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Head and neck cancer (HNC) is the eighth most common cancer in the UK, with over 12,000 new cases every year. The incidence of HNC is predicted to increase by 33% by 2035. Risk modelling produces personalised risk estimates for specific diseases, which can be used to inform education, screening programmes and recruitment to clinical trials. The present study describes the development and validation of the first risk prediction model for absolute risk of HNC, using a nested case‑control study within the UK Biobank dataset. The UK Biobank recruited 502,647 individuals aged 40‑69 years from around the UK. In total, 859 cases of HNC were identified, with 253 incident cases (individuals who developed HNC in the 7 years following recruitment to the UK Biobank study). Logistic regression was used to develop the model, then the model performance was validated using a cohort from the North West of England. Overall, increasing age, male sex, positive history of smoking and alcohol consumption and higher levels of material deprivation were significantly associated with a higher risk of HNC. Consuming at least five portions of fruit and vegetables per day, exercising at least once per week and higher BMI offered a protective effect against HNC. The C‑statistic was 0.69 [95% confidence interval (CI), 0.66‑0.71] and the model displayed good calibration. Upon external validation, the C‑statistic was 0.64 (95% CI, 0.60‑0.68) with reasonable calibration. The model developed and validated in the present study allows calculation of a personalised risk estimate for HNC. This could be used to guide clinicians when counselling individuals on risk behaviour, and there is potential for such models to inform recruitment to screening trials.

Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P

Recent studies show an increased incidence of head and neck cancers worldwide. The present study evaluated the trend in the incidence of head and neck cancers in England during 2002-2011. Data were extracted from the database of Office for National Statistics. The study population was categorised according to age, residential area, gender and cancer sub-types. Overall trend in incidence of head and neck cancer and some subtypes were examined using Poisson regression models. In total, 71,457 head and neck cancers were registered in England between 2002 and 2011 and 68% of patients were males. Statistically significant increases in incidence of 27.0 and 32.4% were documented in males and females, respectively (p

Lung cancer is the most common cancer in the world, therefore creating a huge public health concern. The aim of this study is to determine the change in age-standardised incidence rate trend of lung cancer in England between 2002 and 2011 and use these findings to anticipate the potential burden of the disease by gender in the year 2020. Lung cancer incidence data (ICD-10 code C33-34) from 2002 and 2011 and mid-year population estimates for the same period were obtained from Office of National Statistics. Age-standardised incidence rates were calculated, by gender and region. Poisson regression analysis was used to describe the time incidence trend and projections were estimated up to year 2020. A total of 318, 417 lung cancer cases were identified. Incidence rates decreased in men by an average annual percentage change (AAPC) of -1.0% and increased in women by +1.9%. Projection analysis showed that by year 2020, provided the rates remain the same, English women will have the same lung cancer incidence rates as their male counterparts. This study demonstrated that there would be 5,848 excess lung cancer cases by 2020 with female population accounting for 85% (4,996) of the excess cases. Therefore, in addition to the development of high quality preventive intervention strategies, future public health also needs to prioritise targets at the implementation phase, in a manner that engage women living in regions that have demonstrated very high AAPC values.

In long-term longitudinal cohort studies the dropout of participants occurring as a result of withdrawal or lost to follow-up may have greater impact on the effect estimates, if characteristics of participants who drop out and those still active in the study differ significantly. The study aimed to investigate factors associated with dropout in a 5-year follow-up of individuals at 'high‑risk' of lung cancer. We studied 'high‑risk' group of 1,486 individuals aged 45-79 selected from the Liverpool Lung Prospective (LLP) cohort study using a strategy reflecting only age, smoking duration and history of pulmonary disease. Study subjects were recalled annually from 2005-2009 for follow-up collection of specimens and questionnaire data. The dropout rate over the follow-up time was investigated using the Kaplan‑Meier survival curve and the Cox proportional hazard model. Dropout rate was 31% after an average of 3 annual visits. Female gender hazard ratio (HR) 1.35 (95% CI 1.09-1.66), current smoking 1.26 (1.02-1.57), prior diagnosis of malignant disease 0.54 (0.36-0.79), home visits 0.67 (0.48-0.94) and systolic blood pressure 1.46 (1.10-1.94) were significantly associated with the dropout rate. Nearly 40% of individuals selected into the 'high‑risk' group by the old criteria were low risk with predicted 5-year absolute risk of less than 2.5%. In conclusion, follow-up of individuals is feasible within the LLP, but may be prone to selective withdrawal attributable to patient's state of health and mobility. We recommend future design of 'high‑risk' follow‑up studies to consider home visit as a useful strategy to encourage continued participation.