title: Validating estimates of prevalence of non-communicable diseases based on household surveys: the symptomatic diagnosis study.
abstract: BACKGROUND
  Easy-to-collect epidemiological information is critical for the more accurate estimation of the prevalence and burden of different non-communicable diseases around the world. Current measurement is restricted by limitations in existing measurement systems in the developing world and the lack of biometry tests for non-communicable diseases. Diagnosis based on self-reported signs and symptoms ("Symptomatic Diagnosis," or SD) analyzed with computer-based algorithms may be a promising method for collecting timely and reliable information on non-communicable disease prevalence. The objective of this study was to develop and assess the performance of a symptom-based questionnaire to estimate prevalence of non-communicable diseases in low-resource areas.
  
  
  METHODS
  As part of the Population Health Metrics Research Consortium study, we collected 1,379 questionnaires in Mexico from individuals who suffered from a non-communicable disease that had been diagnosed with gold standard diagnostic criteria or individuals who did not suffer from any of the 10 target conditions. To make the diagnosis of non-communicable diseases, we selected the Tariff method, a technique developed for verbal autopsy cause of death calculation. We assessed the performance of this instrument and analytical techniques at the individual and population levels.
  
  
  RESULTS
  The questionnaire revealed that the information on health care experience retrieved achieved 66.1% (95% uncertainty interval [UI], 65.6-66.5%) chance corrected concordance with true diagnosis of non-communicable diseases using health care experience and 0.826 (95% UI, 0.818-0.834) accuracy in its ability to calculate fractions of different causes. SD is also capable of outperforming the current estimation techniques for conditions estimated by questionnaire-based methods.
  
  
  CONCLUSIONS
  SD is a viable method for producing estimates of the prevalence of non-communicable diseases in areas with low health information infrastructure. This technology can provide higher-resolution prevalence data, more flexible data collection, and potentially individual diagnoses for certain conditions.
authors.forename: Spencer L
authors.initials: SL
authors.lastname: James
authors.forename: Minerva
authors.initials: M
authors.lastname: Romero
authors.forename: Dolores
authors.initials: D
authors.lastname: Ramírez-Villalobos
authors.forename: Sara
authors.initials: S
authors.lastname: Gómez
authors.forename: Kelsey
authors.initials: K
authors.lastname: Pierce
authors.forename: Abraham
authors.initials: A
authors.lastname: Flaxman
authors.forename: Peter
authors.initials: P
authors.lastname: Serina
authors.forename: Andrea
authors.initials: A
authors.lastname: Stewart
authors.forename: Christopher J L
authors.initials: CJ
authors.lastname: Murray
authors.forename: Emmanuela
authors.initials: E
authors.lastname: Gakidou
authors.forename: Rafael
authors.initials: R
authors.lastname: Lozano
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave,, Suite 600, Seattle 98121, WA, USA. bhp3@uw.edu.
authors.forename: Bernardo
authors.initials: B
authors.lastname: Hernandez
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D002423:Cause of Death; D002908:Chronic Disease / Q000453:epidemiology; D057225:Data Mining; D004812:Epidemiologic Methods; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D008800:Mexico / Q000453:epidemiology; D008875:Middle Aged; D009323:Natural Language Processing; D015995:Prevalence; D011795:Surveys and Questionnaires