METHODS: In this multinational, prospective cohort study, we examined associations for 14 potentially modifiable risk factors with mortality and cardiovascular disease in 155 722 participants without a prior history of cardiovascular disease from 21 high-income, middle-income, or low-income countries (HICs, MICs, or LICs). The primary outcomes for this paper were composites of cardiovascular disease events (defined as cardiovascular death, myocardial infarction, stroke, and heart failure) and mortality. We describe the prevalence, hazard ratios (HRs), and population-attributable fractions (PAFs) for cardiovascular disease and mortality associated with a cluster of behavioural factors (ie, tobacco use, alcohol, diet, physical activity, and sodium intake), metabolic factors (ie, lipids, blood pressure, diabetes, obesity), socioeconomic and psychosocial factors (ie, education, symptoms of depression), grip strength, and household and ambient pollution. Associations between risk factors and the outcomes were established using multivariable Cox frailty models and using PAFs for the entire cohort, and also by countries grouped by income level. Associations are presented as HRs and PAFs with 95% CIs.
FINDINGS: Between Jan 6, 2005, and Dec 4, 2016, 155 722 participants were enrolled and followed up for measurement of risk factors. 17 249 (11·1%) participants were from HICs, 102 680 (65·9%) were from MICs, and 35 793 (23·0%) from LICs. Approximately 70% of cardiovascular disease cases and deaths in the overall study population were attributed to modifiable risk factors. Metabolic factors were the predominant risk factors for cardiovascular disease (41·2% of the PAF), with hypertension being the largest (22·3% of the PAF). As a cluster, behavioural risk factors contributed most to deaths (26·3% of the PAF), although the single largest risk factor was a low education level (12·5% of the PAF). Ambient air pollution was associated with 13·9% of the PAF for cardiovascular disease, although different statistical methods were used for this analysis. In MICs and LICs, household air pollution, poor diet, low education, and low grip strength had stronger effects on cardiovascular disease or mortality than in HICs.
INTERPRETATION: Most cardiovascular disease cases and deaths can be attributed to a small number of common, modifiable risk factors. While some factors have extensive global effects (eg, hypertension and education), others (eg, household air pollution and poor diet) vary by a country's economic level. Health policies should focus on risk factors that have the greatest effects on averting cardiovascular disease and death globally, with additional emphasis on risk factors of greatest importance in specific groups of countries.
FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).
METHODS: This study was designed in the form of cross-sectional analysis, in which, cancer survivors were recruited from the Sarawak General Hospital, the largest tertiary and referral public hospital in Sarawak. To capture the financial toxicity of the cancer survivors, the Comprehensive Score for Financial Toxicity (COST) instrument in its validated form was adopted. Multivariable logistic regression analysis was applied to determine the relationship between financial toxicity (FT) and its predictors.
RESULTS: The median age of the 461 cancer survivors was 56 while the median score of COST was 22.0. Besides, finding from multivariable logistic regression revealed that low income households (OR: 6.893, 95% CI, 3.109-15.281) were susceptible to higher risk of financial toxicity, while elderly survivors above 50 years old reported a lower risk in financial toxicity. Also, survivors with secondary schooling (OR:0.240; 95%CI, 0.110-0.519) and above [College or university (OR: 0.242; 95% CI, 0.090-0.646)] suffer a lower risk of FT.
CONCLUSION: Financial toxicity was found to be associated with survivors age, household income and educational level. In the context of cancer treatment within public health facility, younger survivors, households from B40 group and individual with educational attainment below the first level schooling in the Malaysian system of education are prone to greater financial toxicity. Therefore, it is crucial for healthcare policymakers and clinicians to deliberate the plausible risk of financial toxicity borne by the patient amidst the treatment process.