1. The Norfolk Arthritis Register Alan Silman arc Epidemiology Unit University of Manchester UK

2. Manchester

3. The Norfolk Arthritis Register A primary care based inception cohort study of patients with inflammatory polyarthritis

4. Norfolk

5. Why Norfolk? Geographically ‘isolated’ Geographically ‘isolated’ Stable population Stable population Single central major hospital Single central major hospital Excellent links primary to secondary care Excellent links primary to secondary care Local enthusiasm Local enthusiasm

6. Topics The NOAR methodology Key results –Classification of RA –Environmental risk factors –Outcome –Predictors of outcome –Treatment effects

7. Manchester Norwich NOAR : Recruitment Entry criteria -age > 16 years -registered with local GP -swelling of > 2 joints -duration > 4 weeks -onset since 1/1/90 Metrology assessment Apply ACR criteria

8. Metrologist Assessment

9. Data Collected

10. The Norfolk Arthritis Register (NOAR) To establish the incidence of IP and subset with RA To identify risk factors for the development of IP and RA To study the natural history of treated IP and RA To identify predictors of outcome in IP and RA Initial aims

11. The Norfolk Arthritis Register (NOAR) To investigate the epidemiology of cardiovascular disease in patients with IP (risk factors, incidence and outcome) To identify predictors of treatment response and non-response Current Major Aims

12. Key results Incidence of IP and RA

14. Estimates of the incidence of RA: Application of ACR criteria Incidence rate per 100,00 FemalesMales ACR criteria applied at baselineACR criteria applied over 5 years Age

15. Issues of Classification IP vs RA

16. Concept Early IP Recovery Another disease Established RA

17. Concept Early IP Recovery Another disease ? Treatment Established RA

18. Does early RA exist?

19. Are there differences between IP destined to differentiate into RA and other ‘causes’ of IP? Are there differences between IP destined to differentiate into RA and other ‘causes’ of IP?

20. Immunisation X ParvovirusX PsoriasisX Can we distinguish early RA from other forms of early arthritis?

21. Leiden model: prediction of outcome Goal: To discriminate at first visit between patients who will go on to have: self-limiting arthritis self-limiting arthritis persistent non-erosive arthritis persistent non-erosive arthritis persistent erosive arthritis persistent erosive arthritis

22. Leiden model: 7 variables Symptom duration at presentation Symptom duration at presentation Morning stiffness > 1 hour Morning stiffness > 1 hour Arthritis of > 3 joints Arthritis of > 3 joints Bilateral compression pain of MTPs Bilateral compression pain of MTPs Rheumatoid factor Rheumatoid factor Anti-cyclic citrullinated peptide antibody Anti-cyclic citrullinated peptide antibody Erosions in hands or feet Erosions in hands or feet

23. Validation of Leiden model erosive vs non-erosive arthritis In presence of persistence In presence of persistence Radiological criterion omitted Radiological criterion omitted LeidenNOAR (n= 526)(n=486) Prediction model ROC0.830.76 ACR criteria ROC0.770.66

24. Key results Risk factors for the development of IP and RA

25. Sources of Data Descriptive Analysis

26. Local Clustering of RA Silman et al., 1999

27. Jan 1990 June Jan 1991 June Jan 1992 June 25 20 15 10 5 0 30 Month of onset Number of new cases All cases UIP RA Onset of Disease by Month 1990-92 Silman et al., 1997

28. Observed & Expected Events in Relation to Time & Distance Silman et al., 1997 0 -5e7 -1e8 400 300 200 100 D Time 500 1000 1500 Distance

29. Socioeconomic Deprivation vs RA Incidence by census ward Bankhead et al., J Rheum 1996 Indicator rsrsrsrs Households in rented accommodation -0.09 Overcrowded accommodation -0.14 Householders with no CH -0.26 Households with no access to a car -0.06 Male unemployment (age 26- 60) -0.03

30. Socioeconomic Deprivation & RA Bankhead et al., J Rheum 1996 Social Class Incidence /100,000 * IV & V combined for men *

31. Sources of data Case control studies Case control studies 1. Internal NOAR Cases 1992 (n=165) : –aged 18-70 –symptom duration < 12 months Controls: 2 per case from referring primary care

32. Lifestyle Factors SmokingObesity 20 10 5 4 3 2 1

33. Association of Smoking with Severe RA: Rheumatoid Nodules CurrentExNever 20 10 4 2 1 0.4 Harrison et al., Arth Rheum 2003 Odds Ratio (95% CI)

34. Hormonal Risk Factors TerminationOral ContraceptiveMiscarriage 8 6 4 2 1.8.6.4.2.1

35. Symmons et al., 1997 CasesControls 0 5 10 15 20 % Association between Prior Blood Transfusion and RA

36. 2. NOAR EPIC Link

37. Co-occurrence of NOAR & EPIC in same population Area for new cases of IP referred to NOAR EPIC practices

38. European Prospective study of the Incidence of Cancer (EPIC-Norfolk) Baseline assessments Random sample (n= 25,000) 45 – 75 years Recruited 1993 – 1997 Health and lifestyle questionnaire Height and weight

39. Prospective ‘nested’ case control study Free of IP at baseline Free of IP at baseline Subsequent registration with NOAR Subsequent registration with NOAR 2 per case 2 per case Matched: Matched: - age (± 3 years) - gender - gender - within 3 months of baseline assessment baseline assessment 73 Cases Controls

40. EPIC Diet Survey 7 day detailed food diary with portion sizes 7 day detailed food diary with portion sizes

41. Fruit Intake (g) and Development of IP Highest (ref)MiddleLowest 8 6 4 2 1.8.6.4.2 * Adjusted for energy intake, smoking, red meat intake Odds Ratio (95% CI)* Pattison et al., ARD 2004 Tertile

42. Tertiles of Vitamin C Intake (mg) Highest (ref)MiddleLowest 8 6 4 2 1.8.6.4.2 * Adjusted for energy intake, smoking, protein intake Pattison et al., ARD 2004 Odds Ratio (95% CI)* Tertile

43. Tertiles of  -cryptoxanthin Intake (µg) HighestMiddleLowest (ref) 3 2 1.5.3.2.1 Odds Ratio (95% CI)* * Adjusted for energy intake, smoking, protein intake Pattison et al. 2005 Tertile

44. HighestMiddleLowest (ref) 3 2 1.7.5.3.2.1 RA and Dietary Zeaxanthin Intake Odds Ratio (95% CI)* * Adjusted for energy intake, protein, smoking Pattison et al 2005 Tertile

45. Red Meat & Meat Products and Development of IP * Adjusted for energy intake, smoking, fruit intake MiddleLowest (ref)Highest 8 6 4 2 1.8.6 Pattison et al., A & R 2004 Odds Ratio (95% CI)*

46. Are the Diet Effects Independent? Vitamin C mg/day HighMiddleLow 8 6 4 2 1.8.6.4.2.1 Red Meat g/day HighMiddleLow 8 6 4 2 1.8.6.4.2.1 Odds Ratio (95% CI)*

47. Key results The natural history of treated IP and RA

48. Outcomes investigated PersistenceRadiological damage Physical function (HAQ)Economic costs Health status (SF-36)Co-morbidity Work disabilityMortality

49. Work disability Year of onset

51. All cause mortality Seropositive patients Men Women Norfolk 0 1 SMR Inflammatory polyarthritis 0 1 2 SMR MenWomen Norfolk SMR = 1.13 SMR = 1.01 2 SMR =1.51 SMR = 1.41

52. Cardiovascular mortality: Influence of RF Status Males 0.5 1 2 3 Females RF- SMR (95% CI ) RF+

53. Key results Predictors of outcome geneticenvironmentaltreatment

54. X-ray strategy NOAR Time from registration Patients X-rayed 01250125 None  3 ACR criteria at baseline  2 ACR criteria at year one  2 ACR criteria at year two and no erosions on any previous X-rays All patients

55. Timing of first erosions Risk setTime of 1st “erosion free” X-ray Median (IQR) Timing of 2nd X-ray Median (IQR) % erosive at 2nd X-ray Incidence rate of 1st erosions (per 1000 pm) (95% CI) 12341234 18 (16-20) 29 (26-31) 41 (37-45) 18 (16-22) 66 (64-69) 69 (66-73) 75 (70-84) 36 23 28 47 24 (21-29) 5(4-8) 7(5-10) 13(9-19)

56. NOAR: Predicting radiological erosions Risk group RF > 40 Initial duration > 3 months Probability of erosions 12341234 XXXX XXXX 0.79 0.52 0.33 0.10 Overall performance: PPV 61% NPV 74%

57. Role of genetic factors HLA.DRBI HLA.DRBI Cytokine Cytokine –TNF –IL1 etc etc MMP MMP MBL MBL MIF MIF

58. Weak association with shared epitope, less strong than in clinic based studies Weak association with shared epitope, less strong than in clinic based studies Few candidates tested were predictors of presence/severity erosions Few candidates tested were predictors of presence/severity erosions Genetic Factors

59. ? Confounding effect of therapy

60. Propensity models Bias in treatment assignments “Confounding by indication” Variable duration of exposure to treatment Problems Solution Assessing the effect of treatment

61. In observational studies : It is not random who will get DMARD therapy Treated patients have more severe disease Therefore ‘bias in allocation’ occurs Adjustment for this effect is needed

62. Propensity modelling Logistic model used to predict treatment decision Using disease characteristics that inform treatment decision Each individual given probability of being treated = propensity score

63. Distribution of HAQ scores at year 5 Never on DMARDs <6 months6-12 months>12 months Delay from symptom onset to start of first DMARD

64. Odds of moderate disability (HAQ  1.0) at 5 years Delay from onset to start of treatment

65. Odds of moderate disability (HAQ  1.0) at 5 years (Models include propensity scores & hospital referral) Delay from onset to start of treatment

66. Odds of moderate disability (HAQ  1.0) at 5 years Without propensity score With propensity score Delay from onset to start of treatment Never on DMARDs/steroids < 66-12> 12 Months

67. Larsen score at year 5 adjusted for propensity score Delay to start of first DMARD < 6 months6-12 months> 12 months No Treatment

68. Patients treated with DMARDS had worse disease at presentation and worse outcome The greatest benefit of treatment was seen in those treated within six months

69. Jt Principal Investigator :Deborah Symmons Research Fellows :Marwan Bukhari Beverley Harrison Nicola Goodson Research Assistants:Clare Bankhead Nicola Wiles Dorothy Pattison Statisticians: Paul Brennan Mark Lunt Research nurses, consultant rheumatologists Acknowledgements