"Patiently waiting": a multivariate logistic regression analysis to understand waiting times. (30th September 2020) - Rossi, M; Da Re, N R; Forni, G; Campanelli, E; Balconi, R; Bergamaschi, W
Abstract: Background: Outpatient visits waiting time poses a significant challenge in countries facing rising health demands, particularly those that have universal access to public health care. In Italy, despite major improvements, many patients still experience extensive delays accessing specialist care. Our study describes waiting times for the 14 most critical specialist "first" visits provided by the national health care system in the Milan Health Protection Agency territory (3, 48 million inhabitants) and investigates whether specific patient, prescription and hospital variables are associated to an increased risk of delay in waiting time. Methods: A multivariate logistic regression analysis of the relationship between specialty, age, sex, priority class, exemption from prescription charges, hospital organization, patient region, and Hospital district was performed to investigate whether specific variables are associated to the odds ratios (OR) for having to wait more than the maximum time limit. Results: Out of the 1, 174, 283 visits performed in 2019, 90% were provided within the maximum waiting time. Visits were provided beyond maximum time in 20% (median delay=2 days) of priority class 1 visits, 24% (median delay=7 days) of class 2, 22% (median delay=37 days) of class 3, and 4% (median delay=65 days) of class 4. All analysed variables were significantly correlated (p < 0.001) to the OR for having to wait beyond the priority class specific limit. In particular: female sex (OR = 1.074), residing outside Lombardy (OR = 0.696), class 1 priority (OR = 4.939), exemption from prescription charges (OR = 1.107), research Hospital (private: OR = 5.937, public: OR = 5.156) and ophthalmology (OR = 8.822). Conclusions: Our results show that most visits were provided within the time limits. However, waiting times seem to be a major issue when assessing certain specialties, hospitals, and priority classes. This data should guide health policy makers interested in tackling the waiting time issue. Key messages: This study highlights the importance of monitoring outpatient waiting times. Strategies and policies to tackle the problem of waiting times should be made upon reliable data and transparent criteria in order to meet patients' needs.