Conference Publication Details
Mandatory Fields
Sinnott S, McHugh S, Whelton H, Woods N
Society for Social Medicine 58th Annual Scientific Meeting
Predictors of general medical services scheme coverage during a changing economic landscape in Ireland.
2014
September
Published
1
()
Optional Fields
General Medical Services Scheme, Economic Landscape, Ireland
Journal of Epidemiology and Public Health
A65
A66
University of Oxford Keble College, Oxford
10-SEP-14
12-SEP-14
Background The General Medical Services (GMS) scheme is a means tested public health insurance, covering approximately 40% of the Irish population. Eligibility for this scheme depends largely on income; however it is unknown how other demographic and socio-economic factors influence eligibility. We aimed to evaluate how demographic and socio-economic factors predicted GMS coverage from years 2006 (economic boom) to 2008 (start of recession) and 2010 (impact of austerity). Methods Cross-sectional data from the nationally representative EU Survey of Income and Living Conditions (EU-SILC) were used, n = 12,140 for 2006, n = 10,315 for 2008 and n = 8968 for 2010. Response rates were >77% for each year; missing data were generated by children. Variables are available on income, education, employment status, health status, private health insurance status, and multiple demographic factors (age, sex, marital status and nationality). The outcome variable was having GMS coverage or not. Logistic regression models were used to examine how the strength of predictors varied from year to year using R version 2.15.2. Results In univariate models, younger age (1524 years and 2549 years) and educational attainment predicted GMS status to a greater extent in 2010 than in 2006. The strength of unemployment as a predictor decreased over the study period, but this reduction was not significant (change in OR 1.210, 95% CI -3.4112.940). Retired people were less likely to qualify for a medical card in 2010 than in 2006 (change in OR 12.260, 95% CI, 9.44012.790). In multivariate models, only age and income remained predictive across the study period. For 1524 years age category the OR increased each year, 2006 OR=0.021 (95% CI, 0.0160.028), 2008 OR = 0.036 (95% CI, 0.0190.070) and 2010 OR = 0.062 (95% CI, 0.0320.120). For the 2449 years age category in 2006 OR = 0.036 (95% CI, 0.0280.045), in 2008 OR = 0.040 (95% CI, 0.0230.070) and in 2010 OR = 0.078(95% CI, 0.0450.135). While income was strongly predictive in each year, no yearly change in OR was observed. Conclusion Younger age categories increasingly predicted GMS status from 2006 through to 2010. This may be indicative of unemployment and falling income resulting from economic recession, however, unemployment did not appear as a significant predictor. Income was a consistent predictor each year. This is the first study to characterise population characteristics associated with GMS coverage. The results are timely given on-going and planned revisions to eligibility criteria as part of the phased introduction of universal health insurance.
http://jech.bmj.com/content/68/Suppl_1/A65.3
doi:10.1136/jech-2014-204726.142
Grant Details