Assessing the impact of the COVID-19 restrictions on HIV testing services in Malawi: an interrupted time series analysis
Bibliographic record
Abstract
Background: Restrictions on public gatherings and movement to mitigate the spread of COVID-19 may have disrupted access and availability of HIV services in Malawi. We quantified the impact of these restrictions on HIV testing services in Malawi.Methods: We conducted an interrupted time series analysis of routine aggregated programme data from 808 public and private, adult and paediatric health facilities across rural and urban communities in Malawi between January 2018 and March 2020 (pre-restrictions) and April to December 2020 (post restrictions), with April 2020 as the month restrictions took effect. Positivity rates were expressed as the proportion of new diagnoses per 100 persons tested. Data were summarised using counts and median monthly tests stratified by sex, age, type of health facility and service delivery points at health facilities. The immediate effect of restriction and post-lockdown outcomes trends were quantified using negative binomial segmented regression models adjusted for seasonality and autocorrelation.Results: The median monthly number of HIV tests and diagnosed people living with HIV (PLHIV) declined from 261 979 (interquartile range [IQR] 235 654–283 293) and 7 929 (IQR 6 590–9 316) before the restrictions, to 167 307 (IQR 161 122–185 094) and 4 658 (IQR 4 535–5 393) respectively, post restriction. Immediately after restriction, HIV tests declined by 31.9% (incidence rate ratio [IRR] 0.681; 95% CI 0.619–0.750), the number of PLHIV diagnosed declined by 22.8% (IRR 0.772; 95% CI 0.695–0.857), while positivity increased by 13.4% (IRR 1.134; 95% CI 1.031–1.247). As restrictions eased, total HIV testing outputs and the number of new diagnoses increased by an average of 2.3% each month (slope change: 1.023; 95% CI 1.010–1.037) and 2.5% (slope change:1.025; 95% CI 1.012–1.038) respectively. Positivity remained similar (slope change: 1.001; 95% CI 0.987–1.015). Unlike general trends noted, while HIV testing services among children aged <12 months declined 38.8% (IRR 0.351; 95% CI 0.351–1.006) with restrictions, recovery has been minimal (slope change: 1.008; 95% CI 0.946–1.073).Conclusion: COVID-19 restrictions were associated with significant but short-term declines in HIV testing services in Malawi, with differential recovery in these services among population subgroups, especially infants. While efforts to restore HIV testing services are commendable, more nuanced strategies that promote equitable recovery of HIV testing services can ensure no subpopulations are left behind.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".