Evidence map›Paper›PMID 42824380›Full record

ArticleIJID regions2026

Strengthening human immunodeficiency virus surveillance through systematic data cleaning: a national retrospective validation of the human immunodeficiency virus treatment and care registry in Guyana, 2025.

Tariq Jagnarine, Murvin Chalmers

Abstract read
In one paragraph

Article in IJID regions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Tariq JagnarineNational AIDS Programme Secretariat (NAPS), Ministry of Health, Georgetown, Guyana.
Murvin ChalmersNational AIDS Programme Secretariat (NAPS), Ministry of Health, Georgetown, Guyana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurate human immunodeficiency virus (HIV) surveillance is essential for achieving the Joint United Nations Programme on HIV/acquired immunodeficiency syndrome (AIDS) (UNAIDS) 95-95-95 targets. Historically, Guyana's HIV datasets contained duplicate entries, silent mortality, and unverified migration, resulting in inflated denominators and under-reported viral suppression. In 2025, the National AIDS Programme Secretariat conducted the first national HIV data-cleaning and verification exercise to establish a validated treatment cohort. Objectives: 1. To verify and clean historical HIV treatment records.2. To quantify duplicate, deceased, migrated, and defaulter cases.3. To distinguish long- vs short-term defaulters.4. To assess the impact on UNAIDS cascade indicators.5. To model viral suppression gains through re-engagement. Methods: A national retrospective review of 12,824 records included deduplication, outcome verification, and cohort classification. Analytical outputs included Wilson 95% confidence intervals, chi-square distribution testing, and standardized residuals. Re-engagement modeling simulated suppression gains across 15-60% scenarios. Results: We identified 401 duplicates (3.1%), 1308 deaths (10.2%), and 454 migrations (3.5%), producing a validated eligible cohort of 10,661 people living with HIV, correcting 13.7% denominator inflation. Of eligible clients, 7268 (68.2%) were active, 2071 (19.4%) long-term defaulters, and 1322 (12.4%) short-term defaulters. Re-engagement modeling showed improvements of +2.6 percentage points at 25% return and +6.3 percentage points at 60%, indicating strong potential for accelerating the third 95. Conclusion: National data validation substantially improved surveillance precision and clarified programmatic gaps. Re-engagement of short-term defaulters is a high-impact strategy for increasing viral suppression. Guyana's 2025 exercise provides a replicable model for improving regional HIV monitoring and advancing progress toward epidemic control.

Indexed as

Data qualityGuyanaHIV care cascadeHIV surveillanceRetention in careViral suppression

Identifiers

PMID42824380
PMCPMC13627942

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.