Modification of claims-based measures improves identification of comorbidities in non-elderly women undergoing mastectomy for breast cancer: a retrospective cohort study

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.

Validation of body mass index (BMI)‐related ICD‐9‐CM and ICD‐10‐CM administrative diagnosis codes recorded in US claims data

“…Consistent with our results, Lloyd et al found that individuals with more severe obesity were more likely to have a diagnosis coded in the claims data. Nickel et al found that diagnoses recorded in US claims data in 2004 to 2011 had a sensitivity of 17.9% and PPV of 90.9% for obesity in a cohort of women who underwent mastectomy . As in our study, a 1‐year ascertainment period was used for the identification of BMI‐related diagnoses.…”

“…Prior studies in the USA and Canada indicate that International Classification of Diseases (ICD) diagnosis codes for obesity recorded in administrative data have a high positive predictive value (PPV; 74%‐91%) but low sensitivity (18%‐19%) . There is limited evidence from a study in women with breast cancer that the sensitivity of administrative diagnosis codes for overweight/obesity may have improved over time in the 2000s . However, this trend has not been confirmed in a broader population, and prior studies have relied on administrative data records from 2011 and earlier.…”

“…9% and PPV of 90.9% for obesity in a cohort of women who underwent mastectomy 6. As in our study, a 1-year ascertainment period was used for the identification of BMI-related diagnoses.…”

Validated methods for identifying individuals with obesity in health care administrative databases: A systematic review

“…12,[14][15][16][17] In addition, the caseidentification methods used in recent studies have led to variable performance depending on the existence of other comorbidities, the severity of obesity and the type of health care administrative data. 12,[18][19][20][21][22][23] Accurate case-identification methods for health care administrative databases is essential to minimize classification errors, which can represent a significant form of bias related to the use of administrative databases.…”

Identifying Patients With Inflammatory Bowel Diseases in an Administrative Health Claims Database: Do Algorithms Generate Similar Findings?

“…The 2 outpatient diagnoses more than 30 days apart is a commonly used method for identifying chronic conditions in claims databases due to the potential for clustering of care immediately following the first diagnosis. 17,18 As anticipated, there was a large degree of overlap between the patient cohorts captured by these 2 algorithms. The timeframe specified between diagnoses as part of the algorithm B criteria may have helped exclude patients who had only a “rule-out” diagnosis recorded in their claims; it may also have increased the accuracy of detection by filtering out patients receiving multiple diagnoses as part of their initial presentation, thus reducing the number of potentially misclassified patients.…”