Use of artificial sweeteners (AS) such as aspartame, cyclamate, saccharin and sucralose is widespread. We evaluated the association of use of aspartame and other AS with cancer. In total 1881 colorectal, 1510 breast, 972 prostate and 351 stomach cancer and 109 chronic lymphocytic leukaemia (CLL) cases and 3629 population controls from the Spanish Multicase-Control (MCC-Spain) study were recruited (2008)(2009)(2010)(2011)(2012)(2013). The consumption of AS, from table-top sweeteners and artificially sweetened beverages, was assessed through a self-administered and validated food frequency questionnaire (FFQ). Sex-specific quartiles among controls were determined to compare moderate consumers (

There is evidence for the influence of socioeconomic status (SES) on healthy behaviours but the effect of social mobility (SM) is not yet well known. This study aims to analyse the influence of origin and destination SES (O-SES and D-SES) and SM on healthy behaviours and co-occurrence, from an integrated gender and age perspective. Data were obtained from the controls of MCC-Spain between 2008–2013 (3,606 participants). Healthy behaviours considered: healthy diet, moderate alcohol consumption, non-smoking and physical activity. SM was categorized as stable high, upward, stable medium, downward or stable low. Binary and multinomial logistic regression models were adjusted. Those aged <65, with a low O-SES, D-SES and stable low SM are less likely to have healthy behaviours in the case of both women (physically active: OR = 0.65 CI = 0.45–0.94, OR = 0.71 CI = 0.52–0.98, OR = 0.61 CI = 0.41–0.91) and men (non-smokers: OR = 0.44 CI = 0.26–0.76, OR = 0.54 CI = 0.35–0.83, OR = 0.41 CI 0.24–0.72; physically active: OR = 0.57 CI = 0.35–0.92, OR = 0.64 CI = 0.44–0.95, OR = 0.53 CI = 0.23–0.87). However, for those aged ≥65, this probability is higher in women with a low O-SES and D-SES (non-smoker: OR = 8.09 CI = 4.18–15.67, OR = 4.14 CI = 2.28–7.52; moderate alcohol consumption: OR = 3.00 CI = 1.45–6.24, OR = 2.83 CI = 1.49–5.37) and in men with a stable low SM (physically active: OR = 1.52 CI = 1.02–1.26). In the case of men, the same behaviour pattern is observed in those with a low O-SES as those with upward mobility, with a higher probability of co-occurring behaviours (three-to-four behaviours: OR = 2.00 CI = 1.22–3.29; OR = 3.13 CI = 1.31–7.48). The relationship of O-SES, D-SES and SM with healthy behaviours is complex and differs according to age and gender.

This cross-sectional study aimed to analyse the social and gender-related factors influencing sedentariness in women, including occupation and family size (FS). We included women aged 45-70 years participating in the Breast Cancer Screening Programme of the Valencia Region (BCSP-VR) between November 2018 and October 2019 (n = 121,988). The response variable was sedentariness measured by sitting time in hours/day (h/day) (<3 h/day and ≥3 h/day). The explanatory variables were age, educational level, country of origin, living alone, childcare responsibilities, FS, disability, body mass index (BMI) and smoking status. Logistic regression models were adjusted for the whole sample and were stratified by occupation and FS. The variables that increased the odds of sedentariness were age ≥65 years (

Objective To construct an individual socioeconomic status index (ISESI) with information available in the Population Information System of the Region of Valencia, Spain, and use it to analyse inequalities in a colorectal cancer screening programme (CRCSP). Methods Cross-sectional study of men and women aged between 50 and 75 at the time of the study (2020) that were selected from the target population of the Region of Valencia CRCSP. (study sample 1,150,684). First, a multiple correspondence analysis was performed to aggregate information from the Population Information System of the Region of Valencia into an ISESI. Second, data from the 2016 Region of Valencia Health Survey were used for validation, and finally the relationship between CRCSP participation and the ISESI was analysed by logistic regression models. Results The variables included in the index were nationality, employment status, disability, healthcare coverage, risk of vulnerability and family size. The most important categories for determining the highest socioeconomic status were being employed and not being at risk of social vulnerability, and being unemployed and at risk of social vulnerability for determining the lowest socioeconomic status. Index validation demonstrated internal and external coherence for measuring socioeconomic status. The relationship between CRCSP participation and the ISESI categorised by quartile (Q) showed that Q4 (the lowest socioeconomic status) was less likely to participate OR = 0.769 (0.757–0.782) than Q1 (the highest socioeconomic status), and the opposite was found for Q2 OR = 1.368 (1.347–1.390) and Q3 OR = 1.156 (1.137–1.175). Conclusions An ISESI was constructed and validated using Population Information System data and made it possible to evaluate inequalities in colorectal cancer screening.

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