Generic Drug Discount Programs: Are Prescriptions Being Submitted for Pharmacy Benefit Adjudication?

Tungol, Alexandra; Starner, Catherine I.; Gunderson, Brent W.; Schafer, Jeremy A.; Qiu, Yang; Gleason, Patrick P.

doi:10.18553/jmcp.2012.18.9.690

Cited by 18 publications

(26 citation statements)

References 9 publications

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“…Other studies have also found that claims databases fail to capture dispensings . These studies have used a diverse set of designs, data sources, and focus of interest.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies have also found that claims databases fail to capture dispensings. 10 These studies have used a diverse set of designs, data sources, and focus of interest. They include studies using commercial claims databases to compare the proportion of subjects exposed to a specific drug in different time periods to infer missing dispensings 11 ; using the Medical Expenditure Panel Survey to quantify the degree of use of low-cost generic programs to infer the degree of missing dispensing in claims databases 12,13 ; using self-report medication use to assess lack of capture in claims databases 14 ; and examining the impact of drug samples provided by physician offices on drug exposure misclassification.…”

Section: Discussionmentioning

confidence: 99%

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Quantification of missing prescriptions in commercial claims databases: results of a cohort study

Cepeda

Fife

Denarie

et al. 2017

Pharmacoepidemiol Drug Saf

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PurposeThis study aims to quantify the magnitude of missed dispensings in commercial claims databases.MethodsA retrospective cohort study has been used linking PharMetrics, a commercial claims database, to a prescription database (LRx) that captures pharmacy dispensings independently of payment method, including cash transactions. We included adults with dispensings for opioids, diuretics, antiplatelet medications, or anticoagulants. To determine the degree of capture of dispensings, we calculated the number of subjects with the following: (1) same number of dispensings in both databases; (2) at least one dispensing, but not all dispensings, missed in PharMetrics; and (3) all dispensings missing in PharMetrics. Similar analyses were conducted using dispensings as the unit of analysis. To assess whether a dispensing in LRx was in PharMetrics, the dispensing in PharMetrics had to be for the same medication class and within ±7 days in LRx.ResultsA total of 1 426 498 subjects were included. Overall, 68% of subjects had the same number of dispensings in both databases. In 13% of subjects, PharMetrics identified ≥1 dispensing but also missed ≥1 dispensing. In 19% of the subjects, PharMetrics missed all the dispensings. Taking dispensings as the unit of analysis, 25% of the dispensings present in LRx were not captured in PharMetrics. These patterns were similar across all four classes of medications. Of the dispensings missing in PharMetrics, 48% involved a subject who had >1 health insurance plan.ConclusionsCommercial claims databases provide an incomplete picture of all prescriptions dispensed to patients. The lack of capture goes beyond cash transactions and potentially introduces substantial misclassification bias. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

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“…Other studies have also found that claims databases fail to capture dispensings . These studies have used a diverse set of designs, data sources, and focus of interest.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantification of missing prescriptions in commercial claims databases: results of a cohort study

Cepeda

Fife

Denarie

et al. 2017

Pharmacoepidemiol Drug Saf

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“…28 A survey among predominantly insured patients found that only 4.7% of respondents used a GDDP in 2008, 29 and the use of these programs was likely greater in uninsured populations. 27,29 Our exclusion of patients with no prescription claims during the entire study may partially address this potential misclassification bias. Finally, we were not able to detect if patients were nonusers because they were not prescribed a β-blocker or because they chose not to fill a prescription, an important distinction in addressing underutilization of this therapy.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Predictors of β-Blocker Initiation After Myocardial Infarction in Patients With Type 2 Diabetes

Hickson

Brancato

Moga

2016

Journal of Pharmacy Technology

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Background: Beta-blockers remain important for secondary prevention after myocardial infarction (MI). Despite clinical guideline recommendations, underutilization of this pharmacotherapy continues in patients with type 2 diabetes (T2DM) compared to the general post-MI population. Objective: This study aimed to (1) quantify the proportion of T2DM patients utilizing β-blocker therapy within 30 days of hospital discharge after MI and (2) identify clinical and demographic characteristics predicting initiation of β-blocker therapy. Methods: A retrospective cohort of US employed, commercially insured individuals was assembled using de-identified enrollment files, medical claims, and pharmacy claims from 2007 to 2009. Inclusion criteria were the following: (1) type 2 diabetes, (2) ≥18 years old, (3) continuous eligibility, (4) MI. Multivariable logistic regression with adjusted odds ratios (OR adj ) using manual backward elimination was used to identify predictors of β-blocker initiation within 30 days of discharge from index hospitalization. Results: Of 341 T2DM patients, 167 (49.0%) were new users and 174 (51.0%) were nonusers of β-blockers within 30 days of post-MI hospital discharge. Patients on a calcium channel blocker (OR adj 2.63) and patients taking 1 to 5 medications (OR adj 3.59) were more likely to initiate β-blockers post-MI. Patients with heart failure (OR adj 0.45) or an arrhythmia (OR adj 0.44) were less likely to initiate β-blockers as well as patients with renal failure not taking a diuretic (OR adj 0.17). Conclusions: These results confirm previous findings that β-blockers are underutilized in T2DM patients post-MI. Predictors from the regression model can guide future research investigating how this deviation from guidelines is attributed to prescriber versus patient behavior.

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“…4, 15, 16 Based on Medical Expenditure Panel Survey (MEPS) self-reports of medication fills/ expenditures merged with Part D claims, researchers concluded that people tended to over-report the number of fills per medication (possibly due to reporting free samples from providers) but to underreport the number of medications. 17 However, the data were from 2006–2007, which was at the start of the expansion of GDDP plans, and the study excluded veterans since federal pharmacies (including VA pharmacies) do not file Part D claims.…”

Section: Introductionmentioning

confidence: 99%

Assessing Medicare Part D Claim Completeness Using Medication Self-Reports

Zhou

Stearns²,

Thudium³

et al. 2015

Medical Care

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Objective Medicare Part D claims are commonly used for research, but missing claims could compromise their validity. This study assessed two possible causes of missing claims: veteran status and Generic Drug Discount Programs (GDDP). Methods We merged medication self-reports from telephone interviews in the Atherosclerosis Risk in Communities Study (ARIC) with Part D claims for six medications (three were commonly in GDDP in 2009). Merged records (4,468) were available for 2,905 ARIC participants enrolled in Part D. Multinomial logit regression provided estimates of the association of concordance (self-report & Part D, self-report only, or Part D only) with veteran and GDDP status, controlling for participant socio-demographics. Results Sample participants were 74±5 years of age, 68% white and 63% female; 19% were male veterans. Compared to females, male veterans were 11% (95% CI: 7%–16%) less likely to have matched medications in self-report & Part D and 11% (95% CI: 7%–16%) more likely to have self-report only. Records for GDDP versus non-GDDP medications were 4% (95% CI: 1%–7%) more likely to be in self-report & Part D and 3% (95% CI: 1%–5%) less likely to be in Part D only, with no difference in self-report only. Conclusions Part D claims were more likely to be missing for veterans, but claims for medications commonly available through GDDP were more likely to match with self-reports. While researchers should be aware of the possibility of missing claims, GDDP status was associated with a higher rather than lower likelihood of claims being complete in 2009.

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Generic Drug Discount Programs: Are Prescriptions Being Submitted for Pharmacy Benefit Adjudication?

Cited by 18 publications

References 9 publications

Quantification of missing prescriptions in commercial claims databases: results of a cohort study

Quantification of missing prescriptions in commercial claims databases: results of a cohort study

Predictors of β-Blocker Initiation After Myocardial Infarction in Patients With Type 2 Diabetes

Assessing Medicare Part D Claim Completeness Using Medication Self-Reports

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