Understanding Why Patients Of Low Socioeconomic Status Prefer Hospitals Over Ambulatory Care
Abstract
Patients with low socioeconomic status (SES) use more acute hospital care and less primary care than patients with high socioeconomic status. This low-value pattern of care use is harmful to these patients' health and costly to the health care system. Many current policy initiatives, such as the creation of accountable care organizations, aim to improve both health outcomes and the cost-effectiveness of health services. Achieving those goals requires understanding what drives low-value health care use. We conducted qualitative interviews with forty urban low-SES patients to explore why they prefer to use hospital care. They perceive it as less expensive, more accessible, and of higher quality than ambulatory care. Efforts that focus solely on improving the quality of hospital care to reduce readmissions could, paradoxically, increase hospital use. Two different profile types emerged from our research. Patients in Profile A (five or more acute care episodes in six months) reported social dysfunction and disability. Those in Profile B (fewer than five acute care episodes in six months) reported social stability but found accessing ambulatory care to be difficult. Interventions to improve outcomes and values need to take these differences into account.
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People with low socioeconomic status are more likely to delay treatment and rely on emergency-room services rather than visit private offices of primary healthcare providers.
“People with low socioeconomic status are also more likely to delay treatment and rely on emergency-room services rather than visit private offices of primary healthcare providers [39].”
From Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy (2022) · cites this paper as [39]
Variables correlated with age and markers of socioeconomic status such as insurance type have been previously linked to hospital admission.
“that have been previously linked to hospital admission [46–48].”
From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [48]
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- Authors
- Kangovi, S., Barg, F. K., Carter, T., Long, J. A., Shannon, R., & Grande, D.
- Affiliations
- Philadelphia VA Medical Center (US)
- University of Pennsylvania (US)
- Tamala Carter is a community health worker in the Penn Center for Community Health Workers.
- Veterans Health Administration (US)
- Richard Shannon is the Frank Wister Thomas Professor of Medicine in the Department of Medicine, Perelman School of Medicine.
- David Grande is an assistant professor of medicine in the Perelman School of Medicine.
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2013-07
- Volume
- 32
- Issue
- 7
- Pages
- 1196-1203
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- OpenAlex
- W2091720548
- Cited by
- 445
- References
- 3
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 23 · 2023: 29 · 2022: 48 · 2021: 61 · 2020: 50 · 2019: 33 · 2018: 44 · 2017: 45 · 2016: 25 · 2015: 22 · 2014: 21 · 2013: 7
- FWCI
- 54.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Affairs (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Social Sciences › Health › Health disparities and outcomes
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Keywords
- low socioeconomic status, hospital utilization, outpatient care, low-value care, patient preferences, health care access, hospital readmission, social dysfunction, disability, urban patients, accountable care organizations, health care costs
- MeSH terms
- Ambulatory Care, Cost Control, Disability Evaluation, Female, Health Services Accessibility, Health Services Misuse, Hospitalization, Humans, Male, Medicaid, Patient Readmission, Poverty, Quality of Health Care, Social Adjustment, United States, Medically Uninsured, Outcome Assessment, Health Care, Qualitative Research, Patient Preference
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