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Deprivation and mortality: the implications of spatial autocorrelation for health resources allocation

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  • Lorant, Vincent
  • Thomas, Isabelle
  • Deliège, Denise
  • Tonglet, René

Abstract

This paper aims at investigating whether the relationship between mortality and socio-economic deprivation is affected by the spatial autocorrelation of ecological data. A simple model is used in which mortality (all-ages and premature) is the dependent variable, and deprivation, morbidity and other socio-economic indicators are the explanatory variables. Deprivation is measured by the Townsend index; the other socio-economic variables are the median income, unequal income distribution (Gini coefficient) and population density. Morbidity is estimated on the basis of hospital admission rates and overweight prevalence. Spatial autocorrelation is measured by the Moran's I coefficient. All mortality and morbidity variables have significant, positive, and moderate-to-high spatial autocorrelation. Two multivariate models are explored: a weighted least-squares model ignoring spatial autocorrelation and a simultaneous autoregressive model. The paper concludes that spatial autocorrelation has a significant impact on the relationship between mortality and socio-economic variables. Future ecological models intended to inform health resources allocation need to pay greater attention to the spatial dimension of the data used.

Suggested Citation

  • Lorant, Vincent & Thomas, Isabelle & Deliège, Denise & Tonglet, René, 2001. "Deprivation and mortality: the implications of spatial autocorrelation for health resources allocation," Social Science & Medicine, Elsevier, vol. 53(12), pages 1711-1719, December.
    • Handle: RePEc:eee:socmed:v:53:y:2001:i:12:p:1711-1719
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    Cited by:

    1. Min Weng & Jianhua Pi & Bingqing Tan & Shiliang Su & Zhongliang Cai, 2017. "Area Deprivation and Liver Cancer Prevalence in Shenzhen, China: A Spatial Approach Based on Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 317-332, August.
    2. Arif Wismadi & Mark Zuidgeest & Mark Brussel & Martin Maarseveen, 2014. "Spatial Preference Modelling for equitable infrastructure provision: an application of Sen’s Capability Approach," Journal of Geographical Systems, Springer, vol. 16(1), pages 19-48, January.
    3. Herzer, Dierk & Nunnenkamp, Peter, 2011. "Income inequality and health: New evidence from panel data," Kiel Working Papers 1736, Kiel Institute for the World Economy (IfW Kiel).
    4. Ankur Singh & Jane Harford & José Leopoldo Ferreira Antunes & Marco A Peres, 2018. "Area-level income inequality and oral health among Australian adults—A population-based multilevel study," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    5. Norman, Paul & Boyle, Paul & Exeter, Daniel & Feng, Zhiqiang & Popham, Frank, 2011. "Rising premature mortality in the UK’s persistently deprived areas: Only a Scottish phenomenon?," Social Science & Medicine, Elsevier, vol. 73(11), pages 1575-1584.
    6. Moscone, F. & Tosetti, E., 2010. "Testing for error cross section independence with an application to US health expenditure," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 283-291, September.
    7. Baltagi, Badi H. & Moscone, Francesco, 2010. "Health care expenditure and income in the OECD reconsidered: Evidence from panel data," Economic Modelling, Elsevier, vol. 27(4), pages 804-811, July.
    8. Francesco Longo & Luigi Siciliani & Hugh Gravelle & Rita Santos, 2017. "Do hospitals respond to rivals' quality and efficiency? A spatial panel econometric analysis," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 38-62, September.
    9. Zheng, Hui, 2012. "Do people die from income inequality of a decade ago?," Social Science & Medicine, Elsevier, vol. 75(1), pages 36-45.
    10. Moscone, Francesco & Skinner, Jonathan & Tosetti, Elisa & Yasaitis, Laura, 2019. "The association between medical care utilization and health outcomes: A spatial analysis," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 306-314.
    11. Graif, Corina & Arcaya, Mariana C. & Diez Roux, Ana V., 2016. "Moving to opportunity and mental health: Exploring the spatial context of neighborhood effects," Social Science & Medicine, Elsevier, vol. 162(C), pages 50-58.
    12. Alasdair Rae, 2009. "Isolated Entities or Integrated Neighbourhoods? An Alternative View of the Measurement of Deprivation," Urban Studies, Urban Studies Journal Limited, vol. 46(9), pages 1859-1878, August.
    13. Daniel J. Graham & Emma J. McCoy & David A. Stephens, 2013. "Quantifying the effect of area deprivation on child pedestrian casualties by using longitudinal mixed models to adjust for confounding, interference and spatial dependence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(4), pages 931-950, October.
    14. Sridharan, Sanjeev & Tunstall, Helena & Lawder, Richard & Mitchell, Richard, 2007. "An exploratory spatial data analysis approach to understanding the relationship between deprivation and mortality in Scotland," Social Science & Medicine, Elsevier, vol. 65(9), pages 1942-1952, November.
    15. Wilkinson, Richard G & Pickett, Kate E., 2006. "Income inequality and population health: A review and explanation of the evidence," Social Science & Medicine, Elsevier, vol. 62(7), pages 1768-1784, April.
    16. Christopoulos, Konstantinos & Eleftheriou, Konstantinos, 2020. "The fiscal impact of health care expenditure: Evidence from the OECD countries," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 195-202.
    17. Syam Prasad, 2011. "Deprevation and vulnerability among elderly in India," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2011-013, Indira Gandhi Institute of Development Research, Mumbai, India.

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