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Objective: Use of benzodiazepines and Z-drugs (non-benzodiazepine sedative hypnotics) is controversial due to adverse health outcomes in the general population. However, little is known about their use in people with multiple sclerosis (MS). We estimated the incidence and prevalence of benzodiazepine and Z-drug use (jointly BZD) in the MS population as compared to an age-, sex- and geographically-matched population without MS, and examined the association of mood/anxiety disorders with the use of BZD over a twenty-year period. Methods: Using administrative data from Manitoba, Canada, we identified 2,985 persons with incident MS and 14,891 persons without MS matched 5:1 on sex, birth year and region. We applied validated case definitions to identify persons with any mood/anxiety disorder. Dispensations of BZD were identified. To assess the association between MS, mood/anxiety disorders and BZD use we constructed generalized linear models adjusting for age, sex, index year, socioeconomic status, urban/rural residence, physical comorbidities, and health care use. We also examined patterns of BZD use. Results: In 2016, the crude incidence of benzodiazepine use in the MS cohort was 2.10% (95%CI: 1.43–2.98%), 1.49-fold higher than in the non-MS cohort (1.41%; 95%CI: 1.18–1.67%). The crude incidence of Z-drug use in the MS cohort was 1.77% (95%CI: 1.20–2.51%), 1.78-fold higher than in the non-MS cohort (0.99%; 95%CI: 0.81–1.21%). After adjusting for covariates, among individuals without an active mood/anxiety disorder, the MS cohort had a 39% increased incidence rate of benzodiazepine use and a 72% increased incidence rate of Z-drug use as compared to the non-MS cohort. Among individuals with an active mood/anxiety disorder, the incidence of BZD use did not differ between the MS and non-MS cohorts. A higher proportion of people with MS used BZD for ≥6 months than people without MS. Conclusion: Use of BZD is more common in people with MS than in general population controls, and use of these agents is in persons with MS is often chronic. [ABSTRACT FROM AUTHOR]

Background: Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data.Methods: Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling's T2 statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit.Results: The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA.Conclusions: The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data. [ABSTRACT FROM AUTHOR]

Purpose: The Seattle Angina Questionnaire (SAQ) is a widely used patient-reported measure of health status in patients with coronary artery disease. Comparisons of SAQ scores amongst population groups and over time rely on the assumption that its factorial structure is invariant. This study evaluates the measurement invariance of the SAQ across different demographic and clinical groups and over time. Methods: Data were obtained from the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry, a registry of patients who received coronary angiogram in Alberta, Canada. The study cohort consists of adult patients who completed the paper-based version of the 16-item Canadian version of the SAQ (SAQ-CAN) 2 weeks and 1-year post-coronary angiogram between 2009 and 2016. Multi-group confirmatory factor analysis was used to assess configural, weak, strong, and strict measurement invariance across age groups, sex, angina type, treatment, and over time. Model fit was assessed using the comparative fit index and root mean square error of approximation. Results: Of the 8101 patients included in these analysis, 1300 (16.1%) were at least 75 years old, while 1755 (21.7%) were female, 5154 (63.6%) were diagnosed with acute coronary syndrome, 1177 (14.5%) received coronary artery bypass graft treatment, and 3279 had complete data on the SAQ-CAN at both occasions. There was evidence of strict invariance across age, sex, and angina type, and treatment groups, but partial strict invariance was established over time. Conclusion: SAQ-CAN can be used to compare the health status of coronary artery disease patients across population groups and over time. [ABSTRACT FROM AUTHOR]

Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which are characterized by binary, ordinal, or mixed types of response distributions. This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on GEE, and RMDA based on maximum likelihood estimators (MLE) under diverse simulation conditions, which included number of repeated measure occasions, number of responses, sample size, correlation structures, and type of response distribution. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on MLE especially in multivariate non-normal distributions. Three repeatedly measured responses namely severity of epilepsy, current number of anti-epileptic drugs, and parent-reported quality of life in children with epilepsy were used to demonstrate the application of these procedures. [ABSTRACT FROM AUTHOR]

Background: Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts.Methods: Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar's test with Holm-Bonferroni adjustment.Results: The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar's test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence.Conclusions: Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits. [ABSTRACT FROM AUTHOR]

Context: Fracture on therapy should motivate better antifracture medication adherence. Objective: This study aimed to describe osteoporosis medication adherence in women before and following a fracture. Methods: This retrospective cohort analysis of antifracture medication possession ratios (MPR) among women in the Manitoba BMD Registry (1996-2013) included menopausal women who started antifracture drug therapy after a dual-energy x-ray absorptiometry (DXA)-BMD assessment with follow-up for 5 years during which a nontraumatic fracture occurred at least 1 year after starting treatment. Linked prescription records determined medication adherence (estimated by MPR) in 1-year intervals. The variable of interest was MPR in the year before and after the year in which the fracture occurred, with subgroup analyses according to duration of treatment pre-fracture. We chose an MPR of ≥ 0.50 to indicate minimum adherence needed for drug efficacy. Results: There were 585 women with fracture on therapy, 193 (33%) had hip or vertebral fracture. Bisphosphonates accounted for 82.2% of therapies. Median MPR the year prior to fracture was 0.89 (IQR, 0.49-1.0) and 0.69 (IQR, 0.07-0.96) the year following the year of fracture (P < 0.0001). The percentage of women with MPR ≥ 0.5 pre-fracture was 73.8%, dropping to 57.3% post-fracture (P < 0.0001); when restricted to hip/vertebral fracture, results were similar (58.2% to 33.3%; P < 0.002). Among those with pre-fracture MPR < 0.5, only 21.7% achieved a post-fracture MPR ≥ 0.5. Conclusions: Although fracture on therapy may motivate sustained/improved adherence, MPR remains low or even declines after fracture in many. This could reflect natural decline in MPR with time but is paradoxical to expectations. Fracture on therapy represents an important opportunity for clinicians to reemphasize treatment adherence. [ABSTRACT FROM AUTHOR]

Summary: The Fracture Risk Evaluation Model (FREM) identifies individuals at high imminent risk of major osteoporotic fractures. We validated FREM on 74,828 individuals from Manitoba, Canada, and found significant fracture risk stratification for all FREM scores. FREM performed better than age alone but not as well as FRAX® with BMD. Introduction: The FREM is a tool developed from Danish public health registers (hospital diagnoses) to identify individuals over age 45 years at high imminent risk of major osteoporotic fractures (MOF) and hip fracture (HF). In this study, our aim was to examine the ability of FREM to identify individuals at high imminent fracture risk in women and men from Manitoba, Canada. Methods: We used the population-based Manitoba Bone Mineral Density (BMD) Program registry, and identified women and men aged 45 years or older undergoing baseline BMD assessment with 2 years of follow-up data. From linked population-based data sources, we constructed FREM scores using up to 10 years of prior healthcare information. Results: The study population comprised 74,828 subjects, and during the 2 years of observation, 1612 incident MOF and 299 incident HF occurred. We found significant fracture risk stratification for all FREM scores, with AUC estimates of 0.63–0.66 for MOF for both sexes and 0.84 for women and 0.65–0.67 for men for HF. FREM performed better than age alone but not as well as FRAX® with BMD. The inclusion of physician claims data gave slightly better performance than hospitalization data alone. Overall calibration for 1-year MOF prediction was reasonable, but HF prediction was overestimated. Conclusion: In conclusion, the FREM algorithm shows significant fracture risk stratification when applied to an independent clinical population from Manitoba, Canada. Overall calibration for MOF prediction was good, but hip fracture risk was systematically overestimated indicating the need for recalibration. [ABSTRACT FROM AUTHOR]