Travel burden for patients with multimorbidity – Proof of concept study in a Dutch tertiary care center()

OBJECTIVES: To explore travel burden in patients with multimorbidity and analyze patients with high travel burden, to stimulate actions towards adequate access and (remote) care coordination for these patients. DESIGN: A retrospective, cross-sectional, explorative proof of concept study. SETTING AND PARTICIPANTS: Electronic health record data of all patients who visited our academic hospital in 2017 were used. Patients with a valid 4-digit postal code, aged ≥18 years, had >1 chronic or oncological condition and had >1 outpatient visits with >1 specialties were included. METHODS: Travel burden (hours/year) was calculated as: travel time in hours × number of outpatient visit days per patient in one year × 2. Baseline variables were analyzed using univariate statistics. Patients were stratified into two groups by the median travel burden. The contribution of travel time (dichotomized) and the number of outpatient clinic visits days (dichotomized) to the travel burden was examined with binary logistic regression by adding these variables consecutively to a crude model with age, sex and number of diagnosis. National maps exploring the geographic variation of multimorbidity and travel burden were built. Furthermore, maps showing the distribution of socioeconomic status (SES) and proportion of older age (≥65 years) of the general population were built. RESULTS: A total of 14 476 patients were included (54.4% female, mean age 57.3 years ([± standard deviation] = ± 16.6 years). Patients travelled an average of 0.42 (± 0.33) hours to the hospital per (one-way) visit with a median travel burden of 3.19 hours/year (interquartile range (IQR) 1.68 – 6.20). Care consumption variables, such as higher number of diagnosis and treating specialties in the outpatient clinic were more frequent in patients with higher travel burden. High travel time showed a higher Odds Ratio (OR = 578 (95% Confidence Interval (CI) = 353 – 947), p < 0.01) than having high number of outpatient clinic visit days (OR = 237, 95% CI = 144 – 338), p < 0.01) to having a high travel burden in the final regression model. CONCLUSIONS AND IMPLICATIONS: The geographic representation of patients with multimorbidity and their travel burden varied but coincided locally with lower SES and older age in the general population. Future studies should aim on identifying patients with high travel burden and low SES, creating opportunity for adequate (remote) care coordination.