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Submitted
October 11, 2023
Accepted
December 19, 2023
Published
December 29, 2023
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Abstract

Background: Public health interventions may affect a variety of health outcomes. This study developed an Interrupted Time Series model to test its efficacy in evaluating public health interventions. The developed model can be used to forecast future trends in interventions to curb pneumonia.


Methods: This study utilized interrupted time-series analysis (ITS) as the study design. The study population comprised children between two months and five years admitted to Kilifi County Hospital from May 2007 to March 2020. The population included a cohort that received the PCV10 vaccine that was introduced in January 2011 for three months.


Results: The study findings indicated a downward trajectory with regard to the number of pneumonia cases reported. Further, the segmented regression results show that the intercept (β0) = 823.16, coefficient estimate of time (β1) = -2.72, coefficient estimate of PCV10 intervention (β2) = 59.63, and the coefficient estimate of the time after PCV10 intervention (β3) = -6.03. In addition, the results showed that during the post-intervention period, the response variable had an average value of approximately. 422.02. The 95% interval of this counterfactual prediction is [669.64, 821.18]. Therefore, the adverse effects observed during the intervention period are statistically significant.


Conclusion: The overall findings of the segmented regression model imply that public health initiatives in Kilifi County have been successful in enhancing population health outcomes. The study recommends using PCV10 vaccination as an intervention for longevity of good health and reducing the number of pneumonia cases among children under five in Kenya.

Keywords

Interrupted time series segmented regression pneumonia efficacy and public health interventions

Article Details

Author Biographies

Kevin Otieno Ouma, Technical University of Mombasa, Kenya

Mr. Kevin Otieno Ouma is a Master of Science- Applied Statistics student at the Technical University of Mombasa, Department of Mathematics and Physics, with a research interest in Applied Time series Analysis.

Otulo Wandera Cyrilus, Technical University of Mombasa, Kenya

Dr. Otulo Wandera Cyrilus is a Lecturer of Statistics and the current Chair of the Mathematics and Physics Department, Technical University of Mombasa, Kenya. He holds a Ph.D. in Applied Statistics with over ten years of university teaching experience. He has a research interest in mathematical modeling of infectious diseases and experimental designs.

Eric Mugambi Kinyua, Technical University of Mombasa, Kenya

Dr. Eric Mugambi Kinyua is a lecturer at the Technical University of Mombasa, Kenya. He holds a doctorate degree in applied mathematics with over ten years of university teaching experience and research interest in fluid mechanics and mathematical modeling.

How to Cite
Ouma, K. O., Cyrilus, O. W., & Kinyua, E. M. (2023). An Interrupted Time Series Analysis Using Segmented Regression in Evaluating the Efficacy of Public Health Interventions in Kilifi County. Pan-African Journal of Health and Environmental Science, 2(2), 143–154. Retrieved from https://journals.aua.ke/ajhes/article/view/438
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