EXERCISE IN OCCUPATIONAL EXPOSURE ASSESSMENT

EXERCISE IN OCCUPATIONAL EXPOSURE ASSESSMENT

After reviewing the lecture on occupational exposure assessment, students are expected to work through the following exercise. The data is from a study whose objective it was to determine the relationship between respirable dust exposure on coal mines and declines in lung function.

PROBLEM

Coal workers exposed to respirable dust in coalmines are at considerable risk for a variety of respiratory problems. These include chronic bronchitis, coal workers pneumoconiosis and silicosis. Evidence now suggests that such workers are also at risk for developing loss of lung function which may not be clinically symptomatic. In order to investigate this, it is essential to be able to understand the relationship between respirable dust exposure on coal mines and declines in lung function in this workforce. To ensure that your study is satisfactory, your exposure assessments are critical.

Your task, using the dataset provided, is to determine the various methods of assessing exposure, beginning, at what you believe to be crude methods of assessment, and then working toward more sensitive measures.

Download the dataset (this is only a subset of the original research data), and review this together with the coding sheet shown below.

VARIABLES IN DATASET

VARIABLE	DESCRIPTION	CODING
STUDYID	Unique number allocated to each individual
COALJOB	Currently working in a coal job	1=yes, 0=no
COALLVE	If no, reason for leaving	1=laid off; 2=another job; 3=sick; 4=injured; 5=other; 9=still working
COALRESN	If other, reason	NA=not applicable (if previous =1-4)
CL_LDATE	Last date in coalmining	0=still working in coal
CLJB1	First job held in a coal mine
CLJB1MNE	Name of mine	1=Khutala, 2=Koornfontein; 3=Douglas; 4=other
CLJB1STE	Job1 worksite	1=surface; 2=underground face; 3=underground backbye; 4=other
CLJB1STR	Year started first job	0=not applicable
CLJB1END	Year ended first job	0=not applicable
CLJB2	Second job held in a coal mine	0=not applicable
CLJB2MNE	Name of mine	1=Khutala, 2=Koornfontein; 3=Douglas; 4=other; 0=not applicable
CLJB2STE	Job2 worksite	1=surface; 2=underground face; 3=underground backbye; 4=other; 0=not applicable
CLJB2STR	Year started second job	0=not applicable
CLJB2END	Year ended second job	0=not applicable
CLJB3	Third job held in a coal mine	0=not applicable
CLJB3MNE	Name of mine	0=not applicable
CLJB3STE	Job3 worksite	1=Khutala, 2=Koornfontein; 3=Douglas; 4=other; 0=not applicable
CLJB3STR	Year started third job	1=surface; 2=underground face; 3=underground backbye; 4=other; 0=not applicable
CLJB3END	Year ended third job	0=not applicable

1. What would be the crudest method of exposure assessment that you would consider?

RESPONSE:

2. Why would this not be useful in this dataset?

RESPONSE:

3. What other crude method of assessment will be more applicable in this dataset?

RESPONSE:

4. Using the dataset provided, please calculate this crude measure

RESULT:

5. What are the shortcomings of this measure?

RESPONSE:

6. Given the shortcomings of this measure, what other measure can you do to consider differences in exposure?

RESPONSE:

7. Using the dataset provided, please calculate this measure

RESULT

8. What other variable/s could you consider to make your assessment more precise?

RESPONSE 1:

RESPONSE 2:

9. Using the dataset provided, please calculate this measure

RESULT – RESPONSE 1

RESULT – RESPONSE 2

You have now considered the dataset, and worked with the available data, but obviously this dataset lacked quantitative exposure (i.e dust data). This may seriously compromise your ability to determine relationships between dust exposure and lung function. The funders decided to make available additional funds for you to conduct dust sampling. After a series of dust sampling underground and on the surface, the laboratories analysing the samples provide you with the following summary results.

10. How could you now include this in your exposure assessment, if you assume that the average dust levels presented were typical of all years of exposure?

RESPONSE:

11. Using the dataset provided, please calculate this measure

RESULT:

12. How could you, given the different exposure strata in the original data, calculate a Simple Cumulative Exposure measure for this cohort of miners?

RESULT

You now realise that the absence of historical data also compromises your exposure assessment. The mining companies decide to give you full access to all the data that they have collected over the years.

Your statistician explains that in order for you to integrate all this data together with the dust data you have collected yourself, statistical modelling is necessary. The statistician works with the information and is able to provide you with the following matrix derived from the statistical models.

13. How will you make use of this information to increase the precision of your exposure assessment measure?

RESULT

EXPOSURE ASSESSMENT EXERCISES: RESPONSE TO QUESTIONS

QUESTION 1:

What would be the crudest method of exposure assessment that you would consider?

RESPONSE: assessment based on ever employment in industry

Back to Q2

QUESTION 2:

Why would this not be useful in this dataset?

RESPONSE: All participants employed in the coal industry

Back to Q23

QUESTION 3:

What other crude method of assessment will be more applicable in this dataset?

RESPONSE: Assessment based on years employed in industry

Back to Q4

QUESTION 4

Using the dataset provided, please calculate this crude measure

RESULT

Studyid totyr

13211 14

13220 21

13245 21

11002 18

12104 13

12110 8

12116 13

12135 12

12143 9

13212 11

11041 23

11009 26

11016 23

11043 22

Back to Q5

QUESTION 5

What are the shortcomings of this measure?

RESPONSE: This does not take into consideration differences in exposure between different workers in different mines, or different sections of the respective mines.

Back to Q6

QUESTION 6

Given the shortcomings of this measure, what other measure can you do to consider differences in exposure?

RESPONSE: Assessment based on years employed in specific mine

Back to Q7

QUESTION 7

Using the dataset provide, please calculate this measure

RESULT:

Studyid MINE 1 MINE 2 MINE 3 MINE 4

13211 13 0 0 1

13220 11 0 0 10

13245 13 0 0 8

11002 4 0 8 6

12104 13 0 0 0

12110 5 0 0 3

12116 10 0 0 0

12135 12 0 0 0

12143 7 0 2 0

13212 5 0 0 6

11041 13 0 4 6

11009 13 0 0 13

11016 13 0 0 10

11043 13 0 9 0

Back to Q8

QUESTION 8

What other variable/s could you consider to make your assessment more precise?

RESPONSE 1: Assessment based on years worked in specific job section (surface/face/underground backbye)

RESPONSE 2: Assessment based on years worked in a specific job, in a specific section, in a specific mine

Back to Q9

QUESTION 9

Using the dataset provide, please calculate this measure

RESULT – RESPONSE 1:

Studyid FACE BACKBYE SURFACE

13211 0 0 14

13220 0 0 21

13245 0 0 21

11002 4 14 0

12104 6 2 5

12110 8 0 0

12116 2 0 11

12135 8 4 0

12143 7 0 2

13212 1 0 10

11041 16 4 3

11009 26 0 0

11016 23 0 0

11043 20 0 0

RESULT – RESPONSE 2:

	MINE 1			MINE 2			MINE 3
STUDYID	FACE	BACKBYE	SURFACE	FACE	BACKBYE	SURFACE	FACE	BACKBYE	SURFACE
11002
11009
11016
11041
11043
12104
12110
12116
12135
12143
13210
13211
13212
13220
13245

Back to Q10

QUESTION 10

How could you now include this in your exposure assessment, if you assume that the average dust levels presented were typical of all years of exposure?

RESPONSE: Assessment using investigator collected dust data (assuming similar levels through the years) for years worked in the specific mine

Back to Q11

QUESTION 11

RESULT

STUDYID	MINE 1	MINE 2	MINE 3	OTHER
11002	D_m*Y_m
11009
11016
11041
11043
12104
12110
12116
12135
12143
13210
13211
13212
13220
13245

Where D = mean dust level for that mine; Y = years worked at that mine

Studyid MINE 1 MINE 2 MINE 3 MINE 4

13211 12.22 0 0.00 0.93

13220 10.34 0 0.00 9.30

13245 12.22 0 0.00 7.44

11002 3.76 0 4.08 5.58

12104 12.22 0 0.00 0.00

12110 4.70 0 0.00 2.79

12116 9.40 0 0.00 0.00

12135 11.28 0 0.00 0.00

12143 6.58 0 1.02 0.00

13212 4.70 0 0.00 5.58

11041 12.22 0 2.04 5.58

11009 12.22 0 0.00 12.09

11016 12.22 0 0.00 9.30

11043 12.22 0 4.59 0.00

Back to Q12

QUESTION 12

RESULT

You will need to design a matrix as shown below, and calculate the data, using the formula shown in the first cell. The final column provides you with the Simple Cumulative Exposure measure for each participant

	MINE 1			MINE 2			MINE 3
STUDYID	FACE	BACKBYE	SURFACE	FACE	BACKBYE	SURFACE	FACE	BACKBYE	SURFACE	CUMEX
11002	D_sm*Y_sm									10.92
11009										29.51
11016										25.43
11041										20.27
11043										27.11
12104										7.97
12110										8.63
12116										4.30
12135										9.20
12143										8.97
13211										4.26
13212										3.53
13220										5.71
13245										5.87

Where D_sm = mean dust level for a specific section in specific mine; Y_sm = years worked in that section at that mine

Back to Q13

QUESTION 13

How will you make use of this information to increasing the precision of your exposure assessment measure?

RESULT

You will need to design a matrix as shown below, and calculate the data, using the formula shown in the first cell. The final column provides you with the Cumulative Dust Exposure measure for each participant

	MINE 1			MINE 2			MINE 3
STUDYID	FACE	BACKBYE	SURFACE	FACE	BACKBYE	SURFACE	FACE	BACKBYE	SURFACE	CDE
11002	D_sm*Y_sm									44.14
11009										136.50
11016										119.61
11041										90.73
11043										88.04
12104										36.77
12110										41.24
12116										16.06
12135										46.16
12143										33.59
13211										11.18
13212										13.49
13220										17.79
13245										17.55

Where D_sm = mean dust level for a specific section in specific mine, calculated from statistical models; Y_sm = years worked in that section at that mine