Sickness absence and disability pension among women with breast cancer: a population-based cohort study from Sweden.

BACKGROUND: Women's return to work after diagnosis of breast cancer (BC) is becoming more prevalent. However, register-based national investigation on sickness absence (SA) and disability pension (DP) in BC women is lacking. The aim of the study was to explore SA and DP before and after a first BC diagnosis and the possibility to predict new cancer-related SA by using disease-related and sociodemographic factors. METHODS: A longitudinal register study of the 3536 women in Sweden aged 19-64 with a first BC diagnosis in 2010 was conducted by linkage of five nationwide registers. Particularly, detailed information on SA and DP was obtained from the National Social Insurance Agency. Descriptive statistics on SA and DP 2 years before through 3 years after the BC diagnosis were performed. The risk of having a new SA spell due to BC or BC-related diagnoses was modeled using logistic regression. RESULTS: The proportion of women with SA increased during the year following the BC diagnosis date and declined over the next 2 years to proportions before diagnosis. At the time of BC diagnosis, half of the women began a new SA spell > 14 days with cancer, cancer-related, or mental diagnosis. Disease-related and sociodemographic factors including occupational sector, living area, age, cancer stage, educational level, and number of previous SA days showed statistical significance (p < 0.05) in predicting a new SA around BC diagnosis. By using these factors, it was possible to correctly predict 67% of the new SA spell. CONCLUSIONS: SA among women with BC was elevated mainly in the first year after diagnosis. New SA following BC diagnosis can accurately be predicted.

Predicting the duration of sickness absence due to knee osteoarthritis: a prognostic model developed in a population-based cohort in Sweden.

BACKGROUND: Predicting the duration of sickness absence (SA) among sickness absent patients is a task many sickness certifying physicians as well as social insurance officers struggle with. Our aim was to develop a prediction model for prognosticating the duration of SA due to knee osteoarthritis. METHODS: A population-based prospective study of SA spells was conducted using comprehensive microdata linked from five Swedish nationwide registers. All 12,098 new SA spells > 14 days due to knee osteoarthritis in 1/1 2010 through 30/6 2012 were included for individuals 18-64 years. The data was split into a development dataset (70 %, nspells =8468) and a validation data set (nspells =3690) for internal validation. Piecewise-constant hazards regression was performed to prognosticate the duration of SA (overall duration and duration > 90, >180, or > 365 days). Possible predictors were selected based on the log-likelihood loss when excluding them from the model. RESULTS: Of all SA spells, 53 % were > 90 days and 3 % >365 days. Factors included in the final model were age, sex, geographical region, extent of sickness absence, previous sickness absence, history of specialized outpatient healthcare and/or inpatient healthcare, employment status, and educational level. The model was well calibrated. Overall, discrimination was poor (c = 0.53, 95 % confidence interval (CI) 0.52-0.54). For predicting SA > 90 days, discrimination as measured by AUC was 0.63 (95 % CI 0.61-0.65), for > 180 days, 0.69 (95 % CI 0.65-0.71), and for SA > 365 days, AUC was 0.75 (95 % CI 0.72-0.78). CONCLUSION: It was possible to predict patients at risk of long-term SA (> 180 days) with acceptable precision. However, the prediction of duration of SA spells due to knee osteoarthritis has room for improvement.

Predicting the duration of sickness absence spells due to back pain: a population-based study from Sweden.

OBJECTIVES: We aimed to develop and validate a prediction model for the duration of sickness absence (SA) spells due to back pain (International Statistical Classification of Diseases and Related Health Problems 10th Revision: M54), using Swedish nationwide register microdata. METHODS: Information on all new SA spells >14 days from 1 January 2010 to 30 June 2012 and on possible predictors were obtained. The duration of SA was predicted by using piecewise constant hazard models. Nine predictors were selected for the final model based on a priori decision and log-likelihood loss. The final model was estimated in a random sample of 70% of the SA spells and later validated in the remaining 30%. RESULTS: Overall, 64 048 SA spells due to back pain were identified during the 2.5 years; 74% lasted ≤90 days, and 9% >365 days. The predictors included in the final model were age, sex, geographical region, employment status, multimorbidity, SA extent at the start of the spell, initiation of SA spell in primary healthcare and number of SA days and specialised outpatient healthcare visits from the preceding year. The overall c-statistic (0.547, 95% CI 0.542 to 0.552) suggested a low discriminatory capacity at the individual level. The c-statistic was 0.643 (95% CI 0.634 to 0.652) to predict >90 days spells, 0.686 (95% CI 0.676 to 0.697) to predict >180 spells and 0.753 (95% CI 0.740 to 0.766) to predict >365 days spells. CONCLUSIONS: The model discriminates SA spells >365 days from shorter SA spells with good discriminatory accuracy.

A prognostic model for predicting the duration of 20,049 sickness absence spells due to shoulder lesions in a population-based cohort in Sweden.

MAIN OBJECTIVE: Sickness absence duration for shoulder lesion patients is difficult to prognosticate, and scientific evidence for the sick-listing practice is lacking. Our objective was to develop a clinically implementable prediction model for the duration of a sickness absence spell due to shoulder lesions. METHODS: All new sickness absence spells due to shoulder lesions (ICD-10-code: M75) issued in the period January 2010-June 2012 that were longer than 14 days were identified through the nationwide sickness absence insurance register. Information on predictors was linked from four other nationwide registers. Piecewise-constant hazards regression models were fitted to predict duration of sickness absence. The model was developed and validated using split sample validation. Variable selection was based on log-likelihood loss ranking when excluding a variable from the model. The model was evaluated using calibration plots and the c-statistic. RESULTS: 20 049 sickness absence spells were identified, of which 34% lasted >90 days. Predictors included in the model were age, sex, geographical region, occupational status, educational level, birth country, specialized healthcare at start of the spell, number of sickness absence days in the last 12 months, and specialized healthcare the last 12 months, before start date of the index sickness absence spell. The model was satisfactorily specified and calibrated. Overall c-statistic was 0.54 (95% CI 0.53-0.55). C-statistic for predicting durations >90, >180, and >365 days was 0.61, 0.66, and 0.74, respectively. SIGNIFICANCE: The model can be used to predict the duration of sickness absence due to shoulder lesions. Covariates had limited predictive power but could discriminate the very long sickness absence spells from the rest.

Multimorbidity and Sickness Absence/Disability Pension in Patients With Cluster Headache and Matched References: A Swedish Register-Based Study.

BACKGROUND AND OBJECTIVES: Multimorbidity among patients with cluster headache (CH) is considered to be high, but large studies are lacking. The aims were to explore the occurrence of diagnosis-specific multimorbidity among patients with CH and matched references and possible associations of this with their sickness absence and disability pension. METHODS: We performed a register-based study of patients with CH and matched references, regarding their multimorbidity, sickness absence, and disability pension. Data were obtained from 2 nationwide registers: Statistics Sweden's Longitudinal Integration Database for Health Insurance and Labor Market Studies (LISA) (for sociodemographics in 2009, sickness absence, and disability pension in 2010) and The National Board of Health and Welfare's specialized outpatient and inpatient registers for diagnosis-specific health care in 2001-2010 (for identifying patients with CH and multimorbidity, defined by ICD-10 codes). The prevalence and number of net days of sickness absence and/or disability pension in 2010 were calculated, in general and by multimorbidity. Odds ratios (OR) with 95% confidence intervals (CIs) were calculated for comparison of each diagnostic group with references without the chosen morbidity. RESULTS: We analyzed 3,240 patients with CH, aged 16-64 years, and living in Sweden in 2010 and 16,200 matched references. A higher proportion of patients with CH had multimorbidity (91.9%) than of references (77.6%), OR 3.263 (95% CI 2.861-3.721), both in general and regarding all analyzed diagnostic groups. Differences were particularly high for diagnoses relating to the nervous (CH 51.8% vs references 15.4%), OR 5.922 (95% CI 5.461-6.422), and musculoskeletal (CH 39.0% vs references 23.7%), OR 2.057 (95% CI 1.900-2.227), systems. Multimorbidity rates were overall higher among women in patients with CH (96.4% vs men 89.6%). Patients with CH had a higher mean number of days of sickness absence and disability pension compared with references, 63.15 vs 34.08 days. Moreover, multimorbidity was associated with a higher mean number of such days in patients with CH, 67.25, as compared with references, 40.69 days. DISCUSSION: The proportions of multimorbidity were high in both patients with CH and references, however, higher in the patients with CH, who also had higher sickness absence and disability pension levels. In particular, CH patients with multimorbidity and of female sex had high sickness absence and disability pension levels.

A prediction model for duration of sickness absence due to stress-related disorders.

BACKGROUND: Stress-related disorders are leading causes of long-term sickness absence (SA) and there is a great need for decision support tools to identify patients with a high risk for long-term SA due to them. AIMS: To develop a clinically implementable prediction model for the duration of SA due to stress-related disorders. METHODS: All new SA spells with F43 diagnosis code lasting >14 days and initiated between 2010-01-01 and 2012-06-30 were identified through data from the Social Insurance Agency. Information on baseline predictors was linked on individual level from other nationwide registers. Piecewise-constant hazard regression was used to predict the duration of the SA. Split-sample validation was used to develop and validate the model, and c-statistics and calibration plots to evaluate it. RESULTS: Overall 83,443 SA spells, belonging to 77,173 individuals were identified. The median SA duration was 55 days (10% were >365 days). Age, sex, geographical region, employment status, educational level, extent of SA at start and SA days, outpatient healthcare visits, and multi-morbidity in the preceding 365 days were selected to the final model. The model was well calibrated. The overall c-statistics was 0.54 (95% confidence intervals: 0.53-0.54) and 0.70 (95% confidence intervals: 0.69-0.71) for predicting SA spells >365 days. LIMITATIONS: The heterogeneity of the F43-diagnosis and the exclusive use of register-based predictors limited our possibility to increase the discriminatory accuracy of the prediction. CONCLUSION: The final model could be implementable in clinical settings to predict duration of SA due to stress-related disorders and could satisfyingly discriminate long-term SA.