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Peer reviewed: True, OBJECTIVES: Uptake of influenza, pneumococcal and shingles vaccines in older adults vary across regions and socioeconomic backgrounds. In this study, we study the coverage and factors associated with vaccination uptake, as well as refusal in the unvaccinated population and their associations with ethnicity, deprivation, household size and health conditions. DESIGN, SETTING AND PARTICIPANTS: This is a cross-sectional study of adults aged 65 years or older in England, using a large primary care database. Associations of vaccine uptake and refusal in the unvaccinated with ethnicity, deprivation, household size and health conditions were modelled using multivariable logistic regression. OUTCOME MEASURE: Influenza, pneumococcal and shingles vaccine uptake and refusal (in the unvaccinated). RESULTS: This study included 2 054 463 patients from 1318 general practices. 1 711 465 (83.3%) received at least one influenza vaccine, 1 391 228 (67.7%) pneumococcal vaccine and 690 783 (53.4%) shingles vaccine. Compared with White ethnicity, influenza vaccine uptake was lower in Chinese (OR 0.49; 95% CI 0.45 to 0.53), 'Other ethnic' groups (0.63; 95% CI 0.60 to 0.65), black Caribbean (0.68; 95% CI 0.64 to 0.71) and black African (0.72; 95% CI 0.68 to 0.77). There was generally lower vaccination uptake among more deprived individuals, people living in larger household sizes (three or more persons) and those with fewer health conditions. Among those who were unvaccinated, higher odds of refusal were associated with the black Caribbean ethnic group and marginally with increased deprivation, but not associated with higher refusal in those living in large households or those with lesser health conditions. CONCLUSION: Certain ethnic minority groups, deprived populations, large households and 'healthier' individuals were less likely to receive a vaccine, although higher refusal was only associated with ethnicity and deprivation but not larger households nor healthier individuals. Understanding these may inform tailored public health messaging to different communities for equitable implementation of vaccination programmes.

Additional file 1.

Additional file 7: Table S8. Cox regression, risk of all-cause mortality according to switching to or augmenting SSRI with mirtazapine.

Additional file 2: Table S1. Propensity score model: multinomial logistic regression with mirtazapine as the reference group.

BACKGROUND AND RESEARCH AIM: The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings. METHODS: This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25-84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008-30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal-external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated. DISCUSSION: The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits.

Additional file 3: Additional Table 1. Overall p-values for predictors of percentage completeness for high quality studies only (n=10). Additional Table 2. Statistical significant predictors with beta values, standard errors and p-values, for high quality studies (n=10).

Additional file 8. Sensitivity analyses for all outcomes: varying the survival analysis models. Table S10. Sensitivity analyses for all-cause mortality: varying the survival analysis models. Table S11. Sensitivity analyses for deaths due to diseases of the circulatory system: varying the survival analysis models. Table S12. Sensitivity analyses for deaths due to diseases of the respiratory system: varying the survival analysis models. Table S13. Sensitivity analyses for deaths due to neoplasms: varying the survival analysis models.

Additional file 6: Table S7. Cox regression, risk of all-cause mortality after accounting for current antidepressant dose.

Additional file 4: Table S4. Full baseline characteristics.

Additional file 1. Co-investigators and members in the DeLIVER consortium.

Additional file 5. Sensitivity analyses: rates of all-cause mortality and mortality due to neoplasms. Table S5. Rates of all-cause mortality per 1000 person-years according to period of follow-up. Table S6. Sensitivity analysis ��� Rates of death due to neoplasms per 1000 person-years according to period of follow-up. Table S9. Sensitivity analysis ��� Rates of all-cause mortality per 1000 person-years after altering study inclusion criteria.

Additional file 1: Supplementary methods. S1. Drug exposure windows. Fig. S1. Drug data preparation algorithm, with options used highlighted. S2. Other variables. S3. Propensity score model.

Additional file 3: Table S2. Proportion of people with complete data and proportion of deaths recorded in people with complete data. Table S3. Missingness for each variable by study group.

Additional file 1. Sources, search strategy, and study selection.

OBJECTIVE: To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent. MATERIALS AND METHODS: We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models. RESULTS: Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival. CONCLUSION: Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.

Table S1. Numbers of prescriptions for different antidepressant drugs by dose category. Table S2. Adjusted hazard ratios for six adverse outcomes (falls, fracture, upper gastrointestinal bleed, adverse drug reaction, road traffic crash and all-cause mortality) by antidepressant class compared with no use of antidepressants in (A) ages 20â 44 years and (B) 45â 64 years over 5 years of follow-up. Table S3. Unadjusted and adjusted hazard ratios for six adverse outcomes (falls, fracture, upper gastrointestinal bleed, adverse drug reaction, road traffic crash and all-cause mortality) by antidepressant drug, compared with citalopram over 1 year of follow-up. Table S4. Adjusted hazard ratios for six adverse outcomes (falls, fracture, upper gastrointestinal bleed, adverse drug reaction, road traffic crash and all-cause mortality) by antidepressant class compared with no use of antidepressants according to duration of use and time since stopping for each antidepressant class over 5 years of follow-up. Table S5. Adjusted hazard ratios for six adverse outcomes (falls, fracture, upper gastrointestinal bleed, adverse drug reaction, road traffic crash and all-cause mortality) by antidepressant class compared with no use of antidepressants, over (A) total follow-up time and (B) 5 years of follow-up, excluding untreated patients. (DOCX 115 kb)