Browsing by Author "Lix, Lisa M"

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    Differential item functioning of the SF-12 in a population-based regional joint replacement registry
    (2019-07-02) Yadegari, Iraj; Bohm, Eric; Ayilara, Olawale F; Zhang, Lixia; Sawatzky, Richard; Sajobi, Tolulope T; Lix, Lisa M
    Abstract Background Joint replacement, an increasingly common procedure amongst older adults, can substantially improve health-related quality of life (HRQoL). However, differential item functioning (DIF) may affect the accurate interpretation of differences in HRQoL amongst patients with different demographic and health status characteristics but the same underlying (i.e., latent) level of the investigated construct. This study tested for DIF in pre-operative SF-12 physical health (PH) and mental health (MH) sub-scale items amongst patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods Data were from a population-based joint replacement registry from the Canadian province of Manitoba. TKA and THA patients who had surgery between 2009 and 2015 and completed a pre-operative assessment were included. DIF was tested using the multiple indicators multiple causes (MIMIC) method with sex, age group, body weight status, and presence of multiple comorbid conditions (i.e., multimorbidity) as covariates. Analyses were stratified by joint type. Results The study cohort included 8820 patients; 42.1% underwent THA, 57.3% were female, 32.7% were 70+ years, and 52.8% were obese. For each sub-scale, four of the six items exhibited DIF in both THA and TKA groups. Differences in the covariate effect estimates for DIF and No-DIF models on the MH latent variable were largest for age and body weight status for the THA group, and for sex and multimorbidity for the TKA group. All of the differences were small for PH. Multimorbidity had the strongest association with PH and age and sex had the strongest association with MH in the DIF models. Conclusions Demographic and health status characteristics influenced SF-12 PH and MH item responses in joint replacement populations, although the size of the effects were not large for PH. We recommend testing and adjusting for DIF effects to ensure comparability of HRQoL measures in joint replacement populations.
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    Estimating annual prevalence of depression and anxiety disorder in multiple sclerosis using administrative data
    (2017-11-25) Marrie, Ruth A; Walld, Randy; Bolton, James M; Sareen, Jitender; Walker, John R; Patten, Scott B; Singer, Alexander; Lix, Lisa M; Hitchon, Carol A; El-Gabalawy, Renée; Katz, Alan; Fisk, John D; Bernstein, Charles N
    Abstract Objective Researchers have developed case definitions to estimate incidence and lifetime prevalence of depression and anxiety disorders in multiple sclerosis (MS) using administrative data. For policymakers however, the prevalence of a disease requiring ongoing treatment during a given period such as annual period prevalence may be more relevant for decision-making. We tested a case definition for annual period prevalence of depression and anxiety disorders in MS using administrative data. Results Using population-based administrative (health claims) data from Manitoba, Canada we identified 1922 persons with incident MS from 1989 to 2012, and 11,392 age, sex and geographically-matched controls from the general population. As compared to controls, MS patients had an elevated annual prevalence ratio of depression (1.77; 95% confidence interval [CI] 1.64, 1.91), and anxiety disorders (1.46; 95% CI 1.35, 1.58). The annual prevalence of depression in our matched cohort was similar to that observed in the 2012 Canadian Community Health Survey, although the annual prevalence of anxiety was slightly higher. Administrative data can be used to estimate the annual period prevalence of psychiatric disorders in MS.
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    How well does the minimum data set measure healthcare use? a validation study
    (2018-04-11) Doupe, Malcolm B; Poss, Jeff; Norton, Peter G; Garland, Allan; Dik, Natalia; Zinnick, Shauna; Lix, Lisa M
    Abstract Background To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. Methods This retrospective observational cohort study was conducted on all NH residents (N = 8832) from Winnipeg, Manitoba, Canada, between April 1, 2011 and March 31, 2013. Six study measures exist. NH user status (newly admitted NH residents, those who transferred from one NH to another, and those who died) was measured using both MDS and administrative data. Rates of in-patient hospitalizations, emergency department (ED) visits without subsequent hospitalization, and physician examinations were also measured in each data source. We calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV), and overall agreement (kappa, κ) of each measure as captured by MDS using administrative data as the reference source. Also for each measure, logistic regression tested if the level of disagreement between data systems was associated with resident age and sex plus NH owner-operator status. Results MDS accurately identified newly admitted residents (κ = 0.97), those who transferred between NHs (κ = 0.90), and those who died (κ = 0.95). Measures of healthcare use were captured less accurately by MDS, with high levels of both under-reporting and false positives (e.g., for in-patient hospitalizations sensitivity = 0.58, PPV = 0.45), and moderate overall agreement levels (e.g., κ = 0.39 for ED visits). Disagreement was sometimes greater for younger males, and for residents living in for-profit NHs. Conclusions MDS can be used as a stand-alone tool to accurately capture basic measures of NH use (admission, transfer, and death), and by proxy NH length of stay. As compared to administrative data, MDS does not accurately capture NH resident healthcare use. Research investigating these and other healthcare transitions by NH residents requires a combination of the MDS and administrative data systems.
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    Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry
    (2019-06-20) Ayilara, Olawale F; Zhang, Lisa; Sajobi, Tolulope T; Sawatzky, Richard; Bohm, Eric; Lix, Lisa M
    Abstract Background Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient’s perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making. We compared the precision and bias of several missing data methods when estimating longitudinal change in PRO scores. Methods This research conducted analyses of clinical registry data and simulated data. Registry data were from a population-based regional joint replacement registry for Manitoba, Canada; the study cohort consisted of 5631 patients having total knee arthroplasty between 2009 and 2015. PROs were measured using the 12-item Short Form Survey, version 2 (SF-12v2) at pre- and post-operative occasions. The simulation cohort was a subset of 3000 patients from the study cohort with complete PRO information at both pre- and post-operative occasions. Linear mixed-effects models based on complete case analysis (CCA), maximum likelihood (ML) and multiple imputation (MI) without and with an auxiliary variable (MI-Aux) were used to estimate longitudinal change in PRO scores. In the simulated data, bias, root mean squared error (RMSE), and 95% confidence interval (CI) coverage and width were estimated under varying amounts and types of missing data. Results Three thousand two hundred thirty (57.4%) patients in the study cohort had complete data on the SF-12v2 at both occasions. In this cohort, mixed-effects models based on CCA resulted in substantially wider 95% CIs than models based on ML and MI methods. The latter two methods produced similar estimates and 95% CI widths. In the simulation cohort, when 50% of the data were missing, the MI-Aux method, in which a single hypothetical auxiliary variable was strongly correlated (i.e., 0.8) with the outcome, reduced the 95% CI width by up to 14% and bias and RMSE by up to 50 and 45%, respectively, when compared with the MI method. Conclusions Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging.
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    Latent variable mixture models to test for differential item functioning: a population-based analysis
    (2017-05-15) Wu, Xiuyun; Sawatzky, Richard; Hopman, Wilma; Mayo, Nancy; Sajobi, Tolulope T; Liu, Juxin; Prior, Jerilynn; Papaioannou, Alexandra; Josse, Robert G; Towheed, Tanveer; Davison, K. S; Lix, Lisa M
    Abstract Background Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs). Methods Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables. Results The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership. Conclusions This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups.
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    Testing population-based performance measures identifies gaps in juvenile idiopathic arthritis (JIA) care
    (2019-08-14) Barber, Claire E; Lix, Lisa M; Lacaille, Diane; Marshall, Deborah A; Kroeker, Kristine; Benseler, Susanne; Twilt, Marinka; Schmeling, Heinrike; Barnabe, Cheryl; Hazlewood, Glen S; Bykerk, Vivian; Homik, Joanne; Thorne, J. C; Burt, Jennifer; Mosher, Dianne; Katz, Steven; Shiff, Natalie J
    Abstract Background The study evaluates Performance Measures (PMs) for Juvenile Idiopathic Arthritis (JIA): The percentage of patients with new onset JIA with at least one visit to a pediatric rheumatologist in the first year of diagnosis (PM1); and the percentage of patients with JIA under rheumatology care seen in follow-up at least once per year (PM2). Methods Validated JIA case ascertainment algorithms were used to identify cases from provincial health administrative databases in Manitoba, Canada in patients < 16 years between 01/04/2005 and 31/03/2015. PM1: Using a 3-year washout period, the percentage of incident JIA patients with ≥1 visit to a pediatric rheumatologist in the first year was calculated. For each fiscal year, the proportion of patients expected to be seen in follow-up who had a visit were calculated (PM2). The proportion of patients with gaps in care of > 12 and > 14 months between consecutive visits were also calculated. Results One hundred ninety-four incident JIA cases were diagnosed between 01/04/2008 and 03/31/2015. The median age at diagnosis was 9.1 years and 71% were female. PM1: Across the years, 51–81% of JIA cases saw a pediatric rheumatologist within 1 year. PM2: Between 58 and 78% of patients were seen in yearly follow-up. Gaps > 12, and > 14, months were observed once during follow-up in 52, and 34%, of cases, and ≥ twice in 11, and 5%, respectively. Conclusions Suboptimal access to pediatric rheumatologist care was observed which could lead to diagnostic and treatment delays and lack of consistent follow-up, potentially negatively impacting patient outcomes.