Spatial autocorrelation analysis of non-communicable diseases: Unveiling hidden patterns and hotspots of hypertension in the Yogyakarta Special Region
The increasing impact of Non-Communicable Diseases (NCDs), especially hypertension, on global mortality has prompted increased scrutiny, with NCDs disproportionately contributing to epidemiological transitions and economic challenges, especially in low- and middle-income countries in Southeast Asia....
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_02003.pdf |
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| Summary: | The increasing impact of Non-Communicable Diseases (NCDs), especially hypertension, on global mortality has prompted increased scrutiny, with NCDs disproportionately contributing to epidemiological transitions and economic challenges, especially in low- and middle-income countries in Southeast Asia. Hypertension requires dominant health interventions because hypertension cases in the Special Region of Yogyakarta, Indonesia dominate other diseases. Therefore, this study addresses the spatial dynamics of hypertension in the Yogyakarta Special Region using spatial autocorrelation techniques. Total population and number of hypertension sufferers is collected from surveillance data and processed through GeoDa. Descriptive quantitative analysis was conducted on hypertension prevalence and spatial distribution of hypertension through quantile maps, Global Moran’s I, and Local Moran’s I (LISA). Findings show a spatial clustering pattern of hypertension prevalence, both hotspots and spatial outliers which has evolved in the period 2019 to 2022. There was significant spatial clustering of hypertension cases, with high-high and low-low prevalence area patterns providing insight into the geographic distribution of risk factors for hypertension prevalence. This study emphasizes the application of novel spatial analysis in public health surveillance in Indonesia, underscoring the effectiveness of spatial autocorrelation techniques in identifying high-risk areas, and as an important step in developing public health strategies and policies. |
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| ISSN: | 2267-1242 |