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. 2023 Sep 20;22(1):23.
doi: 10.1186/s12942-023-00341-8.

Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach

Eleojo Oluwaseun Abubakar  1 , Niall Cunningham  2
Affiliations

Small-area estimation and analysis of HIV/AIDS indicators for precise geographical targeting of health interventions in Nigeria. a spatial microsimulation approach

Eleojo Oluwaseun Abubakar et al. Int J Health Geogr. .

Abstract

Background: Precise geographical targeting is well recognised as an indispensable intervention strategy for achieving many Sustainable Development Goals (SDGs). This is more cogent for health-related goals such as the reduction of the HIV/AIDS pandemic, which exhibits substantial spatial heterogeneity at various spatial scales (including at microscale levels). Despite the dire data limitations in Low and Middle Income Countries (LMICs), it is essential to produce fine-scale estimates of health-related indicators such as HIV/AIDS. Existing small-area estimates (SAEs) incorporate limited synthesis of the spatial and socio-behavioural aspects of the HIV/AIDS pandemic and/or are not adequately grounded in international indicator frameworks for sustainable development initiatives. They are, therefore, of limited policy-relevance, not least because of their inability to provide necessary fine-scale socio-spatial disaggregation of relevant indicators.

Methods: The current study attempts to overcome these challenges through innovative utilisation of gridded demographic datasets for SAEs as well as the mapping of standard HIV/AIDS indicators in LMICs using spatial microsimulation (SMS).

Results: The result is a spatially enriched synthetic individual-level population of the study area as well as microscale estimates of four standard HIV/AIDS and sexual behaviour indicators. The analysis of these indicators follows similar studies with the added advantage of mapping fine-grained spatial patterns to facilitate precise geographical targeting of relevant interventions. In doing so, the need to explicate socio-spatial variations through proper socioeconomic disaggregation of data is reiterated.

Conclusions: In addition to creating SAEs of standard health-related indicators from disparate multivariate data, the outputs make it possible to establish more robust links (even at individual levels) with other mesoscale models, thereby enabling spatial analytics to be more responsive to evidence-based policymaking in LMICs. It is hoped that international organisations concerned with producing SDG-related indicators for LMICs move towards SAEs of such metrics using methods like SMS.

Keywords: AIDS; Geographic targeting; HIV; Nigeria; Sexual behaviour; Small-Area estimation; Spatial microsimulation.

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Conflict of interest statement

The authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this manuscript.

Figures

Fig. 1
Fig. 1
Overview of steps required to construct a spatial microsimulation model (Clarke and Harding, 2013, p. 262)
Fig. 2
Fig. 2
Some Goodness-of-Fit statistics for three of the constraints used for the SMS of Kogi State, mapped for each synthetic small-area zone of the study area. The greener, the more accurate, while with increasing redness comes increasing relative zonal error
Fig. 3
Fig. 3
Small-area estimates of MICS 5 Indicators 9.1 and 9.6
Fig. 4
Fig. 4
Small-area estimates of MICS 5 Indicators 9.1 and 9.6 disaggregated by key PROGRESS factors
Fig. 5
Fig. 5
Small-Area Estimates of MICS 5 Indicators 9.14 and 9.15
Fig. 6
Fig. 6
Small-Area Estimates of MICS 5 Indicators 9.14 and 9.15 disaggregated by key PROGRESS factors
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