Use of survey data and small area statistics to assess the link between individual morbidity and neighbourhood deprivation.

STUDY OBJECTIVE

The aim of the study was to examine the relationship between sociogeographic factors and health, using a particular social indicator of neighbourhood deprivation.

DESIGN

The study analysed the relationship between health problems (reported by randomly selected respondents to a household survey) and an area social indicator for the neighbourhoods where the respondents lived (based on census data). The area social indicator tested was the underprivileged areas indicator developed by the St Mary's Hospital Department of General Practice, London. Generalised linear interactive modelling with a logistic model was used to test the strength of the relationship between social indicators and morbidity, and to calculate the probability of reporting illness or consultations for survey respondents living in different types of area.

SETTING

The study population was derived from three London health districts and their corresponding census enumeration districts.

PARTICIPANTS

Responses were obtained from 738 households drawn from the local taxation evaluation list (66% of those sampled), and 1384 persons over 16 participated in the survey (94% of eligible adults in households surveyed). Of these, 1221 provided complete data on health problems. The survey population was considered representative of the general population in the areas studied since its characteristics were similar to those reported for the population as a whole in the 1981 census.

RESULTS

Within different age and sex groups, those living in very deprived areas, with high underprivileged area scores, were more likely to consult their doctor and to report various indicators of poor health than those living in privileged areas, with low underprivileged area scores.

CONCLUSIONS

The underprivileged areas index may provide a useful surrogate indicator to estimate morbidity and use of general practitioner services in small areas. It is likely to be most effective in areas where sociodemographic profiles of the local population are highly contrasting.