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      Smoking, drinking, diet and physical activity—modifiable lifestyle risk factors and their associations with age to first chronic disease

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          Abstract

          Background

          This study examined the incidence of a person’s first diagnosis of a selected chronic disease, and the relationships between modifiable lifestyle risk factors and age to first of six chronic diseases.

          Methods

          Ontario respondents from 2001 to 2010 of the Canadian Community Health Survey were followed up with administrative data until 2014 for congestive heart failure, chronic obstructive respiratory disease, diabetes, lung cancer, myocardial infarction and stroke. By sex, the cumulative incidence function of age to first chronic disease was calculated for the six chronic diseases individually and compositely. The associations between modifiable lifestyle risk factors (alcohol, body mass index, smoking, diet, physical inactivity) and age to first chronic disease were estimated using cause-specific Cox proportional hazards models and Fine-Gray competing risk models.

          Results

          Diabetes was the most common disease. By age 70.5 years (2015 world life expectancy), 50.9% of females and 58.1% of males had at least one disease and few had a death free of the selected diseases (3.4% females; 5.4% males). Of the lifestyle factors, heavy smoking had the strongest association with the risk of experiencing at least one chronic disease (cause-specific hazard ratio = 3.86; 95% confidence interval = 3.46, 4.31). The lifestyle factors were modelled for each disease separately, and the associations varied by chronic disease and sex.

          Conclusions

          We found that most individuals will have at least one of the six chronic diseases before dying. This study provides a novel approach using competing risk methods to examine the incidence of chronic diseases relative to the life course and how their incidences are associated with lifestyle behaviours.

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          Most cited references23

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          Causes and consequences of comorbidity: a review.

          A literature search was carried out to identify and summarize the existing information on causes and consequences of comorbidity of chronic somatic diseases. A selection of 82 articles met our inclusion criteria. Very little work has been done on the causes of comorbidity. On the other hand, much work has been done on consequences of comorbidity, although comorbidity is seldom the main subject of study. We found comorbidity in general to be associated with mortality, quality of life, and health care. The consequences of specific disease combinations, however, depended on many factors. We recommend more etiological studies on shared risk factors, especially for those comorbidities that occur at a higher rate than expected. New insights in this field can lead to better prevention strategies. Health care workers need to take comorbid diseases into account in monitoring and treating patients. Future studies on consequences of comorbidity should investigate specific disease combinations.
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            Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records.

            To determine if using a combination of hospital administrative data and ambulatory care physician billings can accurately identify patients with congestive heart failure (CHF), we tested 9 algorithms for identifying individuals with CHF from administrative data. The validation cohort against which the 9 algorithms were tested combined data from a random sample of adult patients from EMRALD, an electronic medical record database of primary care physicians in Ontario, Canada, and data collected in 2004/05 from a random sample of primary care patients for a study of hypertension. Algorithms were evaluated on sensitivity, specificity, positive predictive value, area under the curve on the ROC graph and the combination of likelihood ratio positive and negative. We found that that one hospital record or one physician billing followed by a second record from either source within one year had the best result, with a sensitivity of 84.8% and a specificity of 97.0%. Population prevalence of CHF can be accurately measured using combined administrative data from hospitalization and ambulatory care.
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              Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale.

              Following individuals sampled in a large-scale health survey for the development of diseases and/or death offers the opportunity to assess the prognostic significance of various risk factors. The proportional hazards regression model, which allows for the control of covariates, is frequently used for the analysis of such data. The authors discuss the appropriate time-scale for such regression models, and they recommend that age rather than time since the baseline survey (time-on-study) be used. Additionally, with age as the time-scale, control for calendar-period and/or birth cohort effects can be achieved by stratifying the model on birth cohort. Because, as discussed by the authors, many published analyses have used regression models with time-on-study as the time-scale, it is important to assess the magnitude of the error incurred from this type of incorrect modeling. The authors provide simple conditions for when incorrect use of time-on-study as the time-scale will nevertheless yield approximately unbiased proportional hazards regression coefficients. Examples are given using data from the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Followup Study. Additional issues concerning the analysis of longitudinal follow-up of survey data are briefly discussed.
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                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                February 2020
                26 April 2019
                26 April 2019
                : 49
                : 1
                : 113-130
                Affiliations
                [1 ] Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto , Toronto, ON, Canada
                [2 ] Institute for Clinical Evaluative Sciences , Toronto, ON, Canada
                [3 ] Institute of Health Policy, Management and Evaluation, University of Toronto , Toronto, ON, Canada
                [4 ] Institute for Better Health, Trillium Health Partners , Mississauga, ON, Canada
                Author notes
                Corresponding author. Dalla Lana School of Public Health, University of Toronto, 155 College Street 6th Floor, Toronto, ON M5T3M7, Canada. E-mail: laura.rosella@ 123456utoronto.ca , laura.rosella@ 123456oahpp.ca
                Author information
                http://orcid.org/0000-0003-4867-869X
                Article
                dyz078
                10.1093/ije/dyz078
                7124486
                31329872
                eff8b38d-8d5b-4a9f-aec9-4afa39f3ea2f
                © The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 11 April 2019
                Page count
                Pages: 18
                Funding
                Funded by: Canadian Institutes of Health Research Partnerships for Health System Improvement;
                Award ID: FRN 141803
                Funded by: Ontario Ministry of Health and Long-Term Care, DOI 10.13039/501100000226;
                Award ID: 6717
                Funded by: Institute for Clinical Evaluative Sciences, DOI 10.13039/100012665;
                Funded by: Ontario Ministry of Health and Long-Term Care, DOI 10.13039/501100000226;
                Categories
                Alcohol

                Public health
                alcohol,body mass index,chronic disease,competing risks,diet,life course,multimorbidity,physical activity,smoking

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