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Are the early predictors of long-term work absence following injury time dependent? Results from the Prospective Outcomes of Injury Study

Objectives Few studies examine the influence of early predictors of work absence beyond 12 months following injury or the time-dependent relative importance of these factors. This study aimed to identify the most important sociodemographic, occupational, health, lifestyle and injury predictors of work absence at 12 and 24 months following injury and to examine changes in the relative importance of these over time. Design Prospective cohort study. Setting The Prospective Outcomes of Injury Study, New Zealand. Participants 2626 injured New Zealand workers aged 18–64 years were identified from the Prospective Outcomes of Injury Study recruited form New Zealand’s monopoly injury compensation provider injury claims register: 2092 completed the 12-month interview (80% follow-up) and 2082 completed the 24-month interview (79% follow-up). Primary and secondary outcomes measures The primary outcomes of interest was absence from work at the time of the 12-month and 24-month follow-up interviews. Results Using modified Poisson regression to estimate relative risks, important groups of workers were identified at increased risk of work absence at both 12 and 24 months: males, low-income workers, trade/manual workers, temporary employees, those reporting two or more comorbidities and those experiencing a work-related injury. Important factors unique to predicting work absence at 12 months included financial insecurity, fixed-term employment and long weekly hours worked; unique factors at 24 months included job dissatisfaction, long weekly days worked, a prior injury and sustaining an injury that was perceived to be a threat to life. Conclusions Important early predictors of work absence at 12 or 24 months following injury are multidimensional and have a time dependent pattern. A consistent set of predictors was, however, present at both time periods that are prime for early intervention. Understanding the multidimensional, time-dependent patterns of early predictors of long-term disability is important to optimally target timely interventions to prevent long-term work disability.

Key terms: Work disability; return to work; outcome; multidimensional; socio-demographic; work organisation; health; lifestyle; time dependent predictors.

Strengths and limitations of this study
• Few studies examine the multidimensional influence of pre-injury and injury-related predictors of work absence beyond 12 months following injury or the time-dependent relative importance of these predictors.
• Using longitudinal cohort data we found a multidimensional set of predictors of work absence that differed at 12 and 24 months following injury.
• The strengths of the study include data collection on outcomes at multiple time points following injury independent of the time periods for measurement of exposure measures; a large sample size with acceptable follow-up to 24 months (79%); and examination of a mulidimensional range of risk factors for work absence.
• The results are sensitive to interview and item missingness, however, sensitivity analysis found this had a negligible impact on estimates of effect size. individual's long-term employment and earning prospects (1,2) and health (3), as well as substantial societal costs (4). The longer a worker is absent from the workplace following injury the higher the likelihood that a worker will never return to work and thus the greater the individual and social impacts of injury (1,5,6). Returning to work following injury is a critical step in the rehabilitation process, making longer-term evaluation of vocational outcomes of interest. The early identification of predictors of long-term chronic work absence provides opportunities for timely intervention to prevent the development of prolonged work disability minimising the consequences of injury.
In the musculoskeletal injuries literature, the relative importance of predictive factors has been demonstrated to change across the stages of development of work disability (7,8). The predictors of the acute phase of work disability often differ from the predictors of the chronic phase, suggesting different interventions are needed to address chronic long-term work disability (8). Few studies investigating work participation following traumatic injury have examined the importance of changes in predictive factors with increasing time following injury; studies typically have short follow-up periods of 6 months, or less, following injury (9).
A limited number of studies have examined vocational outcomes following injury from a broad population-based perspective. Previous studies have tended to focus on specific injury types, injury to specific body regions, on specific settings of injury and on injury types typically regarded as severe such as hospitalised injury and/or severe trauma. The Prospective Outcomes of Injury Study (POIS) gives us an opportunity to examine predictors for hospitalised and non-hospitalised acute injuries, and all injury settings including road, home, recreational and work (10). Furthermore, analysis of the large POIS cohort allows us to examine predictors using a broader multidimensional perspective.  6 The POIS study recruited 2856 participants in the period December 2007 to August 2009 (12). This paper is restricted to those POIS participants who were active workforce participants prior to their injury and had completed either, or both, a 12-and 24-month interview. Structured telephone and postal interviews collected self-reported data following injury at 3 months, with simultaneous retrospective baseline recall, and 12-and 24-month intervals following injury.
With the exception of the injury-related variables, which relate to the injury event itself, all explanatory variables examined are pre-injury variables. A priori hypotheses of a relationship with work absence, and/or prior identification in previous studies was the basis of selection of each explanatory variable (7,13). The explanatory variables examine seven dimensions: 1) socio-demographic (age, gender, income, highest qualification, occupation, relationship status, living arrangements, material standard of living, household income adequacy, financial security); 2) physical work (repetitive hand movements, heavy lifting, physical exertion, standing, painful/tiring body positions); 3) psychosocial (job strain, support, security, satisfaction, optimism, self-efficacy, prior depressive episode); 4) work organisation (number of hours and days worked per week, employment contract, multiple job holding) ; 5) lifestyle (alcohol consumption, smoking status, Body Mass Index (BMI), exercise, sleep quantity); 6) health (overall self-assessment for health, co-morbidities, pain/discomfort, prior injury, prior disabling condition, work capacity); 7) injury-related (work-related injury, nature and body region of injuries, intent of injury, hospital admission or at least 3 hours treatment at an Emergency Department within 7 days of injury, anatomical injury severity -NISS), injury perceived as a threat to life, injury perceived as a threat of serious disability, access to health services).  Table 1 for more detailed information about the explanatory variables. Single item questions from the New Zealand Census (29) Occupation grouped into professional (major level 1-3), semiprofessional (4)(5) and trade/manual ( 6-9) using New Zealand Standard Classification of Occupations (30) Adequacy of household income Single item regarding adequacy of total household income to meet every day needs (31) Material standard of living Single item rating standard of living before injury.

Financial insecurity
Single item rating financial security in the next 10 years (22) Repetitive hand movements, heavy lifting, painful/tiring body positions,

standing & physical exertion
Set of five single items regarding amount of time spend doing physical task ranked on 4 point Likert Scale "never" to "all to ¾ of the time" (32) Job strain, job support Score using the Whitehall II study adaptation of Karasek's job content model (33) Job satisfaction Single item rating overall job satisfaction/ dissatisfaction (32) Self-efficacy General Self-Efficacy Scale, scale from 0-40, dichotomised into poor (score ≤25) and good (score >25) (34) Optimism Agreement with expectation of more good things happen than bad, dichotomised yes (agree and strongly agree) and no (strongly disagree, disagree, neutral) (35) Prior depressive episode Two items from DSM-III on depressed mode and loss of interest or pleasure in daily activities for at least 2 weeks in Hours & days worked per week Single items asking number of hours or days worked in main job (32) Type of contract Combination of two items on employment status and, for employees only, the type of employment contract (32) Multiple job holding Single item asking if work only one paying job or more than one job (32) Alcohol consumption Score (0-12) identifying hazardous drinking patterns in the year prior using the brief Alcohol Use Disorders Identification Test (Audit-C) (37) Smoking status Single item asking if smoke regularly.
Body Mass Index (BMI) Calculated using weight and height, categorised underweight/normal (≤25), overweight (25)(26)(27)(28)(29) and obese (≥30) Physical exercise Multiple questions ascertaining how many days over a 7 day period engaged in 15 minutes vigorous activity or 30 minutes moderate activity. Dichotomised into ≤4 or 5-7 days a week (38) Sleep quantity Single item identifying how many nights a week usually obtain at least 7 hours sleep Self-assessment for health Single item rating health in the 4 weeks prior to injury with five point likert scale from excellent to poor (39) Co-morbidities Single item asking if had any of a list of 21 specific chronic conditions lasting or expected to last more than 6 months (40) Prior injury Single item regarding any prior injuries that affect participant Prior disability Single item asking if had a health problem or condition lasting 6 month or more than cased difficulty with either daily activities OR communication/socialised OR any other activity (29) Injury nature and body region 12 binary Y/N variables indicating the presence of common nature and body region combinations were created using ACC At 12-and 24-month interviews a single item "Which of the following best describes your paid work situation now?" was used to ascertain work status. Participants indicating full-time and part-time work for pay were considered to be "working", while those indicating they were receiving a benefit and/or compensation, or were unemployed were considered to be "absent from work".

Data analysis
The relationship between work absence and pre-injury and injury-related explanatory variables was examined using frequency tables, summary statistics and regression analyses.
For each time point adjusted univariate models were created using modified Poisson regression with robust error variance to estimate relative risks (14). Age, gender, anatomical severity of injury (NISS), interview region and all 12 injury nature and body region variables were included in all univariate models as potential covariates. To account for the range in the timing of the 12-and 24- Any variable from the adjusted univariate models for either the 12-or 24-month time point with a p≤0.2 was then entered into a multidimensional model for each time point. A consistent variable approach was used, resulting in both the 12-and 24-month overall multidimensional regression models including a consistent set of variables across both time points prior to stepwise Poisson regression. All multidimensional models were additionally adjusted for age, sex, region of interview, time since injury, anatomical injury severity -NISS and injury type and region with these variable entered as fixed variables. Complete case analysis was undertaken at both time periods. A sensitivity analysis was conducted by applying inverse probability weighting to the data to account for loss to follow-up (15). Analyses were performed using STATA statistical package 13.0 SE (16).

DISCUSSION
In this prospective cohort study of injured New Zealand workers we identified a number of new preinjury socio-demographic, work organisational, health and injury-related factors that are important predictors of work absence at 12-and 24-months following injury, and confirmed predictors identified previously. While a time-dependent pattern of predictors of work absence was also observed, a consistent set of six predictors was identified across both time periods with only two unique predictors at 12 months and four at 24 months following injury.
Comparisons of our findings with previous injury studies examining time-dependent patterns of risk factors for work absence are limited by our study's broader population based design, study sample and longer follow-up. Nevertheless, our findings are broadly consistent with the few studies which have previously demonstrated that the influence and relative importance of risk factors changes with increasing time since injury (9). Our study also provides further support to the importance of considering a multidimensional set of predictors across several time points when following the development of long-term work disability to provide longitudinal insight and to detect risk factors  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60   F  o  r  p  e  e  r  r  e  v  i  e  w  o  n  l  y   22 commonly, or primarily, associated with one or another stage of the disabling process (7)(8)(9)17). As in our earlier paper examining outcomes at 3 months following injury (11), findings from this study indicate a broad range of financial, occupational, psychosocial, work organisational and health supports for employees during recovery from injury are potentially important avenues for early intervention to interrupt prolonged absence from work.
Certain domains examined in our study have had limited, if any attention, in other research examining outcomes following acute injury (13). Work organisational factors were identified in our study as important predictors of work absence. These findings point to the provision of opportunities for recovery from over-demanding work schedules (be it long working hours or weeks) in the long-term following injury as being important for returning to work. Workers with temporary employment contracts were at higher risk of work absence at both time points. Potential mechanisms for this relationship may include the poor quality employment protections and employment relationships between parties with the use of precarious temporary employment contracts, the degree to which substitute workers are available, and the difficulties of realising job accommodations for temporary workers (17,20). While the evidence for a relationship between precarious employment contracts and work absence following injury is lacking, elsewhere, temporary employment has been associated with delays in returning to work following depressionrelated work disability episodes (21). Financial insecurity predicted work absence at 12 months only, suggesting financial security has an important time-dependent influence on work disability in the first year following injury, possibly through feelings of anxiety generated by future economic insecurity (19). Further research confirming these newly identified risk factors and examining potential mechanisms of these for work absence following injury is warranted. interventions focusing on primary and secondary prevention of work-related injury would influence not only work absence in the long-term, but also primary prevention of these common injuries in the first place. By 24 months, the perception of the injury as having been a threat to life and having had a prior injury emerged as important injury-related risk factors. Possible mechanisms for this relationship may lie in the psychological trauma associated with the event, such as through the development of Post-Traumatic Stress Disorder (PTSD). Self-perception of the injury event is rarely examined in studies of work absence and this relationship merits further investigation. Few studies examining outcomes following injury examine previous injury, however the only other study to consider previous injury found higher odds of work disability at 1 year following back injury (27).
Further studies should consider prior injury as a potential risk factor for long-term work disability.
A number of our findings support and extend previous studies findings. Our finding that income and occupation are important predictors of work absence at both 12-and 24-months following injury is consistent with a growing body of longitudinal evidence (8,13,18). Differing financial incentives, commonly substituted low remuneration work skills, the physical nature of work and practical difficulties in providing work accommodations to trade/manual workers are possible income and occupational barriers to work participation. Our finding that the presence of several pre-existing comorbidities is an important health-related risk factor for work absence at both time periods extends the findings of previous studies observing co-morbid conditions associated with a delayed return-towork up to six months following injury (23,24). Workers with several comorbid conditions experience an impaired health state prior to injury, and following the injury itself this may limit a person's ability to engage with the injury rehabilitation process, or to manage their chronic conditions, resulting in difficulties maintaining work. The prevalence of multimorbidity in developed nations is increasing rapidly (25), therefore, further analysis is warranted to examine the long-term influence of co-morbidity on the risk of work disability following injury.
Job satisfaction emerged as the only important pre-injury psychosocial factor predictive of work status at 24 months following injury. Recent evidence that job satisfaction is not predictive of return-to work following injury or musculoskeletal disorders is limited by short follow-up periods up to 12 months following injury (20,22). Our findings suggest studies with follow-up beyond 12 months should consider job satisfaction as a possible risk factor for prolonged work absence.
Psychosocial factors have been identified as predictive of work absence in other injury studies (8,13), however, this evidence is limited by the lack of multivariable analysis and the narrow range of potential pre-injury predictors and co-variates examined, potentially explaining why our findings differ from others.
Finally, changes in the predictive value of risk factors over time has significant implications for the types and timing of interventions required to prevent the development of work disability (8,17).
While our findings highlight the importance of distinguishing time dependent predictors we did identify a consistent set of potentially modifiable pre-injury and injury-related risk factors that are present regardless of the disability phase that are prime for early intervention. Risk factors consistently identified in our current analysis at 12-and 24-months and in our previous analysis at three months following injury (11) include workers with low incomes, trade/manual occupations, temporary employment contracts, over-demanding work schedules and an injury perceived to be a threat to life. Given our study identified few unique time dependent risk factors our findings suggest This study has some limitations. Firstly pre-injury factors and baseline pre-injury work status were collected through self-report at the first interview (on average 3 months following injury) and may be subject to recall bias, particularly in those variables of a subjective nature such as the psychosocial factors. The self-reported pre-injury work absence measure was found to be accurate by verifying self-reported data against ACC injury claims data about paid employment, with greater than 99% concordance. Secondly, use of single item constructs for many psychosocial factors, such as optimism and job security, may be a limitation. However, good reliability and validity has been demonstrated with parsimonious measures while reducing the interview burden on participants (22). Finally, this analysis uses two slightly different groups of individuals followed up at two separate time points following the original injury using complete case analysis. While 90% of participants who completed the 12 month interview also completed the 24 month interview this analysis is sensitive to interview missingness, as well as item missingness for those who completed 12 & 24 month interviews. Sensitivity analyses, undertaken to assess the impact of being included in the final multivariable models in comparison to the baseline interview, found very few variables had a meaningful increase in effect size (ie. > 10% change) and only one, working part-time hours was significant in the final 12-month multivariable model.
Our study offers a number of strengths that overcome some of the limitations of previous studies including: the collection of comprehensive pre-injury information at 12-and 24-months following injury across a broad multi-dimensional perspective; a large sample size with acceptable follow-up to and combined multi-dimensional analysis examining a comprehensive range of risk factors for work disability. A further key strength is the measurement of pre-and early-injury exposures which provide opportunity for identifying early interventions to influence long-term work disability outcomes. The use of two point-in-time measures of work participation is also a strength as it best captures long-term outcomes since follow-up occurs regardless of previous work status and of previous non-response. Finally, we have been able to examine within NZ's universal no-fault injury rehabilitation and compensation scheme the early predictors of long-term work disability for all injured workers, regardless of whether the injury was sustained at work or not. This is a strength as being absent from the workplace, irrespective of where the injury occurred, has substantial socioeconomic costs to individuals, employers and to society. Our findings are generalizable to those injury rehabilitation and compensation systems with similar coverage of work and non-work injury.
In conclusion, in this study we identified a number of known and new socio-demographic, work organisational, health and injury-related factors predictive of work absence at 12-and 24-months following injury in a cohort of New Zealand workers. Our study confirmed and extended previous observations of a time dependent pattern of predictors of work disability following injury, however, there was a consistent set of socio-demographic, work organisational, health and injury-related factors present regardless of the timing of follow-up that are prime for early intervention to prevent the development of long term work disability. Understanding the multi-dimensional and time dependent patterns of predictors of long-term disability and how they may, or may not, differ by time is important if we are to develop and optimally target early interventions to reduce the significant personal and social burden and costs of long-term work disability.

Acknowledgements
We are most grateful to the study participants for sharing their information with us. We thank Dr Ari Samaranayaka, Biostatistician, Department of Preventive and Social Medicine, University of Otago for conducting the sensitivity analyses.

Contributions
RL was the lead author and is guarantor of this paper. RL analysed the data. GD and SD contributed to the study design, interpretation of the results, and the review and editing of the manuscript. RL, GD, SD approved the submitted manuscript.

Competing Interests
None declared.

Ethics approval
The New Zealand Multi-Region Ethics Committee granted ethical approval for this study (MEC/07/07/093). Informed consent was obtained from all participants.

Data sharing statement
We have a data sharing policy and would consider requests on a case by case basis. Please contact sarah.derrett@otago.ac.nz.

Abstract
Objectives: Few studies examine the influence of early predictors of work absence beyond 12 months following injury or the time-dependent relative importance of these factors. This study aimed to identify the most important socio-demographic, occupational, health, lifestyle and injury predictors of work absence at 12-and 24-months following injury and to examine changes in the relative importance of these over time.
Design: Prospective cohort study.

Setting:
The Prospective Outcomes of Injury Study, New Zealand.
Participants: 2626 injured New Zealand workers were identified from the Prospective Outcomes of Injury Study; 2092 completed the 12 month interview (80% follow-up) and 2082 completed the 24 month interview (79% follow-up).

Primary and secondary outcomes measures:
The primary outcomes of interest was absence from work at the time of the 12-and 24-month follow-up interviews.
Results: Using modified Poisson regression to estimate Relative Risks important groups of workers were identified at increased risk of work absence at both 12-and 24-months: males, low income workers, trade/manual workers, temporary employees, those reporting two or more co-morbidities, and those experiencing a work-related injury. Important factors unique to predicting work absence at 12 months included financial insecurity, fixed-term employment and long weekly hours worked; unique factors at 24 months included job dissatisfaction, long weekly days worked, a prior injury and sustaining an injury that was perceived to be a threat-to-life.
Conclusions: Important early predictors of work absence at 12-or 24-months following injury are multidimensional and have a time dependent pattern. A consistent set of predictors was, however, present at both time periods that are prime for early intervention. Understanding the multidimensional, time dependent patterns of early predictors of long-term disability is important to optimally target timely interventions to prevent long-term work disability.

Strengths and limitations of this study
• Few studies examine the multidimensional influence of pre-injury and injury-related predictors of work absence beyond 12 months following injury or the time-dependent relative importance of these predictors.
• Using longitudinal cohort data we found a multidimensional set of predictors of work absence that differed at 12 and 24 months following injury.
• The strengths of the study include data collection on outcomes at multiple time points following injury independent of the time periods for measurement of exposure measures; a large sample size with acceptable follow-up to 24 months (79%); and examination of a multidimensional range of risk factors for work absence.
• The results are sensitive to interview and item missingness, however, sensitivity analysis found this had a negligible impact on estimates of effect size. individual's long-term employment and earning prospects 1,2 and health 3 , as well as substantial societal costs. 4 The longer a worker is absent from the workplace following injury the higher the likelihood that a worker will never return to work and thus the greater the individual and social impacts of injury. 1,5,6 Returning to work following injury is a critical step in the rehabilitation process, making longer-term evaluation of vocational outcomes of interest. The early identification of predictors of long-term chronic work absence provides opportunities for timely intervention to prevent the development of prolonged work disability minimising the consequences of injury.
In the musculoskeletal injuries literature, the relative importance of predictive factors has been demonstrated to change across the stages of development of work disability. 7,8 The predictors of the acute phase of work disability often differ from the predictors of the chronic phase, suggesting different interventions are needed to address chronic long-term work disability. 8 Few studies investigating work participation following traumatic injury have examined the importance of changes in predictive factors with increasing time following injury; studies typically have short follow-up periods of 6 months, or less, following injury. 9 A limited number of studies have examined vocational outcomes following injury from a broad population-based perspective. Previous studies have tended to focus on specific injury types, injury to specific body regions, on specific settings of injury and on injury types typically regarded as severe such as those resulting in hospitalisation. [7][8][9][10] The Prospective Outcomes of Injury Study (POIS) gives us an opportunity to examine predictors for hospitalised and non-hospitalised acute injuries, and all injury settings including road, home, recreational and work. 11 Furthermore, analysis of the large POIS cohort allows us to examine predictors using a broader multidimensional perspective. Our The aim of this study is to examine the combined pre-injury influences of socio-demographic, occupational, health and lifestyle factors, and the injury itself, as predictors of work absence at 12and 24-months following injury for a cohort of New Zealand workers. The focus of this analysis is on potentially identifiable and modifiable factors measured early in the development of long-term work absence. In addition, this study will examine if the relative importance of these early predictors of work absence change with increasing time following injury in this same cohort.

Study setting
Recruitment of the POIS cohort was via New Zealand's universal no-fault Accident Compensation Corporation (ACC) scheme. Compensation covers all injury settings, including the home, workplace and road. Eligible participants included those on the ACC entitlement claims register (indicating their injury would require more than simple acute treatment) who had: 1) sustained an injury between June 2007 and May 2009; 2) were aged between 18-64 years; and 3) lived in one of five regions of New Zealand. Sensitive claims, such as for from victims of abuse, are excluded. The recruitment process and resulting cohort has been described in detail elsewhere. 11,13 Data collection and explanatory variables  Table 1 for more detailed information about the explanatory variables. Job satisfaction Single item rating overall job satisfaction/ dissatisfaction 18 Self-efficacy General Self-Efficacy Scale, scale from 0-40, dichotomised into poor (score ≤25) and good (score >25) 20 Optimism Agreement with expectation of more good things happen than bad, dichotomised yes (agree and strongly agree) and no (strongly disagree, disagree, neutral) 21 Prior depressive episode Two items from DSM-III on depressed mode and loss of interest or pleasure in daily activities for at least 2 weeks in

Outcome measure
At 12-and 24-month interviews a single item "Which of the following best describes your paid work situation now?" was used to ascertain work status. Participants indicating full-time and part-time work for pay were considered to be "working", while those indicating they were receiving a benefit and/or compensation, or were unemployed were considered to be "absent from work".

Data analysis
The relationship between work absence and pre-injury and injury-related explanatory variables was examined using frequency tables, summary statistics and regression analyses.
For each time point adjusted univariate models were created using modified Poisson regression with robust error variance to estimate relative risks. 31 Age, gender, anatomical severity of injury (NISS), interview region and all 12 injury nature and body region variables were included in all univariate models as potential covariates. To account for the range in the timing of the 12-and 24-month interviews after the injury event, time since injury was also included as a continuous variable in all analyses.

RESULTS
Of the 2626 POIS participants who were working prior to their injury, 2092 completed the 12 month interview (79% follow-up) and 2082 completed the 24 month interview (79% follow-up) ( Table 2). At the 12 month interview 329 (16%) reported being absent from work, while 304 (15%) were absent at the 24 month interview. Reasons for work absence were reported at 24 months following injury only and were varied: predominantly the sample was absent from work due to ongoing recovery and rehabilitation (43%) associated with the original injury event. The majority (82% at 12 months, 81% at 24 months) had received earnings-related compensation from the ACC scheme, indicating a period at least 7 days absence from the workplace following injury. There was no evidence of statistically significant differences in patterns of work absence at either 12 or 24 months by receipt of earnings-related compensation therefore it has not been included in our analyses.

Sensitivity analysis
Complete case analysis was under taken for all models. Sensitivity analyses, using inverse probability weighting (IPW), were undertaken to assess the impact of not being included in the multivariable models in comparison to the cohort's baseline interview. Overall, the relative risks obtained using IPW were slightly lower than for the analyses presented in Table 4. Exceptions to this

DISCUSSION
In this prospective cohort study of injured New Zealand workers we identified a number of new preinjury socio-demographic, work organisational, health and injury-related factors that are important predictors of work absence at 12-and 24-months following injury, and confirmed predictors identified previously. While a time-dependent pattern of predictors of work absence was also observed, a consistent set of six predictors was identified across both time periods with only two unique predictors at 12 months and four at 24 months following injury.
Comparisons of our findings with previous injury studies examining time-dependent patterns of risk factors for work absence are limited by our study's broader population based design, study sample and longer follow-up. Nevertheless, our findings are broadly consistent with the few studies which have previously demonstrated that the influence and relative importance of risk factors changes with increasing time since injury. 9 Our study also provides further support to the importance of considering a multidimensional set of predictors across several time points when following the development of long-term work disability to provide longitudinal insight and to detect risk factors  23 commonly, or primarily, associated with one or another stage of the disabling process. [7][8][9]34 As in our earlier paper examining outcomes at 3 months following injury 11 , findings from this study indicate a broad range of financial, occupational, psychosocial, work organisational and health supports for employees during recovery from injury are potentially important avenues for early intervention to interrupt prolonged absence from work.
Certain domains examined in our study have had limited, if any attention, in other research examining outcomes following acute injury. 10 Work organisational factors were identified in our study as important predictors of work absence. These findings point to the provision of opportunities for recovery from over-demanding work schedules (be it long working hours or weeks) in the long-term following injury as being important for returning to work. Workers with temporary employment contracts were at higher risk of work absence at both time points. Potential mechanisms for this relationship may include the poor quality employment protections and employment relationships between parties with the use of precarious temporary employment contracts, the degree to which substitute workers are available, and the difficulties of realising job accommodations for temporary workers. 34,35 While the evidence for a relationship between precarious employment contracts and work absence following injury is lacking, elsewhere, temporary employment has been associated with delays in returning to work following depressionrelated work disability episodes. 36 Financial insecurity predicted work absence at 12 months only, suggesting financial security has an important time-dependent influence on work disability in the first year following injury, possibly through feelings of anxiety generated by future economic insecurity. 37 Further research confirming these newly identified risk factors and examining potential mechanisms of these for work absence following injury is warranted. Injury-related factors displayed a time dependent pattern. Work-related injury was predictive of work status at both time points supporting our previous findings that workers with work-related injuries had poorer vocational outcomes by 12 months following injury. 38 Our findings indicate work-related injury has an important influence on the development of long-term work absence and interventions focusing on primary and secondary prevention of work-related injury would influence not only work absence in the long-term, but also primary prevention of these common injuries in the first place. By 24 months, the perception of the injury as having been a threat to life and having had a prior injury emerged as important injury-related risk factors. Possible mechanisms for this relationship may lie in the psychological trauma associated with the event, such as through the development of Post-Traumatic Stress Disorder (PTSD). Self-perception of the injury event is rarely examined in studies of work absence and this relationship merits further investigation. Few studies examining outcomes following injury examine previous injury, however the only other study to consider previous injury found higher odds of work disability at 1 year following back injury. 39 Further studies should consider prior injury as a potential risk factor for long-term work disability.
A number of our findings support and extend previous studies findings. Our finding that income and occupation are important predictors of work absence at both 12-and 24-months following injury is consistent with a growing body of longitudinal evidence. 8,10,40 Differing financial incentives, commonly substituted low remuneration work skills, the physical nature of work and practical difficulties in providing work accommodations to trade/manual workers are possible income and occupational barriers to work participation. Our finding that the presence of several pre-existing comorbidities is an important health-related risk factor for work absence at both time periods extends the findings of previous studies observing co-morbid conditions associated with a delayed return-towork up to six months following injury. 41,42 Workers with several comorbid conditions experience an impaired health state prior to injury, and following the injury itself this may limit a person's ability to  25 engage with the injury rehabilitation process, or to manage their chronic conditions, resulting in difficulties maintaining work. The prevalence of multimorbidity in developed nations is increasing rapidly 43 , therefore, further analysis is warranted to examine the long-term influence of co-morbidity on the risk of work disability following injury.
Job satisfaction emerged as the only important pre-injury psychosocial factor predictive of work status at 24 months following injury. Recent evidence that job satisfaction is not predictive of return-to work following injury or musculoskeletal disorders is limited by short follow-up periods up to 12 months following injury. 35,17 Our findings suggest studies with follow-up beyond 12 months should consider job satisfaction as a possible risk factor for prolonged work absence. Psychosocial factors have been identified as predictive of work absence in other injury studies 8,10 , however, this evidence is limited by the lack of multivariable analysis and the narrow range of potential pre-injury predictors and co-variates examined, potentially explaining why our findings differ from others.
Finally, changes in the predictive value of risk factors over time has significant implications for the types and timing of interventions required to prevent the development of work disability. 8,34 While our findings highlight the importance of distinguishing time dependent predictors we did identify a consistent set of potentially modifiable pre-injury and injury-related risk factors that are present regardless of the disability phase that are prime for early intervention. Risk factors consistently identified in our current analysis at 12-and 24-months and in our previous analysis at three months following injury include workers with low incomes, trade/manual occupations, temporary employment contracts, over-demanding work schedules and an injury perceived to be a threat to life. 12 Given our study identified few unique time-dependent risk factors our findings suggest that interventions primarily targeting workers with these consistent pre-injury and injury risk factors This study has some limitations. Firstly pre-injury factors and baseline pre-injury work status were collected through self-report at the first interview (on average 3 months following injury) and may be subject to recall bias, particularly in those variables of a subjective nature such as the psychosocial factors. The self-reported pre-injury work absence measure was found to be accurate by verifying self-reported data against ACC injury claims data about paid employment, with greater than 99% concordance. Secondly, use of single item constructs for many psychosocial factors, such as optimism and job security, may be a limitation. However, good reliability and validity has been demonstrated with parsimonious measures while reducing the interview burden on participants. 17 Thirdly, the capture of all types of work absence (more commonly for disability or unemployment) could potentially explain the shift in patterns of risk factors overtime as the longer a worker is absent from work the greater the likelihood of unemployment or redundancy. The relative proportion of officially unemployment in our work-absent participant at 12-(15%) and 24-months (18%) was small and was unlikely to be the substantial driver of the time dependent shift in risk factors that we observed. Finally, this analysis uses two slightly different groups of individuals followed up at two separate time points following the original injury using complete case analysis. While 90% of participants who completed the 12 month interview also completed the 24 month interview this analysis is sensitive to interview missingness, as well as item missingness for those who completed 12 & 24 month interviews. Sensitivity analyses, undertaken to assess the impact of being included in the final multivariable models in comparison to the baseline interview, found very few variables had a meaningful increase in effect size (ie. > 10% change) and only one, working part-time hours was significant in the final 12-month multivariable model.

Acknowledgements
We are most grateful to the study participants for sharing their information with us. We thank Dr Ari Samaranayaka, Biostatistician, Department of Preventive and Social Medicine, University of Otago for conducting the sensitivity analyses.

Contributions
RL was the lead author and is guarantor of this paper. RL analysed the data. GD and SD contributed to the study design, interpretation of the results, and the review and editing of the manuscript. RL, GD, SD approved the submitted manuscript.

Competing Interests
None declared.

Strengths and limitations of this study
• Few studies examine the multidimensional influence of pre-injury and injury-related predictors of work absence beyond 12 months following injury or the time-dependent relative importance of these predictors.
• Using longitudinal cohort data we found a multidimensional set of predictors of work absence that differed at 12 and 24 months following injury.
• The strengths of the study include data collection on outcomes at multiple time points following injury independent of the time periods for measurement of exposure measures; a large sample size with acceptable follow-up to 24 months (79%); and examination of a multidimensional range of risk factors for work absence.
• The results are sensitive to interview and item missingness, however, sensitivity analysis found this had a negligible impact on estimates of effect size.

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For peer review only -http://bmjopen.bmj.com/site/about/guidelines.xhtml 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60   F  o  r  p  e  e  r  r  e  v  i  e  w  o  n  l  y  INTRODUCTION   A prolonged absence from work following injury can have many detrimental effects on an individual's long-term employment and earning prospects 1,2 and health 3 , as well as substantial societal costs. 4 The longer a worker is absent from the workplace following injury the higher the likelihood that a worker will never return to work and thus the greater the individual and social impacts of injury. 1,5,6 Returning to work following injury is a critical step in the rehabilitation process, making longer-term evaluation of vocational outcomes of interest. The early identification of predictors of long-term chronic work absence provides opportunities for timely intervention to prevent the development of prolonged work disability minimising the consequences of injury.

BMJ Open
In the musculoskeletal injuries literature, the relative importance of predictive factors has been demonstrated to change across the stages of development of work disability. 7,8 The predictors of the acute phase of work disability often differ from the predictors of the chronic phase, suggesting different interventions are needed to address chronic long-term work disability. 8 Few studies investigating work participation following traumatic injury have examined the importance of changes in predictive factors with increasing time following injury; studies typically have short follow-up periods of 6 months, or less, following injury. 9 A limited number of studies have examined vocational outcomes following injury from a broad population-based perspective. Previous studies have tended to focus on specific injury types, injury to specific body regions, on specific settings of injury and on injury types typically regarded as severe such as those resulting in hospitalisation. [7][8][9][10] The Prospective Outcomes of Injury Study (POIS) gives us an opportunity to examine predictors for hospitalised and non-hospitalised acute injuries, and all injury settings including road, home, recreational and work. 11 Furthermore, analysis of the large POIS cohort allows us to examine predictors using a broader multidimensional perspective. Our

Recruitment of the POIS cohort was via New
With the exception of the injury-related variables, which relate to the injury event itself, all explanatory variables examined are pre-injury variables. A priori hypotheses of a relationship with work absence, and/or prior identification in previous studies was the basis of selection of each explanatory variable. 7,10 The explanatory variables examine seven dimensions: 1) socio-demographic (age, gender, income, highest qualification, occupation, relationship status, living arrangements, material standard of living, household income adequacy, financial security); 2) physical work (repetitive hand movements, heavy lifting, physical exertion, standing, painful/tiring body positions); 3) psychosocial (job strain, support, security, satisfaction, optimism, self-efficacy, prior depressive episode); 4) work organisation (number of hours and days worked per week, employment contract, multiple job holding) ; 5) lifestyle (alcohol consumption, smoking status, Body Mass Index (BMI), exercise, sleep quantity); 6) health (overall self-assessment for health, co-morbidities, pain/discomfort, prior injury, prior disabling condition, work capacity); 7) injury-related (work-related injury, nature and body region of injuries, intent of injury, hospital admission or at least 3 hours treatment at an Emergency Department within 7 days of injury, anatomical injury severity -NISS), injury perceived as a threat to life, injury perceived as a threat of serious disability, access to health services).

Outcome measure
At 12-and 24-month interviews a single item "Which of the following best describes your paid work situation now?" was used to ascertain work status. Participants indicating full-time and part-time work for pay were considered to be "working", while those indicating they were receiving a benefit and/or compensation, or were unemployed were considered to be "absent from work".

Data analysis
The relationship between work absence and pre-injury and injury-related explanatory variables was examined using frequency tables, summary statistics and regression analyses.
For each time point adjusted univariate models were created using modified Poisson regression with robust error variance to estimate relative risks. 31 Age, gender, anatomical severity of injury (NISS), interview region and all 12 injury nature and body region variables were included in all univariate models as potential covariates. To account for the range in the timing of the 12-and 24-month interviews after the injury event, time since injury was also included as a continuous variable in all analyses.

RESULTS
Of the 2626 POIS participants who were working prior to their injury, 2092 completed the 12 month interview (79% follow-up) and 2082 completed the 24 month interview (79% follow-up) ( Table 2). At the 12 month interview 329 (16%) reported being absent from work, while 304 (15%) were absent at the 24 month interview. Reasons for work absence were reported at 24 months following injury only and were varied: predominantly the sample was absent from work due to ongoing recovery and rehabilitation (43%) associated with the original injury event. The majority (82% at 12 months, 81% at 24 months) had received earnings-related compensation from the ACC scheme, indicating a period at least 7 days absence from the workplace following injury. There was no evidence of statistically significant differences in patterns of work absence at either 12 or 24 months by receipt of earnings-related compensation therefore it has not been included in our analyses. Reasons for work absence -24 months Not recovered yet/rehabilitation  Most variables from the adjusted univariate modelling step (results not shown) were retained and entered into the final multivariable 12-and 24-month models. Variables not included in the final models included multiple job holding, repetitive hand movements, sleep quantity and work capacity. Table 3 presents the final multivariable model for work absence 12 months following injury. Sociodemographic characteristics predictive of increased risk of work absence included being male, those with low levels of personal income or who could not provide an estimate of their personal income, those in trade/manual occupations and those reporting financial insecurity. Variables representing relationship status, living arrangements and adequacy of household income were not retained in the final model.   1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60   F  o  r  p  e  e  r  r  e  v  i  e  w  o  n  l  y   18 Complete case analysis was under taken for all models. Sensitivity analyses, using inverse probability weighting (IPW), were undertaken to assess the impact of not being included in the multivariable models in comparison to the cohort's baseline interview. Overall, the relative risks obtained using IPW were slightly lower than for the analyses presented in Table 3 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59 19 have previously demonstrated that the influence and relative importance of risk factors changes with increasing time since injury. 9 Our study also provides further support to the importance of considering a multidimensional set of predictors across several time points when following the development of long-term work disability to provide longitudinal insight and to detect risk factors commonly, or primarily, associated with one or another stage of the disabling process. [7][8][9]34 As in our earlier paper examining outcomes at 3 months following injury 11 , findings from this study indicate a broad range of financial, occupational, psychosocial, work organisational and health supports for employees during recovery from injury are potentially important avenues for early intervention to interrupt prolonged absence from work.

months
Certain domains examined in our study have had limited, if any attention, in other research examining outcomes following acute injury. 10 Work organisational factors were identified in our study as important predictors of work absence. These findings point to the provision of opportunities for recovery from over-demanding work schedules (be it long working hours or weeks) in the long-term following injury as being important for returning to work. Workers with temporary employment contracts were at higher risk of work absence at both time points. Potential mechanisms for this relationship may include the poor quality employment protections and employment relationships between parties with the use of precarious temporary employment contracts, the degree to which substitute workers are available, and the difficulties of realising job accommodations for temporary workers. 34,35 While the evidence for a relationship between precarious employment contracts and work absence following injury is lacking, elsewhere, temporary employment has been associated with delays in returning to work following depressionrelated work disability episodes. 36 Financial insecurity predicted work absence at 12 months only, suggesting financial security has an important time-dependent influence on work disability in the first year following injury, possibly through feelings of anxiety generated by future economic  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60   F  o  r  p  e  e  r  r  e  v  i  e  w  o  n  l  y   20 insecurity. 37 Further research confirming these newly identified risk factors and examining potential mechanisms of these for work absence following injury is warranted.
Injury-related factors displayed a time dependent pattern. Work-related injury was predictive of work status at both time points supporting our previous findings that workers with work-related injuries had poorer vocational outcomes by 12 months following injury. 38 Our findings indicate work-related injury has an important influence on the development of long-term work absence and interventions focusing on primary and secondary prevention of work-related injury would influence not only work absence in the long-term, but also primary prevention of these common injuries in the first place. By 24 months, the perception of the injury as having been a threat to life and having had a prior injury emerged as important injury-related risk factors. Possible mechanisms for this relationship may lie in the psychological trauma associated with the event, such as through the development of Post-Traumatic Stress Disorder (PTSD). Self-perception of the injury event is rarely examined in studies of work absence and this relationship merits further investigation. Few studies examining outcomes following injury examine previous injury, however the only other study to consider previous injury found higher odds of work disability at 1 year following back injury. 39 Further studies should consider prior injury as a potential risk factor for long-term work disability.
A number of our findings support and extend previous studies findings. Our finding that income and occupation are important predictors of work absence at both 12-and 24-months following injury is consistent with a growing body of longitudinal evidence. 8,10,40 Differing financial incentives, commonly substituted low remuneration work skills, the physical nature of work and practical difficulties in providing work accommodations to trade/manual workers are possible income and occupational barriers to work participation. Our finding that the presence of several pre-existing co-  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60   F  o  r  p  e  e  r  r  e  v  i  e  w  o  n  l  y   21 morbidities is an important health-related risk factor for work absence at both time periods extends the findings of previous studies observing co-morbid conditions associated with a delayed return-towork up to six months following injury. 41,42 Workers with several comorbid conditions experience an impaired health state prior to injury, and following the injury itself this may limit a person's ability to engage with the injury rehabilitation process, or to manage their chronic conditions, resulting in difficulties maintaining work. The prevalence of multimorbidity in developed nations is increasing rapidly 43 , therefore, further analysis is warranted to examine the long-term influence of co-morbidity on the risk of work disability following injury.
Job satisfaction emerged as the only important pre-injury psychosocial factor predictive of work status at 24 months following injury. Recent evidence that job satisfaction is not predictive of return-to work following injury or musculoskeletal disorders is limited by short follow-up periods up to 12 months following injury. 35,17 Our findings suggest studies with follow-up beyond 12 months should consider job satisfaction as a possible risk factor for prolonged work absence. Psychosocial factors have been identified as predictive of work absence in other injury studies 8,10 , however, this evidence is limited by the lack of multivariable analysis and the narrow range of potential pre-injury predictors and co-variates examined, potentially explaining why our findings differ from others.
Finally, changes in the predictive value of risk factors over time has significant implications for the types and timing of interventions required to prevent the development of work disability. 8,34 While our findings highlight the importance of distinguishing time dependent predictors we did identify a consistent set of potentially modifiable pre-injury and injury-related risk factors that are present regardless of the disability phase that are prime for early intervention. Risk factors consistently identified in our current analysis at 12-and 24-months and in our previous analysis at three months  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  This study has some limitations. Firstly pre-injury factors and baseline pre-injury work status were collected through self-report at the first interview (on average 3 months following injury) and may be subject to recall bias, particularly in those variables of a subjective nature such as the psychosocial factors. The self-reported pre-injury work absence measure was found to be accurate by verifying self-reported data against ACC injury claims data about paid employment, with greater than 99% concordance. Secondly, use of single item constructs for many psychosocial factors, such as optimism and job security, may be a limitation. However, good reliability and validity has been demonstrated with parsimonious measures while reducing the interview burden on participants. 17 Thirdly, the capture of all types of work absence (more commonly for disability or unemployment) could potentially explain the shift in patterns of risk factors overtime as the longer a worker is absent from work the greater the likelihood of unemployment or redundancy. The relative proportion of officially unemployment in our work-absent participant at 12-(15%) and 24-months (18%) was small and was unlikely to be the substantial driver of the time dependent shift in risk factors that we observed. Finally, this analysis uses two slightly different groups of individuals followed up at two separate time points following the original injury using complete case analysis. While 90% of participants who completed the 12 month interview also completed the 24 month interview this analysis is sensitive to interview missingness, as well as item missingness for those who completed  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  and combined multi-dimensional analysis examining a comprehensive range of risk factors for work disability. A further key strength is the measurement of pre-and early-injury exposures which provide opportunity for identifying early interventions to influence long-term work disability outcomes. The use of two point-in-time measures of work participation is also a strength as it best captures long-term outcomes since follow-up occurs regardless of previous work status and of previous non-response. Finally, we have been able to examine within NZ's universal no-fault injury rehabilitation and compensation scheme the early predictors of long-term work disability for all injured workers, regardless of whether the injury was sustained at work or not. This is a strength as being absent from the workplace, irrespective of where the injury occurred, has substantial socioeconomic costs to individuals, employers and to society. Our findings are generalizable to those injury rehabilitation and compensation systems with similar coverage of work and non-work injury.