Research Flashcards

Question

RCS Values: Respect

Answer 1

We value every person we come into contact with at the College as an individual, respect their aspirations and commitments in life, and seek to understand and meet their physical and wellbeing needs. * We treat everyone we meet with kindness and integrity and we seek to promote these behaviours in others * We actively seek a range of views and experiences across our work, and we listen to, and make everyone feel, a valued part of the team

Answer 2

We aspire to excellence and success. We share learning from our experiences, apply feedback into practice, and commit to continual improvement. * We work hard to be the best at what we do, recognising and celebrating effort and achievement, and reflecting on our work, so we can learn and improve * We value and invest in research, education and training to drive excellence and put improvements in surgical practice, dentistry and patient care at the heart of our work * We always seek to learn and discover more, valuing knowledge and scientific evidence, basing our decisions on insights, fact and experience

Answer 3

The Centre will have approximately 20 staff from a range of disciplines including statistics, data science, health services research, epidemiology, healthcare quality improvement and clinical audit management. It will be led by Dr Julie Nossiter, who will be the Centre’s Operations Director, and Prof David Cromwell, Dr Kate Walker, and Prof Jan van der Meulen who are public health academics at London School of Hygiene and Tropical Medicine (LSHTM).

Answer 4

The Clinical Effectiveness Unit (CEU) is a collaboration between The Royal College of Surgeons of England and the Department of Health Services Research and Policy of the London School of Hygiene and Tropical Medicine (LSHTM). The work of the CEU involves carrying out national clinical audits, developing audit methodologies and producing evidence on clinical and cost effectiveness. The CEU currently delivers cancer audits in prostate, lung, bowel, oesophageal and stomach cancer, and recently completed an audit of breast cancer in older patients. These audits have helped to identify and address variations in cancer care across England and Wales, and improve outcomes for patients. They have also promoted quality improvement initiatives within NHS cancer services and identified best practice. An essential element of the CEU’s strategy is that it considers audit projects as epidemiological studies of the quality of hospital care. Epidemiological methods are used to generate high quality evidence on the processes and outcomes of hospital care as well as on their determinants. Another important feature of the CEU’s strategy is the emphasis it gives to joint clinical and methodological leadership. The CEU has 20 staff members, of whom 6 are academic staff members of LSHTM. The background of the staff demonstrates the multidisciplinary character of the Unit (medicine, health services research, medical statistics, epidemiology and public health). The Unit’s Director is David Cromwell, Professor of Health Services Research.

Answer 5

* ASSOCIATE PROFESSOR OF MEDICAL STATISTICS * I completed my PhD in 2006 at UCL and worked for a year at Queen Mary University as a research fellow in the Wolfson Institute of Preventive Medicine. I joined LSHTM in 2007 in the Faculty of Epidemiology and Population Health, and started my current role, attached to the Royal College of Surgeons, in February 2011. * I am Deputy Module Organiser for Basic Statistics for Public Health and Policy. I also lecture and run practicals on and Advanced Statistical Methods in Epidemiology (ASME). * I am based in the Clinical Effectiveness Unit, a collaboration with the Royal College of Surgeons. My research includes developing risk models, statistical issues in performance monitoring, linkage of multiple routine datasets and developing clinical indicators from large routine datasets. The clinical area of my research is primarily the management and outcomes of patients with bowel cancer. I am the lead methodologist for the National Bowel Cancer Audit, carrying out health services research using linked data from large clinical and administrative databases, and I am a methodologist for the National Emergency Laparotomy Audit.

Answer 6

* Probabilistic linkage without personal information successfully linked national clinical datasets. 2021. * Surgical Treatment and Outcomes of Colorectal Cancer Patients During the COVID-19 Pandemic: A National Population-based Study in England. 2021.

Answer 7

BACKGROUND: Probabilistic linkage can link patients from different clinical databases without the need for personal information. If accurate linkage can be achieved, it would accelerate the use of linked datasets to address important clinical and public health questions. OBJECTIVE: We developed a step-by-step process for probabilistic linkage of national clinical and administrative datasets without personal information, and validated it against deterministic linkage using patient identifiers. STUDY DESIGN AND SETTING: We used electronic health records from the National Bowel Cancer Audit and Hospital Episode Statistics databases for 10,566 bowel cancer patients undergoing emergency surgery in the English National Health Service. RESULTS: Probabilistic linkage linked 81.4% of National Bowel Cancer Audit records to Hospital Episode Statistics, vs. 82.8% using deterministic linkage. No systematic differences were seen between patients that were and were not linked, and regression models for mortality and length of hospital stay according to patient and tumour characteristics were not sensitive to the linkage approach. CONCLUSION: Probabilistic linkage was successful in linking national clinical and administrative datasets for patients undergoing a major surgical procedure. It allows analysts outside highly secure data environments to undertake linkage while minimizing costs and delays, protecting data security, and maintaining linkage quality.

Answer 8

**Introduction:** Studies reporting lower rates of surgery for older women with early invasive breast cancer (EIBC) have focused on women with oestrogen receptor (ER-) positive tumours. This study examined the factors that influence receipt of breast surgery in older women with ER-positive and ER-negative EIBC. **Methods**: Women aged ≥50yrs with unilateral EIBC (stage 1-3A) diagnosed between 2014-2017 were identified from linked English and Welsh cancer registration and routine hospital datasets. Logistic regression was used to evaluate influence of tumour and patient factors on receipt of surgery. **Results**: Among 83,188 women, 87% had ER-positive and 13% had ER-negative EIBC. This percentage was unaffected by age at diagnosis. In comparison to women with ER-negative EIBC, a higher percentage of women with ER-positive EIBC presented with low risk tumour characteristics: G1 (20% vs. 2%), T1 (61% vs. 44%) and N0 (74% vs. 69%). The percentages of women with any recorded comorbidity (14% vs. 14%) or degree of frailty (25% vs. 26%) were similar among women with ER-positive and ERnegative disease, respectively. In women with ER-positive EIBC aged 70-74yrs, 75-79yrs and ≥80yrs, rates of no surgery were 6%, 11% and 42%, respectively. The rates were 4%, 4% and 12% among women with ER-negative EIBC. The relative lower rate of surgery for ER-positive EIBC persisted in women with good fitness. **Conclusion**: Fewer fit older women in England and Wales receive surgery if diagnosed with ER-positive compared to ER-negative EIBC. Reasons for this variation should be explored, to ensure consistency of treatment decisions among older women.

Answer 9

The population-level cohort study was undertaken as part of the NABCOP. The NABCOP uses pseudonymised patient-level datasets provided by the National Cancer Registration and Analysis Service (NCRAS) in England and Wales Cancer Network for Wales. The datasets included national cancer registrations and extracts from the routine hospital admission databases for NHS hospitals (the English Hospital Episode Statistics (HES) and the Patient Episode Database for Wales (PEDW)). Survival information is recorded in the Civil Registration/Mortality data. .

Answer 10

* Among the 109 018 eligible women in the NABCOP datasets, women with a missing record of ER status (n = 10 951 (10%)) or other key study variables (n = 14 837 (14%)), or who were borderline ER status (n = 42), were excluded from the study. * Information on patient demographics (age, social deprivation), date of diagnosis, method of presentation and tumour characteristics was obtained from the national cancer registration dataset.

Answer 11

* The percentage of patients not receiving surgery was calculated for women with ER-positive and ERnegative EIBC, and among groups with different patient and tumour characteristics. The statistical significance of differences between the groups was assessed using statistical tests appropriate for continuous or categorical variables. Patient and tumour factors of interest included age, deprivation quintile, CCI, SCARF index, tumour grade, tumour size (T stage), the presence of malignant lymph nodes (N stage), human epidermal growth factor receptor 2 (HER2) status and mode of presentation (screen-detected or symptomatic). Short-term surgical outcomes were also calculated for women aged 50-69 or ≥70 years with ER-positive and ER-negative disease. * Multi-variable logistic regression was used to investigate how all the patient and tumour factors were associated with the likelihood of no surgery. Age was included in the model as a continuous variable, and a cubic spline was used to accommodate its non-linear effect. The spline knots were defined at ages 51, 73, 85 and 90 years, which were selected based on the Akaike information criterion (AIC). The model also included Interaction terms to capture differences in the patterns of surgery between women with ER-positive and ER-negative EIBC. The interaction terms were ER-status and age, ERstatus and N-stage, and ER-status and frailty. The performance of the model was evaluated in terms of its calibration and discrimination in the overall cohort and within the two ER subgroups. * As the spline coefficients and the interaction terms are difficult to interpret, predictions from the model were produced to illustrate the relationship between age and the likelihood of no surgery for women with ER-positive and ER-negative EIBC. The predictions were produced for four patient subgroups: women with low risk (grade 1, stage T1 N0) or high risk (grade 3, stage T2 N1) breast cancer, with each stratified by two levels of fitness: good (no comorbidities, not frail) and poor (comorbidity score ≥ 2 and severe frailty). Analysis for this study was conducted as a complete case analysis using Stata 15.1 (StataCorp LP, College Station, Texas USA). All statistical tests were two sided.

Answer 12

* The percentage of patients not receiving surgery was calculated for women with ER-positive and ERnegative EIBC, and among groups with different patient and tumour characteristics. The statistical significance of differences between the groups was assessed using statistical tests appropriate for continuous or categorical variables. Patient and tumour factors of interest included age, deprivation quintile, CCI, SCARF index, tumour grade, tumour size (T stage), the presence of malignant lymph nodes (N stage), human epidermal growth factor receptor 2 (HER2) status and mode of presentation (screen-detected or symptomatic). Short-term surgical outcomes were also calculated for women aged 50-69 or ≥70 years with ER-positive and ER-negative disease. * Multi-variable logistic regression was used to investigate how all the patient and tumour factors were associated with the likelihood of no surgery. Age was included in the model as a continuous variable, and a cubic spline was used to accommodate its non-linear effect. The spline knots were defined at ages 51, 73, 85 and 90 years, which were selected based on the Akaike information criterion (AIC). The model also included Interaction terms to capture differences in the patterns of surgery between women with ER-positive and ER-negative EIBC. The interaction terms were ER-status and age, ERstatus and N-stage, and ER-status and frailty. The performance of the model was evaluated in terms of its calibration and discrimination in the overall cohort and within the two ER subgroups. * As the spline coefficients and the interaction terms are difficult to interpret, predictions from the model were produced to illustrate the relationship between age and the likelihood of no surgery for women with ER-positive and ER-negative EIBC. The predictions were produced for four patient subgroups: women with low risk (grade 1, stage T1 N0) or high risk (grade 3, stage T2 N1) breast cancer, with each stratified by two levels of fitness: good (no comorbidities, not frail) and poor (comorbidity score ≥ 2 and severe frailty). Analysis for this study was conducted as a complete case analysis using Stata 15.1 (StataCorp LP, College Station, Texas USA). All statistical tests were two sided.