Theo Georghiou and Dr Jessica Sheringham: Data and Colorectal Cancer, 30 June 2014

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In this slideshow, Dr Jessica Sheringham, Visiting Fellow, and Theo Georghiou, Senior Research Analyst, Nuffield Trust describe what linked data can tell us about the GPs role in diagnosing colorectal cancer.

Dr Jessica Sheringham and Theo Georghiou spoke at the Nuffield Trust event: The future of the hospital, in June 2014.

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Theo Georghiou and Dr Jessica Sheringham: Data and Colorectal Cancer, 30 June 2014

  1. 1. © Nuffield TrustJune 2014 What can linked data tell us about GPs’ role in diagnosing colorectal cancer? 30 June 2014 Jessica Sheringham & Theo Georghiou
  2. 2. © Nuffield Trust Outline Background: Why colorectal cancer? What we did • Aims & setting • Linkage • Constructing & examining routes to diagnosis Illustrative findings Discussion • Colorectal cancer • Wider applications
  3. 3. © Nuffield Trust16 July 2014 © Nuffield Trust Background
  4. 4. © Nuffield Trust Why colorectal cancer? 4th most common cancer in UK Incidence increasing Most common in older people 55% overall survive 5 years after diagnosis Survival much better if diagnosed at an early stage: • 5-year survival: early stage (“Dukes Stage A”) = 93% • 5-year survival: late stage (“Dukes Stage D”) = 6.6% References: CRUK, 2014; NCIN data briefing, 2009
  5. 5. © Nuffield Trust Reference: Coleman et al. Lancet 2011 Colorectal cancer: Age-standardised 1-year and 5-year relative survival trends 1995–2007, by cancer and country
  6. 6. © Nuffield Trust Improving outcomes for colorectal cancer: points for intervention Screening Symptom awareness Patients & public Prevention
  7. 7. © Nuffield Trust Improving outcomes for colorectal cancer: points for intervention Prevention Screening Symptom awareness Patients & public Secondary care Access to effective treatment Diagnosis
  8. 8. © Nuffield Trust Improving outcomes for colorectal cancer: points for intervention Diagnostic referrals Primary care • 2-week wait referral pathway underpinned by NICE guidance • Decision support tools e.g. RATs(Hamilton 2013), Qrisk (H-Cox 2012, Collins 2012) BUT • Only 24% diagnosed on 2-week wait (2WW) pathway, 24% diagnosed as emergencies(Thorne et al. 2006) • Existing monitoring strategies, e.g. audit, reliant on GP/practice participation – could underestimate variation Access to effective treatment Patients & public Secondary care Prevention Screening Symptom awareness Diagnosis
  9. 9. © Nuffield Trust16 July 2014 © Nuffield Trust The project
  10. 10. © Nuffield Trust Project Aim: Explore the feasibility of examining quality of diagnostic process across the patient pathway using routinely available data Objectives 1. Establish whether linkage of three datasets (primary care, secondary care and cancer registry) possible 2. Apply chosen candidate indicator(s) of quality to examine variations in diagnostic process to identify points for intervention at patient or population level
  11. 11. © Nuffield Trust Time-based • Patient interval: symptoms to presentation • Primary care interval: presentation to diagnosis • Secondary care interval: diagnosis to treatment Event-based • Stage at diagnosis • Route: emergency diagnosis • Short-term survival Candidate indicators: How measure the quality of the diagnostic process? Reference: Lyratzopoulos, 2014
  12. 12. © Nuffield Trust16 July 2014 © Nuffield Trust Methods development
  13. 13. © Nuffield Trust Project setting: Outer North East London 1m population & 4 diverse boroughs RBWF B&D HV Havering (HV) Waltham Forest (WF) Reference: borough profiles, www.london.gov.uk % Population over 65 (2011) Income support claimants (2013) Redbridge (RB) RBWF B&D HV Barking & Dagenham (B&D)
  14. 14. © Nuffield Trust Datasets and linkage Key data: Date of cancer diagnosis Stage of cancer Colorectal cancer diagnoses Four CCGs 2009 – 2011 N = 1,367 Cancer registry data from Public Health England
  15. 15. © Nuffield Trust Datasets and linkage Key data: Date of cancer diagnosis Stage of cancer Colorectal cancer diagnoses Four CCGs 2009 – 2011 N = 1,367 All cancer diagnoses 2005 – 2010 Cancer registry data from Public Health England
  16. 16. © Nuffield Trust Datasets and linkage Identify and remove prior cancers Colorectal cancer diagnoses N = 1,367 All cancer diagnoses Cancer registry data from Public Health England
  17. 17. © Nuffield Trust Datasets and linkage Colorectal cancer Diagnoses 2009-2011 N = 1,150 Cancer registry data from Public Health England Colorectal cancer diagnosis, no prior cancer
  18. 18. © Nuffield Trust Datasets and linkage Colorectal cancer diagnoses 2009-2011 N = 1,150 GP and Hospital data from CCGs For population with recorded colorectal cancer diagnosis during 2007-2012 GP data Four CCGs (registered) 2007-2012 Hospital data: inpatient, outpatient, A&E Key data: Socio demographic information (e.g. age, gender, deprivation) Hospital contacts & procedures GP contacts & Read codes (GP recorded symptoms and activities)
  19. 19. © Nuffield Trust Datasets and linkage Colorectal cancer diagnoses N = 1,150 GP data Hospital data: inpatient, outpatient, A&E GP and Hospital data from CCGs
  20. 20. © Nuffield Trust Datasets and linkage Colorectal cancer diagnoses N = 1,150 GP data Hospital data: inpatient, outpatient, A&E Not all individuals with diagnosis found in CCG data
  21. 21. © Nuffield Trust Colorectal cancer diagnoses 2009-2011 N = 943 Datasets and linkage GP data At least 21 months prior to diagnosis Hospital data: inpatient, outpatient, A&E 82% of Registry records ‘matched’ local data ‘Unmatched’: high % missing stage and higher % of patients over 90 years
  22. 22. © Nuffield Trust Assigning a ‘route’ to diagnosis 1. Looked back at patient records 6 months (starting from the hospital episode closest to date of diagnosis) Reference: Elliss-Brookes et al, 2012
  23. 23. © Nuffield Trust
  24. 24. © Nuffield Trust Assigning a ‘route’ to diagnosis 1. Looked back at patient records 6 months (starting from the hospital episode closest to date of diagnosis) 2. Examined previous activity and referral source (refined to exclude activity NOT connected with colorectal cancer) Reference: Elliss-Brookes et al, 2012
  25. 25. © Nuffield Trust
  26. 26. © Nuffield Trust Referral source = “GP 2WW”
  27. 27. © Nuffield Trust Assigning a ‘route’ to diagnosis 1. Looked back at patient records 6 months starting from the hospital episode closest to date of diagnosis 2. Examined referral source and previous activity Refined to exclude activity NOT connected with colorectal cancer 3. Assigned each patient to one of four routes to diagnosis: Emergency GP – urgent/2WW GP – routine/unknown Consultant, other, unknown Reference: Elliss-Brookes et al, 2012
  28. 28. © Nuffield Trust Analysis at population and individual levels 1. POPULATION: Logistic regression to identify factors associated with increased chance of emergency presentations • Cancer stage at diagnosis: early, vs late/missing • Consultation characteristics: • no. GP visits • relevant symptoms (using Read Codes in GP records: anaemia, rectal bleeding, diarrhoea, constipation, abdominal pain, other, incl. weight loss, fatigue other altered bowel) • Patient demographics: age, gender • Area: borough, deprivation 2. INDIVIDUAL: Characteristics of pathways within each route
  29. 29. © Nuffield Trust16 July 2014 © Nuffield Trust Illustrative findings 1. Cohort 2. Population level 3. Individual level
  30. 30. © Nuffield Trust16 July 2014 © Nuffield Trust Illustrative findings 1. Cohort 2. Population level 3. Individual level
  31. 31. © Nuffield Trust Diagnostic route in our cohort vs. other estimates 31 52 19 26 24 24 24 0% 20% 40% 60% 80% 100% Cohort Thorne et al Emergency GP urgent/2WW Alternative route (Consultant/other/unknown) Alternative route (GP routine/unknown) Cohort, n=943 Thorne et al (2006)
  32. 32. © Nuffield Trust16 July 2014 © Nuffield Trust Illustrative findings 1. Cohort 2. Population level 3. Individual level
  33. 33. © Nuffield Trust Characteristics of emergency presentation vs. other routes Symptoms Ref: no symptom Stage Ref: early Age Ref: 60-69y Borough Ref: “2” Area deprivation Ref: Most deprived 20% Adjustedoddsratio 0.01 0.1 1 10 "Late"/Missing Totalno.GPvisits(12mbefore diagnosis) Abdominal Constipation Rectal 20-59y 70-79y 80+y 1 3 4 20-40% 40-60% 60-80% 20%leastdeprived Missing Logistic regression, adjusted for stage, symptoms, age, borough, deprivation and clustering between practices
  34. 34. © Nuffield Trust Characteristics of emergency presentation (EP) vs. other routes Adjustedoddsratio 0.01 0.1 1 10 "Late"/Missing Totalno.GPvisits(12mbefore diagnosis) Abdominal Constipation Rectal 20-59y 70-79y 80+y 1 3 4 20-40% 40-60% 60-80% 20%leastdeprived Missing Symptoms Ref: no symptom Age Ref: 60-69y Borough Ref: “2” Area deprivation Ref: Most deprived 20% Stage Ref: early Higher odds of emergency presentation for late stage cancers is consistent with: - theory of EP as a marker of diagnostic delay - other literature (McPhail 2013, Downing 2012)
  35. 35. © Nuffield Trust Characteristics of emergency presentation vs. other routes Symptoms Ref: no symptom Stage Ref: early Age Ref: 60-69y Borough Ref: “2” Area deprivation Ref: Most deprived 20% Adjustedoddsratio 0.01 0.1 1 10 "Late"/Missing Totalno.GPvisits(12mbefore diagnosis) Abdominal Constipation Rectal 20-59y 70-79y 80+y 1 3 4 20-40% 40-60% 60-80% 20%leastdeprived Missing Fewer GP visits → EP Abdominal pain & constipation → EP more common Rectal bleeding → EP less likely ?clinical manifestation of emergency cases different?
  36. 36. © Nuffield Trust Characteristics of emergency presentation vs. other routes Age Ref: 60-69y Borough Ref: “2” Area deprivation Ref: Most deprived 20% Adjustedoddsratio 0.01 0.1 1 10 "Late"/Missing Totalno.GPvisits(12mbefore diagnosis) Abdominal Constipation Rectal 20-59y 70-79y 80+y 1 3 4 20-40% 40-60% 60-80% 20%leastdeprived Missing Symptoms Ref: no symptom Age Ref: 60-69y Borough Ref: “2” Area deprivation Ref: Most deprived 20% Significant differences by borough No significant deprivation associations ?? Healthcare system factors??
  37. 37. © Nuffield Trust16 July 2014 © Nuffield Trust Illustrative findings 1. Cohort 2. Population level 3. Individual level
  38. 38. © Nuffield Trust Pathway examples: “Emergency” routes
  39. 39. © Nuffield Trust Pathway examples: Emergency (2)
  40. 40. © Nuffield Trust Pathway examples: GP 2WW referred (1)
  41. 41. © Nuffield Trust Pathway examples: GP 2WW referred (2)
  42. 42. © Nuffield Trust16 July 2014 © Nuffield Trust Summary and discussion points
  43. 43. © Nuffield Trust Summary 1. Linkage: • feasible (not quick – cancer data was rate limiting step) • relatively complete set, (cf 82% cancer cases vs 17% audit participation) BUT • important biases to consider 2. Routes to diagnosis: • distinguishing activity from pathways • POPULATION: important differences between patients, clinical characteristics and boroughs by route to diagnosis • INDIVIDUAL: diversity of healthcare use can identify cases for indepth audit
  44. 44. © Nuffield Trust Discussion points and next steps Variations in colorectal cancer diagnostic pathways can be identified using routine data: • Identifies a) local targets for intervention b) specific cases for indepth audit Next steps • Refine measures/criteria to identify cases for indepth audit Transferable methods, approaches to other clinical areas • Challenges of defining diagnostic interval • Pros and cons of pathways analysis
  45. 45. © Nuffield Trust www.nuffieldtrust.org.uk Sign-up for our newsletter www.nuffieldtrust.org.uk/newsletter Follow us on Twitter: Twitter.com/NuffieldTrust © Nuffield Trust Acknowledgements: • Xavier Chitnis, The Royal Marsden NHS Foundation Trust • Dr Martin Bardsley, Nuffield Trust • Knowledge & Intelligence Team (London), Public Health England: Neil Hanchett and Ashu Sehgal • Rob Meaker, Phil Kozcan, Outer North East London CCGs • Stuart Bond, Health Analytics Acknowledgements
  46. 46. © Nuffield Trust Pathway examples: GP 2WW referred (3)
  47. 47. © Nuffield Trust Pathways: Consultant/other examples

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