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Identifying The Main Predictors Of Length Of Care In Social Care In Portugal.

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Identifying The Main Predictors Of Length Of Care In Social Care In Portugal.

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Identifying the Main Predictors of Length of Care in Social Care in Portugal.

Hugo Lopes^1,2, Gracinda Guerreiro³, Manuel Esquível³

¹NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal.

Portuguese Journal of Public Health

|October 29, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Predicting long-term care length of stay (LOC) in Portugal is possible using patient factors like gender and support, and facility factors like occupancy. This helps optimize resource allocation in home and community-based services (HCBS) and nursing homes (NH).

Keywords:

Dependency levels Home and community-based services Length of care Long-term care Nursing homes Portugal

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Area of Science:

Gerontology
Health Services Research
Public Health Policy

Background:

The Portuguese National Network for Long-Term Integrated Care manages diverse care settings, including home and community-based services (HCBS) and nursing home (NH) units.
Understanding predictors of length of care (LOC) is crucial for resource allocation and cost estimation within this network.
A comprehensive dataset of 20,984 individuals admitted and discharged in 2015 provides a robust basis for analysis.

Purpose of the Study:

To identify key predictors of length of care (LOC) at admission for individuals entering the Portuguese long-term care (LTC) system.
To estimate expected LOC for new individuals, enabling better resource management and policy planning.
To compare predictors across different care settings: HCBS and NH (Short, Medium, Long Stay).

Main Methods:

A generalised linear model (GLM) with a gamma distribution was employed to analyze LOC.
Patient characteristics including age, gender, social support, and dependency in activities of daily living (ADL) were assessed.
External factors such as referral source, facility characteristics (beds, occupancy, maturity), and care setting were incorporated into the models.

Main Results:

Male gender, family/neighbour support, and hospital referral to NH (or primary care to HCBS) were associated with reduced LOC.
Lower facility occupancy rates and fewer operational months correlated with shorter LOC.
Increased dependency in ADL significantly increased LOC, while cognitive status showed a significant inverse relationship only in NH settings.

Conclusions:

Predictive models for LOC in the Portuguese LTC system can be developed using admission data.
Identifying key predictors allows for proactive resource management and cost-effective planning.
The findings support evidence-based policy decisions for optimizing the delivery of long-term care services.