A statin a day keeps the doctor away: comparative proverb assessment modelling study.

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1 Web Appendix. BMJ.2013.f7267 A statin a day keeps the doctor away: comparative proverb assessment modelling study. Adam DM Briggs, Anja Mizdrak, Peter Scarborough Appendix: Description of the PRIME model The Preventable Risk Integrated ModEl (PRIME) is a comparative risk assessment model that is freely available upon request (see Availability below). The PRIME model estimates the change in annual population mortality from twenty-four non-communicable diseases as a result of population changes in behavioural risk factors (ten dietary risk factors, physical activity, alcohol consumption and tobacco smoking), with obesity, blood pressure and blood cholesterol levels as intermediary factors (see Structure below). The model has previously been used for published analyses in the UK [1-6], Ireland [7] and Canada [8]. Structure The structure of the PRIME model is shown in figure A1. Each line in the figure is parameterised by meta-analyses of prospective cohort studies or meta-analyses of randomised trials, with the exception of the link between smoking and health outcomes (parameterised by single large prospective cohort studies) and the link between alcohol consumption and liver disease (parameterised by a meta-analysis of prospective cohort studies and case-control studies). The exact parameters are reported in table A1. With the exception of tobacco smoking, all of the behavioural risk factors in the PRIME model are continuous variables. The PRIME model describes the baseline distribution of each of these risk factors on the basis of parameters entered by the user (e.g. mean and standard deviation within the population of interest), assuming an underlying population distribution (either normal or lognormal). The user then defines the counterfactual scenario for each risk factor by altering the parameters described in the baseline situation. Tobacco smoking is considered a categorical variable, with the population split into proportions that have, are former smokers and are current smokers. The PRIME model estimates a Population Impact Fraction (PIF) for the difference in risk between the baseline and counterfactual scenario using the equation below, where RR(x) is the relative risk of the disease of interest at risk factor level x, P(x) is the number of people in the baseline distribution at risk factor level x, and P (x) is the number of people in the counterfactual scenario at risk factor level x: The PIF is then applied to the annual number of deaths from the disease of interest in the population to estimate the number of deaths that would be delayed or averted in the counterfactual scenario. The structure of the PRIME model has been designed in order to minimise double counting and to ensure that links between risk factors and health outcomes are mutually adjusted for each other to the largest extent that is possible with the extant literature. The PRIME model

2 estimates the result of changing multiple behavioural risk factors simultaneously by combining PIFs using the equation below: Uncertainty The PRIME model can estimate the degree of uncertainty that is a result of parametric uncertainty. This is done by conducting a Monte Carlo uncertainty analysis, where the PRIME model is run 5000 times and the parameters drawn from the literature (see table A1 below) are allowed to vary randomly according to their underlying distribution which can be estimated using the reported 95% confidence intervals. The 5000 results that are generated are an estimate of the distribution of results that are produced from incorporating uncertainty in these parameters. From this distribution, 95% confidence intervals (sometimes called uncertainty intervals or credible intervals ) can be derived. Availability The PRIME model is currently available as an Excel file, which can be run without the need for purchasing any additional software. The PRIME model is available upon request from Peter Scarborough (peter.scarborough@dph.ox.ac.uk). The PRIME model is currently being developed into a web application with an open source Python-based code. This web application will be freely available upon launch, with an estimated launch date of June References 1. Briggs A*, Mytton O*, Kehlbacher A, Tiffin R, Rayner M, Scarborough P. The overall and income specific effect on the prevalence of overweight and obesity of a 20% sugar sweetened beverage tax in the UK: an econometric and comparative risk assessment modelling study. BMJ, 2013;347:f6189 * Joint lead authors. 2. Briggs A, Kehlbacher A, Tiffin R, Garnett T, Rayner M, Scarborough P. Incorporating the societal cost of greenhouse gases into the price of foods could save lives from cardiovascular disease and cancer in England: a comparative risk assessment modelling study. BMJ Open, 2013;3:e Nichols M, Scarborough P, Allender S, Rayner M. Population alcohol consumption and chronic disease mortalities in England: modelling the impact of changes in the percentage of non drinkers and the amount of alcohol consumed by drinkers. BMJ Open, 2012; 2: e Scarborough P, Allender S, Clarke D, Wickramasinghe K, Rayner M. Modelling the health impact of environmentally sustainable dietary scenarios in the UK. European Journal of Clinical Nutrition, 2012; 66(6): Scarborough P, Morgan R, Webster P, Rayner M. Differences in coronary heart disease, stroke and cancer mortality rates between England, Scotland, Wales and Northern Ireland: the role of diet and nutrition. BMJ Open, 2011;1 (1): 1. e doi: /bmjopen Scarborough P, Nnoaham K, Clarke D, Rayner M, Capewell S. Modelling the impact of a healthy diet on cardiovascular disease and cancer mortality. Journal of Epidemiology and Community Health, 2012; 66(5): Epub 2010 Dec Briggs A*, Mytton O*, Madden D, O Shea D, Rayner M, Scarborough P. The impact on obesity of a 10% tax on sugar sweetened beverages in Ireland, a

3 comparative risk assessment modelling study. BMC Public Health, 2013;13:860. doi: / * Joint lead authors. 8. Belanger M, Poireir M, Jbilou J, Scarborough P. Modelling impact of compliance with dietary recommendations on cancer and cardiovascular disease mortality in Canada. Public Health, 2013 (in press). 9. Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta analysis of cohort studies. J Nutr, 2006;136(10): Dauchet L, Amouyel P, Dallongeville J. Fruit and vegetable consumption and risk of stroke: a meta analysis of cohort studies. Neurology. 2005;65(8): World Cancer Research Fund/American Institute for Cancer Research. Food, Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective. Washington DC: AICR, Pereira M, O'Reilly E, Augustsson K, Fraser GE, Goldbourt U, Heitmann BL, Hallmans G, Knekt P, Pietinen P, Spiegelman D, Stevens J, Virtamo J, Willett WC, Ascherio A. Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies. Arch Int Med. 2004;164(4): Threapleton DE, Greenwood DC, Evans CE, Cleghorn CL, Nykjaer C, Woodhead C, Cade JE, Gale CP, Burley VJ. Dietary fiber intake and risk of first stroke: a systematic review and meta analysis. Stroke, 2013; 44: Norat T, Chan D, Lau R, Aune D, Vieira R. WCRF/AICR Systematic Literature Review Continuous Update Project Report: The Associations between Food, Nutrition and Physical Activity and the Risk of Colorectal Cancer. Washington DC: AICR, Prospective studies collaboration. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta analysis of individual data from 61 prospective studies with vascular deaths. Lancet, 2007; 370: Prospective Studies Collaboration. Age specific relevance of usual blood pressure to vascular mortality: a meta analysis of individual data for one million adults in 61 prospective studies. Lancet, 2002; 360: Prospective studies collaboration. Body mass index and cause specific mortality in adults: collaborative analyses of 57 prospective studies. Lancet, 2009; 373: Ronksley PE, Brien SE, Turner BJ, Mukamal KJ, Ghali WA. Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta analysis. BMJ, 2011; Baliunas DO, Taylor BJ, Irving H, Roerecke M, Patra J, Mohapatra S, Rehm J. Alcohol as a risk factor for type 2 diabetes: a systematic review and metaanalysis. Diabetes Care, 2009; 32(11). 20. Rehm J, Taylor B, Mohapatra S, Irving H, Baliunas D, Patra J, Roerecke M. Alcohol as a risk factor for liver cirrhosis: a systematic review and meta analysis. Drug and Alcohol Review, 2010; 29: Thun MJ, Apicella LF, Henley SJ. Smoking vs other risk factors as the cause of smoking attributable deaths: confounding in the courtroom. JAMA, 2000;284(6): Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes: a systematic review and meta analysis. JAMA, 2007;298(22): Gandini S, Botteri E, Iodice S, Boniol M, Lowenfels AB, Maisonneuve P, Boyle P. Tobacco smoking and cancer: a meta analysis. Int J Cancer, 2008;122(1):

4 24. Zhou B, Yang L, Sun Q, Cong R, Gu H, Tang N, Zhu H, Wang B. Cigarette smoking and the risk of endometrial cancer: a meta analysis. Am J Med, 2008, 121(6): e3. doi: /j.amjmed Lee YC, Cohet C, Yang YC, Stayner L, Hashibe M, Straif K. Meta analysis of epidemiologic studies on cigarette smoking and liver cancer. Int J Epidemiol, 2009;38(6): doi: /ije/dyp280. Epub 2009 Aug Manek N, Nichols M, Kelly P, Foster C, Webster P, Rayner M, Scarborough P. Quantifying the association between physical activity and cardiovascular disease, cancer and all cause mortality: a systematic review and meta analyses. Under review. 27. Clarke R, Frost C, Collins R, Appleby P, Peto R. Dietary lipids and blood cholesterol: quantitative analysis of metabolic ward studies. BMJ, 1997; He FJ, Li J, MacGregor GA. Effect of longer term modest salt reduction on blood pressure: Cochrane systematic review and meta analysis of randomised trials. BMJ, 2013;346:f1325. doi: /bmj.f Christiansen E, Garby L. Prediction of body weight changes caused by changes in energy balance. European Journal of Clinical Investigation, 2002; 32:

5 Table A1. Parameters used in the PRIME model Risk factor Outcome Unit of change Relative risk (95% confidence intervals) Source Fruit CHD 106g/day 0.93 (0.89, 0.96) [9] Stroke 106g/day 0.89 (0.85, 0.93) [10] Lung cancer 80g/day 0.94 (0.90, 0.97) [11] Vegetables CHD 106g/day 0.89 (0.83, 0.95) [9] Fibre CHD 10g/day 0.81 (0.72, 0.92) [12] Stroke 7g/day 0.93 (0.88, 0.98) [13] Colorectal cancer 10g/day Men: 0.88 (0.78, 0.99) [14] Women: 0.92 (0.87, 0.98) Serum cholesterol CHD 1mmol/l decrease Under 49: 0.44 (0.42, 0.48) [15] 50-59: 0.58 (0.56, 0.61) 60-69: 0.72 (0.69, 0.74) 70-79: 0.82 (0.80, 0.85) Over 79: 0.85 (0.82, 0.89) Stroke 1mmol/l decrease Under 59: 0.90 (0.84, 0.97) [15] 60-69: 1.02 (0.97, 1.08) 70-79: 1.04 (0.99, 1.09) Over 79: 1.06 (1.00, 1.13) Blood pressure CHD 20mmHg SBP decrease Under 49: 0.49 (0.45, 0.53) [16] 50-59: 0.50 (0.49, 0.52) 60-69: 0.54 (0.53, 0.55) 70-79: 0.60 (0.58, 0.61) Over 79: 0.67 (0.64, 0.70) Stroke 20mmHg SBP decrease Under 49: 0.36 (0.32, 0.40) [16] 50-59: 0.38 (0.35, 0.40) 60-69: 0.43 (0.41, 0.45) 70-79: 0.50 (0.48, 0.52) Over 79: 0.67 (0.63, 0.71) Hypertensive disease 20mmHg SBP decrease 0.22 (0.20, 0.25) [16] Heart failure 20mmHg SBP decrease 0.53 (0.48, 0.59) [16] Pulmonary embolism 20mmHg SBP decrease 0.72 (0.60, 0.87) [16] Rheumatic heart disease 20mmHg SBP decrease 0.74 (0.61, 0.89) [16] Aortic aneurysm 20mmHg SBP decrease 0.55 (0.49, 0.62) [16] Body mass index CHD 5kg/m 2 Men, BMI 15-25: 1.27 (1.16, 1.39) [17] Women, BMI 15-25: 1.01 (0.86, 1.18) Men, BMI 25-50: 1.42 (1.35, 1.48) Women, BMI 25-50: 1.35 (1.28, 1.43) Stroke 5kg/m 2 BMI 15-25: 0.92 (0.82, 1.03) [17] BMI 25-50: 1.39 (1.31, 1.48) Heart failure 5kg/m 2 BMI 15-25: 0.93 (0.66, 1.29) [17] BMI 25-50: 1.86 (1.55, 2.23) Diabetes 5kg/m 2 BMI 15-25: 0.96 (0.59, 1.55) [17] BMI 25-50: 2.16 (1.89, 2.46) Hypertensive disease 5kg/m 2 BMI 15-25: 1.17 (0.77, 1.76) [17] BMI 25-50: 2.03 (1.75, 2.36) Pancreas cancer 5kg/m (1.07, 1.22) [11] Colorectum cancer 1kg/m (1.02, 1.04) [11] Breast cancer 2kg/m 2 Under 60: 0.94 (0.92, 0.95) [11] Over 60: 1.03 (1.01, 1.04) Endometrial cancer 5kg/m (1.35, 1.72) [11] Kidney cancer 5kg/m (1.24, 1.39) [11] Gallbladder cancer 5kg/m (1.15, 1.32) [11] Kidney disease 5kg/m 2 BMI 15-25: 1.14 (0.74, 1.77) BMI 25-50: 1.59 (1.27, 1.99) Liver disease 5kg/m 2 BMI 15-25: 0.73 (0.54, 1.00) BMI 25-50: 1.79 (1.54, 2.08) Alcohol CHD <2.5g/d: 0.92 (0.80, 1.06) zero consumption g/d: 0.79 (0.73, 0.86) 15-30g/d: 0.79 (0.71, 0.88) 30-60g/d: 0.77 (0.72, 0.83) 60+g/d: 0.75 (0.53, 0.89) Stroke Diabetes zero consumption zero consumption <2.5g/d: 1.00 (0.75, 1.34) g/d: 0.86 (0.75, 0.99) 15-30g/d: 1.15 (0.86, 1.54) 30-60g/d: 1.10 (0.85, 1.45) 60+g/d: 1.44 (0.99, 2.10) <6g/d: 0.73 (0.62, 0.86) 6-12g/d: 0.73 (0.62, 0.86) 12-24g/d: 0.66 (0.59, 0.75) 24-48g/d: 0.74 (0.63, 0.88) 48+g/d: 0.93 (0.74, 1.18) M/L/P cancer Per drink per week 1.24 (1.18, 1.30) [11] [17] [17] [18] [18] [19]

6 Colorectal cancer 10g/d 1.09 (1.03, 1.14) [11] Breast cancer 10g/d 1.10 (1.06, 1.14) [11] Liver cancer 10g/d 1.10 (1.02, 1.17) [11] Liver cirrhosis zero consumption Tobacco CHD Stroke Diabetes M/L/P cancer Oesophagus cancer Lung cancer Pancreas cancer Endometrium cancer Kidney cancer Stomach cancer Liver cancer Cervix cancer Bladder cancer COPD Women, <12g/d: 1.90 (1.10, 3.10) Women, 12-24g/d: 5.60 (4.50, 6.90) Women, 24-36g/d: 7.70 (6.30, 9.50) Women, 36-48g/d: (7.50, 13.50) Women, 48-60g/d: (11.00, 19.60) Women. 60+g/d: (17.20, 30.10) Men, <12g/d: 1.00 (0.60, 1.60) Men, 12-24g/d: 1.60 (1.40, 2.00) Men, 24-36g/d: 2.80 (2.30, 3.40) Men, 36-48g/d: 5.60 (4.50, 7.00) Men, 48-60g/d: 7.00 (5.80, 8.50) Men. 60+g/d: (11.70, 16.70) [20] Men, <65, current: 2.60 (2.40, 2.90) [21] Men, <65, former: 1.60 (1.40, 1.70) Men, 65+, current: 1.50 (1.30, 1.60) Men, 65+, former: 1.20 (1.10, 1.30) Women, <65, current: 3.20 (2.80, 3.60) Women, <65, former: 1.40 (1.20, 1.70) Women, 65+, current: 1.70 (1.60, 1.90) Women, 65+, former: 1.40 (1.30, 1.50) Men, <65, current: 2.40 (1.80, 3.00) [21] Men, <65, former: 1.00 (0.80, 1.40) Men, 65+, current: 1.50 (1.20, 1.80) Men, 65+, former: 1.00 (0.90, 1.20) Women, <65, current: 3.80 (3.10, 4.70) Women, <65, former: 1.50 (1.10, 2.00) Women, 65+, current: 1.60 (1.40, 1.90) Women, 65+, former: 1.20 (1.00, 1.40) Current: 1.44 (1.31, 1.58) [22] Former: 1.23 (1.14, 1.33) Current: 6.98 (3.14, 15.50) [23] Former: 4.65 (3.35, 6.45) Current: 3.57 (2.63, 4.48) [23] Former: 1.18 (0.73, 1.91) Current: 8.96 (6.73, 12.10) [23] Former: 3.85 (2.77, 5.34) Current: 1.70 (1.51, 1.91) [23] Former: 1.18 (1.04, 1.33) Current: 0.74 (0.64, 0.84) [24] Former: 0.88 (0.78, 0.99) Current: 1.52 (1.33, 1.74) [23] Former: 1.25 (1.14, 1.37) Current: 1.64 (1.37, 1.95) [23] Former: 1.31 (1.17, 1.46) Current: 1.56 (1.29, 1.87) [25] Former: 1.49 (1.06, 2.10) Current: 1.83 (1.51, 2.21) [23] Former: 1.26 (1.11, 1.42) Current: 2.77 (2.17, 3.54) [23] Former: 1.72 (1.46, 2.04) Men, current: (8.40, 13.90) [21] Men, former: 7.80 (6.10, 9.80) Women, current: (9.90, 15.20) Women, former: 8.90 (7.10, 11.10) 0.81 (0.75, 087) [26] Physical activity CHD Stroke 0.79 (0.68, 0.92) [26] Heart failure 0.86 (0.82, 0.89) [26] Breast cancer 0.91 (0.87, 0.95) [26] Lung cancer 0.74 (0.63, 0.86) [26] Stomach cancer 0.74 (0.64, 0.85) [26] Food component Outcome Unit of change Regression parameter (95% confidence Source intervals Total fat Total serum cholesterol 1% of total calories (0.010, 0.030) [27] Saturated fat Total serum cholesterol 1% of total calories (0.046, 0.058) [27] MUFAs Total serum cholesterol 1% of total calories (-0.001, 0.011) [27] PUFAs Total serum cholesterol 1% of total calories (-0.034, ) [27]

7 Dietary cholesterol Salt Total energy intake / physical activity level Total serum cholesterol Systolic blood pressure (mmhg) Change in body weight (kg) 1mg/d (0.001, 0.001) [27] 6g/day reduction (-2.50, -9.20) [28] 1MJ/PAL Men: 17.7 Women: 20.7 [29]

8 Figure A1. Structure of the PRIME model

FRUIT AND VEGETABLES CONSUMPTION AND PREVENTION OF CHRONIC DIET-RELATED NON- COMMUNICABLE DISEASES. Mike Rayner Chair EHN Expert Group on Nutrition

British Heart Foundation Health Promotion Research Group FRUIT AND VEGETABLES CONSUMPTION AND PREVENTION OF CHRONIC DIET-RELATED NON- COMMUNICABLE DISEASES Mike Rayner Chair EHN Expert Group on Nutrition