Fig 1.
Flow chart of record matching and selection process for the SIHOS study sample.
Table 1.
Definition of specific chronic diseases based on main diagnosis during hospitalisation.
Fig 2.
Cross-classified multilevel data structure.
Fig 3.
Top. Outcome length of stay (left) and mediator comorbidity (right) by age groups and educational attainment; bottom: Outcome length of stay (left) and mediator discharge destination (right) by age groups and household type.
Table 2.
Distribution (N records, %) of demographic and social factors with descriptive statistics (mean (SD), median (IQR)) of length of stay and number of side diagnoses and percentage (%) of transfer to inpatient setting = yes.
Table 3.
Distribution (N records, %) of variables related to health status and hospital stay with descriptive statistics (mean (SD), median (IQR)) of length of stay and number of side diagnoses and percentage (%) of transfer to inpatient setting = yes.
Table 4.
Associations of length of stay with social factors, health status and factors related to hospital stay (linear CCMM A to D).
Table 5.
Associations of length of stay with migration factors (linear CCMM D.1 and D.2).
Fig 4.
Mediation of the effect of educational attainment on length of stay by the number of side diagnoses (educational attainment: Compulsory = a1, c1, c1’; upper secondary = a2, c2, c2’; tertiary = reference).
Indirect effects of educational attainment on length of stay: a1*b = 0.371*0.901 = 0.334 (95% Monte Carlo CI: 0.283, 0.388); a2*b = 0.229*0.901 = 0.206 (0.169, 0.245). Mediation Model with intermediate outcome number of side diagnoses: controlling for clustering on hospital- and on patient-level and adjusted for sex, age, nationality, insurance class, household type, chronic condition, language region of hospital and year of discharge.
Fig 5.
Mediation of the effect of living alone on length of stay by the number of side diagnoses (top triangle: a, b, c and c’ = coefficients of linear CCMM) and transfer to inpatient setting (bottom triangle: d = coefficient of logistic CCMM; e, f and f’ = coefficients of linear CCMM). Indirect effect of living alone on length of stay (via number of side diagnoses): a*b = 0.216*0.901 = 0.194 (95% Monte Carlo CI: 0.149, 0.240); indirect effect of living alone on length of stay via transfer to inpatient setting: (p<0.001). Mediation Model with intermediate outcome number of side diagnoses (top): controlling for clustering on hospital- and on patient-level and adjusted for sex, age, nationality, educational attainment, insurance class, chronic condition, language region of hospital and year of discharge. Mediation Model with intermediate outcome transfer to inpatient setting (bottom): controlling for clustering on hospital- and on patient-level and adjusted for sex, age, nationality, educational attainment, insurance class, chronic condition, number of side diagnoses, psychic comorbidity, hospital ward, need of intensive care, language region of hospital and year of discharge.