BACKGROUND: Previous literature has emphasized the importance of cost sharing, health literacy, socioeconomic status, cognitive function, disease burden, and polypharmacy as some of the determinants of medication adherence. Little research has been published examining disparities in adherence rates when comparing different regions of the United States.OBJECTIVE: To examine the impact of geography, socioeconomic status, and other demographic variables on medication adherence rates in a large national sample of Medicare Part D and commercially insured beneficiaries.METHODS: This study focused on users of oral antidiabetic, antihypertensive, and/or antilipidemic medications. Beneficiaries who had at least 2 antidiabetic, antihypertensive, or antilipidemic prescription fills in 2010, 2011, or 2012 and who were enrolled in a large commercial or Medicare Part D prescription drug plan for at least 80% of one of these years (9.6 months) were included in this study. Results were stratified by year and by benefit type. Logistic regression was used to test for the adherence differences among the 9 U.S. regions as defined by the U.S. Census Bureau. Additional variables included in the model to control for population differences were age, gender, socioeconomic status, and yearly out-of-pocket medication expenses.RESULTS: After meeting all inclusion and exclusion criteria, 379,533 beneficiaries were in the 2012 Medicare cohort, and 659,553 beneficiaries were in the 2012 commercial cohort. New England was statistically the most adherent geographic region in both cohorts (Medicare odds ratio [OR] = 1.512, CI = 1.399-1.635); commercial OR = 1.193, CI = 1.109-1.284). Younger age beneficiaries, lower income beneficiaries, and females were less adherent in both groups.CONCLUSIONS: In the commercial and Medicare populations, geography, socioeconomic status, age, and gender all impact the likelihood of a beneficiary being adherent to chronic medications for hypertension, diabetes, and hyperlipidemia. While this study does not elucidate the specific factors (i.e., health literacy, disease severity) driving geographic and other differences in medication adherence observed between groups, it does highlight the limitations of quality metrics and wellness initiatives that assume relative homogeneity in beneficiary characteristics across the United States.
BACKGROUND: Immunosuppressive medication therapy after organ transplantation is essential for preventing transplant rejection and minimizing the need for re-transplantations. Nonadherence to immunosuppressant therapy has been identified as a major risk factor for acute complications and allograft rejection, as well as late graft rejection, and a return to dialysis after failed renal transplantation, leading to an increase in health care costs and potentially even death.
In Japan, the screening rates for breast and cervical cancers have been lower than in other countries, with rates below 20%. Breast cancer screening has been conducted biennially for over 40 years, and cervical cancer screening has been conducted biennially for over 20 years. Since lack of resources is an important barrier to increasing cancer screening rates, relationships between resources and cancer screening rates were investigated for breast and cervical cancers in Japan. METHODS: Based on the national data from 2008, the resource gap among 47 prefectures was compared. Resources were defined by the number of mammography equipment installations (per 100,000 women) for breast cancer screening and the number of gynecologists (per 100,000 women) for cervical cancer screening. Correlations between the screening rates and the availability of resources were calculated. RESULTS: The national average breast cancer screening rate was 14.7%, varying from 2.5% to 35% among the 47 prefectures. The national average number of mammography equipment installations was 5.88 per 100,000, ranging from 8.81 to 4.41 per 100,000 among the 47 prefectures. The correlation between mammography equipment installations and the screening rate for breast cancer was 0.420 (PϽ0.01). The national average cervical cancer screening rate was 19.4%, varying from 12.1% to 34.8% among the 47 prefectures. The national average number of gynecologists was 18.0 per 100,000, ranging from 13.1 to 25.9 per 100,000 among the 47 prefectures. The correlation between the number of gynecologists and the cervical cancer screening rate was -0.079 (n.s). CONCLUSIONS: Although the breast cancer screening rate shows a close relationship with medical resource availability, there is no relationship for cervical cancer screening. Since medical resources to increase breast cancer screening are limited in local areas, sufficient resources should be provided. In cervical cancer screening, other factors that affect the screening rate should be investigated.
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