Assessing real world vaccine effectiveness: A review of Scotland's approach to monitoring human papillomavirus (HPV) vaccine impact on HPV infection and cervical disease.

High-risk human papillomavirus (HPV) infections can progress to cervical cancer which is the fourth most common cancer in women globally. In Scotland, the incidence of cervical cancer has a strong socioeconomic deprivation gradient disproportionately affecting women from more deprived areas. An HPV vaccination programme was initiated in Scotland in 2008 targeting girls aged 12-13 years with a catch-up campaign running for the first three years for girls aged up to 18 years. The programme has evolved over the last 16 years with changes in the type of vaccine, dosing schedules and the extension of the programme to boys and gay, bisexual and other men who have sex with men. Vaccine uptake in Scotland has historically been high but has gradually decreased over time and disparities exist in women from more deprived areas of Scotland. The ability to link national immunisation and screening databases in Scotland has allowed direct monitoring of the impact of the HPV vaccine on virological and histological outcomes. Analyses of this linked data have demonstrated real-world evidence of high vaccine effectiveness against HPV infection, cervical disease, and cervical cancer with evidence of herd immunity in unvaccinated women. Continued monitoring is crucial to assess the duration of protection, the impact of vaccine and dosing schedules changes and the emergence of potential type replacement. With the World Health Organisation's aim to eliminate cervical cancer as a public health problem by the next century addressing the inequalities in cervical cancer incidence will be crucial. This will require targeted interventions for women most at risk of cervical cancer to ensure elimination is achieved timely for all women in Scotland.

Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.

BACKGROUND: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. OBJECTIVE: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. RESULTS: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. CONCLUSIONS: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

Decision Support in Cardiac Surgery: Early Exploration of Requirements with Cardiac Anesthetists and Surgeons.

Successful implementation of clinical decision support tools is rare, the key barrier being the lack of user involvement during development. Following the idea, development, exploration, assessment, long-term follow-up (IDEAL) framework, this study aims to provide early insights into the current challenges, clinical processes, and priorities when developing new decision support tools in cardiac surgery. Using a qualitative approach, semi-structured interviews were conducted with cardiac anesthetists and surgeons from three Scottish cardiac centers. Thematic analysis identified adverse postoperative outcomes, ageing cardiac patient population and changing surgical procedures to be the main challenges in cardiac surgery. Existing risk prediction tools were largely not used due to a perceived lack of utility and validation. This study underscores the need to shift focus towards predicting postoperative complications, instead of mortality. It emphasizes the importance of early collaboration with clinical experts and stakeholders in developing decision support systems that are fit for purpose. By identifying the priorities of cardiac clinicians, the study lays the groundwork for developing clinically meaningful prediction models.

Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study.

BACKGROUND: Affecting 2-4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia. METHODS: We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (-LR) and positive (+LR) likelihood ratios. FINDINGS: Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76-0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63-0·74]) and categorised women into very low risk (-LR <0·1; eight [0·7%] of 1103 women), low risk (-LR 0·1 to 0·2; 321 [29·1%] women), moderate risk (-LR >0·2 and +LR <5·0; 676 [61·3%] women), high risk (+LR 5·0 to 10·0, 87 [7·9%] women), and very high risk (+LR >10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%). INTERPRETATION: The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers. FUNDING: University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation.

Invasive cervical cancer incidence following bivalent human papillomavirus vaccination: a population-based observational study of age at immunization, dose, and deprivation.

BACKGROUND: High-risk human papillomavirus causes cervical cancer. Vaccines have been developed that significantly reduce the incidence of preinvasive and invasive disease. This population-based observational study used linked screening, immunization, and cancer registry data from Scotland to assess the influence of age, number of doses, and deprivation on the incidence of invasive disease following administration of the bivalent vaccine. METHODS: Data for women born between January 1, 1988, and June 5, 1996, were extracted from the Scottish cervical cancer screening system in July 2020 and linked to cancer registry, immunization, and deprivation data. Incidence of invasive cervical cancer per 100 000 person-years and vaccine effectiveness were correlated with vaccination status, age at vaccination, and deprivation; Kaplan Meier curves were calculated. RESULTS: No cases of invasive cancer were recorded in women immunized at 12 or 13 years of age irrespective of the number of doses. Women vaccinated at 14 to 22 years of age and given 3 doses of the bivalent vaccine showed a significant reduction in incidence compared with all unvaccinated women (3.2/100 000 [95% confidence interval (CI) = 2.1 to 4.6] vs 8.4 [95% CI = 7.2 to 9.6]). Unadjusted incidence was significantly higher in women from most deprived (Scottish Index of Multiple Deprivation 1) than least deprived (Scottish Index of Multiple Deprivation 5) areas (10.1/100 000 [95% CI = 7.8 to 12.8] vs 3.9 [95% CI = 2.6 to 5.7]). Women from the most deprived areas showed a significant reduction in incidence following 3 doses of vaccine (13.1/100 000 [95% CI = 9.95 to 16.9] vs 2.29 [95% CI = 0.62 to 5.86]). CONCLUSION: Our findings confirm that the bivalent vaccine prevents the development of invasive cervical cancer and that even 1 or 2 doses 1 month apart confer benefit if given at 12-13 years of age. At older ages, 3 doses are required for statistically significant vaccine effectiveness. Women from more deprived areas benefit more from vaccination than those from less deprived areas.

Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art.

INTRODUCTION: Intensive care units (ICUs) are high-pressure, complex, technology-intensive medical environments where patient physiological data are generated continuously. Due to the complexity of interpreting multiple signals at speed, there are substantial opportunities and significant potential benefits in providing ICU staff with additional decision support and predictive modeling tools that can support and aid decision-making in real-time.This scoping review aims to synthesize the state-of-the-art dynamic prediction models of patient outcomes developed for use in the ICU. We define "dynamic" models as those where predictions are regularly computed and updated over time in response to updated physiological signals. METHODS: Studies describing the development of predictive models for use in the ICU were searched, using PubMed. The studies were screened as per Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, and the data regarding predicted outcomes, methods used to develop the predictive models, preprocessing the data and dealing with missing values, and performance measures were extracted and analyzed. RESULTS: A total of n = 36 studies were included for synthesis in our review. The included studies focused on the prediction of various outcomes, including mortality (n = 17), sepsis-related complications (n = 12), cardiovascular complications (n = 5), and other complications (respiratory, renal complications, and bleeding, n = 5). The most common classification methods include logistic regression, random forest, support vector machine, and neural networks. CONCLUSION: The included studies demonstrated that there is a strong interest in developing dynamic prediction models for various ICU patient outcomes. Most models reported focus on mortality. As such, the development of further models focusing on a range of other serious and well-defined complications-such as acute kidney injury-would be beneficial. Furthermore, studies should improve the reporting of key aspects of model development challenges.

The physical-mental health interface in the preconception period: analysis of 131 182 women planning pregnancy in the UK.

OBJECTIVE: The physical and mental health of women prior to conception can have a significant impact on pregnancy and child outcomes. Given the rising burden of non-communicable diseases, the aim was to explore the relation between mental health, physical health and health behaviour in women planning a pregnancy. METHODS: Cross-sectional analysis of responses from 131 182 women to a preconception health digital education tool, providing data on physical and mental health and health behaviour. Logistic regression was used to explore associations between mental health and physical health variables. RESULTS: Physical health conditions were reported by 13.1% and mental health conditions by 17.8%. There was evidence for an association between self-reported physical and mental health conditions (odds ratio [OR] 2.22, 95% CI 2.14-2.3). Those with a mental health condition were less likely to engage with healthy behaviour at preconception such as folate supplementation (OR 0.89, 95% CI 0.86-0.92) and consumption of the recommended amount of fruit and vegetables (OR 0.77, 95% CI 0.74-0.79). They were more likely to be physically inactive (OR 1.14, 95% CI 1.11-1.18), smoke tobacco (OR 1.72, 95% CI 1.66-1.78) and use illicit substances (OR 2.4, 95% CI 2.25-2.55). CONCLUSIONS: Greater recognition of mental and physical comorbidities is needed and closer integration of physical and mental healthcare in the preconception period, which could support people to optimise their health during this time and improve long-term outcomes.

Pre-pregnancy health of women with pre-existing diabetes or previous gestational diabetes: Analysis of pregnancy risk factors and behavioural data from a digital tool.

AIMS: To examine health behaviours and risk factors in women with pre-existing diabetes or previous gestational diabetes mellitus who are planning pregnancy. METHODS: Health behaviour, risk factor and demographic data obtained from a digital pregnancy planning advisory tool (Tommy's charity UK) were analysed. Descriptive statistical analysis was performed, stratified by diabetes type. RESULTS: Data from 84,359 women, including 668 with type 1 diabetes, 707 with type 2 diabetes and 1785 with previous gestational diabetes obtained over a 12-month period (September 2019-September 2020) were analysed. 65%, 95%CI (61,68%) of women with type 2 diabetes and 46%, 95%CI (43,48%) with previous gestational diabetes were obese (BMI ≥30 kg/m2 ), compared with 26%, 95%CI (26,26%) without diabetes. Use of folic acid supplements was low; 41%, 95%CI (40,41%) of women without diabetes and 42%, 95%CI (40,45%) with previous gestational diabetes reported taking folic acid (any dose) while 47%, 95%CI (43.50%) women with type 1 diabetes and 44%, 95%CI (40,47%) women with type 2 diabetes respectively reported taking the recommended dose (5 mg). More women with type 1 diabetes and type 2 diabetes reported smoking (20%, 95%CI [17,23%] and 23%, 95%CI [20,26%] respectively) and taking illicit/recreational drugs (7%, 95%CI [6,10%] and 9%, 95% CI [7,11%]) compared to women without diabetes (smoking 17%, 95% CI [16,17%], drug use 5%, 95%CI [5,5%]). Alcohol consumption, low levels of physical activity and of fruit and vegetable intake were also evident. CONCLUSIONS: This study highlights the potential of online pregnancy planning advisory tools to reach high-risk women and emphasises the need to improve pre-pregnancy care for women with pre-existing diabetes and previous gestational diabetes, many of whom are actively seeking advice. It is also the first to describe pre-pregnancy health behaviours in women with previous gestational diabetes.

Development and validation of a digital biomarker predicting acute kidney injury following cardiac surgery on an hourly basis.

Objectives: To develop and validate a digital biomarker for predicting the onset of acute kidney injury (AKI) on an hourly basis up to 24 hours in advance in the intensive care unit after cardiac surgery. Methods: The study analyzed data from 6056 adult patients undergoing coronary artery bypass graft and/or valve surgery between April 1, 2012, and December 31, 2018 (development phase, training, and testing) and 3572 patients between January 1, 2019, and June 30, 2022 (validation phase). The study used 2 dynamic predictive modeling approaches, namely logistic regression and bootstrap aggregated regression trees machine (BARTm), to predict AKI. The mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values across all lead times before the occurrence of AKI were reported. The clinical practicality was assessed using calibration. Results: Of all included patients, 8.45% and 16.66% had AKI in the development and validation phases, respectively. When applied to testing data, AKI was predicted with the mean AUC of 0.850 and 0.802 by BARTm and logistic regression, respectively. When applied to validation data, BARTm and LR resulted in a mean AUC of 0.844 and 0.786, respectively. Conclusions: This study demonstrated the successful prediction of AKI on an hourly basis up to 24 hours in advance. The digital biomarkers developed and validated in this study have the potential to assist clinicians in optimizing treatment and implementing preventive strategies for patients at risk of developing AKI after cardiac surgery in the intensive care unit.

Termination of wanted pregnancy and suicidal ideation in hyperemesis gravidarum: A mixed methods study.

Background: Difficulty accessing medication and poor patient experience have been implicated as risk factors for termination of pregnancy and suicidal ideation in women with hyperemesis gravidarum. We aimed to gain further insight into these factors in order to further inform and improve patient care. Methods: We performed a sub-analysis on quantitative data generated through a UK-wide survey of 5071 participants. A qualitative analysis of free text comments was performed using an inductive thematic approach. Results: 41.2% % of women taking prescribed medications had to actively request them. 'Extremely poor' or 'poor' experiences were described in 39.4% and 30.0% of participants in primary and secondary care respectively. Protective factors for termination of pregnancy and suicidal ideation include holistic support from family, friends and healthcare providers. Conclusion: Optimal care in hyperemesis gravidarum should incorporate timely access to pharmacotherapy, assessment of mental health, consideration of referral to specialist services and care being delivered in a compassionate manner.

Definition and Classification of Postoperative Complications After Cardiac Surgery: Pilot Delphi Study.

BACKGROUND: Postoperative complications following cardiac surgery are common and represent a serious burden to health services and society. However, there is a lack of consensus among experts on what events should be considered as a "complication" and how to assess their severity. OBJECTIVE: This study aimed to consult domain experts to pilot the development of a definition and classification system for complications following cardiac surgery with the goal to allow the progression of standardized clinical processes and systems in cardiac surgery. METHODS: We conducted a Delphi study, which is a well-established method to reach expert consensus on complex topics. We sent 2 rounds of surveys to domain experts, including cardiac surgeons and anesthetists, to define and classify postoperative complications following cardiac surgery. The responses to open-ended questions were analyzed using a thematic analysis framework. RESULTS: In total, 71 and 37 experts' opinions were included in the analysis in Round 1 and Round 2 of the study, respectively. Cardiac anesthetists and cardiac critical care specialists took part in the study. Cardiac surgeons did not participate. Experts agreed that a classification for postoperative complications for cardiac surgery is useful, and consensus was reached for the generic definition of a postoperative complication in cardiac surgery. Consensus was also reached on classification of complications according to the following 4 levels: "Mild," "Moderate," "Severe," and "Death." Consensus was also reached on definitions for "Mild" and "Severe" categories of complications. CONCLUSIONS: Domain experts agreed on the definition and classification of complications in cardiac surgery for "Mild" and "Severe" complications. The standardization of complication identification, recording, and reporting in cardiac surgery should help the development of quality benchmarks, clinical audit, care quality assessment, resource planning, risk management, communication, and research.

Opportunities and challenges when using record linkage of routinely collected electronic health care data to evaluate outcomes of systemic anti-cancer treatment in clinical practice.

The efficacy and safety of cancer medicines as reported from randomised clinical trials do not always translate into similar benefits in routine clinical practice; hence, post-marketing studies are a useful addition to the evidence base. With recent advances in digital infrastructure and the advent of electronically available health records, linkage of routinely collected data has emerged as a promising evaluation method for these studies. This paper discusses the opportunities and challenges when applying an electronic record linkage methodology with respect to systemic anti-cancer therapy by showcasing exemplar studies conducted over a three-year period in Scotland, and highlights some of the potential pitfalls spanning the entire breadth and depth of the research process. Our experiences as an interdisciplinary team indicate that there is scope to conduct large cohort studies to generate results from routine clinical practice within a reasonable time frame; however, close collaboration between researchers, data controllers and clinicians is required in order to obtain valid and meaningful results.

Health behaviours in 131,182 UK women planning pregnancy.

BACKGROUND: A woman's health at the time of conception lays the foundation for a healthy pregnancy and the lifelong health of her child. We investigated the health behaviours of UK women planning pregnancy. METHODS: We analysed survey data from the 'Planning for Pregnancy' online tool (Tommy's, UK). We described all women planning pregnancy and compared the frequency of non-adherence to preconception recommendations in women who had already stopped contraception (active planners) and those who had not (non-active planners). RESULTS: One hundred thirty-one thousand one hundred eighty-two women from across the UK were included, of whom 64.8% were actively planning pregnancy. Of the whole cohort, twenty percent were smokers and less than one third took folic acid supplements (31.5%). Forty two percent engaged in less than the recommended 150 min of weekly physical activity and only 53.3% consumed five portions of fruit or vegetables 4 days a week. Smokers were 1.87 times more likely to be active planners than non-smokers (95% CI 1.79-1.94), and women who took folic acid were 7 times more likely to be active planners (95% CI 6.97-7.59) compared to women who did not. Smoking, drug use and lack of folic acid supplementation were common in younger women and those who were underweight. CONCLUSIONS: This unique survey of UK women has identified poor adherence to preconception recommendations in those planning pregnancies and supports the need for a greater public health focus on preconception health. This study provides a contemporary basis from which to inform preconception health advice and a benchmark to measure changes over time.

Incidence of Clostridioides difficile infection (CDI) related to antibiotic prescribing by GP surgeries in Wales.

BACKGROUND: Clostridioides difficile infection (CDI) is a healthcare-acquired infection (HAI) causing significant morbidity and mortality. Welsh CDI rates are high in comparison with those in England and Scotland. OBJECTIVES: This retrospective ecological study used aggregated disease surveillance data to understand the impact of total and high-risk Welsh GP antibiotic prescribing on total and stratified inpatient/non-inpatient CDI incidence. METHODS: All cases of confirmed CDI, during the financial years 2014-15 to 2017-18, were linked to aggregated rates of antibiotic prescribing in their GP surgery and classified as 'inpatient', 'non-inpatient' or 'unknown' by Public Health Wales. Multivariable negative-binomial regression models, comparing CDI incidence with antibiotic prescribing rates, were adjusted for potential confounders: location; age; social deprivation; comorbidities (estimated from prevalence of key health indicators) and proton pump inhibitor (PPI) prescription rates. RESULTS: There were 4613 confirmed CDI cases, with an incidence (95% CI) of 1.44 (1.40-1.48) per 1000 registered patients. Unadjusted analysis showed that an increased risk of total CDI incidence was associated with higher total antibiotic prescribing [relative risk (RR) (95% CI) = 1.338 (1.170-1.529) per 1000 items per 1000 specific therapeutic group age-sex related GP prescribing units (STAR-PU)] and that high-risk antibiotic classes were positively associated with total CDI incidence. Location, age ≥65 years and diabetes were associated with increased risk of CDI. After adjusting for confounders, prescribing of clindamycin showed a positive association with total CDI incidence [RR (95% CI) = 1.079 (1.001-1.162) log items per 1000 registered patients]. CONCLUSIONS: An increased risk of CDI is demonstrated at a primary care practice population level, reflecting their antibiotic prescribing rates, particularly clindamycin, and population demographics.

The Impact of the COVID-19 Pandemic on Pregnancy Planning Behaviors.

Background: Our understanding of how the coronavirus disease 2019 (COVID-19) pandemic has impacted decision-making for women planning to conceive is unclear. We aimed to investigate how the COVID-19 pandemic has influenced pregnancy planning behaviors. Methods: An online questionnaire of closed- and open-ended questions was utilized to capture pregnancy planning behaviors and reported behavioral changes during the COVID-19 pandemic in women planning pregnancy between January and July 2020. Closed-ended questions were analyzed quantitatively, and thematic framework analysis was utilized for open-ended responses. Results: A total of 504 questionnaires were included for analysis. The majority of respondents lived in the United Kingdom. Ninety-two percent of the women were still planning a pregnancy but over half (n = 267) reported that COVID-19 had affected their plans, with 72% of these (n = 189) deliberately postponing pregnancy. Concerns were predominantly over changes in antenatal care, but also fear of adverse effects of the virus on mother and baby. From the thematic analysis (n = 37), lack of services to remove contraceptive devices and provide fertility treatment were also cited. In contrast, 27% (n = 71) reported bringing their pregnancy plans forward; common themes included recalibration of priorities and cancelled or changed plans. Conclusions: The COVID-19 pandemic influenced pregnancy-planning behaviors with many women reporting postponement of pregnancy. These alterations in behavior could impact the health and wellbeing of women planning pregnancy while having important implications for health care services worldwide. Continued provision of family planning and fertility services should be ensured to mitigate the effect of future outbreaks or pandemics.

Use of record linkage to evaluate treatment outcomes and trial eligibility in a real-world metastatic prostate cancer population in Scotland.

PURPOSE: New treatments are introduced into standard care based on clinical trial results. However, it is not clear if these benefits are reflected in the broader population. This study analysed the clinical outcomes of patients with metastatic castration-resistant prostate cancer, treated with abiraterone and enzalutamide, within the Scottish National Health Service. METHODS: Retrospective cohort study using record linkage of routinely collected healthcare data (study period: February 2012 to February 2017). Overall survival (OS) was analysed using Kaplan-Meier methods and Cox Proportional Hazard models; a subgroup analysis comprised potentially trial-eligible patients. RESULTS: Overall, 271 patients were included and 73.8% died during the study period. Median OS was poorer than in the pivotal trials, regardless of medication and indication: 10.8 months (95% confidence interval [CI] 8.6-15.1) and 20.9 months (95% CI 14.9-29.0) for abiraterone, and 12.6 months (95% CI 10.5-18.2) and 16.0 months (95% CI 9.8-not reached) for enzalutamide, post and pre chemotherapy, respectively. Only 46% of patients were potentially "trial eligible" and in this subgroup OS improved. Factors influencing survival included baseline performance status, and baseline prostate-specific antigen, alkaline phosphatase, and albumin levels. CONCLUSIONS: Poorer prognostic features of non-trial eligible patients impact real-world outcomes of cancer medicines. Electronic record linkage of routinely collected healthcare data offers an opportunity to report outcomes on cancer medicines at scale and describe population demographics. The availability of such observational data to supplement clinical trial results enables patients and clinicians to make more informed treatment decisions, and policymakers to contextualise trial findings.

Head and neck cancer risk calculator (HaNC-RC)-V.2. Adjustments and addition of symptoms and social history factors.

OBJECTIVES: Head and neck cancer (HNC) diagnosis through the 2-week wait, urgent suspicion of cancer (USOC) pathway has failed to increase early cancer detection rates in the UK. A head and neck cancer risk calculator (HaNC-RC) has previously been designed to aid referral of high-risk patients to USOC clinics (predictive power: 77%). Our aim was to refine the HaNC-RC to increase its prediction potential. DESIGN: Following sample size calculation, prospective data collection and statistical analysis of referral criteria and outcomes. SETTING: Large tertiary care cancer centre in Scotland. PARTICIPANTS: 3531 new patients seen in routine, urgent and USOC head and neck (HaN) clinics. MAIN OUTCOME MEASURES: Data collected were as follows: demographics, social history, presenting symptoms and signs and HNC diagnosis. Univariate and multivariate regression analysis were performed to identify significant predictors of HNC. Internal validation was performed using 1000 sample bootstrapping to estimate model diagnostics included the area under the receiver operator curve (AUC), sensitivity and specificity. RESULTS: The updated version of the risk calculator (HaNC-RC v.2) includes age, gender, unintentional weight loss, smoking, alcohol, positive and negative symptoms and signs of HNC. It has achieved an AUC of 88.6% with two recommended triage referral cut-offs to USOC (cut-off: 7.1%; sensitivity: 85%, specificity: 78.3%) or urgent clinics (cut-off: 2.2%; sensitivity: 97.1%; specificity of 52.9%). This could redistribute cancer detection through USOC clinics from the current 60.9%-85.2%, without affecting total numbers seen in each clinical setting. CONCLUSIONS: The use of the HaNC-RC v.2 has a significant potential in both identifying patients at high risk of HNC early thought USOC clinics but also improving health service delivery practices by reducing the number of inappropriately urgent referrals.

Seasonal Influenza Vaccine Effectiveness in People With Asthma: A National Test-Negative Design Case-Control Study.

BACKGROUND: Influenza infection is a trigger of asthma attacks. Influenza vaccination can potentially reduce the incidence of influenza in people with asthma, but uptake remains persistently low, partially reflecting concerns about vaccine effectiveness (VE). METHODS: We conducted a test-negative designed case-control study to estimate the effectiveness of influenza vaccine in people with asthma in Scotland over 6 seasons (2010/2011 to 2015/2016). We used individual patient-level data from 223 practices, which yielded 1 830 772 patient-years of data that were linked with virological (n = 5910 swabs) data. RESULTS: Vaccination was associated with an overall 55.0% (95% confidence interval [CI], 45.8-62.7) risk reduction of laboratory-confirmed influenza infections in people with asthma over 6 seasons. There were substantial variations in VE between seasons, influenza strains, and age groups. The highest VE (76.1%; 95% CI, 55.6-87.1) was found in the 2010/2011 season, when the A(H1N1) strain dominated and there was a good antigenic vaccine match. High protection was observed against the A(H1N1) (eg, 2010/2011; 70.7%; 95% CI, 32.5-87.3) and B strains (eg, 2010/2011; 83.2%; 95% CI, 44.3-94.9), but there was lower protection for the A(H3N2) strain (eg, 2014/2015; 26.4%; 95% CI, -12.0 to 51.6). The highest VE against all viral strains was observed in adults aged 18-54 years (57.0%; 95% CI, 42.3-68.0). CONCLUSIONS: Influenza vaccination gave meaningful protection against laboratory-confirmed influenza in people with asthma across all seasons. Strategies to boost influenza vaccine uptake have the potential to substantially reduce influenza-triggered asthma attacks.

Discontinuation, persistence and adherence to subcutaneous biologics delivered via a homecare route to Scottish adults with rheumatic diseases: a retrospective study.

OBJECTIVES: To understand patterns of subcutaneous (SC) biologics use over time in adults with inflammatory rheumatic musculoskeletal diseases receiving a homecare delivery service. DESIGN: Retrospective cohort. SETTING: Patients in secondary care receiving SC biologics in the largest Scottish Health Board. PARTICIPANTS: A new bespoke cohort was created from routine data gathered as part of a health board Homecare Service Database. Patients over 18 years who received a supply of SC biologic from January 2012 to May 2015 with a diagnosis for rheumatoid arthritis (RA), psoriatic arthritis (PsA) or ankylosing spondylitis (AS) were included. OUTCOMES MEASURED: A standardised framework was applied by measuring discontinuation rates, persistence using Kaplan-Meier analysis and Cox regression and adherence using medication refill adherence (MRA) and compliance rate (CR). RESULTS: 751 patients were identified (AS: 105, PsA: 227, RA: 419) of whom 89.3% had more than one biologic delivery (median days' follow-up: AS: 494; PsA: 544; RA: 529) and 83.2% did not switch biologic. For all conditions, approximately half were persistent on their index biologic (52% AS, 54% PsA, 48%RA). Of patients who discontinued treatment, the majority reinitiated with the same biologic (19% AS, 18% PsA and 21% RA). Overall adherence during the period of treatment was over 80% when calculated using MRA (median %MRA: AS: 84.0%, PsA: 85.0%, RA: 82.4%) or CR (median %CR: AS: 96.6%, PsA: 97%, RA: 96.6%). CONCLUSION: Use of linked routine data is a sustainable pathway to enable ongoing evaluation of biologics use. A more consistent approach to studying use (discontinuation, persistence and adherence metrics) should be adopted to enable comparability of studies.