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Bian Z, Zhang R, Yuan S, Fan R, Wang L, Larsson SC, Theodoratou E, Zhu Y, Wu S, Ding Y, Li X. Healthy lifestyle and cancer survival: A multinational cohort study. Int J Cancer 2024; 154:1709-1718. [PMID: 38230569 DOI: 10.1002/ijc.34846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/11/2023] [Accepted: 12/19/2023] [Indexed: 01/18/2024]
Abstract
Lifestyle factors after a cancer diagnosis could influence the survival of cancer 60 survivors. To examine the independent and joint associations of healthy lifestyle factors with mortality outcomes among cancer survivors, four prospective cohorts (National Health and Nutrition Examination Survey [NHANES], National Health Interview Survey [NHIS], UK Biobank [UKB] and Kailuan study) across three countries. A healthy lifestyle score (HLS) was defined based on five common lifestyle factors (smoking, alcohol drinking, diet, physical activity and body mass index) that related to cancer survival. We used Cox proportional hazards regression to estimate the hazard ratios (HRs) for the associations of individual lifestyle factors and HLS with all-cause and cancer mortality among cancer survivors. During the follow-up period of 37,095 cancer survivors, 8927 all-cause mortality events were accrued in four cohorts and 4449 cancer death events were documented in the UK and US cohorts. Never smoking (adjusted HR = 0.77, 95% CI: 0.69-0.86), light alcohol consumption (adjusted HR = 0.86, 95% CI: 0.82-0.90), adequate physical activity (adjusted HR = 0.90, 95% CI: 0.85-0.94), a healthy diet (adjusted HR = 0.69, 95% CI: 0.61-0.78) and optimal BMI (adjusted HR = 0.89, 95% CI: 0.85-0.93) were significantly associated with a lower risk of all-cause mortality. In the joint analyses of HLS, the HR of all-cause and cancer mortality for cancer survivors with a favorable HLS (4 and 5 healthy lifestyle factors) were 0.55 (95% CI 0.42-0.64) and 0.57 (95% CI 0.44-0.72), respectively. This multicohort study of cancer survivors from the United States, the United Kingdom and China found that greater adherence to a healthy lifestyle might be beneficial in improving cancer prognosis.
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Affiliation(s)
- Zilong Bian
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Rongqi Zhang
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Rong Fan
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lijuan Wang
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Yimin Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Tangshan, China
| | - Yuan Ding
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
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Anema JR, Schellart AJM, Cassidy JD, Loisel P, Veerman TJ, van der Beek AJ. Can cross country differences in return-to-work after chronic occupational back pain be explained? An exploratory analysis on disability policies in a six country cohort study. J Occup Rehabil 2009; 19:419-26. [PMID: 19760488 PMCID: PMC2775112 DOI: 10.1007/s10926-009-9202-3] [Citation(s) in RCA: 149] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
INTRODUCTION There are substantial differences in the number of disability benefits for occupational low back pain (LBP) among countries. There are also large cross country differences in disability policies. According to the Organization for Economic Cooperation and Development (OECD) there are two principal policy approaches: countries which have an emphasis on a compensation policy approach or countries with an emphasis on an reintegration policy approach. The International Social Security Association initiated this study to explain differences in return-to-work (RTW) among claimants with long term sick leave due to LBP between countries with a special focus on the effect of different disability policies. METHODS A multinational cohort of 2,825 compensation claimants off work for 3-4 months due to LBP was recruited in Denmark, Germany, Israel, the Netherlands, Sweden, and the United States. Relevant predictors and interventions were measured at 3 months, one and 2 years after the start of sick leave. The main outcome measure was duration until sustainable RTW (i.e. working after 2 years). Multivariate analyses were conducted to explain differences in sustainable RTW between countries and to explore the effect of different disability policies. RESULTS Medical and work interventions varied considerably between countries. Sustainable RTW ranged from 22% in the German cohort up to 62% in the Dutch cohort after 2 years of follow-up. Work interventions and job characteristics contributed most to these differences. Patient health, medical interventions and patient characteristics were less important. In addition, cross-country differences in eligibility criteria for entitlement to long-term and/or partial disability benefits contributed to the observed differences in sustainable RTW rates: less strict criteria are more effective. The model including various compensation policy variables explained 48% of the variance. CONCLUSIONS Large cross-country differences in sustainable RTW after chronic LBP are mainly explained by cross-country differences in applied work interventions. Differences in eligibility criteria for long term disability benefits contributed also to the differences in RTW. This study supports OECD policy recommendations: Individual packages of work interventions and flexible (partial) disability benefits adapted to the individual needs and capacities are important for preventing work disability due to LBP.
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Affiliation(s)
- J R Anema
- Department of Public and Occupational Health and EMGO Institute, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands.
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