Evaluation of methodologies for small area life expectancy estimation

Study objective: To evaluate methods for calculating life expectancy in small areas, for example, English electoral wards. Design: The Monte Carlo method was used to simulate the distribution of life expectancy (and its standard error) estimates for 10 alternative life table models. The models were combinations of Chiang or Silcocks methodology, 5 or 10 year age intervals, and a final age interval of 85+, 90+, or 95+. Setting: A hypothetical small area experiencing the population age structure and age specific mortality rates of English men 1998–2000. Participants: Routine mortality and population statistics for England. Main results: Silcocks and Chiang based models gave similar estimates of life expectancy and its standard error. For all models, life expectancy was increasingly overestimated as the simulated population size decreased. The degree of overestimation depended largely on the final age interval chosen. Life expectancy estimates of small populations are normally distributed. The standard error estimates are normally distributed for large populations but become increasingly skewed as the population size decreases. Substitution methods to compensate for the effect of zero death counts on the standard error estimate did not improve the estimate. Conclusions: It is recommended that a population years at risk of 5000 is a reasonable point above which life expectancy calculations can be performed with reasonable confidence. Implications are discussed. Within the UK, the Chiang methodology and a five year life table to 85+ is recommended, with no adjustments to age specific death counts of zero

Compendium of Clinical and Health Indicators

The Compendium is a collection of indicators, giving a comprehensive overview of population health at a national, regional and local level. These indicators were previously available on the Clinical and Health Outcomes Knowledge Base website (also known as NCHOD). The indicators within Compendium can be useful for: [1] comparing the profile of your local area with other regions and national averages [2] understanding what the population health challenges are in your area and how they may be changing over time, and [3] exploring the diverse range of factors that influence health inequalities

Clinical and Health Outcomes Knowledge Base

The Clinical and Health Outcomes Knowledge Base was a source of information on health outcomes. It included comparative data for 700 health and local government organisations in England plus advice on how to measure health and the impact of health care. This data was subsequently incorporated into NHS Digital's Compendium of Population Health Indicators and the CHOKB website has closed

Impact of nursing home deaths on life expectancy calculations in small areas

Study objective: The drive to tackle health inequalities at the local level has increased interest in mortality data for small populations. There is some concern that nursing homes may affect measures of mortality for small populations, but there has been little in depth analysis of this. Design and setting: Deaths between 1997 and 2001 and population figures from the GP register (Exeter) database and census 2001 were used to produce life expectancy (LE) figures for all electoral wards in West Sussex. The proportion of those dying within each ward that had been residents of nursing homes was calculated and the relation between these variables and deprivation investigated. Results: There was a significant linear relation between nursing home deaths and LE (p<0.0001), which explained 36% of variation in LE between wards. Deprivation accounted for around 35% of the variation in LE (p<0.0001) but was not correlated with nursing home deaths (p⩾0.0982). Multiple linear regression shows that over 60% of the variation in LE at ward level can be explained by both nursing home deaths and deprivation (p<0.0001) and that the two variables explain similar proportions of this variation. The relation between LE and nursing home deaths within wards grouped by deprivation suggests that the impact of nursing homes is strongest in deprived wards. Conclusions: This finding has important implications for LE calculations in small populations. Further investigation is now needed to examine the impact of nursing homes in other areas, on other mortality measures, and in larger populations