Comparative Spatial Regression Modeling of HIV Cases in Indonesia in 2023
DOI:
https://doi.org/10.54065/likelihood.1687Keywords:
Human Immunodeficiency Virus (HIV), Spatial Regression, Spatial Autoregressive Model, Spatial Autocorrelation, Socioeconomic FactorsAbstract
Reported HIV cases vary substantially across Indonesian provinces and may exhibit spatial dependence that is not adequately represented by conventional regression. This study compared spatial autoregressive (SAR), spatial error (SEM), and General Spatial Model (GSM) specifications for reported HIV cases in 34 Indonesian provincial units in 2023. The response was the annual number of reported HIV cases, and the explanatory variables were population density, the Gini Index, the open unemployment rate, and mean years of schooling. Global Moran's I, Lagrange Multiplier tests, maximum-likelihood estimation, and Akaike information criterion (AIC) were used to assess spatial dependence and compare models. Reported HIV cases exhibited significant positive spatial autocorrelation (I = 0.61893, p < .001). Robust LM-lag was significant (p = .026019), whereas robust LM-error was not significant (p = .950388), providing stronger support for a spatial lag specification. SAR had the lowest AIC (594.07), compared with GSM (595.67) and SEM (596.00), and was therefore selected as the preferred model. In the SAR model, population density, the Gini Index, and the open unemployment rate were positively associated with reported HIV cases, while mean years of schooling was negatively associated; all four explanatory variables were statistically significant. The spatial lag parameter was positive but not significant (rho = 0.21451, p = .10170). These ecological findings support geographically coordinated HIV surveillance and prevention while requiring cautious interpretation because reported case counts reflect population size, testing, and reporting coverage.
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