Dian Mitra Desnawati Silalah
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Maximum Wind Speed, Wind Direction, and Pneumonia Incidence Among Children Under Five: An Ecological Study Dina, Dina Rawan G.Rana; Dian Mitra Desnawati Silalah; Prinawati
Health and Technology Journal (HTechJ) Vol. 4 No. 5 (2026): October 2026
Publisher : KHD Production

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53713/htechj.v4i5.833

Abstract

Childhood pneumonia is influenced by interactions among infectious, individual, environmental, and meteorological factors, yet the contribution of wind characteristics to pneumonia occurrence remains insufficiently understood in tropical urban settings. This study examined the relationship between maximum wind speed, wind direction at maximum speed, and pneumonia incidence among children under five in Palangka Raya City, Indonesia, during 2016–2024. An analytical observational ecological design was applied using aggregate secondary data on childhood pneumonia and meteorological parameters. Descriptive analysis characterized temporal and spatial patterns, while linear regression assessed associations between wind parameters and pneumonia incidence. Pneumonia incidence varied substantially over the observation period, reaching its lowest level in 2021, with 22 cases, and its highest in 2024, with 139 cases. Spatial heterogeneity was observed across Community Health Center catchment areas, with a marked increase in Kereng Bangkirai during 2023–2024. Maximum wind speed showed a strong positive association with pneumonia incidence (β = 0.940, t = 8.827, p < 0.001), while wind direction at maximum speed also demonstrated a significant association (β = 0.972, t = 15.817, p < 0.001). These findings indicate that wind variability may contribute to ecological patterns of childhood pneumonia through atmospheric transport and exposure processes. Causal inference remains limited by the ecological design, unmeasured environmental confounders, and the circular nature of wind-direction data. Future studies should integrate circular statistical methods, air-quality indicators, emission sources, land cover, and spatiotemporal pneumonia data to strengthen environmental risk assessment.