Cargando…

Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India

BACKGROUND: Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modif...

Descripción completa

Detalles Bibliográficos
Autores principales: Nilima, Kamath, Asha, Shetty, Karthik, Unnikrishnan, B., Kaushik, Siddharth, Rai, Shesh N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251155/
https://www.ncbi.nlm.nih.gov/pubmed/30470208
http://dx.doi.org/10.1186/s12889-018-6213-z
_version_ 1783373059903717376
author Nilima
Kamath, Asha
Shetty, Karthik
Unnikrishnan, B.
Kaushik, Siddharth
Rai, Shesh N.
author_facet Nilima
Kamath, Asha
Shetty, Karthik
Unnikrishnan, B.
Kaushik, Siddharth
Rai, Shesh N.
author_sort Nilima
collection PubMed
description BACKGROUND: Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data. METHODS: An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran’s I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0. RESULTS: The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron’s I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007–2008, no much reduction was observed in the period 2015–2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1–33.8% and 1.3–96.1% in the period 2007–2008 and 0.6–29.1% and 10.4–92.0% in the period 2015–2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively. CONCLUSION: Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-018-6213-z) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6251155
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-62511552018-11-26 Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India Nilima Kamath, Asha Shetty, Karthik Unnikrishnan, B. Kaushik, Siddharth Rai, Shesh N. BMC Public Health Research Article BACKGROUND: Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data. METHODS: An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran’s I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0. RESULTS: The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron’s I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007–2008, no much reduction was observed in the period 2015–2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1–33.8% and 1.3–96.1% in the period 2007–2008 and 0.6–29.1% and 10.4–92.0% in the period 2015–2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively. CONCLUSION: Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-018-6213-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-23 /pmc/articles/PMC6251155/ /pubmed/30470208 http://dx.doi.org/10.1186/s12889-018-6213-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Nilima
Kamath, Asha
Shetty, Karthik
Unnikrishnan, B.
Kaushik, Siddharth
Rai, Shesh N.
Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India
title Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India
title_full Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India
title_fullStr Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India
title_full_unstemmed Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India
title_short Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India
title_sort prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in india
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251155/
https://www.ncbi.nlm.nih.gov/pubmed/30470208
http://dx.doi.org/10.1186/s12889-018-6213-z
work_keys_str_mv AT nilima prevalencepatternsandpredictorsofdiarrheaaspatialtemporalcomprehensiveevaluationinindia
AT kamathasha prevalencepatternsandpredictorsofdiarrheaaspatialtemporalcomprehensiveevaluationinindia
AT shettykarthik prevalencepatternsandpredictorsofdiarrheaaspatialtemporalcomprehensiveevaluationinindia
AT unnikrishnanb prevalencepatternsandpredictorsofdiarrheaaspatialtemporalcomprehensiveevaluationinindia
AT kaushiksiddharth prevalencepatternsandpredictorsofdiarrheaaspatialtemporalcomprehensiveevaluationinindia
AT raisheshn prevalencepatternsandpredictorsofdiarrheaaspatialtemporalcomprehensiveevaluationinindia