Public housing and healthcare use: an investigation using linked administrative data.

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.

Changes in healthcare use among individuals who move into public housing: a population-based investigation.

BACKGROUND: Residence in public housing, a subsidized and managed government program, may affect health and healthcare utilization. We compared healthcare use in the year before individuals moved into public housing with usage during their first year of tenancy. We also described trends in use. METHODS: We used linked population-based administrative data housed in the Population Research Data Repository at the Manitoba Centre for Health Policy. The cohort consisted of individuals who moved into public housing in 2009 and 2010. We counted the number of hospitalizations, general practitioner (GP) visits, specialist visits, emergency department visits, and prescriptions drugs dispensed in the twelve 30-day intervals (i.e., months) immediately preceding and following the public housing move-in date. Generalized linear models with generalized estimating equations tested for a period (pre/post-move-in) by month interaction. Odds ratios (ORs), incident rate ratios (IRRs), and means are reported along with 95% confidence intervals (95% CIs). RESULTS: The cohort included 1942 individuals; the majority were female (73.4%) who lived in low income areas and received government assistance (68.1%). On average, the cohort had more than four health conditions. Over the 24 30-day intervals, the percentage of the cohort that visited a GP, specialist, and an emergency department ranged between 37.0% and 43.0%, 10.0% and 14.0%, and 6.0% and 10.0%, respectively, while the percentage of the cohort hospitalized ranged from 1.0% to 5.0%. Generally, these percentages were highest in the few months before the move-in date and lowest in the few months after the move-in date. The period by month interaction was statistically significant for hospitalizations, GP visits, and prescription drug use. The average change in the odds, rate, or mean was smaller in the post-move-in period than in the pre-move-in period. CONCLUSIONS: Use of some healthcare services declined after people moved into public housing; however, the decrease was only observed in the first few months and utilization rebounded. Knowledge of healthcare trends before individuals move in are informative for ensuring the appropriate supports are available to new public housing residents. Further study is needed to determine if decreased healthcare utilization following a move is attributable to decreased access.

A systematic review of the quality of reporting of simulation studies about methods for the analysis of complex longitudinal patient-reported outcomes data.

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.

International Classification of Diseases (ICD)-coded obesity predicts risk of incident osteoporotic fracture.

International Classification of Diseases (ICD) codes have been used to ascertain individuals who are obese. There has been limited research about the predictive value of ICD-coded obesity for major chronic conditions at the population level. We tested the utility of ICD-coded obesity versus measured obesity for predicting incident major osteoporotic fracture (MOF), after adjusting for covariates (i.e., age and sex). In this historical cohort study (2001-2015), we selected 61,854 individuals aged 50 years and older from the Manitoba Bone Mineral Density Database, Canada. Body mass index (BMI) ≥30 kg/m2 was used to define measured obesity. Hospital and physician ICD codes were used to ascertain ICD-coded obesity and incident MOF. Average cohort age was 66.3 years and 90.3% were female. The sensitivity, specificity and positive predictive value for ICD-coded obesity using measured obesity as the reference were 0.11 (95% confidence interval [CI]: 0.10, 0.11), 0.99 (95% CI: 0.99, 0.99) and 0.79 (95% CI: 0.77, 0.81), respectively. ICD-coded obesity (adjusted hazard ratio [HR] 0.83; 95% CI: 0.70, 0.99) and measured obesity (adjusted HR 0.83; 95% CI: 0.78, 0.88) were associated with decreased MOF risk. Although the area under the receiver operating characteristic curve (AUROC) estimates for incident MOF were not significantly different for ICD-coded obesity versus measured obesity (0.648 for ICD-coded obesity versus 0.650 for measured obesity; P = 0.056 for AUROC difference), the category-free net reclassification index for ICD-coded obesity versus measured obesity was -0.08 (95% CI: -0.11, -0.06) for predicting incident MOF. ICD-coded obesity predicted incident MOF, though it had low sensitivity and reclassified MOF risk slightly less well than measured obesity.

Health and social predictors of applications to public housing: a population-based analysis.

BACKGROUND: Residents of public housing are often in poor health. However, it is unclear whether poor health precedes residency in public housing. We compared the health of people who applied to public housing to people who did not apply and had similar socioeconomic characteristics. METHODS: Population-based administrative databases from Manitoba, Canada, containing health, housing and income assistance information were used to identify a cohort of individuals who applied to public housing and a matched cohort from the general population. 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 women, young, urban residents, and received income assistance. A higher per cent of the public housing cohort had physician-diagnosed physical and mental health conditions compared to the matched cohort. Physical health, mental health and health service use were significantly associated with applying to public housing, after controlling for individual and area-level income. CONCLUSIONS: Applicants to public housing were in poorer health compared to people of the same income level who did not apply to public housing. These health issues may affect the long-term stability of their tenancy if appropriate services and supports are not provided. Additionally, preventing ill health, better management of mental health and additional supports may reduce the need for public housing, which, in turn, would alleviate the pressure on governments to provide this form of housing.

Comparison of the epidemiology of laboratory-confirmed influenza A and influenza B cases in Manitoba, Canada.

BACKGROUND: Despite the public health significance of annual influenza outbreaks, the literature comparing the epidemiology of influenza A and B infections is limited and dated and may not reflect recent trends. In Canada, the relative contribution of influenza A and B to the burden of morbidity is not well understood. We examined rates of laboratory-confirmed cases of influenza A and B (LCI-A and LCI-B) in the Canadian province of Manitoba between 1993 and 2008 and compared cases of the two types in terms of socio-demographic and clinical characteristics. METHODS: Laboratory-confirmed cases of influenza A and B in Manitoba between 1993 and 2008 were identified from the Cadham Provincial Laboratory (CPL) Database and linked to de-identified provincial administrative health records. Crude and age-adjusted incidence rates of LCI-A and LCI-B were calculated. Demographic characteristics, health status, health service use, and vaccination history were compared by influenza type. RESULTS: Over the study period, 1,404 of LCI-A and 445 cases of LCI-B were diagnosed, corresponding to an annual age-standardized rate of 7.2 (95% CI: 6.5-7.9) for LCI-A and 2.2 (CI: 1.5 - 3.0) per 100,000 person-years for LCI-B. Annual rates fluctuated widely but there was less variation in the LCI-B rates. For LCI-A, but not LCI-B, incidence was inversely related to household income. Older age, urban residence and past hospitalization were associated with increased detection of LCI-A whereas receipt of the influenza vaccine was associated with decreased LCI-A detection. Once socio-demographic variables were controlled, having a pre-existing chronic disease or immune suppression was not related to influenza type. CONCLUSION: Influenza A and B affected different segments of the population. Older age was associated with increased LCI-A detection, but not with pre-existing chronic diseases. This information may be useful to public health professionals in planning and evaluating new and existing seasonal influenza vaccines.

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

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.

Robust tests for the multivariate Behrens-Fisher problem.

Hotelling's T2 procedure is used to test the equality of means in two-group multivariate designs when covariances are homogeneous. A number of alternatives to T2, which are robust to covariance heterogeneity, have been proposed in the literature. However, all are sensitive to departures from multivariate normality. We demonstrate how to obtain multivariate tests that are robust to covariance heterogeneity and non-normality with estimators of location and scale based on trimming and Winsorizing. The performance of six alternatives to T2 was examined via Monte Carlo methods when characteristics of the research design, degree of covariance heterogeneity, and degree of non-normality were manipulated. We have recently developed a program written in the SAS/IML language that can be used to implement these robust multivariate tests. Recommendations are provided on the specific data-analytic conditions under which these tests should be adopted.