Introduction to
Epidemiology
Craig Jackson
Prof. Occupational Health Psychology
Head of Psychology
BCU
Occupational Epidemiology
“People who work sitting down get paid
more than people who work standing up”
Ogden Nash (1902 - 1971)
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.
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.
Descriptive Epidemiology - Months
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
Work-Related ill-health in UK
Work-Related ill-health in UK
North-South Divide?
Self-Reported Sickness
Objective data?
Objective interpretation?
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
Introduction
Basic science of public health
Quantitative
Based on
probability
statistics
sound research
Uses “causal reasoning”
Practical common sense
Introduction
epi
“on” or “upon”
demos
“people” or “mass”
logos
“study of”
“Epidemiology is the study of the distribution and determinants of healthrelated 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
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
Introduction
“Epidemiology” searched on BBCi on 4th Nov 2003
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
RIASS survey
Comparison of yards by staff size:
a) UK Yard population
b) Survey sample
Scaled up numbers
Sample
Nos
Scaled up
Population Nos
Total No of reported
Accidents
665
1862
Horse related
640
1792
No of yards reporting
accidents
252
352
Required medical
attention
318
996
A&E Attendances
213
596
Soft tissue injuries
364
1019
Fractures
86
240
Lacerations
62
174
Concussion
21
59
Accidents leading to
sickness absence
Resulting Days
Sickness Absence
Cost to industry based
on min wage
261
730
7431
20807
From £1.05
million to 1.207
million
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
occupational skin disease, and on the agents responsible.
of
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)
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
1) Person / Host
2) Place / Environment
3) Time of exposure and symptoms
2227 people exposed to
something and 1522 of them died.
What can we discover
about this event?
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
2227 people exposed to
something and 1522 of them died.
3. Time of exposure and symptoms
•
•
Mid April
Death occurred within hours of exposure
What can we discover
about this event?
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
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
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
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
John Snow, Cholera, and the Broad Street Pump
Mortality from Cholera in the districts of London, 9th Jul – 26th Aug 1854
John Snow, Cholera, and the Broad Street Pump
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
Case Definition
People can be classified as
Cases
Non-Cases
Suspects
?
+
Hospital 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
• Stroke
• Deliberate self harm
• Road traffic injuries
On match day
and 2 days after
match day
Compared with admissions at the same time in 1997 and 1998
Carroll, D et al. 2002
Hospital Admissions and World Cup 1998
England's matches in the 1998 World Cup
15 June
22 June
26 June
30 June
(England 2, Tunisia 0)
(Romania 2, England 1)
(Colombia 0, England 2)
(Argentina 2, England 2)
win
lost
win
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
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
662
856
3308
Day of match
1 day after
2 days after
3 days after
4 days after
5 days after
(2%) for myocardial infarction
(1%) for stroke
(1%) for road traffic injury
(4%) for deliberate self harm
observed / expected
admissions
91 / 72
88 / 72
91 / 71
76 / 74
71 / 74
83 / 72
actual – expected
admissions
19
16
20
2
3
11
ARR
1.25 (0.99 to 1.57)
1.21 (0.96 to 1.57)
1.27 (1.01 to 1.61)
0.99 (0.77 to 1.27)
0.92 (0.71 to 1.19)
1.13 (0.89 to 1.43)
Hospital Admissions and World Cup 1998
Admission
diagnosis
Within 2 days
of win
Within 2 days
of 1-2 loss
Within 2 days of
loss on penalty
P value
M.I
0.99
0.89 - 1.11
0.91
0.78 - 1.07
1.25
1.08 - 1.44
0.007
Stroke
0.87
0.74 - 1.03
0.97
0.79 - 1.19
1.00
0.82 - 1.23
0.42
RTA
0.99
0.85 - 1.14
0.96
0.79 - 1.17
0.85
0.69 - 1.05
0.51
DSH
1.08
1.00 - 1.16
1.01
0.91 - 1.12
1.05
0.95 - 1.16
0.26
•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%.
• No increases in admission were seen for any of the other diagnoses
Hospital 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?
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
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
Descriptive Epidemiology - Years
Descriptive Epidemiology - Seasonal
Descriptive Epidemiology - Days
Fatalities associated with Tractor injuries, by day of week,
Georgia: 1971-1981
Descriptive Epidemiology - Regional
Descriptive Epidemiology - Workspaces
Incidence Rate
No. of new cases of disease over time period
Incidence rate =
No. of population at risk
Prevalence Rate
No. of cases of disease at a given time
Prevalence rate =
No. of total population
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
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
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
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?
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
n=456
Smoking years 13.75
(± 1.5)
Controls
n=456
6.12
(± 2.1)
F
7.5
P
0.04
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
ill
healthy
mobile phone user
292
108
400
non-phone user
89
313
402
381
421
802
Age groups
Sex
Economics of Scale - Solway Harvester
Photo courtesy of Dr Gordon Baird
Solway Harvester
Numerical
%
Isle of Whithorn
7/300
2.3
Wigtownshire
7/20,000
0.03
London
7/6,000,000
0.00001
7 people from Wigtownshire . . . . .
. . . . . Equivalent to 120,000 people from London
Risk & Odds Ratios: Gulf War Syndrome
2 x 2 Tables: Diabetes over 2 years in Builders
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
Recommended Journal Reading 1
Carroll D, Davey Smith G, Sheffield D, Shipley MJ, and Marmot MG. (1995).
Pressor reactions to psychological stress and prediction of future blood
pressure: data from the Whitehall II study. BMJ; 310: 771-775.
Carroll D et al., (2002) Admissions for myocardial infarction and World Cup
football: database survey. BMJ. Dec 21; 325: 1439-42.
Chen C, David AS, Nunnerley H, Michell M, Dawson JL, Berry H, Dobbs J,
and Fahy T. (1995). Adverse life events and breast cancer: case-control
study. BMJ; 311: 1527-1530.
Recommended Journal Reading 2
Kivimäki M, Leino-Arjas P, Luukkonen R, Riihimäki H, Vahtera J, and
Kirjonen J. (2002). Work stress and risk of cardiovascular mortality:
prospective cohort study of industrial employees. BMJ; 325: 857.
Levenstein S. (1998). Stress and peptic ulcer: life beyond helicobacter.
BMJ; 316: 538-541.
Meyer JD. et al. (2000) Occupational contact dermatitis in the UK: a
surveillance report from EPIDERM and OPRA. Occupational Medicine;
50(4): 265-273.
Recommended Book Reading
Altman, D.G. “Designing Research”. In: Altman, D.G., (ed.) Practical
Statistics For Medical Research. London, Chapman and Hall, 1991; 74-106.
Bland, M. “The design of experiments”. In: Bland, M., (ed.) An introduction to
medical statistics. Oxford, Oxford Medical Publications, 1995; 5-25.
Daly, L.E., Bourke, G.J. “Epidemiological and clinical research methods”. In:
Daly L.E., Bourke, G.J., (eds.) Interpretation and uses of medical statistics.
Oxford, Blackwell Science Ltd, 2000; 143-201.
Fitzpatrick M. (2002). Work Stress: The Making of a Modern Epidemic.
Milton Keynes Open University Press.
Jackson, C.A. “Study Design” & “Sample Size and Power”. In: Gao Smith, F.
and Smith, J. (eds.) Key Topics in Clinical Research. Oxford, BIOS scientific
Publications, 2002.