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Deprivation, ill‐health and the ecological fallacy

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  • Gillian Lancaster
  • Mick Green

Abstract

The use of ecological studies in explaining the relationship between deprivation and ill‐health is widespread in many health applications. However, inferences drawn from these studies about individuals are susceptible to serious bias known as the ecological fallacy. Our paper demonstrates the ecological fallacy effect in this context but also shows how it can be considerably reduced by taking into account different population structures at the aggregate level. Two regression analyses of limiting long‐term illness are performed, one at the individual level and one at the electoral ward level, using the 1991 UK census sample of anonymized records and the small area statistics. The analyses compare several measures of deprivation including the standard Carstairs index, with the separate variables which make up the indices, to determine their effectiveness in explaining rates of illness. Two of the deprivation scores are constructed using latent variable modelling techniques which enable a score to be generated at the individual level as well as at the ward level. It is shown that, given the right choice of socioeconomic variables and taking into account the age structure of the population, it should be possible to construct a single aggregate deprivation index that will explain most of the variation in rates of illness across the study region.

Suggested Citation

  • Gillian Lancaster & Mick Green, 2002. "Deprivation, ill‐health and the ecological fallacy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 263-278, June.
  • Handle: RePEc:bla:jorssa:v:165:y:2002:i:2:p:263-278
    DOI: 10.1111/1467-985X.00586
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    Cited by:

    1. RAILEANU SZELES Monica & FUSCO Alessio, 2009. "Item response theory and the measurement of deprivation: Evidence from PSELL-3," IRISS Working Paper Series 2009-05, IRISS at CEPS/INSTEAD.
    2. Carolina Bruzzi & Enrico Ivaldi & Stefano Landi, 2020. "Non-compensatory aggregation method to measure social and material deprivation in an urban area: relationship with premature mortality," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(3), pages 381-396, April.
    3. Enrico Ivaldi, 2016. "Material and social deprivation in Italy: an analysis on a regional basis," Eastern European Business and Economics Journal, Eastern European Business and Economics Studies Centre, vol. 2(3), pages 248-268.
    4. 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.
    5. Alaimo, Leonardo Salvatore & Ivaldi, Enrico & Landi, Stefano & Maggino, Filomena, 2022. "Measuring and evaluating socio-economic inequality in small areas: An application to the urban units of the Municipality of Genoa," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    6. Angela Testi & Enrico Ivaldi, 2011. "Measuring Progress in Health through Deprivation Indexes," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 49-57, April.
    7. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    8. Enrico Ivaldi & Paolo Parra Saiani & Juan José Primosich & Carolina Bruzzi, 2020. "Health and Deprivation: A New Approach Applied to 32 Argentinian Urban Areas," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(1), pages 155-179, August.
    9. Christopher Jackson & And Nicky Best & Sylvia Richardson, 2008. "Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 159-178, January.

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