Sequence analysis of sickness absence and disability pension in the year before and the three years following a bicycle crash; a nationwide longitudinal cohort study of 6353 injured individuals

Abstract

Background Bicyclists are the road user group with the highest number of severe injuries in the EU, yet little is known about sickness absence (SA) and disability pension (DP) following such injuries. Aims To explore long-term patterns of SA and DP among injured bicyclists, and to identify characteristics associated with the specific patterns. Methods A longitudinal register-based study was conducted, including all 6353 individuals aged 18–59 years and living in Sweden in 2009, who in 2010 had incident in-patient or specialized out-patient healthcare after a bicycle crash. Information about sociodemographic factors, the injury, SA (SA spells > 14 days), and DP was obtained from nationwide registers. Weekly SA/DP states over 1 year before through 3 years after the crash date were used in sequence and cluster analyses. Multinomial logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for factors associated with each identified sequence cluster. Results Seven clusters were identified: “No SA or DP” (58.2% of the cohort), “Low SA or DP” (7.4%), “Immediate SA” (20.3%), “Episodic SA” (5.9%), “Long-term SA” (1.7%), “Ongoing part-time DP” (1.7%), and “Ongoing full-time DP” (4.8%). Compared to the cluster “No SA or DP”, all other clusters had higher ORs for women, and higher age. All clusters but “Low SA and DP” had higher ORs for inpatient healthcare. The cluster “Immediate SA” had a higher OR for: fractures (OR 4.3; CI 3.5–5.2), dislocation (2.8; 2.0–3.9), sprains and strains (2.0; 1.5–2.7), and internal injuries (3.0; 1.3–6.7) compared with external injuries. The cluster “Episodic SA” had higher ORs for: traumatic brain injury, not concussion (4.2; 1.1–16.1), spine and back (4.5; 2.2–9.5), torso (2.5; 1.4–4.3), upper extremities (2.9; 1.9–4.5), and lower extremities (3.5; 2.2–5.5) compared with injuries to the head, face, and neck (not traumatic brain injuries). The cluster “Long-term SA” had higher ORs for collisions with motor vehicles (1.9;1.1–3.2) and traumatic brain injury, not concussion (18.4;2.2–155.2). Conclusion Sequence analysis enabled exploration of the large heterogeneity of SA and DP following a bicycle crash. More knowledge is needed on how to prevent bicycle crashes and especially those crashes/injuries leading to long-term consequences.

Sickness absence and disability pension among injured working-aged pedestrians - a population-based Swedish register study

“…This could be seen in EU where pedestrians represent the road user group with the highest proportion of accidents where the injured is admitted to hospital and also the longest average of hospital stay [ 8 ]. Therefore, it should also be of interest to study the SA and DP of pedestrians in a longer perspective as have been done for bicycle crashes [ 19 ].…”

“…A modified version of the Barell matrix [ 15 ] was used to classify the ICD-10 codes into categories of type of injury and injured body region. Similar categorizations were used in recent studies on injuries among car occupants and bicyclists [ 16 – 19 ]. Type of injury was categorized into the following six groups: fracture; dislocation; sprains and strains; internal (brain, spinal cord, and other internal organs); external (open wounds, contusions, and superficial injuries) ( reference group ); and “other and unspecified”.…”

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

“…The type of injury was categorized into six groups: fracture; dislocation; sprains and strains; internal (brain, spinal cord, and other internal organs); external (open wounds, contusions, and superficial injuries) ( reference group ); and “other and unspecified”. Similar categorizations have been used in recent studies on injuries among different groups of road traffic users [ 8 , 12 – 15 ].…”

“…The sociodemographic factors were categorized as: sex (women; men ( reference group )), age group (20–24; 25–34 ( reference group ); 35–44; 45–54; 55–59 years), level of education (elementary school (≤ 9 years including missing); high school (9–12 years); university/college (> 12 years) ( reference group )), country of birth (Sweden ( reference group ); not Sweden), type of living area (cities ( reference group ); town and suburbs; rural areas), marital status (married ( reference group ); not married). These have all been demonstrated to be common risk factors for SA in general [ 16 ] and after a traffic related injury [ 8 , 12 – 15 ].…”

Sustainable Working Life Patterns in a Swedish Twin Cohort: Age-Related Sequences of Sickness Absence, Disability Pension, Unemployment, and Premature Death during Working Life

“…However, although no consensus definition of sustainable working life exists [ 1 , 6 ], there is a need to understand various patterns of working life, with or without interruptions, over the life course. Until now, many earlier studies have focused on SA or DP due to specific diseases or conditions [ 7 , 8 , 9 , 10 ], but to the best of our knowledge, population-based cohort studies are rare for working life patterns identified via sequences with a long follow-up over the life course. Hence, a sequence analysis could provide assessment of the sustainable working life over time and informative clusters [ 11 , 12 ].…”