Evaluating area-based socioeconomic status predictors of pediatric health outcomes in Manitoba

Introduction Socioeconomic 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 Approach This 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). Results In 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/Implications There 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

Modelling Diagnostic Validity Estimates from Administrative Health Data

ABSTRACT Objectives Validation 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. Approach Computer 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. Results Univariate 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. Conclusions We 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

Evaluating the Completness of Physician Billing Claims: A Proof-of-Concept Study

ABSTRACT Objectives An 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. Approach Our 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. Results The 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. Conclusion The 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

Perceptions of Pediatric Nephrologists regarding Timing of Dialysis Initiation in Children in Canada

Background: Significant practice variation exists in Canada with respect to timing of dialysis initiation in children. In the absence of evidence to guide practice, physicians' perceptions may significantly influence decision-making. Objective: The objectives of this study are to (1) evaluate Canadian pediatric nephrologists' perceptions regarding dialysis initiation in children with chronic kidney disease (CKD) and (2) determine the factors guiding practice that may contribute to practice variation across Canada. Design: This study was a cross-sectional online survey. Setting: This study was done in academic pediatric nephrology centers in Canada. Participants: The participants of this study are pediatric nephrologists. Measurements and methods: An anonymous web-based survey was administered to pediatric nephrologists in Canada to evaluate perspectives and practice patterns regarding timing of dialysis initiation. We also explored the importance of estimated glomerular filtration rate (eGFR) vs. symptoms and the role of patient and provider factors influencing decisions. Results: Thirty-five nephrologists (59 %) completed the survey. Most respondents care for advanced CKD patients in a multidisciplinary clinic (86 %) and no centers have a formal policy on timing of dialysis initiation. Seventy-five percent of centers follow 30 patients. Discussions about dialysis initiation are generally informal (75 %) and the decision to start is made by the nephrologist (37 %) or a team (57 %). Fifty percent agreed GFR was important when deciding when to initiate dialysis, 41 % were neutral, and 9 % disagreed. Variability exists in the threshold that nephrologists considered early (vs. late) dialysis initiation: >20 (21 %), >15 (38 %), >12 (26 %), and >10 ml/min/1.73 m 2 (12 %). Practitioners however typically start dialysis in asymptomatic patients at eGFRs of 7–9 (9 %), 10–11 (41 %), 12–14 (38 %), and 15–19 (6 %) ml/min/1.73 m 2 . Patient factors important in the decision to start dialysis for >90 % of nephrologists were fatigue, >10 % weight loss, nausea, increasing missed school, and awaiting a pre-emptive transplant. Age was only a factor for 56 %. Limitations: This study has a 59 % response rate. Conclusions: Variability exists in Canada regarding the importance and threshold of eGFR guiding the decision as to when to start dialysis in children, whereas patient symptoms are almost universally important to pediatric nephrologists' decision-making. Additional studies evaluating outcomes of children starting dialysis earlier vs. later are needed to standardize decision-making and care for children with kidney failure

Testing population-based performance measures identifies gaps in juvenile idiopathic arthritis (JIA) care

Background: The study evaluates Performance Measures (PMs) for Juvenile Idiopathic Arthritis (JIA): The percentage of patients with new onset JIA with at least one visit to a pediatric rheumatologist in the first year of diagnosis (PM1); and the percentage of patients with JIA under rheumatology care seen in follow-up at least once per year (PM2). Methods: Validated JIA case ascertainment algorithms were used to identify cases from provincial health administrative databases in Manitoba, Canada in patients  12 and > 14 months between consecutive visits were also calculated. Results: One hundred ninety-four incident JIA cases were diagnosed between 01/04/2008 and 03/31/2015. The median age at diagnosis was 9.1 years and 71% were female. PM1: Across the years, 51–81% of JIA cases saw a pediatric rheumatologist within 1 year. PM2: Between 58 and 78% of patients were seen in yearly follow-up. Gaps > 12, and > 14, months were observed once during follow-up in 52, and 34%, of cases, and ≥ twice in 11, and 5%, respectively. Conclusions: Suboptimal access to pediatric rheumatologist care was observed which could lead to diagnostic and treatment delays and lack of consistent follow-up, potentially negatively impacting patient outcomes.Medicine, Faculty ofOther UBCNon UBCMedicine, Department ofReviewedFacult

Pearson correlations among distress measures and WPAI impairment.

<p>Pearson correlations among distress measures and WPAI impairment.</p

Quantile regression point estimates 95% confidence intervals for absenteeism (percent of work time missed due to health) (N = 457), presenteeism (percent impairment while at work) (N = 462), and general activity impairment (N = 939).

<p>Quantile regression point estimates 95% confidence intervals for absenteeism (percent of work time missed due to health) (N = 457), presenteeism (percent impairment while at work) (N = 462), and general activity impairment (N = 939).</p

Frequencies, means and medians of characteristics for the employed group compared to the not-working group.

<p>Frequencies, means and medians of characteristics for the employed group compared to the not-working group.</p

Quantile regression point estimates and standardized estimates with 95% confidence intervals for absenteeism (percent of work time missed due to health), presenteeism (percent of impairment at work due to health), and general activity impairment (percent of activity impairment due to health).

<p>Distress variables examined individually in separate regression models.</p