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Search Results: 1 - 10 of 463142 matches for " Jacek A Kopec "
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Is Lifelong Knee Joint Force from Work, Home, and Sport Related to Knee Osteoarthritis?
Charles R. Ratzlaff,Mieke Koehoorn,Jolanda Cibere,Jacek A. Kopec
International Journal of Rheumatology , 2012, DOI: 10.1155/2012/584193
Abstract: Purpose. To investigate the association of cumulative lifetime knee joint force on the risk of self-reported medically-diagnosed knee osteoarthritis (OA). Methods. Exposure data on lifetime physical activity type (occupational, household, sport/recreation) and dose (frequency, intensity, duration) were collected from 4,269 Canadian men and women as part of the Physical Activity and Joint Heath cohort study. Subjects were ranked in terms of the “cumulative peak force index”, a measure of lifetime mechanical knee force. Multivariable logistic regression was conducted to obtain adjusted effects for mean lifetime knee force on the risk of knee OA. Results. High levels of total lifetime, occupational and household-related force were associated with an increased in risk of OA, with odds ratio’s ranging from approximately 1.3 to 2. Joint injury, high BMI and older age were related to risk of knee OA, consistent with previous studies. Conclusions. A newly developed measure of lifetime mechanical knee force from physical activity was employed to estimate the risk of self-reported, medically-diagnosed knee OA. While there are limitations, this paper suggests that high levels of total lifetime force (all domains combined), and occupational force in men and household force in women were risk factors for knee OA. 1. Introduction The promotion of physical activity (PA) is a major public health initiative in many countries due to its protective effect on numerous major health problems [1], including Canada and the US where public health bodies recommend 30 to 60 minutes of moderate-to-vigorous activities per day. However, there has long been a concern that such promotion could lead to a rise in hip and knee OA, the major public health problem in musculoskeletal medicine and a leading cause of chronic disability [2]. While there is a broad agreement that PA is an important determinant of joint health, it is unclear what amount and type of PA are beneficial or pose a risk. In short, despite numerous studies, the association between PA and joint health is complex and poorly understood. While different study designs, case definitions, sampling frames, and size play a role, the wide variation in how PA is defined is the most probable reason for the uncertainty. There is a lack of valid, reliable, and standardized instruments across studies, substantial measurement error, variation in the period and nature of PA measured, and failure to measure the most relevant aspect of PA-joint load [3]. Where accurate and precise measures are available, they are impractical for use in
Self-reported physical and mental health status and quality of life in adolescents: a latent variable mediation model
Richard Sawatzky, Pamela A Ratner, Joy L Johnson, Jacek A Kopec, Bruno D Zumbo
Health and Quality of Life Outcomes , 2010, DOI: 10.1186/1477-7525-8-17
Abstract: The data were obtained via a cross-sectional health survey of 8,225 adolescents in 49 schools in British Columbia, Canada. Structural equation modeling was applied to test the implied latent variable mediation model. The Pratt index (d) was used to evaluate variable importance.Relative to one another, self-reported mental health status was found to be more strongly associated with depressive symptoms, and self-reported physical health status more strongly associated with physical activity. Self-reported physical and mental health status and the five life domains explained 76% of the variance in global QOL. Relatively poorer mental health and physical health were significantly associated with lower satisfaction in each of the life domains. Global QOL was predominantly explained by three of the variables: mental health status (d = 30%), satisfaction with self (d = 42%), and satisfaction with family (d = 20%). Satisfaction with self and family were the predominant mediators of mental health and global QOL (45% total mediation), and of physical health and global QOL (68% total mediation).This study provides support for the validity and relevance of differentiating self-reported physical and mental health status in adolescent health surveys. Self-reported mental health status and, to a lesser extent, self-reported physical health status were associated with significant differences in the adolescents' satisfaction with their family, friends, living environment, school experiences, self, and their global QOL. Questions about adolescents' self-reported physical and mental health status and their experiences with these life domains require more research attention so as to target appropriate supportive services, particularly for adolescents with mental or physical health challenges.Health researchers and providers increasingly recognize the importance of obtaining information about adolescents' perspectives of their quality of life (QOL) [1-10]. Several instruments have been
The Effect of Disease Site (Knee, Hip, Hand, Foot, Lower Back or Neck) on Employment Reduction Due to Osteoarthritis
Eric C. Sayre,Linda C. Li,Jacek A. Kopec,John M. Esdaile,Sherry Bar,Jolanda Cibere
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0010470
Abstract: Osteoarthritis (OA) has a significant impact on individuals' ability to work. Our goal was to investigate the effects of the site of OA (knee, hip, hand, foot, lower back or neck) on employment reduction due to OA (EROA).
Advances in Microsimulation Modeling of Population Health Determinants, Diseases, and Outcomes
Jacek A. Kopec,Kimberley Edwards,Douglas G. Manuel,Carolyn M. Rutter
Epidemiology Research International , 2012, DOI: 10.1155/2012/584739
Abstract:
Advances in Microsimulation Modeling of Population Health Determinants, Diseases, and Outcomes
Jacek A. Kopec,Kimberley Edwards,Douglas G. Manuel,Carolyn M. Rutter
Epidemiology Research International , 2012, DOI: 10.1155/2012/584739
Abstract:
Risk of Type 2 Diabetes among Osteoarthritis Patients in a Prospective Longitudinal Study
M. Mushfiqur Rahman,Jolanda Cibere,Aslam H. Anis,Charlie H. Goldsmith,Jacek A. Kopec
International Journal of Rheumatology , 2014, DOI: 10.1155/2014/620920
Abstract: Objectives. Our aim was to determine the risk of diabetes among osteoarthritis (OA) cases in a prospective longitudinal study. Methods. Administrative health records of 577,601 randomly selected individuals from British Columbia, Canada, from 1991 to 2009, were analyzed. OA and diabetes cases were identified by checking physician’s visits and hospital records. From 1991 to 1996 we documented 19,143 existing OA cases and selected one non-OA individual matched by age, sex, and year of administrative records. Poisson regression and Cox proportional hazards models were fitted to estimate the effects after adjusting for available sociodemographic and medical factors. Results. At baseline, the mean age of OA cases was 61 years and 60.5% were women. Over 12 years of mean follow-up, the incidence rate (95% CI) of diabetes was 11.2 (10.90–11.50) per 1000 person years. Adjusted RRs (95% CI) for diabetes were 1.27 (1.15–1.41), 1.21 (1.08–1.35), 1.16 (1.04–1.28), and 0.99 (0.86–1.14) for younger women (age 20–64 years), older women (age ≥ 65 years), younger men, and older men, respectively. Conclusion. Younger adults and older women with OA have increased risks of developing diabetes compared to their age-sex matched non-OA counterparts. Further studies are needed to confirm these results and to elucidate the potential mechanisms. 1. Introduction Diabetes mellitus is a common chronic health condition worldwide. It is predicted that the global prevalence of this disease among adults will rise from 6.4% in 2010 to 7.7% by 2030 [1]. Diabetes affects an estimated 8.3% of Americans and 8.8% of Canadians [2, 3], resulting in severe damage to the cardiovascular system, kidneys, eyes, and other organs. Metabolic syndrome is a group of conditions such as hypertension, hyperlipidemia, obesity, and elevated blood glucose that are linked with diabetes [4]. Other common risk factors for diabetes include age, sex, family history, ethnicity, socioeconomic status (SES), heart disease, history of gestational diabetes, physical inactivity, alcohol consumption, and diet [5, 6]. As the prevalence of diabetes has risen, it has been imperative to identify determinants beyond these traditional risk factors. Studies have shown that the increased risk of diabetes is caused in part by physical inactivity and that physically active individuals have lower rates of the disease [6, 7]. In addition, muscle strength was found to be significantly lower among adults with type 2 diabetes [8]. Osteoarthritis (OA) is the most common type of rheumatic disease and a leading cause of disability [9–11].
Uncertainty Analysis in Population-Based Disease Microsimulation Models
Behnam Sharif,Jacek A. Kopec,Hubert Wong,Philippe Finès,Eric C. Sayre,Ran R. Liu,Michael C. Wolfson
Epidemiology Research International , 2012, DOI: 10.1155/2012/610405
Abstract: Objective. Uncertainty analysis (UA) is an important part of simulation model validation. However, literature is imprecise as to how UA should be performed in the context of population-based microsimulation (PMS) models. In this expository paper, we discuss a practical approach to UA for such models. Methods. By adapting common concepts from published UA guidelines, we developed a comprehensive, step-by-step approach to UA in PMS models, including sample size calculation to reduce the computational time. As an illustration, we performed UA for POHEM-OA, a microsimulation model of osteoarthritis (OA) in Canada. Results. The resulting sample size of the simulated population was 500,000 and the number of Monte Carlo (MC) runs was 785 for 12-hour computational time. The estimated 95% uncertainty intervals for the prevalence of OA in Canada in 2021 were 0.09 to 0.18 for men and 0.15 to 0.23 for women. The uncertainty surrounding the sex-specific prevalence of OA increased over time. Conclusion. The proposed approach to UA considers the challenges specific to PMS models, such as selection of parameters and calculation of MC runs and population size to reduce computational burden. Our example of UA shows that the proposed approach is feasible. Estimation of uncertainty intervals should become a standard practice in the reporting of results from PMS models. 1. Introduction Computer simulation models are widely used in public health research [1, 2]. Population-based microsimulation (PMS) models are increasingly used to model possible effects of public health interventions at the population level [3–5]. Such models usually represent the population of a country: incorporate multiple cohorts, and model births, deaths, and migration [6–8]. Population-based models differ from models commonly used in cohort-based cost-effectiveness studies that model a single cohort of patients [9]. Unlike macrolevel simulation models (e.g., cell-based [10] or compartmental models [11]), microsimulation (MS) models generate a life history for every individual in a population [12, 13] and provide population-level outcomes by aggregating the individuals’ event histories [14, 15]. In PMS models of chronic, noncommunicable diseases, individuals can be treated as independent units (no interindividual interactions). Examples include models of breast cancer [7, 16], stroke [6, 17], pulmonary disease [18], colon cancer [19], diabetes [20, 21], and other chronic conditions [5, 15]. MS models that incorporate interactions between individuals, often referred to as agent-based models, have been
Comparing the content of participation instruments using the International Classification of Functioning, Disability and Health
Vanessa K Noonan, Jacek A Kopec, Luc Noreau, Joel Singer, Anna Chan, Louise C Masse, Marcel F Dvorak
Health and Quality of Life Outcomes , 2009, DOI: 10.1186/1477-7525-7-93
Abstract: A systematic literature search was conducted to identify instruments that assess participation according to the ICF. Instruments were considered to assess participation and were included if the domains contain content from a minimum of three ICF chapters ranging from Chapter 3 Communication to Chapter 9 Community, social and civic life in the activities and participation component. The instrument content was examined by first identifying the meaningful concepts in each question and then linking these concepts to ICF categories. The content analysis included reporting the 1) ICF chapters (domains) covered in the activities and participation component, 2) relevance of the meaningful concepts to the activities and participation component and 3) context in which the activities and participation component categories are evaluated.Eight instruments were included: Impact on Participation and Autonomy, Keele Assessment of Participation, Participation Survey/Mobility, Participation Measure-Post Acute Care, Participation Objective Participation Subjective, Participation Scale (P-Scale), Rating of Perceived Participation and World Health Organization Disability Assessment Schedule II (WHODAS II). 1351 meaningful concepts were identified in the eight instruments. There are differences among the instruments regarding how participation is operationalized. All the instruments cover six to eight of the nine chapters in the activities and participation component. The P-Scale and WHODAS II have questions which do not contain any meaningful concepts related to the activities and participation component. Differences were also observed in how other ICF components (body functions, environmental factors) and health are operationalized in the instruments.Linking the meaningful concepts in the participation instruments to the ICF classification provided an objective and comprehensive method for analyzing the content. The content analysis revealed differences in how the concept of participat
Assessment of health-related quality of life in arthritis: conceptualization and development of five item banks using item response theory
Jacek A Kopec, Eric C Sayre, Aileen M Davis, Elizabeth M Badley, Michal Abrahamowicz, Lesley Sherlock, J Ivan Williams, Aslam H Anis, John M Esdaile
Health and Quality of Life Outcomes , 2006, DOI: 10.1186/1477-7525-4-33
Abstract: About 1,400 items were drawn from published questionnaires or developed from focus groups and individual interviews and classified into 19 domains of HRQL. We selected the following 5 domains relevant to arthritis and related conditions: Daily Activities, Walking, Handling Objects, Pain or Discomfort, and Feelings. Based on conceptual criteria and pilot testing, 219 items were selected for further testing. A questionnaire was mailed to patients from two hospital-based clinics and a stratified random community sample. Dimensionality of the domains was assessed through factor analysis. Items were analyzed with the Generalized Partial Credit Model as implemented in Parscale. We used graphical methods and a chi-square test to assess item fit. Differential item functioning was investigated using logistic regression.Data were obtained from 888 individuals with arthritis. The five domains were sufficiently unidimensional for an IRT-based analysis. Thirty-one items were deleted due to lack of fit or differential item functioning. Daily Activities had the narrowest range for the item location parameter (-2.24 to 0.55) and Handling Objects had the widest range (-1.70 to 2.27). The mean (median) slope parameter for the items ranged from 1.15 (1.07) in Feelings to 1.73 (1.75) in Walking. The final item banks are comprised of 31–45 items each.We have developed IRT-based item banks to measure HRQL in 5 domains relevant to arthritis. The items in the final item banks provide adequate psychometric information for a wide range of functional levels in each domain.Over the past decade, item response theory (IRT) has been increasingly applied to the assessment of health-related quality of life (HRQL) [1]. IRT can be used to evaluate, modify, link, compare, and score existing measures as well as develop new instruments [1,2]. An important application of IRT is computerized adaptive assessment of HRQL [1-4]. The process is adaptive because it allows different respondents to answer differ
Validation of population-based disease simulation models: a review of concepts and methods
Jacek A Kopec, Philippe Finès, Douglas G Manuel, David L Buckeridge, William M Flanagan, Jillian Oderkirk, Michal Abrahamowicz, Samuel Harper, Behnam Sharif, Anya Okhmatovskaia, Eric C Sayre, M Mushfiqur Rahman, Michael C Wolfson
BMC Public Health , 2010, DOI: 10.1186/1471-2458-10-710
Abstract: We developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.Evidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.As the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.Computer simulation models have been used in health research and policy since the 1960s [1,2]. In a review of simulation modeling in population health and health care delivery prior to 2000, Fone et al identified 182 papers covering a wide range of topics, including hospital scheduling, communicable diseases, screening, cost of illness, and economic evaluation [3]. The authors noted that the quality of published papers was variable and the value of modeling was difficult to assess. One of the features distinguishing high quality papers from lower grade papers was more complete reporting of model validation [3]. While concerns have been raised about the role of modeling in guiding health policies [4,5], the number of published disease simulation mode
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