Impact of FDA Black Box Warning on Psychotropic Drug Use in Noninstitutionalized Elderly Patients Diagnosed With Dementia

2016 ◽  
Vol 29 (5) ◽  
pp. 495-502 ◽  
Author(s):  
Rakesh R. Singh ◽  
Rajesh Nayak

Background: The study seeks to investigate the impact of Food and Drug Administration's black box warning (BBW) on the use of atypical antipsychotics (AAP) and nonantipsychotic psychotropic alternatives in noninstitutionalized elderly population diagnosed with dementia. Method: The Medical Expenditure Panel Survey (2004 through 2007) was utilized as the data source. Medication use in elderly patients (≥65 years) was defined as taking at least 1 medication for dementia. We performed a statistical comparison of prewarning (2004-2005) and postwarning (2006-2007) periods with respect to AAP and nonantipsychotic psychotropic use to examine the impact of labeling changes. Results: A bivariate analysis did not yield statistically significant change in either the AAP or nonantipsychotic psychotropic use, pre- versus postwarning. However, multivariate logistic-regression analyses revealed greater odds for antidementia (odds ratio [OR] = 1.976, P = .0195) and benzodiazepine (OR = 3.046, P = .0227) medication use in postwarning period compared to the prewarning period. Conclusion: The regulatory warnings and labeling changes regarding off-label use of AAPs in dementia treatment showed minimal impact on their actual use in noninstitutionalized populations. A corresponding increase in the use of nonantipsychotic psychotropics explains that BBW might have resulted in a compensatory shift in favor of benzodiazepines and antidementia medications. Additional research should be conducted to examine the long-term impact of BBW on antipsychotic prescribing changes.

2021 ◽  
Author(s):  
Yu Wang ◽  
Joohyun Park ◽  
Rui Li ◽  
Elizabeth Luman ◽  
Ping Zhang

<b>Objective</b> <p>To assess national trends in out-of-pocket (OOP) costs among adults aged 18–64 years with diabetes in the United States. </p> <p><b>Research design and methods</b></p> <p>Using data from the 2001–2017 Medical Expenditure Panel Survey, we estimated total per person annual OOP costs (insurance premiums, prescription drug costs, inpatient and outpatient deductibles, and copays, and other payments not covered by insurance) and high OOP cost rate defined as the percentage of people with OOP spending more than 10% of their family’s pretax income. We examined trends overall, by subgroup (insurance type, income level, insulin use, size of patient’s employer, and whether the patient was enrolled in a high deductible health plan), and by type of service. Changes in trends were identified using joinpoint analysis; costs were adjusted to 2017 US dollars.</p> <p><b>Results</b></p> <p>From 2001 to 2017, OOP costs decreased 4.3%, from $4,328 to $4,139, and high OOP cost rate fell 32%, from 28% to 19% (<i>P</i> < 0.001). Changes in the high OOP cost rate varied by subgroup, declining among those with public or no insurance and those with an income <200% of the federal poverty level (<i>P</i> < 0.001), but remaining stable among those with private insurance and higher income. Drug prescription OOP costs decreased among all subgroups (<i>P</i> < 0.001). Decreases in total (-$58 vs -$37, <i>P</i> < 0.001) and prescription (-$79 vs -$68, <i>P</i> < 0.001) OOP costs were higher among insulin users than noninsulin users. </p> <p><b>Conclusions</b></p> OOP costs among US nonelderly adults with diabetes declined, especially among those least able to afford them. Future studies may explore factors contributing to the decline in OOP costs and the impact on the quality of diabetes care and complication rates.


2021 ◽  
Author(s):  
Yu Wang ◽  
Joohyun Park ◽  
Rui Li ◽  
Elizabeth Luman ◽  
Ping Zhang

<b>Objective</b> <p>To assess national trends in out-of-pocket (OOP) costs among adults aged 18–64 years with diabetes in the United States. </p> <p><b>Research design and methods</b></p> <p>Using data from the 2001–2017 Medical Expenditure Panel Survey, we estimated total per person annual OOP costs (insurance premiums, prescription drug costs, inpatient and outpatient deductibles, and copays, and other payments not covered by insurance) and high OOP cost rate defined as the percentage of people with OOP spending more than 10% of their family’s pretax income. We examined trends overall, by subgroup (insurance type, income level, insulin use, size of patient’s employer, and whether the patient was enrolled in a high deductible health plan), and by type of service. Changes in trends were identified using joinpoint analysis; costs were adjusted to 2017 US dollars.</p> <p><b>Results</b></p> <p>From 2001 to 2017, OOP costs decreased 4.3%, from $4,328 to $4,139, and high OOP cost rate fell 32%, from 28% to 19% (<i>P</i> < 0.001). Changes in the high OOP cost rate varied by subgroup, declining among those with public or no insurance and those with an income <200% of the federal poverty level (<i>P</i> < 0.001), but remaining stable among those with private insurance and higher income. Drug prescription OOP costs decreased among all subgroups (<i>P</i> < 0.001). Decreases in total (-$58 vs -$37, <i>P</i> < 0.001) and prescription (-$79 vs -$68, <i>P</i> < 0.001) OOP costs were higher among insulin users than noninsulin users. </p> <p><b>Conclusions</b></p> OOP costs among US nonelderly adults with diabetes declined, especially among those least able to afford them. Future studies may explore factors contributing to the decline in OOP costs and the impact on the quality of diabetes care and complication rates.


2021 ◽  
Vol 111 (12) ◽  
pp. 2157-2166
Author(s):  
Samuel H. Zuvekas ◽  
David Kashihara

The COVID-19 pandemic caused substantial disruptions in the field operations of all 3 major components of the Medical Expenditure Panel Survey (MEPS). The MEPS is widely used to study how policy changes and major shocks, such as the COVID-19 pandemic, affect insurance coverage, access, and preventive and other health care utilization and how these relate to population health. We describe how the MEPS program successfully responded to these challenges by reengineering field operations, including survey modes, to complete data collection and maintain data release schedules. The impact of the pandemic on response rates varied considerably across the MEPS. Investigations to date show little effect on the quality of data collected. However, lower response rates may reduce the statistical precision of some estimates. We also describe several enhancements made to the MEPS that will allow researchers to better understand the impact of the pandemic on US residents, employers, and the US health care system. (Am J Public Health. 2021;111(12):2157–2166. https://doi.org/10.2105/AJPH.2021.306534 )


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