The epidemiology of the human immunodeficiency virus (HIV) varies in distribution, disease patterns, and risk factors among distinct geographic regions. The HIV-1 group M is responsible for the global pandemic associated with HIV; the HIV-2 strain is mainly found in West Africa, Mozambique, Angola, and southwest India (Eberle & Gürtler, 2012), and is less pathogenic than the HIV-1 strain. Geographic HIV clusters are driven by different biological and behavioural risk factors (Ying et al., 2020). In eastern and southern Africa, HIV prevalence among sex workers is extremely high; adolescent girls and young women account for 30% of new infections, and above 50% of sex workers are living with HIV (UNAIDS, 2020). The three geographical subregions with the largest numbers of people who inject drugs (PWID) –East and South-East Asia, North America, and Eastern Europe— together account for 58 per cent of the global number of PWIDS. Coincidentally, these regions also have the highest prevalence of HIV among PWIDs (United Nations, 2020). Men who have sex with men (MSM) account majorly for the distribution of new HIV infections (Paraskevis et al., 2019); 64% in Western Europe, Central Europe, and the North America region; 44% in Latin America and Asia-Pacific (UNAIDS, 2020). HIV infection also exhibits localised geographic clustering that is interrelated to the socio-demographic circumstances around respective regions; consequently, suggesting inconsistency in exposure to HIV risk (Waruru et al., 2018). Experiences of stigmatisation, discriminatory attitudes, and criminalisation among people living with HIV (PLHIV) –particularly the key populations— can dampen the quest for pertinent HIV prevention, testing, and treatment; thus, contributing to new infections in susceptible regions (Health Policy Project, 2014).
Spatiotemporal analytical methods use data that possess elements of space and time (Porta, 2014); therefore, they characterise the ‘where’ and ‘when’ of health events in epidemiology (McBride et al., 2019). These methods are dependent on both the available data and the purpose of the analysis (Lai et al., 2008). Spatial statistical methods are used to uncover relationships between spatiotemporal disease patterns and host (Lawson, 2018); visualise the distribution of disease using detailed maps; and, identify clusters or hotspots (Anselin et al., 2010). The knowledge of geographical and time-bound factors in HIV research is a vital tool for describing spatial heterogeneity in the epidemiology of HIV; hence, identifying risk factors that lead to increased HIV incidence, and forecasting future health trends in different geographical areas (Stevens & Pfeiffer, 2011). The identification of HIV infection clusters reveals the right regions to target priority-tailored HIV interventions while highlighting interventions of utmost priority appropriate for a specific region. Visualisation of HIV surveillance data can help improve existing HIV treatment programmes; and generate theories concerning transmission pathways (Agustí et al., 2020).
Objectives
The overall objective is to review the methods used in the spatial analysis of HIV epidemiology globally. The diverse types of health data employed in the analysis will also be evaluated.
Specific review questions include
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What spatial approaches are used in the identification of HIV clusters?
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What spatial analytical methods are used in determining the distribution of HIV infection in the human population?
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What methods are used in the investigation and prediction of HIV temporal trends?