Multivariate Contrasts For Repeated Measures Designs Under Assumption Violations

Conventional and approximate degrees of freedom procedures for testing multivariate interaction contrasts in groups by trials repeated measures designs were compared under assumption violation conditions. Procedures were based on either least-squares or robust estimators. Power generally favored test procedures based on robust estimators for non-normal distributions, but was influenced by the degree of departure from non-normality, definition of power, and magnitude of the multivariate effect size

Public housing and healthcare use: Determining whether public housing functions as an intervention using linked population-based administrative data

Introduction Public housing is a form of subsidized housing that is owned and/or managed by government. Previous research suggests that public housing has a positive impact on personal finances and education outcomes, but less is known about if/how it impacts health and healthcare use. Objectives and Approach Using linked administrative health and social data, we tested for changes in healthcare use among a cohort who moved into public housing in 2012 and 2013 in Manitoba, Canada, and compared utilization to a matched general population cohort who did not move into public housing. Generalized linear models with generalized estimating equations tested for differences in numbers of healthcare contacts in the years before and after the move-in date, adjusted for economic, residential mobility, and health characteristics. The data were modeled using a Poisson (rate ratio, RR), negative binomial (incident rate ratio, IRR), or a binomial (odds ratio, OR) distribution. Results There were 2619 residents in the public housing cohort; 99.7% were matched to the general population. The cohort by time interaction was statistically significant for inpatient days (p Conclusion/Implications Public housing residents were more likely to use healthcare services than the matched population, but changes in use were similar in the two cohorts. There is little evidence that public housing impacts healthcare use, but it serves an important function of meeting basic needs for a vulnerable population group

Predicting who applies to Public Housing using Linked Administrative Data

ABSTRACT Objective Public housing residents, who live in low income government rental housing, are often in poorer health than the rest of the population. However, few studies have been able to untangle the relationships between health and public housing residency, and to assess whether health contributes to the decision to apply. We used linked population-based administrative data from one Canadian province to compare the health and health service use of people who applied to public housing to that of people who did not apply. Approach Administrative data housed in the Manitoba Centre for Health Policy’s Population Health Research Data Repository were used to identify a cohort of individuals who applied to public housing in 2005 and 2006. They were matched one-to-one to a cohort from the general population using socio-demographic variables. A population registry provided demographic and geographic characteristics. Economic measures included receipt of income assistance and an area-level measure from the Statistics Canada Census. Measures of health and health service use were derived from hospital, physician, emergency department, and prescription drug databases. Conditional logistic regression was used to test the association between a public housing application and health status and health service use, after controlling for income. Results There were 10,324 individuals in each of the public housing applicant and matched cohorts; the majority were female (72.4%), young (62% less than 40 years), urban residents (61.2%), and received income assistance (52.8%). A higher percent of the public housing applicant cohort had physician-diagnosed physical and mental health conditions and used more health services compared to the matched cohort. Having a physician-diagnosed respiratory illness (odds ratio [OR] = 1.14, 95% confidence interval [CI] 1.05,1.25), diabetes (OR = 1.24, 95% CI 1.09,1.40), schizophrenia (OR = 1.58, 95% CI 1.30,1.92), affective disorders (OR = 1.37, 95% CI 1.27,1.48), and substance abuse disorders (OR = 1.46, 95% CI 1.25,1.71) were associated with an increased likelihood of applying for public housing, while being diagnosed with cancer (OR = 0.76, 95% CI 0.61,0.96) was associated with a decreased likelihood of applying, after controlling for income differences. High health service users were also more likely to apply for public housing, after controlling for income differences. Conclusion Individuals who move into public housing are in poor health before they apply. Health and social service supports that are co-located with public housing facilities may help to ensure that residents have successful tenancies

Residential mobility of individuals with diagnosed schizophrenia: a comparison of single and multiple movers

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 v2 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.<br/

Mental health and the city: intra-urban mobility among individuals with schizophrenia

Intra-urban residential mobility of a cohort with schizophrenia was compared to a matched cohort with no mental illness using population-based administrative data. The percentage of individuals with one or more changes in postal code in the three-year mobility study period was examined, along with measures of the movement between different intra-urban areas. The schizophrenia cohort was more likely to move than the matched cohort; however, this depends on their age, income level, and area of residence at baseline. Age, gender, marital status, income quintile, and use of physicians and hospitalizations were associated with mobility. Individuals in the schizophrenia cohort were significantly more likely to move from the suburb to the inner city, and significantly less likely to move from the inner city to the suburb than those with no mental illness. Implications of the findings and directions for future research are discussed, with particular attention paid to the utility of administrative data for further mental health research

A Systematic Review of the Quality of Reporting of Simulation Studies about Methods for the Analysis of Complex Longitudinal Patient-Reported Outcomes Data

International audiencePURPOSE: 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

Additional file 3: of Changes in healthcare use among individuals who move into public housing: a population-based investigation

Table S3. Model Estimates and 95% Confidence Intervals (CIs). (DOCX 23 kb

Additional file 1: of Changes in healthcare use among individuals who move into public housing: a population-based investigation

Table S1. Unadjusted Point Estimates and 95% Confidence Intervals (CI) for the Healthcare Utilization Measures over Time. (DOCX 19 kb

Additional file 2: of Changes in healthcare use among individuals who move into public housing: a population-based investigation

Table S2. Chi-Square Test Statistics and P-values for Main and Interaction Effects. (DOCX 17 kb