Suchergebnisse

ABSTRACT
ObjectivesValidation studies compare diagnostic information in linked administrative and reference (i.e., gold standard) data; they are an essential tool to develop accurate case definitions, the rules used to identify individuals in administrative data with a specific health condition. Validation studies often estimate the accuracy of multiple case definitions, in order to identify the data features (e.g., diagnosis codes, type of data source) that influence accuracy estimates. Descriptive analyses are commonly used to select a case definition(s) with the greatest accuracy estimates, but fail to account for uncertainty in accuracy estimates. The objectives were to: (1) compare the performance of regression-based approaches to test for differences in diagnostic accuracy estimates, and (2) demonstrate how to apply and use these models.
ApproachComputer simulation was used to compare three regression models: (a) univariate fixed-effects models applied to estimates of sensitivity and specificity; (b) univariate fixed-effects model for Youden's index, the average of sensitivity and the complement of specificity; and (c) bivariate random-effects joint model of sensitivity and specificity. The simulations varied the means and variances of sensitivity and specificity, the correlation between these parameters, and the number of case definitions. Performance was compared using: (a) bias (i.e., difference between estimated and observed mean), (b) mean squared error (MSE), the sum of the estimated variance and bias squared, and (c) 95% confidence interval (CI) coverage, the proportion of times the population mean is contained in the 95% CI. For objective 2, we applied the models to estimates of diagnostic accuracy from a published rheumatoid arthritis (RA) validation study with 61 case definitions.
ResultsUnivariate models of sensitivity and specificity had lower bias than the bivariate model (e.g., univariate=1.8%, bivariate=2.2%). The bivariate model had a smaller MSE than the univariate models when sample size was large and there was a small correlation between sensitivity and specificity (e.g., univariate=3.4%, bivariate=2.6%). Across all scenarios, the univariate model for Youden's index showed small bias (average=2.4%) and MSE (average=2.1%). For objective 2, the univariate models of sensitivity, specificity, and Youden's index revealed multiple case definition features that were associated with estimates of RA diagnostic accuracy: 1+ diagnosis in hospital records, >1 diagnosis in physician claims, and 1+ diagnoses by a specialist physician.
ConclusionsWe recommend the bivariate model when a validation study contains a large number of case definitions. When the data contain a small number of case definitions, univariate models are recommended.

IntroductionSocioeconomic gradients in health exist in Canada. Although multiple Canadian area-based socioeconomic measures (ABSM) have been developed, none have been specifically validated against relevant pediatric outcomes. Our objective was to use key pediatric health outcomes and compare the strength of association with a number of ABSM, including income quintile.
Objectives and ApproachThis is a retrospective cross-sectional assessment of the association between socioeconomic status (SES) measured by ABSM and key pediatric health outcomes at the population level. Data from the Manitoba Population Research Data Repository was used for residents aged 0-19y. The timeframe was 2010-2015. Outcomes included preterm births, birth weight, mortality, vaccination rates and teen pregnancy. Regressions used each outcome against various ABSM (e.g. CAN-Marg, SEFI2, INSPQ) or income quintile. Best model for each outcome was assessed by goodness of fit measure (AIC). Measures of inequality included SII (Slope Index of Inequality) and RII (Relative Index of Inequality, both RIImean and RIIratio).
ResultsIn our regression models, the 4 Can-Marg subcomponents consistently had about 15% lower AICs (best fit) across all 16 key pediatric outcomes compared to INSPQ (Raymond-Pampalon), income quintile or SEFI2 (Socioeconomic Factor Index - Version 2). Sex differences were small and inconsequential. Whether ABSMs were treated as continuous or categorical predictors was of little statistical consequence. Of note, 15 of the 16 outcomes had socioeconomic gradients identified by SII or RII on at least one of the ABSMs. Income quintile detected 12 of 15, CAN-Marg material deprivation detected 9; the combination of CAN-Marg material deprivation and ethnicity detected 13 of 15. SEFI2 detected only 3 and the National INSPQ detected 6.
Conclusion/ImplicationsThere are significant health inequalities in pediatric outcomes in Manitoba (15 of 16 studied). Combining CAN-Marg measures of poverty (material deprivation) and ethnic concentration identified 13/15 cases of documented inequality and was the best ABSM for capturing pediatric health gradients; it was similar to income quintile alone.

ABSTRACTObjectivesAn increasing number of physicians are remunerated by alternative forms of payment, instead of conventional fee-for-service (FFS) payments. Changes in physician remuneration methods can to influence the completeness of physician billing claims databases, because physicians on alternative payments may not consistently complete billing records. However, there is no established technique to estimate the magnitude of data loss. This proof-of-concept study estimated completeness of physician claims by comparing them with prescription drug records. We applied the method to estimate completeness of non-fee-for-service (NFFS) and FFS physician claims data over time in Manitoba, Canada.
ApproachOur method uses information on the date of patient initiation of a new prescription medication, payment method of the prescribing physician, and presence/absence of a physician billing claim prior to the medication initiation date. A billing claim within 7 days of the medication initiation date was defined as a captured claim; if there was no claim in this observation window, it was classified as missed. Our method was applied to annual patient cohorts who initiated a common prescription medication (i.e., anti-hypertensives) between fiscal years 1998/99 and 2012/13. A sensitivity analysis used a 21-day observation window to identify captured/missing claims. Multivariable hierarchical logistic regression models tested patient and prescriber characteristics associated with missing claims.
ResultsThe cohort consisted of 274, 462 individuals with a new anti-hypertensive prescription medication. A total of 9.2% of the cohort had a NFFS prescribing physician in 1998/99; this increased to 20.2% in 2012/13 (linear trend p-value < .0001). The percentage of NFFS prescribers almost doubled, from 10.0% to 17.8%. The percentage of the annual cohorts with a FFS prescribing physician and a missing claim remained close to 13.0%. However, the percentage of the annual cohorts with a NFFS prescribing physician and a missing claim increased from 15.6% to 23.3% (linear trend p-value < .0001), and was always higher than the FFS percentage. Patient age, sex, and comorbidity and physician specialty and practice location were associated with the odds of a missing claim.
ConclusionThe percentage of missing claims was higher for patients with NFFS than FFS prescribing physicians, demonstrating the impact of physician remuneration on database completeness. The trend of greater data loss in later than earlier years suggests that completeness of physician billing claims data may be decreasing. Our method can be applied across jurisdictions to compare the impact of physician payment methods on data quality.