Evidence map›Paper›PMID 40937428›Full record

ArticleBMJ public health2025

Health facility and contextual correlates of HIV test positivity: a multilevel model of routine programmatic data from Malawi.

Miyu Niwa, Dylan Green, Tyler Smith, Brandon Klyn, Yohane Kamgwira, Sara Allinder, Deborah Hoege, Suzike Likumbo, Charles B Holmes, Gift Kawalazira and 1 more

Abstract read
In one paragraph

Article in BMJ public health, 2025. 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Miyu NiwaCooper/Smith, Austin, Texas, USA.ORCID https://orcid.org/0009-0007-9543-9233
Dylan GreenCooper/Smith, Austin, Texas, USA.
Tyler SmithCooper/Smith, Austin, Texas, USA.
Brandon KlynCooper/Smith, Austin, Texas, USA.
Yohane KamgwiraNational AIDS Commission, Lilongwe, Malawi.
Sara AllinderCenter for Innovation in Global Health, Georgetown University, Washington, District of Columbia, USA.
Deborah HoegeCenter for Innovation in Global Health, Georgetown University, Washington, District of Columbia, USA.
Suzike LikumboBlantyre District Health Office, Blantyre District Council, Lilongwe, Malawi.
Charles B HolmesCenter for Innovation in Global Health, Georgetown University, Washington, District of Columbia, USA.
Gift KawalaziraBlantyre District Health Office, Blantyre District Council, Lilongwe, Malawi.
Linley ChewereDepartment of HIV, STI & Hepatitis, Ministry of Health, Lilongwe, Malawi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Innovative and efficient methods are needed to identify remaining people living with HIV unaware of their status. Routine health information system (RHIS) data, widely available in high-burden HIV settings, may help target areas of high risk to deliver timely prevention services. Often underused, RHIS data were leveraged at the facility level to predict changes in HIV test positivity in Malawi. Methods: From District Health Information Software-2 from January 2017 to March 2023, we analysed sexually transmitted infection (STI) cases and HIV tests and test results across 563 health facilities in Malawi. A multilevel model was employed to determine whether changes in STI diagnoses were predictive of changes in HIV test positivity. We considered STI types and their incubation periods, and controlled for facility type, ownership, quarter, season, zonal HIV and STI prevalence (2016 Population-Based HIV Impact Assessment). Results: Among 139 million HIV tests, overall positivity was 2.8%. Blantyre facilities had the highest positivity (6.0%) while those in the central-east zone had the lowest (1.8%). Key variables-changes in syndromic STI counts (lagged and cross-sectional)-showed weak or no associations with HIV positivity (OR: 1.01, CI: 1.01 to 1.01; OR: 1.00, CI: 1.00 to 1.00). However, contextual covariates, including zonal HIV prevalence (OR: 1.04, CI: 1.04 to 1.04), genital ulcers (OR: 1.16, CI: 1.16 to 1.16) and clinical STI diagnoses (OR: 1.29, CI: 1.29 to 1.29), were positively associated with HIV positivity. Conclusions: In settings with high STI screening uptake, RHIS data can be used to monitor changes in STI diagnoses and contextual factors to identify HIV hotspots and guide targeted testing, prevention and treatment services.

Indexed as

EpidemiologyHIVPublic HealthSexually Transmitted Diseases

Identifiers

PMID40937428
PMCPMC12421179

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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.