Social inequalities, length of hospital stay for chronic conditions and the mediating role of comorbidity and discharge destination: A multilevel analysis of hospital administrative data linked to the population census in Switzerland

Abstract

Social factors are recognized determinants of morbidity and mortality and also have an impact on use of medical services. The objective of this study was to assess the associations of educational attainment, social and financial resources, and migration factors with length of hospital stays for chronic conditions. In addition, the study investigated the role of comorbidity and discharge destination in mediating these associations. The study made use of nationwide inpatient data that was linked with Swiss census data. The study sample included n = 141,307 records of n = 92,623 inpatients aged 25 to 84 years, hospitalized between 2010 and 2016 for a chronic condition. Cross-classified multilevel models and mediation analysis were performed. Patients with upper secondary and compulsory education stayed longer in hospital compared to those with tertiary education (β 0.24 days, 95% CI 0.14-0.33; β 0.37, 95% CI 0.27-0.47, respectively) when taking into account demographic factors, main diagnosis and clustering on patient and hospital level. However, these effects were almost fully mediated by burden of comorbidity. The effect of living alone on length of stay (β 0.60 days, 95% CI 0.50-0.70) was partially mediated by both burden of comorbidities (33%) and discharge destination (30.4%). (Semi-) private insurance was associated with prolonged stays, but an inverse effect was observed for colon and breast cancer. Allophone patients had also prolonged hospital stays (β 0.34, 95% CI 0.13-0.55). Hospital stays could be a window of opportunity to discern patients who need additional time and support to better cope with everyday life after discharge, reducing the risks of future hospital stays. However, inpatient care in Switzerland seems to take into account rather obvious individual needs due to lack of immediate support at home, but not necessarily more hidden needs of patients with low health literacy and less resources to assert their interests within the health system.

Fig 1. Flow chart of record matching and selection process for the SIHOS study sample.

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.

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: $\text{zMediation}=\frac{z_d{z}_e}{\widehat{\sigma }{z}_{de}}=12.62$ (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.