Lack of English proficiency is associated with the characteristics of work- related injury and recovery cost in the Victorian working population.

BACKGROUND: Migrant workers have been identified in Europe, North America, Asia and Australia as a particularly vulnerable working population with a higher risk of work-related injury and mortality compared to non-migrant workers. Lack of English language proficiency is associated with an increased risk of work-related injury. Whether lack of English proficiency influences post-injury recovery or return to work outcomes remains unknown. OBJECTIVE: Using administrative data from a population based workers' compensation dataset in the state of Victoria, Australia, we aimed to examine work-related injury rates, worker characteristics and compensation outcomes in workers who were not proficient in English. We hypothesized that the use of an interpreter service would be associated with a poorer post-injury recovery profile and worse return to work outcomes. METHODS: WorkSafe Victoria accepted non-fatal claims for injuries and illnesses reported between January 1, 2003, and December 31, 2012 by workers aged 15 to 74 (n = 402, 828 claims) were analysed. Consistent with prior research, we selected "use of an interpreter service" as the indicator of English language proficiency. The total and categorical compensable cost of recovery was used as recovery outcomes. RESULTS: Of these claims, 16,286 (4%) involved the use of an interpreter service (LOTE workers). Our analysis revealed that Victorian injured LOTE workers have significantly different demographic, occupational and injury characteristics compared to non-LOTE injured workers. Furthermore, we present novel evidence that LOTE status was associated with poorer long-term injury outcomes, observed as a greater healthcare utilisation and larger paid income benefits, after controlling for occupation, employment status and injury type compared to non-LOTE injured workers. CONCLUSIONS: These data suggest that English language proficiency is associated not only with the risk of work-related injury but also to the long-term recovery outcomes. We conclude that despite access to language interpreter services, injured LOTE workers experience English language proficiency dependent, and injury severity independent, recovery barriers which need to be overcome to improve long term recovery outcomes.

Prognostic Role of Demographic, Injury and Claim Factors in Disabling Pain and Mental Health Conditions 12 Months after Compensable Injury.

Identifying who might develop disabling pain or poor mental health after injury is a high priority so that healthcare providers can provide targeted preventive interventions. This retrospective cohort study aimed to identify predictors of disabling pain or probable mental health conditions at 12 months post-injury. Participants were recruited 12-months after admission to a major trauma service for a compensable transport or workplace injury (n = 157). Injury, compensation claim, health services and medication information were obtained from the Victorian Orthopaedic Trauma Outcome Registry, Victorian State Trauma Registry and Compensation Research Database. Participants completed questionnaires about pain, and mental health (anxiety, depression, posttraumatic stress disorder) at 12 months post-injury. One third had disabling pain, one third had at least one probable mental health condition and more than one in five had both disabling pain and a mental health condition at 12 months post-injury. Multivariable logistic regression found mental health treatment 3-6 months post-injury, persistent work disability and opioid use at 6-12 months predicted disabling pain at 12 months post-injury. The presence of opioid use at 3-6 months, work disability and psychotropic medications at 6-12 months predicted a mental health condition at 12 months post-injury. These factors could be used to identify at risk of developing disabling pain who could benefit from timely interventions to better manage both pain and mental health post-injury. Implications for healthcare and compensation system are discussed.

Impact of comorbidity on health outcome after a transport-related injury.

INTRODUCTION: Understanding the impact of comorbidity on health outcomes is important given that comorbidities can affect survival, morbidity, service delivery costs and healthcare utilisation. However, little is known about the types of comorbidities affecting specific health outcomes after minor to moderate road trauma. METHODS: This study involved 1574 participants who claimed injury compensation following transport-related injury. Cross sectional data were collected. Health outcomes were assessed using the EQ-5D-3L specific domains and summary score. Twelve self-reported pre-existing chronic conditions were assessed using a multivariate logistic regression, adjusting for demographic and injury characteristics. RESULTS: Out of 1574 participants, only 17 (1%) participants reported no pre-existing comorbidities, 72% reported one, 13% reported two and 14% reported three or more comorbidities. Hypertension (15%), depression (14%) and anxiety (14%) were the most commonly reported comorbidities, followed by arthritis (13%), chronic pain (11%) and asthma (11%). Participants with a history of arthritis (adjusted odds ratio [AOR] 1.90, 95% CI 1.24 to 2.91); chronic back pain (AOR 1.59, 95% CI, 1.04 to 2.43); other chronic pain (AOR 2.73, 95% CI 1.42 to 4.24); depression (AOR 2.55, 95% CI 1.60 to 4.05) and anxiety (AOR 2.08, 95% CI 1.32 to 3.26) were at increased risk of poorer health outcomes, after controlling for age, gender, type of injury and time since injury. CONCLUSION: This study found that comorbidities such as arthritis, chronic back pain, other chronic pain, depression and anxiety significantly increase the odds of poorer health postinjury, regardless of the time since injury. Regular screening of comorbid conditions may help identify people likely to have poorer outcomes, thereby enabling the implementation of interventions to optimise health despite the presence of comorbidities.

An Interpretable Algorithm on Post-injury Health Service Utilization Patterns to Predict Injury Outcomes.

Purpose Post-injury health service utilization (HSU) contributes to injury outcomes, but limited studies investigated their relationship. This study aims to group injured patients in transport accidents based on minimal historical information of their HSU so that the groups are meaningfully associated with the outcome of interest. Methods The data include 20,692 injured patients who had compensation claims over 3 years. We propose a hybrid approach, combining unsupervised and supervised machine learning methods. Based on the first week post-injury data, we identify a proper clustering of patients best associated with total cost to recovery, as well as the discovery of HSU patterns. This allows developing models to accurately predict the outcome of interest using the discovered patterns. Furthermore, we propose to use decision tree classifiers to accurately classify future patients into the discovered clusters using their first week post-injury information. Results Our hybrid approach has identified eight patient groups. The compactness of the resulted clusters, assessed by Average Silhouette Width metric, is 0.71 indicating well-defined clusters. The resulted patient groups are highly predictive of injury outcomes. They improve the cost predictability more than twice in comparison with predictors such as gender, age and injury type. These groups also have substantial association with patients' recovery. The transparency and interpretability of decision trees allow integrating the resulting classification rules conveniently in operational processes. Conclusions This study provides a framework to discover knowledge and useful insights for health service providers and policy makers to control injury outcomes, and consequently to reduce the severity of transport accidents.

Multi-objective semi-supervised clustering to identify health service patterns for injured patients.

PURPOSE: This study develops a pattern recognition method that identifies patterns based on their similarity and their association with the outcome of interest. The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury. This grouping is based on distinctive patterns in health service use within the first week post-injury. The groups also provide predictive information towards the total cost of medication process. As a result, the group of patients who have undesirable outcomes are identified as early as possible based health service use patterns. METHODS: We propose a multi-objective optimization model to group patients. An objective function is the cost function of k-medians clustering to recognize the similar patterns. Another objective function is the cross-validated root-mean-square error to examine the association with the total cost. The best grouping is obtained by minimizing both objective functions. As a result, the multi-objective optimization model is a semi-supervised clustering which learns health service use patterns in both unsupervised and supervised ways. We also introduce an evolutionary computation approach includes stochastic gradient descent and Pareto optimal solutions to find the optimal solution. In addition, we use the decision tree method to reproduce the optimal groups using an interpretable classification model. RESULTS: The results show that the proposed multi-objective semi-supervised clustering identifies distinct groups of health service uses and contributes to predict the total cost. The performance of the multi-objective model has been examined using two metrics such as the average silhouette width and the cross-validation error. The examination proves that the multi-objective model outperforms the single-objective ones. In addition, the interpretable classification model shows that imaging and therapeutic services are critical services in the first-week post-injury to group injured patients. CONCLUSION: The proposed multi-objective semi-supervised clustering finds the optimal clusters that not only are well-separated from each other but can provide informative insights regarding the outcome of interest. It also overcomes two drawback of clustering methods such as being sensitive to the initial cluster centers and need for specifying the number of clusters.

Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering.

Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use (HSU) of transport accident injured patients within the first week post-injury. Aiming those patterns that are associated with the outcome of interest. To recognize these patterns, we propose a multi-objective optimization model that minimizes the k-medians cost function and regression error simultaneously. Thus, we use a semi-supervised clustering approach to identify patient groups based on HSU patterns and their association with total cost. To solve the optimization problem, we introduce an evolutionary algorithm using stochastic gradient descent and Pareto optimal solutions. As a result, we find the best optimal clusters by minimizing both objective functions. The results show that the proposed semi-supervised approach identifies distinct groups of HSUs and contributes to predict total cost. Also, the experiments prove the performance of the multi-objective approach in comparison with single- objective approaches.

A population-based study of treated mental health and persistent pain conditions after transport injury.

BACKGROUND: Persistent pain and mental health conditions often co-occur after injury, cause enormous disability, reduce social and economic participation, and increase long-term healthcare costs. This study aimed to characterise the incidence, profile and healthcare cost implications for people who have a treated mental health condition, persistent pain, or both conditions, after compensable transport injury. METHODS: The study comprised a population cohort of people who sustained a transport injury (n = 74,217) between 2008 to 2013 and had an accepted claim in the no-fault transport compensation system in Victoria, Australia. Data included demographic and injury characteristics, and payments for treatment and income replacement from the Compensation Research Database. Treated conditions were identified from 3 to 24-months postinjury using payment-based criteria developed with clinical and compensation system experts. Criteria included medications for pain, anxiety, depression or psychosis, and services from physiotherapists, psychologists, psychiatrists, and pain specialists. The data were analysed with Cox Proportional Hazards regression to examine rates of treated conditions, and general linear regression to estimate 24 month healthcare costs. RESULTS: Overall, the incidence of treated mental health conditions (n = 2459, 3.3%) and persistent pain (n = 4708, 6.3%) was low, but rates were higher in those who were female, middle aged (35-64 years), living in metropolitan areas or neighbourhoods with high socioeconomic disadvantage, and for people who had a more severe injury. Healthcare costs totalled more than $A707 M, and people with one or both conditions (7.7%) had healthcare costs up to 7-fold higher (adjusting for demographic and injury characteristics) in the first 24 months postinjury than those with neither condition. CONCLUSIONS: The incidence of treated mental health and persistent pain conditions was low, but the total healthcare costs for people with treated conditions were markedly higher than for people without either treated condition. While linkage with other public records of treatment was not possible, the true incidence of treated conditions is likely to be even higher than that found in this study. The present findings can be used to prioritise the implementation of timely access to treatment to prevent or attenuate the severity of pain and mental health conditions after transport injury.

Patterns and Predictors of Failed and Sustained Return-to-Work in Transport Injury Insurance Claimants.

Purpose To determine the incidence of employed people who try and fail to return-to-work (RTW) following a transport crash. To identify predictors of RTW failure. METHODS: A historical cohort study was conducted in the state of Victoria, Australia. People insured through the state-based compulsory third party transport accident compensation scheme were included. Inclusion criteria included date of crash between 2003 and 2012 (inclusive), age 15-70 years at the time of crash, sustained a non-catastrophic injury and received at least 1 day of income replacement. A matrix was created from an administrative payments dataset that mapped their RTW pattern for each day up to 3 years' post-crash. A gap of 7 days of no payment followed by resumption of a payment was considered a RTW failure and was flagged. These event flags were then entered into a regression analysis to determine the odds of having a failed RTW attempt. RESULTS: 17% of individuals had a RTW fail, with males having 20% lower odds of experiencing RTW failure. Those who were younger, had minor injuries (sprains, strains, contusions, abrasions, non-limb fractures), or were from more advantaged socio-economic group, were less likely to experience a RTW failure. Most likely to experience a RTW failure were individuals with whiplash, dislocations or particularly those admitted to hospital. CONCLUSIONS: Understanding the causes and predictors of failed RTW can help insurers, employers and health systems identify at-risk individuals. This can enable earlier and more targeted support and more effective employment outcomes.

Effectiveness of employer financial incentives in reducing time to report worker injury: an interrupted time series study of two Australian workers' compensation jurisdictions.

BACKGROUND: Early intervention following occupational injury can improve health outcomes and reduce the duration and cost of workers' compensation claims. Financial early reporting incentives (ERIs) for employers may shorten the time between injury and access to compensation benefits and services. We examined ERI effect on time spent in the claim lodgement process in two Australian states: South Australia (SA), which introduced them in January 2009, and Tasmania (TAS), which introduced them in July 2010. METHODS: Using administrative records of 1.47 million claims lodged between July 2006 and June 2012, we conducted an interrupted time series study of ERI impact on monthly median days in the claim lodgement process. Time periods included claim reporting, insurer decision, and total time. The 18-month gap in implementation between the states allowed for a multiple baseline design. In SA, we analysed periods within claim reporting: worker and employer reporting times (similar data were not available in TAS). To account for external threats to validity, we examined impact in reference to a comparator of other Australian workers' compensation jurisdictions. RESULTS: Total time in the process did not immediately change, though trend significantly decreased in both jurisdictions (SA: -0.36 days per month, 95% CI -0.63 to -0.09; TAS: 0.35, -0.50 to -0.20). Claim reporting time also decreased in both (SA: -1.6 days, -2.4 to -0.8; TAS: -5.4, -7.4 to -3.3). In TAS, there was a significant increase in insurer decision time (4.6, 3.9 to 5.4) and a similar but non-significant pattern in SA. In SA, worker reporting time significantly decreased (-4.7, -5.8 to -3.5), but employer reporting time did not (-0.3, -0.8 to 0.2). CONCLUSIONS: The results suggest that ERIs reduced claim lodgement time and, in the long-term, reduced total time in the claim lodgement process. However, only worker reporting time significantly decreased in SA, indicating that ERIs may not have shortened the process through the intended target of employer reporting time. Lack of similar data in Tasmania limited our ability to determine whether this was a result of ERIs or another component of the legislative changes. Further, increases in insurer decision time highlight possible unintended negative effects.

Prescription opioid dispensing and prescription opioid poisoning: Population data from Victoria, Australia 2006 to 2013.

OBJECTIVE: To describe recent trends in opioid prescribing and prescription opioid poisoning resulting in hospitalisation or death in Victoria, Australia. METHOD: This is a population-based ecological study of residents of Victoria, 2006 - 14. Australian Bureau of Statistics residential population data were combined with Pharmaceutical Benefits Scheme (PBS) opioid prescription data, Victorian Admitted Episodes Data (VAED) and cause of death data. RESULTS: Annual opioid dispensings increased by 78% in 2006 - 13, from 0.33 to 0.58 per population. Opioid use increased with age: in 2013, 14% of Victorian residents aged ≥65 years filled at least one oxycodone prescription. In 2006 - 14, prescription opioid related hospital admissions increased by 6.8% per year, from 107 to 187 /1,000,000 person-years; 56% were due to intentional self-poisoning. Annual deaths increased from 21 to 28 /1,000,000 persons, in 2007 - 11. Admissions and deaths peaked at 25-44 years. CONCLUSIONS: Although both opioid prescribing and poisoning have increased, there is discrepancy between the exposed group (dispensings increased with age) and those with adverse consequences (rates peaked at ages 25-44 years). IMPLICATIONS: A better understanding is needed of drivers of prescribing and adverse consequences. Together with monitoring of prescribing and poisoning, this will facilitate early detection and prevention of a public health problem.

Compensation Research Database: population-based injury data for surveillance, linkage and mining.

BACKGROUND: Compensation health research aims to study the influence of compensation systems, processes and practices on health and health-related outcomes. In many jurisdictions, injury compensation authorities collect substantial volumes of case and service level data for the purpose of administering the compensation system. An important secondary use of such data is research and analysis to explore interactions between individuals and organisations in compensation systems, and between compensation and other systems including healthcare and legal systems, in order to understand the role of compensation processes in injury recovery. RESULTS: The Compensation Research Database (CRD) established at the Institute for Safety Compensation and Recovery Research at Monash University, holds over 20 years of population-based data for transport and workplace injury in the state of Victoria, Australia. The CRD is unique in that it is held independently, at arm's length from the compensation authorities that collect the data, and its primary purpose is to support research and analyses to develop new insights into system and individual level outcomes. This paper describes the core elements of the database including the design, process and type of information collected. We review some of the research findings that have been published using the CRD, and describe the ongoing program of research utilising the database. CONCLUSIONS: The CRD is a unique administrative database that supports research into compensation health, with the objective of improving understanding of the interaction between injury compensation systems and injury recovery. The availability of the CRD for independent research is leading to substantial advancements in the compensation health research field and in related areas.

Does time off work after injury vary by jurisdiction? A comparative study of eight Australian workers' compensation systems.

OBJECTIVES: To determine whether the jurisdiction in which a work-related injury compensation claim is made is an independent predictor of duration of time off work following work injury, and if so, the magnitude of the effect. SETTING: Eight Australian state and territory workers' compensation systems, providing coverage for more than 90% of the Australian labour force. Administrative claims data from these systems were provided by government regulatory authorities for the study. PARTICIPANTS: 95 976 Australian workers with workers' compensation claims accepted in 2010 and with at least 2 weeks of compensated time off work. PRIMARY OUTCOME MEASURE: Duration of time lost from work in weeks, censored at 104 weeks. RESULTS: After controlling for demographic, worker, injury and employer factors in a Cox regression model, significant differences in duration of time loss between state and territory of claim were observed. Compared with New South Wales, workers in Victoria, South Australia and Comcare had significantly longer durations of time off work and were more likely to be receiving income benefits at 104 weeks postinjury, while workers in Tasmania and Queensland had significantly shorter durations of time off work. CONCLUSIONS: The jurisdiction in which an injured worker makes a compensation claim has a significant and independent impact on duration of time loss. Further research is necessary to identify specific compensation system policies and practices that promote timely and appropriate return to work and reduce duration of time off work.

The effect of pre-existing health conditions on the cost of recovery from road traffic injury: insights from data linkage of medicare and compensable injury claims in Victoria, Australia.

BACKGROUND: Comorbidity is known to affect length of hospital stay and mortality after trauma but less is known about its impact on recovery beyond the immediate post-accident care period. The aim of this study was to investigate the role of pre-existing health conditions in the cost of recovery from road traffic injury using health service use records for 1 year before and after the injury. METHODS: Individuals who claimed Transport Accident Commission (TAC) compensation for a non-catastrophic injury that occurred between 2010 and 2012 in Victoria, Australia and who provided consent for Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) linkage were included (n = 738) in the analysis. PBS and MBS records dating from 12 months prior to injury were provided by the Department of Human Services (Canberra, Australia). Pre-injury use of health service items and pharmaceuticals were considered to indicate pre-existing health condition. Bayesian Model Averaging techniques were used to identify the items that were most strongly correlated with recovery cost. Multivariate regression models were used to determine the impact of these items on the cost of injury recovery in terms of compensated ambulance, hospital, medical, and overall claim cost. RESULTS: Out of the 738 study participants, 688 used at least one medical item (total of 15,625 items) and 427 used at least one pharmaceutical item (total of 9846). The total health service cost of recovery was $10,115,714. The results show that while pre-existing conditions did not have any significant impact on the total cost of recovery, categorical costs were affected: e.g. on average, for every anaesthetic in the year before the accident, hospital cost of recovery increased by 24 % [95 % CI: 13, 36 %] and for each pathological test related to established diabetes, hospital cost increased by $10,407 [5466.78, 15346.28]. For medical costs, each anaesthetic led to $258 higher cost [174.16, 341.16] and every prescription of drugs used in diabetes increased the cost by 8 % [5, 11 %]. CONCLUSIONS: Services related to pre-existing conditions, mainly chronic and surgery-related, are likely to increase certain components of cost of recovery after road traffic trauma but pre-existing physical health has little impact on the overall recovery costs.

Prescription Opioid and Benzodiazepine Use After Road Traffic Injury.

BACKGROUND: Motor vehicle crash victims with physical injury are likely to receive prescription opioids and benzodiazepines. Potential mental trauma and lack of primary treating physician contribute to the risk of adverse opioid outcomes for this group. The purpose of this study is to characterise opioid and benzodiazepine prescribing after road traffic injury. METHOD: Individuals who claimed Transport Accident Commission compensation for a noncatastrophic injury that occurred between 2010 and 2012 in Victoria, Australia and who provided consent for pharmaceutical benefits scheme (PBS) linkage were included (n = 734). PBS records dating between 12 months preinjury and 18 months postinjury were provided by the Department of Human Services. RESULTS: In the year before injury, 10.5% of participants received prescription opioids; after injury, 45.1% of hospitalized and 21.1% of nonhospitalized participants received opioids. Benzodiazepines were used by 4.8% preinjury, and 7.0% and 7.4% postinjury (with and without hospitalization, respectively). Postinjury, 39% of opioid use and 73% of benzodiazepine use was potentially unrelated to the injury. CONCLUSIONS: Prescription opioid and benzodiazepine before road traffic injury was substantial: the significance of postinjury prescription drug use cannot be established without taking preinjury use into account. It may be beneficial for pain medication to be managed by a pain treatment coordinator, in this injured population with high rates of pre-existing opioid and benzodiazepine use.

Use of antidepressant medication after road traffic injury.

OBJECTIVES: Mental ill health after road traffic injury is common, as is the use of antidepressant medication after injury. Little is known about antidepressant use by injured people prior to their injury. The aim of this study is to describe the nature and extent of antidepressant use before and after road traffic injury. METHODS: Victorian residents who claimed Transport Accident Commission (TAC) compensation for a non-catastrophic injury that occurred between 2010 and 2012 and provided consent for Pharmaceutical Benefits Scheme (PBS) linkage were included (n=734). PBS records dating from 12 months prior through to 12 months post injury were provided by the Department of Human Services (Canberra). PBS and TAC claims data were linked. RESULTS: Among participants, 12% used antidepressants before injury (84.4D efined Daily Doses/1000 person-days) and 17% used antidepressants after injury (114.1DDD/1000p-d). Only 7.7% of the injured cohort commenced antidepressant treatment post injury. Thus, of all post-injury antidepressant use, 45% could potentially be related to the incident injury, with the remaining 55% most probably a continuation of pre-injury use. Pre-injury use was more common among women (109.4 vs. 54.6 DDD/1000p-d, p<0.0001), and those with whiplash injury (119.3 vs. 73.1, p=0.03). Cyclists and motorcyclists were less likely to use antidepressants pre-injury than car drivers (18.3 vs. 16.9 vs. 109.3, respectively; p<0.001). CONCLUSIONS: Less than half of post-injury antidepressant use could potentially be attributable to the incident injury. These results highlight the importance of obtaining information on pre-injury health status before interpreting post-injury health service use to be an outcome of the injury in question.