Literature DB >> 12522486

Diagnosing asthma: the fit between survey and administrative database.

Lisa Huzel1, Leslie L Roos, Nicholas R Anthonisen, Jure Manfreda.   

Abstract

BACKGROUND: Standard methods for population studies of asthma include surveying population samples using questionnaires and examining people in laboratories. These procedures are extremely expensive. It would be helpful if, at least for some purposes, they could be replaced by cheaper techniques with adequate validity.
OBJECTIVES: To determine agreement between survey and database in regard to the prevalence of asthma.
METHODS: Responses to survey questions about asthma symptoms in the past 12 months were linked to physician claims in the Manitoba Population Health Repository.
RESULTS: The overall agreement was moderate (k=0.45 to 0.50) and increased if two years of physician claims were studied (k=0.55 to 0.59); studying additional years had no further effect on agreement. Sex and smoking did not significantly affect the kappa scores.
CONCLUSIONS: There were several plausible reasons for discrepancies. Symptoms recorded on the survey were intrinsically different from those recorded for physician visits. Physicians also used other respiratory codes instead of asthma, and survey participants did not see a physician every year for asthma. The estimates of prevalence derived from the survey and the administrative database included two overlapping groups of people. In each, the diagnosis of asthma seems justifiable, although the agreement between the two groups was only moderate to substantial. Both methods are useful, although they are useful for different purposes. Health care utilization estimates may be particularly useful for studying trends over time.

Entities:  

Mesh:

Year:  2002        PMID: 12522486     DOI: 10.1155/2002/921497

Source DB:  PubMed          Journal:  Can Respir J        ISSN: 1198-2241            Impact factor:   2.409


  20 in total

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9.  Identifying persons with treated asthma using administrative data via latent class modelling.

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10.  Data enhancement for co-morbidity measurement among patients referred for sleep diagnostic testing: an observational study.

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