焦虑
心理健康
婚姻状况
医学
萧条(经济学)
人口
医疗保健
保险不足的
精神科
逻辑回归
老年学
临床心理学
环境卫生
健康保险
内科学
经济
宏观经济学
经济增长
作者
Young Ji Lee,Josh Palmer,Alice Cline,Heeyoung Lee
标识
DOI:10.1177/10783903231197655
摘要
Background: This analysis aimed to examine the factors predictive of service utilization among patients with anxiety and/or depression. Quick and appropriate treatment for anxiety and depression can reduce disease burden and improve social functioning. Currently, less than half of the population with comorbid anxiety and depression receives the recommended treatment. Aims: This analysis aims to identify factors predictive of utilizing mental health treatment for those with anxiety and/or depression by analyzing intrinsic, patient-centered factors. Method: This study is a cross-sectional cohort analysis using National Health Interview Survey (NHIS) 2019 data. The sample size is 7,156 adults aged 18 to 64 with family incomes ≤100% of the federal poverty level. We used multivariate logistic regression analysis to identify factors predictive of care utilization in this population. Variables of interest include scores on Patient Health Questionnaire-8 (PHQ-8) and Generalized Anxiety Disorder-7 (GAD-7), service utilization, level of social functioning, having a usual source for care, and previous mental health care utilization. Additional covariates were age, gender, race, country of origin, education, marital status, and insurance coverage. Results: Twenty-one percent of respondents reported using mental health services. Factors predictive of care utilization were older age, female gender, limited social functioning, having a usual source of care, and insurance coverage. Conclusion: There are significant barriers to receiving quick and appropriate care for anxiety and/or depression. Strategies should focus on reducing barriers for young adults, men, and the uninsured/underinsured. Strategies for integrating mental health services into primary care could increase the percentage of people with anxiety and/or depression who receive services.
科研通智能强力驱动
Strongly Powered by AbleSci AI