Objective: To assess the prevalence of human immunodeficiency virus (HIV) among sexually active women in Ethiopia and explore determinants of higher HIV prevalence rates.
Methodology: The analysis was based on secondary data from the Ethiopian demographic and health survey (EDHS), a community-based cross-sectional survey conducted by the central statistical agency (CSA) of Ethiopia between January 18 and June 27, 2016. The raw data were extracted from household and biomarker questionnaires, with HIV testing performed using dried blood spot specimens collected from eligible women aged 15–49 years.
Results: Of 14,443 women (aged 15–49 years) tested, 278 (1.9%) were HIV-positive. Most respondents (68%) resided in rural areas, while 32% were from urban areas. The mean age was 28 years (Standard deviation (SD)±9.2). HIV prevalence increased with age (Pearson r=0.087, P<0.01). The adjusted odds ratio (AOR) indicated that women aged 15–19 years (AOR = 0.14) and 20–24 years (AOR = 0.20) were significantly less likely to be HIV positive compared to older women (p < 0.001). Widowed and divorced women had higher odds of HIV positivity (70% and 20% greater, respectively) compared to never-married women.
Conclusion: Higher HIV prevalence was observed among women aged 30–34 years and older, with widowed and divorced women showing markedly elevated risk. Tailored HIV prevention, care, and treatment interventions should target these high-risk subpopulations.

Human immunodeficiency virus, Central statistical agency, Biomarker, Women, Prevalence, Survey.

The HIV epidemic in Ethiopia is heterogeneous, with wide regional variation, higher prevalence in urban areas, and distinct transmission pockets among key and priority populations and in some sectors of the general population. The Ethiopian population-based HIV impact assessment (EPHIA) survey in 2018 estimated an HIV prevalence of 3% in urban parts of the country, with regional variation.[1] The latest HIV modelling estimate (2021 Spectrum model) has indicated the number of people living with HIV (PLHIV) in the country to be 612,925. The conflict in the Northern part of Ethiopia, which started in November 2020, is threatening Ethiopia’s continuing momentum to reach epidemic control, and determining the national HIV cascade is limited by the lack of reporting from Tigray and parts of Amhara and Afar regions.[2] Estimates indicate that 84% of PLHIV adults and children know their HIV status; among people living with HIV who know their HIV status, 83% are confirmed to be receiving antiretroviral therapy (ART). Nonetheless, accurate treatment data is currently unavailable from conflict-affected areas in Tigray and parts of the Amhara region. Among adults PLHIV receiving ART who have a documented viral load test, 96% were virally suppressed (VL <1,000 copies/mL).[3-6]

Viral load coverage has increased modestly in the past years due to a combination of clinical, analytic, and data completeness factors, with unknown viral load status of an estimated 100,373 clients. Of people who received HIV testing services in the past year, an estimated 33,988 tested positive for HIV, and 91.4% of those testing positive were linked to treatment. Key population groups and priority populations are all estimated to have significantly higher HIV prevalence rates than the general population.[7-9] It includes people like female sex workers, widowed and divorced people, long-distance truck drivers, adolescent girls and young women (AGYW) engaged in transactional sex, male clients of sex workers (SWs), people who inject drugs (PWID), and those who live along major transport corridors. Gambela Region continues to have the highest HIV prevalence, 3.564, with little distinction between urban and rural residents; some rural areas with high seasonal migrant populations have high HIV prevalence. The overall ART coverage in Ethiopia is 70% with lower ART coverage of 33% among children below 15 years of age, but these estimates are affected by the lack of current reporting from conflict-affected areas in Tigray and parts of the Amhara region. In FY21, despite 31,086 patients newly enrolled in ART, the reported number of patients currently on ART by the end of the year dropped by 48,287 due to incomplete reporting from conflict-affected regions in northern Ethiopia. In sites that had consistent treatment reporting throughout FY21, the treatment cohort was modestly increased.[10-14]

Statement of the problem (Gap statement): Women are disproportionately affected by the HIV/AIDS pandemic globally, in general, and in sub-Saharan Africa in particular. Biological factors, power dynamics, economic vulnerability, health-seeking behavior, and other socio-cultural factors are attributed to the disproportionately higher HIV/AIDS disease burden among women.[15] There is some anecdotal evidence corroborating this phenomenon in Ethiopia.  Moreover, the past four years (2019-2023) represent a particularly challenging time for the Ethiopian HIV program.[16] First with COVID-19 and then with the widespread conflict and disruption of health services and program support in most of the conflict-affected and high HIV prevalence areas of the country, has resulted in remarkable morbidities and mortalities associated with HIV/AIDS.[17,18] Apparently, women are the most vulnerable and affected sub-population groups during such difficult times. In conflict-affected areas, there was widespread damage to infrastructure (Electricity, Telecom, others), loss of information systems, basic equipment, and furniture, and displacement of clients and health workers. These unprecedented challenges over a prolonged period pose continuing constraints on HIV/AIDS program implementation and support.[19-21] Based on some limited program data, lack of targeted testing, identifying positives, linking tested positive cases to ART sites, interruption of treatment (ART), and difficulty returning to treatment initiatives were some of the major challenges in the HIV/AIDS program. Overall, the lack of representative data and evidence to better understand vulnerability factors associated with HIV/AIDS is a critical gap to address the lingering challenges. Paradoxically, this gap is more prominent among women who are the most vulnerable sub-population groups in the context of HIV/AIDS.[22-24]

How to address the gap/challenge: Conducting studies to address the haunting evidence gap among vulnerable sub-population groups, like women of sexually active age groups, is quite important. Subsequently, designing and implementing targeted HIV/AIDS prevention programs based on evidence will make the intervention more effective.  Moreover, interventions need to be tailored to specific vulnerability factors and determinants.[25] Focus should also be made to enhance people-centered services for highly vulnerable sub-populations, virally unsuppressed clients, and clients newly initiated on treatment, ensuring that healthy aging is promoted through management of co-morbidities and coordination of other age-specific health and wellness needs. This will be done in adherence with Ministry of Health (MOH) policies for differentiated service delivery (DSD) models and close collaboration and coordination between facility and community stakeholders. The main driver in increasing the treatment cohort is HIV case finding, and the program will work to strengthen case finding through mixed, targeted modalities, linkage to rapid ART initiation, treatment continuity, and partner services.[26-28] Support a strategic mix of person-centered case finding strategies implemented by community and facility implementing partners.

Capacity strengthening activities will be supported to increase HIV testing services (HTS) capabilities and align efforts with the longer-term vision of sustaining HTS services. The following will be major focus areas: (1) Increase the availability of safe and ethical index case testing (ICT) among newly diagnosed and virally unsuppressed index cases, with the goal of offering index testing services to 100% of eligible clients; (2) Improve risk screening and risk-based testing to advance provider-initiated testing and counselling (PITC); (3) Scale up targeted community-based testing for populations with gaps in the first 95% and/or high HIV incidence, including Key Populations and other priority populations in geographic regions with high incidence; (4) Expand the promotion of and access to HIV self-testing (HIVST), both assisted and unassisted, to reach more populations that would benefit from this service; (5) Empower FSWs with HIV and those at high risk for HIV to serve as seeds for social network testing (SNT) at facility and community levels; (6)  Increase targeted demand creation to harder-to-access populations such as KP through a combination HIVST, ICT, and SNS testing modalities; and (7) Support the implementation of the new HIV testing algorithm and its implementation in health facilities and community settings.[29,30]

Objective:

• To assess determinant factors of high HIV prevalence among sexually active women in Ethiopia and generate evidence to inform HIV prevention, care, and treatment programs.
• To determine the prevalence of HIV among sexually active women.
• To assess socio-demographic and behavioral determinants associated with higher HIV prevalence among the study population.
• To assess the correlation of some predictive variables (factors) with the HIV test outcome

Study design: This study used secondary raw data extracted from the Ethiopian demographic and health survey (EDHS) 2016. Hence, this is a cross-sectional study design using a nationally representative dataset. The CSA of Ethiopia conducted the survey, which is the source of the raw data for the study, from January 18 to June 27, 2016. This was the fourth demographic and health survey conducted in Ethiopia. There were five major sections (questionnaires) on the 2016 EDHS: the household questionnaire, the woman’s questionnaire, the man’s questionnaire, the biomarker questionnaire, and the health facility questionnaire. This study primarily used raw data extracted from the household and biomarker questionnaire for HIV.

Sampling strategy: The sampling frame used for the 2016 EDHS was the Ethiopian population and housing census (PHC), which was conducted in 2007 by the Ethiopian CSA. The census frame was a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. Except for EAs in six zones of the Somali region, each EA has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the EA that help identify the EA. The sample for the 2016 EDHS was designed to provide estimates of key indicators for the country. Urban and rural areas separately, and for each of the nine regions and the two administrative cities. The 2016 EDHS sample was stratified and selected in two stages. In the first stage, a total of 645 EAs (202 in urban areas and 443 in rural areas) were selected with probability proportional to EA size (based on the 2007 PHC) and with independent selection in each sampling stratum. The resulting lists of households served as a sampling frame for the selection of households in the second stage. Some of the selected EAs were large, consisting of more than 300 households. Household listing was conducted only in the selected segment; that is, a 2016 EDHS cluster is either an EA or a segment of an EA. In the second stage of selection, a fixed number of 28 households per cluster were selected with an equal probability systematic selection from the newly created household listing.

Study population: All women and men aged 15-49, who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey, were eligible to be interviewed under the EDHS. Dry blood spot (DBS) samples were collected for HIV testing in the laboratory from women aged 15-49 and men aged 15-59 who consented to testing. However, this study filtered only women aged 15-49 years to be included, and all men were excluded from the study.

Data collection: Interviewers collected finger-prick blood specimens from women aged 15-49 and men aged 15-59 who consented to HIV testing. However, only the women sub-population was included in this study. The protocol for blood specimen collection and analysis was based on the anonymous linked protocol developed for the DHS Program. Interviewers explained the procedure, the confidentiality of the data, and the fact that the test results would not be made available to respondents. If a respondent consented to HIV testing, five blood spots from the finger prick were collected on a filter paper card to which a barcode label unique to the respondent was affixed. A duplicate label was attached to the Biomarker Questionnaire. A third copy of the same barcode was affixed to the dried blood spot transmittal sheet to track the blood samples from the field to the laboratory. Respondents were also asked whether they would consent to having the laboratory store their blood sample for future testing of hepatitis B and C, rubella, and measles. If respondents did not consent to additional testing of their blood sample in the future, their refusal was recorded on the biomarker questionnaire, and the words “no additional testing” were written on the filter paper card. All respondents, irrespective of whether they provided consent, were given an informational brochure on HIV and a list of nearby sites providing HIV counselling and testing (HCT) services. Blood samples were dried overnight and packaged for storage the following morning. Samples were periodically collected from the field and transported to the laboratory at the Ethiopian Public Health Institute (EPHI) in Addis Ababa. Upon arrival at EPHI, each blood sample was logged into the CSPro HIV Test Tracking System database, given a laboratory number, and stored at -20°C until tested.

Data management and quality assurance: As part of the standard demographic and health survey (DHS) practices, several quality control procedures are employed throughout the data collection and testing process. Before testing the 2016 EDHS survey samples began, EPHI and the DHS Program optimized the HIV testing procedures. First, to ensure that the testing procedures, especially those used to adapt the three assays for use on DBS samples, would correctly identify the HIV status of respondents’ DBS samples, a challenge panel of well-classified samples was used. The testing algorithm calls for testing all samples with the first assay, the Genscreen ULTRA Ag/Ab (BioRad) enzyme-linked immunoassay (ELISA I). All samples testing positive on the ELISA I are subjected to a second ELISA (ELISA II), the Bioelisa HIV 1+2 Ag/Ab combination (Biokit). Five percent of the samples that test negative on ELISA I are also subjected to ELISA II, while the other 95% are recorded as negative. Concordant negative results on ELISA I and ELISA II are recorded as negative. If the results of the ELISA I and ELISA II are discordant, the specimen is rendered inconclusive. Concordant positive results on the ELISA I and ELISA II are also subjected to the third confirmatory assay. When both the ELISA I and the ELISA II are positive, the sample is rendered positive if the Inno-Lia is positive and inconclusive if the Inno-Lia is negative or indeterminate. To monitor the quality of HIV testing and assess the validity of test results, two quality control steps were employed.

Laboratory technologists tested the challenge panel samples using each of the three HIV assays without knowing the true status of the samples, and the test results were then compared with the known results for the challenge panel. Second, as mentioned previously, a portion of the ELISA I negative samples were retested on ELISA II to assess assay agreement. Third, to monitor the performance of the HIV assays to correctly classify the HIV status of respondents’ samples, HIV negative, HIV low-positive, and HIV high-positive dried blood spot control materials provided by the CDC were included on each test plate, and their serological classification was compared with the expected value. Test results are exported electronically from the ELISA plate reader into a lab data management program developed by The DHS Program, the CSPro HIV Test Tracking System (CHTTS). CHTTS tracks the results of each sample on each assay, and laboratory technologists, through the program’s user interface, ensure that each sample receives exactly the tests needed to render a final HIV status according to the logic of the testing algorithm. After the HIV testing was completed, the laboratory results were linked to the survey data file via the anonymous unique bar code. Following the linking of laboratory results to the survey data file, sample weights were calculated and applied.

Data analysis: Various summary tables, graphs, and charts were used for descriptive purposes. Measures of central tendency and dispersion were used to describe various relevant findings. Binary logistic regression was used to uncover statistically significant associations between explanatory variables and discrete outcome variables (HIV sero-status). The enter method was used for variable selection in the regression model.

Ethical considerations: Ethical clearance for the EDHS was granted by EPHI, NRERC, and ICF International. Informed consent was obtained before data and blood collection. HIV testing followed an anonymous linked protocol, ensuring confidentiality. Respondents were informed, provided brochures, and referred to nearby HIV counselling and testing sites, regardless of consent.

A total of 14,443 individuals (women aged 15-49 years) were tested for HIV. Two hundred seventy-eight (1.9%) of tested individuals were HIV +ve. Oromia, SNNPR, and Amhara Regional States contributed the highest (12.4%, 12.2% and 11.7%) of tested individuals, respectively. The majority (68%) of tested individuals were from rural areas, while 32% were from urban areas. Respondents’ mean age was 28 years with a standard deviation (SD) of 9.2 years and a range of 15-49 years.

Figure 1: Age distribution of study subjects

The majority of the study subjects (79%) either did not have formal education or only attended primary level education.

Table 1: Education level distribution in the study sample

Majority of the respondents (62%) were married followed by those who have never been in marital union, 26% and divorced 6%.

Figure 2: Marital status of study subjects

Most of the respondents 60% were Christian and Muslims constitute about 39%. About 24% of the respondents were Amhara and ethnic Oromo and Tigrie respondents constitute 23% and 13% respectively. More than half (51.5%) of respondents were not employed, while 17.4% are involved in agricultural and 15.5% in small and petty trade/sales work. About 36% of women (respondents) own mobile phone. Only 18% of respondents have bank or micro-finance accounts in their name. Significant majority 92% of respondents have never been using internet in the past 12 months.

Sero-status (HIV test result): 278 HIV positive individuals were identified from all tested study subjects making the prevalence 1.9%. This prevalence was one of the lowest in sub-Saharan Africa including neighbouring eastern Africa countries like Kenya which has a generalized HIV epidemic with a prevalence among 15–49-year-olds estimated to be 5.9% in 2015 and 4.9% in 2018(“UNODC Eastern Africa – HIV and AIDS,” n.d.). Eastern Africa is the second most affected region by HIV and AIDS in the world after Southern Africa. Countries in southern Africa region (Botswana, Lesotho, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe) has the highest prevalence (more than 10%) in 2017. The HIV prevalence across different socio-demographic variables was quite variable which characterizes the heterogeneity of the HIV epidemic in Ethiopia.  Looking at place of residence among study subjects, the HIV prevalence rate among urban women (4.3%) was five times higher than rural residents (0.8%).  This finding is pretty much consistent with the general population’s urban-rural HIV prevalence rate in Ethiopia. With Ethiopia’s mixed type HIV epidemic, the prevalence in urban setting has been consistently higher than rural residents in the past 20 years ever since the country started HIV bio-behavioral surveys

Age is another important determinant factor in the prevalence of HIV. In this study, the HIV prevalence rate has increased as the age of study subjects increases and reached the maximum among women aged 30-34 years (3.6%). The prevalence leveled off up to age 40-44 and showed a slight decline. The prevalence among adolescent and young women (age 15-19 and 20-24) was lower (0.4% and 0.6% respectively) compared to the overall average (1.9%). This finding is very atypical of most southern and eastern African countries, where there is relatively higher HIV prevalence among adolescent girls and young women.

Table 3: Distribution of HIV status across age groups

Figure 3: HIV prevalence across different age categories (women 15-49 years)

To assess the correlation between the age of respondents and serum HIV status, Pearson’s correlation coefficient was computed. As respondents’ age increases, the prevalence of HIV increases with a Pearson correlation coefficient value of 0.087 and with a statistically significant margin of P<0.01 (2-tailed). Thus, for a single unit (1 year) increase in age, there was a 0.087% increase in the prevalence of HIV. Age at first sex has also shown a correlation with serum HIV test result (status), with a correlation coefficient of 0.055. Logistic regression was also done to further assess the association between age as a predictor variable and the HIV sero-status of women as an outcome variable. Hence, the odds of HIV positivity were 25% and 18%   higher among women of age 30-34 years and 35-39 years, but not statistically significant (P .32 and .47, respectively). However, the AOR for women in the age group 15-19 years was 0.14, and for women in the age group  20-24 years was 0.2, with a statistically significant margin (P<0.001). This regression finding further strengthens the atypically lower HIV risk and prevalence rate among adolescent and young women in Ethiopia. The study finding reinforces the need to do further studies to better understand why these particular age categories were uniquely less vulnerable in Ethiopia compared to some countries in Africa. Moreover, lessons from such studies will help to inform a number of adolescent girls and young women about focused interventions that are widely practiced in many sub-Saharan African countries.

Table 4: HIV prevalence across educational categories

The education status of study subjects showed some variability in their serum HIV status. The highest prevalence was observed among women who reported secondary level education status (3%), while the lowest prevalence was reported among illiterate women (women with no education).

Figure 4: HIV prevalence across different education status categories

Further analysis was done to assess the association between women’s education status and their HIV test outcome. The odds of a positive HIV test outcome were 60% and 40% higher among secondary and primary education level women compared to higher level (tertiary level) educated women. Nonetheless, the adjusted odds ratio (AOR) didn’t show a statistically significant association between education status and serum HIV test outcome (P=0.17 for primary level educated women and P=0.067 for secondary level educated women). Similar studies in sub-Saharan Africa showed that being illiterate (lower education status) may not necessarily predispose one to HIV and contribute to the spread of infection. However, lower-level literacy limits access to written information (Medel-Añonuevo and Cheick, 2007).

Figure 5: Husband/Partner’s educational level and HIV prevalence of women

Furthermore, the husbands’/partners’ education status of women was also assessed to see any association with their HIV test result. A similar pattern was observed in the overall HIV test outcome of women in relation to the education status of their spouses.  Alike the study subjects’ educational status, their spouses (husbands’) education didn’t show a statistically significant association with the HIV test outcome of women, but a relatively higher odds ratio was reported: AOR=3.5 for secondary level educated husbands and AOR=2.5 for higher/tertiary level educated husbands compared to no education.

Figure 6: HIV prevalence vs the highest year of education

Study subjects’ economic status is sometimes hypothesized as a determining factor for HIV risk and vulnerability. This assumption usually applies to women in developing and least developed countries, given their economic vulnerability and engagement in risky sexual behavior as a result. With that understanding, the HIV prevalence rate across various wealth quintiles was assessed. As depicted in the line graph below, the HIV prevalence rate of women increases as their wealth quintile increases. This finding was not consistent with the conventional assumptions that poorer or poorest women are much more vulnerable to HIV.  Further analysis of this finding using logistic regression showed that the odds of HIV positivity were lower 80%, 85%,82% and 68% among the poorest to richest, with the richest as the constant (reference wealth quintile), with statistical significance (p<0.001). This could be explained by the fact that rich women in the study were urban dwellers, where the background HIV prevalence was much higher (five times higher) than that of the rural residents.  However, further study to explore why such higher vulnerability to HIV was associated among economically well-off women is very important.

Figure 7: HIV prevalence across wealth quintiles

Women’s marital status could affect their lifestyle and sexual behavior. Hence, the prevalence of HIV across different marital statuses of the study subjects was examined. Remarkably higher (11%) HIV prevalence was observed among widowed women. This was the highest HIV prevalence finding among any sub-population in the overall study subjects. HIV/AIDS related mortality could be the possible cause of death for some of the spouses of the widowed women. However, this hypothesis needs further study to associate the cause of mortality of spouses with the widowed women.

Figure 8: Marital status and HIV prevalence

Further analysis of marital status with HIV test outcome was done using logistic regression. Being a widowed woman has 70% and divorced women have 20% higher odds of HIV positivity compared to never-married women. Non-married 88% and married couples have 78% less odds of a statistically significant level (P<.0001) than married couples. Relatively higher (4%) HIV prevalence was observed among women who chew Khat (a local addictive substance) compared to non-chewers, 1.7%. HIV prevalence among women who reported alcohol use was slightly higher, 2.4% compared to non-alcohol users (1.7%). However, neither Khat use nor alcohol consumption showed a statistically significant association with HIV positive test outcome.   The overall HIV prevalence among pregnant women was lower (0.6%) compared to non-pregnant women, who had 2%.

Women who reported having multiple concurrent partnerships have 6.2% HIV prevalence compared to those who have a single partner or didn’t have any partner (1.8%). The total lifetime number of partners correlated with the HIV positive test outcome was analyzed using Pearson’s correlation coefficient of 0.034. A prevalence rate increase was observed for several lifetime sexual partners. HIV prevalence was four times higher (4.1%) when the head of the household was a woman than in households headed by men (1%). Women with a history of some emotional violence have 2.8% HIV prevalence compared to those who didn’t report any emotional violence (2.2%). Women who experience sexual violence have slightly higher HIV prevalence (2.7%) compared to those who don’t have a history of sexual violence (2.3%). Women who reported any form of severe violence have relatively higher HIV prevalence (3.2%) compared to those who didn’t encounter severe forms of violence (2.3%).

Limitations: Inherent to the nature of the secondary analysis of existing data, the available data are not collected to address the research question or to test the hypothesis. It is not uncommon that some important third variables were not available for analysis. Another limitation while analyzing existing or secondary data sets was a lack of awareness of study-specific nuances or glitches in the data collection process that may be important to the interpretation of specific variables in the dataset.

Strength: Demographic and health survey methods and materials are globally recognized, high-quality tools and practices tested in multiple countries. The geographic coverage and sample size of the study were representative of drawing scientific conclusions and generalizing study findings. Having such a large-scale population-based survey raw data in a short period of time with no or minimal cost was a huge advantage and strength to effectively spend time on testing hypotheses and thinking about different research approaches rather than collecting primary data.

Recommendations: Targeted HIV interventions should be strengthened for women, as they carry a higher burden compared to men, with particular focus on urban areas where prevalence is disproportionately higher. Age-specific strategies are essential, including behavioral and biomedical interventions for women aged 30–34 years and older, while further research is needed to understand why adolescent girls and young women (15–24 years) show lower vulnerability in Ethiopia compared to other regions. Revitalizing school-based HIV prevention programs, which were once highly effective, could also play a key role in equipping young people with knowledge and protective behaviors against HIV.

Special attention should be given to widowed and divorced women, who exhibit remarkably higher prevalence rates, by ensuring their full inclusion in the Joint United Nations Programme on HIV and AIDS (UNAIDS) 95-95-95 cascade. Further studies are needed to explain the paradoxical link between higher wealth quintiles and HIV prevalence, particularly considering urban residency as a confounding factor. Additionally, prevention strategies should integrate substance use interventions that address alcohol and khat consumption, and promote behavioral change aimed at reducing multiple concurrent sexual partnerships among women of sexually active age groups.

The overall HIV prevalence (1.9%) among sexually active women in Ethiopia is relatively higher compared to men, highlighting the need for gender-specific interventions. Women residing in urban areas experience disproportionately higher HIV prevalence compared to their rural counterparts, suggesting a concentrated epidemic in urban settings. Age-wise, women aged 30–34 years and older exhibit higher prevalence, while adolescent girls and young women (15–24 years) showed unexpectedly low HIV prevalence compared to patterns observed in other sub-Saharan African countries. Widowhood and divorce are strongly associated with higher HIV prevalence, while secondary education shows some degree of association, though not statistically significant. Substance use (alcohol and khat chewing), higher economic status (likely linked with urban residence), and multiple concurrent sexual partnerships were also associated with increased HIV prevalence.

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Corresponding Author:
Mekdem Bisrat
Department of internal medicine
Howard University, Washington DC, USA
Email: mekdembisrat21@gmail.com

Co-Authors:
Wondwossen A Alemayehu
Project Hope
Howard University, Washington DC, USA

Elizabeth Beyene, Rawan Elkomi, Syed Fahad Gillani, Shaheen Alvi, Huda Gasmelseed, Shahnoza Dusmatova, Swetha Mynedi, Miriam Michael
Department of Internal Medicine
Howard University, Washington DC, USA

All authors contributed to the conceptualization, investigation, and data curation by acquiring and critically reviewing the selected articles. They were collectively involved in the writing, original draft preparation, and writing review & editing to refine the manuscript. Additionally, all authors participated in the supervision of the work, ensuring accuracy and completeness. The final manuscript was approved by all named authors for submission to the journal.

Ethical clearance for the EDHS was granted by EPHI, NRERC, and ICF International. Informed consent was obtained before data and blood collection. HIV testing followed an anonymous linked protocol, ensuring confidentiality. Respondents were informed, provided brochures, and referred to nearby HIV counseling and testing sites, regardless of consent.

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https://doi.org/10.63096/medtigo30623311

Bisrat M, Alemayehu WA, Beyene E, et al. Determinants of HIV Prevalence Among Sexually Active Age Group Women in Ethiopia. medtigo J Med. 2025;3(3):e30623311. doi:10.63096/medtigo30623311 Crossref