Socioeconomic determinants of rates of consultation in general practice based on fourth national morbidity survey of general practices

Abstract

Objective: To identify the socioeconomic determinants of consultation rates in general practice. Design: Analysis of data from the fourth national morbidity survey of general practices (MSGP4) including sociodemographic details of individual patients and small area statistics from the 1991 census. Multilevel modelling techniques were used to take account of both individual patient data and small area statistics to relate socioeconomic and health status factors directly to a measure of general practitioner workload. Results: Higher rates of consultations were found in patients who were classified as permanently sick, unemployed (especially those who became unemployed during the study year), living in rented accommodation, from the Indian subcontinent, living with a spouse or partner (women only), children living with two parents (girls only), and living in urban areas, especially those living relatively near the practice. When characteristics of individual patients are known and controlled for the role of “indices of deprivation” is considerably reduced. The effect of individual sociodemographic characteristics were shown to vary between different areas. Conclusions: Demographic and socioeconomic factors can act as powerful predictors of consultation patterns. Though it will always be necessary to retain some local planning discretion, the sets of coefficients estimated for individual level factors, area level characteristics, and for practice groupings may be sufficient to provide an indicative level of demand for general medical services. Although the problems in using socioeconomic data from individual patients would be substantial, these results are relevant to the development of a resource allocation formula for general practice. Key messages Characteristics of individual patients are much more powerful predictors of consulting patterns than the characteristics of the areas in which patients live The effects of individual socioeconomic factors themselves vary in different geographical areas Resource allocation methods based on area of residence (for example, Jarman score) will always be inferior to an approach that takes into account the characteristics of individual patients