DESIGN: Data were derived from the Global Adult Tobacco Survey, Malaysia (GATS-M). GATS-M is a nationwide study that employed a multistage, proportionate-to-size sampling strategy to select a representative sample of 5112 Malaysian adults aged 15 years and above. Multiple logistic regression was used to identify factors associated with support for smoke-free policy in selected public domains that is, workplaces, restaurants, bars, hotels, casinos, karaoke centres, public transport terminals and shopping centres.

RESULTS: The level of support for enactment of a smoke-free policy at selected public domains varied from 37.8% to 94.4%, with the highest support was for gazetted smoke-free domains, namely, shopping centres (94.4%, 95% CI: 93.2% to 95.3%) and public transport terminals (85.2%, 95% CI: 83.3% to 86.9%). Multiple logistic regression revealed that non-smokers were more likely to support smoke-free policy at all domains. In addition, respondents who worked in workplaces with total or partial smoking restrictions were more likely to support a smoke-free policy ((total restriction adjusted OR (AOR): 14.94 (6.44 to 34.64); partial restriction AOR: 2.96 (1.138 to 6.35); non-restriction was applied as a reference).

Method: The guidelines were developed by an appointed workgroup comprising experts in the Asia Pacific region, following reviews of previously published guidelines and recommendations relevant to each section.

Results: It recommends that healthcare facilities review specific risk factors and develop effective prevention strategies, which would be cost effective at local levels. Gaps identified are best closed using a quality improvement process. Surveillance of SSIs is recommended using accepted international methodology. The timely feedback of the data analysed would help in the monitoring of effective implementation of interventions.

METHODS AND FINDINGS: Genetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly (P < 5 × 10-8) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case-control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR-Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence (P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio [OR] per 50% higher odds liability: 1.10, 95% CI 1.06-1.15; P = 6.94 × 10-7) and suggestive evidence that lifetime smoking exposure was associated with an increased risk of invasive epithelial ovarian cancer (OR per unit increase in smoking score: 1.36, 95% CI 1.04-1.78; P = 0.02). In analyses examining histotypes and low malignant potential tumours, the strongest associations found were between height and clear cell carcinoma (OR per SD increase: 1.36, 95% CI 1.15-1.61; P = 0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset: 1.09, 95% CI 1.02-1.16; P = 0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per 50% higher odds liability: 0.89, 95% CI 0.82-0.96; P = 0.002). There was little evidence for an association of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone binding globulin with ovarian cancer or its subtypes. The primary limitations of this analysis include the modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the association of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptive use, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk.