accident patterns by time-of-day and day-of-week of injury occurrence
TRANSCRIPT
Journal of Occupational Accidents, 2 (1979) 159-176 159 o Elsevier Scientific Publishing Company, Amsterdam - Printed in The Netherlands
ACCIDENT PATTERNS BY TIME-OF-DAY AND DAY-OF-WEEK OF INJURY OCCURRENCE
KEITH MASON
Workers’ Compensation Board of British Columbia, Actuarial and Research Department, 5255 Heather Street, Vancouver, B.C. V5Z 3L8 (Canada)
(Received August 23rd, 1978)
ABSTRACT
Mason, K., 1979. Accident patterns by time-of-day and day-of-week of injury occurrence. Journal of Occupational Accidents, 2: 159-176.
Using Workers’ Compensation Board’s claims experience for the years 1971-1974 it is discovered that, with respect to hour of working day and day of working week of injury occurrence, there are definite patterns in the numbers of injuries reported, and that these patterns differ in unexpected ways for strain injuries and non strain injuries. In particular, it is observed that there are many more strain injuries reported as having occurred on Monday morning than would be expected from the patterns exhibited by the rest of the data. This excess of Monday morning strain claims is estimated to total between one and two percent of all claims. The phenomenon is analysed by industrial, occupational and individual factors.
INTRODUCTION
We recently observed (Mason, 1976, 1977) (and it has been elsewhere noted as well) that accidents do not occur with uniform frequency either on the different days of the weeks or in the different hours of the day. The purpose of this study was to further investigate these phenomena using the claims data available at the Workers’ Compensation Board; specifically to attempt measurements of their magnitudes and to search for possible explana- tions.
The statistical coding of claims done at the Board has included, since 1971, the variables: date of injury, time-of-day of injury, time of start-of-shift and weekdays-at-work/weekdays-off-work. We used this coding to select for this study those claimants for whom the weekday work pattern was exactly ‘five consecutive days at work followed by two days off work’, for whom the workday was at least eight hours in length and for whom the work shift began between 6:00 A.M. and noon. This selection was done so that our distributions of accidents by time of day and by day of week would not be distorted by variations in total hazard exposure with respect to the various hours and the various days.
160
For the selected claimants we used the coded data to compute the hour of the working day in which injuries occurred (first hour, second hour, etc.) and the day of the working week in which injuries occurred (first day, second day, etc.). In the remainder of this report, the word ‘Monday’ is used with the more general meaning ‘first day of a five day working week’, ‘Tuesday’ is used as meaning ‘second day of a five day working week’ and so forth.
Data were used for years of injury 1971-1974, including claims that were accepted for payment in the same year as the injury occurred as well as claims that were accepted by the end of the first quarter of the year following the injury year.
‘MONDAY EFFECT’
It was immediately evident by inspection that our data did indeed exhibit a Monday effect. As can be seen* from Table 1, and corresponding Fig.1, there has been a tendency in each of the years 1971-1974 for accidents to occur more frequently on Mondays and furthermore, for accidents to occur with declining frequency throughout the week.
TABLE 1
Distributions of injuries by day of week, 1971-1974
1971 1972 1973 1974 AI1 years
Claim counts Monday 5,041 Tuesday 4,467 Wednesday 4,092 Thursday 3,637 Friday 3,675
Total 20,912
5,722 6,941 7,121 24,825 5,302 6,454 6,982 23,205 4,958 6,075 6,497 21,622 4,519 5,800 5,932 19,888 4,215 4,947 5,207 18,044
24,716 30,217 31,739 107,584
Percentages Monday 24.1 Tuesday 21.4 Wednesday 19.6 Thursday 17.4 Friday 17.6
Total 100.0*
23.2 23.0 22.4 23.1 21.5 21.4 22.0 21.6 20.1 20.1 20.5 20.1 18.3 19.2 18.7 18.5 17.1 16.4 16.4 16.8
100.0s 100.0* 100.0 100.0*
*Percentages do not add to 100.0 because of roundings.
*In this section, formal statistical significance testing is omitted where the magnitude of the deviation from the random hypothesis, and the consistency of that deviation in the different years, makes such formal testing clearly redundant.
161
.__.1971
3000 I I I 1 I
Monday Tuesday Wednesday Thursday Friday
Fig. 1. W.C.B. claims by day of working week of injury occurrence.
HOURLY EFFECT
It was also apparent by inspection that there was a marked pattern of accident occurrence over the various hours of the working day and that the pattern was approximately the same for the different days of the week. This pattern (Fig. 2) essentially consists of: a definite mid morning peak, a mid day trough and a (less definite) mid afternoon peak. The mid day trough, of course, would be largely attributable to a lunch time decrease in accident exposure. (As an aside, it should be noted that, because of the specific nature of the coding done at the Board, the term ‘first hour’ as used in this report actually means only the first three-quarters of an hour, the term ‘second hour’ means the (whole) hour following that first three-quarters of an hour, and so forth. This particular quirk in the data will be of little consequence to us since it is consistently present and will therefore not interfere with our comparison of Monday to the other weekdays. For that matter, the ‘first hour’ total need only be increased (mentally) by one-third to make it comparable to the totals for the other hours.)
STRAIN INJURIES
Early in the study we received the suggestion that the claims be divided into two groups: ‘strain’ injuries* and ‘all other’ injuries. The frequency distributions for these two groups were so obviously different that the rest
*Code 310 in the American National Standards Institute Z-16.2 Nature-of-injury Code System. Slightly more than half of these injuries are back strains.
162
.-.
123456789
Hour of working day
Monday Tuesday Wednesday Thursday Friday
Fig.2. W.C.B. claims by hour of working day and day of working week (1971-1974).
of the investigation was conducted from the premise of this established differ- ence.
From Table 2, we observe that strain injuries have a more pronounced Monday peak and subsequent weekly decline than do other injuries, but that these patterns are present in a modified way for this latter group as well,
In Fig. 3 we show the hourly pattern broken down by strain injuries and other injuries, for the year 1971. This pattern, very consistent over the four years, suggests that, for almost any ‘hour of working day’ by ‘type of injury’ combination, the claim count declines progressively during the week. These declines are generally steeper for strain injuries than for non strain injuries.
Another prominent feature is the seemingly excessive total of Monday morning strain claims.
Also, for both types of injuries, there are similar patterns of mid morning peaks but odd differences in the afternoon configurations. For non strain injuries, the afternoon peaks are fairly consistently similar in magnitude to the morning peaks (except that there seems to be a rise on Friday afternoon), whereas for strain injuries, the morning peak is much larger than the after- noon peak on Mondays, and retains a declining margin throughout the week. Furthermore, for strain injuries, the peak seems to be reached in the second or third last hour whereas for non strains the peak is usually the last hour.
MEASUREMENT
In this section we make some measurements of the extent of the excess of Monday morning strains. We also measure the combination of ‘Monday morning strains and Tuesday morning strains’ (since we had observed that the Tuesday peak seems to be similar in some respects to the Monday peak).
TA
BL
E
2
Dis
trib
uti
ons
of i
nju
ries
by
day
of w
eek
, by
str
ain
/oth
er,
1971
-197
4
~-
1971
19
72
1973
--
Str
ain
O
ther
S
trai
n
Oth
er
Str
ain
_I
_-
Num
ber
of c
laim
s M
onda
y 21
72
2869
24
08
3314
30
13
Tu
esda
y 17
60
2707
20
46
3256
24
56
Wed
nes
day
1572
25
20
1929
30
29
2281
T
hu
rsda
y 14
26
2211
16
24
2895
21
12
Fri
day
1424
22
51
1636
25
79
1951
-
- -
- T
otal
83
54
1255
8 96
43
1507
3 11
813
1974
A
ll y
ears
Oth
er
Str
ain
O
ther
S
trai
n
Oth
er
3928
31
52
3969
39
98
2849
41
33
3794
25
88
3909
36
88
2275
36
57
2996
21
33
3074
1840
4 12
997
1874
2
1074
5 14
080
9111
14
094
8370
13
252
7437
12
451
7144
10
900
-- 42
807
6477
7
Per
cent
ages
0
f cl
aim
s M
onda
y 26
.0
22.8
25
.0
22.0
25
.5
21.3
24
.3
21.2
25
.1
21.7
T
ues
day
21.1
21
.6
21.2
21
.6
20.8
,
21.7
21
.9
22.1
21
.3
21.7
W
edn
esda
y 18
.8
20.1
20
.0
20.1
19
.3
20.6
19
.9
20.9
19
.6
20.5
T
hu
rsda
y 17
.1
17.6
16
.8
19.2
17
.9
20.0
17
.5
19.5
17
.4
19.2
F
rida
y 17
.0
17.9
17
.0
17.1
16
.5
16.3
16
.4
16.4
16
.7
16.8
~
-
- -
- -
____
T
otal
10
0.0
100.
0 10
0.0
100.
0 10
0.0
100.
0a
;oo.
o 10
0.oa
10
0.oa
10
0.oa
*Per
cen
tage
s do
not
add
to
100.
0 be
cau
se o
f ro
un
din
g.
164
5 z Monday Tuesday Wednesday Thursday Friday
123456789 _~___ .___ Hour of working day Of rnjury *cc”frence
Fig. 3. Distribution of W.C.B. ciaims for 1971 injuries by day of week and hour of day of injury occurrence. - Strain injuries; .- - - - other injuries.
A first measurement is presented in Tables 3 and 4. The excess count in Table 3 is calculated by taking the difference between
the observed count of Monday morning strains and the figure that would have been observed if the count of Monday morning strain injuries had born the same relationship to the count of ‘rest of the week’ strain injuries that the count of Monday morning ‘other’ injuries bears to the count of ‘rest of the week’ other injuries. This measure is thus a calculation of the Monday morning effect for strain injuries in excess of the Monday morning effect for ‘other’ injuries. (In other words, if the decline in strain injuries during the week were at the same rate as the decline in non strain injuries, then we woulc be reporting no excess.) The total excess count over the period 1971-1974
TABLE 3
Excess of Monday-morning-strain claims -_..----. _____.
Monday morning
._. -- _-- -~ - .~ -_--- ---- Rest of Excess week __ _-.._ .-^__________.
Count Percentage
1971 Strains 1268 7086 Other 1357 11201 410 2.0%
1972 Strains 1429 8214 Other 1594 13479
458 1.9%
1973 Strains 1734 10079 Other 1843 16561 612 2.0%
1974 Strains 1790 11207 Other 1931 16811 503 1.6%
All years Strains 6221 36586 Other 6725 68052 1983a 1.8%
~~. -~-. _ l_ ._____~
aFigure calculated from ‘all year’ data (not derived by summing yearly excesses).
165
was found to be 1983, and the ‘percentage’ excess, the excess count as a percentage of all claims, was found to be 1.8%.
The calculations in Table 4 are done analogously. The combined ‘Monday morning strain, Tuesday morning strain’ excess was found to be 2946 claims or 2.7%. One difficulty in computing a measurement of an excess of this type is in deciding what to use for a base line; that is, in determining how much of the Monday morning peak in strain injuries is due to some kind of excess phenomena and how much to the existence of a declining injury trend in strain injuries during the week that might be legitimately different from the declining trend in ‘other’ injuries.
TABLE 4
Excess of Monday/Tuesday-morning-strain claims
Monday or Tuesday morning
Rest of week
Excess
Count Percentage
1971 Strains 2190 6164 Other 2610 9948
573 2.7%
1972 Strains 2475 7168 Other 3057 12016
651 2.6%
1973
1974
Strains 3047 8766 Other 3600 14804
Strains 3304 9693
Other 3847 14895
915 3.0%
801 2.5%
All years Strains 11016 31791 Other 13114 51663
2946a 2.7%
aFigure calculated from ‘all year’ data (not derived by summing yearly excesses).
In Fig.4, therefore, to help gain some feeling for this data, we plot the claim counts over the days of the week for: A.M. strains, A.M. other, P.M. strains and P.M. other. (For this purpose, ‘A.M.’ actually means the count of accidents in the first 3.75 hours of work, ‘P.M.’ means the remainder. Again, the arbitrariness of this division is unimportant since it is the variation in A.M./PM counts that we wish to examine.) The shaded areas around each curve represent the statistical uncertainty associated with each curve. That is, for any point on any of the curves, we are 95% certain that the ‘true’ claim count lies in the shaded area; where ‘true’ means the average of claim counts that would result from hypothetical rerunnings of the years 1971-1974 (with only chance varying the results), and where ‘95% certain’ means that we would expect to be right 95 out of every 100 times that we claimed a true point to be within a shaded area (so that we actually expect 19 of the 20 plotted points
166
8000-
7000-
t”
0 ;
z
:
6000 1
I2 t
2
5000
Fig.4. Claim counts for the period 1971-1974 with associated statistical confidence regions. (The shaded areas are statistical confidence regions in which the probability is 95% that the true claim counts are contained.)
to be within the shaded areas and one to be outside). These statistical con- fidence regions were computed under the simplifying assumption that each claim count was binomial and independent of the other counts - an assump tion that will be sufficiently accurate in this instance because of the relatively large number (20) of counts.
There are several conclusions that one can immediately draw from Fig. 4. Firstly, in all four categories of injuries there is a real (i.e. statistically signif- icant) pattern of a Monday peak and a steady, strikingly linear, decline throughout the week. (It is not clear whether the Tuesday rise in P.M. non
167
strains or the Thursday dip in P.M. strains is real; in each case it is possible to trace alternative routes that eliminate these deviations while remaining within the confidence bands.) Secondly, the Monday peak in A.M. strains is more extreme than the Monday peak in the other three categories. Thirdly, for the non strains the P.M. category is larger than the A.M. for each day of the week whereas for the strains this pattern is reversed on Mondays and continuously evolves until it reaches the non strain pattern by Friday.
These observations thus suggest other base line assumptions we might use in estimating a Monday morning excess in strain injuries. As one possibility, we observe that the A.M. non strain data for Tuesday to Friday has a slope that is not statistically different from the A.M. strain data for Tuesday to Friday, and that the Monday A.M. non strain data point is very close to that predicted by its Tuesday-Friday linear approximation whereas the strain Monday A.M. point is not. If we assume, because the parent linear models for the two data sets are so similar, that the Monday A.M. strain point should be near its predicted value as well, then there is an excess of:
Actual count of Monday A.M. strains 6,221 Predicted count of Monday A.M. strains 5,279
Excess 942
Taken as a ratio of all claims, this represents
942/107,807 = 0.9%
(In this approach, we have gone further in the direction of assuming that an excess of Monday morning strains may be normal, by considering the excess to be only that amount by which the difference in A.M. strains between Monday and Tuesday exceeds the difference between Tuesday/Wednesday, Wednesday/Thursday and Thursday/Friday.)
As a second possibility, if we assume that the A.M./P.M. ratio amongst strain injuries should be comparable to that for non strain injuries, so that, amongst the five A.M. strain data points only the Friday value is approximate- ly correct, then the A.M. strain surpluses for Monday to Thursday are:
Monday 1,805 Tuesday 670 Wednesday 423 Thursday 189 Total excess 3,087
As a percentage, the A.M. strain excess calculated in this way is
3,087/107,807 = 2.9%
The Monday A.M. strain component of this excess is
1,805/107,807 = 1.7%
168
INDUSTRY AND OCCUPATIONAL ANALYSIS
In this section we examine the industrial and occupational patterns in our Monday A.M. strain excesses. For each of the 48 industries with at least 50 claims in the 1971-1974 period, we determined whether the excess of Monday A.M. strains was statistically significant. The industries were defined according to the W.C.B. system of subclasses and the excesses were calculated according to the first of the three procedures described in the previous section. The excesses were tested for statistical significance using the usual x: test for a 2 X 2 table. Those 17 industries where the excess was found to be signif- icantly different from zero at the 95% level are listed in Table 5.
Of the 1’7 subclasses in Table 5 with statistically significant excesses, 16 were positive and one, assumed to be an anomaly, was negative. Of the remaining 31 non significant subclasses, 25 had a positive excess and 6 a negative. If these subclasses were in fact all zero-excess subclasses, we would expect to observe equal numbers of positives and negatives. The observed disparity in negatives and positives is thus an indication that there exist some subclasses with real, positive (but too small to be statistically significant) excesses. We estimate that there are 12 subclasses with no excesses (assigning 6 of the 25 positives to match the 6 negatives) and 25 - 6 = 19 subclasses with real, positive (but too small to be statistically significant) excesses.
TABLE 5
Monday A.M. strain excesses by industry
(For industries with statistically significant excesses)
Subclass Description
0102 0105 0109
0607
0620 0624 0654 0659 0706 0707 0721 0726 0801 0851 0906 1401
Logging Sawmills Shingle mills Mfg. of non-alcoholic beverages Bakeries Fruit canning, dairies Wholesalers Garages, auto sales Bldg. construction Steel fabrication, welding Dry dock Road construction Light, gas, utilities Trucking Fish canning Municipalities
~__ Excess of Monday Total of Excess A.M. strain claims 1971-1974 asa for 1971-1974 claims analysed percentage
~~~ 105 10,598 1.0 116 6,495 1.8
23 879 2.6
-29 428 -6.8
49 1,164 4.2 29 902 3.2
105 2,512 4.2 73 3,323 2.2
267 12,180 2.2 293 13,733 2.1
39 2,284 1.7 54 2,739 2.0 32 911 3.5
124 4,501 2.8 21 871 2.4 98 4,248 2.3
169
Our final figure is therefore that there are 35 out of 48 subclasses, or 73%, that have real,-positive excesses, so that the phenomenon is seen to be quite widespread across different industries. It doesn’t seem possible to generalize greatly about the nature of the 16 subclasses with positive, significant ex- cesses although there does seem to be a large proportion of manual labour involved.
We do the analogous analysis by occupation, using only those groups that have at least 50 claims. The occupational groups are defined using the W.C.B. occupational code. Out of the 195 occupations tested, there were 28, shown in Table 6, with statistically significant excesses (all positive). Using the same
TABLE 6
Monday A.M. strain excesses by occupation
(For occupations with statistically significant excesses)
Occupation code
14 16 33 51
56
62 77
121 122 126 129 130 207 284 298 334 393 458 479 492 574 675 711
726
736 800 860 908
Description
Assembly hand Auto mechanic Baker, caterer Block puller, mill hand Bolt cutter, shake splitter Boom man, log swam Bricklayer, mason Carpenter Carpenter’s helper Tractor driver Bookkeeper, typist Cement finisher Diamond driller Fitter, fitterman Furnaceman Groundman Const. labourer
per
Lineman, belt man Machinist, engine fitter Mechanic Poultry cutter/dresser Roofer, shingler Log scaler, scaler Sheet metal worker’s helper Steam shovel operator Surveyor’s assistant Truck driver Warehouseman
Excess of Monday Total of Excess A.M. strain claims 1971-1974 asa for 1971-1974 claims analysed percentage
20 302 6.6 42 1,644 2.6 12 143 8.4 47 2,703 1.7
17 239 7.1
35 499 7.0 19 334 5.7
103 5,480 1.9 25 977 2.6 35 1,549 2.3 22 620 3.5 21 194 10.8
6 66 9.1 36 628 5.7
7 66 10.6 7 51 13.7
61 3,633 1.7 14 300 4.7 26 1,170 2.2 45 2,841 1.6 24 588 4.1 29 709 4.1 13 131 9.9
15
20 18
168 81
282 5.3
239 8.4 389 4.6
6,580 2.6 2,716 3.0
170
procedure as before, we estimate that of the remaining 167 occupations there were 77 with real, positive (but too small to be listed) excesses and a residual 90 with no excesses; so that there appears to be an excess in 105/195 = 54% of the occupational groups.
REGRESSION ANALYSIS
In this section we consider whether Monday A.M. strain claims differ from other claims with respect to the following variables (which are coded from claims): age of claimant, sex, marital status, length of time on job, number of previous claims, wage, location of firm (Vancouver or not), size of firm, days lost from work as a result of the injury and time between date of injury and date of first payment of claim.
Our procedure is to run four different types of regression, running each type for each of the four years 1971-1974. In the first type of regression we include only Monday A.M. strains and Monday P.M. strains in the analysis, giving the former group a dependent variable value equal to one and the latter group a dependent variable value equal to zero, and using the 10 variables previously listed as the independent variables. The effect of this regression is therefore to determine if there are any statistically significant differences between Monday A.M. strains and Monday P.M. strains with respect to the listed variables.
We use this multiple regression procedure (rather than considering each of the independent variables in turn) so that any resulting relationships are properly attributed. For this reason, we actually also use an eleventh indepen- dent variable, occupation, in all these regressions. (We already know from the previous section that there is variation by occupation, variation we want to allow for before examining other variables.) Accordingly, we only consider claims from the 20 most numerous occupations, and then use that categoriza- tion (into one of the 20 groups) as one independent variable. If we then find, for example, that Monday A.M. strains differ from Monday P.M. strains with respect to the distribution of male versus female injury victims, we would know that that difference was not actually due to males being engaged in dif- ferent types of occupations than females, since any occupational difference would already be accounted for by that variable.
In the second set of regressions, we compare Monday A.M. strains to Wednesday-to-Friday A.M. strains, again using only those claims from the 20 most numerous occupations. In the third set we compare Tuesday A.M. strains to Wednesday-to-Friday A.M. strains and in the fourth set Monday ‘other’ injuries to Friday ‘other’ injuries.
In Table 7, the categories used for the 11 independent variables are given. When an independent variable is treated in this categorical sense, it means that the actual value of the variable is not being used, rather just the category it falls into, so that any observed relationships are between the dependent variable and those particular categories of the independent variable. This is
171
not a particular disadvantage, since if there is any relationship between the two variables it will appear, providing the categories are not unreasonable. On the contrary, the procedure has the advantage of not forcing a particular functional form (i.e. linear) on any relationships.
In analysing results from these regressions, we consider not only the usual statistical t tests for significant relationships but we look for patterns as well. That is, if a relationship is not large enough to be statistically significant in any one year, but appears in a consistent fashion in all four years, we make note of that as being evidence of the existence of a small, but real relation- ship. (This procedure merely amounts to significance testing of a different sort, of course, since there is only a 12% chance that a relationship would appear in the same direction in each of four years if there were actually no relationship. The basic t test, in contrast, is set at a 5% chance.) The regression results are presented in Table 8.
From Table 8 we note that, regarding ‘Monday other’ versus ‘Friday other’ injuries, the ‘Monday other’ injuries are clearly occurring most often to workers under the age of 40 and are clearly most often of the ‘less than 10 days lost from work’ type. There is also evidence (somewhat less certain) that they are the type of claim that is paid faster than the ‘Friday other’ claims. They thus seem to be less serious injuries occurring to younger workers.
The results for ‘Tuesday AM strains’ versus ‘Wednesday-Friday AM strains’ are more ambiguous. The strongest finding is that the ‘Tuesday AM strains’ are proportionately more often of the ‘11-25 days lost from work’ type. They are also seen to occur more frequently to single workers and tend to be paid faster than do ‘Wednesday-Friday AM strains’. This set of character- istics makes an unclear composite.
The ‘Monday AM strains’ seem to occur more frequently to males than do either ‘Monday PM strains’ or ‘Wednesday-Friday AM strains’, and seem to be paid faster than do either of the other two types. The evidence also suggests that ‘Monday AM strains’ occur more frequently to workers with more than one year on the job than do ‘Wednesday-Friday AM strains’. It is again dif- ficult to draw a clear composite from these characteristics.
CONCLUSIONS
(1) There is a daily pattern of injury occurrence that involves morning and afternoon peaks. The two types of injuries, strains and ‘all others’, demonstrate differences regarding the relative magnitudes of their morning/afternoon peaks, and the hour in which the afternoon peak occurs.
(2) Counts of reported injuries decline steadily over the course of the five day working week. The decline is evident for each of the four following sub groups of injuries: strain injuries occurring before lunch, strain injuries occur- ring after lunch, ‘other’ injuries occurring before lunch and ‘other’ injuries occurring after lunch.
172
TABLE 7
Description of independent variables used in regression
Variable Categories Comment
Occupation Orderlies Chefs/cooks Janitors Labouring occupations,
service industries Timber cutting Log hoisting, sorting Labouring occupations, logging Meat canning, cutting, curing Labouring occupations,
wood processing Wood processing, occupations,
N.E.C. Welders Auto mechanics Machinery repair Labouring occupations in
grading, paving Carpenters Labouring occupations in
construction Truck drivers Longshoremen Materials handling equipment
operators Labouring occupations, N.E.C.
Age Under 24 25-40 Over 40
Sex Male Female
Marital status Married Single Other
Length of time on job
Less than one year One year--five years More than five years
Number of 0 previous claims 1
2 or more
Wage O-$125 $125~$225 $225-
This is time in the same occupa- tion with the same firm.
These are claims made while in the current occupation and with with current firm.
Claimant’s weekly salary at the time of injury.
size of firm
Days foe from l-10 work 11-25
26-
Lag ‘Lsetwem O-28 injury date a& W- payment date
28 c-&s was clta~en shY3 it is a rnu~ti~~e uk 7, and thus there L no hias in the reporting and mailing d&q.5 with r~~~~~~~~ to day of week of injury (i.e. afl. claims have the same nmnber of Mondays, Tuesdays, etc. in which to get the injury qxxted, adjudicated)*
-- ,“..W pL__II
(3) In the ease of strain injuries oc~u~g before hmch, however, the Monday total is in excess of what would be consistent with the Tuesday- Friday decline, We measure this excess using three different ~~rn~~uus*
[4) Firstly, if we assume that tbe total of strain injuries offing on IvIou- day morning should bear the same propu~ion to the total of strain injuries occurring during the rest of the week as ‘&her’ injuries occurring on midday morning do to ‘other’ enjoy occurring during the rest of the week, then the excc?s~, of injuries of the Monday morning strain type is 1.8% of ali ckdms,
(5) ~e~~~d~~~ if we assume that the total of strain injuries accurring on Monday morning should properly exceed the count of strain injuries aecur- ring on Tuesday morning, but only in the same way that Tuesday’s count exceeds W~~~esday’s, W~n~day’s exceeds Thursday’s, etc., then the excess of injuries of the Monday morning strain type is 0.9% of aYh cfains.
(6) Tbirdfy, we note that for %tber’ injur&s, the cuant of af%emour? a~~~d~~~ exceeds t&e ~~rn~g count; in a ~~ns~~~~~ way fur al). wea~da~~* Fur strain inj4.&2s, the reverse is true. The count of rno~~~~ str&ns exceeds the count af afternoon strains by a Iarge msqin on Monday, and cont~u~ly lesser rn~s on Tuesday, Wednesday and Thu~day, until by Friday the count of afte~oon strains exceeds the count of morning strains. If we take the Friday ratio of morning strains to afternoon strains as the norm, we find that the total excess of morning strains for Monday to Thursday is 2.9% of all claims. The component of this total due to the excess of Monday morning strains is 1+7%.
(7) The p~~a~~~enon of an excess of M~~d~~ morning shuns is quite wide spread with respect to industria.I and ~~u~a~on~ ~~~sifi~atiu~. It exists in
Reg
ress
ion
resu
lts
Mon
A
M s
trai
ns
Mon
L
&I
stra
ins
vs.
VS
.
Non
PM
str
ains
W
ed-F
ri
AM
str
ains
_. ~
._
~ __
__.-
.---
T
ue
AM
str
ains
M
on
othe
r vs
. V
S.
Wed
-Fri
A
M
stra
ins
Fri
othe
r
Age
In
al
l fo
ur
year
s w
orke
rs
unde
r 40
ha
ve
prop
or-
tiona
tely
m
ore
‘Mon
ot
her’
in
juri
es,
over
40
hav
e m
ore
‘Fri
ot
her’
in
juri
es
(all
four
ye
ars
sign
ific
antly
so
)
Sex
Mar
ital
stat
us
Len
gth
of
time
on
job
(197
1,19
72,1
973
data
on
ly
In
all
four
ye
ars
mal
es
had
prop
ortio
nate
ly
mor
e ‘M
on
AM
str
ains
’ (n
o ye
ars
sign
ific
antly
so)
In
all
four
ye
ars
mal
es
had
prop
ortio
nate
ly
mor
e ‘M
an
AM
str
ains
’ (t
wo
year
s si
gnif
ican
tly
so)
In a
ll fo
ur
year
s si
ngle
w
orke
rs
had
prop
or-
tiona
tely
m
ore
‘Tue
A
M s
trai
ns’
(one
ye
ar
sign
ific
antly
so
)
In
all
thre
e ye
ars
the
‘les
s th
an
one
year
’ w
orke
rs
had
few
est
‘Mon
A
M s
trai
ns’
(no
year
si
gnif
ican
tly
so)
Num
ber
of p
revi
ous
clai
ms
(197
4 d
ata
only
)
Wag
e
Loc
atio
n
Size
of
firm
Day
s lo
st f
rom
wor
k
Lag
betw
een
dat
e of
in
jury
and
dat
e of
fi
rst
paym
ent
of c
laim
In a
ll fo
ur y
ears
‘T
ue
AM
str
ains
’ w
ere
pro-
po
rtio
nate
ly
mor
e in
the
‘1
1-25
’ ca
tego
ry
(thr
ee
year
s si
gnif
ican
tly
so)
In a
ll fo
ur y
ears
‘M
on
AM
str
ains
’ w
ere
paid
fa
ster
(no
yea
rs s
igni
f-
ican
tly
so)
In a
ll fo
ur y
ears
‘M
on
AM
str
ains
’ w
ere
paid
fa
ster
(o
ne y
ear
sign
if-
ican
tly
so)
In a
ll fo
ur y
ears
‘T
ue
In t
hree
ye
ars
out
of f
our,
A
M s
trai
ns’
wer
e pa
id
‘Mon
oth
er’
wer
e pa
id
fast
er
(one
yea
r si
gnif
- fa
ster
(tw
o ye
ars
sign
if-
ican
tly
so)
ican
tly
so)
In a
ll fo
ur y
ears
‘M
on
othe
r’
wer
e pr
opor
tion
ate-
ly
mor
e in
the
‘l-
10
cate
gory
an
d ‘
Fri
othe
r’
in t
he ‘
25--
’ ca
tego
ry
(all
four
yea
rs s
igni
fica
nt-
1Y so
)
176
an estimated 73% of the industrial classifications and in an estimated 54% of the occupational classifications.
(8) Monday morning strain injuries, in contrast to both strain injuries on Monday afternoon and strain injuries on Wednesday-Friday mornings, occur significantly more often to males and are the type of claim that tends to be paid more quickly. They also occur more frequently to workers with more than one year on the job than do Wednesday-Friday morning strain injuries.
(9) In contrast to Friday ‘other’ injuries, Monday ‘other’ injuries occur more often to workers under 40 years old, are more often of the ‘less than ten days lost from work’ type and result in a type of claim that tends to be paid faster.
REFERENCES
Mason, K., 1976. Notes on accident patterns (Internal W.C.B. report). Mason, K., 1977. The effect of piecework on accident rates in the logging industry
(incorporating a different approach to the exposure problem). J. Occupational Accidents, 1: 281-294.