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      Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada

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          Abstract

          Over the last 30 years, public investments in Canada and many other countries have created clinical and administrative health data repositories to support research on health and social services, population health and health policy. However, there is limited capacity to share and use data across jurisdictional boundaries, in part because of inefficient and cumbersome procedures to access these data and gain approval for their use in research. A lack of harmonization among variables and indicators makes it difficult to compare research among jurisdictions. These challenges affect the quality, scope, and impact of work that could be done. The purpose of this paper is to compare and contrast the data access procedures in three Canadian jurisdictions (Manitoba, Alberta and British Columbia), and to describe how we addressed the challenges presented by differences in data governance and architecture in a Canadian cross-jurisdictional research study. We characterize common stages in gaining access to administrative data among jurisdictions, including obtaining ethics approval, applying for data access from data custodians, and ensuring the extracted data is released to accredited individuals in secure data environments. We identify advantages of Manitoba’s flexible ‘stewardship’ model over the more restrictive ‘custodianship’ model in British Columbia, and highlight the importance of communication between analysts in each jurisdiction to compensate for differences in coding variables and poor quality data. Researchers and system planners must have access to and be able to make effective use of administrative health data to ensure that Canadians continue to have access to high-quality health care and benefit from effective health policies. The considerable benefits of collaborative population-based research that spans jurisdictional borders have been recognized by the Canadian Institutes for Health Research in their recent call for the creation of a National Data Platform to resolve many of the issues in harmonization and validation of administrative data elements.

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          A standard procedure for creating a frailty index

          Background Frailty can be measured in relation to the accumulation of deficits using a frailty index. A frailty index can be developed from most ageing databases. Our objective is to systematically describe a standard procedure for constructing a frailty index. Methods This is a secondary analysis of the Yale Precipitating Events Project cohort study, based in New Haven CT. Non-disabled people aged 70 years or older (n = 754) were enrolled and re-contacted every 18 months. The database includes variables on function, cognition, co-morbidity, health attitudes and practices and physical performance measures. Data came from the baseline cohort and those available at the first 18-month follow-up assessment. Results Procedures for selecting health variables as candidate deficits were applied to yield 40 deficits. Recoding procedures were applied for categorical, ordinal and interval variables such that they could be mapped to the interval 0–1, where 0 = absence of a deficit, and 1= full expression of the deficit. These individual deficit scores were combined in an index, where 0= no deficit present, and 1= all 40 deficits present. The values of the index were well fit by a gamma distribution. Between the baseline and follow-up cohorts, the age-related slope of deficit accumulation increased from 0.020 (95% confidence interval, 0.014–0.026) to 0.026 (0.020–0.032). The 99% limit to deficit accumulation was 0.6 in the baseline cohort and 0.7 in the follow-up cohort. Multivariate Cox analysis showed the frailty index, age and sex to be significant predictors of mortality. Conclusion A systematic process for creating a frailty index, which relates deficit accumulation to the individual risk of death, showed reproducible properties in the Yale Precipitating Events Project cohort study. This method of quantifying frailty can aid our understanding of frailty-related health characteristics in older adults.
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            Prevalence of frailty in community-dwelling older persons: a systematic review.

            To systematically compare and pool the prevalence of frailty, including prefrailty, reported in community-dwelling older people overall and according to sex, age, and definition of frailty used. Systematic review of the literature using the key words elderly, aged, frailty, prevalence, and epidemiology. Cross-sectional data from community-based cohorts. Community-dwelling adults aged 65 and older. In the studies that were found, frailty and prefrailty were measured according to physical phenotype and broad phenotype, the first defining frailty as a purely physical condition and the second also including psychosocial aspects. Reported prevalence in the community varies enormously (range 4.0-59.1%). The overall weighted prevalence of frailty was 10.7% (95% confidence interval (CI) = 10.5-10.9; 21 studies; 61,500 participants). The weighted prevalence was 9.9% for physical frailty (95% CI = 9.6-10.2; 15 studies; 44,894 participants) and 13.6% for the broad phenotype of frailty (95% CI = 13.2-14.0; 8 studies; 24,072 participants) (chi-square (χ(2) ) = 217.7, degrees of freedom (df)=1, P < .001). Prevalence increased with age (χ(2) = 6067, df = 1, P < .001) and was higher in women (9.6%, 95% CI = 9.2-10.0%) than in men (5.2%, 95% CI = 4.9-5.5%; χ(2) = 298.9 df = 1, P < .001). Frailty is common in later life, but different operationalization of frailty status results in widely differing prevalence between studies. Improving the comparability of epidemiological and clinical studies constitutes an important step forward. © 2012, Copyright the Authors Journal compilation © 2012, The American Geriatrics Society.
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              Accumulation of Deficits as a Proxy Measure of Aging

              This paper develops a method for appraising health status in elderly people. A frailty index was defined as the proportion of accumulated deficits (symptoms, signs, functional impairments, and laboratory abnormalities). It serves as an individual state variable, reflecting severity of illness and proximity to death. In a representative database of elderly Canadians we found that deficits accumulated at 3% per year, and show a gamma distribution, typical for systems with redundant components that can be used in case of failure of a given subsystem. Of note, the slope of the index is insensitive to the individual nature of the deficits, and serves as an important prognostic factor for life expectancy. The formula for estimating an individual's life span given the frailty index value is presented. For different patterns of cognitive impairments the average within-group index value increases with the severity of the cognitive impairment, and the relative variability of the index is significantly reduced. Finally, the statistical distribution of the frailty index sharply differs between well groups (gamma distribution) and morbid groups (normal distribution). This pattern reflects an increase in uncompensated deficits in impaired organisms, which would lead to illness of various etiologies, and ultimately to increased mortality. The accumulation of deficits is as an example of a macroscopic variable, i.e., one that reflects general properties of aging at the level of the whole organism rather than any given functional deficiency. In consequence, we propose that it may be used as a proxy measure of aging.
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                Author and article information

                Journal
                Int J Popul Data Sci
                Int J Popul Data Sci
                IJPDS
                International Journal of Population Data Science
                Swansea University
                2399-4908
                5 October 2018
                2018
                : 3
                : 3
                : 437
                Affiliations
                [1 ] Manitoba Centre for Health Policy Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, Room 408-727 McDermot Ave. University of Manitoba, Winnipeg, MB R3E 3P5 Canada
                [2 ] Centre for Health Services and Policy Research, School of Population and Public Health and School of Nursing, University of British Columbia, 2206 East Mall Vancouver, BC Canada V6T 1Z3
                [3 ] Department of Community Health Sciences, Faculty of Medicine University of Calgary, TRW Building, 3rd Floor, 3280 Hospital Drive NW, Calgary, Alberta CANADA T2N 4Z6
                [4 ] Manitoba Primary Care Research Network, Department of Family Medicine S100, 750 Bannatyne Avenue, University of Manitoba, Winnipeg, MB R33 0W2
                [5 ] Centre for Health Services and Policy Research, School of Population and Public Health and School of Nursing, University of British Columbia, 2206 East Mall Vancouver, BC Canada V6T 1Z3
                [6 ] Health Research Methods and Analytics, Alberta Health Services, University of Alberta
                Author notes
                [*] [* ] Corresponding author: A Katz. akatz@ 123456cpe.umanitoba.ca

                Statement on Conflict of Interest: The authors declare that there are no conflicts of interest.

                Article
                3:3:6 S2399490818004378
                10.23889/ijpds.v3i3.437
                8142948
                34095523
                37c0f9eb-d3b9-4e95-bf1e-c9e1d56f6878

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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                Population Data Science

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