Factors Associated With In-Hospital Death Among Pneumonia Patients in US Hospitals From 2016∼2019open access
- Authors
- Kim, Sun Jung; Medina, Mar; Zhong, Lixian; Chang, Jongwha
- Issue Date
- Jan-2023
- Publisher
- KERMAN UNIV MEDICAL SCIENCES
- Keywords
- Pneumonia; NIS Sample; In-Hospital Death; Health Disparity
- Citation
- INTERNATIONAL JOURNAL OF HEALTH POLICY AND MANAGEMENT, v.12, no.1
- Journal Title
- INTERNATIONAL JOURNAL OF HEALTH POLICY AND MANAGEMENT
- Volume
- 12
- Number
- 1
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/25452
- DOI
- 10.34172/ijhpm.2023.7390
- ISSN
- 2322-5939
2322-5939
- Abstract
- Background: Pneumonia is one of the leading causes of hospital admission in the United States with a global health burden of about 6.8 million hospitalizations and 1.1 million deaths in patients over 65 years old in 2015. This study aimed to identify possible patient and hospital-related risk factors for in-hospital pneumonia death across US hospitals. Methods: The National Inpatient Sample (NIS) was used to identify nationwide pneumonia patients (n = 374 766, weighted n = 1 873 828) from 2016 to 2019. We examined the characteristics of the study sample and their association with in-hospital death. Multivariate survey logistic regression models were used to identify risk factors. Results: During the study periods, in-hospital death rates continuously decreased (2.45% in 2016 to 2.19% in 2019). Descriptive statistics showed that patient and hospital factors had varied in-hospital death rates. Survey logistic regression results suggested that male, very low income, non-Medicare, government hospitals, rural hospitals, and specific hospital regions were associated with higher in-hospital death rates than their reference groups. Conclusion: Socioeconomic factors, including income and insurance, are associated with pneumonia mortality. Census region, hospital ownership, and rural location are also related to in-hospital mortality. Such findings in underserved, impoverished, and rural areas to identify possible health disparities.
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