Tyler J Lane1, Alex Collie1. 1. School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Abstract
OBJECTIVES: To identify geographic hotspots and coldspots of work disability burden and their associated sociodemographic factors in Australia. METHODS: Using Australian workers' compensation and census data, we calculated weeks of compensated time off work per 1000 labor force, an indicator of work disability burden, at Statistical Area Level 4, the smallest level of labor force data produced by the Australian Bureau of Statistics. Records included all claims with at least 1 day of compensated time off work lodged between 2010 and 2015. Work disability burden was z-transformed by state and territory and mapped across Australia. Statistical Areas that were more than 1 standard deviation from the state or territory mean were considered hotspots and coldspots. We tested several sociodemographic factors as predictors of work disability burden. RESULTS: Work disability burden hotspots were concentrated in lower socioeconomic suburbs and exurbs of state capitals, in addition to several regional areas. Coldspots were primarily in wealthy central urban and suburban areas. Factors associated with greater work disability burden include higher area socioeconomic disadvantage, rurality, lower labor force participation, higher unemployment, higher proportion of work in production industries and in blue-collar occupations, and higher numbers of those with core activity limitations, those aged 65+ years, and foreign-born residents. CONCLUSIONS: Work disability burden is unequally distributed across Australia and strongly influenced by sociodemographic and occupational factors. The findings can guide more efficient allocation of resources for work disability prevention and rehabilitation.
OBJECTIVES: To identify geographic hotspots and coldspots of work disability burden and their associated sociodemographic factors in Australia. METHODS: Using Australian workers' compensation and census data, we calculated weeks of compensated time off work per 1000 labor force, an indicator of work disability burden, at Statistical Area Level 4, the smallest level of labor force data produced by the Australian Bureau of Statistics. Records included all claims with at least 1 day of compensated time off work lodged between 2010 and 2015. Work disability burden was z-transformed by state and territory and mapped across Australia. Statistical Areas that were more than 1 standard deviation from the state or territory mean were considered hotspots and coldspots. We tested several sociodemographic factors as predictors of work disability burden. RESULTS: Work disability burden hotspots were concentrated in lower socioeconomic suburbs and exurbs of state capitals, in addition to several regional areas. Coldspots were primarily in wealthy central urban and suburban areas. Factors associated with greater work disability burden include higher area socioeconomic disadvantage, rurality, lower labor force participation, higher unemployment, higher proportion of work in production industries and in blue-collar occupations, and higher numbers of those with core activity limitations, those aged 65+ years, and foreign-born residents. CONCLUSIONS: Work disability burden is unequally distributed across Australia and strongly influenced by sociodemographic and occupational factors. The findings can guide more efficient allocation of resources for work disability prevention and rehabilitation.