D Deliège

Equity in prevention and health care

Study objective: There is an increasing body of evidence about socioeconomic inequality in preventive use, mostly for cancer screening. But as far as needs of prevention are unequally distributed, even equal use may not be fair. Moreover, prevention might be unequally used in the same way as health care in general. The objective of the paper is to assess inequity in prevention and to compare socioeconomic inequity in preventive medicine with that in health care. Design: A cross sectional Health Interview Survey was carried out in 1997 by face to face interview and self administered questionnaire. Two types of health care utilisation were considered (contacts with GPs and with specialists) and four preventive care mostly delivered in a GP setting (flu vaccination, cholesterol screening) or in a specialty setting (mammography and pap smear). Setting: Belgium. Participants: A representative sample of 7378 residents aged 25 years and over (participation rate: 61%). Outcome measure: Socioeconomic inequity was measured by the HIwvp index , which is the difference between use inequality and needs inequality. Needs was computed as the expected use by the risk factors or target groups. Main results: There was significant inequity for all medical contacts and preventive medicine. Medical contacts showed inequity favouring the rich for specialist visits and inequity favouring the poor for contacts with GPs. Regarding preventive medicine, inequity was high and favoured the rich for mammography and cervical screening; inequity was lower for flu immunisation and cholesterol screening but still favoured the higher socioeconomic groups. In the general practice setting, inequity in prevention was higher than inequity in health care; in the specialty setting, inequity in prevention was not statistically different from inequity in health care, although it was higher than in the general practice setting. Conclusions: If inequity in preventive medicine is to be lowered, the role of the GP must be fostered and access to specialty medicine increased, especially for cancer screening.

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

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 Morans 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.

Socio-economic differences in psychiatric in-patient care

Objective:  We seek to investigate socio‐economic differences in psychiatric in‐patient care regarding admission, treatment and outcome.