BMJ No 7037 Volume 312. This Week in BMJ

Many studies have shown an association between economic disadvantage and mortality and ill health. Less is known about the relation between inequalities in income distribution and health, and the question of whether there is a link between ill health and the width of the distribution of income is hotly disputed. Two papers from the United States add to a growing body of evidence that health is influenced by income distribution. On p 999 Kaplan et al report their study of the correlation between the share of total household income received by the poorer 50% of households in each state and death rates in that state. The most unequal states had the highest death rates. The wider the income distribution the smaller the decline in mortality from 1980 to 1990. They found similar correlations for various other health and social outcomes, such as work disability, unemployment, and violent crime. Kennedy et al also examined the relation between income distribution and mortality within the US (p 1004), but using two different measures of inequality in income distribution - the Robin Hood index and the Gini coefficient. They found strong correlations between the Robin Hood index, but not the Gini coefficient, and total mortality and several treatable causes of death. Both groups conclude that economic policies that affect wealth inequality have an important impact on populations' health.

Socioeconomic influences on general practitioner workload

Much of our information about consultations in general practice comes from the national morbidity surveys carried out every 10 years since 1961. On p 1008 Carr-Hill et al present analyses from the fourth survey. They found important effects of age, social class, unemployment, housing status, cohabiting status, and ethnicity on consultation patterns. Using new statistical techniques they showed that the characteristics of individual patients predict general practitioner workload much more strongly than the characteristics of areas in which patients live. The effects of individual socioeconomic factors themselves varied in different geographical areas. The findings of this study are relevant to the possibility of developing a resource allocation formula for general practice. It is clear that any method (like the current Jarman score) based on area of residence will be greatly inferior to an approach that could take more account of the characteristics and needs of individual patients.

Change in patients' health can be measured quickly

Practitioners in primary care base much of their everyday practice on the changes in subjective health reported to them by their patients, but they have no way of measuring these changes. Generic health status instruments provide a useful profile of health but there are few data on how they respond to change. A new, short, patient generated instrument designed by Paterson (p 1016) requires patients to choose, in their own words and on the basis of what is most important to them, one or two symptoms and an activity that is restricted by the problem, and to score severity on a seven point scale. Patients also score their general feeling of wellbeing. Paterson tested MYMOP (the measure yourself medical outcome profile) on 265 patients attending general practitioners and complementary practitioners in her practice and compared it with the SF-36 health survey. The patient generated measure was highly responsive to change. Its use in the consultation helped the practitioners to be more patient centred, and charts of the scores provided a novel way of following up patients with chronic disease. The results confirm the hypothesis that, though brief, patient generated measures may be responsive. MYMOP shows promise as an outcome measure for primary care.