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Are sample medicines hurting the uninsured?
John Zweifler1, Susan Hughes, Sean Schafer
1University of California, San Francisco-Fresno Medical Education Family Practice Residency Program, Fresno 93702-2907, USA.
The Journal of the American Board of Family Practice
|September 28, 2002
Summary
Uninsured patients with hypertension who received free medication samples had higher diastolic blood pressure. This highlights potential risks associated with sample medication use in vulnerable populations.
Area of Science:
- Health Services Research
- Clinical Pharmacy
- Hypertension Management
Background:
- Physician distribution of free sample medications is common.
- Sample medication use may lead to suboptimal disease control in chronic conditions like hypertension.
- The study investigated links between sample medicine use, hypertension, and healthcare payment sources.
Purpose of the Study:
- To examine the association between free sample medication use and hypertension.
- To explore the relationship between sample medication use and healthcare payment sources.
- To identify factors influencing sample medication access among hypertensive patients.
Main Methods:
- Conducted telephone interviews and chart reviews at two California community health centers.
- Included adult patients with hypertension, at least three clinic visits in the past year, and lacking insurance or having Medicare/Medicaid.
- Analyzed data from 71 participants to assess medication use and health outcomes.
Main Results:
- Lack of health insurance was significantly associated with the use of sample medications (P < .01).
- No significant differences in medication changes were observed between groups.
- Uninsured patients receiving sample medications exhibited higher diastolic blood pressure (P = .01).
Conclusions:
- Health insurance status, particularly lack of insurance, is a key predictor of sample medication use.
- Higher diastolic blood pressure was linked to sample medication use among uninsured hypertensive patients.
- The study's cross-sectional design and variable covariance necessitate cautious interpretation of findings.