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A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace. [ABSTRACT FROM AUTHOR]

Smoking has been identified as the second greatest risk factor for global death and disability and has impacts on the oral cavity from aesthetic changes to fatal diseases such as oral cancer. The paper presents a secondary analysis of the National Adult Dental Health Survey (2009). The analysis used descriptive statistics, bivariate analyses and logistic regression models to report the self-reported oral health status and dental attendance of smokers and non-smokers in England. Of the 9,657 participants, 21% reported they were currently smoking. When compared with smokers; non-smokers were more likely to report ‘good oral health’ (75% versus 57% respectively, p<0.05). Smokers were twice as likely to attend the dentist symptomatically (OR = 2.27, CI = 2.02–2.55) compared with non-smoker regardless the deprivation status. Smokers were more likely to attend symptomatically in the most deprived quintiles (OR = 1.99, CI = 1.57–2.52) and perceive they had poorer oral health (OR = 1.77, CI = 1.42–2.20). The present research is consistent with earlier sub-national research and should be considered when planning early diagnosis and management strategies for smoking-related conditions, considering the potential impact dental teams might have on smoking rates. [ABSTRACT FROM AUTHOR]

England’s national Improving Access to Psychological Therapies (IAPT) programme advocates stepped care as its organizational delivery of psychological therapies to common mental health problems. There is limited evidence regarding the efficacy of stepped care as a service delivery model, heterogeneity of definition and differences in model implementation in both research and routine practice, hence outcome comparison in terms of effectiveness of model is difficult. Despite sound evidence of the efficacy of low intensity interventions there appears to be a perpetuation of the notion that severity and complexity should only be treated by a high intensity intervention through the continuation of a stratified care model. Yet no psychotherapy treatment is found to be more superior to another, and not enough is known about what works for whom to aid the matching of treatment decision. In the absence of understanding precise treatment factors optimal for recovery, it may be useful to better understand the impact of a service delivery model, and whether different models achieve different outcomes. This study aims to contribute to the discussion regarding the stepped care definition and delivery, and explores the impact on clinical outcomes where different types of stepped care have been implemented within the same service. An observational cohort study analysed retrospective data (n = 16,723) over a 4 year period, in a single IAPT service, where delivery changed from one type of stepped care model to another. We compared the outcomes of treatment completers with a stratified care model and a progression care model. We also explored the assumption that patients who score severe on psychological measures, and therefore are potentially complex, would achieve better outcomes in a stratified model. Outcomes in each model type were compared, alongside baseline factor variables. A significant association was observed between a recovery outcome and model type, with patients 1.5 times more likely to recover in the progression delivery model. The potential implications are that with a progression stepped care model of service delivery, more patients can be treated with a lower intensity intervention, even with initial severe presentations, ensuring that only those that need high intensity CBT or equivalent are stepped up. This could provide services with an effective clinical model that is efficient and potentially more cost effective. [ABSTRACT FROM AUTHOR]

Many disciplines of scholarship are interested in the Relative Age Effect (RAE), whereby age-banding confers advantages on older members of the cohort over younger ones. Most research does not test this relationship in a manner consistent with theory (which requires a decline in frequency across the cohort year), instead resorting to non-parametric, non-directional approaches. In this article, the authors address this disconnect, provide an overview of the benefits associated with Poisson regression modelling, and two managerially useful measures for quantifying RAE bias, namely the Indices of Discrimination and Wastage. In a tutorial-like exposition, applications and extensions of this approach are illustrated using data on professional soccer players competing in the top two tiers of the “Big Five” European football leagues in the search to identify paragon clubs, leagues, and countries from which others may learn to mitigate this form of age-discrimination in the talent identification process. As with OLS regression, Poisson regression may include more than one independent variable. In this way we test competing explanations of RAE; control for unwanted sources of covariation; model interaction effects (that different clubs and countries may not all be subject to RAE to the same degree); and test for non-monotonic versions of RAE suggested in the literature. [ABSTRACT FROM AUTHOR]

Aim: To gain a better understanding of the complex relationships of different measures of social position, educational level and income with alcohol consumption in England. Method: Between March 2014 and April 2018 data were collected on n = 57,807 alcohol drinkers in England taking part in the Alcohol Toolkit Study (ATS). Respondents completed the AUDIT-C measure of frequency of alcohol consumption, amount consumed on a typical day and binge drinking frequency. The first two questions were used to derive a secondary measure of quantity: average weekly unit consumption. Socio-economic factors measured were: social-grade (based on occupation), employment status, educational qualifications, home and car ownership and income. Models were constructed using ridge regression to assess the contribution of each predictor taking account of high collinearity. Models were adjusted for age, gender and ethnicity. Results: The strongest predictor of frequency of alcohol consumption was social-grade. Those in the two lowest occupational categories of social grade (e.g. semi-skilled and unskilled manual workers, and unemployed, pensioners, casual workers) has fewer drinking occasions than those in professional-managerial occupations (β = -0.29, 95%CI -0.34 to -0.25; β = -0.31, 95%CI -0.33 to -0.29). The strongest predictor of consumed volume and binge drinking frequency was lower educational attainment: those whose highest qualification was an A-level (i.e. college/high school qualification) drank substantially more on a typical day (β = 0.28, 95%CI 0.25 to 0.31) and had a higher weekly unit intake (β = 3.55, 95%CI 3.04 to 4.05) than those with a university qualification. They also reported a higher frequency of binge drinking (β = 0.11, 95%CI 0.09 to 0.14). Housing tenure was a strong predictor of all drinking outcomes, while employment status and car ownership were the weakest predictors of most outcomes. Conclusion: Social-grade and educational attainment appear to be the strongest socioeconomic predictors of alcohol consumption indices in England, followed closely by housing tenure. Employment status and car ownership have the lowest predictive power. [ABSTRACT FROM AUTHOR]

Background: We have assessed whether the linkage between routine primary and secondary care records provided an opportunity to develop an improved population based co-morbidity score with the combined information on co-morbidities from both health care settings. Methods: We extracted all people older than 20 years at the start of 2005 within the linkage between the Hospital Episodes Statistics, Clinical Practice Research Datalink, and Office for National Statistics death register in England. A random 50% sample was used to identify relevant diagnostic codes using a Bayesian hierarchy to share information between similar Read and ICD 10 code groupings. Internal validation of the score was performed in the remaining 50% and discrimination was assessed using Harrell’s C statistic. Comparisons were made over time, age, and consultation rate with the Charlson and Elixhauser indexes. Results: 657,264 people were followed up from the 1st January 2005. 98 groupings of codes were derived from the Bayesian hierarchy, and 37 had an adjusted weighting of greater than zero in the Cox proportional hazards model. 11 of these groupings had a different weighting dependent on whether they were coded from hospital or primary care. The C statistic reduced from 0.88 (95% confidence interval 0.88–0.88) in the first year of follow up, to 0.85 (0.85–0.85) including all 5 years. When we stratified the linked score by consultation rate the association with mortality remained consistent, but there was a significant interaction with age, with improved discrimination and fit in those under 50 years old (C = 0.85, 0.83–0.87) compared to the Charlson (C = 0.79, 0.77–0.82) or Elixhauser index (C = 0.81, 0.79–0.83). Conclusions: The use of linked population based primary and secondary care data developed a co-morbidity score that had improved discrimination, particularly in younger age groups, and had a greater effect when adjusting for co-morbidity than existing scores. [ABSTRACT FROM AUTHOR]

Background: Coronary Heart Disease (CHD) remains a major cause of mortality in the United Kingdom. Yet predictions of future CHD mortality are potentially problematic due to population ageing and increase in obesity and diabetes. Here we explore future projections of CHD mortality in England & Wales under two contrasting future trend assumptions. Methods: In scenario A, we used the conventional counterfactual scenario that the last-observed CHD mortality rates from 2011 would persist unchanged to 2030. The future number of deaths was calculated by applying those rates to the 2012–2030 population estimates. In scenario B, we assumed that the recent falling trend in CHD mortality rates would continue. Using Lee-Carter and Bayesian Age Period Cohort (BAPC) models, we projected the linear trends up to 2030. We validate our methods using past data to predict mortality from 2002–2011. Then, we computed the error between observed and projected values. Results: In scenario A, assuming that 2011 mortality rates stayed constant by 2030, the number of CHD deaths would increase 62% or approximately 39,600 additional deaths. In scenario B, assuming recent declines continued, the BAPC model (the model with lowest error) suggests the number of deaths will decrease by 56%, representing approximately 36,200 fewer deaths by 2030. Conclusions: The decline in CHD mortality has been reasonably continuous since 1979, and there is little reason to believe it will soon halt. The commonly used assumption that mortality will remain constant from 2011 therefore appears slightly dubious. By contrast, using the BAPC model and assuming continuing mortality falls offers a more plausible prediction of future trends. Thus, despite population ageing, the number of CHD deaths might halve again between 2011 and 2030. This has implications for how the potential benefits of future cardiovascular strategies might best be calculated and presented. [ABSTRACT FROM AUTHOR]

Background: South Asians in England have an increased risk of childhood cancer but incidence by their individual ethnicities using self-assigned ethnicity is unknown. Our objective was to compare the incidence of childhood cancer in British Indians and Whites in Leicester, which has virtually complete, self-assigned, ethnicity data and the largest population of Indians in England. Methods: We obtained data on all cancer registrations from 1996 to 2008 for Leicester with ethnicity obtained by linkage to the Hospital Episodes Statistics database. Age-standardised incidence rates were calculated for childhood cancers in Indians and Whites as well as rate ratios, adjusted for age. Results: There were 33 cancers registered among Indian children and 39 among White children. The incidence rate for Indians was greater compared to Whites for all cancers combined (RR 1.82 (95% CI 1.14 to 2.89); p = 0.01), with some evidence of increased risk of leukaemia (RR 2.20 (0.95 to 5.07); p = 0.07), lymphoma (RR 3.96 (0.99 to 15.84); p = 0.04) and central nervous system tumours (RR 2.70 (1.00 to 7.26); p = 0.05). Rates were also higher in British Indian children compared to children in India. Conclusions: British Indian children in Leicester had an increased risk of developing cancer compared to White children, largely due to a higher incidence of central nervous system and haematological malignancies. [ABSTRACT FROM AUTHOR]