We calculated the prices paid per HRR for the 3 most common generic products and the 3 most common branded products in each of the three categories (i.e., 6 products per category and 18 products overall). HRRs. Most (75.9%) of that difference was attributable to the cost per prescription ($53 vs. $63). Regional differences in cost per prescription explained 87.5% of expenditure variation for ACE inhibitors and ARBs and 56.3% for statins but only 36.1% for SSRIs and SNRIs. The ratio of branded-drug to total prescriptions, which correlated highly with cost per prescription, ranged across HRRs from 0.24 to 0.45 overall and from 0.24 to 0.55 for ACE inhibitors and ARBs, 0.29 to 0.60 for statins, and 0.15 to 0.51 for SSRIs and SNRIs. CONCLUSIONS Regional variation in Medicare Part D spending results largely from differences in the cost of drugs selected rather than prescription volume. A reduction in branded-drug use in some regions through modification of Part D plan benefits might lower costs without reducing quality of care. (Funded by the National Institute on Aging as well as others.) There is considerable geographic variation in health care spending across the United States,1C5 and a recent study showed regional variation in prescription-drug spending for Medicare Part D enrollees.6 However, the sources of regional variation in drug spending are not well understood. Prescription-drug use and expenditures could be higher in regions with more seriously ill patient populations requiring more medications. Alternatively, expenditures could be higher in regions with greater use of expensive brand-name drugs rather than lower-cost generic equivalents.7,8 Knowledge of whether variation in Medicare drug spending arises principally from differences in volume or medication choice could inform interventions to improve the quality CYM 5442 HCl of prescribing for older adults and to reduce drug costs. We used Medicare Part D data to investigate sources of variation in drug spending. After adjusting for demographic, socioeconomic, and health-status differences, we measured regional variation in pharmaceutical expenditures overall and in three drug categories: angiotensin-convertingCenzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs), 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins), and newer antidepressants (selective serotonin-reuptake inhibitors [SSRIs] and serotoninCnorepinephrine reuptake inhibitors [SNRIs]). CYM 5442 HCl We decomposed regional differences in total and category-specific prescription-drug expenditures into two components: annual prescription volume and the cost of filling each prescription per month. In addition, we hypothesized that this proportion of prescriptions filled as branded products in each region would be strongly associated with cost per prescription. METHODS DATA CYM 5442 HCl SOURCES AND SAMPLE From a 40% random CYM 5442 HCl sample of the 2008 Medicare Denominator file, we identified beneficiaries 65 years of age or older who were continuously enrolled in fee-for-service Medicare and a stand-alone Part D prescription-drug plan (PDP). Medicare Prescription Drug Event files do not contain Medicare Advantage PDP enrollee data; thus, we excluded these beneficiaries. Medicare Prescription Drug Event and Pharmacy Characteristics files include the National Drug Code (NDC), the date the prescription was filled, the quantity dispensed, the number of days of supply, the type of pharmacy (e.g., retail or long-term care), and the amount paid to the pharmacy by the PDP and the beneficiary. The Lexi-Data Basic database (Lexicomp) was used to obtain the drug name, dose, brand or generic status, and active ingredient according to the NDC.9 From the 2008 Medicare Provider Analysis and Review (MEDPAR), Outpatient, Carrier, and Denominator files, we obtained outpatient and inpatient diagnoses, beneficiaries demographic characteristics and ZIP Code, and Part D low-income subsidy (LIS) status. ZIP CodeClevel income and proportion of the population living in poverty were obtained from 2000 Census data.10 We measured individual-level prescription-drug use and expenditures overall and for three drug categories BIMP3 that are widely used by the elderly and that account for a large share of spending, lack over-the-counter substitutes, and include generic options: ACE inhibitors and ARBs, which are close substitutes11; statins; and newer antidepressants (SSRIs and SNRIs). Prescriptions were standardized to a 30-day (considered 1 month).
Categories
- 11??-Hydroxysteroid Dehydrogenase
- 45
- 5-HT6 Receptors
- 7-TM Receptors
- 7-Transmembrane Receptors
- Acetylcholine Nicotinic Receptors, Non-selective
- Adrenergic ??1 Receptors
- Adrenergic Related Compounds
- AHR
- Aldosterone Receptors
- Androgen Receptors
- Antiprion
- AT2 Receptors
- ATPases/GTPases
- Atrial Natriuretic Peptide Receptors
- Calcineurin
- CAR
- Carboxypeptidase
- Casein Kinase 1
- Corticotropin-Releasing Factor
- CysLT1 Receptors
- Dardarin
- Deaminases
- Death Domain Receptor-Associated Adaptor Kinase
- Delta Opioid Receptors
- DMTs
- DNA-Dependent Protein Kinase
- Dual-Specificity Phosphatase
- Dynamin
- eNOS
- ER
- G Proteins (Small)
- GAL Receptors
- General
- GLT-1
- Glucagon and Related Receptors
- Glycine Receptors
- Growth Factor Receptors
- Growth Hormone Secretagog Receptor 1a
- GTPase
- Guanylyl Cyclase
- KDM
- Kinesin
- Lipid Metabolism
- Main
- MAPK
- MCH Receptors
- Muscarinic (M2) Receptors
- NaV Channels
- Neurotransmitter Transporters
- NFE2L2
- Nitric Oxide Precursors
- Nitric Oxide Signaling
- NPFF Receptors
- Opioid
- Other
- Other MAPK
- Other Peptide Receptors
- Other Transferases
- OX1 Receptors
- OX2 Receptors
- OXE Receptors
- PAO
- Phosphatases
- Phosphoinositide 3-Kinase
- Phosphorylases
- Pim Kinase
- Polymerases
- Purine Transporters
- Sec7
- Serine Protease
- Sodium/Calcium Exchanger
- Sphingosine Kinase
- V2 Receptors
-
Recent Posts
- [PubMed] [Google Scholar] 52
- Methods and Material 2
- It has been well established that harboring the allele enhances dementia associated with Alzheimers disease (AD), and several studies have supported a role of proteolysis as an important factor that may contribute to this risk [2,3C10]
- [PubMed] [Google Scholar]Xiao YF, Ke Q, Wang SY, Auktor K, Yang Con, Wang GK, Morgan JP, Leaf A
- Although passively-administered hyperimmune serum conferred protection in intact birds [15,17,18], the contribution of innate defenses and cell-mediated immunity to the control of APEC in the avian host remains ill-defined
Tags
- 68521-88-0
- a 105-120 kDa heavily O-glycosylated transmembrane glycoprotein expressed on hematopoietic progenitor cells
- Ankrd11
- Capn1
- Carboplatin cost
- DKFZp781B0869
- HA6116
- Hdac11
- IGF2R
- INK 128 supplier
- JTK4
- LRP2
- Masitinib manufacturer
- MDA1
- Mouse monoclonal to CD34.D34 reacts with CD34 molecule
- Mouse monoclonal to ERBB3
- Mouse monoclonal to INHA
- order NVP-AEW541
- PECAM1
- Rabbit Polyclonal to AML1
- Rabbit polyclonal to AML1.Core binding factor CBF) is a heterodimeric transcription factor that binds to the core element of many enhancers and promoters.
- Rabbit Polyclonal to AQP12
- Rabbit Polyclonal to C-RAF phospho-Ser301)
- Rabbit Polyclonal to C-RAF phospho-Thr269)
- Rabbit polyclonal to CD80
- Rabbit Polyclonal to Claudin 3 phospho-Tyr219)
- Rabbit Polyclonal to CYSLTR1
- Rabbit polyclonal to DDX20
- Rabbit Polyclonal to EDG4
- Rabbit Polyclonal to FGFR2
- Rabbit Polyclonal to GAS1
- Rabbit Polyclonal to GRP94
- Rabbit polyclonal to INMT
- Rabbit Polyclonal to KAPCB
- Rabbit Polyclonal to MMP-2
- Rabbit Polyclonal to MT-ND5
- Rabbit Polyclonal to OR52E2
- Rabbit polyclonal to PHC2
- Rabbit Polyclonal to RAB31
- Rabbit Polyclonal to SLC25A31
- Rabbit Polyclonal to ZC3H13
- Rabbit polyclonal to ZNF268
- TNFRSF13C
- VAV1
- Vegfa