• Pain Med · Nov 2008

    Influence of prescription benefits on reported pain in cancer patients.

    • Margarette Bryan, Nila De La Rosa, Ann Marie Hill, William J Amadio, and Robert Wieder.
    • Division of Medical Oncology, UMDNJ-New Jersey Medical School/University Hospital Cancer Center, Newark, New Jersey 07103, USA.
    • Pain Med. 2008 Nov 1;9(8):1148-57.

    ObjectiveTo identify associations between prescription coverage and cancer pain and its sequelae in indigent patients.Design And SettingA retrospective chart review at UMDNJ-University Hospital.Patients And Outcome MeasuresCharts from 20 patients with Medicaid and 20 patients categorized as Self-pay/Charity Care were analyzed for the influence of insurance coverage on reported pain at the time of a hospital discharge and at three subsequent clinic visits. Patient and disease characteristics, pain regimens, doses, reported pain and its impact were determined.ResultsThe groups were statistically indistinguishable except for age and ethnicity. The Medicaid group was younger and had a majority of African Americans while the Self-pay/Charity Care patients had a majority of Hispanics. Lower doses of transdermal fentanyl were prescribed to Self-pay/Charity Care patients. Self-pay/Charity Care patients tended to report higher pain levels, but this was statistically significant only at the second clinic visit. The clinical significance of differences in pain intensity was reflected in differences in unscheduled visits and admissions. Adherence to pain regimens improved in the Medicaid group and diminished in the Self-pay/Charity Care group, but the differences did not achieve statistical significance. Lack of funds as the reason for non-adherence was only given by Self-pay/Charity Care patients.ConclusionIndigent patients without prescription coverage trended toward reporting more cancer pain, received lower doses of transdermal fentanyl, and trended to lower adherence to pain regimens due to financial reasons. The trends observed in this pilot study will guide the design of a hypothesis-driven regression analysis.

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