www.monash.edu.au

THE HEALTH WATCH STUDYAustralian petroleum industry cohort

A/Prof Deborah Glass and Prof Malcolm SimMonash Centre Occupational Environmental Health, Department of Epidemiology and Preventive Medicine

www.monash.edu.au

2

Health Watch

• Set up 1980• Prospective cohort study of mortality and

cancer incidence• Australian petroleum industry workers

– Upstream sites– Refineries– Terminals– Airports

• Funded Australian Institute of Petroleum (AIP)– Large companies not small independents

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3

Health Watch Cohort

• 95% of blue collar employees interviewed– except those at sites with <10 employees

• >5 years in industry• Actively followed & re-interviewed every 5 years

until 2000

• Surveys inc. job histories, smoking and drinking

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4

Cohort is ageing

• Over 30 years

• 16,623 men and 1,375 women

• 2004: 1,473 men and 34 women died

– 289,275 person-years of observation in men

– 19,347 person-years in women

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5

Update to mortality and cancer incidence

• Matched to national death data – end 2004

• Matched to Cancer Registry data– end 2002

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Strong healthy worker effect

All major causes of death are low

SexOverall SMR

(95% C.I.)Cancer SMR

(95% C.I.)Cancer SIR

(95% C.I.)

Male 0.72 (0.68-0.76)

0.81 (0.75-0.88)

0.99 (0.94-1.04)

Female 0.65(0.45-0.91)

0.88 (0.54-1.34)

0.89 (0.68-1.15)

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7

Women in Health Watch

Too few women to do many analyses

• 21/34 deaths were from cancer– SMR for cancer as expected

• 58 cancers– SIR for cancer as expected

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8

Mortality among men in Health Watch

Cause SMR (95% C.I.)Cancer (Malignant) 0.81 (0.75-0.88)

Ischaemic heart disease 0.77 (0.69-0.85)

Stroke 0.60 (0.46-0.77)

Respiratory disease 0.73 (0.59-0.89)

All diseases of the digestive system 0.57 (0.42-0.77)

External Causes (accidents, violence, suicide) 0.64 (0.53-0.77)

All other causes 0.55 (0.47-0.64)

All causes 0.72 (0.68-0.76)

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For men in Health Watch

There is no evidence of increasing cancer incidence or increasing cancer mortality with:

• increasing duration of employment; • increasing time since first employment;• time period of first employment.

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Cancer among men in Health Watch

• Significantly excess:– Mesothelioma - 1.29 (1.13 - 1.48) – Melanoma - 1.76 (1.12 - 2.65)

• Leukaemia, prostate cancer and bladder cancer are no longer in excess

• Kidney cancer raised but not in significant excess in cohort or drivers

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Health Watch lymphohaematopoetic (LH) cancers over time

0.2

0.7

1.2

1.7

2.2

2.7

3.2

3.7

1987 1990 1993 1996 1999 2002

Year of analyses

SIR

for

men

non Hodgkin lymphoma (NHL)Multiple myeloma (MM)Leukaemia

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Nested case-control questions

• Is benzene exposure associated with increases in:– Leukaemia & sub-types?– Non-Hodgkin lymphoma (NHL)?– Multiple myeloma (MM)?

• Is there a latent period?

• Does exposure rate (peaks) matter?

• Are smoking and alcohol risk factors?

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Nested case-control study

79 LH Cancer 395 Controls5:1 age matched

Health WatchCohort

(~16,000 men)

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Quantitative exposure assessment

• Detailed job histories from cohort records– Interview

– Company records

• Site history

• Contemporary colleague– Structured case-blind interview

> tasks> products> technology

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Exposure model

• Exposure measurements – Company & supplementary data

Base Estimates for tasks (ppm)

• Exposure modifiers– eg technology factors

• Individual exposure estimates– work history + algorithm

individual exposure estimates (ppm & ppm-years)

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Base estimates

• 54 BEs, 49 used in study

• 36 based on local data– Based on measured personal exposure to benzene– Data from Australian petroleum industry – Data from Australian sites – More than 3870 data points– Identified task/job – Routine exposure– Used AM of data

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Rail car loading

Observed Value

6420-2-4-6

Exp

ect

ed

No

rma

l3

2

1

0

-1

-2

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Laboratory worker (lubes, R&D)

Observed Value

10-1-2-3-4-5-6

Exp

ect

ed

No

rma

l3

2

1

0

-1

-2

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Exposure metrics

• Duration (years)• Intensity (average daily ppm)

– Highest or longest job

• Cumulative exposure (ppm-years)

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2020

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Other Upstream

Office

Upstream Operations

Supervision

Other Refinery

Other Terminal

Road Tanker Driving

Refinery Operations

Aircraft Refuelling

Wharf & JettyOperations

Tank FarmOperations

Laboratory

Road Tanker Loading

Fitting

Drum Laundry & Preparation

Vehicle Maintenance

Rail Car Loading

Drum Filling

Average Intensity of Exposure(ppm)

Job group and exposure

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Years of employment

mean exposure period of 20 years (range 4-42)

0%

10%

20%

30%

40%

<10 10-20 20-30 30-40 >40

Cases

Controls

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Health Watch case-control study

4-8 > 16

0.1

1.0

10

100

1000

NHL/MM

1-2<1 8-16 > 16

0.1

1.0

10

100

1000

Cumulative Lifetime Benzene Exposure midpoint (ppm-years)

Odd

s ra

tio (

log

scal

e) 86 63

Leukaemia

7

98

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Combining two lowest exposure groups

Exposure (ppm-years)

Cases

OR (95% CI)

< 2 9 1.0

2 - 4 8 2.9 (1.0 – 8.5)

4 - 8 3 1.2 (0.3 – 5.0)

8 - 16 6 3.1 (0.9 – 10.6)

> 16 7 51.9 (5.6 – 477)

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2424

Leukaemia latency

Odd

s R

atio

(lo

g sc

ale)

Cumulative Exposure (ppm-years)

Leukaemia < 15 years

0.05

1

10

100

1000

Leukaemia > 15 years

0 5 10 15 0.05

1

10

100

1000

34.12

6.18

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Od

ds

Ra

tio (

log

sca

le)

Cumulative Exposure (ppm-years)0 20 40 60

0.5

1

2

5

10

20

50

100

Leukaemia exposure groups

Horizontal bars indicate the range of exposure in each group

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Leukaemia risk by sub-type

OTHER2 ALL1 hairy cell leukaemia 2 unspecified lymphocytic

Exposurequintile

Odds Ratio( 95%CI)

AML 1-3 1.004 0.00 (0.00)5 8.89 (0.95-82.84)

ANLL 1-3 1.00(AML+AUL) 4 1.11 (0.18-6.96)

5 8.29 (1.31-52.36)CML 1-3 1.00

4 0.00 (0.00)5 0.78 (0.07-9.06)

CLL 1-3 1.004 2.25 (0.34-14.76)5 7.15 (1.29-39.70)

Cases405425501425

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What is a peak?

• Highest job?

• Highest day?

• Highest hour?

• Highest 15 minutes?

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Evidence for effects of peak exposure

0 10 20 30 40

0.5

1

10

100

500

1000

Odd

s R

atio

(lo

g sc

ale)

With CB/BTX cases

Without CB/BTX cases

Cumulative Exposure (ppm-years)

98

39

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2929

High exposures

• 12 subjects were exposed to concentrated

benzene or BTX

• 5 developed leukaemia, no NHL or MM

• 5/12 exposed >32 ppm-years

• 4 developed leukaemia

• No cases among office workers

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Adding possible high exposures (PHEs)C

umul

ativ

e E

xpos

ure

with

PH

Es

adde

d lo

g sc

ale

(ppm

/yea

rs)

12

48

1632

6412

8

1 2 4 8 16 32 64 128

Cumulative Exposure log scale (ppm/years)

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Odds ratios reduced

7.8 (2.3 - 25.9)7>16

2.7 (0.7 - 10.1)6>8-16

1.2 (0.3 - 5.2)3>4-8

3.1 (1.0 - 9.3)8>2-4

1.09≤2

Cumulative exposure and PHEs

OR (95% CI)

Number of cases

Benzene exposure

(ppm-years)

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3232

Summary of case-control results

• NHL MM - not associated with benzene exposure • Leukaemia - strongly positive

– ANNL & CLL ~ positive

• Significant excess risk at >16 ppm-years– Cum exp and intensity too close to separate

• Latency period 10-15 years • Effect of “peaks” – some evidence• No association with smoking or alcohol

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THE POOLED PETROLEUM INDUSTRY CASE-CONTROL STUDY

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Pooled study (published online JNCI 30/10/12)

• 3 case-control studies (Canadian, UK, Australian) nested in petroleum industry cohorts

• Each updated with new cases and pooled• more power for leukaemia subtypes• use WHO classification of LH cancers

• Similar design, case and control identification, exposure assessment and analytical methods

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35

Aims of the study

To investigate the relationship between exposure to benzene and risk of leukaemia

– Evaluate dose-response overall– Evaluate by WHO subtype– Include leukaemias, MDS and MPD

– Explore influence on dose-response relationships of study, site type, job, lag/latency, exposure metrics

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Pooling three nested case control studies

U.K.2

inconsistent dose response, depending on subgroups and different

exposure metrics

based on 31 LH cancers based on 90 leukaemias based on 79 LH cancers

before pooling data update studies

Canada1

no consistent dose response, but

small study

Australia3

strong dose response, for ANNL & CLLRefs:

1. Schnatter et al. 1996 53:773-781.

2. Rushton et al. 1997 54: 152-166.

3. Glass et al. 2003 14: 569-577.

60 LH cancersIncl. 5 MDS

193 LH cancersIncl. 11 MDS

117 LH cancersIncl. 13 MDS

370 LH cancers

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37

Pooled study steps

• Ethical approvals, identify new cases & controls, obtain work histories, carry out exposure assessment

• Ensure consistency of disease classification• Certainty of diagnosis

• Assess consistency of exposure assessments– Development of common job groups

– Development of peak and skin exposure metrics

– Certainty of exposure

– Comparability of background exposure

– Rationalization of differences across studies

• Statistical Analyses

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38

Traditional Paradigm: Anatomy• LEUKAEMIAS (in peripheral blood)• LYMPHOMAS (in lymph system)

New Paradigm: Cell of Origin• MYELOID tumours

– Myeloproliferative Disease (MPD)– Myelodysplastic Syndrome (MDS)– Acute Myeloid Leukaemias (AML)

• LYMPHOID tumours– B-cells – T-cells

Checked lymphohaematopoietic (LH) cancer classification

(leukaemias and lymphomas)

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39

Quantitative exposure assessment

• Individual job histories

• Site histories

• Exposure of new cases and controls

assessed by original study method

• Exposure intensity (ppm) for each job• Confidence score for each estimate

– L, M, H

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Pooled study exposure assessment

• Team of study hygienists and external hygienist• Peak exposure metric

– Prob. >3ppm for 15-60 mins at least weekly

• Skin exposure metric– 0, L, M, H prob of at least weekly skin exposure

• Prepared common list of job categories– Allocated each job for each individual

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Exposure assessment rationalisation

• Compared job estimates between studies• Estimates in each job category by era

– pre 1945, 1945-1960, 1960s & 1970s and 1980+ – AM, GM, n, max & min

• If mean were same (within 10%)- no change• If different

– Checked job/technology/products/conditions– If no apparent local explanation, adjust

• Some job cats. had no other study comparison– Different industry sector or period

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42

Study exposure estimates

Little change from original estimates

Little difference between original and revised

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43

Statistical analyses

• Risk as ORs by exposure : – cumulative benzene (ppm-years)

> ppm-years within relevant window (lag/latency)

– average & maximum intensity (ppm)– peaks & skin

• Job category, industry sector

• Sensitivity analyses: study, job confidence, exposure confidence

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44

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45

1.73

4.333.47

11.6

0.1

1

10

100

>0.348 and<2.93

>2.93 >0.348 and<2.93

>2.93

MDS, cumulative benzene exposure and certainty of diagnosis

Odd

s R

atio

s

All Subjects More Certain Diagnoses

Cumulative Exposure (ppm-years)

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46

Pooled analysis MDS cases and controls

Suggests MDS cases over-represented at approx 1+ ppm

Currentexposure zone

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CONCAWE study findings

MDS associated with low benzene exposure MDS may be a more sensitive outcome than AML

AML: several ORs were >1, few statistically sig. perhaps higher benzene exp. for sig. risks of AML some cases formerly classified as AML were MDS

CML: several ORs >1, but no clear exposure response pattern

CLL and MPD: no strong relationship

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48

Putting the evidence together

• Epidemiology assesses risk for the group Can it be applied to other groups?

• Risk estimates from single studies

• Pooled data or meta-analyses needed for conclusions about causation

wobbly

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49

Population risk vs individual risk

• Attribution at a population level– Benzene exposure increases the incidence of

leukaemia

• Attribution for an individual– Benzene exposure caused leukaemia in this person

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50

Acknowledgements

• Australian Institute of Petroleum• Institute of Petroleum (UK)• American Petroleum Institute• Conservation for Clean Air and Water

Europe (CONCAWE)• Aromatic Producers Association• Energy Institute• Canadian Petroleum Products Institute