Workshop: Chronic diseases and employment in Europe-increasing gap? Glasgow, Scotland, UK. 19 - 22 November 2014 Multimorbidity as a determinant of incident sickness absence M Ubalde-Lopez (1,2,3), G L Delclos (1, 2,3,4), D Gimeno (1, 2,3,5), E Calvo-Bonacho (6) , F G Benavides (1,2,3) (1) (2) (3) (4) (5) (6) CISAL-Center for Research in Occupational Health, Pompeu Fabra University (UPF),Barcelona, Spain CIBERESP-CIBER in Epidemiology and Public Health, Madrid, Spain IMIM-Hospital del Mar Medical Research Institut, Barcelona, Spain The University of Texas School of Public Health at Houston, Texas, USA The University of Texas School of Public Health, San Antonio Regional Campus, Texas, USA Department of Health Projects. Ibermutuamur, Madrid, Spain The authors have no conflicts of interest to declare Background Working Population vs Chronic health Conditions Aging + working longer ↑Chronic health Conditions (40% Western working populations) HRQL work limitations/impairment productivity loss work-related absences Sickness Absence (SA) co-existence of chronic conditions Background Holistic approach, patient focused MULTIMORBIDITY (MMB) Co- ocurrence ≥2 health conditions None index disease Cluster-non randomly Single disease approach COMORBIDITY Co-ocurrence medical conditions aditional to an index disease Treatment Treatment PATIENT Index disease Assessment of effectiveness QOL, functioning Index disease activity Background Multimorbidity measures Epidemiology of multimorbidity ↑Hospitalization rates, treatments and costs ↑Need for improved primary care services Large heterogeneity Target populations: general population patients (hospitalized, primary care) elderly (>70 years) Applicability poorly studied in: younger populations better health indicators lower prevalence of chronic conditions higher prevalence of health-related risk factors Working populations Background Recovery Healthy worker Health problem Sickness absence RTW Disability Prognostic factors (for duration) Risk factors (for incidence) Social conditions Work environment Proximal risk factors Job/occupation Work organization Multimorbidity (health status) Demands/control/support Age Marital status Gender Having children Educational level Economic activity Employee-employer relations Health region Distal risk factors Labor market Healthcare system Social benefits system HYPOTHESIS Multimorbidity (MMB) is associated with an increased incidence of SA episodes Methods 1. Calculation of a multimorbidity score (MMBS) • • • Cross-sectional study Study population N= 372,370 workers covered by a national health insurance company underwent standardized medical evaluation in 2006 Information sources Medical/physical examination (occupational physician) Self-reported questionnaire Chronic health conditions Hypertension Hyperlipidemia Diabetes Venous thrombosis Coronary artery disease Cerebrovascular disease Peripheral vascular disease Body mass index (BMI) Health-related behaviors Physical activity Tobacco consumption Alcohol consumption Symptoms Fatigue Headache Neck pain Low back pain Sleep disturbances • Statistical analysis Multiple correspondence analysis (MCA) Algorithm to calculate multimorbidity score (MMBS) Methods 2. Incidence of sickness absence (SA) episodes Baseline sample (Medical evaluation, 2006) Men = 269,083 (72,4%) Women = 103,287 (27.6%) Excluded, not eligible* [Men = 18,789(7.0%) Women =7,049 (6.7%)] Excluded, missing key variables [Men =13,794 (5.1%) Women = 4,799 (4.7%)] Sample for SA incidence [Men = 236,500 (87.9%) Women =91,439 (88.6%)] Flow chart for sample selection * coverage ended or on SA leave Methods Follow up 2 years 1rst SA Prior SA 2 years 2006 18 months of SA Cox survival analysis : aHR (CI95%) 1rst SA incident episode Variables: Main independent MMBS (low, medium, high) Covariates Age groups Occupational social-class No of 2-year prior SA (0,1-4,<5) Stratifying variables Sex Diagnosis groups/pathologies (ICD9) Musculoskeletal (dorsopathies) Mental (CMD) CVD (Ischemic) Results Multiple correspondence analysis (MCA) results Cardiovascular pattern Pain-related pattern DIMENSION 1 Hyperlipidemia Hypertension Diabetes Coronary artery disease Normal weight Obesity Former smoker DIMENSION 2 Headache Low back pain Total inertia Dimension Inertia Sleep disturbances Median absolute contribution 0.09 0.15 0.04 Category absolute contribution 0.171 0.278 0.081 0.65 0.83 0.18 Neck pain DIMENSION 1 Venous thrombosis Coronary artery disease Cerebrovascular disease Peripheral vascular disease DIMENSION 2 Headache Low back pain Neck pain MEN (n=108,178) a Variable Category Category absolute relative inertia contribution contribution 0.114 0.186 0.972 0.184 0.297 0.975 0.056 0.083 0.936 0.055 0.037 0.047 0.08 0.131 0.015 0.063 0.083 0.072 0.04 0.100 0.855 0.879 0.90 0.887 0.03 0.072 0.050 0.075 0.079 0.072 0.344 0.877 0.951 0.04 0.17 0.072 0.338 0.448 0.947 WOMEN (n=15,632 ) b 0.22 0.444 0.03 0.078 0.144 0.909 0.07 0.14 0.081 0.154 0.936 0.08 0.154 0.090 0.161 0.884 0.08 0.161 0.174 0.305 0.862 0.15 0.305 0.028 0.110 0.112 0.084 0.332 0.347 0.846 0.856 0.860 0.04 0.17 0.17 0.084 0.332 0.337 0.034 0.069 0.569 0.02 0.046 0.49 0.77 0.28 a Non-significant variables for men: venous thrombosis, cerebrovascular disease, peripheral vascular disease, fatigue, alcohol consumption, sleep disturbances. b Non-significant variables for women: hypertension, hyperlipidemia, diabetes, BMI, fatigue, tobacco and alcohol consumption. MMBS = [(∑ AbsC*InertiaD1) + (∑ AbsC*InertiaD2)]*100 Results Descriptive Distribution of the Multimorbidity Score (MMBS>0) among men and women MMBS Range Mean (SD) P50 (P25-P75) Tertiles Low Medium High Total Men (N=269,083) Score n 2.23-100 17.90 (14.50) 8.98 (7.38-24.50) < 8.98 8.23-13.43 >13.43 100 56,499 23,319 28,360 108,178 % - Women (N=103,287) Score n 2.72-100 9.69 (10.59) 2.72 (2.72-19.16) - 52.2 21.6 26.2 100 < 2.72 9,563 2.72-7.62 862 >7.62 5,207 100 15,632 % - - - 61.2 4.34 33.3 100 Under-representation of women (28%) Lower prevalence of MMB ( 15%) in women than in men (40%) Lower MMB levels among women than men Results Risk for incident sickness absence by MMB levels among MEN 1,2 log a HR (95%CI) 1 0,8 0,6 0,4 0,2 0 -0,2 -0,4 Low (n=54,366) Medium (n=42,626) MMB Levels High (n=85,889) No of cases Overall SA 17,192 Dorsopathies 845 CMD 390 Ischemic 90 log aHR: Logarithm of hazard ratio adjusted for age, number of 2 years prior SA and occupational social class Results Risk for incident sickness absence by MMB levels among WOMEN 1,2 1 log a HR (95%CI) 0,8 0,6 0,4 0,2 0 -0,2 -0,4 -0,6 -0,8 No of cases Low (n=8,391) Medium (n=964) MMB Levels Overall SA High (n=4,418) 7,310 Dorsopathies 418 CMD 366 CVD 86 log aHR: Logarithm of hazard ratio adjusted for age, number of 2 years prior SA and occupational social class Discussion MAJOR LIMITATIONS • Lack of other high-cost chronic conditions (mental, musculoskeletal, respiratory or tumors) Questionnaire Not designed for the study purpose Mostly on CVD & risk factors • Selection bias Towards male gender Self-selection: Reason for attending medical evaluation Healthier and more motivated for their health-status Not on sick leave Discussion STRENGTHS • Large sample size • Broad geographic representation • Study population distribution representative of Spanish workforce • Few studies in worker populations (younger and healthier) First step a global approach of disease (MMB) impact on workforce health indicators Main messages and next steps • Multimorbidity (MMB) increases the risk of overall future sickness absence and for diagnosis-specific pathologies. • Next steps: To assess MMB effect by including measurements of work-related factors. To continue targeting the effect of MMB on relevant occupational outcomes. MANY THANKS [email protected] Center for Research in Occupational Health (CISAL) Barcelona, Catalonia (Spain)
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