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 increase 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 self-rated 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.