1. Predicting return to work among sickness certified patients in general practice Properties of two assessment tools Anna-Sophia von Celsing MD, PhD Department of Public Health and Caring Sciences, Family Medicine and Preventive Medicine Section Uppsala University, Sweden anna-sophia.von.celsing@pubcare.uu.se

2. Aim To analyse the properties of two models for the assessment of return to work after sickness certification, one based on clinical judgement including non-measureable information (”gut feeling”), and a computer-based one

3. Method Population:all sickness certified patients, 18 − 63 years, at one PHC (n=943) Inclusion period:8 months 2004 Follow-up:1 year before − 3 years after study start Exclusion: rehabilitation initiated 2003 Data sources: information from sickness certificate, registry data from the Swedish Social Insurance Agency, Employment Agency, Community

4. Outcome number of days until return to work Registry data from the Swedish Social Insurance Agency

5. Study population WomenMen Number (%)482 (51)461(49) Age years (mean)39 Employment (%)7677 Sick leave before baseline, days (mean) 55 (SD 106)49 (SD 94) Full-time sick leave at baseline (%) 8993 Diagnosis M (%) (ICD-10) 3133 Diagnosis F (%) (ICD-10) 2719 Diagnosis J (%) (ICD-10) 1815

6. AgeDays of sick leave before baseline F diagnosis J diagnosis Return to work (n=943)

7. Manual model Sick leave >28 days last year Diagnosis F or M Age > 45 years Unemployed Women Full-time sick leave ”gut feeling” Computer-based model Number of sick leave days last year Diagnosis Age Employment status Sex Degree of sick leave Manual model 73 – 76% Computer-based model 86 – 89% Concordance between actual RTW within 28, 90 and 180 days and RTW predicted by the assessments models

8. ”Heavy diagnoses” (-2) ”Light diagnoses” (+3) F G I K M Others A B O L N H J Diagnostic labels of ICD-10 diagnoses -2 -1 0 +1 +2 +3 F Psychiatric disease G Nervous system disease I Cardiovascular disease K Digestive system disease M Musculoskeletal disease J Respiratory disease H Eye or ear disease N Genitourinary system diease L Dermatology diease O Pregnancy, childbirth AB Infectious-parasite disease C Cancer D Blood disease E Endocrine-metabolic disease R Symptoms and signs S Injury, poisoning

9. Age ICD-10 diagnosis code Sick leave days last year 8098879296 384553617078 16 2025 30 36 43 18 63 0 I, K, MF, GAll otherNH, JA, B, O, L 72 54 25 63 31 7179 37 44 8692 5260 1316 19 2429 10 Return to work (%), days 0-28 27 36 45 54 73 146 219 292 365

10. 38% 70%

11. Women Men No sick leave Sick leave Kvinnors / mäns skattning jämfört med rehabteamets skattning och utfall vid dag 180

12. Red flags of sick leave Age Sick leave history Diagnosis Patients’ assessment?

13. THANK YOU