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Worker compensation injuries among the Aboriginal population of British Columbia, Canada: incidence, annual trends, and ecological analysis of risk markers, 1987–2010

  • Andrew Jin1,
  • M Anne George2Email author,
  • Mariana Brussoni3 and
  • Christopher E Lalonde4
BMC Public Health201414:710

DOI: 10.1186/1471-2458-14-710

Received: 19 November 2013

Accepted: 26 June 2014

Published: 10 July 2014

Abstract

Background

Aboriginal people in British Columbia (BC) have higher injury incidence than the general population, but information is scarce regarding variability among injury categories, time periods, and geographic, demographic and socio-economic groups. Our project helps fill these gaps. This report focuses on workplace injuries.

Methods

We used BC’s universal health care insurance plan as a population registry, linked to worker compensation and vital statistics databases. We identified Aboriginal people by insurance premium group and birth and death record notations. We identified residents of specific Aboriginal communities by postal code. We calculated crude incidence rate and Standardized Relative Risk (SRR) of worker compensation injury, adjusted for age, gender and Health Service Delivery Area (HSDA), relative to the total population of BC. We assessed annual trend by regressing SRR as a linear function of year. We tested hypothesized associations of geographic, socio-economic, and employment-related characteristics of Aboriginal communities with community SRR of injury by multivariable linear regression.

Results

During the period 1987–2010, the crude rate of worker compensation injury in BC was 146.6 per 10,000 person-years (95% confidence interval: 146.4 to 146.9 per 10,000). The Aboriginal rate was 115.6 per 10,000 (95% CI: 114.4 to 116.8 per 10,000) and SRR was 0.88 (95% CI: 0.87 to 0.89). Among those living on reserves SRR was 0.79 (95% CI: 0.78 to 0.80). HSDA SRRs were highly variable, within both total and Aboriginal populations. Aboriginal males under 35 and females under 40 years of age had lower SRRs, but older Aboriginal females had higher SRRs. SRRs are declining, but more slowly for the Aboriginal population. The Aboriginal population was initially at lower risk than the total population, but parity was reached in 2006. These community characteristics independently predicted injury risk: crowded housing, proportion of population who identified as Aboriginal, and interactions between employment rate and income, occupational risk, proportion of university-educated persons, and year.

Conclusions

As employment rates rise, so has risk of workplace injury among the Aboriginal population. We need culturally sensitive prevention programs, targeting regions and industries where Aboriginal workers are concentrated and demographic groups that are at higher risk.

Keywords

Occupational injuries (MeSH) Workers’ compensation (MeSH) Indians North American (MeSH) Indigenous population (MeSH) “First Nations” British Columbia (MeSH) Canada (MeSH) Epidemiology (MeSH) Population surveillance (MeSH) Socioeconomic factors (MeSH)

Background

Aboriginal people in British Columbia (BC) have higher incidences of severe injuries (as recorded in the BC Trauma Registry) [1] or death due to injury [26] than the general population. However, the absolute numbers of deaths and trauma-team cases occurring among Aboriginal people in the province are small, limiting ability to break down results and make meaningful comparisons between sub-populations. This can lead to over-generalization of findings and stigmatization of Aboriginal British Columbians as a group [1]. Also, within the Aboriginal population, limited information about variability in incidence rates among injury categories, geographic regions, and demographic and socio-economic groups hampers efforts to identify risk factors and develop targeted prevention programs. The project Injury in British Columbia’s Aboriginal Communities: Building Capacity while Developing Knowledge[7] seeks to overcome these limitations by studying a broader range of injury morbidity events.

This report focuses on injuries claimed for worker compensation. Previous researchers in Canada have measured the incidence of worker compensation injuries among the general populations of the provinces of Ontario [8, 9] and BC [10], using population-based registries [810] or longitudinal cohort methods [9]. Another study measured incidence, among workers in BC employed in a specific industry, by linking employment records with the injury registry [11]. The population-based studies described variations of incidence rates by gender, age, time period, and geographic location, but study of other risk markers is difficult because such information is not usually available for both individual members of the population base and individuals recorded in the injury registry. The ecological approach, where the unit of observation is a geographic unit, can help overcome this limitation, because both injury incidence, and a broad range of socio-economic, geographic, and employment-related markers can be measured at the level of the geographic unit. A previous ecologic study of predictors of risk of worker compensation injury did this among 46 regions of Ontario [12].

This report describes incidence rates, annual trends, and predictors of risk of worker compensation injury among the Aboriginal population of BC. We found no previously published report on these topics regarding the Aboriginal population of any province of Canada. We consider such information to be important to broaden the understanding of both the health status of Aboriginal British Columbians and their participation in the economic life of the province.

Methods

Ethics review and permission for data access

The University of British Columbia Behavioural Research Ethics Board reviewed and approved our methods. Data Stewards representing the BC Ministry of Health Services and Work Safe BC approved the data access requests. Population Data BC linked the data files and made the client records anonymous, before our analysis.

Population counts

We obtained one-day extracts of the consolidated registration and premium billing files of the Medical Services Plan of BC (MSP, the province’s universal health care insurance program), at the mid-point of each fiscal year, 1985–1986 through 2010–2011. We took these to represent the total resident population of BC. Within this population, we marked as “Aboriginal” any person with:
  1. a)

    Membership in MSP Premium Group 21 (indicating insurance premiums paid by First Nations and Inuit Health Program, Health Canada, for reason of Aboriginal status), OR

     
  2. b)

    One or both parents with Aboriginal status or resident on an Indian Reserve, as indicated on the Vital Statistics birth record, OR

     
  3. c)

    Aboriginal status or resident of an Indian Reserve, as indicated on the Vital Statistics death record).

     

For purposes of ecologic analysis (see below), within the population we identified Aboriginal “communities”. In BC there are 199 First Nations and Indian Bands recognized by and registered with the government of Canada. More than 1,000 parcels of land in BC have been designated as “reserves”, each set apart for the collective use and benefit of the members of a specified First Nation or Indian Band. Some 498 of these reserves are currently inhabited. Approximately 44% of the Aboriginal people in BC reside on a reserve (“on-reserve”) and 56% do not reside on a reserve (“off-reserve”). Conceptually, we defined a community as all the Aboriginal people residing on the reserves of one band. Operationally, we delineated each community by aggregating the postal codes of the reserves belonging to a band, and we assigned Aboriginal people to the community according to their postal code of residence. By this method, we identified 177 Aboriginal communities in BC. In fiscal year 2006–2007, total population of the communities was 62,059 and mean population per community was 351, with standard deviation of 419. The number of communities is fewer than the number of bands, because in rural areas, due to low population density, full 6-digit postal codes correspond to large areas, containing both reserves and non-reserve areas, and sometimes containing the reserves of more than one band. Thus, in practice, the identified Aboriginal communities include both Aboriginal reserve residents and off-reserve Aboriginal persons living near by, and some communities contain more than one band. Although this does not perfectly match our conceptual definition, it suffices, because it is consistent with our underlying intention, which is to identify culturally homogenous clusters of Aboriginal people living in close proximity to one another.

We aggregated the 177 identified Aboriginal communities to create a subcategory of the Aboriginal population which we called “reserve”. We classified all other Aboriginal persons as “not reserve”.

There are sixteen Health Service Delivery Areas (HSDAs) in BC. The 2011 Census of Canada found that 62.3% of the population of BC resided in urban centres with populations greater than 100,000. If more than 62.3% of the 2011 population of an HSDA resided in such an urban centre then we classified the entire HSDA (and all its residents) as “urban” [13]. In this way we classified as urban six HSDAs containing 62.7% of the 2011 population of the province [14]: HSDAs 22 (Fraser North), 23 (Fraser South), 31 (Richmond), 32 (Vancouver), 33 (North Shore/Coast Garibaldi), and 41 (South Vancouver Island). Within these six HSDAs, 88.8% of the population resided in urban centres with populations greater than 100,000. We classified all other HSDAs (and their residents) as “not urban”. Within these ten HSDAs, 17.8% of the population resided in urban centres with populations greater than 100,000. Figure 1 is a map of BC showing the 16 HSDAs in the province. The six urban HSDAs are marked with the ¶ symbol.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-14-710/MediaObjects/12889_2013_Article_6836_Fig1_HTML.jpg
Figure 1

Standardized Relative Risk of worker compensation injury among Aboriginal populations of Health Service Delivery Areas. Adapted/reproduced with permission from the map illustration entitled "British Columbia Health Service Delivery Areas, Prepared by BC Stats, July 2008".Copyright Province of British Columbia. All Rrights reserved.

We tabulated population counts by fiscal year, gender, 5-year age group, Aboriginal status, community, reserve residence, HSDA, and urban residence.

Worker compensation injuries

We tabulated counts of worker compensation injuries among residents of BC, occurring from January 1, 1987 through December 31, 2010. We defined “worker compensation injury” as an injury registered for a claim with Work Safe BC (the province’s workplace injury compensation program), with an ICD-9 numeric code diagnosis in the range 800 through 999. This definition excludes some chronic conditions recognized as injuries by Work Safe BC, for example, tendonitis, carpal tunnel syndrome, noise-induced hearing loss, occupational lung diseases, and occupational cancers. Work Safe BC provides compensation for injury or disease that arises out of and in the course of employment, or is due to the nature of employment. Employers are required by law to register with Work Safe BC to provide coverage to their employees. Aboriginal subsistence activities (e.g., hunting, fishing, trapping, gathering wild plants, cutting trees) may be covered, if the individual registers with Work Safe BC and pays insurance premiums for the optional personal protection available to self-employed persons. In Canada, Aboriginal subsistence includes a right to earn a moderate living by selling the products of one’s labour. Unpaid domestic labour is not considered employment. Injury occurring while travelling between one’s place of residence and place of employment does not meet the test of “arising out of and in the course of employment, or due to the nature of employment”. Full-time or part-time labour does not influence acceptance of an injury claim, though it does influence the amount of compensation.

We classified worker compensation injuries by injury type (trauma, poisoning, burn or other) using ranges of the ICD-9 numeric code diagnosis. We tabulated counts of injuries by injury type, calendar year (of injury occurrence), gender, 5-year age group, Aboriginal status, reserve residence, HSDA, and urban residence.

Incidence rates of injury

We calculated the crude rate of worker compensation injuries as the number of injuries divided by the person-years of observation (the sum of the annual population counts) during the same time period. We considered the crude rate to be a binomial proportion, and we estimated standard errors of the proportion, and 95% confidence intervals of the proportion, using the method of Agresti and Coull [15]. Consistent with Statistics Canada policy [16, 17], we suppressed reporting of the crude rate in a cell if the coefficient of variation (the standard error of the crude rate divided by the crude rate) exceeded 0.333.

We calculated rates of worker compensation injury using person-years of population as the denominator, because we consider such rates to be indicators of population health status (limited to one specific category of health outcome). Other researchers have used person-years of employment as the rate denominator, which would be appropriate if one thinks of injury risk in the manner of an insurer seeking to justify premiums levied on employers according to the size of the workforce. But that was not our intention. Also, our population counts are more reliable than estimates of numbers of employed persons derived from survey samples, which would also have had to be adjusted for intensity of employment (i.e., full-time or part-time employment) with even more propagation of random measurement error.

We calculated Standardized Relative Risk (SRR) of worker compensation injury relative to the risk of injury in the reference population (95,457,166 person-years, the combined total population of BC from January 1, 1987 through December 31, 2010) using the method of indirect standardization [18], adjusting for gender and age, or gender, age and HSDA, as appropriate for the intended comparisons. We suppressed reporting of the SRR in a cell if the coefficient of variation (the standard error of the expected number of injuries divided by the expected number) exceeded 0.333.

The error bars in Figure 2 depict 95% confidence intervals. Comparing two crude rates or two SRRs, we considered the difference to be “statistically significant” if the 95% confidence intervals did not overlap. This indicates p < 0.006, if the standard errors are equal, or p < 0.021 if one of the standard errors is up to five times larger than the other [19].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2458-14-710/MediaObjects/12889_2013_Article_6836_Fig2_HTML.jpg
Figure 2

Worker compensation injuries, British Columbia, 1987–2010, Standardized Relative Risk by year.

We assessed annual trend as a linear function with year as the independent variable, and SRR as the dependent variable. We considered the trend to be “statistically significant” if the 95% confidence interval of the regression coefficient (the slope) did not include zero.

Predictors of risk

We expected that the individual-level analysis methods above would describe heterogeneity among age and gender groups, among geographic regions, among fiscal years of observation, between Aboriginal and non-Aboriginal, and between on-reserve Aboriginal and off-reserve Aboriginal populations, but would not explain why the heterogeneities exist. Therefore, to elucidate possible explanatory factors, we studied risk markers for worker compensation injury among the Aboriginal population using an ecological approach, where the unit of observation was the “community” (as defined above). As hypothesized risk factors, we selected socio-economic, housing, and geographic indicators that had previously been developed by Statistics Canada and Aboriginal Affairs and Northern Development Canada, which are used to allocate federal government resources to health care, education, housing, and economic development programs for Aboriginal people. We wanted to test if these markers had predictive validity with respect to risk of worker compensation injury, which is indicative of both health status and economic development.

Within communities, risk of injury among Aboriginal people is calculated using our own definition of “Aboriginal”, derived from health insurance premium group and notations on birth and death records. However, every First Nation band makes its own residency rules, and not all residents of reserves would meet our definition of Aboriginal. We wanted to test if variability in the ethnic composition of reserve populations would introduce biases into our ecologic analysis, and if so, to correct such biases. Therefore, we included in the analysis two Census-derived ecologic indicators describing ethnic composition.

From the 2001 and the 2006 Censuses of Canada we obtained customized data tabulations for all enumerated First Nation reserves, settlements or self-government districts in BC, aggregated by First Nation band. The Census long-form (usually administered to a 20% sample of the population) was administered to 100% of residents of First Nation reserves, settlements and self-government districts. From these data, for as many communities as possible, we tabulated the following hypothesized socio-economic markers of injury risk:

  • Total Income per capita,

  • Community Well-Being Income Score [20], calculated as: Log10[(Total Income per capita)/2000] / Log10[20] × 100,

  • Proportion of population, age 25+ years with at least a high school certificate,

  • Proportion of population, age 25+ years with university degree, bachelors or higher,

  • Average population per room (an index of the degree of crowding in the community’s housing), calculated as the number of residents divided by the number of habitable rooms (not counting bathrooms, halls, vestibules and rooms used solely for business purposes) in the dwelling,

  • Proportion of dwellings in need of major repairs (defective plumbing or electrical wiring, structural repairs to walls, floors or ceilings, etc., does not include desirable remodelling or additions),

  • Proportion of population, age 25+ years, in the labour force (in the week before the census, employed, temporarily absent, looking for work, or starting work within 4 weeks),

  • Proportion of population, age 25+ years, employed (any work for pay or self-employment in the week before the census),

  • Proportion of population who identified themselves as “an Aboriginal person, that is, North American Indian, Métis or Inuit (Eskimo)”,

  • Proportion of population who gave only one response to the ethnic origin question, and it was a group that could be classified as North American Indian.

Some calculated proportions exceeded 100% because Statistics Canada rounds cell counts to the nearest multiple of five, to protect privacy. If a community contained more than one First Nation band, then we calculated the community’s marker as the population-weighted mean of the First Nation bands’ markers. Statistics Canada reports only the total population count for aggregations with population less than 40, and suppresses income data for aggregations with population less than 250. We were able to calculate the two income-related markers for 79 (of 177) communities in Census year 2001, and 73 communities in 2006. We were able to calculate the other markers listed above for 151 communities in 2001, and 127 communities in 2006.

Rates of worker compensation claims differ among occupational [21] and industrial categories [22], and these factors (and size of payroll and previous claims experience) determine the insurance premiums that Work Safe BC levies upon employers. We hypothesized that the distribution of the community’s labour force among occupational and industrial categories would help explain the community’s risk of worker compensation injury. We invented two statistics that summarize the hypothesized hazardousness of the community’s labour force distribution. Each statistic is the mean risk of work injury claim among the occupational or industrial categories in the total population of BC, weighted by the number of persons in each occupational or industrial category in the community. Combining Work Safe BC injury claims statistics and Census data, we calculated the following work-related statistics of injury risk for each community:

  • Risk of work injury claim, relative to the population of BC, expected from occupational categories [21], among labour force aged 15+ years,

  • Risk of work injury claim, relative to the population of BC, expected from industry categories [22], among labour force aged 15+ years.

The Government of Canada’s Department of Aboriginal Affairs and Northern Development has a classification system for calculating funding allocations to First Nation bands [23]. From this system, we assigned to communities the following hypothesized geographic markers of injury risk:

  • Remoteness Index (higher score means more remote), and

  • Environmental Index (higher score means more environmentally challenging).

These indices are numeric scores, based on geographic latitude, availability of year-round road access, and distance to the nearest “service centre” (a city or town having government services, banks and suppliers). If a community contained more than one First Nation band, then we calculated the community’s index as the population-weighted mean of the bands’ indices.

Worker compensation injury can only occur to employed people. It is plausible that risk factors for such injury would apply only to the fraction of the population who are employed. Therefore, for each of the above hypothesized socio-economic, work-related, and geographic risk markers we also created an employment-interaction variable, calculated as the risk marker multiplied by the proportion of the population in each community who were employed.

Ecological analysis

For each community, we calculated the age, gender and HSDA-adjusted SRR of worker compensation injury during the period 1999 through 2003 (a 5-year period centred about the Census year 2001) and during the period 2004 through 2008 (centred about the Census year 2006), relative to the total population of BC during the same time period. Logarithmic transformation approximately normalized the distribution of the SRRs (Kolmogorov-Smirnov statistic 0.058, Shapiro-Wilk statistic 0.988, df = 319, p = 0.012); therefore we used the natural logarithm of SRR as the dependent (Y) variable for regression analysis.

We tested hypotheses of association by performing least-squares linear regressions. We tested census year, hypothesized socio-economic, work-related and geographic markers, and their employment-interaction variables, in turn as the single independent variable. Variables that had statistically significant association (p < 0.05) with SRR of worker compensation injury in univariate analysis were included in subsequent multivariable regression analysis. We used stepwise backwards elimination of variables to arrive at the best-fitting multivariable model. At each step, the variable with the largest p-value was eliminated. Elimination stopped when all independent variables had regression coefficients significantly different from zero (p < 0.05).

In the best-fitting model, “B” is the regression coefficient of each independent variable, representing the change in the dependent variable Ln (SRR) that is associated with a unit change in the independent variable. The relative risk associated with a one standard deviation change (SD) in the independent variable is calculated as the antilogarithm of BxSD. Repeating the calculation with the lower and upper 95% confidence limits of B gives the confidence limits of the relative risk.

Results

Aboriginal status and reserve residence

Table 1 shows crude rates and SRRs of injuries claimed for worker compensation, during the period 1987–2010, among the total population of BC, the Aboriginal population, the Aboriginal population residing on reserve, and the Aboriginal population residing off-reserve. Table 1 also separates injuries into broad ranges of the ICD-9 numeric classification: trauma, poisoning, burn, and other. Because 96% of worker compensation injuries are in the category of trauma, we combined all injury categories for the remainder of the description and analysis.
Table 1

Worker compensation injuries [1], British Columbia, 1987–2010 [2]

Injury Category [3]

P-years [4]

Obs [5]

Exp [6]

Rate [7]

95% CI for Rate

SRR [8]

95% CI for SRR

BC

           
 

Total, All injuries

95,457,166

1,399,661

1,399,659

146.6

146.4

to

146.9

1.00

1.00

to

1.00

 

. Trauma

95,457,166

1,343,044

1,343,042

140.7

140.5

to

140.9

1.00

1.00

to

1.00

 

. Poisoning

95,457,166

6,469

6,469

0.7

0.7

to

0.7

1.00

0.98

to

1.02

 

. Burn

95,457,166

45,612

45,612

4.8

4.7

to

4.8

1.00

0.99

to

1.01

 

. Other

95,457,166

4,536

4,536

0.5

0.5

to

0.5

1.00

0.97

to

1.03

BC, Aboriginal

           
 

Total, All injuries

3,091,021

35,736

40,608

115.6

114.4

to

116.8

0.88

0.87

to

0.89

 

. Trauma

3,091,021

34,504

38,826

111.6

110.5

to

112.8

0.89

0.88

to

0.90

 

. Poisoning

3,091,021

180

202

0.6

0.5

to

0.7

0.89

0.78

to

1.02

 

. Burn

3,091,021

903

1,429

2.9

2.7

to

3.1

0.63

0.60

to

0.67

 

. Other

3,091,021

149

151

0.5

0.4

to

0.6

0.99

0.84

to

1.16

BC, Aboriginal, off-reserve

           
 

Total, All injuries

1,688,590

20,983

21,898

124.3

124.3

to

122.6

0.96

0.95

to

0.97

 

. Trauma

1,688,590

20,202

20,931

119.6

119.6

to

118.0

0.97

0.95

to

0.98

 

. Poisoning

1,688,590

98

106

0.6

0.6

to

0.5

0.92

0.76

to

1.12

 

. Burn

1,688,590

597

781

3.5

3.5

to

3.3

0.76

0.71

to

0.82

 

. Other

1,688,590

86

79

0.5

0.5

to

0.4

1.08

0.87

to

1.35

BC, Aboriginal, on-reserve

           
 

Total, All injuries

1,393,652

14,641

18,595

105.1

105.1

to

103.4

0.79

0.78

to

0.80

 

. Trauma

1,393,652

14,195

17,787

101.9

101.9

to

100.2

0.80

0.79

to

0.81

 

. Poisoning

1,393,652

81

95

0.6

0.6

to

0.5

0.85

0.70

to

1.04

 

. Burn

1,393,652

302

643

2.2

2.2

to

1.9

0.47

0.43

to

0.51

 

. Other

1,393,652

63

71

0.5

0.5

to

0.4

0.89

0.70

to

1.13

Notes:

1. “Injury” defined as Diagnosis in the range ICD9:800–999.

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Injuries classified by ICD9 numeric code.

4. Person-years is the sum of annual population counts during the observation period.

5. Observed number of injuries.

6. Expected number, indirectly standardized, based on age, gender and HSDA-specific rates in the total population of BC.

7. Crude Rate per 10,000 person-years.

8. Standardized Relative Risk (compared to the total population of BC) = Observed/Expected.

Table 1 shows a pattern of the lowest incidence among the Aboriginal population on or near a reserve, higher incidence among the Aboriginal population off-reserve, and highest incidence in the total population of BC. Standardization by age, gender and HSDA reduces but does not eliminate the disparities among the three population groups. In particular, the gap between the off-reserve Aboriginal population and the total population of BC (i.e., the reference population) is small, but remains statistically significant.

HSDAs and urban residence

Tables 2 and 3 show crude rates and age and gender-adjusted SRRs of injuries claimed for worker compensation, during the period 1987–2010, within the total populations (Table 2) and the Aboriginal populations (Table 3) of the HSDAs. Depending on the HSDA, Aboriginal people may be at higher (SRR > 1), lower (SRR < 1), or the same risk (SRR = 1) of injury as the total population of the province (Figure 1). There are differences in risk of worker compensation injury between HSDAs, but these differences do not necessarily apply to both the Aboriginal and the total populations. For example, within the total population, the highest age- and gender-standardized risks of worker compensation injury occur in HSDAs 21, 22 and 23, but within the Aboriginal population, the highest risks occur in HSDAs 22, 23 and 31. Within the total population, urban and not urban residents had the same age- and gender-standardized risks of worker compensation injury, but within the Aboriginal population, urban residents had higher age- and gender-standardized risk of worker compensation injury (SRR = 0.95, 95% confidence interval: 0.93 to 0.96) than those who were not urban (SRR = 0.79, 95% CI: 0.78 to 0.80). However, as shown in Table 3 and Figure 1, not all urban HSDAs showed above-average risks among their Aboriginal populations: HSDAs 22, 23 and 31 did (lower 95% confidence limit of SRR was above one), but HSDAs 32 and 33 clearly did not (upper 95% confidence limit of SRR was below one).
Table 2

Worker compensation injuries [1], British Columbia, 1987–2010 [2], by Health Service Delivery Area

HSDA

P-years [3]

Obs [4]

Exp [5]

Rate [6]

95% CI for Rate

SRR [7]

95% CI for SRR

11

1,847,429

22,605

25,976

122

121

to

124

0.87

0.86

to

0.88

12

1,878,968

24,204

25,856

129

127

to

130

0.94

0.92

to

0.95

13

7,129,280

92,766

93,549

130

129

to

131

0.99

0.99

to

1.00

14

4,987,600

59,730

70,736

120

119

to

121

0.84

0.84

to

0.85

21

5,455,829

94,435

74,278

173

172

to

174

1.27

1.26

to

1.28

22

11,998,748

211,048

183,029

176

175

to

177

1.15

1.15

to

1.16

23

13,344,187

251,995

191,340

189

188

to

190

1.32

1.31

to

1.32

31

3,979,078

51,080

59,421

128

127

to

129

0.86

0.85

to

0.87

32

13,897,287

170,380

224,694

123

122

to

123

0.76

0.76

to

0.76

33

6,104,957

69,383

88,384

114

113

to

114

0.79

0.78

to

0.79

41

7,873,455

104,328

109,547

133

132

to

133

0.95

0.95

to

0.96

42

5,507,969

77,846

73,643

141

140

to

142

1.06

1.05

to

1.06

43

2,656,173

42,981

37,237

162

160

to

163

1.15

1.14

to

1.17

51

2,034,014

28,103

30,192

138

137

to

140

0.93

0.92

to

0.94

52

3,562,522

44,853

53,539

126

125

to

127

0.84

0.83

to

0.84

53

1,551,472

17,162

23,369

111

109

to

112

0.73

0.73

to

0.74

Urban [8]

57,197,712

858,214

856,415

150

150

to

150

1.00

1.00

to

1.00

Not [9]

36,611,256

504,685

508,375

138

137

to

138

0.99

0.99

to

1.00

Notes:

1. “Injury” defined as any diagnosis in the range ICD9:800–999

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Person-years is the sum of annual population counts during the observation period.

4. Observed number of injuries.

5. Expected number, indirectly standardized, based on age- and gender-specific rates in total population of BC.

6. Crude Rate per 10,000 person-years.

7. Standardized Relative Risk (compared to the total population of BC) = Observed/Expected.

8. Urban: aggregation of HSDAs 22, 23, 31, 32, 33 and 41, where > 62.3% of the HSDA population live in a large population centre.

9. Not urban: aggregation of HSDAs 11, 12, 13, 14, 21, 42, 43, 51, 52, 53.

Table 3

Worker compensation injuries [1], Aboriginal BC, 1987–2010 [2], by Health Service Delivery Area

HSDA

P-years [3]

Obs [4]

Exp [5]

Rate [6]

95% CI for Rate

SRR [7]

95% CI for SRR

11

38,313

443

512

116

105

to

127

0.87

0.79

to

0.94

12

13,647

165

169

121

104

to

141

0.98

0.84

to

1.14

13

161,664

2,177

2,184

135

129

to

140

1.00

0.96

to

1.04

14

404,410

3,821

5,738

94

92

to

98

0.67

0.65

to

0.68

21

196,605

2,393

2,612

122

117

to

127

0.92

0.88

to

0.95

22

111,440

1,967

1,526

177

169

to

184

1.29

1.23

to

1.36

23

122,044

1,927

1,437

158

151

to

165

1.34

1.27

to

1.41

31

17,062

404

229

237

215

to

261

1.76

1.55

to

2.01

32

261,269

2,916

4,152

112

108

to

116

0.70

0.68

to

0.72

33

233,561

2,868

3,360

123

118

to

127

0.85

0.83

to

0.88

41

156,312

2,090

2,160

134

128

to

140

0.97

0.93

to

1.01

42

329,123

3,711

4,353

113

109

to

116

0.85

0.83

to

0.88

43

157,943

2,108

2,130

133

128

to

139

0.99

0.95

to

1.03

51

490,310

5,348

6,854

109

106

to

112

0.78

0.76

to

0.80

52

275,145

2,279

3,706

83

80

to

86

0.61

0.60

to

0.63

53

98,686

846

1,277

86

80

to

92

0.66

0.63

to

0.70

Urban [8]

901,688

12,172

12,864

135

133

to

137

0.95

0.93

to

0.96

Not [9]

2,165,846

23,291

29,534

108

106

to

109

0.79

0.78

to

0.80

Notes:

1. “Injury” defined as any diagnosis in the range ICD9:800–999.

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Person-years is the sum of annual population counts during the observation period.

4. Observed number of injuries.

5. Expected number, indirectly standardized, based on age- and gender-specific rates in total population of BC.

6. Crude Rate per 10,000 person-years.

7. Standardized Relative Risk (compared to the total population of BC) = Observed/Expected.

8. Urban: aggregation of HSDAs 22, 23, 31, 32, 33 and 41, where > 62.3% of the HSDA population live in a large population centre.

9. Not urban: aggregation of HSDAs 11, 12, 13, 14, 21, 42, 43, 51, 52, 53.

Age and gender

Tables 4 and 5 show crude rates and SRRs of injuries claimed for worker compensation, among the total population (Table 4) and the Aboriginal population (Table 5) of BC, by age and gender categories. Crude rates (age- and gender-specific) of worker compensation injury are higher among males than among females in all age groups. Among males, injury rates are highest among men aged 20 to 29 years, and decline steadily as age increases. Among females, worker compensation injury rates are highest among women aged 40 to 54 years.
Table 4

Worker compensation injuries [1], British Columbia, 1987–2010 [2], by Age and Gender

Gender

Age

P-years [3]

Obs [4]

Exp [5]

Rate [6]

95% CI for Rate

SRR [7]

95% CI for SRR

F

15-19

3,091,296

18,913

18,913

61

60

to

62

1.00

0.99

to

1.01

F

20-24

3,215,407

39,379

39,379

122

121

to

124

1.00

0.99

to

1.01

F

25-29

3,478,049

42,537

42,537

122

121

to

123

1.00

0.99

to

1.01

F

30-34

3,702,923

45,675

45,675

123

122

to

124

1.00

0.99

to

1.01

F

35-39

3,861,158

50,660

50,660

131

130

to

132

1.00

0.99

to

1.01

F

40-44

3,830,469

53,141

53,141

139

138

to

140

1.00

0.99

to

1.01

F

45-49

3,525,752

50,340

50,340

143

142

to

144

1.00

0.99

to

1.01

F

50-54

3,024,714

40,801

40,801

135

134

to

136

1.00

0.99

to

1.01

F

55-59

2,558,851

26,963

26,963

105

104

to

107

1.00

0.99

to

1.01

F

60-64

2,186,965

11,050

11,050

51

50

to

51

1.00

0.98

to

1.02

F

65-69

1,912,893

1,279

1,279

7

6

to

7

1.00

0.95

to

1.06

F

70-74

1,670,886

225

225

1

1

to

2

1.00

0.88

to

1.14

M

15-19

3,256,059

51,250

51,250

157

156

to

159

1.00

0.99

to

1.01

M

20-24

3,186,968

137,742

137,742

432

430

to

434

1.00

0.99

to

1.01

M

25-29

3,377,301

154,893

154,893

459

456

to

461

1.00

1.00

to

1.00

M

30-34

3,611,964

155,457

155,457

430

428

to

432

1.00

1.00

to

1.00

M

35-39

3,797,595

142,295

142,295

375

373

to

377

1.00

0.99

to

1.01

M

40-44

3,806,541

122,056

122,056

321

319

to

322

1.00

0.99

to

1.01

M

45-49

3,542,795

97,188

97,188

274

273

to

276

1.00

0.99

to

1.01

M

50-54

3,056,634

73,977

73,977

242

240

to

244

1.00

0.99

to

1.01

M

55-59

2,591,967

51,784

51,784

200

198

to

201

1.00

0.99

to

1.01

M

60-64

2,179,698

25,923

25,923

119

117

to

120

1.00

0.99

to

1.01

M

65-69

1,821,029

4,182

4,182

23

22

to

24

1.00

0.97

to

1.03

M

70-74

1,479,753

1,024

1,024

7

7

to

7

1.00

0.94

to

1.06

Notes:

1. “Injury” defined as any diagnosis in the range ICD9:800–999.

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Person-years is the sum of annual population counts during the observation period.

4. Observed number of injuries.

5. Expected number, indirectly standardized, based on age, gender and HSDA-specific rates in total population of BC.

6. Crude Rate per 10,000 person-years.

7. Standardized Relative Risk (compared to the total population of BC) = Observed/Expected.

Table 5

Worker compensation injuries [1], Aboriginal BC, 1987–2010 [2], by Age and Gender

Gender

Age

P-years [3]

Obs [4]

Exp [5]

Rate [6]

95% CI for Rate

SRR [7]

95% CI for SRR

F

15-19

135,848

385

790

28

26

to

31

0.49

0.45

to

0.52

F

20-24

127,128

890

1,445

70

66

to

75

0.62

0.58

to

0.65

F

25-29

129,776

1,098

1,497

85

80

to

90

0.73

0.70

to

0.77

F

30-34

129,199

1,317

1,496

102

97

to

108

0.88

0.84

to

0.93

F

35-39

122,522

1,405

1,522

115

109

to

121

0.92

0.88

to

0.97

F

40-44

108,519

1,397

1,419

129

122

to

136

0.98

0.93

to

1.04

F

45-49

89,472

1,175

1,192

131

124

to

139

0.99

0.93

to

1.04

F

50-54

68,418

865

846

126

118

to

135

1.02

0.96

to

1.09

F

55-59

51,343

589

488

115

106

to

124

1.21

1.10

to

1.32

F

60-64

38,327

226

176

59

52

to

67

1.28

1.11

to

1.49

F

65-69

27,860

29

20

10

7

to

15

1.45

0.93

to

2.28

F

70-74

19,479

7

3

4

2

to

8

2.66

0.80

to

19.00

M

15-19

138,807

1,117

2,012

80

76

to

85

0.56

0.53

to

0.58

M

20-24

122,074

3,753

5,001

307

298

to

317

0.75

0.73

to

0.77

M

25-29

124,279

4,807

5,552

387

376

to

398

0.87

0.84

to

0.89

M

30-34

122,053

4,834

5,073

396

385

to

407

0.95

0.93

to

0.98

M

35-39

114,734

4,095

4,115

357

346

to

368

1.00

0.97

to

1.03

M

40-44

100,165

3,026

3,066

302

292

to

313

0.99

0.95

to

1.02

M

45-49

81,440

2,063

2,130

253

243

to

264

0.97

0.93

to

1.01

M

50-54

61,645

1,416

1,412

230

218

to

242

1.00

0.95

to

1.06

M

55-59

46,051

787

853

171

159

to

183

0.92

0.86

to

0.99

M

60-64

33,729

339

379

101

90

to

112

0.89

0.81

to

0.99

M

65-69

24,066

65

61

27

21

to

34

1.06

0.83

to

1.37

M

70-74

16,365

16

12

10

6

to

16

1.28

0.73

to

2.30

Notes:

1. “Injury” defined as any diagnosis in the range ICD9:800–999.

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Person-years is the sum of annual population counts during the observation period.

4. Observed number of injuries.

5. Expected number, indirectly standardized, based on age, gender and HSDA-specific rates in total population of BC.

6. Crude Rate per 10,000 person-years.

7. Standardized Relative Risk (compared to the total population of BC) = Observed/Expected.

SRRs (adjusted for age, gender and HSDA) show that younger Aboriginal persons (males under 35 years and females under 40 years of age) have lower risk of worker compensation injury compared to persons of the same age and gender in the total population. Older Aboriginal males have about the same risk of worker compensation injury as males in the total population. Older Aboriginal females have higher risk of worker compensation injury than females in the total population.

Annual trends

Tables 6 and 7 show crude rates and SRRs of injuries claimed for worker compensation, during the period 1987–2010, among the total population (Table 6) and the Aboriginal population (Table 7), by year. Figure 2 depicts comparisons of SRRs between these populations, regarding all injuries combined. SRRs in both the tables and figures have been adjusted for age, gender, and HSDA. Recall that the reference population is the combined total population of BC during the entire period (1987 through 2010). Thus, the SRR for the total population of BC in a particular year can be higher or lower than one, but the average of the SRRs for the total population of BC, over all the years, will be one.
Table 6

Worker compensation injuries [1], British Columbia, 1987–2010 [2], by Year

Year

P-years [3]

Obs [4]

Exp [5]

Rate [6]

95% CI for Rate

SRR [7]

95% CI for SRR

1987

3,121,318

56,943

44,364

182

181

to

184

1.28

1.27

to

1.30

1988

3,165,022

62,293

45,153

197

195

to

198

1.38

1.37

to

1.39

1989

3,245,277

68,314

46,509

211

209

to

212

1.47

1.46

to

1.48

1990

3,339,763

72,124

48,247

216

214

to

218

1.49

1.48

to

1.51

1991

3,421,459

67,786

49,492

198

197

to

200

1.37

1.36

to

1.38

1992

3,515,345

66,197

50,970

188

187

to

190

1.30

1.29

to

1.31

1993

3,649,925

64,624

53,506

177

176

to

178

1.21

1.20

to

1.22

1994

3,771,519

65,575

55,449

174

173

to

175

1.18

1.17

to

1.19

1995

3,856,183

61,873

56,649

160

159

to

162

1.09

1.08

to

1.10

1996

3,959,300

60,944

58,338

154

153

to

155

1.04

1.04

to

1.05

1997

4,040,687

58,697

59,628

145

144

to

146

0.98

0.98

to

0.99

1998

4,087,714

61,689

60,256

151

150

to

152

1.02

1.02

to

1.03

1999

4,115,601

49,584

60,640

120

119

to

122

0.82

0.81

to

0.82

2000

4,114,815

49,678

60,464

121

120

to

122

0.82

0.82

to

0.83

2001

4,160,615

58,317

61,161

140

139

to

141

0.95

0.95

to

0.96

2002

4,211,443

52,979

61,890

126

125

to

127

0.86

0.85

to

0.86

2003

4,285,095

50,983

63,310

119

118

to

120

0.81

0.80

to

0.81

2004

4,335,962

52,636

64,324

121

120

to

122

0.82

0.81

to

0.82

2005

4,383,639

55,255

65,001

126

125

to

127

0.85

0.84

to

0.86

2006

4,414,528

57,497

65,192

130

129

to

131

0.88

0.88

to

0.89

2007

4,476,436

58,476

66,025

131

130

to

132

0.89

0.88

to

0.89

2008

4,546,001

58,183

67,066

128

127

to

129

0.87

0.86

to

0.87

2009

4,607,365

45,298

67,875

98

97

to

99

0.67

0.66

to

0.67

2010

4,632,154

43,716

68,149

94

93

to

95

0.64

0.64

to

0.65

Notes:

1. “Injury” defined as any diagnosis in the range ICD9:800–999.

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Person-years is the population count during the specified year.

4. Observed number of injuries.

5. Expected number, indirectly standardized, based on age, gender and HSDA-specific rates in total population of BC during entire observation period.

6. Crude Rate per 10,000 person-years.

7. Standardized Relative Risk (compared to the total population of BC during the entire observation period) = Observed/Expected.

Table 7

Worker compensation injuries [1], Aboriginal BC, 1987–2010 [2], by Year

Year

P-years [3]

Obs [4]

Exp [5]

Rate [6]

95% CI for Rate

SRR [7]

95% CI for SRR

1987

96,252

1,174

1,207

122

115

to

129

0.97

0.92

to

1.03

1988

99,507

1,336

1,266

134

127

to

142

1.06

1.00

to

1.11

1989

102,607

1,567

1,327

153

145

to

160

1.18

1.12

to

1.25

1990

104,866

1,639

1,381

156

149

to

164

1.19

1.13

to

1.25

1991

108,471

1,564

1,437

144

137

to

151

1.09

1.03

to

1.15

1992

111,758

1,499

1,489

134

128

to

141

1.01

0.96

to

1.06

1993

116,061

1,560

1,558

134

128

to

141

1.00

0.95

to

1.05

1994

119,614

1,609

1,608

135

128

to

141

1.00

0.95

to

1.05

1995

122,026

1,416

1,640

116

110

to

122

0.86

0.82

to

0.91

1996

124,891

1,365

1,681

109

104

to

115

0.81

0.77

to

0.85

1997

126,909

1,488

1,704

117

111

to

123

0.87

0.83

to

0.92

1998

128,332

1,478

1,718

115

109

to

121

0.86

0.82

to

0.90

1999

128,945

1,318

1,720

102

97

to

108

0.77

0.73

to

0.80

2000

130,683

1,243

1,732

95

90

to

101

0.72

0.68

to

0.75

2001

133,025

1,457

1,755

110

104

to

115

0.83

0.79

to

0.87

2002

135,727

1,446

1,781

107

101

to

112

0.81

0.78

to

0.85

2003

139,955

1,370

1,845

98

93

to

103

0.74

0.71

to

0.78

2004

142,881

1,485

1,877

104

99

to

109

0.79

0.76

to

0.83

2005

145,834

1,687

1,907

116

110

to

121

0.88

0.85

to

0.92

2006

148,458

1,759

1,932

118

113

to

124

0.91

0.87

to

0.95

2007

151,609

2,023

1,964

133

128

to

139

1.03

0.99

to

1.08

2008

154,876

1,769

1,993

114

109

to

120

0.89

0.85

to

0.93

2009

158,252

1,265

2,030

80

76

to

84

0.62

0.60

to

0.65

2010

159,482

1,219

2,055

76

72

to

81

0.59

0.57

to

0.62

Notes:

1. “Injury” defined as any diagnosis in the range ICD9:800–999.

2. Injuries occurring during the observation period 1987-Jan-01 to 2010-Dec-31.

3. Person-years is the population count during the specified year.

4. Observed number of injuries.

5. Expected number, indirectly standardized, based on age, gender and HSDA-specific rates in total population of BC during entire observation period.

6. Crude Rate per 10,000 person-years.

7. Standardized Relative Risk (compared to the total population of BC during the entire observation period) = Observed/Expected.

SRR trends (Figure 2) show that risks of injury are declining, although the rate of decline has been greater for the total population (mean change in SRR was −0.033 per year, 95% confidence interval: −0.039 to −0.027) than for the Aboriginal population (mean change in SRR was −0.016 per year, 95% CI: −0.022 to −0.009). The Aboriginal population was at lower risk than the total population at the start of the period (1987), but parity was reached (the trend lines converged) in 2006. The risk of worker compensation injury among the Aboriginal population increased during the years 2003 through 2007, then declined markedly.

Ecological analysis of predictors of risk

Our analysis of custom data from the Census showed that Aboriginal people residing on reserves have lower employment rates than the total population of BC (45.4% vs. 61.1% in 2001, and 46.2% vs. 62.4% in 2006); on the other hand, when they are employed, they are more likely to work in hazardous occupations (expected relative risk of worker compensation claim, “RR” was 1.10 in 2001, increasing to 1.14 in 2006) or industries (RR = 1.08 in both 2001 and 2006). Compared to the male labour force of BC, the Aboriginal male labour force residing on reserves are more concentrated in “trades, transport and equipment operators and related occupations”, “occupations unique to primary industry”, and “occupations unique to processing, manufacturing and utilities” (i.e., the proportion of the Aboriginal labour force in each of these categories was greater than the proportion of the BC general population labour force in the same category.) These are “blue-collar” occupational groups, with relatively higher rates of worker compensation claims [21]. The Aboriginal male labour force is also more concentrated in “occupations in social science, education, government service and religion.” This is generally an occupational category with a low risk of worker compensation claim [21]. However, on Aboriginal reserves, operations of the band government represent a disproportionately large amount of economic activity, and “government service” may have a different meaning than elsewhere. Compared to the female labour force of BC, the Aboriginal female labour force residing on reserves are more concentrated in the high-risk occupational categories of “trades, transport and equipment operators and related occupations”, and “occupations unique to primary industry. The Aboriginal female labour force is also more concentrated in the medium-risk category of “sales and service occupations”. Like Aboriginal males, the Aboriginal female labour force is more concentrated in the generally low-risk category of “occupations in social science, education, government service and religion.” By industry category, the Aboriginal male labour force is more concentrated in “agriculture, forestry, fishing and hunting”, “construction” and “manufacturing”. These are industries with relatively higher rates of worker compensation claims. The Aboriginal male labour force is also more concentrated in “mining and oil and gas extraction” (medium-risk), and “public administration”, and industry with a relatively low rate of worker compensation claims [22]. Again, on Aboriginal reserves, operations of the band government represent a disproportionately large amount of economic activity, and “public administration” may have a different meaning than elsewhere. The Aboriginal female labour force is more concentrated in the high-risk industrial categories of “agriculture, forestry, fishing and hunting”, and “construction”. The Aboriginal female labour force is also more concentrated in “mining and oil and gas extraction” (medium-risk), and “public administration”, and industry with a relatively low rate of worker compensation claims [22].

Tables 8 and 9 show regression statistics from the preliminary regression models with a single independent (X) variable. “P” is the probability of the null hypothesis that R2 is equal to zero. If “P” was less than 0.05, then the independent variable was retained for subsequent multivariable regression analysis.
Table 8

Ecological analysis of worker compensation injury risk among BC Aboriginal communities, 1999–2008, Regression [1] statistics from models with one independent (X) variable

X Variable

units

min

max

mean [2]

SD [2]

N

R2

B

SE

P

RR Ratio per SD [2]

L95CL

U95CL

Census

1 year

2001

2006

2003.5

2.5

319

0.012

0.020

0.010

0.049

1.053

1.000

1.108

Income Per Capita 1000

$1,000

5.3

50.9

13.1

5.9

147

0.067

0.022

0.007

0.002

1.135

1.051

1.226

Income Score

1

32.6

108.1

60.2

12.7

147

0.087

0.010

0.003

0.000

1.142

1.064

1.226

High School

1%

0.0

116.7

55.7

17.4

261

0.021

0.004

0.002

0.018

1.080

1.013

1.150

University Degree

1%

0.0

34.3

3.9

5.8

261

0.040

0.016

0.005

0.001

1.098

1.038

1.161

Pop Per Room

1

0.30

1.11

0.53

0.11

260

0.069

−1.294

0.296

0.000

0.866

0.811

0.924

Need Major Repairs

1%

0.0

120.0

32.7

19.2

261

0.005

−0.002

0.002

0.267

0.963

0.900

1.030

Labour Force

1%

9.9

100.0

61.7

12.3

261

0.005

−0.003

0.003

0.235

0.959

0.894

1.028

Employed

1%

7.7

77.3

47.3

11.0

261

0.027

0.008

0.003

0.008

1.094

1.024

1.169

Occupation Risk

RR

0.00

2.71

1.12

0.36

261

0.008

0.148

0.105

0.159

1.054

0.979

1.135

Industry Risk

RR

0.00

3.92

1.11

0.34

261

0.005

0.157

0.141

0.265

1.054

0.961

1.157

Remoteness

1

0.08

1.35

0.23

0.22

317

0.008

−0.183

0.113

0.108

0.961

0.916

1.009

Environ Index

1

0.40

3.00

0.65

0.38

317

0.012

−0.133

0.068

0.051

0.950

0.902

1.000

Aboriginal

1%

5.7

100.0

84.7

23.2

261

0.060

−0.005

0.001

0.000

0.892

0.844

0.943

NAIndian

1%

5.6

103.1

81.5

23.8

261

0.054

−0.005

0.001

0.000

0.896

0.847

0.948

Notes:

1. The dependent (Y) variable is Ln (SRR of worker compensation injury, total of all injuries), and regression is weighted by person-years.

2. Unweighted mean and standard deviation.

Table 9

Ecological analysis of worker compensation injury risk among BC Aboriginal communities, 1999–2008, Regression [1] statistics from models with one independent (X) variable

X Variable, Interaction term

units

min

max

mean [2]

SD [2]

N

R2

B

SE

P

RR Ratio per SD [2]

L95CL

U95CL

Census_Employed

1 year

153.9

1550

947.2

220.4

261

0.027

0.000

0.000

0.007

1.095

1.025

1.169

IncomePerCapita1000_Employed

$1,000

1.2

39.0

6.1

4.1

147

0.061

0.032

0.010

0.003

1.139

1.047

1.238

IncomeScore_Employed

1

5.5

82.7

27.7

10.4

147

0.089

0.014

0.004

0.000

1.153

1.070

1.243

HighSchool_Employed

1%

0.0

72.9

27.1

12.2

261

0.026

0.007

0.003

0.009

1.091

1.022

1.165

UniversityDegree_Employed

1%

0.0

26.3

2.0

3.5

261

0.038

0.028

0.009

0.002

1.102

1.038

1.171

PopPerRoom_Employed

1

0.03

0.47

0.25

0.07

260

0.000

−0.146

0.470

0.756

0.990

0.927

1.056

NeedMajorRepairs_Employed

1%

0.0

60.0

15.4

9.3

261

0.001

0.002

0.004

0.574

1.020

0.951

1.094

LabourForce_Employed

1%

0.8

66.7

30.2

11.4

261

0.004

0.003

0.003

0.311

1.036

0.967

1.109

OccupationRisk_Employed

RR

0.00

1.34

0.52

0.19

261

0.041

0.663

0.200

0.001

1.134

1.052

1.222

IndustryRisk_Employed

RR

0.00

1.12

0.52

0.17

261

0.030

0.631

0.224

0.005

1.115

1.033

1.204

Remoteness_Employed

1

0.01

0.75

0.11

0.10

261

0.000

−0.044

0.246

0.859

0.995

0.947

1.047

EnvironIndex_Employed

1

0.03

1.41

0.30

0.18

261

0.001

−0.076

0.145

0.601

0.986

0.936

1.039

Aboriginal_Employed

1%

1.1

75.0

40.2

13.9

261

0.008

−0.003

0.002

0.150

0.956

0.899

1.016

NAIndian_Employed

1%

1.0

68.8

38.8

14.0

261

0.007

−0.003

0.002

0.169

0.958

0.900

1.019

Notes:

1. The dependent (Y) variable is Ln (SRR of worker compensation injury, total of all injuries), and regression is weighted by person-years.

2. Unweighted mean and standard deviation.

Table 10 shows regression statistics from the best-fitting regression model with multiple independent (X) variables. The best-fitting model identified the following as statistically significant predictors of worker compensation injury risk: population per room, proportion of the population who identified themselves as Aboriginal, income score multiplied by employment, occupational risk multiplied by employment, proportion of university educated persons multiplied by employment, and Census year multiplied by employment. The entire model explained 32.5% of the variance among communities in SRR of worker compensation injury (R2 = 0.325, p < 0.0005).
Table 10

Ecological analysis of worker compensation injury risk among BC Aboriginal communities, 1999–2008, Regression [1] statistics from the best-fitting model with multiple independent (X) variables

X Variable

units

min

max

mean [2]

SD [2]

N

B

SE

P

RR Ratio per SD [2]

L95CL

U95CL

(Constant)

     

147

0.285

0.282

0.313

   

PopPerRoom

1

0.30

1.11

0.53

0.11

147

−1.878

0.528

0.001

0.811

0.722

0.911

Aboriginal

1%

5.7

100.0

84.7

23.2

147

−0.007

0.002

0.000

0.847

0.777

0.923

IncomeScore_Employed

1

5.5

82.7

27.7

10.4

147

−0.048

0.012

0.000

0.606

0.472

0.777

OccupationRisk_Employed

RR

0.00

1.34

0.52

0.19

147

1.801

0.369

0.000

1.407

1.225

1.615

UniversityDegree_Employed

1%

0.0

26.3

2.0

3.5

147

0.050

0.014

0.001

1.189

1.077

1.313

Census_Employed

1 year

154

1550

947.2

220.4

147

0.002

0.000

0.002

1.395

1.133

1.717

Notes:

Multivariable model statistics: R2 = 0.325, F = 11.232, p = 0.000

1. The dependent (Y) variable is Ln (SRR of worker compensation injury, total of all injuries), and regression is weighted by person-years.

2. Unweighted mean and standard deviation.

Discussion

It has been asserted that Aboriginal people in BC are at higher risk of injury than the total population, but our descriptive statistics offer a more varied perspective. In the category of worker compensation injury, Aboriginal people generally have lower risk. There are exceptions: in some HSDAs, and among women aged 50 years and older, Aboriginal people are at higher risk. Disparities in worker compensation injury risk might result from the competing effects of employment rates, occupations and industries. Aboriginal people have lower employment rates than the general population, but are more likely to work in hazardous occupations and industries. During the period 1987–2010, worker compensation injury rates declined for both the Aboriginal and the total populations, probably reflecting a secular trend towards safer work environments, but the decline was less among Aboriginal people. During the economic “boom” (measured in 2002 dollars, during the 5 years from 2002 to 2007, the Gross Domestic Product (GDP) of BC grew 19.0%, a year-over-year average of 3.5%), risk of worker compensation injury increased among Aboriginal people and went higher than the risk among the total population. In contrast, during the subsequent economic “bust” (during the two years from 2007 to 2009, GDP shrank 1.7%) [24], risk among Aboriginal people declined sharply to below the level of risk among the total population. As shown by our own analysis of Census data (see above), between the census years 2001 and 2006 the employment rate among Aboriginal reserve residents increased, and so did the hazardousness of their occupations. The jobs were insecure, because economic fluctuations were more severe in the industrial sectors where Aboriginal workers are concentrated. In “agriculture, forestry, fishing and hunting”, measured in 2002 dollars, during the 5 years from 2002 to 2007, the GDP grew by 4.3%, then in the subsequent two years from 2007 to 2009, GDP shrank disastrously by 16.2%. During the same periods respectively, in “construction” the GDP grew by an astonishing 43.8% then shrank markedly by 5.3% [24].

Our ecological analysis of hypothesized socio-economic, work-related, and geographic risk markers demonstrates some interesting associations, and may provide clues regarding the web of causation surrounding risk of worker compensation injury among the Aboriginal population.

The best-fitting model indicated that increased household population per room, and increased proportion of the population who identified as Aboriginal were associated with decreased risk of injury. We are accustomed to associating crowded housing and Aboriginal ethnicity with socio-economic disadvantage. However, in a multivariable model (controlling for employment, occupation, income and education), population per room and Aboriginal identity may reflect family structure and cultural adherence, rather than disadvantaged economic conditions. It is plausible that living in communities where people value extended family relationships and have strong identification with Aboriginal heritage could have psychological benefits that lower the risk of injury among community members [5].

In the descriptive, individual-level analysis, we observed that among the Aboriginal population, urban residents were at higher risk of worker compensation injury than Aboriginal people who were not urban. But the ecologic analysis tested two geographic variables, “remoteness” and “environmental index” that were derived from distance to the nearest urban centre, and neither was independently associated with risk of worker compensation injury. This suggests that the higher risk among urban residents is due to intervention by one or some combination of the variables retained in the final model. Likely the variable was “employment”, and it is plausible that urban dwelling Aboriginal people are more at risk for worker compensation injury because they are more likely to be employed.

The best-fitting model included the proportion of the population who were employed, but not as an independent variable with a directly proportional association with injury risk. Employment interacts multiplicatively with income, occupational risk, and university education. Increased occupational risk and increased employment, interacting together multiplicatively, are strongly associated with increased risk of worker compensation injury. Increased income and increased employment, interacting together multiplicatively, are strongly associated with decreased risk of worker compensation injury. These findings are plausible, as well as empirical. But it seems paradoxical that increased proportion of the population who are university-educated, and increased employment, interacting together multiplicatively, are associated with increased risk of worker compensation injury. Perhaps this indicates that university-educated people can better access the worker compensation system. Alternatively, this may indicate that among Aboriginal people, having university education may lead to mismatching of educational level with job category, increasing the risk of worker compensation injury. Or, the paradox may be ecological: increased proportion with university education among the population may indicate a more unequal social order, with increased injury risk to those in the lower strata.

Time (as measured by Census year) and increased employment, interacting together multiplicatively, are associated with increasing risk of worker compensation injury. This is disturbing, yet intriguing, as it suggests that among Aboriginal communities, there are other time-related factors that we have not measured, that are pushing worker compensation injury rates upwards, or preventing them from declining as much as injury rates in the total population.

Our ecological multivariable analysis studied only Aboriginal communities. We did not include any non-Aboriginal communities. Therefore, the findings only apply to Aboriginal communities, and cannot be used to explain the observed differences in worker compensation injury rates between the Aboriginal and total populations of BC. This matter invites future research, that includes both Aboriginal and non-Aboriginal communities in an ecological analysis.

Data quality

BC’s universal health care insurance program is the best available registry of the province’s population. Using this registry, in fiscal year 2006–2007 we counted 4,266,070 people in BC, which is 103.7% of the number (4,113,487) enumerated in BC by the 2006 Census of Canada. The slight excess may represent persons who were deceased or no longer resident in the province, but who had not yet been removed from the insurance registry.

Using the insurance registry and our definition of “Aboriginal” (derived from insurance premium group and notations on birth and death records), in fiscal year 2006–2007 we counted 148,458 people in BC whom we considered “Aboriginal”, which is 75.8% of the number (196,070) enumerated in BC who identified themselves as “an Aboriginal person, that is, North American Indian, Métis or Inuit (Eskimo)” in the 2006 Census of Canada. Our definition of “Aboriginal” is admittedly restrictive, and largely, if indirectly, based on legally recognized Indian status, as defined by the Indian Act of Canada. Some might say that we should have determined Aboriginality using the federal government’s Indian Status Registry, but due to privacy issues and political considerations, it was not possible for us to get access to the Indian Status Registry. However, we consider our definition of “Aboriginal” to be superior to presence in the Indian Status Registry, because our definition includes residence in BC, whereas the Indian Status Registry reflects membership in a recognized First Nation or Indian band located in BC, regardless of where the individual in fact resides. Also, our definition is more likely to include children who are eligible for Indian status because of their parents’ Indian status, but who have not yet applied to be included in the Indian Status Registry.

We counted injuries registered for claims with the provincial worker compensation system. Work Safe BC’s database is the reference standard. There is no better. We have confidence in its accuracy because compensation payments depend on this database, and people who do not get the payments to which they are entitled will take action to claim their due. Some may argue that limiting our analysis to injuries registered for worker compensation claims imposes an overly restrictive definition of occupational injury. However, limiting our definition helps to protect the internal validity of our analysis.

Conclusions

As an increasing proportion of Aboriginal people became employed with pay, over the past decade incidence of worker compensation injury among the Aboriginal population has reached parity with, or even exceeded that among the general population. We need culturally sensitive workplace injury prevention programming, particularly in geographic regions and industries where Aboriginal workers are concentrated. Targets for prevention programs should include older Aboriginal people, especially women. It is conventional wisdom that employment is good for health, but our analysis suggests the effects may be mixed. This challenge can be met with further knowledge and better-informed planning.

Authors’ information

AJ is self-employed as an epidemiology consultant. MAG is an Associate Professor in the Department of Pediatrics, Faculty of Medicine, University of British Columbia, and Scientist Level 1 at the Child and Family Research Institute. MB is an Assistant Professor in the Department of Pediatrics, Faculty of Medicine, University of British Columbia, and Scientist Level 1 at the Child and Family Research Institute. CEL is a Professor in the Department of Psychology, Faculty of Social Sciences, University of Victoria.

Abbreviations

BC: 

British Columbia

GDP: 

Gross Domestic Product

HSDA: 

Health Service Delivery Area

MSP: 

Medical Services Plan of British Columbia

SRR: 

Standardized Relative Risk.

Declarations

Acknowledgements

This research was funded by the Canadian Institutes of Health Research (Funding reference: AHR # 81043), by the British Columbia Region, First Nations and Inuit Health, Health Canada, and by the Child and Family Research Institute.

The authors thank Anna Low, Sherylyn Arabsky and Kelly Alke of Population Data BC for assistance with data access and linkage. The authors thank Dr. Rod McCormick for his contributions to the study design.

Authors’ Affiliations

(1)
(2)
University of British Columbia and Child & Family Research Institute, University of Northern BC
(3)
University of British Columbia and Child & Family Research Institute, Child and Family Research Institute, BC Children’s Hospital
(4)
Department of Psychology, University of Victoria

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