Abstract
BackgroundAccurate identification of underlying health conditions is important to fully adjust for confounders in studies using insurer claims data. Our objective was to evaluate the ability of four modifications to a standard claims-based measure to estimate the prevalence of select comorbid conditions compared with national prevalence estimates.MethodsIn a cohort of 11,973 privately insured women aged 18–64 years with mastectomy from 1/04–12/11 in the HealthCore Integrated Research Database, we identified diabetes, hypertension, deficiency anemia, smoking, and obesity from inpatient and outpatient claims for the year prior to surgery using four different algorithms. The standard comorbidity measure was compared to revised algorithms which included outpatient medications for diabetes, hypertension and smoking; an expanded timeframe encompassing the mastectomy admission; and an adjusted time interval and number of required outpatient claims. A χ2 test of proportions was used to compare prevalence estimates for 5 conditions in the mastectomy population to national health survey datasets (Behavioral Risk Factor Surveillance System and the National Health and Nutrition Examination Survey). Medical record review was conducted for a sample of women to validate the identification of smoking and obesity.ResultsCompared to the standard claims algorithm, use of the modified algorithms increased prevalence from 4.79 to 6.79 % for diabetes, 14.75 to 24.87 % for hypertension, 4.23 to 6.65 % for deficiency anemia, 1.78 to 12.87 % for smoking, and 1.14 to 6.31 % for obesity. The revised estimates were more similar, but not statistically equivalent, to nationally reported prevalence estimates. Medical record review revealed low sensitivity (17.86 %) to capture obesity in the claims, moderate negative predictive value (NPV, 71.78 %) and high specificity (99.15 %) and positive predictive value (PPV, 90.91 %); the claims algorithm for current smoking had relatively low sensitivity (62.50 %) and PPV (50.00 %), but high specificity (92.19 %) and NPV (95.16 %).ConclusionsModifications to a standard comorbidity measure resulted in prevalence estimates that were closer to expected estimates for non-elderly women than the standard measure. Adjustment of the standard claims algorithm to identify underlying comorbid conditions should be considered depending on the specific conditions and the patient population studied.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-016-1636-7) contains supplementary material, which is available to authorized users.
Highlights
Accurate identification of underlying health conditions is important to fully adjust for confounders in studies using insurer claims data
Modifications to a standard comorbidity measure resulted in prevalence estimates that were closer to expected estimates for non-elderly women than the standard measure
We found the greatest overall percent improvement compared to algorithm 1 in the youngest age group (18–47 years) for diabetes, hypertension, and obesity, while improvement was greatest for deficiency anemia and smoking in the middle age group (48–55 years)
Summary
Accurate identification of underlying health conditions is important to fully adjust for confounders in studies using insurer claims data. Adjusting for comorbidities in observational studies is essential to account for underlying differences in populations under investigation. A second commonly used comorbidity measure with claims data developed by Elixhauser includes 29 medical conditions [4, 5]. These measures were developed and validated in hospitalized patients, and may be more applicable to older, sicker populations. Many studies have concluded that that these measures perform well [7, 9,10,11], while others have found the Elixhauser classification improved prediction of in-hospital [8, 12] and longerterm mortality [6, 12]. Several investigators have expanded the parameters of these two comorbidity measures to include physician [10, 13,14,15], outpatient, and auxiliary claims [10, 14, 15] and different lookback periods relative to the index event (e.g., one or two years of prior data and/or including the index admission) [10, 12, 14, 15]
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