An ecological study of regional variation in work injuries among young workers

Background on young workers

Many young Canadians are engaged in paid employment. Employment rates from the Labour Force Survey show that 44.7% of Canadian adolescents (15 to 19 years old) and 70.9% of young adults (20 to 24 years olds) were employed in any given month during 2004 [16].

Young workers occupy a particular niche in the Canadian labour market that is characterized by part-time, temporary work, and concentration in certain jobs and industries. Canadian adolescents and young adults work an average of 21.2 and 32.4 hours per week, respectively [16]. Even during the school year (September to May), 41.7% of Canadian adolescents and 29.2% of young adults reported having worked in their current job for six months or less [17].

Canadian adolescents are most likely to hold sales and service jobs such as food and beverage, cashier, and retail sales (67%) and clerical/administration jobs (8.5%). Nevertheless, there is some representation in more hazardous jobs such as trades/transportation (e.g., construction; 7.7%) and primary industry (e.g., agriculture; 7.8%) [17]. Young adults are less concentrated in sales and service jobs (37.6%), and more likely to work in clerical/administration (12.8%), trades/transportation (12.8%) and primary industry (9.6%) jobs than adolescents. These similarities among adolescent and young adult workers are partly due to the fact that the school to work transition is extending into the mid to late 20's [18]. Consequently, both age groups provide valuable information about the initial stages of work life.

Work injuries and illnesses among young people 15 to 24 years old entering the formal labour market are a public health concern. In developed countries, both teenagers and young adults have rates of work injury that are typically 1.2 to two times that of older workers [19–21]. This elevated risk is more marked for young males than young females. Because young workers are more likely to work part-time or seasonally, injury rates based on number of hours worked (rather than per worker) show an elevated youth risk more consistently [22, 23].

Many of the work injuries youth sustain have a clear health and economic consequence. Fifteen to 26% of injured adolescent workers surveyed reported permanent impairments such as chronic pain, scarring, sensory loss, and loss of range of motion. [24, 25]. Sixteen to 24 year olds who sustained a work injury showed significantly lower earnings in the year after the injury than their uninjured counterparts [26].

Among young workers, individual characteristics are associated with claim rates. For example, young males sustain work injuries at about twice the rate of young females [27–34]. In seven studies examining adolescent and young adult claim rates by industry, the retail/wholesale, manufacturing, agriculture, and construction industrial sectors typically had higher claim rates than the service sector [27–34]. With regard to occupational groups, young workers in manual jobs such as handlers/laborers, janitors/cleaners and food service exhibited higher work injury rates than youth in sales or administrative/clerical jobs [27, 29, 30]. Previous research has shown that Ontario young workers have a similar set of demographic and work-related factors associated with their risk of work injury as found in young worker studies elsewhere in North America [34].

Geographic variation in youth work injury rates, and the factors underlying these variations, are just beginning to be explored. A Canada-wide survey showed that even when demographic and work factors were statistically controlled, 15 to 24 year olds in a western province were twice as likely to be injured at work compared to youth in Ontario, a large eastern province [35]. Brooks and Davis [36] conducted an analysis of Massachusetts workers' compensation claims for teenagers in 45 regions in the state. The highest claim rates were found in the southeast region of the state. Compositional differences in jobs were suggested because hazardous industries such as apparel manufacturing are concentrated in this region. Also suggesting potential regional differences in deprivation, the southeast region of the state had a lower per capita income and higher unemployment rate than the state as a whole. However, there was no attempt in this study to control for compositional differences and formally assess the association between region-level indicators and variation in adjusted claim rates.

Rationale for level of aggregation

One conceptual and methodological issue for geographic studies is the level of aggregation of areas [3]. The census division, the level of aggregation for the present study, is a "general term applying to areas established by provincial law, which are intermediate geographical areas between the municipality (census subdivision) and the province. Usually they are created to facilitate regional planning and the provision of services which can be more effectively delivered on a scale larger than a municipality" [37].

Examining regional variation at this subprovincial level is appropriate for occupational health data as labour markets are significantly shaped by geography [38]. Aggregating to this level makes it possible to capture a mix of industries within the local regional economy, as compared to a detailed neighbourhood analysis where that diversity would be attenuated. Moreover, census divisions (CDs) will, on average, approximate regional labour markets, although in the largest urban areas the labour market may consist of several CDs because of the size of the city and density of employers. Regional measures of the labour market and socioeconomic status also offer the potential advantage of greater stability than neighbourhood- or workplace-level indicators [39].

Indicators and pathways for geographic variation

Regional variation in work injury rates can be due to both compositional and contextual factors. Variation can occur because regions may have demographically different subgroups of young workers or young workers hold different types of jobs. For example, the economy in more rural regions of the country lead young workers to have proportionally more jobs in the agricultural sector than in urban areas [17].

Geographic variation can also occur due to contextual factors. Contextual factors refer to the physical, social or economic aspects of a workplace or region (.([40]. The development of indices to summarize the socio-economic status of whole regions is a significant scholarly enterprise, with indices such at the Carstairs index, the Townsend deprivation score and others now widely used [41]. For the purposes of this study, we adopted the material and social deprivation indices developed in Quebec. According to Pampalon and Raymond [42], two forms of area deprivation, material and social, are significant for health at the regional level and we hypothesize that these same factors could influence work injury rates. Material deprivation involves the lack of economic resources to provide or access goods and services relevant to health, and is reflected in level of education and income indices. Relative material deprivation in a region could limit the capability of local businesses to invest in safer equipment and to set up the infrastructure to effectively disseminate work safety information.

Social deprivation refers to the quality of relationships in the workplace and community [42]. Common indicators of this construct are the transience of residents in the area and family structure (e.g., single parent families). With regard to workplaces, relevant aspects of the social environment include workplace size and the degree of unionization [43, 44], which reflects the capability for collective action. Relative social deprivation can exert influence on the workplace and health through a weak safety culture and lack of adherence to safety practices. Less socially cohesive areas may also lead to higher manager/employee turnover and poorer safety education and training.

The main analytic focus of the research was on geographic variation of youth work injuries and the relationship between that variation and area social/economic deprivation. Substantial geographic variation even after controlling for compositional differences raises the possibility of the presence of contextual factors. Using compensation claims data of 15 to 24 year old workers from a Canadian province, the present study had three objectives: (a) to describe the geographic variation of work injuries; (b) to determine whether geographic variation remained after controlling for relevant demographic and job characteristics; (c) to identify the region-level factors that correlate with the geographic variation.

Data Sources

The Canadian Census is conducted every 5-years by Statistics Canada. The Census contains both short form and long form questionnaires. The short form contains seven questions: the respondent's name, sex, age, marital and common-law status, family and household relationships, and mother tongue. The long form includes the seven questions from the short questionnaire plus 52 additional questions. These include questions on the average number of weeks worked in the previous year, type of occupation and industry each respondent works in, information on family composition, socio-demographic composition, education, income and geographic mobility in the last year and over the last five years. Four in five Canadian households receive the short form, with the other 20% receiving the long form.

The Labour Force Survey follows a complex, rotating panel sample design to efficiently estimate monthly changes in the Canadian labour force [45]. The sample size covered by the Labour Force Survey is approximately 54,000 households each month. Each household is surveyed monthly, for six months. The Labour Force Survey contains information on industry, occupation, and hours of work in the previous week

Begun in 1993, the SLID is a longitudinal survey of representative samples of Canadian households. The survey consists of a series of six-year panels, with a new panel being introduced every three years to replace the oldest panel. Each panel contains approximately 15,000 households with about 31,000 respondents aged 16 years old and over. The population for each SLID panel is recruited from participants to the Labour Force Survey.

The Education, Quality and Accountability Office website published the results of the Ontario Secondary School Literacy test (OSSLT). The OSSLT assesses the reading and writing skills that students are expected to have acquired, according to the Ontario school curriculum, by the end of grade nine. The OSSLT is administered to students within all schools in Ontario.

Estimates for lost-time injury claims

Lost-time injury claims information for 15 to 24 year olds that occurred in 2000 was gathered from the Ontario Workplace Safety and Insurance Board (WSIB). The present study combined teenage and young adult claims because: a) both age groups are in the school to work transition (as noted above); b) previous research has shown comparable work injury rates for both age groups [21, 34, 46].; and c) the precision and stability of the claim rate estimates by region was aided by using both age groups.

The WSIB is the sole provider of workers compensation in Ontario and covers approximately 65% – 70% of Ontario labour force participants [47]. The remaining 30 to 35% not covered included those self-employed, domestic workers, federal government workers, the finance industry, and workers associated with inter-provincial commerce.

All WSIB covered workers in Ontario are required to submit lost-time claims for any workplace injury that requires health care and/or (1) results in an absence from their regular work, (2) requires modified duties at less than regular pay, (3) requires modified duties at regular pay for more than seven calendar days after their accident, (4) results in earning less than regular pay at regular work. Injuries requiring only first aid, such as cleaning minor cuts, scrapes or scratches, applying bandages or dressings, or applying splints, do not require submission of a lost-time claim.

Along with socio-demographic information such as age and gender, and workplace related information such as industrial sector, each lost-time claim submitted to the WSIB contains information on the census division in which the claimant lived and the location where the injury occurred. Injuries were classified into census divisions first using the living location of each claimant; if this information was not available the location where the injury occurred was used (1.4% of claims).

Estimates of Full-time-equivalents worked in 2000

Estimates of the average number of weeks worked, stratified by census division, gender, full-time or part-time status, industry (goods and services) and occupation (manual, mixed and non-manual) were obtained for all 15 – 24 year olds in Ontario, working in industries with mandatory coverage from the Ontario WSIB (for further details on exclusions to WSIB coverage see [48]. For details on the occupational categories see [49]. Unfortunately, the Census only contains information on the hours of work in the previous week (in 2001). As this number of hours may not be applicable to each respondent's working hours in the previous year, similar data on the average number of hours worked per week for all 15 – 24 year olds in Ontario was obtained from the Labour Force Survey, by level of economic region (of which there are 11 in Ontario). These estimates were applied to all census divisions within a given economic region to estimate the number of full-time equivalents (FTEs), gender, occupation and industry within each census division.

We adjusted the claim rate for each census by occupation and gender using direct standardization [50]. That is, injury rates presented have taken into account differences in the gender and occupational composition of the labour force between census divisions. We decided not to further adjust for industrial composition, given the percent of the labour force working in goods-producing industries was highly correlated with the percent of the labour force working in manual occupations.

The map of regional claim rates was constructed using MapInfo. The grouping of regions by claim rate represented in the maps was derived using a software algorithm that computes the natural breaks in the distribution of the variable.

With regard to the types of injuries, the Canadian Work Injury Standard codes were used to classify the nature of the injury [51] into the following groups: cut, burns, fractures, amputations, sprains and strains, contusions, and other (e.g., concussions, exposures, multiples injuries).

Reduction of predictors

The original data set contained 47 different variables at the Census Division level. An initial analytic step was to examine the correlations between each of these variables. Items with high correlations (> 0.85) with other items were removed due to probable redundancy. The revised data set contained 21 variables at the census division level:

1. Population density (Census)

2. Percent of total labour force with coverage by a union or collective agreement (SLID)

3. Percent of total labour force working in workplaces with less than 20 employees (SLID)

4. Percent of 15 – 24 year old labour force working in workplaces with less than 20 employees (SLID)

5. Average labour market earnings (Census)

6. Percent of 15 – 24 year olds not currently studying with less than high school education (Census)

7. Percent of 25+ year olds not currently studying with less than high school education (Census)

8. Unemployment rate for 15 – 24 year olds (Census)

9. Percent of tenant households who spend 30% or more on rent (Census)

10. Percent of owner households who spend 30% or more on household expenses (Census)

11. Percent of school children who successfully completed the Ontario Secondary School Literacy Test (OSSLT) in 2000–01 (Education, Quality and Accountability Office website)

12. Percent of families with a single mother and children under the age of 14 years (Census)

13. Percent of private dwellings that are owned (Census)

14. Average value of dwellings (Census)

15. Ratio of population aged over 65 years to the population aged 15 – 64 years (Census)

16. Percent of population who have lived at the same address for the last five years (Census)

17. Percent of the population who have lived at the same address for the last year (Census)

18. Percent of population who lived outside of Canada five years ago (Census)

19. Population growth between 1996 and 2001 (Census)

20. Percent of population with only French language ability (Census)

21. Percent of population with Aboriginal identity (Census)

The variables for population density, French only language ability and aboriginal identity were skewed and kurtotic. Therefore they were each log transformed before correlation analysis and factor analysis were undertaken.

We first examined if census divisions could be characterized by combinations of indicators of deprivation and labour market variables. The initial exploratory factor analysis revealed five factors. The percent of population with French only language was dropped as it loaded, by itself, onto only one factor. In a subsequent model the percentage of tenant households who spend more than 30% on rent only loaded onto one factor, by itself, and was subsequently removed as well. Our final factor analysis revealed four factors.

We then created factor scores for each of the four factors by taking only those variables with a loading of greater than 0.5 on a given factor. In two cases variables did not have loadings of over 0.50 with any factor. In these situations (for union membership and education for 25+ yrs) these variables were included in the factor that they loaded most strongly on. In two further cases (the percentage of household spending 30% or more of household income on expenses and the percent of dwellings that were owned) variables loaded onto two factors. We then performed four additional factor analyses, taking only the variables assigned to a given factor and constraining the number of factors to one, to produce factor scores, using PROC FACTOR in SAS version 9.1.

Regression analyses

We then performed two regression analysis; the first examined the relationship between each of the factors and the adjusted injury rate within a census division; and the second examined the relationship between each of the variables contained within a given factor and the adjusted injury rate separately, with a final model including only the variables that were significantly associated with the adjusted injury rate. Because of the different size of each census divisions all regression and correlation analyses were weighted, using a relative weight based on the size of the 15 – 24 year old labour force within a census division.

Examination of influence statistics demonstrated the larger labour markets of Peel (10% of total labour force), Toronto (17%) and York (6%) regions were influential over the correlations obtained, as assessed by Cooks D statistics. While we could remove such influential observations, given we are examining how census division level variables influence work-related lost-time claim rates, and the largest number of workers are in these divisions, we decided to keep them in our analyses, as conditions within these divisions would be expected to be influential on overall work-injury rates. Note that removal of these census divisions, while changing correlation estimates, only changed the significant relationships reported to non-significant relationships, and vice versa in three situations (single mother families would be significant at p < 0.10, value of dwellings would be significant at p < 0.05 and ratio of population aged over 65 to population aged 15 – 64 years would reduce in significance to p < 0.10).