Study design

We designed a study using national Chilean data from multiple sources. We retrieved the distribution of six lifestyle risk factors (alcohol consumption, high body mass index - BMI, low fruits and vegetables consumption, lack of physical activity, tobacco smoking and passive smoking) by sex using data from a national representative survey in Chile (Table 1) [6]. Relative risks (RR) of each risk factor-cancer pair by sex were retrieved from published meta-analysis and pooled data analysis of cohort studies [9, 14, 18, 20–35]. Estimated number of cancer cases and deaths (excluding nonmelanoma skin cancer) in adults 20 years or older in Chile in 2018 by sex and cancer site were retrieved from the GLOBOCAN 2018 [3]. Similar methodological approach has been used to estimate the burden of cancer attributable to modifiable risk factors in other countries [8, 9, 11–15].

Distribution (proportion or mean and 95% confidence intervals) of lifestyle risk factors associated with cancer incidence and mortality in Chile

Importantly, we considered in our estimates only lifestyle risk factors with strong/convincing evidence for increasing the risk of cancer according to the International Agency for Research on Cancer (IARC) [17, 18] and the World Cancer Research Fund (WCRF) [19], and for which exposure data were available in Chile and dose-response relationship of exposures and site-specific cancers were well-defined (Table ​(Table11).

We used data from the National Health Survey of Chile 2016–2017 (Encuesta Nacional de Salud - ENS), a national representative, population-based, household survey that enrolled 6233 participants over 15 years old [6]. ENS 2016–2017 sampling strategy considered a stratified, multistage and clustered random sample of households at the national, regional (15 Chilean geographical regions), urban and rural level. One participant per household was randomly selected. The sample size was calculated with a relative sampling error of less than 30% and an absolute sampling error of 2.6% to the national level. The data collection was performed between August 2016 and March 2017 [6]. In this study we included 5834 adults aged ≥20 years who responded to a self-reported questionnaire about alcohol consumption, weight and height, fruits and vegetables consumption, physical activity, tobacco smoking, and passive smoking. The ENS 2016–2017 was funded by the Chilean Ministry of Health and approved by the Ethics Research Committee of the School of Medicine at the Pontificia Universidad Católica de Chile (No. 16–019). Participants signed an informed consent to take part in the study. Details about ENS 2016–2017 are available elsewhere [6].

Alcohol consumption was assessed through average number of drinks in a regular day. One drink of beer, one glass of wine or one shot of distilled spirit was assumed to have 12.5 g of pure alcohol. Self-reported weight and height were obtained to calculate the BMI. Fruits and vegetables consumption were calculated based on the average frequency (days/week) and number of servings per day (i.e., each serving was defined as 80 g). Physical activity was assessed through the Global Physical Activity Questionnaire which include self-reported frequency (days/week) and duration (minutes) of active transport (walking and cycling), and moderate and vigorous recreational and occupational activities. We assigned the following metabolic equivalent tasks (MET) to each of these activities: 4 for active transport (walking and cycling), 3.8 for moderate and 7.8 for vigorous occupational activities; and 3 for moderate and 6 for vigorous recreational activities; and then calculated total physical activity (MET-minutes/week) [36]. Smoking was assessed based on current and prior tobacco use (never, former and current). Passive smoking among never smokers (yes, no) was defined based on regular exposure to smoke at home. Although lifestyle risk factors data were available by age-group, we decided to calculate the prevalence estimates by sex only in order to align with relative risk and estimated cancer occurrence data.