
Wood (2010) [54]	NR	Total: 14 788 Adults: 12 097 Children (5–15): 1 640 Children (<5): 1 051	Total: 1 289 (8.7) Adults: 670 (5.5) Children (5–15): 45 (2.7) Children (<5): 86 (8.2)	Total population: 1909 [1799–2018] per 100 000 PY Adults: (culture-confirmed): 1347 [1 108–1 437] per 100 000 PY Adults (culture-confirmed): 5 539 people with TB per 100 000 Children (5–15): 546 [346–546] per 100 000 PY Children (<5): 1522 [1419–1533] per 100 000 PY	Various	Individual
Naidoo (2014) [55]	positive	969	54 (5.6)	4.5 [3.3–5.8] cases per 100 PY	Various	Individual
Ilunga (2010) [56]	NR	184 351	788 (0.4)	427 people with TB per 100 000	Self-report	Individual
b. LTBI INCIDENCE
Middelkoop (2014) [49]	Negative	67	16 (23.9)	23 881 people with TB per 100 000	TST	Individual
Martinez (2017) [53]	Negative^*	915	147 (16)	11.8 [10–13.8] per 100 PY	TST	Individual

Open in a new tab

Abbreviations: TB = Tuberculosis; PY = Person-years; NR = Not reported; LTBI = Latent Tuberculosis; TST = Tuberculin Skin Test.

*The 2 (<1) children living with HIV were not separately analysed and included in the cohort of children without HIV; all TB incidents occurred in children living without HIV.

†Sex-stratified outcome estimates are not provided due to the limited number of studies reporting TB/LTBI prevalence separately for each sex.

Pooled results

Primary analysis

A meta-analysis of eight eligible studies [35–41, 43] yielded a pooled active TB prevalence of 15.4% (95% CI 7.7–25.3) among PLWH, 2.7% (95% CI 0.1–8.5) among those living without HIV, and 7.9% (95% CI 0.4–23) for studies including both PLWH and those living without HIV (S1 Fig). The I² was >95% in all three groups. The small number of eligible studies prevented us from making conclusions about publication bias for any of the subgroups, although the Egger test failed to reject the null hypothesis of symmetry (S2–S4 Figs, S5 Table). Meta-analysis of four eligible studies [47–50] yielded a pooled LTBI prevalence of 43.4% (95% CI 39.5–47.3) in people living without HIV, with an I² of 71.6% (S5 Fig). LTBI prevalence was 33% (95% CI 22.6–44.4) in the one study conducted among PLWH [47] and 55% (95% CI 53.3–55.9) in the one study including both PLWH and those living without HIV [51]. The Egger test for asymmetry was not significant (S5 Table). Due to the small number of eligible studies and their heterogeneity, we could not draw any conclusions about publication bias from the funnel plot (S6 Fig). Additionally, because of the insufficient number of qualifying studies reporting summary estimates using consistent incidence units (<2 within each meta-analysis group), we did not conduct meta-analyses for active TB disease incidence or for LTBI incidence.

Sensitivity and subgroup analyses

After removing two studies [39, 41] that were major contributors to overall heterogeneity because of differences in study design and participant characteristics, meta-analysis of the remaining four studies yielded a pooled active TB prevalence of 22.7% (95% CI 15.8–30.4) among PLWH. In the group of studies including both people living with and without HIV, we excluded one study [40] that was a statistically significant outlier (Cook’s distance = 1.1). The remaining three studies had a pooled active TB prevalence of 2.9% (95% CI 1.5–4.8; Fig 2). The I² remained high at > 90%. Excluding one study [47], a statistically significant outlier (Cook’s distance = 0.47), from the studies including people living without HIV resulted in a pooled LTBI prevalence of 44.8% (95% CI 42.5–47) in the remaining three studies, with the I² reduced to 14.6% (Fig 3). S6 Table compares pooled prevalence estimates obtained before and after excluding outliers.

Fig 2 — **Abbreviations**: HIV = Human Immunodeficiency Virus. *The ‘People living with and without HIV’ group includes studies for which outcomes were not stratified by HIV status.

Fig 3 — **Abbreviations**: HIV = Human Immunodeficiency Virus. *The ‘People living with and without HIV’ group includes studies for which outcomes were not stratified by HIV status.

We were unable to conduct formal subgroup analyses by potential drivers of heterogeneity such as diagnostic tool, age, sex, ethnicity, or study setting used because these variables were only reported in <10 studies or reported inconsistently (e.g., differing age categories) [25, 26]. We were also unable to conduct sensitivity analyses based on the risk-of-bias assessment results because only one study reporting TB prevalence and only three studies reporting LTBI prevalence were considered to have a low risk of bias.

Prevalence ratios for active TB disease and LTBI relative to the general population

People living without HIV in underserved populations had an almost 4-fold greater risk of active TB disease (PR = 3.8) than the overall South African population. Populations including both PLWH and people living without HIV also had an almost 4-fold higher risk (PR = 3.9) of active TB disease. PLWH were at a 31-fold increased risk (PR = 30.7). For LTBI, we predicted people living without HIV to be at a 3.3-fold increased risk compared to the overall population. Based on a single study estimate, we estimated a PR of 4 in a population with both PLWH and those living without HIV [51]. The prevalence estimate for LTBI in the one study of PLWH [47] was almost the same as the national estimate, yielding a PR of 1.

Discussion

In this updated systematic review of the literature published between 2010–2023, we found that underserved populations living without HIV had an almost 4-fold increased risk of active TB disease and a 3.3-fold increased risk of LTBI compared to the general population in South Africa. The risk of active TB was even greater in underserved PLWH. Our findings are, however, limited by substantial heterogeneity among a small number of studies distributed over a long period of time during which TB and HIV policies and practices have been rapidly changing. In addition, the generally low quality of study reporting further limits our confidence in these results.

Our findings are in line with previous research conducted among underserved populations worldwide. A prior systematic review conducted in South Africa and including studies published between 2000 and 2011 reported a 5.8-fold increased risk of active TB disease in informal settlements [14]. In contrast, a modelling study on population-level risk factors for TB in South Africa in 2010 failed to find a significantly higher active TB disease risk among those living in informal settlements [57]. However, because TB prevalence was derived from laboratory reports of detected TB incidents as a proxy for prevalence and not from a population-based sample, the true TB disease burden in informal settlements was likely substantially underestimated. An international review of the TB risk in slum households, an alternative term for those living in informal settlements, also found that the incidence of smear-positive TB in slums was 2.96 times higher than the national TB incidence [58]. Likewise, a population-based, cross-sectional study conducted in slum settings in Uganda in 2019 found that the TB prevalence was four times the national estimate [59]. Finally, a prospective implementation study of active TB case finding in Nigerian slums in 2012 yielded a TB prevalence twice that of the national average, of which 22.6% were living with HIV [60].

There were several limitations to the studies included in this review. Firstly, most studies were not designed primarily to measure incidence and prevalence, but to address other research questions. As a result, they were limited by small sample sizes and non-population-based sampling, leading to uncertain and biased estimates of the true TB and LTBI prevalence among underserved populations in South Africa. Conducting population-based studies in resource-limited settings is particularly challenging given constraints in diagnostic availability and associated costs [61, 62]. Consequently, we identified only one low risk-of-bias study among the studies reporting TB prevalence that employed population-based sampling [41]. A recent comprehensive overview of systematic reviews on TB prevalence and incidence in underserved populations worldwide also underscored deficiencies in the quality of included studies, as assessed by the same risk-of-bias assessment tool employed in our review [12]. Secondly, our broad definition of underserved populations, the long study inclusion period, demographic differences, and diverse diagnostic tools across studies may have introduced heterogeneity, as reflected in the high I² values in the pooled prevalence estimates. Unfortunately, the limited number of studies prevented more detailed subgroup analyses to explore heterogeneity. Subgroup analyses based on sex and age would have been particularly relevant due to significant variations in LTBI prevalence across different age groups and between men and women [31]. Two out of four studies that reported sex-stratified TB prevalence estimates found slightly higher numbers in women than in men. This contrasts with nationwide estimates, which show higher TB prevalence and mortality in men than in women [63]. All other studies that provided sex-specific outcomes reported lower prevalence and incidence estimates of active TB disease and LTBI in women compared to men. Thirdly, assessing publication bias was challenging due to the limited power of funnel plots and the Egger test with small study numbers [26]. Furthermore, the high concentration of study samples from around Cape Town limits the generalisability of our findings, emphasising the need for additional research from other cities and provinces, including rural areas to provide a more representative view of TB epidemiology in South Africa. Further research is needed not only in different geographical areas but also among other at-risk populations, including but not limited to rural poor communities, people who are incarcerated, and refugee and migrant populations [14]. Also, few studies investigated other risk factors like diabetes mellitus, tobacco smoking, alcohol use, or malnutrition in relation to overall TB risk. Given their high prevalence in socioeconomically disadvantaged populations and potential impacts on pathogenesis and treatment, quantifying their prevalence in individuals with TB would be beneficial. Lastly, data on the impacts of the COVID-19 pandemic on the TB burden in underserved populations is scarce [64]. These populations face increased vulnerability to the pandemic’s consequences due to a loss of household income without a financial safety net and poor access to social assistance programmes, testing, and healthcare services [65].

Despite the rigorous methodology employed in this review, there are several weaknesses. First, the risk for LTBI among PLWH appears comparable to that of the general population in the primary analysis, as indicated by a single study estimate. However, this finding should be interpreted with caution, as the prevalence of LTBI remains higher among PLWH compared to those living without HIV. This is despite the implementation of TB preventive therapy among household contacts of infectious pulmonary TB and TST-confirmed LTBI cases, for which uptake remains poor [66, 67]. The credibility of this finding is further diminished by the low sensitivity of both the TST and IGRA in detecting LTBI among PLWH [66]. Second, we used the most up-to-date 2018 national TB prevalence estimate as the denominator for active TB prevalence ratios [30]. However, because many studies contributing to the numerator were published around 2010 when TB prevalence was higher, the 2018 national estimate likely underestimated the national TB prevalence in earlier years, potentially inflating our estimates of the increased TB risk in underserved populations. Further, the use of modelled national LTBI estimates from 2014 as the denominator for LTBI prevalence ratios introduces similar limitations [31]. Two of the three studies contributing to the numerator including people without HIV—the only subgroup with more than one study–were published in 2010 and 2011, during a period when TB prevalence was higher than in 2014. This may have resulted in an overestimation of the LTBI risk in underserved people without HIV. Finally, the risk-of-bias assessment tool we used was designed for prevalence studies and does not consider the importance of length of follow-up to obtain valid cumulative incidence estimates [23].

Our study also has several strengths. First, this review employed a rigorous and systematic search of both South African and international databases. This is reflected in the substantial number of studies incorporated into our review, the majority of which were not captured in the previously conducted scoping review on TB prevalence and incidence in informal settlements in South Africa [14]. This suggests a significant expansion in the pool of available studies since then, and that our search strategy successfully captured a broad spectrum of studies. Moreover, to enhance precision in our pooled prevalence estimates, our meta-analysis only incorporated prevalence estimates obtained using WHO-approved diagnostic tools. Other review strengths are the use of a risk-of-bias assessment tool specifically tailored for prevalence studies, and the thorough review process, with two independent reviewers conducting all stages of study screening, data extraction and risk-of-bias assessment.

The results from our analysis have contributed to updated TB prevalence estimates for people living in informal settlements in South Africa’s National TB Strategic Plan 2023–2028 [5]. Their population size has been steadily growing over the past 20 years in South Africa and many other countries worldwide, and is projected to further increase in the coming decades [68, 69]. Consequently, informal settlements are expected to remain a significant contributor to both the global TB prevalence and incidence. To effectively curb these figures, it is imperative to implement a diverse array of testing modalities that are acceptable, feasible, cost-effective, and can be seamlessly integrated into targeted case-finding initiatives that focus on ‘hard-to-reach’ populations [70–73]. For example, a community-based active case-finding initiative using rapid point-of-care (POC) GeneXpert testing in mobile clinics within peri-urban informal settlements of Cape Town has shown promise, by decreasing time to treatment initiation and increasing the percentage of people receiving TB treatment compared to facility-based testing using sputum smear microscopy [74]. Strategies like these may be especially beneficial in informal settings where laboratory facilities and qualified medical personnel are scarce, by helping reach individuals who do not present to healthcare facilities [75, 76]. The National TB Strategic Plan 2023–2028 identifies informal settlements as a priority population for novel POC diagnostic tools, such as digital chest x-ray, tongue swabs, and self-screening [5]. Additionally, it notes that treatment modalities like community-based care and adherence support should be considered, bolstered by laboratory data and geo-spatial mapping [5]. In addition, the expansion of existing surveillance systems plays a pivotal role in evaluating the effectiveness of targeted TB interventions. In the long-term, changes to urban infrastructure and integration of healthcare services are needed to sustainably decrease TB prevalence and promote health equity. Importantly, this cannot be achieved without addressing the enduring economic and social repercussions of apartheid, which continue to perpetuate racial inequities in housing and other social determinants of health in South Africa [77].

Conclusions

This review adds to a pool of evidence that emphasises the key contribution of underserved populations to the South African TB epidemic. Thus, this review can serve as an important resource for national stakeholders and TB programs in assessing the relative contributions of various populations to the TB epidemic in South Africa and other TB-endemic settings.

Supporting information

S1 Checklist. PRISMA 2020 checklist [1].

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S1 Table. Search term.

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S2 Table. Data extraction form.

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S3 Table. Risk-of-bias assessment form [2].

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S4 Table

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S5 Table. Egger tests: P-values.

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S6 Table. Pooled prevalence before and after sensitivity analysis.

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S1 Data. Sensitivity analysis.

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S2 Data. Prevalence ratios.

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S3 Data. Screened studies, broken down into excluded and included studies.

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S1 Fig. Pooled active TB disease prevalence among underserved populations in South Africa, stratified by HIV status.

Abbreviations: HIV = Human Immunodeficiency Virus. *The ‘People living with and without HIV’ group includes studies for which outcomes were not stratified by HIV status.

(DOCX)

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S2 Fig. Funnel plot: Pooled active TB disease prevalence among underserved populations in South Africa (‘People living with HIV’ subgroup).

Abbreviations: TB = Tuberculosis; HIV = Human Immunodeficiency Virus.

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S3 Fig. Funnel plot: Pooled active TB disease prevalence among underserved populations in South Africa (‘People living without HIV’ subgroup).

Abbreviations: TB = Tuberculosis; HIV = Human Immunodeficiency Virus.

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S4 Fig. Funnel plot: Pooled active TB disease prevalence among underserved populations in South Africa (‘People living with and without HIV’ subgroup).

Abbreviations: TB = Tuberculosis; HIV = Human Immunodeficiency Virus.

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S5 Fig. Pooled LTBI prevalence among underserved populations in South Africa, stratified by HIV status.

Abbreviations: HIV = Human Immunodeficiency Virus. *The ‘People living with and without HIV’ group includes studies for which outcomes were not stratified by HIV status.

(DOCX)

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S6 Fig. Funnel plot: Pooled LTBI prevalence among underserved populations in South Africa (‘People living without HIV’ subgroup).

Abbreviations: LTBI = Latent Tuberculosis; HIV = Human Immunodeficiency Virus.

(DOCX)

pgph.0003753.s016.docx^{(212KB, docx)}

Data Availability

All relevant data has been included in the article, appendix, or the supplementary materials. Additional datasets and analytical code can be accessed under https://osf.io/uzj65/?view_only=e562685a34804ebb8a8f0286804ae4ce.

Funding Statement

The authors received no specific funding for this work.

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0003753.r001

Decision Letter 0

Marguerite Massinga Loembe

3 May 2024

PGPH-D-24-00621

Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis

PLOS Global Public Health

Dear Dr. Holtgrewe,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’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.

While we appreciate the merit of the manuscript submitted, we would please request that the authors specfically address the following comments from the reviewers:

Based on the various criteria presented in tables S4, clarify how a study was considered low vs high quality and included or excluded from the review as per table S5. Consider moving this information to the main manuscript as suggested by the reviewer.
Define what were considered "ineligible measures of disease burden"

Further, with regards to WHO endorsed diagnostic tools (line 128-129): no mention is made of the LF-LAM assay. Could the authors please clarify why this WHO endorsed diagnostic tool, and also recommended by South Africa NTP since 2021, was not included ? Particularly in the view that 6 studies included in this review focused on TB in PLWH only.

Please submit your revised manuscript by Jun 17 2024 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 globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ 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 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'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Marguerite Massinga Loembe, PhD

Academic Editor

PLOS Global Public Health

Journal Requirements:

1. Please provide separate figure files in .tif or .eps format.

For more information about figure files please see our guidelines: LINK

https://journals.plos.org/globalpublichealth/s/figures

https://journals.plos.org/globalpublichealth/s/figures#loc-file-requirements

Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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

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

PLOS Global Public Health 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

**********

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: Thank you for the opportunity to review this interesting study. This is an important analysis that attempts to estimate the burden of tuberculosis (TB) among underserved populations in South Africa–a high TB burden country. In the systematic review, the authors identified 22 studies for inclusion and found that TB burden was substantially greater among underserved populations compared to the overall population. I have the following comments to improve the clarity of the study:

Introduction:

Lines 51–56: The authors mention the WHO End TB Targets. For readers who are unfamiliar, the authors should briefly define the targets.

Line 52: Consider also citing the latest Global Burden of Disease report illustrating that the End TB targets were not achieved at the global level.

https://pubmed.ncbi.nlm.nih.gov/38518787/

Please use PLWH for people living with HIV rather than PWH.

Methods:

Lines 86 – 88: Why wasn’t PubMed also queried? This is important because prior work has shown that a combination of Embase and PubMed yields high coverage.

https://pubmed.ncbi.nlm.nih.gov/29208034/

Study selection: Please include additional information for inclusion criteria such as types of study designs were included, sampling methods, populations, outcome measurement, etc. This is important because I’m very unclear on the exact inclusion criteria for the systematic review.

Quality assessment: Please include 2-3 sentences on which metrics were used to evaluate study quality and why. Please also reference Table S4 here to point to additional details for readers. In addition, what is considered to be high-quality vs low-quality?

Sensitivity analyses: Was it possible to conduct sub-group analyses by sex? It may be interesting to see if the male to female ratio among underserved population differs compared to other estimates.

Other effect estimates: Have the authors considered using estimates from the Global Burden of Disease study as the comparator for the overall national-level TB prevalence in South Africa?

http://ihmeuw.org/6e62

Results:

Lines 169: What are ineligible measures of disease burden?

Table 1: The authors mention that many studies are not population-based but recruited. This is an important point. Can the authors include an additional column in Table 1 for recruitment strategy/sampling?

Discussion:

Lines 367–376: Can the authors comment on previous interventions that have been shown to improve case finding and TB outcomes among underserved populations. Since the goal of the study was to help inform the national TB program in South Africa, this provide valuable insights.

Can the authors briefly comment how the COVID-19 pandemic may have impact TB burden among underserved populations in South Africa?

Reviewer #2: Summary: A systematic review and meta-analysis of studies undertaken to assess burden of TB ( active TB prevalence and incidence and LTBI prevalence and incidence ) in underserved populations in South Africa . The review found a high burden of TB in this population compared with the South African general population (four and 31 fold higher risk of active TB in those without HIV and those with HIV respectively).

Strengths: The review is of high quality and conforms with accepted norms for the conduct of a systematic review and meta-analysis including in methodological approach, literature search strategy, assessment of bias, measures/ascertainment of outcomes etc.

Weakness:

1. A description of the burden of TB in South Africa, including incidence and prevalence is not provided to allow readers of this paper to have a better understanding of the TB situation in this country. The introduction only includes a statement about TB treatment coverage in South Africa (line 52-55).

2. While the review was focused on underserved” populations including people living in informal settlements, townships and impoverished communities only the population of people living in informal settlements is clearly defined (Lines 101-103). It is not clear if living in informal settlement is the same as living in a township. The measure of impoverishment in South Africa is also not defined.

3. It is noted that included studies were mostly carried out among township populations ( 9 of 12 studies for prevalence of active TB, 4 out of 5 studies for LTBI prevalence, 5 of 5 studies for TB incidence and 2 of 2 studies for LTBI incidence) and mostly in Cape Town. While the authors acknowledge this limitation, there is no apparent push or argument to conduct research studies similar to those included in this systematic review in other regions of the country and also in other populations considered at risk for TB ( for example rural poor populations, people deprived of liberty and others). It is therefore uncertain how the results of this study will inform the development of South Africa's National TB Strategy.

4. The “global value” of the results obtained by this systematic review and meta-analysis appears limited – what this systematic review and meta-analysis reveals has been known for a long time.

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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.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Jeremiah Chakaya

**********

[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.

PLOS Glob Public Health. 2024 Oct 3;4(10):e0003753. doi: 10.1371/journal.pgph.0003753.r002

Author response to Decision Letter 0

23 Jun 2024

Attachment

Submitted filename: Response to the Editor and Reviewers.docx

pgph.0003753.s017.docx^{(83.3KB, docx)}

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0003753.r003

Decision Letter 1

Marguerite Massinga Loembe

29 Aug 2024

PGPH-D-24-00621R1

Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis

PLOS Global Public Health

Dear Dr. Holtgrewe,

Thank you for submitting your revised manuscript to PLOS Global Public Health and for comprehensively responding to the reviewers' comments. After careful consideration, two (2) minor comments would need to be addressed. Therefore, we invite you to submit a revised version of the manuscript that addresses these pending points raised during the second review process.

Please submit your revised manuscript by Sep 28 2024 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 globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ 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 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'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Marguerite Massinga Loembe, PhD

Academic Editor

PLOS Global Public Health

Journal Requirements:

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Line 424 of the revised manuscript with track changes:

Authors cite the article by Kubjane, M., Cornell, M., Osman, M., Boulle, A., & Johnson, L. F. (reference 63). This paper indicated that "the M:F ratios for tuberculosis incidence and mortality rates persisted above 1.0, and the figures reached 1.70 and 1.65, respectively, by the end of 2019" and further that "the 2019 estimated tuberculosis prevalence in males was 1.06% (95% CI 1.0–1.12%) and 0.58% (95% CI 0.56–0.62%) in females".

These observations appear to be contradicting the authors' statement on line 424 that "..nationwide estimates, ... show higher TB prevalence and mortality in women than in men". Could this please be cross checked for consistency ?

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

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

**********

2. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I thank the authors for their diligent edits to the manuscript. The authors have addressed all my comments. The paper provides a valuable contribution to understanding the burden of TB in underserved populations in a high TB burden country.

Reviewer #2: The revised manuscript has provided satisfactory responses to the comments made in the previous review. I have only a very minor comment. On page 7 line 54 it is stated that TB treatment coverage (TC) increased from 57% to 77% in 2022. Could you please provide the year when the the TB treatment coverage was 57% - is the baseline year 2015?

**********

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.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLOS Glob Public Health. 2024 Oct 3;4(10):e0003753. doi: 10.1371/journal.pgph.0003753.r004

Author response to Decision Letter 1

1 Sep 2024

Attachment

Submitted filename: Rebuttal letter.pdf

pgph.0003753.s018.pdf^{(186.4KB, pdf)}

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0003753.r005

Decision Letter 2

Marguerite Massinga Loembe

3 Sep 2024

Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis

PGPH-D-24-00621R2

Dear Miss Holtgrewe,

We are pleased to inform you that your manuscript 'Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis' has been provisionally accepted for publication in PLOS Global Public Health.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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 globalpubhealth@plos.org.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Global Public Health.

Best regards,

Marguerite Massinga Loembe, PhD

Academic Editor

PLOS Global Public Health

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Reviewer Comments (if any, and for reference):

Associated Data

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

Supplementary Materials

S1 Checklist. PRISMA 2020 checklist [1].

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S1 Table. Search term.

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S2 Table. Data extraction form.

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S3 Table. Risk-of-bias assessment form [2].

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S4 Table

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S5 Table. Egger tests: P-values.

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S6 Table. Pooled prevalence before and after sensitivity analysis.

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S1 Data. Sensitivity analysis.

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S2 Data. Prevalence ratios.

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S3 Data. Screened studies, broken down into excluded and included studies.

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S1 Fig. Pooled active TB disease prevalence among underserved populations in South Africa, stratified by HIV status.

Abbreviations: HIV = Human Immunodeficiency Virus. *The ‘People living with and without HIV’ group includes studies for which outcomes were not stratified by HIV status.

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S2 Fig. Funnel plot: Pooled active TB disease prevalence among underserved populations in South Africa (‘People living with HIV’ subgroup).

Abbreviations: TB = Tuberculosis; HIV = Human Immunodeficiency Virus.

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S3 Fig. Funnel plot: Pooled active TB disease prevalence among underserved populations in South Africa (‘People living without HIV’ subgroup).

Abbreviations: TB = Tuberculosis; HIV = Human Immunodeficiency Virus.

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S4 Fig. Funnel plot: Pooled active TB disease prevalence among underserved populations in South Africa (‘People living with and without HIV’ subgroup).

Abbreviations: TB = Tuberculosis; HIV = Human Immunodeficiency Virus.

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S5 Fig. Pooled LTBI prevalence among underserved populations in South Africa, stratified by HIV status.

Abbreviations: HIV = Human Immunodeficiency Virus. *The ‘People living with and without HIV’ group includes studies for which outcomes were not stratified by HIV status.

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S6 Fig. Funnel plot: Pooled LTBI prevalence among underserved populations in South Africa (‘People living without HIV’ subgroup).

Abbreviations: LTBI = Latent Tuberculosis; HIV = Human Immunodeficiency Virus.

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Attachment

Submitted filename: Response to the Editor and Reviewers.docx

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Attachment

Submitted filename: Rebuttal letter.pdf

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Data Availability Statement

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PERMALINK

Burden of tuberculosis in underserved populations in South Africa: A systematic review and meta-analysis

Lydia M L Holtgrewe

Ann Johnson

Kate Nyhan

Jody Boffa

Sheela V Shenoi

Aaron S Karat

J Lucian Davis

Salome Charalambous

Roles

Author summary

Introduction

Methods

Study protocol and search strategy

Study selection

Data extraction

Risk-of-bias assessment

Data synthesis

Primary analyses

Sensitivity and subgroup analyses

Other effect estimates

Results

Search process and study selection

Fig 1. PRISMA flow diagram, showing the results of study search and screening procedures.

Study characteristics

Table 1. Characteristics of included studies.

Risk-of-bias assessment

Table 2. Risk-of-bias assessment of included studies.

Results of individual studies

Prevalence of active TB disease and LTBI

Table 3. Prevalence of active TB disease and LTBI in included studies.

Incidence of active TB disease and LTBI

Table 4. Incidence of active TB disease and LTBI in included studies.

Pooled results

Primary analysis

Sensitivity and subgroup analyses

Fig 2. Pooled active TB disease prevalence among underserved populations in South Africa, stratified by HIV status.

Fig 3. Pooled LTBI prevalence among underserved populations in South Africa, stratified by HIV status.

Prevalence ratios for active TB disease and LTBI relative to the general population

Discussion

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Marguerite Massinga Loembe

Roles

Author response to Decision Letter 0

Decision Letter 1

Marguerite Massinga Loembe

Roles

Author response to Decision Letter 1

Decision Letter 2

Marguerite Massinga Loembe

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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