Neighborhood Influences on the Geography of Type 2 Diabetes in Malaysia: A Geospatial Modelling Study

Abstract

Type 2 diabetes (T2D) often exhibits long-standing disparities across populations. Spatial regression models can identify areas of epidemiological conformity and transitions between local neighborhoods to inform timely, localized public health interventions. We identified areal-level distributions of T2D rates across Malaysia and synthesized prediction models to estimate local effects and interactions of different neighborhood covariates affecting local T2D burden. We obtained aggregated counts of national level T2D cases data by administrative-districts between 2016-2020 and computed district-wise crude rates to correlate with district-level neighborhood demographic, socio-economic, safety, fitness, access to built-environments, and urban growth indicators from various national sources and census data. We applied simultaneous spatial autoregressive (SAR) models coupled with two-way interaction analyses to account for spatial autocorrelation and estimate risk factors for district-level T2D rates in Malaysia. The variation in spatial lag estimates of T2D rates by districts was influenced by the proportion of households living below 50% of the median income (β = 0.009, p = 0.002) and national poverty line (β = - 0.012, p = 0.001), income inequalities (β = - 2.005, p = 0.004), CCTV coverage per 1000 population (β = 0.070, p = 0.023), average property crime index per 1000 population (β = 0.014, p = 0.033), access to bowling centers (β = - 0.003, p = 0.019), and parks (β = 0.007, p = 0.001). Areal-level district-wise crude T2D rate estimates were influenced by neighborhood socio-economic vulnerabilities, neighborhood safety, and neighborhood access to fitness facilities, after accounting for residual spatial correlation via SAR models.