This chapter summarises the results of the preceding sections, which estimate the fraction of cancers occurring in the UK in 2010 that can be attributed to sub-optimal, past exposures of 14 lifestyle and environmental risk factors. For each of 18 cancer types, we present the percentage of cases attributable to one or all of the risk factors considered (tobacco, alcohol, four elements of diet (consumption of meat, fruit and vegetables, fibre, and salt), overweight, lack of physical exercise, occupation, infections, radiation (ionising and solar), use of hormones, and reproductive history (breast feeding)).Exposure to less than optimum levels of the 14 factors was responsible for 42.7% of cancers in the UK in 2010 (45.3% in men, 40.1% in women) – a total of about 134 000 cases.Tobacco smoking is by far the most important risk factor for cancer in the UK, responsible for 60 000 cases (19.4% of all new cancer cases) in 2010. The relative importance of other exposures differs by sex. In men, deficient intake of fruits and vegetables (6.1%), occupational exposures (4.9%) and alcohol consumption (4.6%) are next in importance, while in women, it is overweight and obesity (because of the effect on breast cancer) – responsible for 6.9% of cancers, followed by infectious agents (3.7%).Population-attributable fractions provide a valuable quantitative appraisal of the impact of different factors in cancer causation, and are thus helpful in prioritising cancer control strategies. However, quantifying the likely impact of preventive interventions requires rather complex scenario modelling, including specification of realistically achievable population distributions of risk factors, and the timescale of change, as well as the latent periods between exposure and outcome, and the rate of change following modification in exposure level.

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We describe an approach for quantifying and characterizing the extent to which sudden and unexpected infant deaths (SUIDs) result from unsafe sleep environments (e.g., prone position, bedsharing, soft bedding); and present data on sleep-related infant deaths in NYC. Using a combination of vital statistics and medical examiner data, including autopsy and death scene investigation findings, we analyzed any death due to accidental threat to breathing (ATB) (ICD-10 W75 & W84), and deaths of undetermined intent (UND) (Y10-Y34) between 2000 and 2003 in NYC for the presence of sleep-related factors (SRF). Homicide deaths were excluded as were SIDS, since in NYC SIDS is not a certification option if environmental factors were possibly contributors to the death. All 19 ATB and 69 (75%) UND had SRFs as per the OCME investigation. Black infants and infants born to teen mothers had higher SRF death rates for both ATB and UND deaths. Bedsharing was the most common SRF (53%-ATB; 72%-UND deaths); the majority of non-bedsharing infants were found in the prone position (60%-ATB; 78%-UND deaths). We found a high prevalence of SRFs among ATB and UND deaths. This is the first local study to illustrate the importance of knowing how SUIDs are certified in order to ascertain the prevalence of infant deaths with SRFs. Advancing the research requires clarity on the criteria used by local medical examiners to categorize SUIDs. This will help jurisdictions interpret their infant mortality statistics, which in turn will improve education and prevention efforts.

P1-276 Table 1 Respiratory diagnoses and prescriptions Drug misusers Controls Crude OR (95% CI) Adjusted OR (95% CI) Asthmaz 1590 (17.1%) 1009 (10.9%)* 1.695 (1.557 to 1.845)y 1.633 (1.485 to 1.796)y COPD (chronic obstructive pulmonary disease)z 219 (2.4%) 74 (0.8%)* 3.007 (2.307 to 3.920)y 1.752 (1.336 to 2.297)y SABA (short acting beta agonist) prescribedx 1520 (16.4%) 736 (7.9%)* 2.274 (2.071 to 2.496)y 1.998 (1.803 to 2.214)y LABA (long acting beta agonist) prescribedx 92 (1%) 39 (0.4%)* 2.373 (1.630 to 3.454)y 1.883 (1.255 to 2.825)y ICS (inhaled corticosteroid) prescribedx 987 (10.6%) 702 (7.6%)* 1.454 (1.314 to 1.609)y 1.500 (1.339 to 1.681)y *p<0.0001, c 2. yp<0.001 binary logistic regression. zDiagnosis ever recorded. xPrescriptions in 2008. Conclusion These data suggest drug misusers have a significantly higher prevalence of respiratory diseases and are prescribed significantly more respiratory medications than matched controls. This exploratory study has set the scene for future work to explore possible reasons for this association.