Antiretroviral drug switches to zidovudine‐based regimens and loss to follow‐up during the first COVID‐19 lockdown in Bali, Indonesia
Bibliographic record
Abstract
OBJECTIVES: International lockdowns during the COVID-19 pandemic impacted antiretroviral drug supplies in Indonesia. We assessed the impact of antiretroviral treatment (ART) provision and being lost to follow-up (LTFU) on people living with HIV, attending a key population-focused HIV clinic in Denpasar, Bali. METHODS: This was a retrospective note review of anonymized data from adult Indonesian patients living with HIV. We collected demographic data and information on being LTFU, and assessed the numbers of patients impacted by ART switches from fixed-dose combination (FDC) tenofovir/lamivudine/efavirenz to multi-pill zidovudine-based regimens, during the first international lockdown from March 2020. RESULTS: Records of 260 Indonesian adult patients registered for HIV care and prescribed ART were reviewed; 240 (92.3%) were men, and 90% were men who have sex with men. Between 13 March and 28 May 2020, 214 (87%) out of 247 patients (previously diagnosed with HIV) had to switch to individual, multi-pill zidovudine-based regimens from their FDC. The switch lasted a mean of 35 days (range 10-85). Twenty-five patients (10%) were LTFU; patients who switched were more likely to remain in care. Data on viral load status and toxicity are lacking as laboratory testing requires self-payment. CONCLUSIONS: The majority of patients living with HIV had no choice but to switch to multi-pill, zidovudine-based regimens. Despite significant efforts to minimize the impact of lockdown on care, 10% of patients were LTFU. Patients switching ART required greater clinic attention and support, improving retention. Complete national data are needed to understand the impact of ART stockouts on virological suppression and drug resistance throughout Indonesia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".