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20. Effect of Smoke-Free Legislation on Adult Smoking Behaviour in England in the 18 Months following Implementation

Author

Lee, John Tayu, Glantz, Stanton A, and Millett, Christopher

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Clinical Research, Prevention, Tobacco, Tobacco Smoke and Health, 3.1 Primary prevention interventions to modify behaviours or promote wellbeing, Prevention of disease and conditions, and promotion of well-being, Respiratory, Stroke, Cardiovascular, Cancer, Adolescent, Adult, Aged, Cross-Sectional Studies, England, Female, Humans, Male, Middle Aged, Smoking, Young Adult, General Science & Technology
Abstract

BackgroundComprehensive smoke-free legislation covering all enclosed public places and workplaces was implemented in England on 1 July 2007. This study examines the impact of this legislation on smoking prevalence, number of cigarettes smoked and location of smoking, controlling for secular trends through the end of 2008.Method and findingsRepeat cross sectional survey using nationally representative data from the Health Survey for England (HSE). In total there are 54,333 respondents from 2003-2008. Logit and linear regression models were used to examine the effect of the legislation on smoking prevalence and the number of cigarettes smoked daily among continuing smokers which took the underlying trend into account. Our finding suggest that smoking prevalence (current smoker) decreased from 25% in 2003 to 21% in 2008 (AOR = 0.96 per year, 95% CI = 0.95-0.98, P

Published
2011

22. Functional limitation as a mediator of the relationship between multimorbidity on health-related quality of life in Australia: evidence from a national panel mediation analysis

Author

Lee, John Tayu, primary, Ishida, Marie, additional, Haregu, Tilahun, additional, Pati, Sanghamitra, additional, Zhao, Yang, additional, Palladino, Raffaele, additional, Anindya, Kanya, additional, Atun, Rifat, additional, Oldenburg, Brian, additional, and Marthias, Tiara, additional

Published
2023
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34. Low physical activity is associated with adverse health outcome and higher costs in Indonesia: A national panel study

Author

Anindya, Kanya, primary, Marthias, Tiara, additional, Zulfikar Biruni, Muhammad, additional, Hage, Sophia, additional, Ng, Nawi, additional, Laverty, Anthony A., additional, McPake, Barbara, additional, Millett, Christopher, additional, Haregu, Tilahun Nigatu, additional, Hulse, Emily S. G., additional, Cao, Yingting, additional, and Lee, John Tayu, additional

Published
2022
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37. Epidemiology of physical–mental multimorbidity and its impact among Aboriginal and Torres Strait Islander in Australia: a cross-sectional analysis of a nationally representative sample

Author

Carman, William, primary, Ishida, Marie, additional, Trounson, Justin S, additional, Mercer, Stewart W, additional, Anindya, Kanya, additional, Sum, Grace, additional, Armstrong, Gregory, additional, Oldenburg, Brian, additional, McPake, Barbara, additional, and Lee, John Tayu, additional

Published
2022
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39. Provincial heterogeneity in the management of care cascade for hypertension, diabetes, and dyslipidaemia in China: Analysis of nationally representative population-based survey

Author

Zhao, Yang, primary, Anindya, Kanya, additional, Atun, Rifat, additional, Marthias, Tiara, additional, Han, Chunlei, additional, McPake, Barbara, additional, Duolikun, Nadila, additional, Hulse, Emily, additional, Fang, Xinyue, additional, Ding, Yimin, additional, Oldenburg, Brian, additional, and Lee, John Tayu, additional

Published
2022
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45. Additional file 1 of The relative impact of underweight, overweight, smoking, and physical inactivity on health and associated costs in Indonesia: propensity score matching of a national sample

Author

Ihyauddin, Zulfikar, Marthias, Tiara, Anindya, Kanya, Ng, Nawi, Dewi, Fatwa Sari Tetra, Hulse, Emily S. G., Aji, Reza Pandu, Putri, Dwi Astuti Dharma, and Lee, John Tayu

Abstract

Additional file 1: Figure S1. Flowchart of sampling selection for independent variable BMI. Figure S2. Flowchart of sampling selection for independent variable tobacco consumption. Figure S3. Flowchart of sampling selection for independent variable physical activity. Figure S4. Flowchart of sampling selection for independent variable ageing. Table S1. List of variables for 2014 IFLS analysis. Table S2. Sample characteristics stratified by age groups, before matching. Table S3. Sample characteristics stratified by tobacco consumption groups, before matching. Table S4. Sample characteristics stratified by BMI groups, before matching. Table S5. Sample characteristics stratified by physical activity (PA) groups, before matching. Table S6. Mean biases of covariates after matching using individual t-test (age group 50-59 vs 40-49). Table S7. Mean biases of covariates after matching using individual t-test (age group 60-69 vs 40-49). Table S8. Mean biases of covariates after matching using individual t-test (age group 70+ vs 40-49). Table S9. Mean biases of covariates after matching using individual t-test (former vs never use tobacco). Table S10. Mean biases of covariates after matching using individual t-test (light user vs never use tobacco). Table S11. Mean biases of covariates after matching using individual t-test (moderate user vs never use tobacco). Table S12. Mean biases of covariates after matching using individual t-test (heavy user vs never use tobacco). Table S13. Mean biases of covariates after matching using individual t-test (overweight vs normal BMI). Table S14. Mean biases of covariates after matching using individual t-test (obesity vs normal BMI). Table S15. Mean biases of covariates after matching using individual t-test (underweight vs normal BMI). Table S16. Mean biases of covariates after matching using individual t-test (low vs high physical activity. Table S17. Mean biases of covariates after matching using individual t-test (moderate vs high physical activity). Table S18. The ATT of the number of chronic conditions across different matching algorithms. Table S19. The ATT of multimorbidity presence across different matching algorithms. Table S20. The ATT of the number of outpatient visits across different matching algorithms. Table S21. The ATT of the number of inpatient visits across different matching algorithms. Table S22. The ATT of CHE >25% of total household expenditure across different matching algorithms. Table S23. The ATT of CHE >40% of total non-food expenditure across different matching algorithms. Table S24. The ATT of labour participation across different matching algorithms. Table S25. The ATT of the number of days primary activity missed across different matching algorithms. Table S26. Logistic and ZINB regression for the number of chronic condition and presence of multimorbidity. Table S27. Logistic and ZINB regression for the number of outpatient and inpatient visits. Table S28. Logistic regression for CHE >25% and 40%. Table S29. Logistic and ZINB regression for the productivity loss.

Published
2022
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47. Impact of the National Health Service Health Check on cardiovascular disease risk: a difference-in-differences matching analysis

Author

Chang, Kiara Chu-Mei, Lee, John Tayu, Vamos, Eszter P., Soljak, Michael, Johnston, Desmond, Khunti, Kamlesh, Majeed, Azeem, and Millett, Christopher

Subjects
Medical records -- Usage, Risk assessment -- Analysis, Cardiovascular diseases -- Research -- Care and treatment, Health, World Health Organization -- Standards
Abstract

Background: The National Health Service Health Check program in England is the largest cardiovascular risk assessment and management program in the world. We assessed the effect of this program on modelled risk of cardiovascular disease, individual risk factors for cardiovascular disease, prescribing of relevant medications and diagnosis of vascular disease. Methods: We obtained retrospective electronic medical records for a randomly selected sample of 138 788 patients aged 40-74 years registered with 462 English general practices participating in the Clinical Practice Research Datalink between 2009 and 2013. We used a quasi-experimental design of difference-indifferences matching analysis to compare changes in outcomes between Health Check attendees and nonattendees, with a median follow-up time of 2 years. Results: Overall, 21.4% of the eligible population attended a Health Check. After matching (n = 29 672 in each group), attendees had a significant absolute reduction in modelled risk for cardiovascular disease (-0.21%, 95% confidence interval [CI] -0.24% to -0.19%) and individual risk factors: systolic blood pressure (-2.51 mm Hg, 95% CI -2.77 to -2.25 mm Hg), diastolic blood pressure (-1.46 mm Hg, 95% CI -1.62 to -1.29 mm Hg), body mass index (-0.27, 95% CI -0.34 to -0.20) and total cholesterol (-0.15 mmol/L, 95% CI -0.18 to -0.13 mmol/L). Statins were prescribed for 39.9% of attendees who were at high risk for cardiovascular disease. The program resulted in significantly more diagnoses of selected vascular diseases among attendees, with the largest increases for hypertension (2.99%) and type 2 diabetes mellitus (1.31%). Interpretation: The National Health Service Health Check program had statistically significant but clinically modest impacts on modelled risk for cardiovascular disease and individual risk factors, although diagnosis of vascular disease increased. Overall program performance was substantially below national and international targets, which highlights the need for careful planning, monitoring and evaluation of similar initiatives internationally., The World Health Organization has set a target to reduce premature death from cardiovascular disease by 25% by 2025, (1) a goal that was affirmed in the recently published Sustainable [...]

Published
2016
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