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 sociodemographic
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.