Application of multivariate binary logistic regression grouped outlier statistics and geospatial logistic model to identify villages having unusual health-seeking habits for childhood malaria in Malawi.

Autor: Hamuza GA; National Statistical Office of Malawi, Zomba, Malawi. gahamuza@gmail.com., Singogo E; University of North Carolina Project, Lilongwe, Malawi.; Department of Mathematical Sciences, School of Natural and Applied Sciences, University of Malawi, Zomba, Malawi., Kaombe TM; Department of Mathematical Sciences, School of Natural and Applied Sciences, University of Malawi, Zomba, Malawi.
Jazyk: angličtina
Zdroj: Malaria journal [Malar J] 2024 Aug 16; Vol. 23 (1), pp. 246. Date of Electronic Publication: 2024 Aug 16.
DOI: 10.1186/s12936-024-05070-2
Abstrakt: Background: Early diagnosis and prompt treatment of malaria in young children are crucial for preventing the serious stages of the disease. If delayed treatment-seeking habits are observed in certain areas, targeted campaigns and interventions can be implemented to improve the situation.
Methods: This study applied multivariate binary logistic regression model diagnostics and geospatial logistic model to identify traditional authorities in Malawi where caregivers have unusual health-seeking behaviour for childhood malaria. The data from the 2021 Malawi Malaria Indicator Survey were analysed using R software version 4.3.0 for regressions and STATA version 17 for data cleaning.
Results: Both models showed significant variability in treatment-seeking habits of caregivers between villages. The mixed-effects logit model residual identified Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole as outliers in the model. Despite characteristics that promote late reporting of malaria at clinics, most mothers in these traditional authorities sought treatment within twenty-four hours of the onset of malaria symptoms in their children. On the other hand, the geospatial logit model showed that late seeking of malaria treatment was prevalent in most areas of the country, except a few traditional authorities such as Mwakaboko, Mwenemisuku, Mwabulambya, Mmbelwa, Mwadzama, Zulu, Amidu, Kasisi, and Mabuka.
Conclusions: These findings suggest that using a combination of multivariate regression model residuals and geospatial statistics can help in identifying communities with distinct treatment-seeking patterns for childhood malaria within a population. Health policymakers could benefit from consulting traditional authorities who demonstrated early reporting for care in this study. This could help in understanding the best practices followed by mothers in those areas which can be replicated in regions where seeking care is delayed.
(© 2024. The Author(s).)
Databáze: MEDLINE
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