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Purpose: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items. Methods: Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients' characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression. Results: Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status. Conclusion: PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics. Plain English summary: Patient-reported depression screening measures are prone to differential item functioning (DIF), which occurs when patients with the same levels of depression respond to and interpret the questions on the measures differently, as a result of different socio-demographic characteristics. Heterogeneity in how epilepsy patients interpret and respond to depression measures, if not identified, could lead to measurement biases that threaten the validity of inferences from patient-reported outcome measure scores to inform clinical and healthcare decisions for epilepsy management. Using data from a clinical registry of patients with epilepsy, the tree-based item response theory model was used to examine the presence of DIF in the items comprising the patient-reported Neurological Disorders Depression Inventory in Epilepsy (NDDI-E) scale. Four subgroups of epilepsy patients were noted to exhibit DIF on NDDI-E items. These groups were defined by interactions among employment status, age and sex. These characteristics affect the interpretation of NDDI-E item scores. It is recommended that clinicians carefully scrutinize responses to individual items alongside the overall NDDI-E score in different patient groups to inform clinical decisions for epilepsy management. [ABSTRACT FROM AUTHOR]

Summary: We found that a standardized order set after hip fracture increased initiation of anti-osteoporosis medication and increased persistence at 1 year, but did not reduce secondary fractures. Background: A treatment gap exists after osteoporosis-related fractures. Introducing standardized care can improve treatment. We evaluated the impact of a hip fracture order set (OS) on anti-osteoporosis medication (AOM) initiation, persistence, and secondary fracture prevention. Methods: In 2015, one hospital in Manitoba, Canada, introduced a hip fracture OS including recommendations for the initiation of AOM (OS group). A control group was identified from the other hospitals in the same region. A retrospective cohort study was conducted using linked administrative health data. All individuals 50 + years with surgical treatment for low-energy hip fracture between 2010 and 2019 were included and followed for AOM initiation, medication persistence at 1 year, and secondary fractures. Between-group differences for each year were assessed using chi-square tests. Logistic regression models tested the impact of socio-demographic and clinical factors on initiation, persistence of AOM. Cox regression tested the risk of secondary fracture. Results: No baseline differences between OS group (813 patients) and control group (2150 patients) were observed in demographics, socioeconomic factors, or comorbidities. An increase in post-fracture AOM initiation was seen with OS introduction (OS group year before 16.7% versus year after 48.6%, p < 0.001). No change was seen in the control group. Persistence on AOM also increased (OS group year before 17.7% versus year after 28.4%, p < 0.001). No difference in secondary fractures was observed (OS group 19.8% versus control group 18.8%, p = 0.38). Conclusion: Introduction of a hip fracture OS significantly increased AOM initiation and persistence at 1-year post-fracture. There was no significant difference in secondary fractures. [ABSTRACT FROM AUTHOR]

Summary: Osteoporosis Canada 2023 clinical practice guidelines increase the number of individuals recommended or suggested for anti-osteoporosis pharmacotherapy by refining treatment guidance for those who fell within the 2010 guidelines' moderate-risk category. Purpose: In 2023, Osteoporosis Canada updated its 2010 clinical practice guidelines based upon consideration of fracture history, 10-year major osteoporotic fracture (MOF) risk, and BMD T-score in conjunction with age. The 2023 guidelines eliminated risk categories, including the moderate-risk group that did not provide clear treatment guidance. The current study was performed to appreciate the implications of the shift from 2010 risk categories to 2023 treatment guidance. Methods: The study population consisted of 79,654 individuals age ≥ 50 years undergoing baseline DXA testing from January 1996 to March 2018. Each individual was assigned to mutually exclusive categories based on 2010 and 2023 guideline recommendations. Treatment qualification, 10-year predicted and 10-year observed MOF risk were compared. Results: Treatment reclassification under the 2023 guidelines only affected 33.8% of individuals in the 2010 moderate-risk group, with 13.0% assigned to no treatment, 14.4% to suggest treatment, and 6.4% to recommend treatment. During the mean follow-up of 7.2 years, 6364 (8.0%) individuals experienced one or more incidents of MOF. The observed 10-year cumulative incidence of MOF in the study population was 10.5% versus the predicted 10.7% (observed to predicted mean calibration ratio 0.98, 95% CI 0.96–1.00). Individuals reclassified from 2010 moderate risk to 2023 recommend treatment were at greater MOF risk than those in the 2010 moderate-risk group assigned to 2023 suggest treatment or no treatment, but at lower risk than those in the 2010 high-risk group. Conclusions: Osteoporosis Canada 2023 clinical practice guidelines affect individuals within the 2010 moderate-risk category, increasing the number for whom anti-osteoporosis pharmacotherapy is recommended or suggested. Increased treatment could reduce the population burden of osteoporotic fractures, though moderate-risk individuals now qualifying for treatment have a lower predicted and observed fracture risk than high-risk individuals recommended for treatment under the 2010 guidelines. [ABSTRACT FROM AUTHOR]

Peak bone mineral density (BMD) is one of the most important factors influencing the development of osteoporosis. It was predicted that a 10% increase in peak BMD will delay the onset of osteoporosis by 13 years. However, changes in peak BMD over time are unknown. This study aimed to investigate secular trends in peak BMD among young adults in the United States. Based on the National Health and Nutrition Examination Survey from 1999–2018, 3,975 males aged 19–28 years and 2370 females aged 31–40 years were our target population for estimating peak lumbar spine BMD. BMD was measured by dual-energy X-ray absorptiometry. Generalized linear models adjusted for multiple covariates were used to examine the secular trends in peak BMD in males and females, respectively. Secular trends for peak lumbar spine BMD from 1999–2000 to 2017–2018 were not statistically significant in males or females (all Plinear and Pquadratic > 0.05). Similar results were observed in race/ethnicity subgroups (all Plinear and Pquadratic > 0.05). However, in stratified analyses by obesity category, peak lumbar spine BMD in obese males and females increased from 1999–2000 to 2009–2010 and then decreased until 2017–2018, while peak lumbar spine BMD in non-obese females decreased from 1999–2000 to 2005–2006 and then increased until 2017–2018 (all Pquadratic < 0.05). Peak lumbar spine BMD was greater in obese males and females than in non-obese males and females up to 2009–2010, but not from 2011–2012 onwards. Overall, there were no significant secular trends in peak lumbar spine BMD. However, secular trends differed between obese and non-obese groups. [ABSTRACT FROM AUTHOR]

Purpose: Unsupervised item-response theory (IRT) models such as polytomous IRT based on recursive partitioning (IRTrees) and mixture IRT (MixIRT) models can be used to assess differential item functioning (DIF) in patient-reported outcome measures (PROMs) when the covariates associated with DIF are unknown a priori. This study examines the consistency of results for IRTrees and MixIRT models. Methods: Data were from 4478 individuals in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry who received cardiac angiography in Alberta, Canada, and completed the Hospital Anxiety and Depression Scale (HADS) depression subscale items. The partial credit model (PCM) based on recursive partitioning (PCTree) and mixture PCM (MixPCM) were used to identify covariates associated with differential response patterns to HADS depression subscale items. Model covariates included demographic and clinical characteristics. Results: The median (interquartile range) age was 64.5(15.7) years, and 3522(78.5%) patients were male. The PCTree identified 4 terminal nodes (subgroups) defined by smoking status, age, and body mass index. A 3-class PCM fits the data well. The MixPCM latent classes were defined by age, disease indication, smoking status, comorbid diabetes, congestive heart failure, and chronic obstructive pulmonary disease. Conclusion: PCTree and MixPCM were not consistent in detecting covariates associated with differential interpretations of PROM items. Future research will use computer simulations to assess these models' Type I error and statistical power for identifying covariates associated with DIF. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this systematic review was to describe the prevalence and magnitude of response shift effects, for different response shift methods, populations, study designs, and patient-reported outcome measures (PROM)s. Methods: A literature search was performed in MEDLINE, PSYCINFO, CINAHL, EMBASE, Social Science Citation Index, and Dissertations & Theses Global to identify longitudinal quantitative studies that examined response shift using PROMs, published before 2021. The magnitude of each response shift effect (effect sizes, R-squared or percentage of respondents with response shift) was ascertained based on reported statistical information or as stated in the manuscript. Prevalence and magnitudes of response shift effects were summarized at two levels of analysis (study and effect levels), for recalibration and reprioritization/reconceptualization separately, and for different response shift methods, and population, study design, and PROM characteristics. Analyses were conducted twice: (a) including all studies and samples, and (b) including only unrelated studies and independent samples. Results: Of the 150 included studies, 130 (86.7%) detected response shift effects. Of the 4868 effects investigated, 793 (16.3%) revealed response shift. Effect sizes could be determined for 105 (70.0%) of the studies for a total of 1130 effects, of which 537 (47.5%) resulted in detection of response shift. Whereas effect sizes varied widely, most median recalibration effect sizes (Cohen's d) were between 0.20 and 0.30 and median reprioritization/reconceptualization effect sizes rarely exceeded 0.15, across the characteristics. Similar results were obtained from unrelated studies. Conclusion: The results draw attention to the need to focus on understanding variability in response shift results: Who experience response shifts, to what extent, and under which circumstances? [ABSTRACT FROM AUTHOR]

Purpose: Our aim is to advance response shift research by explicating the implications of published syntheses by the Response Shift – in Sync Working Group in an integrative way and suggesting ways for improving the quality of future response shift studies. Methods: Members of the Working Group further discussed the syntheses of the literature on definitions, theoretical underpinnings, operationalizations, and response shift methods. They outlined areas in need of further explication and refinement, and delineated additional implications for future research. Results: First, the proposed response shift definition was further specified and its implications for the interpretation of results explicated in relation to former, published definitions. Second, the proposed theoretical model was further explained in relation to previous theoretical models and its implications for formulating research objectives highlighted. Third, ways to explore alternative explanations per response shift method and their implications for response shift detection and explanation were delineated. The implications of the diversity of the response shift methods for response shift research were presented. Fourth, the implications of the need to enhance the quality and reporting of the response shift studies for future research were sketched. Conclusion: With our work, we intend to contribute to a common language regarding response shift definitions, theory, and methods. By elucidating some of the major implications of earlier work, we hope to advance response shift research. [ABSTRACT FROM AUTHOR]

Purpose: Mixture item response theory (MixIRT) models can be used to uncover heterogeneity in responses to items that comprise patient-reported outcome measures (PROMs). This is accomplished by identifying relatively homogenous latent subgroups in heterogeneous populations. Misspecification of the number of latent subgroups may affect model accuracy. This study evaluated the impact of specifying too many latent subgroups on the accuracy of MixIRT models. Methods: Monte Carlo methods were used to assess MixIRT accuracy. Simulation conditions included number of items and latent classes, class size ratio, sample size, number of non-invariant items, and magnitude of between-class difference in item parameters. Bias and mean square error in item parameters and accuracy of latent class recovery were assessed. Results: When the number of latent classes was correctly specified, the average bias and MSE in model parameters decreased as the number of items and latent classes increased, but specification of too many latent classes resulted in modest decrease (i.e., < 10%) in the accuracy of latent class recovery. Conclusion: The accuracy of MixIRT model is largely influenced by the overspecification of the number of latent classes. Appropriate choice of goodness-of-fit measures, study design considerations, and a priori contextual understanding of the degree of sample heterogeneity can guide model selection. [ABSTRACT FROM AUTHOR]

Summary: We assessed post-fracture mortality in a population-based cohort of 122,045 individuals with cancers. Major fractures (hip, vertebrae, humerus, and forearm) were associated with early and long-term increased all-cause mortality. Introduction: Currently, there are no population-based data among cancer patients on post-fracture mortality risk across a broad range of cancer diagnoses. Our objective was to estimate the association of fracture with mortality in cancer survivors. Methods: Using Manitoba Cancer Registry data from the province of Manitoba, Canada, we identified all women and men with cancer diagnosed between January 1, 1987, and March 31, 2014. We then linked cancer data to provincial healthcare administrative data and ascertained fractures after cancer diagnosis and mortality to March 31, 2015. Hazard ratios for all-cause mortality in those with versus without fracture were estimated from time-dependent Cox proportional hazards models adjusted for multiple covariates. Results: The study cohort consisted of 122,045 cancer patients (median age 68 years, IQR 58–77, 49.2% female). During the median follow-up of 5.8 years from cancer diagnosis, we ascertained 7120 (5.8%) major fractures. All fracture sites, except for the forearm, were associated with increased mortality risk, even after multivariable adjustment. Excess mortality risk associated with a major fracture was greatest in the first year after fracture (HR 2.42, 95% CI 2.30–2.54) and remained significant > 5 years after fracture (HR 1.60, 95% CI 1.50–1.70) and for fractures occurring > 10 years after cancer diagnosis (HR 1.93, 95% CI 1.79–2.07). Conclusion: Fractures among cancer patients are associated with increased all-cause mortality. This excess risk is greatest in the first year and persists more than 5 years post-fracture; increased risk is also noted for fractures occurring up to and beyond 10 years after cancer diagnosis. [ABSTRACT FROM AUTHOR]

Summary: Fracture risk calculators may not be accurate for all ethnicity groups. The Manitoba bone density registry was used to test the Canadian CAROC tool for predicting fracture risk in Asian-Canadian women. The tool significantly over-estimated fracture risk, suggesting that it may not be ideal for Asian-Canadian patients. Purpose: Health risk prediction tools based on largely White populations may lead to treatment inequity when applied to non-White populations where outcome rates differ. We examined the calibration of the Canadian Association of Radiologists-Osteoporosis Canada (CAROC) fracture risk prediction tool in self-identified Asian-Canadian women. Methods: Retrospective cohort study of women over age 50 using the Manitoba BMD Registry. At first BMD, the intake questionnaire collected self-identification of ethnicity and fracture risk factors. 10-year fracture risk was estimated using CAROC and categorized into low, medium, or high fracture risk. Linked administrative databases identified incident osteoporotic fractures. Observed fracture rates were compared between White and Asian-Canadians and compared to the original CAROC risk stratification. Results: There were 63,632 and 1703 women who self-identified as White-Canadian or Asian-Canadian, respectively, covering approximately 600,000 patient-years follow-up. There were 6588 incident fractures; a similar percentage of patients were assigned to each risk stratum at baseline by CAROC. A progressive rise in 10-year observed fracture rates occurred for each CAROC stratum in the White-Canadian population but much lower fracture rates than predicted in Asian-Canadian patients (p < 0.001). Fracture incidence rate ratios were 1.9–2.6 fold higher in White- vs Asian-Canadian patients for all strata (p < 0.001). In the CAROC moderate and high-risk categories, observed fracture rates in Asian-Canadian patients were typically lower than predicted, indicating poor model calibration. Conclusion: In Asian-Canadian women, observed osteoporosis fracture rates are lower than predicted when using the CAROC tool. Over-estimation of fracture risk may influence shared decision-making discussions. [ABSTRACT FROM AUTHOR]

Purpose: Item non-response (i.e., missing data) may mask the detection of differential item functioning (DIF) in patient-reported outcome measures or result in biased DIF estimates. Non-response can be challenging to address in ordinal data. We investigated an unsupervised machine-learning method for ordinal item-level imputation and compared it with commonly-used item non-response methods when testing for DIF. Methods: Computer simulation and real-world data were used to assess several item non-response methods using the item response theory likelihood ratio test for DIF. The methods included: (a) list-wise deletion (LD), (b) half-mean imputation (HMI), (c) full information maximum likelihood (FIML), and (d) non-negative matrix factorization (NNMF), which adopts a machine-learning approach to impute missing values. Control of Type I error rates were evaluated using a liberal robustness criterion for α = 0.05 (i.e., 0.025–0.075). Statistical power was assessed with and without adoption of an item non-response method; differences > 10% were considered substantial. Results: Type I error rates for detecting DIF using LD, FIML and NNMF methods were controlled within the bounds of the robustness criterion for > 95% of simulation conditions, although the NNMF occasionally resulted in inflated rates. The HMI method always resulted in inflated error rates with 50% missing data. Differences in power to detect moderate DIF effects for LD, FIML and NNMF methods were substantial with 50% missing data and otherwise insubstantial. Conclusion: The NNMF method demonstrated comparable performance to commonly-used non-response methods. This computationally-efficient method represents a promising approach to address item-level non-response when testing for DIF. [ABSTRACT FROM AUTHOR]

Summary: Epidemiological studies show an inconsistent association between cancer and osteoporosis. In this nationally representative population-based study, we found that a prior cancer diagnosis was not associated with osteoporosis. This finding may primarily apply to cancer survivors seen many years after their cancer diagnosis. Background: Epidemiological studies show an inconsistent association between cancer and osteoporosis. We examined the association between a prior cancer diagnosis and osteoporosis in population-based data. Methods: We performed an age- and sex-matched case–control study (1:2 matching ratio) using the National Health and Nutrition Examination Survey, 2011–2018. Cases were determined by self-reported prior diagnosis of cancer; all controls were free of cancer at the time of bone density measurement with dual-energy x-ray absorptiometry. We defined osteoporosis as a T-score ≤ − 2.5 at femoral neck, total hip, or lumbar spine. Unconditional multivariable logistic regression was used to test the association between a prior cancer diagnosis and osteoporosis. Results: We identified 246 prior cancer cases and 492 controls (mean age: 65.8 years) in females, and 243 prior cancer cases and 486 controls (mean age: 68.0 years) in males. The most common types of cancer in females and males were breast cancer and prostate cancer, respectively. Osteoporosis prevalences were comparable between cases and controls among females (19.1% in cases vs. 18.7% in controls; P = 0.894) and males (5.8% in cases vs. 6.8% in controls; P = 0.594). After adjusting for covariates, a prior cancer diagnosis was not associated with osteoporosis in females (odds ratio [OR]: 0.83; 95% confidence interval [CI]: 0.54–1.29) or males (OR: 1.09; 95% CI: 0.51–2.30). Results were unaffected by cancer severity, cancer type, or time since cancer diagnosis. Conclusions: A prior cancer diagnosis was not associated with osteoporosis in this nationally representative population. [ABSTRACT FROM AUTHOR]

Summary : The Danish Fracture Risk Evaluation Model (FREM) was found to predict fracture risk independent of 10-year fracture probability derived with the FRAX® tool including bone mineral density from DXA. Introduction : FREM was developed from Danish public health registers without DXA information to identify high imminent risk of major osteoporotic fracture (MOF) and hip fracture (HF), while FRAX® estimates 10-year fracture probability from clinical risk factors and femoral neck bone mineral density (BMD) from DXA. The FREM algorithm showed significant 1- and 2-year fracture risk stratification when applied to a clinical population from Manitoba, Canada. We examined whether FREM predicts 10-year fracture risk independent of 10-year FRAX probability computed with BMD. Methods: Using the Manitoba BMD Program registry, we identified women and men aged ≥ 45 years undergoing baseline BMD assessment. We calculated FREM and FRAX scores, and identified incident fractures over 10 years. Hazard ratios (HRs) for incident fracture were estimated according to FREM quintile, adjusted for FRAX probability. We compared predicted with observed 10-year cumulative fracture probability estimated with competing mortality. Results: The study population comprised 74,446 women, mean age 65.2 years; 7945 men, mean age 67.5 years. There were 7957 and 646 incident MOF and 2554 and 294 incident HF in women and men, respectively. Higher FREM scores were associated with increased risk for MOF (highest vs middle quintile HRs 1.49 women, 2.06 men) and HF (highest vs middle quintile HRs 2.15 women, 2.20 men) even when adjusted for FRAX. Greater mortality with higher FREM scores attenuated its effect on 10-year fracture probability. In the highest FREM quintile, observed slightly exceeded predicted 10-year probability for MOF (ratios 1.05 in women, 1.49 in men) and HF (ratios 1.29 in women, 1.34 in men). Conclusions: Higher FREM scores identified women and men at increased fracture risk even when adjusted for FRAX probability that included BMD; hence, FREM provides additional predictive information to FRAX. FRAX slightly underestimated 10-year fracture probability in those falling within the highest FREM quintile. [ABSTRACT FROM AUTHOR]

Diabetes increases fracture and falls risks. We evaluated the performance of the Garvan fracture risk calculator (FRC) in individuals with versus without diabetes. Using the population-based Manitoba bone mineral density (BMD) registry, we identified individuals aged 50-95 years undergoing baseline BMD assessment from 1 September 2012, onwards with diabetes and self-reported falls in the prior 12 months. Five-year Garvan FRC predictions were generated from clinical risk factors, with and without femoral neck BMD. We identified non-traumatic osteoporotic fractures (OF) and hip fractures (HF) from population-based data to 31 March 2018. Fracture risk stratification was assessed from area under the receiver operating characteristic curves (AUROC). Cox regression analysis was performed to examine the effect of diabetes on fractures, adjusted for Garvan FRC predictions. The study population consisted of 2618 women with and 14,064 without diabetes, and 636 and 2201 men with and without the same, respectively. The Garvan FRC provided significant OF and HF risk stratification in women with diabetes, similar to those without diabetes. Analyses of OF in men were limited by smaller numbers; no significant difference was evident by diabetes status. Cox regression showed that OF risk was 23% greater in women with diabetes adjusted for Garvan FRC including BMD (hazard ratio [HR] 1.23, 95% confidence interval [CI] 1.01-1.49), suggesting it slightly underestimated risk; a non-significant increase in diabetes-related HF risk was noted (HR 1.37, 95% CI 0.88-2.15). Garvan FRC shows similar fracture risk stratification in individuals with versus without diabetes, but may underestimate this risk. [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]

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]

Purpose: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods. Methods: Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified. Results: We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points. Conclusion: No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward. [ABSTRACT FROM AUTHOR]

Purpose: The Working Group undertook a critical, comprehensive synthesis of the response shift work to date. We aimed to (1) describe the rationale for this initiative; (2) outline how the Working Group operated; (3) summarize the papers that comprise this initiative; and (4) discuss the way forward. Methods: Four interdisciplinary teams, consisting of response shift experts, external experts, and new investigators, prepared papers on (1) definitions and theoretical underpinnings, (2) operationalizations and response shift methods, (3) implications for healthcare decision-making, and (4) on the published magnitudes of response shift effects. Draft documents were discussed during a two-day meeting. Papers were reviewed by all members. Results: Vanier and colleagues revised the formal definition and theory of response shift, and applied these in an amended, explanatory model of response shift. Sébille and colleagues conducted a critical examination of eleven response shift methods and concluded that for each method extra steps are required to make the response shift interpretation plausible. Sawatzky and colleagues created a framework for considering the impact of response shift on healthcare decision-making at the level of the individual patient (micro), the organization (meso), and policy (macro). Sajobi and colleagues are conducting a meta-analysis of published response shift effects. Preliminary findings indicate that the mean effect sizes are often small and variable across studies that measure different outcomes and use different methods. Conclusion: Future response shift research will benefit from collaboration among diverse people, formulating alternative hypotheses of response shift, and conducting the most conclusive studies aimed at testing these (falsification). [ABSTRACT FROM AUTHOR]

Summary: Site-specific X-ray procedure codes are a useful ancillary source of information for identifying fractures in healthcare administrative and claims data. Introduction: Real-world evaluation of fracture epidemiology at the population level from electronic healthcare information, such as administrative data, requires comprehensive data sources and validated case definitions. Only hip fractures are routinely hospitalized, and the identification of most osteoporosis-related fractures which are non-hospitalized fractures remains challenging. Plain X-rays (radiographs) are first-line tests for fracture diagnosis and are frequently repeated to monitor fracture healing, and claims data related to radiologic procedures are available in many healthcare systems. We hypothesized that temporal clustering in plain X-ray procedure codes might be an ancillary source of fracture data. Methods: We identified individuals age 40 years and older in Manitoba Bone Mineral Density (BMD) Registry with a fracture diagnosis (hip, forearm, humerus, clinical vertebral) before or following a BMD test. A subset underwent detailed review of X-rays to verify an acute fracture. We examined the association between fracture diagnosis and numbers of site-specific X-ray procedures. Results: The registry cohort included 7793 individuals with a fracture in the previous 5 years and 8417 incident fractures. The X-ray review cohort included 167 radiologically-verified fractures. The number of site-specific X-ray codes was greater in those with vs without fracture (all P < 0.001). The number of days with site-specific X-rays was strongly associated with a fracture diagnosis (area under the curve 0.90 to 0.99 for all non-vertebral fractures, 0.66 to 0.75 for clinical vertebral fractures). There was good agreement between the date of fracture diagnosis and the first X-ray at all non-vertebral fracture sites (Spearman correlation range 0.65 to 0.99), but this was lower for clinical vertebral fractures (range 0.29 to 0.59). Conclusions: Temporal clustering in site-specific X-ray procedures was associated with a corresponding fracture diagnosis in administrative medical records. Non-vertebral fracture sites were more strongly associated with X-ray procedures than clinical vertebral fractures. [ABSTRACT FROM AUTHOR]

Summary: The best screening strategy to identify treatment qualification based upon indicators of high fracture risk (low-trauma fractures of the hip, spine, or multiple fracture episodes at other sites; high fracture probability with the Canadian fracture risk assessment [FRAX®] tool major osteoporotic fracture [MOF] computed with bone mineral density [BMD] > 20%; or vertebral fracture on vertebral fracture assessment [VFA]) was FRAX-MOF without BMD using a cutoff of ≥ 10%. Purpose: To inform clinical practice guidelines in Canada, we compared multiple screening tools using the population-based Manitoba BMD Program registry. Methods: The study populations consisted of (a) 28,906 individuals > 50 years or older, and (b) 15,429 women age > 65 years undergoing baseline BMD assessment (2010–2018). We considered two treatment qualifications: Treatment Approach 1: prior high-risk fracture, high fracture probability (FRAX-MOF with BMD > 20%), or vertebral fracture on VFA; Treatment Approach 2: Approach 1 or an osteoporotic BMD T score. Candidate screening tools were FRAX-MOF without BMD, age alone, weight alone, SCORE, ORAI, SOFSURF, OSIRIS, ABONE, and OST. Healthcare records were assessed for the presence of incident fracture diagnoses. Results: Among all individuals, FRAX-MOF without BMD demonstrated the best ability to identify those satisfying Treatment Approach 1 (area under the curve [AUC 0.863]) and was significantly better than all other screening tools (P < 0.001). For identification of individuals satisfying Treatment Approach 2, FRAX-MOF without BMD showed moderate stratification (AUC 0.735), slightly lower than OSIRIS (AUC 0.752, P < 0.05), similar to SCORE (AUC 0.739, P > 0.05) and significantly better than all other screening tools (P < 0.05). For prediction of incident MOF, FRAX-MOF without BMD achieved the highest performance (AUC 0.652), and was significantly better than all other screening tools except OSIRIS. AUCs among women age > 65 years tended to be greater with a similar ranking, and no tool outperformed FRAX-MOF without BMD. Based upon a summary score, the highest ranked strategy was FRAX-MOF without BMD using a cutoff of 10%. Conclusions: All screening tools show some ability to identify individuals qualifying for treatment and stratify risk for incident fracture. For treatment based upon indicators of high fracture risk, the best performing strategy was FRAX-MOF without BMD using a cutoff of ≥ 10%. [ABSTRACT FROM AUTHOR]

Summary: During median follow-up 6.0 years in 9622 individuals, prior loss in estimated total body lean mass (TBLM), but not total body fat mass loss (TBFM), was associated with increased fracture risk, particularly for hip fracture. Introduction: Weight loss, and especially muscle loss, adversely affects skeletal health. The FRAX® tool considers baseline body mass index, but not body composition nor changes in its components over time. Our aim was to compare the independent associations between prior loss in DXA-estimated TBLM and TBFM and subsequent fracture risk. Methods: We identified women and men age 40 years or older with two DXA assessments at least 1 year apart (median interval 3.3 years). TBLM and TBFM were estimated from weight, sex, and DXA of the spine and hip. Incident fractures and deaths were ascertained from linked population-based health service data after the date of the second DXA. Hazard ratios (HRs) from Cox regression models were used to study time to fracture from prior loss in TBLM and TBFM adjusted for FRAX-related covariates. Results: The study population consisted of 9622 individuals (mean age 67 [SD 10] years, 95% female). We identified 692 subjects with incident major osteoporotic fracture [MOF] and 194 with hip fracture. Mean TBLM loss was significantly greater in those with incident MOF and hip fracture (P < 0.001) while TBFM loss was only significantly greater in those with incident hip fracture (P < 0.001). Each SD greater TBLM loss was associated with 10–13% increased MOF risk and 29–38% increased hip fracture risk, adjusted for TBFM loss and other covariates. Prior TBFM loss was not associated with fractures when adjusted for TBLM loss. Conclusions: Prior loss in total body lean mass, but not in fat mass, is associated with increased fracture risk, particularly hip fracture, independent of other risk factors. This is consistent with the hypothesis that muscle loss (sarcopenia) adversely impacts skeletal health and fracture risk. [ABSTRACT FROM AUTHOR]

Summary: Among 39,475 women, age 65 years and older, use of fracture history, major osteoporotic fracture (MOF) probability from FRAX®, vertebral fracture assessment (VFA), and bone mineral density (BMD) T-score stratified women into different levels of risk. The majority of women identified as being at high risk from fracture history, FRAX MOF-BMD > 20%, or vertebral fracture on VFA had a BMD T-score in the osteoporotic range. Purpose: To inform criteria for pharmacologic treatment in women age 65 years and older, we examined subgroups defined from fracture history, MOF calculated with BMD (MOF-BMD), VFA, and BMD T-score using the population-based Manitoba BMD Program registry. Methods: The study population consisted of women age > 65 years was divided into mutually exclusive subgroups based upon fracture history, MOF-BMD ≥ 20%, vertebral fracture on VFA, and osteoporotic BMD T-score. Healthcare records were assessed for the presence of fracture diagnosis codes occurring after DXA assessment. For each subgroup, we estimated the proportion of individuals with BMD T-score in the osteoporotic range, predicted versus observed 10-year MOF probability, hazard ratio (HR) for MOF, and number needed to treat (NNT) for 3 years to prevent a fracture event. Results: The study population consisted of 39,475 women (median age 72 years). The majority of women (76.8%) selected as being at high risk based on fracture history, MOF-BMD > 20%, or vertebral fracture on VFA had a BMD T-score in the osteoporotic range. During a median follow-up of 8 years, 5169 (13.1%) sustained one or more incident MOF. Fracture rates and HRs generally paralleled the FRAX prediction, except in women with a positive VFA where predicted risk based upon clinical risk factors prior to VFA underestimated the observed risk. NNT differed by the risk subgroup, and showed a gradient of decreasing NNT (consistent with greater benefit) in individuals with the highest fracture risk. Conclusions: Fracture history, fracture probability from FRAX, targeted vertebral fracture assessment (VFA), and BMD T-score can stratify older women into different levels of risk and treatment benefit. These results are expected to inform clinical practice guidelines in Canada. [ABSTRACT FROM AUTHOR]

Summary: In 50,330 women undergoing bone mineral density (BMD) testing, adjusting FRAX® major osteoporotic fracture (MOF) probability for T-score differences between lumbar spine and femoral neck had a small impact on treatment qualification, with only 1.1% reclassified from below to above the 20% MOF intervention threshold. Purpose: Discordance between lumbar spine and femoral neck T-score measurements are frequently encountered in clinical practice. The FRAX tool accepts femoral neck BMD as an optional input but does not consider lumbar spine BMD, though adjustments based upon T-score difference have been proposed. This analysis was undertaken to examine change in treatment qualification using the lumbar spine T-score adjustment to FRAX assuming an intervention threshold of 20% MOF. Methods: Women aged > 50 years with baseline FRAX MOF probability computed with femoral neck BMD of ≥ 20% but without previous high-risk fracture or high-risk medication use were identified in the province of Manitoba BMD registry. If lumbar spine T-score was lower than the femoral neck T-score, MOF probability was recalculated, and treatment qualification, reassessed. Results: The study population consisted of 50,300 women (mean age 64.2 ± 8.6 years). During mean follow-up of 9.5 years, 4550 sustained incident MOF. The baseline mean T-score difference (femoral neck minus lumbar spine) was − 0.2 ± 1.1. Recalculated MOF probability using the T-score difference significantly improved overall net reclassification index for incident MOF prediction (+ 0.017, p < 0.001). A total of 561 (1.1%) of these women were reclassified from below to above 20% MOF probability based upon the T-score difference. No individuals with MOF probability less than 15% were reclassified to the higher risk category. Risk reclassification increased with lower lumbar spine T-score (15.6% reclassification for lumbar spine T-score of − 3.5 or lower, 4.0% of the study population) and larger T-score differences (9.7% reclassification for T-score difference of 2.5 or more, < 1% of the study population). Conclusions: Very few women showed risk reclassification based upon adjusting FRAX MOF probability for lumbar spine T-score. Reclassification occurred only in those with baseline MOF probability > 15%. [ABSTRACT FROM AUTHOR]

Summary: Vertebral fracture assessment (VFA) provides incremental information in identifying women and men aged 70 years and older qualifying for anti-osteoporosis treatment compared with FRAX® major osteoporotic fracture (MOF) probability computed with bone mineral density (BMD). Purpose: This analysis was performed to inform appropriate use of VFA testing as part of Osteoporosis Canada's Guidelines Update, assuming vertebral fracture is an indication for pharmacotherapy in women and men. Methods: Women and men aged 70 years and older without previous high-risk fracture (i.e., hip, spine, or multiple fractures) were identified in a BMD registry for the province of Manitoba, Canada. MOF probability with BMD was computed using the Canadian FRAX® tool. VFA was performed in those with a minimum BMD T-score of −1.5 or lower. Results: The study population consisted of 7289 women (mean age 76.7 ± 5.6 years) and 1323 men (77.9 ± 5.8 years). More women than men qualified for VFA testing (48.7% vs 25.4%, respectively, p < 0.001). Among those undergoing VFA, a vertebral fracture was more commonly detected among men than women (22.9% vs 13.3%, p < 0.001), and vertebral fracture prevalence increased with lower BMD T-score (both p trend <0.001). The number needed to screen with VFA to detect a vertebral fracture was 8 for women and 4 for men. MOF probability was substantially lower in men than in women, and fewer men than women (3.3% vs 20.2%, p < 0.001) met a treatment threshold of MOF 20% or greater. In those with MOF probability <20%, VFA identified an incremental 5.4% of men and 3.4% of women for treatment based upon vertebral fracture. Conclusions: The number needed to screen to identify a previously unappreciated vertebral fracture is low and further improves with lower BMD T-score. VFA identified more men as qualifying for treatment than MOF probability. In women, treatment qualification was predominantly from MOF probability. [ABSTRACT FROM AUTHOR]

Summary: This comparison of osteoporosis treatment strategies and intervention thresholds highlights tradeoffs in terms of number of individuals qualifying for treatment and estimated fractures prevented. Purpose: The current analysis was performed to inform the following key question as part of the Osteoporosis Canada's Osteoporosis Guidelines Update: "What is the best strategy to identify those at high fracture risk for pharmacotherapy in order to prevent the most fractures, considering both population and patient perspectives?" Methods: The study population consisted of 66,878 women age 50 years and older (mean age 66.0 ± 9.7 years) with documented fracture probability assessment (FRAX) and fracture outcomes. Fractures over the next 5 years were identified through linked administrative healthcare data. We estimated the fraction of the population that would warrant treatment and the number of fractures avoided per 1000 person-years according to multiple strategies and thresholds. Strategies were then rank ordered using 19 metrics. Results: During mean 4.4 years, 863 (3.5%) sustained one or more major osteoporotic fractures (MOF), 212 (0.8%) sustained a hip fracture, and 1210 (4.9%) sustained any incident fracture. For woman age 50–64 years, the highest ranked strategy was treatment based upon total hip T score ≤ −2.5, but several other strategies fell within 0.5 overall ranking. For women age 65 years and older, MOF > 20% was the highest ranked strategy with no closely ranked strategies. Pooling both age subgroups gave MOF > 20% as the highest ranked strategy, with several other strategies within 0.5 overall ranking. Conclusions: Choice of treatment strategy and threshold for osteoporosis management strongly influences the number of individuals for whom pharmacologic treatment would be recommended and on estimated fracture rates in the population. This evidence-based approach to comparing these strategies will help to inform guidelines development in Canada and may be on interest elsewhere. [ABSTRACT FROM AUTHOR]

Purpose: Statistical approaches have been developed to detect bias in individual trials, but guidance on how to detect systematic differences at a meta-analytical level is lacking. In this paper, we elucidate whether author bias can be detected in a cohort of randomized trials included in a meta-analysis.Methods: We utilized mortality data from 35 trials (10,880 patients) included in our previously published meta-analysis. First, we linked each author with their trial (or trials). Then we calculated author-specific odds ratios using univariate cross table methods. Finally, we tested the effect of authorship by comparing each author's estimated odds ratio with all other pooled estimated odds ratios using meta-regression.Results: The median number of investigators named as authors on the primary trial reports was six (interquartile range: 5-8, range: 2-32). The results showed that the slope of author effect for mortality ranged from - 1.35 to 0.71. We identified only one author team showing a marginally significant effect (- 0.39; 95% confidence interval, - 0.78 to 0.00). This author team has a history of retractions due to data manipulations and ethical violations.Conclusion: When combining trial-level data to produce a pooled effect estimate, investigators must consider sources of potential bias. Our results suggest that systematic errors can be detected using meta-regression, although further research is needed to examine the sensitivity of this model. Systematic reviewers will benefit from the availability of methods to guard against the dissemination of results with the potential to mislead decision-making. [ABSTRACT FROM AUTHOR]

Purpose: This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift.Methods: Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines.Synthesis: A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method.Conclusions: While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored. [ABSTRACT FROM AUTHOR]

Purpose: Patient-reported outcome measures (PROMs) are frequently used in heterogeneous patient populations. PROM scores may lead to biased inferences when sources of heterogeneity (e.g., gender, ethnicity, and social factors) are ignored. Latent variable mixture models (LVMMs) can be used to examine measurement invariance (MI) when sources of heterogeneity in the population are not known a priori. The goal of this article is to discuss the use of LVMMs to identify invariant items within the context of test construction.Methods: The Draper-Lindely-de Finetti (DLD) framework for the measurement of latent variables provides a theoretical context for the use of LVMMs to identify the most invariant items in test construction. In an expository analysis using 39 items measuring daily activities, LVMMs were conducted to compare 1- and 2-class item response theory models (IRT). If the 2-class model had better fit, item-level logistic regression differential item functioning (DIF) analyses were conducted to identify items that were not invariant. These items were removed and LVMMs and DIF testing repeated until all remaining items showed MI.Results: The 39 items had an essentially unidimensional measurement structure. However, a 1-class IRT model resulted in many statistically significant bivariate residuals, indicating suboptimal fit due to remaining local dependence. A 2-class LVMM had better fit. Through subsequent rounds of LVMMs and DIF testing, nine items were identified as being most invariant.Conclusions: The DLD framework and the use of LVMMs have significant potential for advancing theoretical developments and research on item selection and the development of PROMs for heterogeneous populations. [ABSTRACT FROM AUTHOR]

Background: Response shift (RS) has been defined as a change in the meaning of an individual's self-evaluation of his/her health status and quality of life. Several statistical model- and design-based methods have been developed to test for RS in longitudinal data. We reviewed the uptake of these methods in patient-reported outcomes (PRO) literature.Methods: CINHAHL, EMBASE, Medline, ProQuest, PsycINFO, and Web of Science were searched to identify English-language articles about RS published until 2016. Data on year and country of publication, PRO measure adopted, RS detection method, type of RS detected, and testing of underlying model assumptions were extracted from the included articles.Results: Of the 1032 articles identified, 101 (9.8%) articles were included in the study. While 54.5 of the articles reported on the Then-test, 30.7% of the articles reported on Oort's or Schmitt's structural equation modeling (SEM) procedure. Newer RS detection methods, such as relative importance analysis and random forest regression, have been used less frequently. Less than 25% reported on testing the assumptions underlying the adopted RS detection method(s).Conclusions: Despite rapid methodological advancements in RS research, this review highlights the need for further research about RS detection methods for complex longitudinal data and standardized reporting guidelines. [ABSTRACT FROM AUTHOR]

Purpose: Response shift (RS) may mask true change in health-related quality of life in longitudinal studies. People with chronic conditions may experience RS as they adapt to their disease, but it is unknown whether fluctuations in disease activity will influence the presence of RS. The study purpose was to test for RS in individuals with inflammatory bowel disease (IBD), a condition characterized by periods of symptom flares and remission.Methods: Data were from the Manitoba IBD Cohort Study (N = 388). Multi-group confirmatory factor analysis (MG-CFA) and a RS detection method based on structural equation modeling were used to test for reconceptualization, reprioritization, and recalibration RS in participants with consistent active, consistent inactive, and inconsistent disease activity over a 6-month period on the SF-36.Results: The MG-CFA revealed that a weak invariance model with equal factor loadings across groups was the best fit to the baseline SF-36 data. Reconceptualization, uniform recalibration, and non-uniform recalibration RS was detected in the consistent active group, but effect sizes were small. For the consistent inactive group, recalibration RS was observed and effect sizes were small to moderate. For the inconsistent disease activity group, small-to-moderate recalibration RS effects were observed. There was no evidence of reprioritization.Conclusions: Individuals with a chronic disease may exhibit RS even if they are not actively experiencing symptoms on a consistent basis. Heterogeneity in the type and magnitude of RS effects may be observed in chronic disease patients who experience changes in disease symptoms. [ABSTRACT FROM AUTHOR]

Accurate predictive models of costs for episodes of healthcare utilization associated with acute and chronic conditions can be used to develop non-fee-for-service provider remuneration systems. We examined the performance of eight predictive models for costs associated with episodes of care for chronic obstructive pulmonary disease (COPD) exacerbations: ordinary least squares (OLS) regression on untransformed costs, OLS regression on log-transformed costs with Duan’s retransformation, OLS regression on log-transformed costs with heteroscedastic retransformation, OLS regression on log-transformed costs with normal retransformation, robust regression, generalized linear model (GLM) with a Poisson distribution and log link function, GLM with a Gamma distribution and identity link function and GLM with a Gamma distribution and log link function. Administrative health data from Saskatchewan, Canada, including hospital records, physician billing claims, prescription drug records and home care service records were linked to identify individuals with diagnosed COPD and measure their episodes of health service utilization and costs. Cross-validation results showed that none of the models consistently resulted in the best prediction; the OLS regression model on log-transformed costs with normal retransformation had the highest R2, but the OLS model on untransformed costs and the robust regression model had the best prediction accuracy based on root mean square error and mean absolute prediction error, respectively. Based on these findings, we recommend that researchers consider adopting one of these three models for predicting costs of healthcare use in episodes of care, but also emphasize that further comparisons of model performance are warranted. [ABSTRACT FROM AUTHOR]

Background: When patients experience a substantial health state change, they may undergo changes in the underlying meaning of their self-report to standardized outcome measures. These response shifts can reflect changes in the patient’s internal standards, values or conceptualization of quality of life. We investigated the presence of changing values (reprioritization response shift) in a longitudinal cohort of spine surgery patients. Methods: Spinal decompression surgery patients (mean age 52 years; 39 % female, 36 % working) provided visual analogue scale (VAS) back and leg pain items, the Short-Form-36 (SF-36v1), and the Oswestry Disability Index (ODI) data pre- and post-surgery ( npre = 169; n6weeks = 102; n3months = 106; n6months = 68). Improved and No-Effect patient groups were compared using the VAS minimally important difference (±15 points) as a cutoff. Reprioritization response shift detection was based on change in the relative importance of the SF-36 domains for group discrimination pre- and post-surgery. Results: The Improved group evidenced significant post-surgery differences from the No-Effect group on bodily pain, general health, physical functioning, social functioning, vitality, and the ODI. The relative importance analysis showed a differential effect with bodily pain ( p < 0.01) and physical functioning ( p < 0.05) becoming more important, and role physical ( p < 0.01) becoming less important post-surgery in distinguishing the Improved group as compared to the No-Effect group. The Improved patients also evidenced stronger associations between bodily pain and physical functioning, vitality and general health ( p < 0.05). The No-Effect group evidenced increased inter-correlations of bodily pain with social functioning, mental health, and general health ( p < 0.05). Conclusions: Patients who report clinically significant change in leg and back pain post-surgery using VAS pain scores are also distinguished by increased importance of bodily pain and physical functioning, and decreased importance of role physical. Bodily pain is primarily reflective of physical item response post-surgery among Improved patients, but reflects physical, social, and emotional item response among No-Effect patients. These changes in values may reflect a “moving goal post” in outcome assessment that complicates the interpretation of mean differences over time on standard spine outcome measures. [ABSTRACT FROM AUTHOR]

OBJECTIVES:A single-item indicator of disease activity over an extended period of time, the Manitoba Inflammatory Bowel Disease Index (MIBDI), is introduced and compared against several standard measures for assessing activity in patients with Crohn's disease (CD) and ulcerative colitis (UC).METHODS:Participants enrolled in the Manitoba IBD Cohort Study, a population-based longitudinal cohort study (N=353), were assessed semiannually by survey, clinical interview, and blood sample during a 2-year period. The MIBDI is based on patient self-reports of symptom persistence for the previous 6 months, using a 6-level response format.RESULTS:The MIBDI had good sensitivity compared with the Harvey–Bradshaw Index (HB; 0.88), Powell–Tuck Index (PT; 0.84), and Inflammatory Bowel Disease Questionnaire (IBDQ; 0.89), which was maintained at two subsequent annual measurements. Test–retest reliability was also strong (Spearman's r=0.81). Discriminant function analyses identified common discriminating variables of active disease for CD and UC that included HB, PT, and IBDQ subscales of bowel and systemic symptoms, prolonged symptom severity (e.g., abdominal and joint pain, tiredness, diarrhea), and recent persistent pain related to IBD. Unique discriminators included weight problems (CD) and blood in stool (UC).CONCLUSIONS:A single-item, patient-defined disease activity measure, the MIBDI, showed a high degree of sensitivity for classifying individuals with regard to disease status over time compared with the existing disease activity measures, and strong convergent validity with expected proxy measures of disease. These relationships remained consistent over time. Thus, the MIBDI shows promise as a valid, brief tool for measuring disease activity over an extended period.Am J Gastroenterol 2009; 104:1754–1763; doi:10.1038/ajg.2009.197; published online 19 May 2009 [ABSTRACT FROM AUTHOR]

To test the measurement equivalence (i.e., invariance) of osteoporosis-specific and general health-related quality-of-life (HRQOL) instruments in Canadian Aboriginal and non-Aboriginal women. A total of 258 Aboriginal and 181 non-Aboriginal women were recruited to the First Nations Bone Health Study from rural and urban sites in the province of Manitoba, Canada. The Medical Outcomes Study Short Form-36 (SF-36) and the mini-Osteoporosis Quality of Life Questionnaire (mini-OQLQ) were administered to study participants by trained interviewers. Confirmatory factor analysis techniques were adopted to test hypotheses about four forms of invariance for the two groups using likelihood ratio tests and other goodness-of-fit indices. For the mini-OQLQ instrument configural and metric invariance were satisfied, indicating that both groups have the same conceptualization of osteoporotic quality of life and the concepts have equivalent meaning. However, scalar and complete invariance were not satisfied for this instrument. The SF-36 exhibited complete invariance in the two groups. Measurement equivalence, which is required to conduct valid group comparisons, was not demonstrated for the disease-specific quality-of-life instrument but was supported for the general instrument. Ethnicity appears to influence responses about the effects of osteoporosis on quality of life. [ABSTRACT FROM AUTHOR]

Background: Several studies have compared the residential mobility of individuals with schizophrenia to mobility of individuals with other mental disorders or with no mental disorders. Little research has been undertaken to describe differences between single (i.e., infrequent) and multiple (i.e., frequent) movers with schizophrenia, and the association between frequency of mobility and health and health service use.Methods: The data source is population-based administrative records from the province of Manitoba, Canada. Hospital separations and physicians claims are linked to health registration files to identify a cohort with diagnosed schizophrenia and track changes in residential postal code over time. Single movers (N = 736), who had only one postal code change in a 2.5-year observation period, are compared to multiple movers (N = 252), who had two or more postal code changes. Differences in demographic, socioeconomic, and geographic characteristics, measures of health service use, and the prevalence of several chronic diseases were examined using chi(2) tests, logistic regression, and generalized linear regression.Results: Multiple movers were significantly more likely to be young, live in socioeconomically disadvantaged neighborhoods, and reside in the urban core. The prevalence of a co-occurring substance use disorder and arthritis was higher for multiple than single movers. Use of acute and ambulatory care for schizophrenia, other mental disorders, as well as physical disorders was generally higher for multiple than single movers.Conclusions: Frequency of mobility should be considered in the development of needs-based funding plans and service delivery interventions. Other opportunities to use record-linkage techniques to examine residential mobility are considered. [ABSTRACT FROM AUTHOR]

Background: Researchers often use conventional parametric procedures to test hypotheses of health-related quality of life (HRQL) mean equality across patient groups. However, these techniques are sensitive to the presence of skewed distributions and unequal group variances, which may characterize many HRQL measures.Purpose: To conduct a sensitivity analysis of conventional and robust approaches to test hypotheses of mean equality on HRQL measures for hematopoietic stem cell transplantation survivors and a healthy comparison group.Methods: The methods applied were the conventional parametric procedure of least-squares analysis of variance applied to the raw scores, the conventional parametric procedure applied to transformed data, and a robust approximate degrees of freedom parametric procedure utilizing trimmed means and Winsorized variances.Results: The choice of analysis method affected the conclusions about the null hypothesis of mean equality. More commonly observed, however, was a substantial difference in the value of the F-statistic and standard errors which was particularly evident in the measures with greater degrees of skewness and heterogeneity of variances.Conclusions: Robust statistical tests should be incorporated into sensitivity analyses when analyzing HRQL data. [ABSTRACT FROM AUTHOR]

The article refers to the affect of residential mobility on severe mental illness, schizophrenia. The frequent shuffling in location of residence causes problems like discontinuity in health care treatment. It is one of the most serious issues for health professionals. There are favorable chances of having negative affect on the illness of an individual with stress involved in shifting from one place to another. Three groups of inpatients were examined by the study including those who were diagnosed with bipolar affective disorder, unipolar depression and psychiatric condition.

Background: Recently, First Nations people were shown to be at high fracture risk compared with the general population. However, factors contributing to this risk have not been examined. This analysis focusses on geographic area of residence, income level, and diabetes mellitus as possible explanatory variables since they have been implicated in the fracture rates observed in other populations. Methods: A retrospective, population-based matched cohort study of fracture rates was performed using the Manitoba administrative health data (1987-1999). The First Nations cohort included all Registered First Nations adults (20 years or older) as indicated in either federal and/or provincial files (n=32,692). Controls (up to three for each First Nations subject) were matched by year of birth, sex and geographic area of residence. After exclusion of unmatched subjects, analysis was based upon 31,557 First Nations subjects and 79,720 controls. Results: Overall and site-specific fracture rates were significantly higher in the First Nations cohort. Income quintile, geographic area of residence, and diabetes were fracture determinants but the excess fracture risk of First Nations ethnicity persisted even after adjustment for these factors. Conclusion: First Nations people are at high risk for fracture but the causal factors contributing to this are unclear. Further research is needed to evaluate the importance of other potential explanatory variables. [ABSTRACT FROM AUTHOR]

Objective: In the province of Manitoba, Canada, given that latent tuberculosis infection (LTBI) treatment is provided at no cost to the patient, treatment completion rates should be optimal. The objective of this study was to estimate LTBI treatment completion using prescription drug administrative data and identify patient characteristics associated with completion., Methods: Prescription drug data (1999-2014) were used to identify individuals dispensed isoniazid (INH) or rifampin (RIF) monotherapy. Treatment completion was defined as being dispensed INH for ≥ 180 days (INH180) or ≥ 270 days (INH270) or RIF for ≥ 120 days (RIF120). Logistic regression models tested socio-demographic and comorbidity characteristics associated with treatment completion., Results: The study cohort comprised 4985 (90.4%) persons dispensed INH and 529 (9.6%) RIF. Overall treatment completion was 60.2% and improved from 43.1% in 1999-2003 to 67.3% in 2009-2014. INH180 showed the highest completion (63.8%) versus INH270 (40.4%) and RIF120 (27.0%). INH180 completion was higher among those aged 0-18 years (68.5%) compared with those aged 19+ (61.0%). Sex, geography, First Nations status, income quintile, and comorbidities were not associated with completion., Conclusions: Benchmark 80% treatment completion rates were not achieved in Manitoba. Factors associated with non-completion were older age, INH270, and RIF120. Access to shorter LTBI treatments, such as rifapentine/INH, may improve treatment completion.

Objective: This study investigated whether a move to public housing affects people's use of healthcare services., Method: Using administrative data from Manitoba, the number of hospitalizations, general practitioner (GP), specialist and emergency department (ED) visits, and prescription drugs dispensed in the years before and after the housing move-in date (2012/2013) were measured for a public housing and matched cohort. Generalized linear models with generalized estimating equations tested for differences between the cohorts in utilization trends. The data were modeled using Poisson (rate ratio, RR), negative binomial (incident rate ratio, IRR), and binomial (odds ratio, OR) distributions., Results: GP visits (IRR = 1.04, 95% CI 1.01-1.06) and prescriptions (IRR = 1.04, 95% CI 1.02-1.05) increased, while ED visits (RR = 0.90, 95% CI 0.82-1.00) and hospitalizations (OR = 0.95, 95% CI 0.93-0.96) decreased over time. The public housing cohort had a significantly higher rate of GP visits (IRR = 1.08, 95% CI 1.04-1.13), ED visits (RR = 1.18, 95% CI 1.01-1.37), and prescriptions (IRR = 1.09, 95% CI 1.05-1.13), and was more likely to be hospitalized (OR = 1.39, 95% CI 1.21-1.61) compared to the matched cohort. The rate of inpatient days significantly decreased for the public housing cohort, but did not change for the matched cohort., Conclusion: Healthcare use changed similarly over time (except inpatient days) for the two cohorts. Public housing provides a basic need to a population who has a high burden of disease and who may not be able to obtain and maintain housing in the private market.

Objective: Administrative health databases are increasingly used to conduct population-based health research and surveillance; this has resulted in a corresponding growth in studies about their quality. Our objective was to describe the characteristics of published Canadian studies about administrative health database quality., Methods: PubMed, Scopus, and Google Advanced were searched, along with websites of relevant organizations. English-language studies that evaluated the quality of one or more Canadian administrative health databases between 2004 and 2014 were selected for inclusion. Extracted information included data quality concepts and measures, year and type of publication, type of database, and geographic origin., Synthesis: More than 3,000 publications were identified fromthe search. Twelve reports and 144 peer-reviewed papers were included. The majority (53.5%) of peer-review publications used databases from Ontario and Alberta, while 67% of the non-peer-review publications used data from multiple provinces/ territories. Almost all peer-reviewed papers (97.2%) were validation studies. Hospital discharge abstracts and physician billing claims were the most frequently validated databases. Approximately half of the publications (53.0%) validated case definitions and 37.7% focused on a chronic physical health condition., Conclusion: Gaps in the Canadian administrative data quality literature include a limited number of studies evaluating data from the Maritimes and across multiple jurisdictions, newer data sources, validating methods for identifying individuals with mental illness, and assessing the completeness and serviceability of the data. Data quality studies can aid researchers to understand the strengths and limitations of the data.

Objectives: The purpose of this study is to estimate and compare smoking prevalence over two time periods in a Manitoba First Nation community., Methods: Data fromtwo independent Diabetes Screening Studies in Sandy Bay First Nation, collected in 2002/2003 (n = 482) and 2011/2012 (n = 596),were used. Crude prevalence of current and ever smoking as well as current smoke exposure was estimated. Change over time was tested using a χ² statistic., Results: The crude prevalence of current smoking was 74.0% (95% confidence interval [CI]: 70.1, 78.0) in 2002/2003 and 80.0% (95% CI: 76.8, 83.2) in 2011/2012. The crude prevalence of ever smoking was 83.0% in 2002/2003 and 91.4% in 2011/2012. The prevalence of both current smoking status and ever smoking were significantly higher in 2011/2012 compared to 2002/2003 (p = 0.020 and p < 0.001 respectively). Among participants who were not current smokers, 58.5% (95% CI: 49.6, 67.4) and 76.5% (95% CI: 68.9, 84.1) reported at least one person who smoked in the home in 2002/2003 and 2011/2012 respectively (p = 0.003). In 2011/2012, 96.5% (95% CI: 94.8, 98.2) of those who reported having any children under the age of 18 living in the home were either a current smoker and/or reported that someone else smoked in the home., Conclusion: Public health and policy initiatives are needed to address the increase in smoking prevalence in the study community.

Objective: Chronic diseases are the leading cause of death and disability worldwide and place considerable burden on the Canadian health care system. This research investigates the self-reported prevalence of major chronic diseases and risk factors in northern Canadian populations and compares their prevalence to southern Canadian populations over time., Methods: Canadian Community Health Survey (CCHS) cycle 1.1 (2000/01) and 3.1 (2005) data were used for the analyses. Respondents 20 years old or greater in the Yukon, Northwest Territories, and Nunavut comprised the northern Canadian sample. Respondents in the same age group in the rest of Canada comprised the southern Canadian sample. Unadjusted and adjusted weighted prevalence estimates and confidence intervals were calculated and tested for significance using z-tests., Results: Northern Canadian respondents had significantly lower crude prevalence of self-reported hypertension, arthritis/rheumatism, diabetes, heart disease and stroke than southern Canadian respondents, although these associations did not remain significant after adjusting for age and sex. Northern Canadian respondents had significantly lower adjusted prevalence of any chronic disease. However, northern Canadian respondents had significantly higher adjusted prevalence of obesity and smoking than southern Canadian respondents at both time periods. The prevalence of any chronic disease increased significantly from 2000/01-2005 for both northern and southern Canadian respondents., Discussion: The higher prevalence of key chronic disease risk factors in northern Canadian populations and the increasing prevalence for many chronic diseases in both southern and northern populations signal a need for continual monitoring of chronic diseases and the development of appropriate prevention and management strategies.