Aim: To explore the impact of structural and intermediary social determinants of health (SDoH) on Californian adults' mental health during the early phase of the COVID-19 pandemic.

Design: This cross-sectional study used data from the 2020 cycle of the California Health Interview Survey, the largest US state-level population health survey.

Methods: Descriptive statistics and logistic regression were used to analyse the data. Using a general social determinant of health framework, we operationalized different survey questions to measure structural and intermediary determinants of mental health.

Results: Mental health during the early phase of COVID-19 among adults in California was associated with age, gender, health conditions, delayed care, employment status (loss of job or reduced income) and discrimination. People in higher social strata were more likely to have better mental health for many of these factors.

Conclusion: This study supports the assertion that material circumstances (such as employment status) and discrimination are associated with experiencing mental health issues among adults in California during COVID-19. Racism is a public health issue, and as nurses, addressing racism is critical. In addition, much work is needed to address SDoH to improve health outcomes, especially among marginalized populations.

Impact: This study addressed the knowledge gap concerning the social determinants of mental health among Californian adults during the early phase of the COVID-19 pandemic. Those who had reduced income and those who lost their jobs during the COVID-19 pandemic were 46% and 56%, respectively, more likely to report mental health problems. Those who experienced discrimination in healthcare were 304% more likely to report mental health issues. This research will increase the understanding of the social determinants of health, particularly for those with chronic illnesses and mental health issues during the COVID-19 pandemic.

Patient Or Public Contribution: No patient or public contribution, as we used an existing US state dataset. However, California Health Interview Survey is the largest state health survey in the United States and interviews more than 20,000 households each year representing the health care needs of Californians.

Download full-text PDF

Source
http://dx.doi.org/10.1111/jan.15803DOI Listing

Publication Analysis

Top Keywords

mental health
36
health
20
social determinants
16
determinants mental
12
adults california
12
early phase
12
phase covid-19
12
health issues
12
mental
10
cross-sectional study
8

Similar Publications

The current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.

View Article and Find Full Text PDF

The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy.

View Article and Find Full Text PDF

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.

View Article and Find Full Text PDF

Personalized Clustering for Emotion Recognition Improvement.

Sensors (Basel)

December 2024

Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.

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!