(), Jørgen Lauridsen
(), Martha Emneus
(), Anders Green
() and Peter Bjødstrup Jensen
Camilla Sortsø: COHERE, Postal: Department of Business and Economics, University of Southern Denmark
Jørgen Lauridsen: COHERE, Postal: Department of Business and Economics, University of Southern Denmark
Martha Emneus: Institute of Applied Economics and Health Research (ApEHR), Postal: Copenhagen, Denmark
Anders Green: Odense Patient Data Explorative Network (OPEN), Postal: Odense University Hospital and University of Southern Denmark, and Institute of Applied Economics and Health Research (ApEHR), Copenhagen, Denmark
Peter Bjødstrup Jensen: Odense Patient Data Explorative Network (OPEN), Postal: Odense University Hospital and University of Southern Denmark
Abstract: Measurement of socioeconomic inequalities in health and health care, and understanding the determinants of such inequalities, are critical for achieving higher equity in health care through targeted health intervention strategies. The aim of the paper is to quantify inequality in diabetes morbidity patterns, survival and health care service usage and understand determinants of these inequalities in relation to socio-demographic and clinical morbidity factors. Further, to compare income level and educational level as proxies for Socio Economic Status (SES). Data on the entire Danish diabetes population in 2011 were applied. Patients’ unique personal identification number enabled individual patient data from several national registers to be linked. Cox survival method and a concentration index decomposition approach are applied. Results indicate that lower socioeconomic status is associated with higher morbidity, mortality and lower survival. Differences in diabetes patients’ morbidity patterns, time of diagnosis and health state at diagnosis as well as health care utilization patterns suggest that despite the Danish universal health care system use of services differ among patients of lower and higher SES. Especially outpatient services, rehabilitation and specialists in primary care show different usage patterns according to SES. Comparison of educational level and income level as proxy for patients’ SES indicate important differences in inequality estimates. This is a result of reversed causality between diabetes morbidity and income as well as income related inequality to a higher extent being explained by morbidity.
70 pages, February 10, 2016
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