Suchergebnisse

IntroductionThe validity of chronic disease case definitions for administrative health data may change over time due to changes in data quality. Trend control charts to identify out-of-control (OOC; i.e., unexpected) observations in a time series may indicate where disease estimates are influenced by changes in data quality.
ObjectiveApply and compare trend control charts methods for multiple sclerosis (MS) incidence and prevalence estimates using previously-validated case definitions for Manitoba, Canada.
MethodsEight case definitions were identified from published literature and applied to Manitoba administrative health data from January 1, 1972 to December 31, 2018. Incidence and prevalence trends were modeled using negative binomial and generalized estimating equation models, respectively. Trend control charts were used to plot predicted case counts against observed case counts. Control limits to identify OOC observations were calculated using two methods: predicted case count ±0.8*standard deviation (0.8*SD) and predicted case count ±2*standard deviation (2*SD). Differences in proportion of OOC observations across case definitions was assessed using McNemar's test.
ResultsThe proportion of OOC observations ranged from 0.71 to 0.90 for incidence and 0.72 to 0.98 for prevalence when using the 0.8*SD control limits. A lower proportion of OOC observations (0.46 to 0.74 for incidence; 0.30 to 0.74 for prevalence) was observed for the 2*SD control limits. Neither method resulted in significant differences in OOC observations across case definitions.
ConclusionsThe proportion of OOC observations in trend control charts varied with the control limit method adopted, but statistical significance did not. Trend control charts are a potentially useful tool for developing surveillance methods, but may benefit from disease-specific calibrated control limits.

ABSTRACTObjectivesMuch clinical knowledge about multiple sclerosis (MS) has been gained from patients who attend MS specialty clinics. However, there is limited information about whether these patients are representative of the wider MS population. The objective of this study was to compare incident MS cases who were MS clinic users to non-users of the specialty MS clinics in British Columbia, Canada.
ApproachThis was a retrospective record linkage cohort study using prospectively collected data from the BCMS database and province-wide health administrative databases. Incident MS cases were identified in the general population using a validated algorithm of hospital and physician diagnostic codes.
Results There were 2,841 incident MS cases between 1996 and 2004 including 1,648 (58%) that had registered at an MS clinic ('clinic cases') and 1,193 (42%) that had not ('non-clinic cases'). Sex and socioeconomic status distributions were similar; however, non-clinic cases were older, accessed health services more frequently, and had a higher burden of comorbidity than clinic cases. Only 1% of the non-clinic cases had filled a prescription for an MS-specific disease-modifying therapy, compared to 51% of the clinic cases.
ConclusionOur findings have several important implications: even within a publicly funded healthcare system, a high proportion of individuals with MS may not access a specialty MS clinic; the needs of MS patients managed in the community may differ from those referred to an MS clinic; findings from studies involving clinic-based MS cohorts may not always be generalizable to the wider MS population; and access to population-based health administrative data offers the opportunity to gain a broader understanding of MS.

OBJECTIVE: Knowledge is limited regarding the longitudinal course and predictors of mental health problems, suicide, and physical health outcomes among military and veterans. Statistics Canada, in collaboration with researchers at the University of Manitoba and an international team, conducted the Canadian Armed Forces Members and Veterans Mental Health Follow-Up Survey (CAFVMHS). Herein, we describe the rationale and methods of this important survey. METHOD: The CAFVMHS is a longitudinal survey design with 2 time points (2002 and 2018). Regular Force military personnel who participated in the first Canadian Community Health Survey Cycle 1.2—Mental Health and Well-Being, Canadian Forces Supplement (CCHS-CFS) in 2002 (N = 5,155) were reinterviewed in 2018 (n = 2,941). The World Mental Health Survey–Composite International Diagnostic Interview was used with the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria. RESULTS: The CAFVMHS includes 2,941 respondents (66% veterans; 34% active duty) and includes data on mental disorder diagnoses, physical health conditions, substance use, medication use, general health, mental health services, perceived need for care, social support, moral injury, deployment experiences, stress, physical activity, military-related sexual assault, childhood experiences, and military and sociodemographic information. CONCLUSIONS: The CAFVMHS provides a unique opportunity to further understand the health and well-being of military personnel in Canada over time to inform intervention and prevention strategies and improve outcomes. The data are available through the Statistics Canada Research Data Centres across Canada and can be used cross-sectionally or be longitudinally linked to the 2002 CCHS-CFS data.

OBJECTIVE: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. METHODS: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. RESULTS: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. CONCLUSION: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions.

OBJECTIVE: The current study used the Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (CAFVMHS) to (1) examine the incidence and prevalence of mental disorders and (2) estimate the comorbidity of mental disorders over the follow-up period. METHOD: The CAFVMHS (2018) is a longitudinal study with two time points of assessment. The sample is comprised of 2,941 Canadian Forces members and veterans who participated in the 2002 Canadian Community Health Survey: Canadian Forces Supplement. The World Health Organization Composite International Diagnostic Interview (WHO-CIDI) was utilized to diagnose Diagnostic and Statistical Manual-IV post-traumatic stress disorder (PTSD), major depressive episode (MDE), generalized anxiety disorder, social anxiety disorder (SAD), and alcohol abuse and dependence. Self-report health professional diagnoses were assessed for attention deficit hyperactivity disorder (ADHD), mania, obsessive compulsive disorder (OCD), and personality disorder. We established weighted prevalence of mental disorders and examined the association between mental disorders using logistic regression. RESULTS: In 2018, lifetime prevalence of any WHO-CIDI-based or self-reported mental disorder was 58.1%. Lifetime prevalence of any mood or anxiety disorder or PTSD was 54.0% in 2018. MDE (39.9%), SAD (25.7%), and PTSD (21.4%) were the most common mental disorders. There was a substantial increase in new onset or recurrence/persistence of mental disorders between the two measurement points (16-year assessment gap); 2002–2018 period prevalences were 43.5% for mood and anxiety disorder and 16.8% for alcohol abuse or dependence. The prevalence of self-reported ADHD, OCD, any personality disorder, and mania were 3.3%, 3.0%, 0.8%, and 0.8%, respectively. Comorbidity between mental disorders increased over the follow-up. CONCLUSIONS: This study demonstrates a high burden of mental disorders among a large Canadian military and veteran cohort. These findings underscore the importance of prevention ...

Objectives: Depression symptom questionnaires are not for diagnostic classification. Patient Health Questionnaire-9 (PHQ-9) scores >= 10 are nonetheless often used to estimate depression prevalence. We compared PHQ-9 >= 10 prevalence to Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) major depression prevalence and assessed whether an alternative PHQ-9 cutoff could more accurately estimate prevalence. Study Design and Setting: Individual participant data meta-analysis of datasets comparing PHQ-9 scores to SCID major depression status. Results: A total of 9,242 participants (1,389 SCID major depression cases) from 44 primary studies were included. Pooled PHQ-9 >= 10 prevalence was 24.6% (95% confidence interval [CI]: 20.8%, 28.9%); pooled SCID major depression prevalence was 12.1% (95% CI: 9.6%, 15.2%); and pooled difference was 11.9% (95% CI: 9.3%, 14.6%). The mean study-level PHQ-9 >= 10 to SCID-based prevalence ratio was 2.5 times. PHQ-9 >= 14 and the PHQ-9 diagnostic algorithm provided prevalence closest to SCID major depression prevalence, but study-level prevalence differed from SCID-based prevalence by an average absolute difference of 4.8% for PHQ-9 >= 14 (95% prediction interval: -13.6%, 14.5%) and 5.6% for the PHQ-9 diagnostic algorithm (95% prediction interval: -16.4%, 15.0%). Conclusion: PHQ-9 >= 10 substantially overestimates depression prevalence. There is too much heterogeneity to correct statistically in individual studies. ; Canadian Institutes of Health Research (CIHR) KRS-134297 PCG155468 PJT-162206 Fonds de recherche du Quebec -Sante (FRQS) Postdoctoral Training Fellowships FRQS Research Institute of the McGill University Health Centre G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University Vanier Canada Graduate Scholarship Canadian Institutes of Health Research (CIHR) Cumming School of Medicine, University of Calgary Alberta Health Services through the Calgary Health Trust Hotchkiss Brain Institute Alberta Innovates Health Solutions Canada Research Chair in Neurological Health Services Research AIHS Population Health Investigator Award Department of Education (NIDRR) H133B080025 National Multiple Sclerosis Society MB 0008 Lundbeck International Tehran University of Medical Sciences M-288 Canadian Institutes of Health Research (CIHR) THC-135234 Crohn's and Colitis Canada Bingham Chair in Gastroenterology Waugh Family Chair in Multiple Sclerosis UK Department for International Development 201446 Department of Education, National Institute on Disability and Rehabilitation Research, Spinal Cord Injury Model Systems: University of Washington H133N060033 Baylor College of Medicine H133N060003 University of Michigan System H133N060032 Grand Challenges Canada 0087-04 United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) R24MH071604 R34 MH072925 K02 MH65919 P30 DK50456 R24 MH56858 RO1-MH069666 R24 MH071604 MH014592-38 MH103210 United States Department of Health & Human Services Centers for Disease Control & Prevention - USA R49 CE002093 Spanish Ministry of Health's Health Research Fund (Fondo de Investigaciones Sanitarias) 97/1184 US National Center for Medical Rehabilitation Research RO1 HD39415 Federal Ministry of Education & Research (BMBF) 01GY1150 University of Technology Sydney Duke Global Health Institute 453-0751 Macao (SAR) Government, through the University of Macau RSKTO grants MYRG-2014-111 United States Agency for International Development (USAID) AID-DFD A-00-08-00308 UK National Institute for Health Research under its Programme Grants for Applied Research Programme RP-PG0606-1142 Consejo Nacional de Ciencia y Tecnologia (CONACyT) CB-2009133923-H National Health Research Institutes - Taiwan NHRI-EX97-9706PI Reitoria de Pesquisa da Universidade de Sao Paulo 09.1.01689.17.7 Banco Santander 10.1.01232.17.9 Pfizer medical faculty of the University of Heidelberg, Germany 121/2000 Research Manitoba Chair in Multiple Sclerosis Canadian Institutes of Health Research (CIHR) Niigata Seiryo University Productivity Grants (PQ-CNPq-2) 301321/2016-7 Ministry of Health, Italy United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) U10CA21661 U10CA180868 U10CA180822 U10CA37422 Pennsylvania Department of Health United Kingdom National Health Service Lothian Neuro-Oncology Endowment Fund United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) K07 CA 093512 Lance Armstrong Foundation United States Department of Health & Human Services United States Health Resources & Service Administration (HRSA) R40MC07840 United States Department of Health & Human Services National Institutes of Health (NIH) - USA T32 GM07356 United States Department of Health & Human Services Agency for Healthcare Research & Quality R36 HS018246 United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Research Resources (NCRR) TL1 RR024135 medical faculty, University of Leipzig Hunter Medical Research Institute Netherlands Organization for Health Research and Development (ZonMw) Mental Health Program 100.003.005 100.002.021 Academic Medical Center/University of Amsterdam Fund for Innovation and Competitiveness of the Chilean Ministry of Economy, Development and Tourism, through the Millennium Scientific Initiative IS130005 Research Manitoba Chair