Abstract
Malaria remains a leading cause of morbidity and mortality in the developing world, particularly in sub-Saharan Africa. Spatial variability significantly influences efforts to control malaria and its incidence, which remains a serious public health concern in Ethiopia. Using geostatistical methods, this study investigates the environmental factors and spatial distribution of malaria incidence in the Hadiya Zone across woredas in 2022 and 2023. Descriptive analyses revealed consistent spatial heterogeneity, with high incidence rates in Shashogo, Soro, and Misrak Badawacho. Global spatial autocorrelation measures Moran’s I (0.558 in 2022 and 0.483 in 2023; p < 0.01) and Geary’s C (0.63 and 0.69, respectively) confirmed statistically significant clustering of malaria cases. Local Moran’s I analysis identified hot spots in Shashogo, Soro, and Misrak Badawacho, and cold spots in Misha, Duna, and Gombora, indicating localized spatial dependence. Spatial regression analysis, comparing Ordinary Least Squares (OLS) and Spatial Autoregressive (SAR) models, highlighted average maximum temperature (β = 0.945, p = 0.017) and proportion of highland terrain (β = 0.543, p = 0.040) as key predictors of malaria incidence. The SAR model showed superior fit, evidenced by lower AIC and higher log-likelihood values, confirming the influence of spatial dependence. These findings support geographically targeted malaria interventions in high-risk woredas. Limitations include the short study period (2022–2023) and the absence of socioeconomic variables due to lack of household survey and secondary data.
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Data availability
The datasets generated and/or analyzed during the current study are not publicly available and must be obtained from the corresponding author upon reasonable request. The data were sourced from the Hadiyya Zone Health Bureau, the SNNPR Meteorological Center, and the Hadiyya Zone Agricultural and Finance Bureaus, and access requires official permission from the respective agencies.
Abbreviations
- SAR:
Spatial autoregressive model
- OLS:
Ordinary least squares
- AIC:
Akaike information criterion
- LM:
Lagrange multiplier
- SNNPR:
Southern nations, nationalities, and peoples’ region
- WHO:
World health organization
- GIS:
Geographic information system
- ARC GIS:
A specific GIS software platform
- ITN:
Insecticide-treated nets
- IRS:
Indoor residual spraying
- RF:
Rainfall
- MIT:
Minimum temperature
- MAT:
Maximum temperature
- LL:
Log-likelihood
- GMI:
Global Moran’s I
- LCI:
Local Moran’s I
- Geary’s C:
Geary’s contiguity ratio
- API:
Annual parasite incidence
- MoH:
Ministry of health
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Acknowledgements
The author gratefully acknowledges Wachemo University (WCU) for granting ethical approval and institutional support for this study. Appreciation is extended to the Hadiyya Zone Health Bureau, the SNNPR Meteorological Center, and the Hadiyya Zone Agricultural and Finance Bureaus for providing the necessary data. Special thanks are due to the health facility staff and local administrators in Hadiyya Zone for their cooperation during data collection.
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This research was conducted without any external funding support.
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S.S.A. conceived and designed the study, acquired and analyzed the data, interpreted the results, prepared all figures and tables, and wrote the entire manuscript. S.S.A. also reviewed and approved the final version of the manuscript.
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Ethical approval
Ethical clearance for this study was obtained from the Institutional Review Board (IRB) of Wachemo University (WCU 17.2023). The study was based on secondary data without personal identifiers. All methods were performed in accordance with the relevant guidelines and regulations of Wachemo University and national research ethics standards. All necessary permissions were secured from relevant health and administrative offices in the Hadiyya Zone to ensure the ethical use of the data.
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As the study utilized secondary, de-identified data and did not involve direct interaction with human participants, informed consent was not applicable. However, permission to use the data was formally obtained from the appropriate authorities.
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Abdo, S.S. Spatial analysis of malaria incidence and environmental determinants in Hadiya Zone, Ethiopia.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33236-8
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DOI: https://doi.org/10.1038/s41598-025-33236-8
Keywords
- Malaria
- Incidence
- Geostatistics and spatial autocorrelation
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