The Ethics of Postmarketing Observational Studies of Drug Safety Under Section 505(o)(3) of the Food, Drug, and Cosmetic Act

2012 ◽  
Vol 38 (4) ◽  
pp. 577-606 ◽  
Author(s):  
Barbara J. Evans

In 2007, Congress granted the Food and Drug Administration (FDA) new powers to order pharmaceutical companies to conduct drug safety studies and clinical trials in the postmarketing period after drugs are approved. The methodologies include observational studies that examine patients' insurance claims data and clinical records to infer whether drugs are safe in actual clinical practice. Such studies offer a valuable tool for improving drug safety, but they raise ethical and privacy concerns because they would entail widespread use of patients' health information in commercial research by drug manufacturers. This is the first article to explore the ethics of these section 505(o)(3) observational studies, so named after the section of the Food, Drug, and Cosmetic Act that authorizes them.Data access problems threaten to make the FDA's section 505(o)(3) study requirements unenforceable. Under existing federal privacy regulations, it appears highly unlikely that pharmaceutical companies will have reliable access to crucial data resources, such as insurance claims data and healthcare records, to use in these studies. State privacy laws present another potential barrier to data access. If pharmaceutical companies do manage to gain access to the needed data, this will raise serious privacy concerns because section 505(o)(3) observational studies do not appear to be covered by any of the major federal regulations that afford ethical and privacy protections to persons whose data are used in research.If the FDA's program of section 505(o)(3) observational studies fails because of the above problems, this failure will have a number of bad consequences: the public will be exposed to avoidable drug safety risks; taxpayers may be forced to bear the costs of having the FDA conduct drug safety investigations that would have been funded by drug manufacturers if data had been available; and, perhaps most troubling, the FDA may be forced to order postmarketing clinical trials to answer questions that could have been answered using observational studies. Problems with access to data for section 505(o)(3) studies thus could directly imperil human research subjects by forcing a needless over-reliance on risky postmarketing drug safety trials.This Article concludes by describing a promising new legal pathway for resolving these problems. Congress has provided the FDA a new set of powers that if skillfully exercised will allow the agency: (1) to facilitate pharmaceutical companies' appropriate access to data for use in section 505(o)(3) observational studies, (2) to impose strict ethical and privacy protections for persons whose data are used in these studies, and (3) to mobilize private-sector funding to generate much-needed evidence of the safety of FDA-approved drugs.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20568-e20568
Author(s):  
Michael Gregory Cushion ◽  
Bhavani Krishnan ◽  
Jeff Paul Hodge ◽  
Jaya Chandra Balusu ◽  
Joseph Wagner ◽  
...  

e20568 Background: Many targeted therapy clinical trials require a somatic gene mutation/alteration for eligibility. We assessed the feasibility of leveraging Real-World Data (RWD) to enrol NSCLC patients into clinical trials. Methods: US insurance claims data were extracted to identify lung cancer patients. These data were matched with EMR data also containing NSCLC patients’ details regarding the occurrence and results of molecular testing for EGFR, ALK, ROS1, JAK2, HER2 and RET somatic alterations, achieving a level of granular detail beyond that available in each individual dataset. A one-year extraction period was applied, with no gender or age restrictions. Results: Results for the matched dataset are summarised in the table below - the overall patient record match was 89.6%. Conclusions: The observed prevalence correlated reasonably well with literature reported prevalence for the molecular biomarkers associated commercially available targeted therapies in NSCLC (EGFR, ALK, ROS1). The sample size for the remaining biomarkers was too small to draw conclusions, though the presence of data correlating to these is of interest, considering that there are no currently approved targeted therapies in NSCLC tailored to these predictive biomarkers. This approach could be expanded upon to recruit patients into targeted therapy clinical trials as the dataset is fully linkable to sites and investigators. With the emergence of broad genomic profiling, the availability of molecular data to support clinical trial enrolment is also expected to grow.[Table: see text]


2021 ◽  
Vol Volume 13 ◽  
pp. 969-980
Author(s):  
Khulood Al Mazrouei ◽  
Asma Ibrahim Almannaei ◽  
Faiza Medeni Nur ◽  
Nagham Bachnak ◽  
Ashraf Alzaabi

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser

Abstract Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.


2006 ◽  
Vol 48 (10) ◽  
pp. 1054-1061 ◽  
Author(s):  
Mark R. Cullen ◽  
Sally Vegso ◽  
Linda Cantley ◽  
Deron Galusha ◽  
Peter Rabinowitz ◽  
...  

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