Background Mitochondrial disease has been linked to mental health disorder in clinical cohorts and post-mortem studies. However, a lack of population-level studies examining the relationship between mitochondrial disease and mental health has resulted in an evidence gap and creates a challenge for identifying and addressing care needs for the mitochondrial disease population. Using multiple linked population health databases in a single-payer health system that covers the full population, this study aimed to investigate the prevalence of mood disorders and other mental health conditions in patients with mitochondrial disease and to examine the joint impact of mitochondrial disease and mental health conditions on healthcare use and health system costs. To contextualize these findings, a clinical comparator cohort of multiple sclerosis (MS) patients was analyzed. Results Overall, co-prevalent mental health conditions are common in the mitochondrial popualtion. Double the proportion of patients in the mitochondrial disease cohort had a co-prevalent mental health illness as compared to the MS population (18% vs 9%). Healthcare utilization was highest among patients with co-prevalent mitochondrial disease and mental illness, with 49% hospitalized within 1 year prior to cohort entry (compared to 12% of MS patients with no mental health condition). Costs were likewise highest among mitochondrial disease patients with mental health conditions. Conclusions This study presents the first comprehensive, population-wide cohort study of mitochondrial disease and co-prevalent mental health conditions. Our findings demonstrate a high burden of mental health conditions among mitochondrial disease patients, with high associated health care needs. We also find that patients with concurrent mental illness and mitochondrial disease represent a high-burden, high-cost population in a single-payer health insurance setting.

IntroductionTo develop and validate the Premature Mortality Population Risk Tool (PreMPoRT), a population-based risk algorithm that predicts the 5-year incidence of premature mortality among the Canadian adult population.MethodsRetrospective cohort analysis used six cycles of the Canadian Community Health Survey linked to the Canadian Vital Statistics Database (2000–2017). The cohort comprised 500 870 adults (18–74 years). Predictors included sociodemographic factors, self-perceived measures, health behaviours and chronic conditions. Three models (minimal, primary and full) were developed. PreMPoRT was internally validated using a split set approach and externally validated across three hold-out cycles. Performance was assessed based on predictive accuracy, discrimination and calibration.ResultsThe cohort included 267 460 females and 233 410 males. Premature deaths occurred in 1.40% of females and 2.05% of males. Primary models had 12 predictors (females) and 13 predictors (males). Shared predictors included age, income quintile, education, self-perceived health, smoking, emphysema/chronic obstructive pulmonary disease, heart disease, diabetes, cancer and stroke. Male-specific predictors were marital status, Alzheimer’s disease and arthritis while female-specific predictors were body mass index and physical activity. External validation cohort differed slightly in demographics. Female model performance: split set (c-statistic: 0.852), external (c-statistic: 0.856). Male model performance: split set and external (c-statistic: 0.846). Calibration showed slight overprediction for high-risk individuals and good calibration in key subgroups.ConclusionsPreMPoRT achieved the strongest discrimination and calibration among existing prediction models for premature mortality. The model produces reliable estimates of future incidence of premature mortality and may be used to identify subgroups who may benefit from public health interventions.

Introduction Avoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data. Methods and analysis The derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18–74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Abstract Database (1999/2000–2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000–2006 compared to 2007–2012. We will assess measures of overall predictive performance (Nagelkerke R 2 ), calibration (calibration plots), and discrimination (Harrell’s concordance statistic). Development of the model will be informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement. Ethics and dissemination This study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.

Background: Climate change has significant consequences on human health. Cities are especially vulnerable, where air pollution is a major environmental health risk. Premature mortality (i.e., deaths before age 75) is a robust population health outcome amenable to targeted policy and programmatic interventions. We used the Premature Mortality Population Risk Tool augmented with environmental data (PreMPoRT-ENV) to predict the 5-year incidence of premature deaths under air pollution reduction policies. Methods: PreMPoRT-ENV is a sex-specific Weibull accelerated failure time survival model that uses the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Death Database and environmental data. We applied PreMPoRT-ENV to the 2016–2017 CCHS cycles and simulated Canadian Ambient Air Quality Standards targets to predict their impact on premature mortality across Canadian census metropolitan areas. We simulated capping annual mean particulate matter 2.5 microns or less in diameter (PM2.5) and nitrogen dioxide (NO2), as well as reducing air pollutants by 10% and 25% plus capping. Results: The weighted sample included 9,240,000 females and 9,260,000 males. Capping PM2.5 to 8.8 μg/m3 and NO2 to 12.0 ppb resulted in 12 per 100,000 fewer predicted premature deaths than observed exposures over 5 years (1,110 fewer absolute premature deaths). Reducing air pollutants by 10% and 25% plus capping resulted in even fewer predicted premature deaths. Conclusion: Our study highlights how to use a model that predicts premature mortality to provide estimates of the health impacts of environmental vulnerabilities. Results suggest that more aggressive targets may be needed to further realize population health benefits.

Objective To compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality.Study Design and Setting Sex-specific Weibull accelerated failure time survival models were run on four separate datasets using complete case, mode, single and multiple imputation to impute missing values. Six performance measures were compared to access predictive accuracy (Nagelkerke R2, integrated brier score), discrimination (Harrell's c-index, discrimination slope) and calibration (calibration in the large, calibration slope).Results The highest proportion of missingness for a single variable was 10.86% for the female model and 8.24% for the male model. Comparing the performance measures for complete case, mode, single and multiple imputation: the Nagelkerke R2 values for the female model was 0.1084, 0.1116, 0.1120 and 0.111-0.1120 with the male model exhibited similar variation of 0.1050, 0.1078, 0.1078 and 0.1078-0.1081. Harrell's c-index also demonstrated small variation with values of 0.8666, 0.8719, 0.8719 and 0.8711-0.8719 for the female model and 0.8549, 0.8548, 0.8550 and 0.8550-0.8553 for the male model.Conclusion In the scenarios examined in this study, mode imputation performed well when using a population health survey compared to single and multiple imputation when predictive performance measures is the main model goal. To generate unbiased hazard ratios, multiple imputation methods were superior. This study shows the need to consider the best imputation approach for a predictive model development given the conditions of missing data and the goals of the analysis.

ABSTRACT Background Work-related traumatic brain injury (wr-TBI) is on the rise. The pre-injury period, a significant consideration for preventive initiatives, is largely unexplored. Objectives To identify Person–Environment–Occupation (PEO) variables associated with wr-TBI to inform sex-specific primary prevention. Methods Retrospective chart review data were analyses. Two-tailed t-test and chi-squared tests were used to study sex differences. Multivariate logistic regression models of wr-TBI were fit with a priori defined PEO variables. Results The sample comprised 330 consecutive workers with wr-TBI (40.8 ± 11.1 years old, 71% male). Sex differences were observed across PEO variables. In multivariable logistic regression analyses the odds of sustaining a wr-TBI from a fall increased with the presence of a mood disorder and participation in non-labourer occupations (odds ratio (OR) 2.89 (95% CI 1.06–7.89) and OR 2.89 (95% CI 1.06–7.89), respectively) and decreased being a male (OR 0.31 (95% CI 0.17–0.54)). The odds of sustaining a wr-TBI from being striken by an object was greater in workers with prior head injury (OR 2.8 (95% CI 1.24–6.45)). None of the variables studied were associated with wr-TBI sustained from being striken against an object. Conclusions Workers’ health status pre-injury is associated with external causes of wr-TBI. Sex differences across PEO categories warrant further study.

IntroductionWe investigated the association between sleep disorders (SDs) and incident dementia in adults with traumatic brain injury (TBI). MethodsAdults with a TBI between 2003 and 2013 were followed until incident dementia. Sleep disorders at TBI were predictors in Cox regression models, controlling for other dementia risks. ResultsOver 52 months, 4.6% of the 712,708 adults (59% male, median age 44, <1% with SD) developed dementia. An SD was associated with a 26% and a 23% of increased risk of dementia in male and female participants (hazard ratio [HR] 1.26, 95% confidence interval [CI] 1.11-1.42 and HR 1.23, 95% CI 1.09-1.40, respectively). In male participants, SD was associated with a 93% increased risk of early-onset dementia (HR 1.93, 95% CI 1.29-2.87); this did not hold in female participants (HR 1.38, 95% CI 0.78-2.44). DiscussionIn a province-wide cohort, SDs at TBI were independently associated with incident dementia. Clinical trials testing sex-specific SD care after TBI for dementia prevention are timely. HighlightsTBI and sleep disorders are linked to each other, and to dementia.It is unclear if sleep disorders pose a sex-specific dementia risk in brain injury.In this study, presence of a sleep disorder increased dementia risk in both sexes.The risk differed by type of sleep disorder, which differed between the sexes.Sleep disorder awareness and care in persons with brain injury is vital for dementia prevention.

Abstract Introduction We aimed to examine the association between sleep disorders and dementia risk in a population-based cohort of adult male and female patients with traumatic brain injury (TBI). Methods We studied a province-wide retrospective cohort of all adult patients (≥ 18 years) free of dementia at the admission to the emergency department or acute care hospital with diagnoses of TBI between May 2003 and April 2013. All patients were followed through until May 2016. The primary exposure was a sleep disorder, and the primary outcome was dementia, both defined by the International Classification of Diseases, tenth revision diagnosis. Associations of sleep disorders with dementia were analyzed in multivariate Cox Proportional Hazard modeling. Results In total, 712,708 patients with TBI of all severities were included in this study. Their median age was 44 years, 59% were males. Over a median follow-up of 52 months (interquartile range, 19–86 months), 32,834 (4.6%) developed dementia. Controlling for age, sex, income level, injury severity, and known comorbidity risks, diagnosed sleep disorder was a significant predictor of incident dementia: hazard ratio (HR), 1.250 [95% CI, 1.146–1.363]. When results were stratified by sex, the association of sleep disorder with dementia remained significant in male: HR 1.255 [95% CI, 1.112–1.415] and in female patients: HR 1.234 [95% CI, 1.088–1.400]. Sensitivity analyses on Alzheimer’s disease case definition and using Fine and Gray competing risk models confirmed the association between sleep disorder and dementia in both sexes. Conclusion In both sexes, sleep disorders were independently associated with dementia onset (adjusted HRs>1.2). Thus, screening for sleep disorders should be part of regular care for TBI patients, as with the steady increase of TBI survivorship and life expectancy, undiagnosed sleep disorders can initiate a new cascade of cognitive deficits independent from TBI. Support (if any) This work was supported by the postdoctoral research grant from the Alzheimer’s Association (AARF-16-442937) to T.Mollayeva. The authors were also supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R21HD089106 and the Canadian Institutes for Health Research Grant–Institute for Gender and Health (#CGW-126580).

An increasing number of patients are able to survive traumatic brain injuries (TBIs) with advanced resuscitation. However, the role of their pre-injury health status in mortality in the following years is not known. Here, we followed 77,088 consecutive patients (59% male) who survived the TBI event in Ontario, Canada for more than a decade, and examined the relationships between their pre-injury health status and mortality rates in excess to the expected mortality calculated using sexand age-specific life tables. There were 5792 deaths over the studied period, 3163 (6.95%) deaths in male and 2629 (8.33%) in female patients. The average excess mortality rate over the follow-up period of 14 years was 1.81 (95% confidence interval = 1.76-1.86). Analyses of follow-up time windows showed different patterns for the average excess rate of mortality following TBI, with the greatest rates observed in year one after injury. Among identified pre-injury comorbidity factors, 33 were associated with excess mortality rates. These rates were comparable between sexes. Additional analyses in the validation dataset confirmed that these findings were unlikely a result of TBI misclassification or unmeasured confounding. Thus, detection and subsequent management of pre-injury health status should be an integral component of any strategy to reduce excess mortality in TBI patients. The complexity of pre-injury comorbidity calls for integration of multidisciplinary health services to meet TBI patients' needs and prevent adverse outcomes.

INTRODUCTION:Evidence of the effect of comorbid spinal cord injury (SCI) on cognitive outcomes in persons undergoing rehabilitation following newly diagnosed traumatic brain injury (TBI) is limited. We conducted a population-based study to investigate this effect.OBJECTIVE:To compare cognitive outcomes in patients with TBI with and without a comorbid SCI.SETTING/PARTICIPANTS:Adult patients diagnosed with TBI were identified and followed for 1 year through provincial health administrative data; those who entered inpatient rehabilitation were studied.DESIGN:A retrospective matched cohort study using the National Rehabilitation Reporting System data of all acute care and freestanding rehabilitation hospitals in Ontario, Canada.MAIN MEASURES:The exposure was a comorbid SCI in patients with diagnosed TBI. Exposed patients were matched to unexposed (TBI-only) on sex, age, injury severity, and income, in a ratio of one to two. Gain differences in the cognitive subscale of the Functional Independence Measure were compared between exposed and unexposed patients using multivariable mixed linear model, controlling for comorbidity propensity score, gains in motor function, and rehabilitation care indicators.RESULTS:Over the first year post injury, 12 750 (0.84%) of all TBI patients entered inpatient rehabilitation, of whom 1359 (10.66%) had a comorbid SCI. A total of 1195 exposed patients (65.4% male, mean age 50.9 ± 20.6 for male and 61.8 ± 21.8 for female patients) were matched to 2390 unexposed patients. Controlling for confounding, exposed patients had lower cognitive gain (beta -0.43; 95% CI -0.72, -0.15), for both male (beta -0.39; 95% CI -0.75, -0.03) and female (beta -0.51; 95% CI -0.97, -0.05) patients. The adverse effects of comorbid SCI were driven largely by lower gains in problem solving and comprehension.CONCLUSIONS:Adult patients with TBI and comorbid SCI showed a lower cognitive domain response to inpatient rehabilitation than patients with TBI alone. Identifying patients at risk for worse cognitive outcomes may facilitate the development of targeted strategies that improve cognitive outcomes.

Objective To understand how pre-injury health status present five-years preceding traumatic brain injury (TBI) affects direct medical cost two years post-injury. Methods Patients age ≥19 years in the emergency department (ED) or acute care for a TBI between April 1, 2007 and March 31, 2014 in Ontario, Canada (N = 55,669) were identified from population-based health administrative data. Forty-three factors of pre-injury health status (i.e., comorbidities and personal, social, and environmental factors) that were internally validated for the TBI population were assessed in this study. The outcome of interest was direct medical cost within two years of discharge. Sex-specific multivariable linear regressions were conducted to understand the associations between direct medical cost within two years of discharge and pre-injury health status. Results Patients who received care in the ED (81.9% of total sample) incurred a median cost of $2,492/male patient (average $12,342/patient) and $3,508/female patient (average $65,285/patient) within two years of injury; 37 pre-injury factors were significantly associated with increased direct medical costs. Patients who first received care for their TBI in acute care (18.1%) incurred a median cost of $25,081/male patient (average $63,060/patient) and $30,277/female patient (average $65,285/patient) within two years of injury; 21 factors were significantly associated with increased direct medical costs. Among more prevalent factors, those associated with increased medical cost by at least 50% included mental health disorders, substance abuse, disorders or medical conditions frequently observed among the elderly, cardiovascular disorders, stroke and emergencies involving the brain, metabolic disorders and abdominal symptoms, conditions and symptoms of abdomen and pelvis, genitourinary disorders and disorders of prostate, and pulmonary abdominal and other emergencies. Conclusions Direct medical costs two years post-TBI differed significantly between patients with and without adverse pre-existing health status. Interdisciplinary teams to promote early identification of pre-existing health conditions and appropriate management and integration of these conditions in TBI care across the continuum of healthcare may be opportunities to reduce direct medical costs post-injury.

AbstractIntroductionDespite evidence that central nervous system (CNS) trauma, including traumatic brain injury and spinal cord injury, can cause sustained neurocognitive impairment, it remains unclear whether trauma‐related variables are associated with incident dementia independently of other known risk factors.MethodsAll adults without dementia entering the health‐care system with diagnoses of CNS trauma were examined for occurrence of dementia. All trauma‐related variables were examined as predictors in sex‐specific Cox regression models, controlling for other known risk factors.ResultsOver a median follow‐up of 52 months, 32,834 of 712,708 patients (4.6%) developed dementia. Traumatic brain injury severity and spinal cord injury interacted with age to influence dementia onset; women were at a greater risk of developing dementia earlier than men, all other factors being equal.DiscussionRisk stratification of patients with CNS trauma by sex is vital in identifying those most likely to develop dementia and in understanding the course and modifying factors.