Factors associated with unawareness of HIV-positive status in urban Ethiopia: Evidence from the Ethiopia population-based HIV impact assessment 2017-2018

Sileshi Lulseged; Wudinesh Belete; Jelaludin Ahmed; Terefe Gelibo; Habtamu Teklie; Christine W West; Zenebe Melaku; Minilik Demissie; Mansoor Farhani; Frehywot Eshetu; Sehin Birhanu; Yimam Getaneh; Hetal Patel; Andrew C Voetsch; EPHIA Study Team

doi:10.1371/journal.pone.0255163

. 2021 Aug 11;16(8):e0255163. doi: 10.1371/journal.pone.0255163

Factors associated with unawareness of HIV-positive status in urban Ethiopia: Evidence from the Ethiopia population-based HIV impact assessment 2017-2018

Sileshi Lulseged ^1,^*,^#, Wudinesh Belete ^2,^#, Jelaludin Ahmed ^3,^‡, Terefe Gelibo ^1,^#, Habtamu Teklie ^2,^#, Christine W West ^4,^#, Zenebe Melaku ^1,^#, Minilik Demissie ^2,^#, Mansoor Farhani ^5,^#, Frehywot Eshetu ^3,^#, Sehin Birhanu ^4,^‡, Yimam Getaneh ^2,^#, Hetal Patel ^4,^‡, Andrew C Voetsch ^4,^#; EPHIA Study Team^¶

Editor: José Antonio Ortega⁶

¹ICAP in Ethiopia, Mailman School of Public Health, Columbia University, Addis Ababa, Ethiopia

²Department of HIV and Tuberculosis Research, Ethiopia Public Health Institute, Addis Ababa, Ethiopia

³United States Centers for Disease Control and Prevention, Addis Ababa, Ethiopia

⁴United States Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America

⁵ICAP. Mailman School of Public Health, Columbia University, New York, New York, United States of America

⁶University of Salamanca, SPAIN

Competing Interests: The authors have declared that no competing interests exist.

‡ These authors also contributed equally to this work.

¶ Membership of the EPHIA Study Group is provided in the Acknowledgments.

^✉

* E-mail: sl2883@cumc.columbia.edu

Contributed equally.

Roles

Sileshi Lulseged: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Wudinesh Belete: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Jelaludin Ahmed: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Terefe Gelibo: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Habtamu Teklie: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Christine W West: Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Zenebe Melaku: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Minilik Demissie: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Mansoor Farhani: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Frehywot Eshetu: Conceptualization, Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Sehin Birhanu: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Yimam Getaneh: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Hetal Patel: Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Andrew C Voetsch: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

José Antonio Ortega: Editor

PMCID: PMC8357455 PMID: 34380145

Abstract

Background

The HIV epidemic in Ethiopia is concentrated in urban areas. Ethiopia conducted a Population-based HIV Impact Assessment (EPHIA) in urban areas between October 2017 and April 2018 to measure the status of the country’s response to the epidemic.

Methods

We conducted field data collection and HIV testing in randomly selected households using the national, rapid testing algorithm with laboratory confirmation of seropositive samples using a supplemental assay. In addition to self-report on HIV diagnosis and treatment, all HIV-positive participants were screened for a set of HIV antiretroviral (ARV) drugs indicative of the first- and second-line regimens. We calculated weighted frequencies and 95% confidence intervals to assess regional variation in participants’ level of unawareness of their HIV-positive status (adjusted for ARV status).

Results

We interviewed 20,170 survey participants 15–64 years of age, of which 19,136 (95%) were tested for HIV, 614 (3.2%) tested positive, and 119 (21%) of HIV-positive persons were unaware of their HIV status. Progress towards the UNAIDS first 90 target (90% of people living with HIV would be aware of their HIV status by 2020) substantially differed by administrative region of the country. In the bivariate analysis using log binomial regression, three regions (Oromia, Addis Ababa, and Harari), male gender, and young age (15–24 years) were significantly associated with awareness of HIV positive status. In multivariate analysis, the same variables were associated with awareness of HIV-positive status.

Conclusion

One-fifth of the HIV-positive urban population were unaware of their HIV-positive status. The number of unaware HIV-positive individuals has a different distribution than the HIV prevalence. National and regional planning and monitoring activities could address this potentially substantial source of undetected HIV infection by increasing HIV testing among young people, men and individuals who do not use condoms.

Introduction

In 2014, The Joint United Nations Programme on HIV/AIDS (UNAIDS) set the 90-90-90 targets for the year 2020: 90% of people living with HIV would know their HIV status (first 90), 90% of people with diagnosed HIV infection would receive sustained antiretroviral treatment (ART) (second 90), and 90% of people receiving ART would have viral suppression (third 90) [1]. Huge resources have been invested and various innovations employed in HIV testing and treatment programs since the initiative was launched, and much has been achieved. Country progress towards achieving these targets varied over time across regions and by sex and age, although the disparities are most pronounced in the first and second 90s, and more so in the first 90 [2].

Ethiopia, the second-most populous country in Africa, is heavily affected by the HIV epidemic [3]. The 2011 and 2016 Ethiopian Demographic and Health Surveys (EDHS) [4, 5] estimated the national HIV prevalence among women and men (15–49 years) at 1.5% and 0.9%, respectively. HIV prevalence was seven times higher in urban compared to rural areas, 2.9% versus 0.4% [4]. In 2017, the urban population of Ethiopia constituted 20.4% [6]. The EDHS data indicate that there is a remarkable variation in HIV prevalence across geographic and other sub-population groups [3] Despite persistent efforts to control the epidemic, HIV transmission continues, particularly among the urban population, predominantly being transmitted through unprotected heterosexual sex. A series of isolated surveys have provided data concerning HIV knowledge, attitude, and practice (KAP), as well as sexual behaviour. Key drivers of the epidemic include multiple and concurrent sexual partners, low and inconsistent use of male and female condoms, and mobility and labour migration [5].

According to the EDHS 2016, knowledge about HIV did not vary much by background characteristics except for education; those with no education were less likely to be knowledgeable about HIV. However, awareness about HIV-positive status was limited among the general population, and valid epidemiological evidence was scarce [7]. This would affect achievement of first 90 UNAIDS target, thereby affecting antiretroviral treatment (ART), and viral suppression. By the end of 2017, it was estimated that 75% of HIV-positive people knew their status globally [8], but information on awareness was lacking in Ethiopia. We analysed the Ethiopia Population-based HIV Impact Assessment (EPHIA) data collected in 2017/2018 to determine the prevalence of unawareness of HIV-positive status in adults and its variation by region and other potential determinants.

Materials and methods

Study setting and population

Ethiopia has an estimated population of 105 million people in 2018 and the country was administratively divided into nine regional states—Tigray, Afar, Amhara, Oromia, Somali, Benishangul Gumuz, SNNPR, Gambella, and Harari, and two city administrations—Addis Ababa and Dire Dawa. EPHIA was conducted in urban areas across the country. The study population included women and men 0–64 years of age, and this analysis on unawareness of HIV-positive status focused on the age group 15–64 years.

Data collection procedures

The survey used a household-based cross-sectional study design. We conducted HIV testing in selected households using the national rapid diagnostic testing algorithm with laboratory confirmation of seropositive samples using a supplemental assay. Data collection was conducted from October 2017 to April 2018. Qualitative screening for a detectable concentration of antiretroviral (ARV) drugs was conducted on all participants who were HIV-positive [9]. The adult questionnaire was administered to all eligible participants aged 15 years and older during face-to-face interviews using tablets. Demographic, behavioral and clinical data were collected electronically in the field. Self-reported awareness of HIV-positive status was collected through interviewer-administered questionnaires.

Sampling procedure

EPHIA used a two-stage, stratified cluster sampling design. The sampling frame for first stage sampling was all urban enumeration areas (EAs) in the country, based on the 2007 Population and Housing Census [3]. Using a probability proportional to size method, 393 EAs were selected randomly. During the second stage, a sample of households was randomly selected within each EA, using an equal probability method, where the average number of households selected per EA was 30 and the actual number of households selected per cluster ranged from 15 to 60, for a total number of 11,810 households.

Definition of variables

The variables included in this analysis are selected from the EPHIA dataset based on the literature and other scientific evidence to examine their relationship with unawareness. The dependent (outcome) variable for this study is unawareness of HIV-positive status among HIV-positive respondents 15–64 years of age. The outcome was defined as unaware if the respondent reported being unaware of their HIV-positive status and had no detectable ARVs and defined as aware if the respondent were aware of their HIV-positive status or had detectable ARVs. The independent variables included region, socio-demographic and behavioural risk factors.

Eligibility for the study

Participants were eligible to participate if they lived or slept in the household the night before the interview. Of 12,618 eligible women and 8,920 eligible men, 96.1% of eligible women and 89.6% of eligible men were interviewed, and among these, 95.2% of women and 93.6% of men also had their blood drawn and tested for HIV.

Data analysis

Data were analyzed using the sample survey procedures and analysis weights were calculated to account for sample selection probabilities and adjusted for non-response and non-coverage using Jackknife replication method, a method that estimates the variance/standard and bias of a large population using a sample data by involving a leave-one-out strategy of the estimation of a parameter in a dataset [10]. All categorical variables were summarized using frequencies and proportions. We calculated weighted frequencies and 95% confidence intervals to assess the association between selected explanatory factors and unawareness of HIV-positive status. In the bivariate analysis, the associations between participants’ unawareness of their current HIV-positive status and the potential explanatory factors were tested using the Chi-square test. Variables with Chi-square P-values of ≤0.10 were included in the log binomial regression model. Crude and adjusted prevalence ratios are estimated using log binomial regression model. Age group, education, marital status and sex were included in the log binomial model as confounders. An independent factor was excluded from the multivariable model when the change in the adjusted log-likelihood ratio was not significant with its addition or removal. For collinear factors assessed by looking at the variance inflation factor (VIF) such as involvement in high-risk sex and having multiple sexual partners, only the variable that improved the model more than the other was included in the multivariable model. The analyses were done using STATA version 14.

Ethical considerations

The survey protocol, screening forms, refusal forms, referral forms, recruitment materials and questionnaires, consent forms, and digital documentation of consent obtained ethical clearance from the respective institution review boards of the Ethiopian Public Health Institute, Centers for Disease Control and Prevention, and Columbia University. As part of the informed consent procedure, all potential participants were informed that participation was voluntary and that they did not need to disclose personal information, which they were uncomfortable sharing, and that they could withdraw from the survey at any time. Prior to initiation of any survey procedures, all potential participants were given a printed copy of the consent form in one of six survey languages depending upon their preference. For illiterate participants, an impartial witness chosen by the participant was involved. Potential participants who did not speak any of the six survey languages were considered ineligible. Respondents who consented to participate for the interview were asked to consent separately for biomarker testing. Written parental/guardian permission was obtained for assenting minors. At each stage of the process, consent was indicated by signing or making a mark on the consent form in the tablet and a printed copy. All participants retained written copies. A designated head of household provided written consent for household members to participate in the survey, after which individual members were rostered during the household interview. Participants aged 15–64 years and emancipated minors aged 13–17 then provided the written consent for an interview and for participation in the biomarker component of the survey, including home-based testing and counselling, with return of HIV-test results. Receipt of tests results was a requirement for participation in the biomarker component. If an individual did not want to receive his or her HIV test result, the individual was considered a refusal and excluded from the survey.

Results

Participants’ characteristics

There were 20,170 survey participants aged 15–64 years in EPHIA, of which 19,136 (95%) were tested and 614 (3.2%) were HIV-positive. As shown in Table 1, 355 (77.2%) of the HIV-positive participants were from three regions, Oromia, Amhara, and Addis Ababa. Among HI- positive participants, 67.9% were females and 39% were 35–44 years of age. Nearly one-half (48.1%) of the HIV-positive participants were married or living together, 49.3% reported to have primary education, and 50.6% were formally employed in the past 12 months. Four in ten (39.5%) of HIV- positive participants did not use condom in the past 12 months and 88.8% reported they did not have first sex before 15 years of age.

Table 1. Demographic, socioeconomic and behavioural characteristics of HIV-positive participants aged 15–64 in urban Ethiopia.

Background characteristics	Tested N (%)	HIV-Positive (n)	Weighted %	95% CI^*
Region
Tigray	1369 (7.2)	39	6.3	4.6–8.6
Afar	821 (1.3)	32	1.8	1.2–2.5
Amhara	2999 (18.8)	118	25.7	21.8–30.0
Oromia	4510 (33.5)	149	33.3	29.0–37.8
Somali	926 (1.3)	8	0.3	0.2–0.7
Benishangul Gumuz	798 (1.3)	20	1.1	0.7–1.7
SNNPR	2665 (16.1)	49	9.4	7.1–12.3
Gambella	788 (0.6)	44	1.1	0.8–1.5
Harari	697 (0.7)	32	1.0	0.7–1.4
Addis Ababa	2780 (18)	88	18.2	14.9–21.9
Dire Dawa	783 (1.2)	35	1.9	1.3–2.6
Total	19136 (100)	614
Sex
Female	11599 (50.1)	461	67.9	63.1–72.3
Male	7537 (49.9)	153	32.1	27.7–36.9
Total	19136 (100)	614
Age group
15–24 years	7547 (34.8)	62	8.4	6.3–11.1
25–34 years	5664 (30.5)	175	26.1	22.3–30.2
35–44 years	3136 (18.9)	234	39.0	34.6–43.6
45–54 years	1651 (10.1)	104	20.3	16.7–24.3
55–64 years	1138 (5.7)	39	6.3	4.5–8.9
Total	19136 (100)	614
Marital status
Never married	7103 (35.6)	71	11.4	8.8–14.6
Married or living together	9418 (52)	285	48.1	43.6–52.8
Divorced or separated	1723 (8.6)	144	21.9	18.5–25.8
Widowed	772 (3.8)	112	18.6	15.3–22.3
Total	19016 (100)	612
Education level
No education	2400 (11.9)	121	20.2	16.8–24.2
Primary	6803 (35.3)	291	49.3	44.7–53.9
Secondary	5488 (28.7)	141	22.6	19.0–26.6
More than secondary	4376 (24.1)	58	7.9	5.8–10.6
Total	19067 (100)	611
Employment status last 12month
Did not work	10955(52.7)	313	49.4	44.8–54.0
Worked	8154 (47.3)	298	50.6	46.0–55.2
Total	19096 (100)	611
Number of sexual partners last 12 months
No sexual partner	3689 (27.7)	241	44.0	39.2–48.9
One sexual partner	8778 (68.2)	270	52.6	47.6–57.4
Two or more sexual partners	497 (4.1)	25	3.5	2.2–5.4
Total	12964 (100)	536
Condom use at last sex in past 12 months
Used condom	793 (6.5)	83	16.3	12.8–20.4
Did not use condom	8192 (65.1)	203	39.5	34.7–44.4
Had no sex	3689 (28.4)	241	44.3	39.4–49.2
Total	12674 (100)	527
Age at first sexual encounter
Did not have sex before age 15	17735 (95)	536	88.8	85.7–91.3
Had first sex before 15	1014 (5)	70	11.2	8.7–14.3
Total	18749 (100)	606

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* Confidence interval.

Testing history

Of all HIV-positive participants aged 15–64 years, 90.4% had ever been tested for HIV and received their results which varied by region, ranging from 84.1% in Afar to 100% Somali and Benishangul Gumuz Region. Among HIV-positive participants who were ever tested for HIV, one-fifth (20.8%) were tested and received results in the past 12 months (Fig 1).

Self-reported HIV status

Among HIV-positive participants, 74% (466) self-reported they were HIV-positive, 15% (95% CI: 12.0–19.1) self-reported HIV negative status, and 11% (95% CI: 8.1–14.1) self-reported they had never been tested or never received a result.

Among HIV-positive participants, the highest proportion (25.1%, 95% CI: 14.2–40.4) of self-reported HIV negative participants were from the Gambella region (Fig 2). In Afar, Addis Ababa, and Dire Dawa, more people never tested than self-reported negative, whereas in the rest of the other regions, more people self-reported negative.

Unawareness of HIV-positive status

Combining self-reported awareness and adjustment of ARV status, among HIV-positive participants aged 15–64 years, 21% (95% CI: 17.3–25.3) were found unaware of their HIV-positive status. A significantly higher proportion of men (30%, 95% CI: 21.9–39.4) were unaware of their HIV-positive status compared to women (16.7%, 95% CI: 13.1–21.0) (Table 2). Unawareness was highest among HIV-positive participants aged 15–24 years. Unawareness among those 15–24 years of age was 37% (95% CI: 24.2–51.9) compared to 14.5% (95% CI: 6.0–30.8) among those 55–64 years; however, the difference was not statistically significant. Unawareness was significantly higher among those who did not use a condom (30%, 95% CI: 22.7–38.6), compared to those who used a condom in the last sexual encounter in the past 12 months (8.7%, 95% CI: 4.0–17.8). Unawareness was 28.1% (95% CI: 21.3–36.2) among male headed households versus 16.4% (95% CI: 4.0–17.8) among female headed households. Unawareness was 22.1% (95% CI: 18.0–26.8) among those who reported first sex at or after 15 years compared to 11.2% (95% CI: 5.5–21.5) among those who stated first sex before 15 years. Among those who were unaware of their HIV status, 84% had never been tested and 14% had been tested for HIV, a difference that was significant (p = 0.0001). Unawareness was 79.3% (95% CI: 71.2–85.6) among those who had no ARV detected in their blood (Table 2).

Table 2. ARV adjusted estimates of unawareness by socio demographic and behavioral characteristics among HIV-positive participants aged 15–64 years, EPHIA 2017/2018.

Characteristics	HIV-Positive (N)	Unaware		95% CI^*	P-value
Characteristics	HIV-Positive (N)	n	%	95% CI^*	P-value
Region
Tigray	38	4	11	[3.9–25.3]	0.138
Afar	32	8	25	[12.8–42.9]
Amhara	118	15	15	[8.8–23.8]
Oromia	148	29	24	[17.0–32.8]
Somali	8	-	0	-
Benishangul Gumuz	20	1	5.8	[0.8–31.7]
SNNPR	49	13	27	[15.9–41.1]
Gambella	44	14	33	[20.7–49.1]
Harari	32	9	27	[14.3–44.5]
Addis Ababa	86	21	25	[16.8–36.0]
Dire Dawa	34	5	16	[6.8–33.7]
Sex
Female	456	78	17	[13.1–21.0]	0.003
Male	153	41	30	[21.9–39.4]	0.003
Age group
15–24 years	62	23	37	[24.2–51.9]	0.135
25–34 years	172	31	20	[13.3–29.2]
35–44 years	233	43	21	[14.9–28.1]
45–54 years	104	17	18	[10.9–27.6]
55–64 years	38	5	15	[6.0–30.8]
Marital status
Never married	71	18	24	[14.2–37.0]	0.461
Married or living together	285	57	23	[17.4–29.9]
Divorced or separated	140	26	20	[12.8–28.9]
Widowed	111	17	15	[8.9–24.5]
Education level
No education	120	19	14	[8.0–22.8]	0.165
Primary	288	58	24	[18.1–30.5]
Secondary or higher	198	41	21	[14.8–28.4]
Religion
Muslim	82	16	20	[10.7–34.2]	0.911
Christian	522	99	21	[16.7–25.2]	0.911
Wealth quintile
Lowest	102	20	26	[15.6–38.8]	0.286
Second	107	17	13	[7.0–23.5]
Middle	142	23	18	[11.5–26.7]
Fourth	145	33	25	[17.2–33.6]
Highest	113	26	24	[15.9–34.0]
Gender of household head
Female headed	396	67	16	[12.5–21.1]	0.005
Male headed	213	52	28	[21.3–36.2]
Condom use at last sex in past 12 months
Used condom	83	8	8.7	[4.0–17.8]	0.001
Did not use condom	201	51	30	[22.7–38.6]
No sex in the past 12 month	238	39	18	[12.6–24.6]
Age at first sexual encounter
First sex 15 + years	532	105	22	[18.0–26.8]	0.047
First sex before 15	69	11	11	[5.5–21.5]	0.047
Ever been tested for HIV
Never tested	51	44	84	[69.4–92.4]	0.0001
Ever tested	556	73	14	[10.9–18.2]
ARV detected in their blood
No	150	119	79.3	[71.2–85.6]	0.0001
Yes	459	0	0
Total	609	119	21	[17.3–25.3]

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* Confidence interval.

The level of unawareness varied across the regions, ranging from zero in the Somali region to 33.4% in Gambella (Table 2). Progress towards the first 90 target substantially differed by region in urban Ethiopia, where the highest burden (78%) were from two most populous regions (Amhara, Oromia) and Addis Ababa, the capital city, while the lowest burden (1.8%) was in Gambella (a small region), though the region had the highest HIV prevalence (Fig 3).

Factors associated with unawareness of HIV-positive status

In a bivariate analysis using the log binomial regression, being from Afar, Oromia, SNNPR, Gambella, Harari, or Addis Ababa region, age 15–24, male gender, primary education level, male headed household, not using condom in the last sexual encounter in the past 12 months, and age less than 15 at first sexual encounter were significantly associated with unawareness of HIV-positive status. In the multivariable log binomial regression model, education level, gender of head of the household and age at first sex were not significantly associated with unawareness (Table 3).

Table 3. Factors associated with weighted unawareness status among HIV-positive participants aged 15–64 years, EPHIA 2017/2018.

Characteristics	CPR^* (95% CI)	P-value	APR^** (95% CI)	P-value
Region ^⸹
Benishangul Gumuz	1		1
Tigray	1.8 (0.4–8.06)	0.439	0.7 (0.2–2.21)	0.507
Afar	4.3 (1.08–16.91)	0.039	1.5 (0.68–3.45)	0.301
Amhara	2.5 (0.6–10.7)	0.205	1.2 (0.68–2.26)	0.495
Oromia	4.1 (1.09–15.44)	0.036	1.9 (1.25–2.77)	0.002
SNNPR	4.6 (1.16–17.99)	0.030	1.4 (0.74–2.7)	0.291
Gambella	5.7 (1.42–23.07)	0.014
Harari	4.6 (1.13–18.63)	0.034	2.2 (1.06–4.39)	0.034
Addis Ababa	4.3(1.16–16.09)	0.029	2.2 (1.27–3.83)	0.005
Dire Dawa	2.8 (0.63–12.1)	0.178	1.3 (0.51–3.32)	0.574
Sex
Female	1		1
Male	1.8 (1.3–2.49)	0.0001	1.3 (1.22–2.69)	0.009
Age group
15–24 years	2.5 (1.09–5.99)	0.031	1.8 (1.32–2.98)	0.003
25–34 years	1.3 (0.58–3.33)	0.458	1.3 (0.68–4.85)	0.238
35–44 years	1.4 (0.66–3.15)	0.362	0.7 (0.61–4.15)	0.07
45–54 years	1.2 (0.52–2.92)	0.636	0.6 (0.38–2.0)	0.56
55–64 years	1		1
Education level
No education	1		1
Primary education	1.7 (1.02–2.89)	0.041	1.2 (0.7–2)	0.537
Secondary and above education	1.3 (0.74–2.34)	0.355	1.1 (0.6–2)	0.715
Condom use at last sex in past 12 months
Used condom	1
Did not use condom	3.4 (1.39–8.54)	0.008	2.8 (1.4–6)	0.006
No sex in the past 12 month	2.0 (0.89–4.65)	0.089	1.2 (0.8–1.7)	0.371

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* Crude prevalence ratio

**Adjusted prevalence ratio.

^⸹ Somali region was not included in the model due to a small number of cases.

Table 3 provides the results of a log binomial regression analysis. Some demographic and behavioral factors were independently associated with higher probability of unawareness. After controlling for marital status, educational level, age and sex using multivariable log binomial regression analysis, three regions (Oromia, Addis Ababa and Harari), male gender, age group (15–24 years) and condom non-use in the past 12 month were significantly associated with unawareness of HIV positive status. Three regions Oromia (APR 1.9, 95% CI: 1.3–2.8), Harari (APR 2.2, 95% CI: 1.1–4.4) and Addis Ababa city administration (APR 2.2, 95% CI: 1.3–3.8) had higher prevalence of unawareness as compared with Benishangul Gumuz region. The probability of HIV-positive status unawareness was higher among males (APR 1.3, 95% CI: 1.2–2.7) compared to females.

There was a greater prevalence of being unaware of HIV-positive status among young people in the age group 15–24 years (APR1.8, 95% CI: 1.3–3.0). The prevalence of unawareness was higher among individuals who did not use condom in the past 12 months (APR 2.8, 95% CI: 1.4–6.0) compared with those who used condom during the last sexual encounter in the past 12 months.

Discussion

We found more than one-fifth (21%) of HIV-positive infected participants were unaware of their HIV status, which is less than the 25% global estimate in 2017 [11]. However, the unawareness level identified in EPHIA is based on urban population and the EPHIA plausibility interval (95% CI, 17.3–25.3) covers the global estimate of 25%. In Ethiopia, a country with huge population size, one-fifth of which is urban population [6], where HIV prevalence is seven times higher in urban areas than in rural areas (2.9% versus 0.4%) [12], being unaware of their HIV-positive status indicates that a substantial number of HIV-positive people are not getting tested, seeking care and treatment and remain a potential source of transmission of HIV infection. The HIV-positive status unawareness reported in this study is lower than that reported in other countries in the region, 52% in Uganda [13], 39% in Mozambique [14], 38.3% in Malawi [15], 44.0% in Zambia [16], 32.9% in Tanzania [17] and 62.8% in Cote d’Ivoire [18]. This difference could partly be explained by the difference in the source and study population, including our study, which focused on urban population, where there was a better access to information, care and treatment services than rural settings. As reported by Deribew et al. [19], the overall capacity score for HIV diagnosis and treatment, which was estimated based on the assessment of structure, process and overall capacity framework constructed by taking the average of all indicators and rescaling it to 100, was higher in urban facilities (57.1%) than that of the rural health facilities (38.2%). The prevalence of unawareness in this study was higher than the finding reported in Kenya 16.2% [20, 21], 16.2% in Rwanda [22], and 15% in South Africa [23] and almost comparable with another report from KwaZulu-Natal province in South Africa (24.8%) [24].

As our findings suggest that 15.2% of HIV-positive persons self-reported not to be infected based on their last HIV test, a potential respondent bias could have contributed to the difference. Detailed analysis of data from household surveys in Africa indicated that, even after adjusting for expected seroconversions, one-quarter to one-third of HIV positive respondents intentionally misreported their HIV-positive status as negative [25, 26]. Raymond, et al. [27] highlighted the gaps in HIV diagnosis, which might be unattainable under the ambitious UNAIDS 90–90–90 targets given the current trends. This could explain most of the observed variation in addition to methodological differences. No single method may be fully effective to increase progress towards the first 90, but when they are used in combination and supported by structural changes they could be more effective [8]. Studies in Ethiopia also indicate that Voluntary Counselling and Testing (VCT) utilization varies by geographic regions of the country in both men and women [28].

Based on our findings, distinguishing between the burden of unaware population and the prevalence of HIV stratified by administrative regions could be useful when designing programs and targeting interventions. The progress towards the first 90 target substantially differed by region in urban Ethiopia. Based on our survey, over three-quarters of those unaware of their HIV- positive status were from three regions (Oromia, Amhara, and Addis Ababa), posing a barrier to HIV prevention, care, and treatment efforts in country (Table 2). Of the estimated 79,827 unaware adult HIV-positive population in urban Ethiopia, 62,183 (77.9%) were from these three regions. In contrast, though Gambella was the region with highest prevalence, among all HIV-positive individuals who were unaware of their HIV-positive status, only 1,443 (1.8%) were those unaware of their HIV-positive status. Identification and meeting administrative region-specific situations could help in finding those with undetected infection and being on track to end AIDS as a pandemic by 2030. These and measures like improving availability of testing service, which currently stands at 70%-76.6% [19] could help in HIV case identification.

The adaptation of community directed intervention approaches used elsewhere [29] and those that are culturally appropriate and less costly in low resource communities can be considered as an alternative strategy to expand coverage. For example, expanding the population coverage of evidence-based interventions with health extension workers such as door-to-door HIV testing during the provision of home visit services. Identification and working with community structures have shown to promote trust, equity and respect, and enhance delivery of essential services to every eligible member of the community [29]. The availability of HIV testing services is currently lower (45%) in Gambella than the national average (74.5%) [19]; this might also contribute to the low awareness of HIV-positive status.

A region-specific analysis showed that the highest proportion of people who were unaware of their HIV-positive status was from Gambella, the region with the highest HIV prevalence. An increase in HIV-positive status unawareness among the population has an impact on increased HIV infection. Consequently, the high percentage of unaware population in Gambella region might have contributed to the high prevalence of HIV in that region. The high rates of undiagnosed infection may suggest limited coverage of testing services and a relatively higher incidence. A study in Ethiopia indicated that HIV testing service was available in 74.5% of health facilities, which varied by region, ranging across facilities from 44.4% in Benishangul Gumuz to 88% in Tigray and Afar [19].

Studies elsewhere have shown that the HIV transmission rate among persons unaware of their HIV- positive status was three to seven times higher than the rate among those aware of their status [30]. Others have reported that for every percentage point increase in HIV-positive status awareness, the HIV incidence in monogamous populations decreases by 0.27% for women and 0.63% for men [31], indicating a HIV risk increase with level of unawareness. These are useful observations and need to be elaborated through modelling analysis in the future.

Knowledge of one’s HIV positive status presumably leads to behaviour change and results in HIV- positive people taking measures to reduce the spread of the virus to uninfected persons. In Gambella, targeted interventions involving sexually active men and women could reduce transmission from persons who were unaware of their HIV seropositivity. It could also help to convey the opportunity to increase Voluntary Medical Male Circumcision (VMMC) in Gambella in the context of the high prevalence of uncircumcised men there. Administrative regions could consider their specific sociodemographic and behavioural factors in the planning for interventions targeting the first 90 and in monitoring progress towards the 2020 goals.

Three regions, Oromia (APR 1.9, 95% CI: 1.3–2.8), Harari (APR 2.2, 95% CI, 1.1–4.4) and Addis Ababa (APR 2.2, 95% CI: 1.3–3.8) had higher prevalence of unawareness as compared with Benishangul Gumuz region. Studies revealed that there was significant difference in the uptake of VCT by region, which partly reflect the multicultural characteristics of the country and difference in the pace of implementation of the health extension program (HEP) [28]. There were significant regional disparities in ART coverage as well, 63% in Amhara and 43% in Oromia [19].

Male gender was associated with increased unawareness among HIV-positive participants (APR 1.3, 95% CI: 1.2–2.7). A higher prevalence of HIV-positive status unawareness among male respondents in our survey was consistent with study reports from South Sudan [8] and Uganda [13], which documented that women were more likely to report that they knew their HIV-positive status than their male counterparts [32]. Similarly, in Mozambique, men had twice the odds of being unaware of their serostatus compared with women [14]. Another study also indicated that unawareness was more common among men than women, 32.7% vs 22.3% [24]. A study in Uganda indicated that females had a 1.26 times higher odds of awareness of HIV-positive status than males [13]. The gender difference in awareness of HIV-positive status could be due to efforts made to increase HIV testing and counselling, which might have benefited women more than men in accessing services. Integration of HIV and antenatal services affords an opportunity for women of childbearing age to access routine HIV testing. The higher level of unawareness of HIV-positive status among adult males suggests a need to utilize alternative HIV testing approaches for this group. As shown elsewhere in Sub-Saharan Africa, door-to-door HIV testing and counselling may be an option for increasing access to testing for male adults in Ethiopia [33]. The other option could be self-testing. Pregnant women and lactating mothers who test positive at antenatal care and mother-and-child centres are provided self-testing kits to give to their male sexual partners to know their status [34].

As in Mozambique [14], HIV-positive people who reported not using a condom during their last sexual intercourse were more likely to be unaware of their HIV-positive status in our study. Unawareness of HIV-positive status was associated with non-condom use in the past 12 months in urban Ethiopia (APR 2.8, 95% CI: 1.4–6.0). Risky sexual behavior seems to be associated with factors linked to poor health-seeking behavior, which may have negative implications for HIV testing and treatment as well as prevention [24]. A previous study reported that perceived low risk of HIV infection is a major barrier to uptake of HIV testing and may undermine the benefits of increasing ART availability in sub-Saharan Africa [35]. Individuals often assume that they are at low risk of infection if they are currently abstinent, have a steady partner, are not part of a high-risk group, or do not have physical symptoms of illness.

Among the sociodemographic and economic factors, age was significantly associated with unawareness. Young people aged 15–24 years had a significantly higher prevalence of unawareness than the older adults (p = 0.003). This is consistent with other studies, where young people were more likely to be unaware than older groups [24]. Educational level and age at first sexual encounter were associated with HIV-positive status unawareness in a bivariate analysis, but we could not demonstrate an independent association for these variables. Wealth quintile was not associated with unawareness of HIV-positive status. However, studies indicated that HIV-positive people from households in the richest wealth quintile were more likely to be aware of their HIV- positive status than those in the middle wealth quintile [14]. In urban Ethiopia, economic inequalities may be minimal among the survey participants.

Limitations

Our study covers urban areas of Ethiopia and there are socio-economic and behavioural factors that were not controlled for in our study. Some regions such as Somali and Benishangul Gumuz had a relatively small number of HIV-positive people, which may raise questions related to accuracy of unawareness estimates in these regions. This study also has had the inherent limitation of a cross-sectional study design, which does not allow examining cause and effect relationships. Furthermore, the survey was conducted only in urban areas and might miss the full effect of population dynamics in the country, including mobility, migration, and transmission risks in the rural population.

Conclusions

As shown by our study, Ethiopia was lagging behind the UNAIDS first 90 target by 2018. There was a significant variation in HIV-positive status unawareness by region, male gender, and young age and HIV risk factors such as condom non-use. The high rates of undiagnosed infection may suggest limited coverage of testing services and relatively high incidence. The number of unaware HIV-positive individuals has a different distribution than the HIV prevalence or percent unaware, which is a critical distinction to control the epidemic. The results of this study can be used to inform how administrative regions use available evidence to make program decisions. The national program could improve the HIV testing programs in increasing awareness among men, individuals who do not use condom and those 15–24 years of age. Further analysis on the level of unawareness of HIV-positive status and service uptake are needed to better understand how individual, community and structural factors contribute to the regional variation.

Supporting information

S1 File. The EPHIA study team.

(DOCX)

Click here for additional data file.^{(14.3KB, docx)}

Acknowledgments

We would like to extend our thanks to the leadership at the Ministry of Health, EPHI, the regional health bureaus (RHBs) and their sub-regional units, CDC and ICAP for their administrative support in organizing and conducting the survey. Our thanks also go to field coordinators, supervisors and data collectors for their dedicated work and all study participants for providing the necessary information.

The EPHIA study team

A list of the study team (survey investigators) and is available from: https://phia.icap.columbia.edu/wp-content/uploads/2020/11/EPHIA_Report_280820_High-Res.pdf; and also uploaded as Supporting Information.

Lead author of the group: Andrew C. Voetsch

E-mail: aav6@cdc.gov

Data Availability

The EPHIA 2017-2018 public release data is now posted on the ICAP PHIA data website (https://phia-data.icap.columbia.edu/files#ethiopia). Dataset documentation is available for immediate download and datasets are available upon request by registering for an account and submitting the request form.

Funding Statement

This project was conducted using the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) funds obtained though the U.S Center for Disease Control and Prevention (CDC) under the term of cooperative agreement #U2GGH001226. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agency.

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PLoS One. doi: 10.1371/journal.pone.0255163.r001

Decision Letter 0

José Antonio Ortega

14 May 2021

PONE-D-21-05302

Factors associated with Unawareness of HIV Positive Status in Urban Ethiopia: Evidence from the Ethiopia Population-based HIV Impact Assessment 2017-2018

PLOS ONE

Dear Dr. Lulseged,

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.

There was a change of editor, and one high-quality review on the research is available that suggests that the paper can fulfill PLOS ONE publication criteria with minor changes. This is also the opinion of the new academic editor. In addition to the detailed comments by the reviewer please take note of:

Data availability statement: You argue that “No - some restrictions will apply” regarding data availability but “All relevant data are within the manuscript and its Supporting Information files.”. Those two statements are not compatible. Please following PLOS data policy provide the URL of the public repository and the conditions of access.
PLOS ONE being a multidisciplinary journal written for a general audience, the authors should not take for granted knowledge of what the 90-90-90 targets are as in line 68. That should be provided as background stating that the paper is concerned about the first 90. Instead, it does not to be fully described later in the results (l. 209).
A third concern is the way that unawareness is measured as a proportion of all HIV+. As the reviewer states, the policy implications of this research is to look for those unaware of their HIV+ status. While this focus is not wrong and corresponds to the first 90 target, it should go together with the complementary perspective: the proportion HIV+ unaware people among those declaring not to be HIV+, since this has implications regarding testing costs and cost-benefit of a testing programme. There is also no information regarding whether HIV+ unaware people had previously tested. Please provide at least descriptive measures in these respects, maybe a table similar to table 1 for those HIV+ unaware as proportion of those declaring to be HIV and some tabulation of those unaware HIV+ according to previous testing. This is a concern due to the effects that testing can have on behaviour, please check Gong, E. (2015), “HIV Testing and Risky Sexual Behaviour”. Econ J, 125: 32-60. https://doi.org/10.1111/ecoj.12125.

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Reviewer #1: Reviewer Report for “Factors associated with Unawareness of HIV Positive Status in Urban Ethiopia: Evidence from the Ethiopia Population-based HIV Impact Assessment (2017-2018)”

Overall Comments:

The paper focuses on people who are HIV-positive and unaware of their status, broken down by different epidemiologic factors. Overall, this is an important study in guiding future testing and treatment efforts. In that vein, one of the strongest claims in the paper is that future efforts should focus on where there are more people living with HIV who are unaware of their status (Addis Ababa, Harari, Oromio), rather than where there is high prevalence (Gambella). However, the paper loses focus toward the end as the authors expound on every finding rather than only the main claims. There are several limitations to the study including that only urban enumeration areas which the authors state is 20% of the population, and possibly that ARV testing was only done for participants who reported being HIV-positive. Despite these limitations, the study uses national data to draw conclusions on how to increase HIV awareness. This paper makes an important contribution to the literature and help guide future HIV testing and treatment policies in Ethiopia.

Major Compulsory Revisions

Abstract:

Line 18: This line implies that all people who tested positive for HIV were also screened for ARVs, whereas later (Line 87), it is implied that only individuals who self-report HIV positive status were screened for ARVs. Please clarify.

Also, throughout the paper, the terms “HIV positive,” “HIV positive status,” “HIV+,” “HIV infected,” and “PLHIV” are used. Unclear to me if this is intentional, but would benefit from consistency.

Results:

Line 188: I think ARV data could be a very important aspect of this study. Was ARV testing only done for participants who reported being HIV-positive? Readers would be very interested in the data on ARV testing among all HIV-positive individuals. It would change our interpretation of the positivity data as well as risk factors such as condom use.

Line 203: Please clarify the column headings in Table 2. Three of them are labeled “(N)” and it’s unclear what are the numerators and denominators for the “%” column.

Line 214: It’s unclear to me what the left y-axis of this graph is. I would assume it is percentage of people who tested HIV-positive who are unaware of their status, but these numbers are different from those in Table 2.

Minor Essential Revisions

Abstract:

Line 23: Unclear to me why a percentage isn’t placed after the number tested HIV positive (3.2%). Is it due to the sampling strategy?

Line 34: “One fifth of the urban population were” should be “One-fifth of the HIV-positive urban population were.”

Introduction:

Lines 62-63: The first estimate of 75% refers to PLHIV knowing their HIV status, whereas the second numbers of 97% and 99% refer to knowledge of HIV. I’d consider either changing this to make the numbers more consistent or focus on framing each number to explain the train of logic.

Line 68: You mention the “first 90 UNAIDS and country targets” but I don’t think the targets have been clearly introduced yet.

Methods:

Line 77: Missing a comma between Afar and Amhara.

Line 78: Do not need a comma between “Addis Ababa” and “and Dire Dawa”

Line 77-78: Consider using an m-dash (—) instead of colons given the use of two colons in the sentence.

Line 105: There is an extra space between “HIV” and “+”

Line 114: “Data was analyzed using” should be changed to “Data were analyzed using”

Line 122: Please clarify what is meant by “priority confounders.”

Line 124: How was collinearity assessed?

Line 124: “was not significant on its” should be changed to “was not significant with its”

Line 125: Please clarify what is meant by “only the variable that improve the model was included.” I would assume that if two variables are collinear and one improves the model, then would both improve the model. Presumably one could improve the model more than the other.

Results:

Line 145: Is there a reason no percentage is placed after the number HIV positive?

Lines 146-147: “Among HIV positive participants, females constituted” should be changed to “Among HIV positive participants, 67.9% were females and 39% were”

Line 164: There is some inconsistent bolding in the table. The word “sex” in “Had no sex” is bolded. “N=606” is not bolded. Two words in “Did not have sex before age 15” appear bolded.

Line 166: The sub-heading “Self-reported HIV Status” does not have a colon at the end. Throughout the paper, some sub-headings have a colon and others do not.

Line 170: In Figure 1, is there a reason why Somali has a greater proportion that has tested and received results in the last 12 months than the proportion that ever tested and received results? I’m guessing this is due to incomplete data but should clarify.

Line 173: Why does this figure only include ages 25-64 when all others are 15-64?

Line 177: “74% self-reported they were HIV” should be changed to “74% self-reported they were HIV-positive”

Line 178: “11% reported they had” should read “11% self-reported they had”

Line 180: I think in general, the order of parentheses and brackets should be with parentheses on the outside and brackets on the inside. This is done multiple times throughout the paper.

Line 189: “ARV status, HIV positive” should read “ARV status, among HIV positive”

Line 192: Some of the brackets/parentheses are mixed up in this line.

Line 203: The last row presumably is the total among everyone. This row could be labeled more obviously or be closer to the top of the table.

Line 218: Table 3 actually shows quite a few other variables that are associated with unawareness of HIV-positive status in bivariate log binomial regression, for example certain regions and primary education. It’s possible that these were not included due to a correction for multiple hypothesis testing but it could be helpful to clarify why those positive results in the table are not included in the text.

Line 225: It may help the reader to clarify that the Crude and Adjusted Prevalence Ratios are estimated using a log binomial model. For less statistically-inclined readers (including myself), there are a lot of terms in the methods that could be confusing.

Line 230: Why were these three variables controlled for? They were not the “priority confounders” as stated in line 121, nor are they significant variables in Table 2.

Line 232: the word “statistically” is unnecessary here as it already says “significantly”

Line 233: Previously in the Results section, 95% CI were followed by a colon, but in these next two paragraphs, they are followed by a comma in some cases, colon in others, and no punctuation as well.

Discussion:

Line 246: one fifth should be “one-fifth” – applies throughout the paper.

Line 263: “Kuwazulu Natal” should be “KwaZulu-Natal province”

Line 287: There is a typo in “2030These and measures”

Line 292: “door to door” should be hyphenated

Line 296: “contribute for the” should be “contribute to the”

Line 317: “Voluntary Medical ale Circumcision” should be “Voluntary Medical Male Circumcision”

Line 318: There is an extra period

Line 327: “HIV positives participants” should be “HIV positive participants”

Line 350: “A previous study report indicated” should read “A previous study reported”

Line 353: “have a steady partner, and are not part” should read “have a steady partner, are not part”

Limitations:

Line 371: “Furthermore;” should read “Furthermore,”

Conclusions:

Line 379: There is an extra period at the end

I would add as one of the major conclusions that the number of unaware HIV+ individuals has a different distribution than the HIV prevalence or % unaware, which is a critical distinction to control the epidemic.

Discretionary Revisions

Abstract:

Lines 26-33: Could probably be shortened to something along the lines of: “In the bivariate analysis…, three regions (Oromia, Addis Ababa, and Harari), male gender, and young age (15-24 years) were significantly associated with HIV awareness of HIV status. In multivariate analysis, the same variables were associated with awareness of HIV status.”

Line 31: The Adjusted Prevalence Ratios are actually risks and not odds. (Unless a prevalence odds ratio was used, which doesn’t appear to be the case)

Introduction:

Lines 42-49: This paragraph jumps from discussing mortality (“mortality declined by 44%”) to infections (“infections decreased by 28%”), and then back to mortality (“The global decline in deaths from AIDS-related illness”). I would consider centering the introduction on the UNAIDS 2020 targets regarding awareness of HIV status.

Methods:

Line 116: May want to describe Jackknife replication. I’m unsure if it’s commonly used but readers may be more familiar with bootstrapping.

Results:

Line 144: A table of characteristics of the general sample, including both HIV-positive and HIV-negative could be useful for comparison’s sake.

Lines 181-184: This sentence is a little unclear. May want to consider changing the wording to something along the lines of “In Afar, Addis Ababa, and Dire Dawa, more people never tested than self-reported negative, whereas in the rest of the other regions, more people self-reported negative.”

Line 206: Figure 3 seems to show in graphical representation the data in the first portion of Table 2. Is there a particular reason for the geographic representation of the unawareness? If not, can consider removing this figure.

Discussion:

Line 259: Please explain the overall capacity score.

Line 268: Just a note, this is exactly why the reader would be very interested in the ARV data!

Line 298: Although this is true regarding Gambella, as you stated in line 283, this would be a low-yield strategy.

Line 308: The authors discuss the difference in transmission between PLHIV who are aware and unaware of their HIV status. They should consider a modeling analysis in the future!

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PLoS One. 2021 Aug 11;16(8):e0255163. doi: 10.1371/journal.pone.0255163.r002

Author response to Decision Letter 0

17 Jun 2021

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PLoS One. doi: 10.1371/journal.pone.0255163.r003

Decision Letter 1

José Antonio Ortega

12 Jul 2021

Factors associated with Unawareness of HIV Positive Status in Urban Ethiopia: Evidence from the Ethiopia Population-based HIV Impact Assessment 2017-2018

PONE-D-21-05302R1

Dear Dr. Lulseged,

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.

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Reviewer #1: All comments have been addressed

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

Reviewer #1: Yes

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

Reviewer #1: Yes

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

Reviewer #1: Yes

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

Reviewer #1: Yes

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6. Review Comments to the Author

Reviewer #1: Thank you for addressing my prior concerns. My remaining comment is that the Discussion is quite long and unfocused. I have two suggestions for shortening. One is that comparisons to other studies can be shortened. For example, lines 255-256 are well summarized whereas lines 330-336 could be shortened without sacrificing information. The second suggestion is that the discussion can be more targeted. The discussion begins with a comparison of the unawareness rate in Ethiopia compared to other countries, and then hypothesizes several reasons why this is so. It then suggests why unawareness may be more important than prevalence. It then switches to geographic variations in testing strategies, then consequences of unawareness, geographic variation again, and then risky sexual activity. Although all sections are relevant and important, the reader would benefit from guidance regarding the most salient results rather than all.

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PLoS One. doi: 10.1371/journal.pone.0255163.r004

Acceptance letter

José Antonio Ortega

30 Jul 2021

PONE-D-21-05302R1

Factors associated with unawareness of HIV-positive status in urban Ethiopia: Evidence from the Ethiopia Population-based HIV Impact Assessment 2017-2018

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Data Availability Statement

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PERMALINK

Factors associated with unawareness of HIV-positive status in urban Ethiopia: Evidence from the Ethiopia population-based HIV impact assessment 2017-2018

Sileshi Lulseged

Wudinesh Belete

Jelaludin Ahmed

Terefe Gelibo

Habtamu Teklie

Christine W West

Zenebe Melaku

Minilik Demissie

Mansoor Farhani

Frehywot Eshetu

Sehin Birhanu

Yimam Getaneh

Hetal Patel

Andrew C Voetsch

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Study setting and population

Data collection procedures

Sampling procedure

Definition of variables

Eligibility for the study

Data analysis

Ethical considerations

Results

Participants’ characteristics

Table 1. Demographic, socioeconomic and behavioural characteristics of HIV-positive participants aged 15–64 in urban Ethiopia.

Testing history

Fig 1. Distribution of self-reported HIV testing coverage by region among HIV-positive participants aged 15–64 years, 2017–18.

Self-reported HIV status

Fig 2. HIV-positive status by history among HIV-positive participants aged 15–64 years in urban Ethiopia, EPHIA 2017–2018.

Unawareness of HIV-positive status

Table 2. ARV adjusted estimates of unawareness by socio demographic and behavioral characteristics among HIV-positive participants aged 15–64 years, EPHIA 2017/2018.

Fig 3. The burden of unawareness status by region among HIV-positive participants aged 15–64 in urban Ethiopia, 2017/18.

Factors associated with unawareness of HIV-positive status

Table 3. Factors associated with weighted unawareness status among HIV-positive participants aged 15–64 years, EPHIA 2017/2018.

Discussion

Limitations

Conclusions

Supporting information

Acknowledgments

The EPHIA study team

Data Availability

Funding Statement

References

Decision Letter 0

José Antonio Ortega

Roles

Author response to Decision Letter 0

Decision Letter 1

José Antonio Ortega

Roles

Acceptance letter

José Antonio Ortega

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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