Journal of Data Science

Abstract: Risks for many chronic diseases (coronary heart disease, can cer, mental illness, diabetes, asthma, etc) are strongly linked both to socio economic and ethnic group and so prevalence varies considerably between areas. Variations in prevalence are important in assessing health care needs and in comparing health care provision (e.g. of surgical intervention rates) to health need. This paper focuses on estimating prevalence of coronary heart disease and uses a Bayesian approach to synthesise information of dif ferent types to make indirect prevalence estimates for geographic units where prevalence data are not otherwise available. One source is information on prevalence risk gradients from national health survey data; such data typ ically provide only regional identifiers (for confidentiality reasons) and so gradients by age, sex, ethnicity, broad region, and socio-economic status may be obtained by regression methods. Often a series of health surveys is available and one may consider pooling strength over surveys by using information on prevalence gradients from earlier surveys (e.g. via a power prior approach). The second source of information is population totals by age, sex, ethnicity, etc from censuses or intercensal population estimates, to which survey based prevalence rates are applied. The other potential data source is information on area mortality, since for heart disease and some other major chronic diseases there is a positive correlation over areas be tween prevalence of disease and mortality from that disease. A case study considers the development of estimates of coronary heart disease prevalence in 354 English areas using (a) data from the Health Surveys for England for 2003 and 1999 (b) population data from the 2001 UK Census, and (c) area mortality data for 2003.