1. Principles of Epidemiology Occupational Application Dr. Craig Jackson Honorary Senior Lecturer in Occupational Psychology Institute of Occupational & Environmental Medicine Research Director The University of Birmingham Research Director Health Research Consultants www.researchconsultants.co.uk/notes

2. Objectives Describe key features of descriptive data Understand:mean, mode, median, variance, standard deviation Calculate:mean, mode, median ratios proportions rates mortality rates prevalence & incidence Understand:tables, charts, plots Understand:public health surveillance

3. Background to Statistics Distributions Data collection Data presentation Dr. Craig Jackson Honorary Senior Lecturer in Occupational Psychology Institute of Occupational & Environmental Medicine Research Director The University of Birmingham Research Director Health Research Consultants www.researchconsultants.co.uk/notes

4. 33% 123 Problem: stick with initial choice or choose another door ? 50% ? Solution: probability says that you stand a better chance of finding the cash if you SWAP The Monty Hall Problem

5. Door 1Door 2Door 3 Never swap WIN LOSE LOSE Always swap LOSE WIN WIN Marilyn vos Savant The Monty Hall Problem

6. Dispersion RangeSpread of data MeanArithmetic average MedianLocation ModeFrequency SDSpread of data about the mean Range50-112 mmHg Mean82mmHgMedian82mmHgMode82mmHg SD± 10mmHg

7. Types of Data / Variables ContinuousDiscrete BPChildren HeightNo. colds in last 12 months WeightAge last birthday Age OrdinalNominal Grade of conditionSex Positions 1 st 2 nd 3 rd Hair colour “Better - Same – Worse”Blood group Height groupsEye colour Age groups

8. Conversion & Re-classification Easier to summarise Ordinal / Nominal data Cut-off Points(who decides this?) Allows Continuous variables to be changed into Nominal variables BP> 90mmHg=Hypertensive BP=< 90mmHg=Normotensive Easier clinical decisions Categorisation reduces quality of data Statistical tests may be more sensational Good for summariesBad for analyses

9. Histograms and Bar-Charts Distinction is often lost Histograms The distribution of a continuous variable No gaps between the bars Bar-Chart Spaces between the bars Distribution of discrete / categorical data

10. values have no “real” meaning values have “real” meaning Categorical Data NOMINAL DATA values that the data may have do not have specific order values act as labels with no real meaning e.g. hair colourbrown =1blond =2black =100 ORDINAL DATA values with some kind of ordering data that has been measured or counted e.g. social class:upper1middle = 2working = 3 e.g. glioblastoma tumor grade:12345 e.g. position in a race:1 st 2 nd 3 rd

11. Quantitative Data DISCRETE distinct or separate parts, with no finite detail e.g children in family CONTINUOUS between any two values, there would be a third e.g between meters there are centimetres INTERVAL equal intervals between values and an arbitrary zero on the scale e.g temperature gradient RATIO equal intervals between values and an absolute zero e.g body mass index

12. White Hot Red Hot Cold “Dangerous” “Unpleasant” “Uncomfortable” “Tolerable” “Comfortable” “Cold” 80 o C 60 o C 40 o C 20 o C 10 o C Unsafe Safe Levels of Variables Temperature

13. 5’6” 5’7” 5’8” 5’9” 5’10” 5’11” 6’ 6’1” 6’2” 6’3” 6’4” 5’6” 5’7” 5’8” 5’9” 5’10” 5’11” 6’ 6’1” 6’2” 6’3” 6’4”Height % of population Distributions Sir Francis Galton (1822-1911) Alumni of Birmingham University 9 books and > 200 papers Fingerprints, correlation of calculus, twins, neuropsychology, blood transfusions, travel in undeveloped countries, criminality and meteorology) Deeply concerned with improving standards of measurement

14. Introduction Who gets disease and Why? Study sick people and healthy people to determine crucial difference between those who get ill and those who do not RATESCOMPARESBALANCESCONTRASTSNUMERATOR The no. of people to whom something happened e.g got sick DENOMINATOR The population at risk e.g.the entire population

16. Common Popular Headlines

19. “High Effort Low Reward” “High Demand Low Control” 2x Substance abuse 2-3x Injuries 2-3x Infections 3x Back pain 5x Certain cancers 2-3x Conflicts 2-3x Mental health problems 3x Cardiovascular problems Potential Health Risks Shain & Kramer 2004

20. Introduction Basic science of public health Quantitative Based on probability; statistics; sound research; nomotheses Uses “causal reasoning” Practical common sense e.g. Monitoring communicable diseases in workplaces Dietary intake exacerbating development of cancers Effectiveness of smoking cessation programmes at work Declining disease incidence as OELs are reduced

21. Introduction epi “on” or “upon” demos“people” or “mass” logos “study of” “Epidemiology is the study of the distribution and determinants of health- related states or events in specified populations, and the application of this study to the control of health problems.” Last 1988 TIME:annual, seasonal, daily, hourly PLACE:geographic variation, urban vs. rural, workplaces, schools PERSONAL:age, race, sex, class, occupation, behaviour

22. Introduction

23. Regional Picture Self-reporting? Who’s best off? Who’s worse off?

24. What is an Epidemic? 1. Person / Host Men, Women and children all at risk Majority were wealthy young men aged 18-50 2. Place / Environment All cases were within 1 square mile of each other Climate was cold 3. Time of exposure and symptoms Mid April Death occurred within hours of exposure When there are significantly more cases of a disease / death than past experience would have predicted

25. Determinants Causes or factors of incidence of ill-health Health-related states or eventschronic disease injuries birth defects child health occupational health environmental health Specified Populations ExposuresOthers exposedSpreadInterventions

26. Case Definition People can be classified as Cases + Non-Cases - Suspects?

27. Modern Example..... Personal Details Cluster of 5 cases of rare pneumonia All 5 were young males Aged between 29-36 2 of the 5 reported frequent homosexual contact All 5 used “poppers” Location Details 5 cases were in Los Angeles Similar cases in NY and SF Time Details 1981 All 5 deaths between Oct 1980 – May 1981 4 weeks after, 67 more cases reported

28. Descriptive Epidemiology - Years

29. Descriptive Epidemiology - Months

30. Descriptive Epidemiology - Seasonal

31. Descriptive Epidemiology - Days Fatalities associated with Tractor injuries, by day of week, Georgia: 1971-1981

32. Descriptive Epidemiology - Regional

33. Descriptive Epidemiology - Workspaces

34. Age groups – data considerations

35. Incidence Rate No. of new cases of disease over time period Incidence rate = No. of population at risk No. of population at risk

36. Prevalence Rate No. of cases of disease at a given time Prevalence rate = No. of total population

37. Risk & Relative Risk “Risk Ratios” can inform how “risky” certain exposures / behaviours are Implications for likelihood of developing certain diseases “Risky” behaviours can be avoided or prohibited Incidence of Parkinson’s Disease among retired welders = R.R = R.R Incidence of Parkinson’s Disease among retired workers 100 cases (per 1000) for ex-welders = 4 = 4 25 cases (per 1000) for retired workers

38. Case Fatality Why are people more scared of a diagnosis of Mesothelioma than Arthritis? Some diseases have a higher Fatality Rate No. of deaths by disease in timeframe No. of deaths by disease in timeframe Fatality Rate = X 100 No. of cases of the disease in timeframe 60 deaths due to Mesothelioma in last month 44%=X 100 135 cases of Mesothelioma recorded in last month

39. Crude Death Rate No. of deaths in calendar year C.D.R = X 1000 No. of population at mid-year Expressed as Deaths per 1000 500,000 deaths in calendar year 8.3 deaths / 1000 = X 1000 60,000,000 population at mid-year

40. Risk & Odds Ratios: Gulf War Syndrome

41. 2 x 2 Tables: Diabetes over 2 years

42. Case Control Study: Lung Cancer Cases have Lung Cancer + Smoking Exposure Controls could be other hospital patients (other disease) or “normals” Matched Cases & Controls for age & gender Smoking years of Lung Cancer cases and controls (matched for age and sex) CasesControls n=456n=456 FP Smoking years13.756.127.50.04 (± 1.5)(± 2.1)

43. Cohort Study: Mobile phones and Ill-Health Subjects classified into 2 (or more groups) e.g. exposed vs non exposed End point: groups compared for health status Comparison of general health between users and non-users of mobile phones illhealthy mobile phone user292108400 non-phone user89313402 381421802

44. Work Related Ill-Health in the UK 33 Million days lost per year Males lose more working days than females Days lost increase with age Low managerial / professionals had highest rate of absence Most sickly occupations are health & social welfare, construction, teaching, and research

45. Work Related Ill-Health in the UK Bakers appear highly with occupational asthma Metal workers appear highly with upper limb problems Mesothelioma deaths high in shipbuilders and asbestos workers Stress, depression and anxiety highest in: Public admin. DefenceEducation Health work Social work

46. Economics of Scale - Solway Harvester Photo courtesy of Dr Gordon Baird

47. Numerical% Isle of Whithorn7/3002.3 Wigtownshire7/20,0000.03 London7/6,000,0000.00001 Solway Harvester 7 people from Wigtownshire Equivalent to 120,000 people from London

49. Occupational Epidemiology of Birmingham ? Health and Safety Executive (THOR) www.hse.gov.uk/statistics Office of National Statistics www.statistics.gov.uk Traditional Industries New and Emerging Industries Environmental Aspects Transport Features Migrant Populations

50. Admissions and World Cup 1998 Examine hospital admissions for range of diagnoses on days surrounding England's 1998 World Cup football matches Hospital admissions obtained from English hospital episode statistics Pop. Aged 15 – 64 years Admissions for Acute MI On match day Acute MI On match day Stroke and 5 days after Stroke and 5 days after Deliberate self harm match day Deliberate self harm match day Road traffic injuries Road traffic injuries Compared with admissions at the same time in 1997 and 1998 Carroll, D et al. 2002

51. Admissions and World Cup 1998 England's matches in the 1998 World Cup 15 June (England 2, Tunisia 0)win 22 June (Romania 2, England 1)lost 26 June (Colombia 0, England 2)win 30 June (Argentina 2, England 2) lost: penalties 4-2 Extracted hospital admissions data for acute myocardial infarction, stroke, deliberate self harm, and road traffic injuries among men and women aged 15 to 64 Games all took place in late evening Examined the same associations using only the two days after the match omitting the day of the match as the exposed condition

52. Admissions and World Cup 1998 During the period of England's World Cup matches (15 June to 1 July) 81,433 emergency admissions occurred: 1348 (2%) for myocardial infarction 662 (1%) for stroke 662 (1%) for stroke 856 (1%) for road traffic injury 856 (1%) for road traffic injury 3308 (4%) for deliberate self harm observed / expectedactual – expectedARR admissionsadmissions Day of match91 / 72191.25 (0.99 to 1.57) 1 day after88 / 72161.21 (0.96 to 1.57) 2 days after91 / 71201.27 (1.01 to 1.61) 3 days after76 / 74 20.99 (0.77 to 1.27) 4 days after71 / 74 30.92 (0.71 to 1.19) 5 days after83 / 72111.13 (0.89 to 1.43)

53. Admissions and World Cup 1998 Admission Within 2 days Within 2 days Within 2 days of P value diagnosisof winof 1-2 loss loss on penalty M.I0.990.911.250.007 0.89 - 1.110.78 - 1.071.08 - 1.44 Stroke0.870.971.000.42 0.74 - 1.030.79 - 1.190.82 - 1.23 RTA0.990.960.850.51 0.85 - 1.140.79 - 1.170.69 - 1.05 DSH1.081.011.050.26 1.00 - 1.160.91 - 1.120.95 - 1.16 Periods after a win (Tunisia, Columbia) and 1st first loss (Romania) were not associated with increased admissions Periods after a win (Tunisia, Columbia) and 1st first loss (Romania) were not associated with increased admissions On match day, and two days after match against Argentina with a penalty shoot-out, admissions for acute MI increased by 25%. On match day, and two days after match against Argentina with a penalty shoot-out, admissions for acute MI increased by 25%. No increases in admission were seen for any of the other diagnoses. No increases in admission were seen for any of the other diagnoses.

54. Admissions and World Cup 1998 Major environmental events, whether physical catastrophes or cultural disappointments, are capable of triggering myocardial infarction. If the triggering hypothesis is true, preventive efforts should consider strategies for dealing with the effects of acute physical and psychosocial upheavals. “Perhaps the national lottery or even the penalty shoot-out should be abandoned on public health grounds.” Limitations: Harvesting effect?Reporting tendency?Sudden deaths?