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Implementation of a pharmacy-based blood lead testing program: A retrospective analysis
Background:
In 2014, a change in the water source in Flint, Michigan, exposed residents to dangerous levels of lead in their drinking water. The concern for lead toxicity created the opportunity for pharmacies to provide quick and accessible blood lead testing services.
Objective:
The primary objective was to create a pharmacy-based blood lead testing program through collaboration with the Michigan Department of Health & Human Services and community pharmacies in Flint, Michigan.
Practice Description:
The partnership was established in 2016 and Magellan LeadCare II blood analyzers were placed at three pharmacies. Walk-in testing was offered from June 2016 to August 2016.
Practice Innovation:
Community pharmacists aimed to provide more accessible lead screening services to limit health disparities and medical complications.
Evaluation Methods:
A retrospective analysis using descriptive statistics was conducted to assess the benefits of early blood lead screening and to determine when a patient should seek further evaluation. Blood lead screening values were based on the Center for Disease Control (CDC) and Prevention's blood lead reference value (BLRV). The BLRV at the time of the study was ≥ 5 μg/dL, but this was eventually changed to ≥3.5 μg/dL.
Results:
A total of 77 individuals were screened in the pharmacies. Four (5%) individuals had lead levels ≥5 μg/dL. Of these, two patients were <6 years of age. Fifteen (19%) patients had lead levels ≥3.5 μg/dL. The highest lead level detected was 24.2 μg/dL in a 70-year-old patient. If the result was ≥5 μg/dL, patients were instructed to follow up with their physician or the Genesee County Health Department. All patients with elevated blood levels were referred to the appropriate health care provider.
Conclusion:
This model demonstrates the feasibility of a public health/community pharmacy collaborative lead testing program. This program can serve as a template for future crisis-focused collaborative models.
