Further validation that claims data are a useful tool for epidemiologic research on hypertension

BMC Public Health. 2013 Jan 18;13:51. doi: 10.1186/1471-2458-13-51.


Background: The practice of using medical service claims in epidemiologic research on hypertension is becoming increasingly common, and several published studies have attempted to validate the diagnostic data contained therein. However, very few of those studies have had the benefit of using actual measured blood pressure as the gold standard. The goal of this study is to assess the validity of claims data in identifying hypertension cases and thereby clarify the benefits and limitations of using those data in studies of chronic disease etiology.

Methods: Disease status was assigned to 19,150 employees at a U.S. manufacturing company where regular physical examinations are performed. We compared the presence of hypertension in the occupational medical charts against diagnoses obtained from administrative claims data.

Results: After adjusting for potential confounders, those with measured blood pressure indicating stage 1 hypertension were 3.69 times more likely to have a claim than normotensives (95% CI: 3.12, 4.38) and those indicating stage 2 hypertension were 7.70 times more likely to have a claim than normotensives (95% CI: 6.36, 9.35). Comparing measured blood pressure values identified in the medical charts to the algorithms for diagnosis of hypertension from the claims data yielded sensitivity values of 43-61% and specificity values of 86-94%.

Conclusions: The medical service claims data were found to be highly specific, while sensitivity values varied by claims algorithm suggesting the possibility of under-ascertainment. Our analysis further demonstrates that such under-ascertainment is strongly skewed toward those cases that would be considered clinically borderline or mild.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blood Pressure
  • Confounding Factors, Epidemiologic
  • Databases, Factual
  • Epidemiologic Research Design*
  • Humans
  • Hypertension / diagnosis*
  • Industry
  • Insurance Claim Review / standards*
  • United States