Lopes, H. and Guerreiro, G. and Esquível, M. and Mateus, C. (2021) Identifying the Main Predictors of Length of Care in Social Care in Portugal. Portuguese Journal of Public Health, 39 (1). pp. 21-35.
Full text not available from this repository.Abstract
In this paper, we aim to identify the main predictors at admission and estimate patients' length of care (LOC), within the framework of the Portuguese National Network for Long-Term Integrated Care, considering two care settings: (1) home and community-based services (HCBS) and (2) nursing home (NH) units comprising Short, Medium, or Long Stay Care. This study relied on a database of 20,984 Portuguese individuals who were admitted to the official long-term care (LTC) system and discharged during 2015. A generalised linear model (GLM) with gamma distribution was adjusted to HCBS and NH populations. Two sets of explanatory variables were used to model the random variable, LOC, namely, patient characteristics (age, gender, family/neighbour support, dependency levels at admission for locomotion, cognitive status, and activities of daily living [ADL]) and external factors (referral entity, number of beds/treatment places per 1,000 inhabitants ≥65 years of age), maturity and occupancy rate of the institution, and care setting. The features found to most influence the reduction of LOC are: male gender, having family/neighbour support, being referred by hospitals to NH (or by primary care to HCBS), and being admitted to units with a lower occupancy rate and with fewer months in operation. Regarding the dependency levels, as the number of ADL considered "dependent"increases, LOC also increases. As for the cognitive status, despite the opposite trend, it was only statistically significant for NH. Furthermore, two additional models were applied by including "death,"although this feature is not observable upon admission. By creating a model that allows for an estimate of the expected LOC for a new individual entering the Portuguese LTC system, policy-makers are able to estimate future costs and optimise resources.