Abstract
Childhood diarrhoea accounts for over 15% of all under-five deaths in Africa. The disease is exacerbated by social vulnerability. This study operationalizes social vulnerability by using three indicators: water poverty, sanitation and assets, to capture social disadvantage, which measures individual or community resources to prevent or mitigate health effects. We particularly investigated the relationship between childhood diarrhoea and risks emanating from multiple stressors: water poverty, poor sanitation and low wealth status, which define social vulnerability. Using data from the 2013/14 Malawi MDG Endline Survey (MMES), we fitted spatial models assuming that the combined effect of social vulnerability indicators, together with individual covariates, exhibit spatial correlation and heterogeneity on the outcome-diarrhoea status. Findings showed evidence of spatially varying risk imposed by social vulnerability indicators on childhood diarrhoea. We established a positive relationship between diarrhoea and water poverty, and negative association with poor sanitation and low wealth status. Spatial characterization of health effects of social vulnerability presents an important step towards targeted interventions in diarrhoea management. Our use of district level mapping provides for optimal planning and implementation, particularly, for the lowly placed individuals who are geographically located in high risk areas, since most decentralized decision making processes are made at this level.
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Ali, M., Farron, M., Dilip, T. R., & Folz, R. (2018). Assessment of family planning service availability and readiness in 10 African countries. Global Health: Science and Practice, 6, 473–83.
Asselin, L.-M. (2009). Composite indicator of poverty. In L.-M. Asselin (Ed.), Analysis of multidimensional poverty: Theory and case studies (pp. 19–53). Springer.
Aubrecht, C., Steinnocher, K., Köstl, M., Züger, J., & Loibl, W. (2013). Long-term spatial-temporal social vulnerability variation considering health-related climate change parameters particularly affecting elderly. Natural Hazards, 68, 1371–1384.
Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics, 43, 1–59.
Bizimana, J. P., Twarabamenye, E., & Kienberger, S. (2015). Assessing the social vulnerability to malaria in Rwanda. Malaria Journal, 14, 2. https://doi.org/10.1186/1475-2875-14-2
Black, R. E., Morris, S. S., & Bryce, J. (2003). Where and why are 10 million children dying every year? Lancet, 361, 2226–34.
Brezger, A., Kneib, T., & Lang, S. (2005). BayesX: Analyzing Bayesian structured additive regression models. Journal of Statistical Software, 14, 11.
Boyer, C., Jackson, E., Bawah, A., Schmitt, M., Awoonor-Williams, J., & Phillips, J. (2015). Estimating indices of health system readiness: An example from rural northern Ghana. The Lancet Global Health, 3, S14.
Carter, R., Mendis, K. N., & Roberts, D. (2000). Spatial targeting of interventions against malaria. Bulletin of the World Health Organization, 78, 1401–1411.
Cutter, S. L., Boruff, B. L., & Shirley, W. L. (2000). Social vulnerability to environmental hazards. Social Science Quarterly, 84, 243–261.
deFur, P. L., Evans, G. W., & Hubal, E. A. C. (2007). Vulnerability as a function of individual and group resources in cumulative risk assessment. Environmental Health Perspectives, 115, 817–824.
Fahrmeir, L., & Lang, S. (2001). Bayesian inference for generalized additive mixed models based on Markov random field priors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 50, 201–220.
Few, R., & Tran, P. G. (2010). Climatic hazard, health risk and response in Vietman: Case studies on social dimensions of vulnerability. Global Environmental Change, 20, 29–38.
Gatrell, A. C. (2002). Geographies of health: An introduction. Blackwell.
Gelfand, A. E., & Vounatsou, P. (2003). Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics, 4, 11–25.
Gentry, E. P., & Viscusi, W. K. (2016). The fatality and morbidity components of the value of statistical life. Journal of Health Economics, 46, 90–99.
Gondwe, K. W., Walker, R. J., Mkandawire-Valhmu, L., Dressel, A., Ngui, E. M., Kako, P. M., & Egede, L. (2021). Predictors of wealth index in Malawi—Analysis of Malawi demographic Health Survey 2004–2015/16. Public Health in Practice. https://doi.org/10.1016/j.puhip.2020.100059
Hahn, M. B., Riederer, A. M., & Foster, S. O. (2009). The livelihood vulnerability index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique. Global Environmental Change, 19, 74–88.
Howe, L. D., Hargreaves, J. R., & Gabrysch, S. (2009). Is the wealth index a proxy for consumption expenditure? A systematic review. Journal of Epidemiology & Community Health, 63, 871–877.
Kasperson, J. X., Kasperson, R. E., & Turner, B. L. (1995). Regions at risk: Comparisons of threatened environments. United Nations University Press.
Kazembe, L. N., Muula, A. S., Appleton, C. C., & Kleinschmidt, I. (2007). Modelling the effect of malaria endemicity on spatial variations in childhood fever, Diarrhoea and pneumonia in Malawi. International Journal of Health Geographics, 6, 33.
Knorr-Held, L., & Best, N. G. (2001). A shared component model for detecting joint and selective clustering of two diseases. Journal of the Royal Statistical Society: Series A (Statistics in Society), 164, 73–85.
Lindenberg, M. (2002). Measuring household livelihood security at the family and community level in the developing world. World Development, 30, 301–318.
McMichael, R., Woodruff, R., & Hales, M. (2006). Climate change and human health: Present and future risks. The Lancet, 367, 859–869.
Menne, B., & Ebi, K. (Eds.). (2006). Climate change and adaptation strategies for human health. World Health Organization Office for Europe.
Morano, J. P., & Holt, D. A. (2017). The social determinants of health contextualized for the Zika virus. International Journal of Infectious Diseases, 65(142–143), 2017.
Moser, C. (1998). The asset vulnerability framework: Re-assessing urban poverty reduction strategies. World Development Review, 26, 1–19.
National Statistical Office. (2015). Malawi endline assessment millennium development goals 2013/14: Preliminary report. NSO.
O’Brien, K., Leichenko, R., & Kelkar, U. (2004). Mapping vulnerability to multiple stressors: Climate change and globalization in India. Global Environmental Change, 140, 303–313.
O’Neil, M. S., Zanobetti, A., & Schwartz, J. (2003). Modifiers of the temperature and mortality association in seven US cities. American Journal of Epidemiology, 157, 1074–1082.
Reckien, D. (2018). What is in an index? Construction method, data metric, and weighting scheme determine the outcome of composite social vulnerability indices in New York City. Regional Environmental Change, 18(5), 1439–1451.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series b (Statistical Methodology) (With Discussion), 64, 583–639.
Ssempiira, J., Kasirye, I., Kissa, J., Nambuusi, B., Mukooyo, E., Opigo, J., et al. (2018). Measuring health facility readiness and its effects on severe malaria outcomes in Uganda. Scientific Reports, 8, 17928.
Su, J. G., Morrello-Frosch, R., Jesdale, B. M., Kyle, A. D., Shamasunder, B., & Jennett, M. (2009). An index assessing demographic inequalities in cumulative hazards with application to Los Angeles, California. Environmental Science and Technology, 43, 7626–7634.
Verschuuren, M., & van Oers, H. (2020). Population health monitoring: An essential public health field in motion. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz, 63(9), 1134–1142.
Wongbusarakum, S., & Loper, C., (2011). Indicators to assess community-level social vulnerability: An addendum to SocMon and SEM-Pasifika regional socio-economic monitoring guidelines. Conservation Gateway.
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We acknowledge permission granted by UNICEF to use the 2013/14 Malawi MEMICS data.
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Kazembe, L.N. Social Vulnerability and Childhood Health: Bayesian Spatial Models to Assess Risks from Multiple Stressors on Childhood Diarrhoea in Malawi. Spat Demogr 10, 209–227 (2022). https://doi.org/10.1007/s40980-021-00101-x
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DOI: https://doi.org/10.1007/s40980-021-00101-x