Measuring potential health problems caused by occupational and environmental exposures…..

….from The Institute of Occupational Medicine

GenStat is well known and highly regarded throughout the world in its historical core area of biosciences, and specifically in agricultural research.

“……beautifully simple to program.” Dr Brian Miller

The breadth of statistical analysis covered is well documented on websites, review articles and the like. As are the importance of its pedigree, developed, tried, tested and used by agricultural statisticians; the birthplace of GenStat (Rothamsted Experimental Station) being also the birth place of modern statistics with the likes of Sir Ronald Fisher, Frank Yates and Professor John Nelder all giving GenStat a certain kudos in statistical circles and the bioscientist’s world.

More and more disciplines are relying on statistics to uncover trends, causes and to better understand relationships between various factors. One area that has always understood the importance of statistics is epidemiology – the study of factors affecting the health and well-being of populations. Epidemiology is a vital discipline underpinning evidence-based medicine, for identifying risk factors for diseases and health effects.

The epidemiologist’s work ranges from investigations into disease outbreaks, clusters and exposure-response relationships, which may include the development of regression models to test hypotheses and estimate risk coefficients. The epidemiologists’ work at the Institute of Occupational Medicine in Edinburgh is designed to provide reliable information about health effects and risks for occupational and environmental hazards, with a view to addressing public and industry concerns, and providing a scientific basis for policies to limit disease. So it’s easy to see how a statistical analysis system such as GenStat is a vital tool for these researchers.

The IOM has been using GenStat for several decades in their studies on public health in the UK. Originally set up as a charity in 1969 to research coalminers’ lung disease, to continue a research programme set up by the National Coal Board’s medical service, the charity has been independent since 1990, and now provides research, consultancy, laboratory and measurement services in relation to potential health problems caused by occupational and environmental exposures. All the research reports published by the IOM since 1969 are available for free download from the online library at www.iom-world.org.

GenStat has been used in a variety of different analyses, including epidemiological or observational data, which typically requires a regression model of some kind (linear, GLM, GAM, LMM, GLMM etc). It is also used for analysing data sets from designed toxicology experiments and for analysing cause-specific mortality data in comparison with reference rates.

A recent study looked at mortality rates in a group of almost 18,000 coalworkers from 10 collieries recruited from the 1950s onwards and followed up until the present time, of whom about two thirds are now deceased. One aim of the study was to compare the observed rates from certain causes of death with the male population rates for those causes in the regions where the coal pits are located. The calculations produce standardised mortality ratios (SMR’s) and their standard errors, using standard epidemiological methods.

GenStat used each individual’s entry and death or censoring dates to amass the person-years in the cohort, tabulating them by region, year and age (using GenStat’s option for sequential tabulation). The SMR calculations then used GenStat’s table manipulation functions to organise observed deaths and calculate expected numbers, ratio of observed to expected (SMR) and its standard error, etc. The outputs included overall SMR, plus a breakdown on 5 year-time groups that show how the healthy worker effect exists in the early part of the follow-up. The study has also been able to show that the risks of developing certain respiratory diseases increase with increased exposure to dust. Detailed results are available in a final report, downloadable from the website http://www.iom-world.org/pubs/IOM_TM0706.pdf.

Table 5.1 Summary results of comparisons of mortality in cohort with external reference rates. The table shows, for chosen cause groups, numbers of deaths, age- year- and region-standardised mortality ratios (SMR) and 95% confidence interval.

Cause of death

Observed deaths

SMR %

Confidence bounds

Lower

Upper

All causes

10698

100.9

99

102.9

All external causes

278

87.5

77.8

98.4

All internal causes

10421

103.7

101.7

105.7

Tuberculosis

16

77.8

47.6

126.9

All cancer

2732

98.0

94.4

101.8

Stomach Cancer

318

129.0

115.6

144.0

Lung Cancer

958

98.7

92.6

105.1

Cardiovascular Disease:

4890

97.8

95.1

100.6

Ischaemic Heart Disease

3298

100.2

96.8

103.7

Acute PHD

28

71.1

49.1

102.9

Non-Malignant Respiratory Disease

1966

138.2

132.3

144.5

COPD

849

115.5

108.0

123.6

Chronic Bronchitis

500

138.9

127.3

151.7

Emphysema

70

164.4

130.1

207.8

Pneumoconiosis

288

NA

NA

NA

CWP

222

NA

NA

NA

Silicosis

10

NA

NA

NA

Mortality ratio by Time period

Figure 5.1 Standardised Mortality Ratio (SMR) for all internal causes over the length of the follow-up period, with years grouped. The solid line is the SMR while the dashed lines represent the 95% confidence interval. The dotted line shows the SMR equal to 100%.

For any complex statistical calculations a software programme that is easy to use and reliable is crucial, but specifically in this instance GenStat’s table functions make the SMR calculations “beautifully simple to program.” (Dr Brian Miller).

The ability to understand the causes of health issues, what factors may lead to ill health or mortality in populations are of critical importance world-wide: so a sound, reliable data analysis system such as GenStat is vital to assist with analysis and help produce scientifically based recommendations and policies.

Our thanks to Dr Brian Miller of The Institute of Occupational Medicine for his help in producing this feature. More information on the IOM can be found at www.iom-world.org

Images/Tables with permission from IOM research report TM/07/06, available at www.iom-world.org/pubs/IOM_TM0706.pdf

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