Aynslie Hinds

Residential mobility and severe mental illness: a population-based analysis.

This research uses population-based administrative data linking health service use to longitudinal postal code information to describe the residential mobility of individuals with a severe mental illness (SMI), schizophrenia. This group is compared to two cohorts, one with no mental illness, and one with a severe physical illness of inflammatory bowel disease. The percentage of individuals with one or more changes in postal code in a 3-year period is examined, along with measures of rural-to-rural regional migration and rural-to-urban migration. Demographic, socioeconomic, and health service use characteristics are examined as determinants of mobility. The odds of moving were twice as high for the SMI cohort as for either of the other two cohorts. There were no statistically significant differences in rural-to-rural or rural-to-urban migration among the cohorts. Marital status, income quintile, and use of physicians are consistent determinants of mobility. The results are discussed from the perspectives of health services planning and access to housing.

Robust tests for the multivariate Behrens-Fisher problem

Hotellings 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.

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

BackgroundDespite 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.MethodsLaboratory-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.ResultsOver 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.ConclusionInfluenza 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.

Quality of administrative health databases in Canada: A scoping review.

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.RésuméOBJECTIF: On utilise de plus en plus les bases de données administratives sur la santé dans la recherche et la surveillance populationnelles en santé; le nombre d’études sur la qualité de ces bases de données croît lui aussi. Nous avons cherché à décrire les caractéristiques des études canadiennes publiées portant sur la qualité des bases de données administratives sur la santé.MÉTHODE: Nous avons interrogé PubMed, Scopus et Google Advanced, ainsi que les sites Web d’organismes pertinents. Nous avons inclus les études en anglais évaluant la qualité d’une ou de plusieurs bases de données administratives canadiennes sur la santé entre 2004 et 2014. Nous en avons extrait: les concepts et les indicateurs de la qualité des données; l’année et le type de publication; le type de base de données; et l’origine géographique.SYNTHÈSE: La recherche a permis de répertorier plus de 3 000 publications. Douze rapports et 144 communications évaluées par des pairs ont été inclus. La majorité (53,5 %) des publications à comité de lecture utilisaient des bases de données de l’Ontario et de l’Alberta, tandis que 67 % des publications sans comité de lecture utilisaient des données de plusieurs provinces ou territoires. Presque toutes les communications évaluées par des pairs (97,2 %) étaient des études de validation. Les registres des sorties des hôpitaux et les demandes de paiement des médecins étaient les bases de données les plus fréquemment validées. Environ la moitié des publications (53,0 %) validaient des définitions de cas et 37,7 % portaient sur un problème de santé physique chronique.CONCLUSION: La documentation sur la qualité des données administratives canadiennes comporte des lacunes, car un nombre limité d’études évaluent les données des provinces maritimes ou de plusieurs provinces ou territoires; évaluent les nouvelles sources de données; valident les méthodes d’identification des personnes atteintes de maladies mentales; et évaluent l’exhaustivité et la fonctionnalité des données. Les études sur la qualité des données peuvent aider les chercheurs à comprendre les forces et les contraintes des données.

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.

HEALTH STATUS AND SERVICE USE IN HOMELESS INDIVIDUALS WITH MENTAL ILLNESS: CONSISTENCY BETWEEN SELF-REPORT AND ADMINISTRATIVE HEALTH RECORDS IN THE AT HOME/CHEZ SOI MULTI-SITE TRIAL

Objective Homeless individuals with poor health frequently use healthcare services. However, studies using self-reported data may be subject to biases. We examined health status, healthcare and drug use among mentally ill homeless individuals, comparing self-report and administrative data claims to estimate the degree of agreement between the two sources. Methods Baseline survey data from 100 participants of the Winnipeg site of the Mental Health Commission of Canadas At Home/Chez Soi research project were linked to deidentified administrative health records stored in the Repository at the Manitoba Centre for Health Policy. Demographic characteristics, homelessness histories and health service use were analysed, as well as disease status for asthma, hypertension, arthritis and diabetes (using previously validated definitions). Participants were similarly classified using their survey responses. The degree of agreement between the two data sources was evaluated using cross-tabulations and the κ statistic. Results There was 100% linkage of surveyed homeless people with the Repository data. In 1 year, 97% of participants had at least one ambulatory physician visit, with an age- and sex-adjusted rate of 14.82 per person-year (Manitoba rate=4.99 per person-year). 34% had an inpatient hospitalisation (adjusted hospital separation rate=491 per thousand person-years vs the Manitoba rate of 137 per thousand person-years). 95% filled at least one prescription, with 65% of drugs targeting the nervous system (majority were psycholeptics). The degree of agreement between the data sources ranged from a κ of 0.27 for arthritis to 0.57 for hypertension. Individuals were more likely to be classified as having one of the four conditions based on the administrative data than on the survey data. Conclusions Compared with the general population, participants had high health service use, and high prescription drug use. There was poor to moderate agreement between the two data sources. Researchers studying homeless persons with mental illness should consider using multiple data sources to estimate disease prevalence and health service use.

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.

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

PurposeThis 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.MethodsScopus, 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.SynthesisA 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.ConclusionsWhile 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.

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

BackgroundResidence 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.MethodsWe 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).ResultsThe 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.ConclusionsUse 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.