Introduction to Epidemiology Craig Jackson Prof. Occupational Health Psychology Head of Psychology BCU

2. Introduction to Epidemiology Craig Jackson Prof. Occupational Health Psychology Head of Psychology BCU

3. Occupational Epidemiology “People who work sitting down get paid more than people who work standing up” Ogden Nash (1902 - 1971)

4. Aims • Highlight basic concepts of “cause and effect” • Describe basic data needed for epidemiological investigation • Demonstrate pictorial epidemiology • Prove epidemiology is not the sole preserve of statisticians • Epidemiology is achievable to anyone with PC and web connection • Show that epidemiology can be small, medium or large scale • Although uses objective statistics, interpretation still subjective • Calculations: incidence rate, death rate, fatality rate etc.

5. Learning Outcomes • Identify sources of epidemiological data and relate these to the occupational setting. • Have a critical insight into the language, methodological approaches and the nature of findings associated with epidemiological investigations. • Reflect on how epidemiological data may inform a dutyholder’s management of occupational health and hygiene issues.

6. Descriptive Epidemiology - Months

7. Work-Related ill-health in UK • 33 Million days lost per year • Males lose more working days than females • Days lost increase with age • Low managerial / professionals have highest rate of absence • Most sickly occupations: Health & Social welfare • Public Admin. • Construction • Teaching • Dress-makers have youngest age-at-death of any occupation

8. Work-Related ill-health in UK

9. Work-Related ill-health in UK

10. North-South Divide? Self-Reported Sickness Objective data? Objective interpretation?

11. Objectives of Epidemiology 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

12. Introduction Basic science of public health Quantitative Based on probability statistics sound research Uses “causal reasoning” Practical common sense

13. 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, behaviours

14. Introduction Epidemiological info used to promote and protect public health Both science and public health come together “Applied Epidemiology” e.g. Monitoring communicable diseases in communities Dietary intake influencing development of cancers Effectiveness and impact of cholesterol awareness programmes Analyse historical and current data to find resource needs

15. Introduction “Epidemiology” searched on BBCi on 4th Nov 2003

16. Introduction Who gets disease? (objective) Why do they get diseases? (conjecture) Study sick people and healthy people to determine crucial difference between those who get ill and those who do not RATES COMPARES BALANCES CONTRASTS NUMERATOR The no. of people to whom something happened e.g got sick DENOMINATOR The population at risk e.g.the entire population

17. RIASS survey

18. Comparison of yards by staff size: a) UK Yard population b) Survey sample

19. Scaled up numbers

20. Epi-derm / THOR The aim of the scheme is to determine the extent of occupational skin disease in UK industry and therefore to take steps to reduce the incidence of occupational disease and to monitor changes. Reports of cases of occupational skin disease from consultant dermatologists since 1993. Funded to continue collecting data for this work until end 2006. Produced valuable information on the incidence of occupational skin disease, and on the agents responsible.

21. Epi-derm / THOR Approximately 188 members of the British Association of Dermatologists take part 22 are core reporters who report every month) 166 are sample reporters who are sampled at random and report for one month only each year. The major category of cases reported consists of contact dermatitis, followed by neoplasia (cancers) 1993 - 1999 total of 12,574 new cases of occupational skin disease were reported by consultant dermatologists 9937 of which were contact dermatitis (79% of total cases)

22. What is an Epidemic? • When there are significantly more cases of a disease than than past • experience would have predicted • Three Things Investigated in Epidemics • Person / Host • Place / Environment • Time of exposure and symptoms 2227 people exposed to something and 1522 of them died. What can we discover about this event?

23. What is an Epidemic? • 1. Person / Host • Men, Women and children all at risk • Majority were working men aged 18-50 • 2. Place / Environment • All male 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 2227 people exposed to something and 1522 of them died. What can we discover about this event?

24. Personal Details Inherent Characteristics age, race, sex Acquired Characteristics marital status, immune status Activities occupation, leisure, drugs Domestic socio-economic status, GP access Try to break ill-health down into these categories Age and Sex are the most critical

25. Age Single most important personal attribute Almost all health-related event or state varies with age Other factors are behind this association e.g. Susceptibility Exposure Latency / Incubation periods Physiological response Use narrow age groups to detect any patterns Age groups may not show enough detail

26. Sex Males have higher rate of mortality and morbidity than females Genetic, Hormonal, Anatomical or other Inherent sex differences Differences effect physiological responses and susceptibility e.g. heart disease lower in pre-menopausal women (oestrogen levels) Sex also effects exposure levels and occupational ill-health e.g. Occupation, Task and Repetitive Strain Injury

27. Determinants Causes or factors of incidence of ill-health Health-related states or events chronic disease injuries birth defects child health occupational health environmental health Specified Populations Exposures Others exposed Spread Interventions

28. John Snow, Cholera, and the Broad Street Pump Mortality from Cholera in the districts of London, 9th Jul – 26th Aug 1854

29. John Snow, Cholera, and the Broad Street Pump

30. Case Definition Set of standard criteria Decides if person has disease / state Objective Allows reliable comparisons across (i) time, (ii) people, (iii) areas Clinical criteria and limitations, symptoms, and signs

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

32. Hospital Admissions and World Cup 1998 • Examine hospital admissions for rangeof diagnoses on days surrounding England's 1998 World Cup footballmatches • Hospital admissions obtained fromEnglish hospital episodestatistics • Pop. Aged 15 – 64 years • Admissions for • Acute MI On match day • Stroke and 2 days after • Deliberate self harm match day • Road traffic injuries • Compared with admissions at the same time in 1997 and 1998 Carroll, D et al. 2002

33. Hospital 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, deliberateself harm, and road traffic injuries among men and womenaged 15 to 64 Games all took place in late evening Examined the same associations using only the two days afterthe match omitting the day of the match as the exposedcondition

34. Hospital 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%) formyocardial infarction 662  (1%) for stroke 856  (1%) for roadtraffic injury 3308  (4%) for deliberate self harm observed / expected actual – expected ARR admissions admissions Day of match 91 / 72 19 1.25 (0.99 to 1.57) 1 day after 88 / 72 16 1.21 (0.96 to 1.57) 2 days after 91 / 71 20 1.27 (1.01 to 1.61) 3 days after 76 / 74 2 0.99 (0.77 to 1.27) 4 days after 71 / 74 3 0.92 (0.71 to 1.19) 5 days after 83 / 72 11 1.13 (0.89 to 1.43)

35. Hospital Admissions and World Cup 1998 • Admission Within 2 days Within 2 days Within 2 days of P value • diagnosis of win of 1-2 loss loss on penalty • M.I 0.99 0.91 1.25 0.007 • 0.89 - 1.11 0.78 - 1.07 1.08 - 1.44 • Stroke 0.87 0.97 1.00 0.42 • 0.74 - 1.03 0.79 - 1.19 0.82 - 1.23 • RTA 0.99 0.96 0.85 0.51 • 0.85 - 1.14 0.79 - 1.17 0.69 - 1.05 • DSH 1.08 1.01 1.05 0.26 • 1.00 - 1.16 0.91 - 1.12 0.95 - 1.16 • Periodsafter a win (Tunisia, Columbia) and 1st first loss (Romania) were not associated with increasedadmissions • On match day, and two days after match against Argentinawith a penalty shoot-out, admissions for acute MIincreased by 25%. • No increases in admission were seen for anyof the other diagnoses

36. Hospital Admissions and World Cup 1998 Major environmental events, whether physical catastrophes or cultural disappointments,are capable of triggering myocardial infarction. If the triggeringhypothesis is true, preventive efforts should consider strategiesfor dealing with the effects of acute physical and psychosocialupheavals. “Perhaps the national lottery or even the penalty shoot-out should be abandoned on publichealthgrounds.” Limitations: Harvesting effect? Reporting tendency? Sudden deaths?

37. Modern Example: AIDS Personal Details 1981 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

38. Modern Example: AIDS Location Details 5 cases were in Los Angeles Similar cases in NY and SF Time Details All 5 deaths between Oct 1980 – May 1981 4 weeks after, 67 more cases reported

39. Descriptive Epidemiology - Years

40. Descriptive Epidemiology - Seasonal

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

42. Descriptive Epidemiology - Regional

43. Descriptive Epidemiology - Workspaces

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

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

46. Standard Mortality Ratio Observed number of deaths SMR = Expected number of deaths 6 deaths (per 1000) for truckers 3 = 2 deaths (per 1000) for all occupations

47. Risk Ratios “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 lung cancer among smokers = R.R Incidence of lung cancer among non smokers 600 cases (per 1000) for smokers = 24 25 cases (per 1000) for non-smokers

48. Case Fatality Why are people more scared of a diagnosis of Cancer than Arthritis? Some diseases have a higher Fatality Rate No. of deaths by disease in timeframe Fatality Rate = X 100 No. of cases of the disease in timeframe 60 deaths due to SARS in last month 69.7% X 100 86 cases of SARS recorded in last month

49. 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 Why might different regions of the UK have different CDRs?

50. 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) Cases Controls n=456 n=456 F P Smoking years 13.75 6.12 7.5 0.04 (± 1.5) (± 2.1)