Impacts of the COVID-19 pandemic on access to HIV and reproductive health care among women living with HIV (WLHIV) in Western Kenya: A mixed methods analysis
Bibliographic record
Abstract
Introduction The COVID-19 pandemic has impacted access to health services. Our objective was to understand the pandemic's impact on access to HIV, pregnancy, and family planning (FP) care among women living with HIV (WLHIV). Methods Data were collected after June 2020, when questions about the pandemic were added to two ongoing mixed methods studies using telephone surveys and in-depth interviews among WLHIV in western Kenya. The Chaguo Langu (CL) study includes primarily non-pregnant WLHIV receiving HIV care at 55 facilities supported by AMPATH and the Opt4Mamas study includes pregnant WLHIV receiving antenatal care at five facilities supported by FACES. Our outcomes were self-reported increased difficulty refilling medication, accessing care, and managing FP during the pandemic. We summarized descriptive data and utilized multivariable logistic regression to evaluate predictors of difficulty refilling medication and accessing care. We qualitatively analyzed the interviews using inductive coding with thematic analysis. Results We analyzed 1,402 surveys and 15 in-depth interviews. Many (32%) CL participants reported greater difficulty refilling medications and a minority (14%) reported greater difficulty accessing HIV care during the pandemic. Most (99%) Opt4Mamas participants reported no difficulty refilling medications or accessing HIV/pregnancy care. Among the CL participants, older women were less likely (aOR = 0.95, 95% CI: 0.92–0.98) and women with more children were more likely (aOR = 1.13, 95% CI: 1.00–1.28) to report difficulty refilling medications. Only 2% of CL participants reported greater difficulty managing FP and most (95%) reported no change in likelihood of using FP or desire to get pregnant. Qualitative analysis revealed three major themes: (1) adverse organizational/economic implications of the pandemic, (2) increased importance of pregnancy prevention during the pandemic, and (3) fear of contracting COVID-19. Discussion The two unique participant groups included in our study encountered overlapping problems during the COVID-19 epidemic. Access to HIV services and antiretrovirals was interrupted for a large proportion of non-pregnant WLHIV in western Kenya, but access to pregnancy/family planning care was less affected in our cohort. Innovative solutions are needed to ensure HIV and reproductive health outcomes do not worsen during the ongoing pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".