Impact of the COVID-19 pandemic on regional and national uptake of HIV testing services in Sierra Leone: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: A key milestone in the reduction of the global HIV burden is reaching the UNAIDS 95-95-95 target by 2025. The COVID-19 pandemic may have affected the ability of countries to achieve this target, but data to describe this impact is limited. This study assessed national HIV testing service uptake in Sierra Leone during three periods of the COVID-19 pandemic. METHODS: We conducted a retrospective cross-sectional study using secondary program data of all patients tested for HIV in all 16 districts of Sierra Leone. Data from March 2019 to February 2020 (pre-COVID-19); March 2020 to February 2021 (during COVID-19); and March 2021 to February 2022 (post-COVID-19) were extracted from DHIS-2 and descriptive analyses were performed using Stata (15.1, StataCorp LLC, College Station, TX). RESULTS: The median number of HIV tests was 58,588 (IQR 54,232 to 62,077) in the pre-COVID phase, 55,141 (IQR 52,975 to 57,689) during the COVID-19 phase, and 74,954 (IQR 72,166 to 76,250) in the post-COVID phase. This shows that HIV testing rate decreased by 6.0% during the COVID-19 period and increased substantially by 36.0% in the post-COVID-19 period. Twice more women than men were tested for HIV across all periods-pre-COVID-19 (38,825 vs. 19,789), during COVID-19 (36,923 vs. 17,755), and post-COVID-19 (49,205 vs. 25,472) phases. CONCLUSION: Our study shows that HIV testing was significantly disrupted during the COVID-19 pandemic but recovered quickly after the pandemic. These findings highlight that lessons learned from previous epidemics may influence adaptive strategies to maintain essential health services during public health emergencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".