Geospatial patterns of progress towards UNAIDS '95-95-95' targets and community vulnerability in Zambia: insights from population-based HIV impact assessments

Abstract

Introduction: In sub-Saharan Africa, HIV/AIDS remains a leading cause of death. The UNAIDS established the '95-95-95' targets to improve HIV care continuum outcomes. Using geospatial data from the Zambia Population-based HIV Impact Assessment (ZAMPHIA), this study aims to investigate geospatial patterns in the '95-95-95' indicators and individual-level determinants that impede HIV care continuum in vulnerable communities, providing insights into the factors associated with gaps.

Methods: This study used data from the 2016 ZAMPHIA to investigate the geospatial distribution and individual-level determinants of engagement across the HIV care continuum in Zambia. Gaussian kernel interpolation and optimised hotspot analysis were used to identify geospatial patterns in the HIV care continuum, while geospatial k-means clustering was used to partition areas into clusters. The study also assessed healthcare availability, access and social determinants of healthcare utilisation. Multiple logistic regression models were used to examine the association between selected sociodemographic and behavioural covariates and the three main outcomes of study.

Results: Varied progress towards the '95-95-95' targets were observed in different regions of Zambia. Each '95' displayed a unique geographical pattern, independent of HIV prevalence, resulting in four distinct geographical clusters. Factors associated with gaps in the '95s' include younger age, male sex, and low wealth, with younger individuals having higher odds of not being on antiretroviral therapy and having detectable viral loads.

Conclusions: Our study revealed significant spatial heterogeneity in the HIV care continuum in Zambia, with different regions exhibiting unique geographical patterns and levels of performance in the '95-95-95' targets, highlighting the need for geospatial tailored interventions to address the specific needs of different subnational regions. These findings underscore the importance of addressing differential regional gaps in HIV diagnosis, enhancing community-level factors and developing innovative strategies to improve local HIV care continuum outcomes.

Keywords: AIDS; Epidemiology; Geographic information systems; HIV.

Figure 1

Prevalence distributions of (A) HIV prevalence; (B) prevalence of HIV positive individuals aware of their status; (C) prevalence of HIV positive individuals aware of their status on ART treatment and (D) prevalence of HIV positive individuals aware of their status on ART treatment that are viral load suppressed. ART, antiretroviral therapy.

Figure 2

Hotspot maps of (A) HIV prevalence; (B) prevalence of HIV positive individuals aware of their status; (C) prevalence of HIV positive individuals aware of their status on ART treatment and (D) prevalence of HIV positive individuals aware of their status on ART treatment that are viral load suppressed. ART, antiretroviral therapy.

Figure 3

Spatial structure of the HIV epidemic and HIV care continuum in Zambia.

Figure 4

Results from the multivariable regressions for the three different models, (A) Model 1: HIV status awareness, the survey data were subsampled to include HIV-positive individuals only; (B) for model 2: ART status, the survey data were subsampled to include HIV-positive individuals aware of their status only and (C) for model 3: viral load suppression, the survey data were subsampled to include HIV-positive individuals aware of their status and on ART treatment. ART, antiretroviral therapy.