Material deprivation as marker of health need
DOI:
https://doi.org/10.6092/issn.1973-2201/3590Abstract
A relationship between socio-economic status and health has been widely documented both by individual-level and ecological regression studies. We addressed the problem known in the literature as using a material deprivation index as predictor of health needs and comparing results when adjusting or not the health outcome and the deprivation index for the same confounding variables. We focus on non-linear hierarchical models. We take as example the the issue of introducing socio-economic indicators in national or regional resources allocation formulas. We fitted a series of models with different data hierarchies to evaluate both the individual effect and the aggregate (census block) effect of material deprivation on heath status, disentagling the individual from the contextual effects. Individual mortality records came from the Florence census cohort 1991-1995 which is part of the Tuscan Longitudinal Study. Data on socio-economic factors derived from individual records of the 1991 census. Our results suggested that after adjusting for age, material deprivation is a good predictor of health needs both at individual and at aggregate level (census block). The presence of a contextual effect increases the interest in using deprivatin in the allocation formula, since it would permit a better distribution of resources to disadvantaged micro-areas. In the present paper, we stress the need to estimate the association between deprivation and health appropriately adjusting for age. The ideal goal would be having information at small geographical level on the joint distribution of age and deprivation to age-standardize both the response and the predictor. A temporary solution should be to regress crude mortality rates on deprivation and age. The current common practice, in absence of individual data, to regress standardized mortality on material deprivation may be inappropriate.References
ARMITAGE P. (1955), Test for linear trend in proportions and frequencies. Biometrics; 11: 375-86.
BIGGERI A, GORINI G, DREASSI E, KALALA N, LISI C (2001), Condizione socio-economica e mortalità in Toscana, Studi e Ricerche, n. 7, Edizioni Regione Toscana, Centro Stampa Giunta Regionale, Firenze.
BRESLOW NE, DAY NE (1975), Indirect standardization and multiplicative models for rates, with reference to the age adjustment of cancer incidence and relative frequency data. Journal of Chronic Diseases 28(5,6), 289-303.
CARR-HILL RA, SHELDON TA, SMITH PC, MARTIN S, PEACOCK S, HARDMAN G (1994), Allocating resources to health authorities: development of method for small area analysis of use of inpatient services, BMJ, 309: 1046-1049.
CATELAN D, BIGGERI A, DREASSI E, LAGAZIO C (2006), Space-cohort Bayesian models in ecological studies. Statistical Modelling; 6: 1-15.
COCHRAN WG (1954), Some methods for strengthening the common chi-squared tests. Biometrics; 10: 417-54.
COSTA G, SPADEA T, CARDANO M (2004), (a cura di) Diseguaglianze di salute in Italia. Epid Prev, 28 (3).
CRONBACH LJ, WEBB J (1975), Between-class and within-class effects in a reported aptitude X treatment interaction. J. Educ. Psycology; 67, 6:717.
DREASSI E, BIGGERI A, CATELAN D (2005), Space-time models with time dependent covariates for the analysis of the temporal lag between socio-economic factors and lung cancer mortality. Statist. Med.; 24: 1-19.
FIREBAUGH G (1978), A rule for inferring individual-level relationships from aggregate data. American Sociological Review. 43, 557-572.
HIGGINS JPT, THOMPSON SG, DEEKS JJ, ALTMAN DG (2003), Measuring inconsistency in metaanalyses. British Medical Journal, 327: 557-560.
JARMAN B (1983), Identification of underprivileged areas. Britisch Medical Journal, 1705-1709.
MINISTERO DELLA SALUTE (1998), Piano Sanitario Nazionale 1998-2000. Un patto di solidarietà per la salute. Roma.
MORGENSTERN H (2008), Ecologic Studies. In Rothman KJ, Greenland S, Lash TL Modern Epidemiology – third edition. Lippincott Williams & Wilkins, Philadelphia.
ROSENBAUM P R, RUBIN D B (1984), Difficulties with regression analyses of age-adjusted rates. Biometrics, 40, 2: 437-443.
STONE M, GALBRAITH J (2006), How not fund hospital and community health services in England. J. R. Statist. Soc. A, 169 Part 1, 143-164.
THOMPSON SG, SHARP SJ (1999), Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18: 2693-2708.
WAKEFIELD J (2007), Disease mapping and spatial regression with count data. Biostatistics, 8, 2, 158-183.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2010 Statistica
Copyrights and publishing rights of all the texts on this journal belong to the respective authors without restrictions.
This journal is licensed under a Creative Commons Attribution 4.0 International License (full legal code).
See also our Open Access Policy.