Evidence map›Paper›PMID 39939893›Full record

ArticleBMC public health2025

Improving HIV case finding using spatial data infrastructures in Anambra State, Nigeria: a pre-post intervention study.

Kevin O Ukueku, Bonaventure M Ukoaka, Emmanuel A Ugwuanyi, Keziah U Ajah, Faithful M Daniel, Monica A Gbuchie, John A Alawa, Emmanuel A Essien, Philip Imohi

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

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

9 authors.

Kevin O Ukueku *Monitoring and Evaluation Unit, Achieving Health Nigeria Initiative, Awka, Nigeria.
Bonaventure M Ukoaka *Prevention, Care, and Treatment Unit, Achieving Health Nigeria Initiative, Awka, Nigeria. bonaventureukoaka@gmail.com.
Emmanuel A UgwuanyiPrevention, Care, and Treatment Unit, Achieving Health Nigeria Initiative, Awka, Nigeria.
Keziah U AjahFaculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Awka, Nigeria.
Faithful M DanielCommunity and Clinical Research Division, First On-Call Initiative, Port Harcourt, Nigeria.
Monica A GbuchieDepartment of Sexual and Reproductive Health Research, Act4Her Health Initiative, Yenagoa, Nigeria.
John A AlawaLaboratory Unit, Excellence Community Education Welfare Scheme, Calabar, Nigeria.
Emmanuel A EssienFederal Neuropsychiatry Hospital, Calabar, Nigeria.
Philip ImohiPrevention, Care, and Treatment Unit, Family Health International (FHI 360) Regional Headquarters, Abuja, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe heightened HIV prevalence in Nigeria is partly associated with challenges in accessing people living with HIV in geographically isolated and unidentified regions. Spatial Data Infrastructure (SDI) is an innovation that has shown promise for HIV case-finding in unidentified settlements. This study reports the use of SDI to improve HIV case identification in Anambra North Senatorial District, Nigeria.

methodsThis study utilised a pre-post intervention study design to analyse data from the implementation of HIV testing services (HTS). Settlements for HTS were identified in the district using SDIs, such as microplans and hotspot maps. Community teams captured areas' names and geolocations using a custom application. Geographical Information Systems technology was overlayed on coordinates to generate microplans and hotspot maps, which were used for targeted tests and new case identification.

resultsOur study showed varying trends across the periods when SDIs were utilised and when they were not. The use of SDI greatly enhanced HIV case identification and provided a strategic framework for HTS implementation. Overall, the period when SDI was used recorded relatively higher new cases than before. Local Government Areas with more rural settlements that leveraged SDI significantly upscaled their case identification.

conclusionsSDI can facilitate HIV case identification. Our study revealed twice as many cases identified across the periods compared. Our pioneering use of SDI for HIV case finding in Nigeria offers promise for efficient HTS implementation in high-burden and yet-to-be-identified locations.

Indexed as

Geographic Information SystemsHIV InfectionsHIV TestingHumansNigeriaSpatial AnalysisGISHIV case-findingHIV testing services (HTS)Hotspot mapsMicroplansSpatial data infrastructure

Identifiers

PMID39939893
PMCPMC11823078

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Registered trials

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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.