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Prevalence and clustering of diarrhoea within households in India: some evidence from NFHS-4, 2015–16

Published online by Cambridge University Press:  04 March 2020

Bevin Vijayan Open the ORCID record for Bevin Vijayan [Opens in a new window]  and
Mala Ramanathan Open the ORCID record for Mala Ramanathan [Opens in a new window]
Bevin Vijayan
Affiliation:
Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
Mala Ramanathan*
Affiliation:
Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
*
*Corresponding author. Email: mala@sctimst.ac.in
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Abstract

Diarrhoeal disease is one of the major causes of morbidity and mortality in children and is usually measured at individual level. Shared household attributes, such as improved water supply and sanitation, expose those living in the same household to these same risk factors for diarrhoea. The occurrence of diarrhoea in two or more children in the same household is termed ‘diarrhoea clustering’. The aim of this study was to examine the role of improved water supply and sanitation in the occurrence of diarrhoea, and the clustering of diarrhoea in households, among under-five children in India. Data were taken from the fourth round of the National Family and Health Survey (NFHS-4), a nationally representative survey which interviewed 699,686 women from 601,509 households in the country. If any child was reported to have diarrhoea in a household in the 2 weeks preceding the survey, the household was designated a diarrhoeal household. Household clustering of diarrhoea was defined the occurrence of diarrhoea in more than one child in households with two or more children. The analysis was done at the household level separately for diarrhoeal households and clustering of diarrhoea in households. The presence of clustering was tested using a chi-squared test. The overall prevalences of diarrhoea and clustering of diarrhoea were examined using exogenous variables. Odds ratios, standardized to allow comparison across categories, were computed. The household prevalence of diarrhoea was 12% and that of clustering of diarrhoea was 2.4%. About 6.5% of households contributed 12.6% of the total diarrhoeal cases. Access to safe water and sanitation was shown to have a great impact on reducing diarrhoeal prevalence and clustering across different household groups. Safe water alone had a greater impact on reducing the prevalence in the absence of improved sanitation when compared with the presence of improved sanitation. It may be possible to reduce the prevalence of diarrhoea in households by targeting those households with more than one child in the under-five age group with the provision of safe water and improved sanitation.

Keywords

DiarrhoeaSanitationClustering
Type
Research Article
Information
Journal of Biosocial Science , Volume 53 , Issue 1 , January 2021 , pp. 108 - 120
Copyright
© The Author(s) 2020. Published by Cambridge University Press

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References

Arulampalam, W and Bhalotra, S (2006) Sibling death clustering in India: state dependence versus unobserved heterogeneity. Journal of the Royal Statistical Society: Series A (Statistics in Society) 169(4), 829–848.CrossRefGoogle Scholar
Cairncross, S, Hunt, C, Boisson, S, Bostoen, K, Curtis, V, Fung, IC and Schmidt, WP (2010) Water, sanitation and hygiene for the prevention of diarrhoea. International Journal of Epidemiology 39 (Supplement 1), i193–205.CrossRefGoogle ScholarPubMed
Das Gupta, M (1990) Death clustering, mothers’ education and the determinants of child mortality in rural Punjab, India. Population Studies 44(3), 489–505.Google Scholar
Edvinsson, S and Janssens, A (2012) Clustering of deaths in families: infant and child mortality in historical perspective. Biodemography and Social Biology 58(2), 75–86.Google ScholarPubMed
Gupta, MD (1997) Socio-economic status and clustering of child deaths in rural Punjab. Population Studies 51(2), 191–202.CrossRefGoogle Scholar
IIPS and ICF (2017) India National Family Health Survey NFHS-4 2015–16. IAKR74DT.DTA, IAHR74DT.DTA. International Institute for Population Sciences and ICF. URL: http://dhsprogram.com (accessed 28th August 2018).Google Scholar
Katz, J, Carey, VJ, Zeger, SL and Sommer, A (1993) Estimation of design effects and diarrhea clustering within households and villages. American Journal of Epidemiology 138(11), 994–1006.Google ScholarPubMed
Lee, J (1994) Odds ratio or relative risk for cross-sectional data? International Journal of Epidemiology 23(1), 201–203.CrossRefGoogle ScholarPubMed
Ministry of Drinking Water and Sanitation (2017) Guidelines for Swachh Bharat Mission (Gramin). URL: http://swachhbharatmission.gov.in/sbmcms/writereaddata/images/pdf/Guidelines/Complete-set-guidelines.pdf.Google Scholar
Ministry of Jal Shakthi (2019) Jal Jeevan Mission (JJM). Department of Drinking Water and Sanitation. URL: https://jalshakti-ddws.gov.in/sites/default/files/JJM_note.pdf (accessed 7th December 2019).Google Scholar
Nandi, A, Megiddo, I, Ashok, A, Verma, A and Laxminarayan, R (2017) Reduced burden of childhood diarrheal diseases through increased access to water and sanitation in India: a modeling analysis. Social Science & Medicine 180, 181–192.CrossRefGoogle ScholarPubMed
Nilima, Kamath A, Shetty, K, Unnikrishnan, B, Kaushik, S and Rai, SN (2018) Prevalence, patterns, and predictors of diarrhea: a spatial-temporal comprehensive evaluation in India. BMC Public Health 18(1), 1288.CrossRefGoogle ScholarPubMed
R Core Team (2017) R Development Core Team. URL: https://www.r-project.org/.Google Scholar
Ramanathan, M and Vijayan, B (2019) Covariates of diarrhoea among under-five children in India: are they level dependent? PLoS One 14(8), e0221200.CrossRefGoogle ScholarPubMed
Ronsmans, C (1995) Patterns of clustering of child mortality in a rural area of Senegal. Population Studies 49(3), 443–461.Google Scholar
Saha, UR and van Soest, A (2011) Infant death clustering in families: magnitude, causes, and the influence of better health services, Bangladesh 1982–2005. Population Studies 65(3), 273–287.Google ScholarPubMed
Shabani, J, Lutambi, AM, Mwakalinga, V and Masanja, H (2010) Clustering of under-five mortality in Rufiji Health and Demographic Surveillance System in rural Tanzania. Global Health Action 3(1), 5264.CrossRefGoogle ScholarPubMed
StataCorp (2018) Stata Statistical Software: Release 15. Stata Corporation.Google Scholar
Thompson, ML, Myers, JE and Kriebel, D (1998) Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done? Occupational and Environmental Medicine 55(4), 272–277.CrossRefGoogle ScholarPubMed
UNICEF (2018) UNICEF Data: Monitoring the Situation of Children and Women. URL: https://data.unicef.org/topic/child-health/diarrhoeal-disease/ (accessed 2nd November 2018).Google Scholar
Wickham, H (2009) Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York. URL: http://ggplot2.org.Google Scholar
WHO and UNICEF (2018) JMP Methodology : 2017 Update & SDG Baselines. WHO, Geneva. URL: https://washdata.org/sites/default/files/documents/reports/2018-04/JMP-2017-update-methodology.pdf (accessed 2nd November 2018).Google Scholar
Zocchetti, C, Consonni, D and Bertazzi, PA (1997) Relationship between prevalence rate ratios and odds ratios in cross-sectional studies. International Journal of Epidemiology 26(1), 220–223.CrossRefGoogle ScholarPubMed