Objective: To examine the effect of Primary Health Care (PHC) on the association between multimorbidity and emergency service utilization among adults in Brazil.

Methods: This is a cross-sectional, nationwide household-based study using data from the 2019 National Health Survey. Poisson regression was used to assess emergency service utilization among individuals with multimorbidity. The interaction of variables such as Family Health coverage and orientation to PHC in these associations was also evaluated.

Results: The prevalence of multimorbidity was 31.2% (95%CI 30.9-31.5), Family Health coverage was 71.8% (95%CI 71.4-72.0), and low orientation of services toward PHC was 70% (95%CI 69.1-70.9). Emergency service utilization had a prevalence of 2.0% (95%CI 1.9-2.0), being twice as high among individuals with multimorbidity (3.1; 95%CI 2.9-3.3) compared to those without this condition (1.4; 95%CI 1.3-1.5). However, individuals with multimorbidity and Family Health coverage had a 20% lower prevalence of emergency service utilization than those without Family Health coverage (PR 0.8; 95%CI 0.6-0.9). The association between emergency service utilization and multimorbidity was not modified by the evaluation of the service as highly oriented toward PHC (p=0.956).

Conclusion: The study showed that Family Health coverage exerted a positive effect on the association between multimorbidity and emergency service utilization.

Download full-text PDF

Source
http://dx.doi.org/10.1590/1980-549720240062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654288PMC

Publication Analysis

Top Keywords

emergency service
28
service utilization
28
family health
20
health coverage
20
association multimorbidity
12
multimorbidity emergency
12
individuals multimorbidity
12
primary health
8
health care
8
multimorbidity
8

Similar Publications

Background: Early rapid sequence induction of anaesthesia (RSI) and tracheal intubation for patients with airway or ventilatory compromise following major trauma is recommended, with guidance suggesting a 45-min timeframe. Whilst on-scene RSI is recommended, the potential time benefit offered by Helicopter Emergency Medical Services (HEMS) has not been studied. We compared the time from 999/112 emergency call to delivery of RSI between patients intubated either in the Emergency Department or pre-hospital by HEMS.

View Article and Find Full Text PDF

Introduction: Ambulance staff play a crucial role in responding to mental health crises. However, negative regard toward patients with mental health conditions can hinder care. The Medical Condition Regard Scale (MCRS) assesses regards or attitudes but has not previously been validated for educated ambulance staff and has never been translated into Norwegian.

View Article and Find Full Text PDF

Conflict-affected regions face severe reproductive health challenges that disproportionately impact adolescent girls and young women (AGYW) and children, who are especially vulnerable due to the breakdown of healthcare systems and limited access to essential services. AGYW are at heightened risk due to restricted access to family planning, prenatal care, and emergency obstetric services, while children face malnutrition, disease outbreaks, and developmental delays. These challenges have profound long-term consequences for both their physical and psychological well-being.

View Article and Find Full Text PDF

Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.

Sci Rep

December 2024

The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, School of Computer and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!