principles of epidemiology occupational application dr. craig jackson honorary senior lecturer in...

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

“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


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


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

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


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


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)


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.


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?

principles of epidemiology occupational application dr. craig jackson honorary senior lecturer in...

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