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Principles of EpidemiologyPrinciples of Epidemiology
Occupational ApplicationOccupational Application
Dr. Craig JacksonDr. Craig JacksonHonorary Senior Lecturer in Occupational PsychologyHonorary Senior Lecturer in Occupational Psychology
Institute of Occupational & Environmental MedicineThe University of Birmingham
Research DirectorResearch DirectorHealth Research Consultants
www.researchconsultants.co.uk/notes
ObjectivesObjectives
Describe key features of descriptive dataDescribe key features of descriptive data
Understand:Understand: mean, mode, median, variance, standard deviationmean, mode, median, variance, standard deviation
Calculate:Calculate: mean, mode, medianmean, mode, medianratiosratiosproportionsproportionsrates rates mortality ratesmortality ratesprevalence & incidenceprevalence & incidence
Understand:Understand: tables, charts, plotstables, charts, plots
Understand:Understand: public health surveillance public health surveillance
Background to StatisticsBackground to StatisticsDistributions Distributions
Data collection Data collection Data presentation Data presentation
Dr. Craig JacksonDr. Craig JacksonHonorary Senior Lecturer in Occupational PsychologyHonorary Senior Lecturer in Occupational Psychology
Institute of Occupational & Environmental MedicineThe University of Birmingham
Research DirectorResearch DirectorHealth Research Consultants
www.researchconsultants.co.uk/notes
33% 33% 33%
1 2 3
Problem:Problem:stick with initial choice or choose another door ?stick with initial choice or choose another door ?
50% ? 50% ?
Solution:Solution:probability says that you stand a better chance of finding the cash if you SWAPprobability says that you stand a better chance of finding the cash if you SWAP
The Monty Hall ProblemThe Monty Hall Problem
Door 1Door 1 Door 2Door 2 Door 3Door 3Never swapNever swap WINWIN
LOSELOSE
LOSELOSE
Always swapAlways swap LOSELOSE
WINWIN
WINWIN
Marilyn vos Savant
The Monty Hall ProblemThe Monty Hall Problem
DispersionDispersion
RangeRange Spread of dataSpread of data
MeanMean Arithmetic averageArithmetic average
MedianMedian LocationLocation
ModeMode FrequencyFrequency
SDSD Spread of dataSpread of dataabout the meanabout the mean
RangeRange 50-112 mmHg50-112 mmHgMeanMean 82mmHg82mmHg MedianMedian 82mmHg82mmHg ModeMode 82mmHg82mmHgSDSD ± 10mmHg± 10mmHg
Types of Data / VariablesTypes of Data / Variables
ContinuousContinuous DiscreteDiscrete
BPBP ChildrenChildrenHeightHeight No. colds in last 12 monthsNo. colds in last 12 monthsWeightWeight Age last birthdayAge last birthdayAgeAge
OrdinalOrdinal NominalNominal
Grade of conditionGrade of condition SexSexPositions 1Positions 1stst 2 2ndnd 3 3rdrd Hair colourHair colour““BetterBetter - Same – Worse” - Same – Worse” Blood groupBlood groupHeight groupsHeight groups Eye colourEye colourAge groupsAge groups
Conversion & Re-classificationConversion & Re-classification
Easier to summarise Ordinal / Nominal dataEasier to summarise Ordinal / Nominal data
Cut-off PointsCut-off Points (who decides this?)(who decides this?)
Allows Continuous variables to be changed into Nominal variables Allows Continuous variables to be changed into Nominal variables
BPBP > 90mmHg> 90mmHg == HypertensiveHypertensive
BPBP =< 90mmHg=< 90mmHg == NormotensiveNormotensive
Easier clinical decisionsEasier clinical decisions
Categorisation reduces quality of dataCategorisation reduces quality of data
Statistical tests may be more sensationalStatistical tests may be more sensational
Good for summariesGood for summaries Bad for analysesBad for analyses
Histograms and Bar-ChartsHistograms and Bar-Charts
Distinction is often lostDistinction is often lost
HistogramsHistograms
The distribution of a continuous variableThe distribution of a continuous variable
No gaps between the barsNo gaps between the bars
Bar-ChartBar-Chart
Spaces between the barsSpaces between the bars
Distribution of discrete / categorical dataDistribution of discrete / categorical data
values have no “real” meaning
values have “real”
meaning
Categorical DataCategorical Data
NOMINAL DATANOMINAL DATA
values that the data may have do not have specific ordervalues that the data may have do not have specific ordervalues act as labels with no real meaningvalues act as labels with no real meaninge.g. hair coloure.g. hair colour brown =1brown =1 blond =2blond =2 black =100black =100
ORDINAL DATAORDINAL DATA
values with some kind of ordering values with some kind of ordering data that has been measured or counteddata that has been measured or counted
e.g. social class:e.g. social class: upperupper 11 middle = 2middle = 2 working = 3working = 3
e.g. glioblastoma tumor grade:e.g. glioblastoma tumor grade: 11 22 33 44 55
e.g. position in a race:e.g. position in a race: 1 1 stst 2 2 ndnd 3 3 rdrd
Quantitative DataQuantitative Data
DISCRETEDISCRETEdistinct or separate parts, with no finite detaildistinct or separate parts, with no finite detaile.g children in familye.g children in family
CONTINUOUSCONTINUOUSbetween any two values, there would be a thirdbetween any two values, there would be a thirde.g between meters there are centimetrese.g between meters there are centimetres
INTERVALINTERVALequal intervals between values and an arbitrary zero on the scaleequal intervals between values and an arbitrary zero on the scalee.g temperature gradiente.g temperature gradient
RATIORATIOequal intervals between values equal intervals between values andand an absolute zero an absolute zeroe.g body mass indexe.g body mass index
White HotWhite Hot
Red HotRed Hot
ColdCold
““Dangerous”Dangerous”
““Unpleasant”Unpleasant”
““Uncomfortable”Uncomfortable”
““Tolerable”Tolerable”
““Comfortable”Comfortable”
““Cold”Cold”
80 80 ooCC
60 60 ooCC
40 40 ooCC
20 20 ooCC
10 10 ooCC
UnsafeUnsafe
SafeSafe
Levels of VariablesLevels of Variables
TemperatureTemperature
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 Height
% o
f pop
ulat
ion
% o
f pop
ulat
ion
DistributionsDistributions
Sir Francis Galton (1822-1911) Alumni of Birmingham UniversitySir Francis Galton (1822-1911) Alumni of Birmingham University
9 books and > 200 papers 9 books and > 200 papers Fingerprints, correlation of calculus, twins, neuropsychology, blood Fingerprints, correlation of calculus, twins, neuropsychology, blood transfusions, travel in undeveloped countries, criminality and meteorology)transfusions, travel in undeveloped countries, criminality and meteorology)
Deeply concerned with improving standards of measurementDeeply concerned with improving standards of measurement
Introduction Introduction
Who gets disease and Why?Who gets disease and Why?
Study sick people and healthy peopleStudy sick people and healthy peopleto determine crucial difference between to determine crucial difference between those who get ill and those who do notthose who get ill and those who do not
RATESRATESCOMPARESCOMPARESBALANCES BALANCES CONTRASTSCONTRASTS
NUMERATORNUMERATORThe no. of people to whom something happened e.g got sickThe no. of people to whom something happened e.g got sick
DENOMINATORDENOMINATORThe population at risk e.g.the entire populationThe population at risk e.g.the entire population
Common Popular HeadlinesCommon Popular Headlines
Common Popular HeadlinesCommon Popular Headlines
“High EffortLow Reward”
“High DemandLow Control”
2x Substance abuse
2-3x Injuries
2-3x Infections
3x Back pain
5x Certain cancers
2-3x Conflicts
2-3xMental health problems
3xCardiovascular problems
Potential Health RisksPotential Health Risks
Shain & Kramer 2004Shain & Kramer 2004
Introduction Introduction
Basic science of public healthBasic science of public health
QuantitativeQuantitative
Based on Based on probability; statistics; sound research; nomothesesprobability; statistics; sound research; nomotheses
Uses “causal reasoning”Uses “causal reasoning”
Practical common sensePractical common sense
e.g. e.g. Monitoring communicable diseases in workplacesMonitoring communicable diseases in workplaces
Dietary intake exacerbating development of cancersDietary intake exacerbating development of cancers
Effectiveness of smoking cessation programmes at Effectiveness of smoking cessation programmes at workwork
Declining disease incidence as OELs are reduced Declining disease incidence as OELs are reduced
Introduction Introduction
epi epi “on” or “upon”“on” or “upon”
demosdemos “people” or “mass”“people” or “mass”
logos logos “study of”“study of”
““Epidemiology is the Epidemiology is the studystudy of the of the distribution distribution and and determinants of health-determinants of health-related states or eventsrelated states or events in in specified populationsspecified populations, and the , and the application application of this of this study to the control of health problems.”study to the control of health problems.”
Last 1988Last 1988
TIME:TIME: annual, seasonal, daily, hourlyannual, seasonal, daily, hourly
PLACE:PLACE: geographic variation, urban vs. rural, workplaces, schoolsgeographic variation, urban vs. rural, workplaces, schools
PERSONAL:PERSONAL: age, race, sex, class, occupation, behaviourage, race, sex, class, occupation, behaviour
Introduction Introduction
Regional PictureRegional Picture
Self-reporting?Self-reporting?
Who’s best off?Who’s best off?
Who’s worse off?Who’s worse off?
What is an Epidemic?What is an Epidemic?
1. Person / Host1. Person / HostMen, Women and children all at riskMen, Women and children all at riskMajority were wealthy young men aged 18-50Majority were wealthy young men aged 18-50
2. Place / Environment2. Place / EnvironmentAll cases were within 1 square mile of each otherAll cases were within 1 square mile of each otherClimate was coldClimate was cold
3. Time of exposure and symptoms3. Time of exposure and symptomsMid AprilMid AprilDeath occurred within hours of exposureDeath occurred within hours of exposure
When there are significantly more cases of a disease / When there are significantly more cases of a disease / death than past experience would have predicteddeath than past experience would have predicted
DeterminantsDeterminants
Causes or factors of incidence of ill-healthCauses or factors of incidence of ill-health
Health-related states or eventsHealth-related states or events chronic diseasechronic diseaseinjuriesinjuriesbirth defectsbirth defectschild healthchild healthoccupational healthoccupational healthenvironmental healthenvironmental health
Specified PopulationsSpecified Populations
ExposuresExposures Others exposedOthers exposed SpreadSpread InterventionsInterventions
Case DefinitionCase Definition
People can be classified as People can be classified as CasesCases ++Non-CasesNon-Cases - -SuspectsSuspects ??
Modern Example . . . . .Modern Example . . . . .
Personal DetailsPersonal DetailsCluster of 5 cases of rare pneumoniaCluster of 5 cases of rare pneumoniaAll 5 were young malesAll 5 were young malesAged between 29-36Aged between 29-362 of the 5 reported frequent homosexual contact2 of the 5 reported frequent homosexual contactAll 5 used “poppers”All 5 used “poppers”
Location DetailsLocation Details5 cases were in Los Angeles5 cases were in Los AngelesSimilar cases in NY and SFSimilar cases in NY and SF
Time DetailsTime Details19811981All 5 deaths between Oct 1980 – May 1981All 5 deaths between Oct 1980 – May 19814 weeks after, 67 more cases reported4 weeks after, 67 more cases reported
Descriptive Epidemiology - YearsDescriptive Epidemiology - Years
Descriptive Epidemiology - MonthsDescriptive Epidemiology - Months
Descriptive Epidemiology - SeasonalDescriptive Epidemiology - Seasonal
Descriptive Epidemiology - DaysDescriptive Epidemiology - Days
Fatalities associated with Tractor injuries, by day of week, Georgia: 1971-1981Fatalities associated with Tractor injuries, by day of week, Georgia: 1971-1981
Descriptive Epidemiology - RegionalDescriptive Epidemiology - Regional
Descriptive Epidemiology - WorkspacesDescriptive Epidemiology - Workspaces
Age groups – data considerationsAge groups – data considerations
Incidence RateIncidence Rate
No. of new cases of disease over time periodNo. of new cases of disease over time periodIncidence rate = Incidence rate =
No. of population at riskNo. of population at risk
Prevalence RatePrevalence Rate
No. of cases of disease at a given time No. of cases of disease at a given time Prevalence rate = Prevalence rate =
No. of total population No. of total population
Risk & Relative RiskRisk & Relative Risk
““Risk Ratios” can inform how “risky” certain exposures / behaviours areRisk Ratios” can inform how “risky” certain exposures / behaviours are
Implications for likelihood of developing certain diseasesImplications for likelihood of developing certain diseases
““Risky” behaviours can be avoided or prohibitedRisky” behaviours can be avoided or prohibited
Incidence of Parkinson’s Disease among retired welders Incidence of Parkinson’s Disease among retired welders = R.R= R.R
Incidence of Parkinson’s Disease among retired workersIncidence of Parkinson’s Disease among retired workers
100 cases (per 1000) for ex-welders 100 cases (per 1000) for ex-welders = 4= 4
25 cases (per 1000) for retired workers25 cases (per 1000) for retired workers
Case FatalityCase Fatality
Why are people more scared of a diagnosis of Mesothelioma than Arthritis?Why are people more scared of a diagnosis of Mesothelioma than Arthritis?
Some diseases have a higher Fatality RateSome diseases have a higher Fatality Rate
No. of deaths by disease in timeframeNo. of deaths by disease in timeframeFatality Rate =Fatality Rate = X 100X 100
No. of cases of the disease in timeframeNo. of cases of the disease in timeframe
60 deaths due to Mesothelioma in last month60 deaths due to Mesothelioma in last month44%44% == X 100X 100
135 cases of Mesothelioma recorded in last month135 cases of Mesothelioma recorded in last month
Crude Death RateCrude Death Rate
No. of deaths in calendar yearNo. of deaths in calendar yearC.D.R =C.D.R = X 1000X 1000
No. of population at mid-yearNo. of population at mid-year
Expressed as Deaths per 1000Expressed as Deaths per 1000
500,000 deaths in calendar year500,000 deaths in calendar year8.3 deaths / 10008.3 deaths / 1000 = = X 1000 X 1000
60,000,000 population at mid-year60,000,000 population at mid-year
Risk & Odds Ratios: Gulf War SyndromeRisk & Odds Ratios: Gulf War Syndrome
2 x 2 Tables: Diabetes over 2 years2 x 2 Tables: Diabetes over 2 years
Case Control Study: Lung CancerCase Control Study: Lung Cancer
Cases have Lung Cancer + Smoking ExposureCases have Lung Cancer + Smoking Exposure
Controls could be other hospital patients (other disease) or “normals”Controls could be other hospital patients (other disease) or “normals”
Matched Cases & Controls for age & genderMatched Cases & Controls for age & gender
Smoking years of Lung Cancer cases and controls Smoking years of Lung Cancer cases and controls (matched for age and sex)(matched for age and sex)
CasesCases ControlsControlsn=456n=456 n=456n=456
FF PPSmoking yearsSmoking years 13.7513.75 6.126.12 7.57.5 0.040.04
(± 1.5)(± 1.5) (± 2.1)(± 2.1)
Cohort Study: Mobile phones and Ill-HealthCohort Study: Mobile phones and Ill-Health
Subjects classified into 2 (or more groups)Subjects classified into 2 (or more groups)e.g. exposed vs non exposede.g. exposed vs non exposed
End point: End point: groups compared for health statusgroups compared for health status
Comparison of general health between users and non-users of mobile Comparison of general health between users and non-users of mobile phonesphones
illill healthyhealthy
mobile phone usermobile phone user 292292 108108 400400
non-phone usernon-phone user 8989 313313 402402
381381 421421 802802
Work Related Ill-Health in the UKWork Related Ill-Health in the UK
33 Million days lost per year33 Million days lost per year
Males lose more working days than femalesMales lose more working days than females
Days lost increase with ageDays lost increase with age
Low managerial / professionals had highest rate of absenceLow managerial / professionals had highest rate of absence
Most sickly occupations are health & social welfare, construction, teaching,Most sickly occupations are health & social welfare, construction, teaching,and researchand research
Work Related Ill-Health in the UKWork Related Ill-Health in the UK
Bakers appear highly with occupational asthmaBakers appear highly with occupational asthma
Metal workers appear highly with upper limb problemsMetal workers appear highly with upper limb problems
Mesothelioma deaths high in shipbuilders and asbestos workersMesothelioma deaths high in shipbuilders and asbestos workers
Stress, depression and anxiety highest in:Stress, depression and anxiety highest in:Public admin. Public admin. DefenceDefenceEducationEducationHealth workHealth workSocial workSocial work
Economics of Scale - Solway HarvesterEconomics of Scale - Solway Harvester
Photo courtesy of Dr Gordon Baird Photo courtesy of Dr Gordon Baird
Numerical %
Isle of Whithorn 7/300 2.3
Wigtownshire 7/20,000 0.03
London 7/6,000,000 0.00001
Solway HarvesterSolway Harvester
7 people from Wigtownshire7 people from Wigtownshire
Equivalent to 120,000 people from LondonEquivalent to 120,000 people from London
Occupational Epidemiology of Birmingham ?Occupational Epidemiology of Birmingham ?
Health and Safety Executive (THOR)Health and Safety Executive (THOR) www.hse.gov.uk/statisticswww.hse.gov.uk/statistics
Office of National StatisticsOffice of National Statistics www.statistics.gov.ukwww.statistics.gov.uk
Traditional IndustriesTraditional Industries
New and Emerging IndustriesNew and Emerging Industries
Environmental AspectsEnvironmental Aspects
Transport FeaturesTransport Features
Migrant PopulationsMigrant Populations
Admissions and World Cup 1998Admissions and World Cup 1998
Examine hospital admissions for rangeExamine hospital admissions for range of diagnoses on days surrounding of diagnoses on days surrounding England's 1998 World Cup footballEngland's 1998 World Cup football matchesmatches
Hospital admissions obtained fromHospital admissions obtained from English hospital episodeEnglish hospital episode statisticsstatistics
Pop. Aged 15 – 64 yearsPop. Aged 15 – 64 years
Admissions for Admissions for • Acute MIAcute MI On match dayOn match day• StrokeStroke and 5 days afterand 5 days after• Deliberate self harmDeliberate self harm match daymatch day• Road traffic injuriesRoad traffic injuries
Compared with admissions at the same time in 1997 and 1998Compared with admissions at the same time in 1997 and 1998
Carroll, D Carroll, D et al.et al. 2002 2002
Admissions and World Cup 1998Admissions and World Cup 1998
England's matches in the 1998 World Cup England's matches in the 1998 World Cup
15 June 15 June (England 2, Tunisia 0)(England 2, Tunisia 0) winwin22 June 22 June (Romania 2, England 1)(Romania 2, England 1) lostlost26 June26 June (Colombia 0, England 2)(Colombia 0, England 2) winwin30 June 30 June (Argentina 2, England 2) (Argentina 2, England 2) lost: penalties 4-2lost: penalties 4-2
Extracted hospital admissions data for acute myocardial infarction, stroke, Extracted hospital admissions data for acute myocardial infarction, stroke, deliberatedeliberate self harm, and road traffic injuries among men and womenself harm, and road traffic injuries among men and women aged aged 15 to 6415 to 64
Games all took place in late eveningGames all took place in late evening
Examined the same associations using only the two days afterExamined the same associations using only the two days after the match the match omitting the day of the match as the exposedomitting the day of the match as the exposed conditioncondition
Admissions and World Cup 1998Admissions and World Cup 1998
During the period of England's World Cup matches (15 June to 1 July)During the period of England's World Cup matches (15 June to 1 July)81,433 emergency admissions occurred: 81,433 emergency admissions occurred:
1348 1348 (2%) for(2%) for myocardial infarction myocardial infarction 662 662 (1%) for stroke(1%) for stroke 856 856 (1%) for road(1%) for road traffic injurytraffic injury3308 3308 (4%) for deliberate self harm(4%) for deliberate self harm
observed / expectedobserved / expected actual – expectedactual – expected ARRARRadmissionsadmissions admissionsadmissions
Day of matchDay of match 91 / 7291 / 72 1919 1.25 (0.99 to 1.57)1.25 (0.99 to 1.57)1 day after1 day after 88 / 7288 / 72 1616 1.21 (0.96 to 1.57)1.21 (0.96 to 1.57)2 days after2 days after 91 / 7191 / 71 2020 1.27 (1.01 to 1.61)1.27 (1.01 to 1.61)3 days after3 days after 76 / 7476 / 74 22 0.99 (0.77 to 1.27)0.99 (0.77 to 1.27)4 days after4 days after 71 / 7471 / 74 33 0.92 (0.71 to 1.19)0.92 (0.71 to 1.19)5 days after5 days after 83 / 7283 / 72 1111 1.13 (0.89 to 1.43)1.13 (0.89 to 1.43)
Admissions and World Cup 1998Admissions and World Cup 1998
Admission Admission Within 2 days Within 2 days Within 2 days Within 2 days Within 2 days of Within 2 days of P valueP valuediagnosisdiagnosis of winof win of 1-2 loss of 1-2 loss loss on penalty loss on penalty
M.IM.I 0.990.99 0.910.91 1.251.25 0.0070.0070.89 - 1.110.89 - 1.11 0.78 - 1.070.78 - 1.07 1.08 - 1.441.08 - 1.44
StrokeStroke 0.870.87 0.970.97 1.001.00 0.420.420.74 - 1.030.74 - 1.03 0.79 - 1.190.79 - 1.19 0.82 - 1.230.82 - 1.23
RTARTA 0.990.99 0.960.96 0.850.85 0.510.510.85 - 1.140.85 - 1.14 0.79 - 1.170.79 - 1.17 0.69 - 1.050.69 - 1.05
DSHDSH 1.081.08 1.011.01 1.051.05 0.260.261.00 - 1.161.00 - 1.16 0.91 - 1.120.91 - 1.12 0.95 - 1.160.95 - 1.16
•PeriodsPeriods after a win (Tunisia, Columbia) and 1st first loss (Romania) were not after a win (Tunisia, Columbia) and 1st first loss (Romania) were not associated with increasedassociated with increased admissionsadmissions
• On match day, and two days after match against ArgentinaOn match day, and two days after match against Argentina with a penalty with a penalty shoot-out, admissions for acute MIshoot-out, admissions for acute MI increased by 25%. increased by 25%.
• No increases in admission were seen for anyNo increases in admission were seen for any of the other diagnoses. of the other diagnoses.
Admissions and World Cup 1998Admissions and World Cup 1998
Major environmental events, whether physical catastrophes or cultural Major environmental events, whether physical catastrophes or cultural disappointments,disappointments, are capable of triggering myocardial infarction.are capable of triggering myocardial infarction.
If the triggeringIf the triggering hypothesis is true, preventive efforts should consider hypothesis is true, preventive efforts should consider strategiesstrategies for dealing with the effects of acute physical and psychosocialfor dealing with the effects of acute physical and psychosocial
upheavals. upheavals.
““Perhaps the national lottery or even the penalty shoot-out Perhaps the national lottery or even the penalty shoot-out should be abandoned on publicshould be abandoned on public healthhealth grounds.”grounds.”
Limitations:Limitations:
Harvesting effect?Harvesting effect? Reporting tendency?Reporting tendency? Sudden deaths?Sudden deaths?