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Background <p>Type 2 diabetes (T2D) often exhibits long-standing disparities across populations. Spatial regression models can identify associations between local rates and observed neighborhood factors to inform timely, localized public health interventions. We identified area-level distributions of T2D rates across Malaysia and estimate associations between different neighborhood covariates and local T2D burden.</p> Methods <p>We obtained aggregated counts of national level T2D cases data by administrative-districts between 2016 and 2020 and computed district-wise crude rates to correlate with district-level neighborhood demographic, socio-economic, safety, physical activity and resources access, and urbanization process indicators from various national sources and census data. We applied a simultaneous spatial autoregressive (SAR) modeling strategy to account for spatial autocorrelation and estimate risk factors for district-level logged T2D crude rates in Malaysia.</p> Results <p>District-level logged T2D crude rates were associated with the proportion of households living below 50% of the median income (β = 0.009, <i>p</i> = 0.003) and below the national poverty line (β = ˗0.011, <i>p</i> = 0.001), income inequalities (β = ˗1.778, <i>p</i> = 0.015), CCTV coverage per 1000 population (β = 0.060, <i>p</i> = 0.049), and perceived access to recreational parks (β = 0.007, <i>p</i> = 0.001) and to bowling centers (β = ˗0.003, <i>p</i> = 0.009).</p> Conclusion <p>Area-level district-wise logged T2D crude rate estimates were associated with neighborhood socio-economic vulnerabilities, neighborhood safety, and neighborhood perceived access to physical activity facilities, after accounting for residual spatial autocorrelation via a SAR model.</p>

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Neighborhood factors and the geography of type 2 diabetes in Malaysia: an ecological based geospatial modelling study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Kurubaran Ganasegeran,
  • Mohd Rizal Abdul Manaf,
  • Nazarudin Safian,
  • Lance A. Waller,
  • Feisul Idzwan Mustapha,
  • Khairul Nizam Abdul Maulud,
  • Muhammad Faid Mohd Rizal

摘要

Background

Type 2 diabetes (T2D) often exhibits long-standing disparities across populations. Spatial regression models can identify associations between local rates and observed neighborhood factors to inform timely, localized public health interventions. We identified area-level distributions of T2D rates across Malaysia and estimate associations between different neighborhood covariates and local T2D burden.

Methods

We obtained aggregated counts of national level T2D cases data by administrative-districts between 2016 and 2020 and computed district-wise crude rates to correlate with district-level neighborhood demographic, socio-economic, safety, physical activity and resources access, and urbanization process indicators from various national sources and census data. We applied a simultaneous spatial autoregressive (SAR) modeling strategy to account for spatial autocorrelation and estimate risk factors for district-level logged T2D crude rates in Malaysia.

Results

District-level logged T2D crude rates were associated with the proportion of households living below 50% of the median income (β = 0.009, p = 0.003) and below the national poverty line (β = ˗0.011, p = 0.001), income inequalities (β = ˗1.778, p = 0.015), CCTV coverage per 1000 population (β = 0.060, p = 0.049), and perceived access to recreational parks (β = 0.007, p = 0.001) and to bowling centers (β = ˗0.003, p = 0.009).

Conclusion

Area-level district-wise logged T2D crude rate estimates were associated with neighborhood socio-economic vulnerabilities, neighborhood safety, and neighborhood perceived access to physical activity facilities, after accounting for residual spatial autocorrelation via a SAR model.