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. 2025 Apr 9;5(1):106.
doi: 10.1038/s43856-025-00824-8.

Assessing regional variations and sociodemographic barriers in the progress toward UNAIDS 95-95-95 targets in Zimbabwe

M D Tuhin Chowdhury  1 , Anna Bershteyn  2 , Masabho Milali  2 , Daniel T Citron  2 , Sulani Nyimbili  2   3 , Godfrey Musuka  4 , Diego F Cuadros  5
Affiliations

Assessing regional variations and sociodemographic barriers in the progress toward UNAIDS 95-95-95 targets in Zimbabwe

M D Tuhin Chowdhury et al. Commun Med (Lond). .

Abstract

Background: The HIV/AIDS epidemic remains critical in sub-Saharan Africa, with UNAIDS establishing "95-95-95" targets to optimize HIV care. Using the 2020 Zimbabwe Population-based HIV Impact Assessment (ZIMPHIA) geospatial data, this study aimed to identify patterns in these targets and determinants impacting the HIV care continuum in underserved Zimbabwean communities.

Methods: Analysis techniques, including Gaussian kernel interpolation, optimized hotspot, and multivariate geospatial k-means clustering, were utilized to establish spatial patterns and cluster regional HIV care continuum needs. Further, we investigated healthcare availability, access, and social determinants and scrutinized the association between socio-demographic and behavioral covariates with HIV care outcomes.

Results: Disparities in progress toward the "95-95-95" targets were noted across different regions, with each target demonstrating unique geographic patterns, resulting in four distinct clusters with specific HIV care needs. Key factors associated with gaps in achieving targets included younger age, male gender, employment, and minority or no religious affiliation.

Conclusions: Our study uncovers significant spatial heterogeneity in the HIV care continuum in Zimbabwe, with unique regional patterns in "95-95-95" targets. The spatial analysis of the UNAIDS targets presented here could prove instrumental in designing effective control strategies by identifying vulnerable communities that are falling short of these targets and require intensified efforts. We provide insights for designing region-specific interventions and enhancing community-level factors, emphasizing the need to address regional gaps and improve HIV care outcomes in vulnerable communities that lag behind.

Plain language summary

UNAIDS, a part of the United Nations, leads global efforts to reduce the impact of HIV and AIDS. HIV (human immunodeficiency virus) is the virus that causes AIDS, a serious illness that weakens the immune system. This study examines Zimbabwe’s progress toward the UNAIDS 95-95-95 targets, which aim for 95% of people with HIV to be diagnosed, 95% of those diagnosed to receive treatment, and 95% of those on treatment to have the virus under control. We found that some areas, especially those with younger populations and fewer healthcare services, are falling behind. More people in these regions are unaware of their HIV status, not receiving treatment, or not achieving viral suppression. To close these gaps, targeted healthcare efforts, such as expanding HIV testing and improving treatment access, are needed. Strengthening healthcare in these areas will help Zimbabwe make greater progress in controlling the HIV epidemic.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Maps of HIV prevalence and 95-95-95 targets in Zimbabwe.
District level maps with percentage of (a) HIV prevalence, b HIV positive population who are unaware of their status, c people who are aware of their status but not on antiretroviral therapy (ART), d people who are on ART but not virally suppressed. Maps were created using ArcGIS Pro by ESRI version 2.8 (http://www.esri.com).
Fig. 2
Fig. 2. Hotspot maps of HIV prevalence and 95-95-95 targets in Zimbabwe.
Hotspot and coldspot clusters of (a) HIV prevalence, b HIV positive population who are unaware of their status, c people who are aware of their status but not on antiretroviral therapy (ART), d people who are on ART but not virally suppressed.
Fig. 3
Fig. 3. K-means cluster map showing clusters of HIV prevalence and care continuum patterns.
This map provides clusters combining HIV prevalence, HIV positive population who are unaware of their status, people who are aware of their status but not on antiretroviral therapy (ART), people who are on ART but not virally suppressed.
Fig. 4
Fig. 4. Bivariate maps of health index and UNAIDS 95-95-95 targets.
Maps of health index with (a) HIV positive population who are aware of their HIV status, b people who are aware of their status and on antiretroviral therapy (ART), c people who are on ART and virally suppressed. Maps were created using ArcGIS Pro by ESRI version 2.8 (http://www.esri.com).
Fig. 5
Fig. 5. Multivariable logistic regression models.
Odds ratio of socio-demographic and behavioral factors associated with HIV-positive people (a) who are unaware of their HIV status—Model 1, b who are aware of their status but not on antiretroviral therapy (ART)—Model 2, c who are on ART but not virally suppressed—Model 3.
See this image and copyright information in PMC

References

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