Quantifying non-communicable diseases' burden in Egypt using State-Space model.

The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Met...

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Autores principales:	Somaya El-Saadani, Mohamed Saleh, Sarah A Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df6c3d36f4d54677a9e0284a18b3ee5d
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spelling	oai:doaj.org-article:df6c3d36f4d54677a9e0284a18b3ee5d2021-12-02T20:15:06ZQuantifying non-communicable diseases' burden in Egypt using State-Space model.1932-620310.1371/journal.pone.0245642https://doaj.org/article/df6c3d36f4d54677a9e0284a18b3ee5d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0245642https://doaj.org/toc/1932-6203The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs' health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, the Central Agency for Public Mobilization and Statistics (CAPMAS) Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease's severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.Somaya El-SaadaniMohamed SalehSarah A IbrahimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0245642 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Somaya El-Saadani Mohamed Saleh Sarah A Ibrahim Quantifying non-communicable diseases' burden in Egypt using State-Space model.
description	The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs' health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, the Central Agency for Public Mobilization and Statistics (CAPMAS) Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease's severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.
format	article
author	Somaya El-Saadani Mohamed Saleh Sarah A Ibrahim
author_facet	Somaya El-Saadani Mohamed Saleh Sarah A Ibrahim
author_sort	Somaya El-Saadani
title	Quantifying non-communicable diseases' burden in Egypt using State-Space model.
title_short	Quantifying non-communicable diseases' burden in Egypt using State-Space model.
title_full	Quantifying non-communicable diseases' burden in Egypt using State-Space model.
title_fullStr	Quantifying non-communicable diseases' burden in Egypt using State-Space model.
title_full_unstemmed	Quantifying non-communicable diseases' burden in Egypt using State-Space model.
title_sort	quantifying non-communicable diseases' burden in egypt using state-space model.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/df6c3d36f4d54677a9e0284a18b3ee5d
work_keys_str_mv	AT somayaelsaadani quantifyingnoncommunicablediseasesburdeninegyptusingstatespacemodel AT mohamedsaleh quantifyingnoncommunicablediseasesburdeninegyptusingstatespacemodel AT sarahaibrahim quantifyingnoncommunicablediseasesburdeninegyptusingstatespacemodel
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Quantifying non-communicable diseases' burden in Egypt using State-Space model.

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