Life expectancy calculations for postcode sectors and their use for monitoring inequalities in the nation's health.
OBJECTIVE
To describe how life expectancy calculations can be used to demonstrate differences in mortality between populations of different socioeconomic status at each population census.


METHODS
Population data were obtained in five-year age-sex and deprivation groups at postcode sector level for the Greater Glasgow Health Board area for the censuses of 1981 and 1991. The numbers of deaths for the same groups were obtained for the three-year periods 1980-82 and 1990-92. Life expectancy tables were derived by applying mortality rates calculated from these data to a synthetic cohort of individuals. Regression analyses were applied to the tables thus derived to examine the relationships between census period, level of deprivation and life expectancy.


RESULTS
Over the period 1980/82 to 1990/92 life expectancy of the age group 0-4 years increased by 2.1 years in males and 2.9 years in females, increases being greater in the more affluent areas. Differences in life expectancy between the geographically defined most deprived and most affluent areas increased from 7.4 to 9.0 years in males and from 5.9 to 6.0 years in females.


CONCLUSIONS
Life expectancy is increasing in all socioeconomic groups, but particularly in the most affluent. We suggest that life expectancy values should be published routinely for different socioeconomic groupings. Over time these would illustrate more clearly the relationships between health policy and health outcomes, and would demonstrate whether policies are reducing inequalities and generally improving health.
Bremner
S
S
Department of Public Health Sciences, St George's Hospital Medical School, London.
Gordon
A
A
Watt
G
G
Womersley
J
J
D000368:Aged; D019352:Censuses; D002675:Child, Preschool; D015331:Cohort Studies; D005260:Female; D006304:Health Status; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008017:Life Expectancy / Q000639:trends*; D008297:Male; D011177:Postal Service; D012044:Regression Analysis; D012606:Scotland / Q000453:epidemiology; D012923:Social Class; D012959:Socioeconomic Factors