Impact of the <scp>COVID</scp>‐19 pandemic on the <scp>HIV</scp> care continuum and associated factors in middle‐income countries: A mixed‐methods systematic review
Bibliographic record
Abstract
INTRODUCTION: The HIV care continuum during the COVID-19 era faced specific challenges. The pandemic, affecting the delivery of HIV care, exacerbated existing healthcare inequities and vulnerabilities in middle-income countries with limited financial resources. This study aims to set the stage for the systematic review, focusing on the impact of COVID-19 on HIV care in middle-income countries with a focus on barriers and facilitators. METHODS: A systematic search of relevant literature, including electronic databases and manual assessment of references, was done. The review included quantitative, qualitative and mixed-methods studies conducted in middle-income countries, with no age or gender restrictions. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for reporting the results. RESULTS: In the course of our systematic review, a comprehensive examination of the pertinent literature published between 2020 and 2024 yielded a total of 76 studies. This adverse impact was prominently attributed to an amalgamation of factors intrinsically associated with pandemic-induced restrictions, fear of contracting the COVID-19 and fear of disclosing HIV status. Moreover, an emergent theme observed in select studies underscored the enduring trend of HIV treatment continuity, which was facilitated by the burgeoning utilization of telemedicine within this context. DISCUSSION: The pandemic negatively affected income and increased vulnerability to HIV across all phases of the HIV care continuum, except for viral suppression. Prevention measures, such as pre-exposure prophylaxis (PrEP), were compromised, leading to increased risky behaviours and compromised mental health among people living with HIV. HIV testing and diagnosis faced challenges, with reduced access and frequency, particularly among key populations. The pandemic also disrupted linkage and retention in care, especially in urban areas, exacerbating barriers to accessing necessary HIV treatment. Additionally, this review highlights the complex and multifaceted landscape of the pandemic's impact on HIV medical appointments, adherence and treatment engagement, with various barriers identified, including fear of COVID-19, economic constraints and disruptions in healthcare services. CONCLUSIONS: The coexistence of pandemics has had negative effects on the HIV care continuum, with restrictions on services, an increase in care gaps and a break in the transmission chain in middle-income countries.
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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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".