Professor Maria Feychting Institute of Environmental Medicine...Maria Feychting 29 marzo 2008 21 Wire-codes Originates from Wertheimer & Leeper, 1979 Takes into account distance and

Exposure indices in ELFepidemiology

Professor Maria Feychting

Institute of Environmental Medicine

29 marzo 2008Maria Feychting 2

Overview of presentation

Sensitivity and specificity of exposure classification

Exposure assessment in occupational studies

Job-titles

”Electrical occupations”

Job-exposure matrices

Exposure assessment in residential studies

Distance or wire codes

Calculated fields

Measurements

Aspects of exposure assessment in overall

assessment of evidence


Sensitivity:

Probability that an exposed subject is classified as

exposed

No. of exposed subjects classified as exposed

Total number of exposed subjects

Specificity:

Probability that an unexposed subject is classified as

unexposed

No. of unexposed subjects classified as unexposed

Total number of unexposed subjects


Misclassification of exposure

Non-differential: independent of the disease

Leads to diluted effect estimates: RR 1

NB: non-differential exposure misclassification has no effect on

relative risk estimates if there is no true association between

exposure and disease risk

Magnitude of bias depends on:

prevalence of the exposure in studied population & sensitivity

and specificity of exposure assessment method


Non-differential exposure misclassification

Example: 2% exposure prevalence, true RR=2

1.0 0.8 0.6 0.4 0.2

1.0 2.00 1.99 1.98 1.98 1.97

0.8 1.09 1.07 1.05 1.02 1.00

0.6 1.05 1.03 1.02 1.00

0.4 1.03 1.02 1.00

0.2 1.02 1.00

Specificity

Sensitivity

Norell SE, 1987


Non-differential exposure misclassification

UnexposedHighexp.


When exposure is rare

High specificity of exposure estimate very important

Specificity = probability that unexposed individual

is classified as unexposed

Even a slight reduction in specificity may dilute risk

estimate considerably


When exposure is rare

Not important to find everyone who is exposed, i.e.

high sensitivity is not needed

Misclassification of exposed subjects as unexposed does

not affect results much

A high sensitvity improves the statistical power of the

study

Affects number of exposed subjects

A high sensitivity is essential for estimation of

exposure prevalence and public health impact


Differential exposure misclassification

i.e. misclassification related to disease

Can affect risk estimate i any direction

Exposure information must be collected in a similar

way for both cases and controls

“Recall bias” may lead to differential exposure

misclassification

Disease influence recall among cases

”Objective” source of information better, i.e. registry


ELF electric and magnetic field exposure

50 or 60 Hz fields (power frequency fields)

Frequencies in occupational settings may vary

Magnetic fields penetrate buildings, trees, humans

Electric fields are shielded by walls, trees, etc.

Epidemiological studies have focused on magnetic

fields – very few have included electric fields


ELF magnetic fields

Generated when electricity is transported or used

Magnetic field directly proportional to the current

Fall of rapidly with distance to the source

Fields are imperceptable – exposure information

cannot easily be obtained through self reports

Need to rely on direct measurements or proxies


ELF magnetic field exposure

Three major groups of exposure sources:

Occupational, residential, electrical appliances

Examples of sources:

Power lines, transformers, substations, various industrial

equipment, public transportation (train, subway), unbalanced

return currents, electric bed heating devices, microwave

ovens, electric razor, etc.


Relevant exposure metric unknown

No known biological mechanism at low levels of

exposure

Time weighted average ?

Peak exposure ?

Intermittent exposure ?

No one is unexposed!


Example: same cumulative dose –

different distribution over time

Duration

Intensity


Time-weighted average (TWA)

Majority of studies have focused on TWA

Some have estimated cumulative exposures

( Thours)

For population exposure: high specificity (91-98%)

when using TWA as an estimate of other metrics

e.g. time above 0.4 T, time above 1.6 T, maximum fields,

sudden changes >0.1 T, intermittence, length of time in a

constant field above 0.2 T


Categorization of exposure

If no prior studies, and no clues from biology –

categorization often based on exposure distribution

among controls, e.g. quartiles

If prior studies available – make sure to present

comparable cut-points

Always present reasons for choice of cut-points

Not valid to search for the cut-points with highest

effect estimates


Residential exposure - high exposures rare

0

10

20

30

40

50

60

70

0.1 0.1-0.19 0.2-0.29 0.3-0.39 0.4-

Magnetic field exposure distribution in the NCI studyLinet et al. 1997

%

T


Residential epxosure - proportion

exposed to >0.4 T

Only 0.4% in the UK Childhood Cancer Study

Similar proportion in Sweden

1% in the US (NCI-study)

3% in a study from Canada (McBride)


Residential magnetic field exposure

assessment

Distance

Wire-codes

Calculated fields

Measurements

Spot measurements

24-48 h measurements

Personal measurements


Distance to power line

Crude estimate of magnetic field level in the home

Correct only very close to high voltage transmission

lines

i.e. less than 40-50 meters from largest transmission lines

(>200 kV)

Few people live close to high voltage transmission

lines

Analysed categories have been within 100 or 200 meters

Low specificity – majority of persons categorized as exposed

will be unexposed


Wire-codes

Originates from Wertheimer & Leeper, 1979

Takes into account distance and type of ELF field

source, e.g. if transmission line, distribution line,

buried cables etc.

Higher specificity than distance alone - but

Does not take variation in power line load into consideration

Measurements have shown that fields are low in most

categories of wire-codes


Wire-codes and magnetic field levels in

studies of EMF and childhood leukemia

Under -

ground

Very

low

Ordinary

low

Ordinary

high

Very high

Savitz Mean

Median

0.05

0.03

0.05

0.03

0.07

0.05

0.12

0.09

0.21

0.22

London Mean

Median

0.05

0.04

0.05

0.04

0.06

0.06

0.07

0.07

0.12

0.11

Linet Mean

Median

0.06 0.05

0.08 0.05

0.12 0.08

0.14 0.10

0.21

0.13

McBride

Mean

0.09

0.08

0.11

0.17

0.26

Green Mean

0.07

(sd 0.06)

0.04

(sd 0.02)

0.14

(sd 0.11)

0.18

(sd 0.15)

0.28 (sd 0.26)


Calculated fields

Takes into account:

Distance to power line

Power line load – historical estimates

Construction of power lineHeight of towers,

Distance between conductors,

Ordering of phases,

Direction of current,

All nearby power lines

Does not take into account:

Other sources of ELF exposure, e.g. unbalancedreturn currents


Calculated fields, cont.

Allows for a quantification of dose-response

relationships

Use of the same unit of measurement (e.g. T)

allows for a better comparability between studies and

between exposure situations

e.g. residential vs occupational

Has primarily improved the specificity of the

exposure assessment


Measurements

+ Captures all sources of exposure

+ Allows for a quantification of dose-response

relationships

- Cannot be made historically

- May vary over time - less reproducible

- Sensitive to time of the day and

- Season of the year


Measurements

Short term spot measurements

May not be representative of long term historical exposure

Highly sensitive to variation of the exposure during the day

and year

24-48 hour measurements

Better representativity than spot-measurements, but may still

not capture historical exposures

Sensitive to exposure variation over different seasons

Personal measurements

For use in prospective cohort studies

Case-control studies: changes in habits after disease

occurrence may influence exposure assessment


Appliances

Magnetic fields falls of rapidly with distance

Can give high localized peak exposures

Electric razors (>0.4 T)

Microwave ovens (0.1-20 T)

Vacuum cleaner (2-20 T)

Small contribution to TWA 24-h exposure

Historically unstable – need to rely on self reports

Recall bias a potential problem


Occupational exposures

Occupational exposures higher than residential

High exposure levels rare – around 4% with >0.5 T

Exposure estimates:

Individual job titles

Grouping of job titles into “electrical occupations”

Job-exposure matrices (JEM)


Job-titles

Underlying exposure information in majority of

occupational studies – also in JEM-based studies

Information from census data or death certificates

One point in time, or several time-points with no information

for intermediate years

Independent of case-control status

Self-reported occupational history

Quality of information may differ between cases and controls

Exposure may vary considerably within the same

occupational title and over time


Electrical occupations

On average higher magnetic field levels than non-

electrical occupations

Electrical & electronic technicians and engineers, repairers of

electronic equipment, telephone & telephone line installers

and repairers, electricians, electric power installers and

repairers, supervisors of electricians and power transmission

installers, power plant operators, motion picture projectionists

Some of the highest exposed occupations are not

included

Welders

Train drivers

Sheet metal workers


Job-exposure matrices (JEM)

Improved exposure assessment – JEMs based on

personal measurements during workday

Aggregated estimates based on several

measurements per occupational title

Summarized into different measures of central tendency

Other metrics

Allows for quantification of dose-response

relationships

Has identified highly exposed occupations other than

”electrical occupations”


Job-exposure matrices

Arithmetic mean – commonly used

Sensitive to outliers

Geometric mean – also frequently used

Not so sensitive to outliers

Median

Not sensitive to outliers

Rate of change

Percent time above a certain exposure level


Weakness of JEMs

Cannot capture historical exposures

Exposure situation has changed in many occupations –

often increased exposures, e.g. librarians, cashiers

Does not take into account individual variations within

occupations

Mainly based on measurements for men – until

recently little was known about female occupations

Greater exposure misclassification for women

Larger number of occupations with no exposure information

for women – reduces statistical power


Further developments of JEMs

Extended from job-title to job-tasks

Takes into account variations within job-titles

Detailed mapping of exposures associated with different job-

tasks

Combines exposure level with estimates of time spent on

each job-task

Increases specificity of exposure estimates


Aspects of exposure assessment in

overall assessment of evidence

First generation of studies used the more crude

exposure assessment methods

Likely to have more extensive exposure misclassification

Later studies have improved exposure assessment

considerably

Likely to have less exposure misclassification

If true association between exposure and disease

–effect estimates should become stronger with

improved exposure assessment

Less dilution of effect estimates from exposure misclassification

Documents

Professor Maria Feychting Institute of Environmental Medicine...Maria Feychting 29 marzo 2008 21 Wire-codes Originates from Wertheimer & Leeper, 1979 Takes into account distance and