Work absence following road traffic crash in Victoria, Australia: A population-based study

Sickness absence and disability pension after road traffic accidents, a nationwide register-based study comparing different road user groups with matched references

Background

Being injured in a road traffic accident may affect individuals' functional ability and in turn lead to sickness absence (SA) and disability pension (DP). Knowledge regarding long-term consequences in terms of SA and DP following a road traffic accident is lacking, especially comparing different groups of road users and compared to the general population. The aim was to estimate excess diagnosis-specific SA and DP among individuals of different road user groups injured in a road traffic accident compared to matched references without such injury.

Methods

A nationwide register-based study, including all working individuals aged 20-59 years and living in Sweden who in 2015 had in- or specialized outpatient healthcare after a new traffic-related injury (n = 20 177) and population-based matched references (matched on: sex, age, level of education, country of birth, living in cities) without any traffic-related injury during 2014-2015 (n = 100 885). Diagnosis-specific (injury and other diagnoses) SA and DP were assessed during 5 years: 1 year before and 4 years following the accident. Mean SA and DP net days/year for each road user group and mean differences of (excess) SA and DP net days/year compared with their matched references were calculated with independent t-tests with bootstrapped 95% confidence intervals (CIs).

Results

A third of all injured road users were bicyclists, 31% were car occupants, 16% were pedestrians (including fall accidents), and 19% were other and unspecified accidents. Pedestrians and other road users were the groups with the highest mean number of SA days during the first year following the accident (51 and 49 days/year respectively). The matched references had between 8 and 13 SA days/year throughout the study period. The excess SA days/year were elevated for all road user groups all five studied years. Excess SA due to injury diagnoses was 15-35 days/year during the first year following the accident. Excess SA due to diagnoses other than injuries were about eight days/year during the whole study period for pedestrians and car occupants and about zero for the bicyclists. The excess DP was low, although it increased every year after the accident for pedestrians and car occupants; for bicyclists no excess DP was seen.

Conclusion

Higher levels of SA due to injury diagnoses were seen among all road user groups during the first year after the accident compared to their references. Pedestrians and car occupants had more excess SA due to other diagnoses and more excess DP four years after the accident than bicyclists and other road users.

Diagnosis-specific sickness absence among injured working-aged pedestrians: a sequence analysis

BACKGROUND

The knowledge about the long-term consequences in terms of sickness absence (SA) among pedestrians injured in a traffic-related accident, including falls, is scarce. Therefore, the aim was to explore diagnosis-specific patterns of SA during a four-year period and their association with different sociodemographic and occupational factors among all individuals of working ages who were injured as a pedestrian.

METHODS

A nationwide register-based study, including all individuals aged 20-59 and living in Sweden, who in 2014-2016 had in- or specialized outpatient healthcare after a new traffic-related accident as a pedestrian. Diagnosis-specific SA (> 14 days) was assessed weekly from one year before the accident up until three years after the accident. Sequence analysis was used to identify patterns (sequences) of SA, and cluster analysis to form clusters of individuals with similar sequences. Odds ratios (ORs) with 95% confidence intervals (CIs) for association of the different factors and cluster memberships were estimated by multinomial logistic regression.

RESULTS

In total, 11,432 pedestrians received healthcare due to a traffic-related accident. Eight clusters of SA patterns were identified. The largest cluster was characterized by no SA, three clusters had different SA patterns due to injury diagnoses (immediate, episodic, and later). One cluster had SA both due to injury and other diagnoses. Two clusters had SA due to other diagnoses (short-term and long-term) and one cluster mainly consisted of individuals with disability pension (DP). Compared to the cluster "No SA", all other clusters were associated with older age, no university education, having been hospitalized, and working in health and social care. The clusters "Immediate SA", "Episodic SA" and "Both SA due to injury and other diagnoses" were also associated with higher odds of pedestrians who sustained a fracture.

CONCLUSIONS

This nationwide study of the working-aged pedestrians observed diverging patterns of SA after their accident. The largest cluster of pedestrians had no SA, and the other seven clusters had different patterns of SA in terms of diagnosis (injury and other diagnoses) and timing of SA. Differences were found between all clusters regarding sociodemographic and occupational factors. This information can contribute to the understanding of long-term consequences of road traffic accidents.

Biopsychosocial sequelae of chronically painful injuries sustained in motor vehicle accidents contributing to non-recovery: A retrospective cohort study

BACKGROUND

Claimants with chronically painful injuries sustained in motor vehicle accidents (MVAs) undergo assessment and management influenced by insurance and medico-legal processes defined by a biomedical paradigm which is discordant with best evidence. We aim to demonstrate the impact of biopsychosocial factors on post-MVA sequelae which contribute to non-recovery.

METHODS

This was a retrospective cohort study of medico-legal documents and reports on 300 consecutive claimants referred to a pain medicine physician over 7 years (2012-2018) for assessment of painful musculoskeletal injuries post-MVA. One hundred data items were extracted from the medico-legal documents and reports for each claimant and entered into an electronic database. Post-MVA sequelae were analysed using chi-square analysis (OR >2) for significant associations with demographic, pre-MVA and post-MVA variables. Factors with significant associations were entered into a logistic regression model to determine significant statistical predictors of post-MVA sequelae contributing to non-recovery.

RESULTS

The claimants were aged 17 to 80 years (mean age 42 years), and approximately half (53%, n=159) were female. The time from MVA to interview averaged 2.5 years. Widespread pain was present in 18% (n=54), and widespread somatosensory signs implying central sensitisation (OR=9.85, p<.001) was the most significant multivariate association. Long-term opiate use post-MVA (32%) was predicted by pre-MVA sleep disturbance (OR=5.08, p=.001), post-MVA major depressive disorder (MDD) (OR=3.02, p=.003) and long-term unemployment (OR=2.22. p=.007). Approximately half (47%, n=142) required post-MVA support from a psychologist or psychiatrist. Post-traumatic stress disorder (PTSD) was diagnosed by a psychiatrist or psychologist in 20% (n=59), yet early identification of risk of PTSD was rare. Pre-MVA, 89.4% (n=268) were studying or employed. Permanent unemployability post-MVA occurred in 35% (n=104) and was predicted by MDD (OR=3.59, p=.001) and antidepressant use (OR=2.17, p=.005). Major social change post-MVA (70%) was predicted by older age (OR=.966, p=.003), depressive symptoms (OR=3.71, p<.001) and opiate use (OR=2.00, p=.039).

CONCLUSIONS

Biomedical factors, including older age, impaired sleep and indicators of widespread central sensitisation, and psychological factors, including stress, anxiety and depression, were the most prominent multivariate associations as statistical predictors of major adverse sequelae contributing to non-recovery for claimants with chronic pain post-MVA.