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

Abstract

BackgroundThe 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.MethodsDisease 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.ResultsAfter 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%.ConclusionsThe 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.

Contribution of health status and prevalent chronic disease to individual risk for workplace injury in the manufacturing environment

“…As with possible job differences, however, it is unlikely those with chronic disease worked, on average, longer than those without, so this bias too is far more likely to result in underestimate of the effect size. Although all our data are administrative, and maintained for business purposes, we cannot exclude the possibility that some of our results could spuriously emerge from incomplete reporting of both injuries and health; despite routine screening at work, we have evidence that men discovered at work to have hypertension or asthma, for example, often delay seeking medical attention22 29 rendering our classification flawed. We have no reason, however, to expect such behaviour would be differential with respect to subsequent injury.…”

“…Data were obtained from a variety of sources including the employer's human resources databases, medical insurance claims data capturing each personal medical service, procedure, test or treatment provided and physician diagnosis for same, and a real-time incident management system established in 1989 in which details of every near-miss, first aid, or OSHA-recordable injury or incident are recorded within 48 h of occurrence. These sources are described in greater detail in previous publications 17 22 23. None of the data included are obtained from self-report.…”

Relationship between paternal somatic health and assisted reproductive technology outcomes

“…As such, diagnoses may have been missed if not evaluated. However, prior data suggest good specificity for ICD-9 codes in identifying men with a given disease, and there is little reason to believe that the coding was dependent on IVF outcomes (27). Moreover, several hypotheses were tested, as was the goal for our exploratory analysis, thereby increasing the potential for false-positive associations.…”

Occupational injury risk by sex in a manufacturing cohort

“…Data collected include smoking history and body mass index. Detailed descriptions of these datasets and the linkage system have been previously published 26 – 28…”