Suboptimal HIV status ascertainment at antenatal clinics and the impact on HIV prevalence estimates.
Wangara, F.; Estill, J.; Kipruto, H.; Wools-Kaloustian, K.; Chege, W.; Manguro, G.; Keiser, O.
Show abstract
IntroductionHIV prevalence estimates is a key indicator to inform the coverage and effectiveness of HIV prevention measures. Many countries including Kenya transitioned from sentinel surveillance to the use of routine antenatal care data to estimate the burden of HIV. Countries in Sub Saharan Africa reported several challenges of this transition, including low uptake of HIV testing and sub national / site-level differences in HIV prevalence estimates. MethodsWe examine routine data from Kwale County, Kenya, for the period January 2015 to December 2019 and predict HIV prevalence among women attending antenatal care (ANC) at 100% HIV status ascertainment. We estimate the bias in HIV prevalence estimates as a result of imperfect uptake of HIV testing and make recommendations to improve the utility of ANC routine data for HIV surveillance. We used a generalized estimating equation with binomial distribution to model the observed HIV prevalence as explained by HIV status ascertainment and region (Sub County). We then used marginal standardization to predict the HIV prevalence at 100% HIV status ascertainment. ResultsHIV testing at ANC was at 91.3%, slightly above the global target of 90%. If there was 100% HIV status ascertainment at ANC, the HIV prevalence would be 2.7% (95% CI 2.3-3.2). This was 0.3% lower than the observed prevalence. Similar trends were observed with yearly predictions except for 2018 where the HIV prevalence was underestimated with an absolute bias of -0.2%. This implies missed opportunities for identifying new HIV infections in the year 2018. ConclusionsImperfect HIV status ascertainment at ANC overestimates HIV prevalence among women attending ANC in Kwale County. However, the use of ANC routine data may underestimate the true population prevalence. There is need to address both community level and health facility level barriers to the uptake of ANC services. Key questionsO_ST_ABSWhat is already known?C_ST_ABS{blacksquare} HIV surveillance estimates from antenatal clinics (ANC) can serve as a useful proxy for HIV prevalence trends in the general female population. {blacksquare}Kenya has conducted multiple studies which have shown that national HIV prevalence estimates from sentinel surveillance and those from routine program data to be similar. {blacksquare}However, these studies have also revealed ongoing challenges to the suitability of using routine data as compared to sentinel surveillance including sub optimal uptake of HIV testing and sub national/ site-level differences in HIV prevalence estimates. What are the new findings?{blacksquare} HIV positive pregnant women are more likely to be tested at ANC as compared to HIV negative women, leading to higher HIV prevalence estimates among women attending ANC. {blacksquare}Health facility level HIV prevalence estimates are lower than that of the general population. What do the new findings imply?{blacksquare} HIV positive women are underrepresented in antenatal clinics. {blacksquare}In Kwale County (and similar contexts), use of routine ANC data is still not a reliable method to estimate HIV prevalence, both at facility and community level.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Predictors of HIV Testing among Youth 15-24 Years in urban Ethiopia, 2017-2018 Ethiopia Population-based HIV Impact Assessment 98%
- Strengthening Health System’s Capacity for Linkage to HIV Care for adolescent girls and young women and adolescent boys and young men in South Africa (SheS’Cap-Linkage): Protocol for a mixed methods study in KwaZulu-Natal, South Africa 97%
- Viral suppression among patients in HIV/AIDS care at healthcare facilities in Ethiopia: Same-day antiretroviral initiation 97%
Similar papers in this journal
- Cervical Cancer Screening Outcomes for HIV-positive Women in the Lubombo and Manzini regions of Eswatini – Prevalence and Predictors of a Positive Visual Inspection with Acetic Acid (VIA) Screen 97%
- Factors influencing the use of multiple HIV prevention services among Transport workers in a City in Southwestern Uganda 97%
- Trends of attrition from HIV care and its predictors among Adolescent Girls and Young Women with inconsistent viral load suppression results in Mainland Tanzania, 2016-2024. 95%
Similar papers in this journal
- “ Bringing testing closer to you ” – Barriers and Facilitators in Implementing HIV Self-Testing among Filipino Men-Having-Sex-with-Men and Transgender Women in National Capital Region (NCR), Philippines: A Qualitative Study 95%
- Age-dependent inequalities in HIV/STI burden and care receipt among men and transgender persons who have sex with men in Nairobi 95%
- Use of health care services during the Covid-19 pandemic in Ethiopia: Evidence from a health facility survey 95%
Similar papers in this journal
- Assisted partner services for people who inject drugs: Index characteristics associated with untreated HIV in partners 95%
- Differentiated HIV Service Delivery vs Conventional Care: Tuberculosis Preventive Therapy Outcomes for People Living with HIV in Sub-Saharan Africa 95%
- Quantifying delay in first contact with HIV programs among young women engaged in sex work in Mombasa, Kenya: a time-to-event analysis 94%
Similar papers in this journal
- Predictors and consequences of HIV status disclosure to adolescents living with HIV in Eastern Cape, South Africa 95%
- Missed opportunities for HIV testing among those who accessed sexually transmitted infection (STI) services, tested for STIs and diagnosed with STIs: a systematic review and meta-analysis 95%
- Preferences and Acceptability for Long-Acting PrEP Agents Among Pregnant and Postpartum Women with Experience Using Daily Oral PrEP in South Africa and Kenya 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.