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Objective: The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland., Methods: We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021., Results: Our validation dataset comprised 465 058 individuals, aged 19-100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell's C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death., Conclusions: We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated., Competing Interests: Competing interests: JH-C reports grants from MRC, grants from Wellcome Trust, grants from NIHR, during the conduct of the study; JH-C is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019. ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. JH-C was chair of the NERVTAG risk stratification subgroup and a member of SAGE COVID-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in COVID-19 infection. AS reports grants from NIHR, grants from MRC, grants from HDR UK, during the conduct of the study. All other authors report no conflict of interest., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

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., Competing Interests: Competing interests: PST reports previous consultation with AstraZeneca and Duke-NUS outside the submitted work. KK is a Member of the Scientific Advisory Group for Emergencies (SAGE), Member of Independent SAGE, Director of the University of Leicester Centre for Black Minority Health and Trustee of the south Asian Health Foundation. JH-C is a member of several SAGE committees and chair of the risk stratification subgroup of the NERVTAG. She is an unpaid director of QResearch and founder and former medical director of ClinRisk Ltd (outside the submitted work). MP, AKC, HD-M, DS, TAR, FZ, BRS, SJG, CC, CG have no interests to declare., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Dysmenorrhoea affects up to 70%-91% of adolescents who menstruate, with approximately one-third experiencing severe symptoms with impacts on education, work and leisure. Dysmenorrhoea can occur without identifiable pathology, but can indicate underlying conditions, including congenital genital tract anomalies or endometriosis. There is a need for evidence about the management and incidence of dysmenorrhoea in primary care, the impact of treatments in adolescence on long-term outcomes and when to consider the possibility of endometriosis in adolescence., Methods and Analysis: This study aims to improve the evidence base for adolescents presenting to primary care with dysmenorrhoea. It comprises three interlinked studies. Using the QResearch Database, the study population includes all female at birth participants aged 10-19 years any time between 1 January 2000 and 30 June 2021. We will undertake (1) a descriptive study documenting the prevalence of coded dysmenorrhoea in primary care, stratified by demographic variables, reported using descriptive statistics; (2) a prospective open cohort study following an index cohort of all adolescents recorded as attending primary care with dysmenorrhoea and a comparator cohort of five times as many who have not, to determine the HR for a diagnosis of endometriosis, adenomyosis, ongoing menstrual pain or subfertility (considered singly and in combination) anytime during the study period; and (3) a nested case-control study for adolescents diagnosed with endometriosis, using conditional logistic regression, to determine the OR for symptom(s) preceding this diagnosis., Ethics and Dissemination: The project has been independently peer reviewed and received ethics approval from the QResearch Scientific Board (reference OX46 under REC 18/EM/0400).In addition to publication in peer-reviewed academic journals, we will use the combined findings to generate a resource and infographic to support shared decision-making about dysmenorrhoea in community health settings. Additionally, the findings will be used to inform a subsequent qualitative study, exploring adolescents' experiences of menstrual pain., Competing Interests: Competing interests: KV has received research funding from Bayer Healthcare and honoraria for talks and consultancy fees from Bayer Healthcare, AbbVie and Eli Lilly. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JHC is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside the scope of this work) into clinical computer systemsThe remaining authors declare no relevant conflict of interest., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Introduction: The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm., Methods and Analysis: We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell's C, Brier Score, R 2 and Royston's D., Ethics and Dissemination: Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal., Competing Interests: Competing interests: AS reports grants from NIHR, grants from MRC, and grants from HRR UK, during the conduct of the study. JL and RL report grants from UKRI Medical Research Council, during the conduct of the study. JH-C reports grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z), grants from NIHR, during the conduct of the study; personal fees and other from ClinRisk, outside the submitted work; and JH-C is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. Carol Coupland reports personal fees from ClinRisk, outside the submitted work. JH-C, AS and CC were members of the research team involved in the development of the QCOVID risk prediction algorithm. All other authors report no conflict of interest, (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening., Methods and Analysis: The study will use data for women aged 20-90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the 'combined' risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with 'apparent' performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups., Ethics and Dissemination: Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals., Competing Interests: Competing interests: JH-C is an unpaid director of QResearch (a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database) and is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019 (ClinRisk produces open and closed source software to implement clinical risk algorithms (including a breast cancer prediction algorithm) into clinical computer systems., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Hormone replacement therapy (HRT) can help women experiencing menopausal symptoms, but usage has declined due to uncertainty around risks of cancer and some cardiovascular diseases (CVD). Moreover, improved cancer survival rates mean that more women who survive cancer go on to experience menopausal symptoms. Understanding these relationships is important so that women and their clinicians can make informed decisions around the risks and benefits of HRT. This study's primary aim is to determine the association between HRT use after cancer diagnosis and the risk of cancer-specific mortality. The secondary aims are to investigate the risks of HRT on subsequent cancer, all-cause mortality and CVD., Methods and Analysis: We will conduct a population-based longitudinal cohort study of 18-79 year-old women diagnosed with cancer between 1998 and 2020, using the QResearch database. The main exposure is HRT use, categorised based on compound, dose and route of administration, and modelled as a time-varying covariate. Analysis of HRT use precancer and postcancer diagnosis will be conducted separately. The primary outcome is cancer-specific mortality, which will be stratified by cancer site. Secondary outcomes include subsequent cancer diagnosis, CVD (including venous thrombo-embolism) and all-cause mortality. Adjustment will be made for key confounders such as age, body mass index, ethnicity, deprivation index, comorbidities, and cancer grade, stage and treatment. Statistical analysis will include descriptive statistics and Cox proportional hazards models to calculate HRs and 95% CIs., Ethics and Dissemination: Ethical approval for this project was obtained from the QResearch Scientific Committee (Ref: OX24, project title 'Use of hormone replacement therapy and survival from cancer'). This project has been, and will continue to be, supported by patient and public involvement panels. We intend to the submit the findings for peer-reviewed publication in an academic journal and disseminate them to the public through Cancer Research UK., Competing Interests: Competing interests: JH-C is an unpaid director of the Qresearch database (partnership between the University of Oxford and EMIS, commercial supplier of GP computer systems). She is shareholder and former director of ClinRisk Ltd outside the submitted work. PST reports consulting with AstraZeneca and Duke-NUS outside the submitted work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Recent evidence suggests that ethnic minority groups are disproportionately at increased risk of hospitalisation and death from SARS-CoV-2 infection. Population-based evidence on potential explanatory factors across minority groups and within subgroups is lacking. This study aims to quantify the association between ethnicity and the risk of hospitalisation and mortality due to COVID-19., Methods and Analysis: This is a retrospective cohort study of adults registered across a representative and anonymised national primary care database (QResearch) that includes data on 10 million people in England. Sociodemographic, deprivation, clinical and domicile characteristics will be summarised and compared across ethnic subgroups (categorised as per 2011 census). Cox models will be used to calculate HR for hospitalisation and COVID-19 mortality associated with ethnic group. Potential confounding and explanatory factors (such as demographic, socioeconomic and clinical) will be adjusted for within regression models. The percentage contribution of distinct risk factor classes to the excess risks seen in ethnic groups/subgroups will be calculated., Ethics and Dissemination: The study has undergone ethics review in accordance with the QResearch agreement (reference OX102). Findings will be disseminated through peer-reviewed manuscripts, presentations at scientific meetings and conferences with national and international stakeholders., Competing Interests: Competing interests: JH-C is founder and director of QResearch database, which is a not-for-profit organisation with EMIS (leading commercial supplier of IT for 55% of general practices in the UK). JH-C is co-owner of ClinRisk and was a paid director until June 2019. ClinRisk develops open and closed source software to ensure the reliable and updatable implementation of clinical risk equations within clinical computer systems to help improve patient care, outside the submitted work. KK is national lead for NIHR ARC ethnicity and diversity, Director for the University of Leicester Centre for BME Health, Trustee of the South Asian Health Foundation and member of the Independent SAGE. The authors declare that no support from any organisation and no financial relationships have influenced the submitted work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: The validity of bullous pemphigoid and pemphigus vulgaris recording in routinely collected healthcare data in the UK is unknown. We assessed the positive predictive value (PPV) for bullous pemphigoid and pemphigus vulgaris primary care Read codes in the Clinical Practice Research Datalink (CPRD) using linked inpatient data (Hospital Episode Statistics (HES)) as the diagnostic benchmark., Setting: Adult participants with bullous pemphigoid or pemphigus vulgaris registered with HES-linked general practices in England between January 1998 and December 2017. Code-based algorithms were used to identify patients from the CPRD and extract their benchmark blistering disease diagnosis from HES., Primary Outcome Measure: The PPVs of Read codes for bullous pemphigoid and pemphigus vulgaris., Results: Of 2468 incident cases of bullous pemphigoid and 431 of pemphigus vulgaris, 797 (32.3%) and 85 (19.7%) patients, respectively, had a hospitalisation record for a blistering disease. The PPV for bullous pemphigoid Read codes was 93.2% (95% CI 91.3% to 94.8%). Of the bullous pemphigoid cases, 3.0% had an HES diagnosis of pemphigus vulgaris and 3.8% of another blistering disease. The PPV for pemphigus vulgaris Read codes was 58.5% (95% CI 48.0% to 68.9%). Of the pemphigus vulgaris cases, 24.7% had an HES diagnosis of bullous pemphigoid and 16.5% of another blistering disease., Conclusions: The CPRD can be used to study bullous pemphigoid, but recording of pemphigus vulgaris needs to improve in primary care., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Objectives: To describe implementation of a new national preventive programme to reduce cardiovascular morbidity., Design: Observational study over 4 years (April 2009--March 2013)., Setting: 655 general practices across England from the QResearch database., Participants: Eligible adults aged 40-74 years including attendees at a National Health Service (NHS) Health Check., Intervention: NHS Health Check: routine structured cardiovascular check with support for behavioural change and in those at highest risk, treatment of risk factors and newly identified comorbidity., Results: Of 1.68 million people eligible for an NHS Health Check, 214 295 attended in the period 2009-12. Attendance quadrupled as the programme progressed; 5.8% in 2010 to 30.1% in 2012. Attendance was relatively higher among older people, of whom 19.6% of those eligible at age 60-74 years attended and 9.0% at age 40-59 years. Attendance by population groups at higher cardiovascular disease (CVD) risk, such as the more socially disadvantaged 14.9%, was higher than that of the more affluent 12.3%. Among attendees 7844 new cases of hypertension (38/1000 Checks), 1934 new cases of type 2 diabetes (9/1000 Checks) and 807 new cases of chronic kidney disease (4/1000 Checks) were identified. Of the 27,624 people found to be at high CVD risk (20% or more 10-year risk) when attending an NHS Health Check, 19.3% (5325) were newly prescribed statins and 8.8% (2438) were newly prescribed antihypertensive therapy., Conclusions: NHS Health Check coverage was lower than expected but showed year-on-year improvement. Newly identified comorbidities were an important feature of the NHS Health Checks. Statin treatment at national scale for 1 in 5 attendees at highest CVD risk is likely to have contributed to important reductions in their CVD events., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Introduction: Risk thresholds for using statins to prevent cardiovascular disease (CVD) have recently been lowered, so an increasing number of patients are now prescribed these drugs. Although the safety of long-term statin use has been generally established, concerns about the balance of risks and benefits of statins still exist for some medical professionals and patients, and issues concerning their side effects are occasionally widely publicised. This study will report the rates of stopping for statins and also identify any patient groups more likely to stop using statins, so possibly increasing their risk of cardiovascular events., Methods and Analysis: A prospective open cohort study between 1 January 2002 and 30 September 2014 will be based on the general population of people prescribed statins, using records from UK general practices contributing to the Clinical Practice Research Database (CPRD). Participants aged 25-84 years will enter the cohort on the date of their first prescription for a statin and leave on the earliest date of: a cardiovascular event; death; leaving the practice; the last practice upload date or the study end date. If there are no prescriptions within 90 days after the expected finishing date of a prescription, a patient will be defined as a stopper with the discontinuation outcome date as the expected finishing date. Rates of statin discontinuation will be calculated by calendar year, type and dose of statin, age, and morbidities. Cox proportional regression analyses will be run to identify the most important factors associated with discontinuation. Analyses will be run separately for patients without CVD (primary prevention) and with diagnosed CVD (secondary prevention)., Ethics and Dissemination: The protocol has been reviewed and approved by Independent Scientific Advisory Committee for MHRA Database Research. The results will be published in a peer-reviewed journal., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Objective: To develop and externally validate risk prediction equations to estimate the 10-year risk of heart failure in patients with diabetes, aged 25-84 years., Design: Cohort study using routinely collected data from general practices in England between 1998 and 2014 contributing to the QResearch and Clinical Research Practice Datalink (CPRD) databases., Setting: We used 763 QResearch practices to develop the equations. We validated it in 254 different QResearch practices and 357 CPRD practices., Participants: 437,806 patients in the derivation cohort; 137,028 in the QResearch validation cohort, and 197,905 in the CPRD validation cohort., Measurement: Incident diagnosis of heart failure recorded on the patients' linked electronic General Practitioner (GP), mortality, or hospital record. Risk factors included age, body mass index (BMI), systolic blood pressure, cholesterol/ high-density lipoprotein (HDL) ratio, glycosylated haemoglobin (HbA1c), material deprivation, ethnicity, smoking, diabetes duration, type of diabetes, atrial fibrillation, cardiovascular disease, chronic renal disease, and family history of premature coronary heart disease., Methods: We used Cox proportional hazards models to derive separate risk equations in men and women for evaluation at 10 years. Measures of calibration, discrimination, and sensitivity were determined in 2 external validation cohorts., Results: We identified 25,480 cases of heart failure in the derivation cohort, 8189 in the QResearch validation cohort, and 11,311 in the CPRD cohort. The equations included: age, BMI, systolic blood pressure, cholesterol/HDL ratio, HbA1c, material deprivation, ethnicity, smoking, duration and type of diabetes, atrial fibrillation, cardiovascular disease, and chronic renal disease. The equations had good performance in CPRD for women (R(2) of 41.2%; D statistic 1.71; and receiver operating characteristic curve (ROC) statistic 0.78) and men (38.7%, 1.63; and 0.77 respectively)., Conclusions: We have developed and externally validated risk prediction equations to quantify absolute risk of heart failure in men and women with diabetes. These can be used to identify patients at high risk of heart failure for prevention or assessment of the disease., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objective: To derive and validate a set of clinical risk prediction algorithm to estimate the 10-year risk of 11 common cancers., Design: Prospective open cohort study using routinely collected data from 753 QResearch general practices in England. We used 565 practices to develop the scores and 188 for validation., Subjects: 4.96 million patients aged 25-84 years in the derivation cohort; 1.64 million in the validation cohort. Patients were free of the relevant cancer at baseline., Methods: Cox proportional hazards models in the derivation cohort to derive 10-year risk algorithms. Risk factors considered included age, ethnicity, deprivation, body mass index, smoking, alcohol, previous cancer diagnoses, family history of cancer, relevant comorbidities and medication. Measures of calibration and discrimination in the validation cohort., Outcomes: Incident cases of blood, breast, bowel, gastro-oesophageal, lung, oral, ovarian, pancreas, prostate, renal tract and uterine cancers. Cancers were recorded on any one of four linked data sources (general practitioner (GP), mortality, hospital or cancer records)., Results: We identified 228,241 incident cases during follow-up of the 11 types of cancer. Of these 25,444 were blood; 41,315 breast; 32,626 bowel, 12,808 gastro-oesophageal; 32,187 lung; 4811 oral; 6635 ovarian; 7119 pancreatic; 35,256 prostate; 23,091 renal tract; 6949 uterine cancers. The lung cancer algorithm had the best performance with an R(2) of 64.2%; D statistic of 2.74; receiver operating characteristic curve statistic of 0.91 in women. The sensitivity for the top 10% of women at highest risk of lung cancer was 67%. Performance of the algorithms in men was very similar to that for women., Conclusions: We have developed and validated a prediction models to quantify absolute risk of 11 common cancers. They can be used to identify patients at high risk of cancers for prevention or further assessment. The algorithms could be integrated into clinical computer systems and used to identify high-risk patients., Web Calculator: There is a simple web calculator to implement the Qcancer 10 year risk algorithm together with the open source software for download (available at http://qcancer.org/10yr/)., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objectives: To validate the performance of a set of risk prediction algorithms developed using the QResearch database, in an independent sample from general practices contributing to the Clinical Research Data Link (CPRD)., Setting: Prospective open cohort study using practices contributing to the CPRD database and practices contributing to the QResearch database., Participants: The CPRD validation cohort consisted of 3.3 million patients, aged 25-99 years registered at 357 general practices between 1 Jan 1998 and 31 July 2012. The validation statistics for QResearch were obtained from the original published papers which used a one-third sample of practices separate to those used to derive the score. A cohort from QResearch was used to compare incidence rates and baseline characteristics and consisted of 6.8 million patients from 753 practices registered between 1 Jan 1998 and until 31 July 2013., Outcome Measures: Incident events relating to seven different risk prediction scores: QRISK2 (cardiovascular disease); QStroke (ischaemic stroke); QDiabetes (type 2 diabetes); QFracture (osteoporotic fracture and hip fracture); QKidney (moderate and severe kidney failure); QThrombosis (venous thromboembolism); QBleed (intracranial bleed and upper gastrointestinal haemorrhage). Measures of discrimination and calibration were calculated., Results: Overall, the baseline characteristics of the CPRD and QResearch cohorts were similar though QResearch had higher recording levels for ethnicity and family history. The validation statistics for each of the risk prediction scores were very similar in the CPRD cohort compared with the published results from QResearch validation cohorts. For example, in women, the QDiabetes algorithm explained 50% of the variation within CPRD compared with 51% on QResearch and the receiver operator curve value was 0.85 on both databases. The scores were well calibrated in CPRD., Conclusions: Each of the algorithms performed practically as well in the external independent CPRD validation cohorts as they had in the original published QResearch validation cohorts., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Introduction: Many studies have found an increased risk of venous thromboembolism (VTE) associated with the use of combined hormonal contraceptives, but various methodologies have been used in the study design relating to definition of VTE event and the selection of appropriate cases for analysis. This study will focus on common oral hormonal contraceptives, including compositions with cyproterone because of their contraceptive effect and will perform a number of sensitivity analyses to compare findings with previous studies., Methods and Analysis: 2 nested case-control studies will be based on the general population using records from UK general practices within the QResearch and Clinical Practice Research Datalink databases. Cases will be female patients aged 15-49 with primary VTE diagnosed between 2001 and 2013. Each case will be matched by age, year of birth and practice to five female controls, who are alive and registered with the practice at the time of diagnosis of the case (index date). Exposure to different hormonal contraceptives will be defined as at least one prescription for that contraceptive in the year before the index date. The effects of duration and the length of any gap since last use will also be investigated. Conditional logistic regression will be applied to calculate ORs adjusted for smoking, ethnicity, comorbidities and use of other medications. Possible indications for prescribing hormonal contraceptives, such as menstrual disorders, acne or hirsutism will be included in the analyses as confounding factors. A number of sensitivity analyses will be carried out., Ethics and Dissemination: The initial protocol has been reviewed and approved by ISAC (Independent Scientific Advisory Committee) for Medicine and Healthcare Products Regulatory Agency Database Research. The project has also been reviewed by QResearch and meets the requirements of the Trent Research Ethics Committee. The results will be published in a peer-reviewed journal.

Objective: To develop and externally validate a risk algorithm (QAdmissions) to estimate the risk of emergency hospital admission for patients aged 18-100 years in primary care., Design: Prospective open cohort study using routinely collected data from general practice linked to hospital episode data during the 2-year study period 1 January 2010 to 31 December 2011., Setting: 405 general practices in England contributing to the national QResearch database to develop the algorithm. Two validation cohorts to validate the algorithm (1) 202 different QResearch practices and (2) 343 practices in England contributing to the Clinical Practice Research DataLink (CPRD). All general practices had data linked to hospital episode statistics at the individual patient level., Participants: We studied 2 849 381 patients aged 18-100 years in the derivation cohort with over 4.6 million person-years of follow-up. 265 573 of these patients had one or more emergency admissions during follow-up. For the QResearch validation cohort, we identified 1 340 622 patients aged 18-100 years with over 2.2 million person-years of follow-up. Of these patients, 132 723 had one or more emergency admissions during follow-up. The CPRD cohort included 2 475 360 patients aged 18-100 years with over 3.8 million person-years of follow-up. 234 204 of these patients had one or more emergency admissions during follow-up. We excluded patients without a valid NHS number and a valid Townsend score., Endpoint: First (ie, incident) emergency admission to hospital in the next 2 years as recorded on the linked hospital episodes records., Risk Factors: Candidate variables recorded on the general practitioner computer system including (1) demographic variables (age, sex, strategic health authority, Townsend deprivation score, ethnicity); (2) lifestyle variables (smoking, alcohol intake); (3) chronic diseases; (4) prescribed medication; (5) clinical values (body mass index, systolic blood pressure); (6) laboratory test results (haemoglobin, platelets, erythrocyte sedimentation rate, ratio of total serum cholesterol to high density lipoprotein cholesterol concentrations, liver function tests). We also included the number of emergency admissions in the preceding year based on information recorded on the linked hospital episodes records., Results: The final QAdmissions algorithm incorporated 30 variables. When applied to the QResearch validation cohort, it explained 41% of the variation in women and 43% of that in men. The D statistic for QAdmissions was 1.7 in women and 1.8 in men. The receiver operating curve statistic was 0.78 for men and 0.77 for women. QAdmissions had good performance on all measures of discrimination and calibration. The positive predictive value for emergency admissions for the top tenth of patients at highest risk was 42% and the sensitivity was 39%. The results for the CPRD validation cohort were similar., Conclusions: The QAdmissions model provided a valid measure of absolute risk of emergency admission to hospital in the general population as shown by its performance in a separate validation cohort. Further research is needed to evaluate the cost-effectiveness of using these algorithms in primary care.

Introduction Bisphosphonates are becoming a common treatment for osteoporosis particularly after discovery of the association between hormone replacement therapy and increased risk of breast cancer. As osteoporosis develops with age, treatment is a long-term intervention. Randomised control trials typically have limited follow-up times, which restricts investigation of the effects of the drugs on risk of primary cancers. A few observational studies have demonstrated a reduced risk of breast cancer and possibly of endometrial cancer in bisphosphonate users. Two epidemiological studies have studied the effect of the drugs on oesophageal cancer but did not reach any definite conclusions. So far, no effects on colorectal and stomach cancer have been shown. This study will investigate the association of bisphosphonates with risks of the 10 most common primary cancers. Methods and analysis A series of nested case-control studies will be based on the general population using records from 660 UK general practices within the QResearch Database. Cases will be patients with primary cancers diagnosed between 1996 and 2011. Each case will be matched by age, sex, practice and calendar year to five controls, who are alive and registered with the practice at the time of diagnosis of the case. Exposure to bisphosphonates will be defined as at least one prescription during the study period. For the most common cancers with substantial numbers of observations, the effect of the duration of the treatment and different types of bisphosphonates will be studied. Conditional logistic regression will be applied to produce ORs adjusted for smoking status, socioeconomic status, ethnicity, cancer-specific co-morbidities and use of other medications.