Determinants of Dengue Hemorrhagic Fever in Aceh: A Panel Regression Approach
Abstract
Dengue Hemorrhagic Fever (DHF) exhibits substantial variation across districts and over time in Aceh Province, making it suitable for analysis within a panel data framework. This study models district-level DHF incidence using applied econometric techniques based on non-spatial panel data regression, employing a balanced panel dataset of 23 districts/cities observed from 2020 to 2022. The Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM) are estimated and formally compared using the Chow test, Hausman test, and Lagrange Multiplier test, with results consistently indicating that the Fixed Effect Model is the most appropriate specification due to the presence of unobserved, time-invariant district-specific effects. Diagnostic testing identifies heteroskedasticity in the error structure; therefore, the selected FEM is re-estimated using White cross-section robust standard errors to ensure reliable statistical inference. Empirical results show that population density is positively and statistically significantly associated with DHF cases, while the number of health workers is negatively and significantly associated, whereas rainfall, number of hospitals, sanitation coverage, and poverty level do not exhibit statistically significant effects in the final robust specification. The selected model explains approximately 86% of the within-district variation in DHF incidence, demonstrating the importance of appropriate model specification and robust variance estimation in panel data regression applied to epidemiological outcomes, while emphasizing that the estimated relationships represent statistical associations rather than causal effects.
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DOI: http://dx.doi.org/10.30829/zero.v9i3.26784
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