Original Article

International Journal of Public Health

, Volume 58, Issue 3, pp 435-447

First online:

Measuring the prevalence of chronic diseases using population surveys by pooling self-reported symptoms, diagnosis and treatments: results from the World Health Survey of 2003 for South Asia

  • J.-F. LevesqueAffiliated withCentre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM)Institut National de Santé Publique du QuébecUniversité de Montréal Email author 
  • , S. MukherjeeAffiliated withInstitute of Development Studies Kolkata (IDSK)
  • , D. GrimardAffiliated withAgence de la santé et des services sociaux de Montréal
  • , A. BoivinAffiliated withUniversité de Sherbrooke
  • , S. MishraAffiliated withIndira Gandhi Institute of Development Research (IGIDR)

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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.


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.


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.


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.


Chronic disease Self-reported health Population surveys Disease prevalence