Measuring the prevalence of chronic disease using population surveys: pooling reported symptoms and treatment to complement self-reported diagnosis

Objectives: Measuring disease prevalence poses challenges in countries where information systems are poorly developed. Population surveys soliciting information on self-reported diagnosis also have limited capacity since they are influenced by informational and recall biases. Our aim is to propose a method to assess the prevalence of chronic disease by combining information on self-reported diagnosis, self-reported treatment and highly suggestive symptoms. Methods: An expanded measure of prevalence was developed using data from the World Health Survey for Bangladesh, India and Sri Lanka. Algorithms were constructed for six chronic diseases. Results: The expanded measures of chronic disease increases the prevalence estimates. Prevalence varies across socio-demographic characteristics such as age, education, socioeconomic status (SES), and country. Finally, the association, as also risk factor, between chronic disease status and poor selfrated health descriptions increases significantly when one takes into account highly suggestive symptoms of diseases. Conclusions: Our expanded measure of chronic disease could form a basis for surveillance of chronic diseases in countries where health information systems have been poorly developed. It represents an interesting trade-off between the bias associated with usual surveillance data and costs.