The amplitude of the brain response to a repeated auditory stimulus is diminished as compared to the response to the first tone (T1) for interstimulus intervals (ISI) lasting up to hundreds of milliseconds. This adaptation process, called auditory sensory gating (ASG), is altered in various psychiatric diseases including schizophrenia and is classically studied by focusing on early evoked cortical responses to the second tone (T2) using 500-ms ISI. However, mechanisms underlying ASG are still not well-understood. We investigated ASG in awake mice from the brainstem to cortex at variable ISIs (125–2000 ms) using high-density EEG and intracerebral recordings. While ASG decreases at longer ISIs, it is still present at durations (500–2000 ms) far beyond the time during which brain responses to T1 could still be detected. T1 induces a sequence of specific stable scalp EEG topographies that correspond to the successive activation of distinct neural networks lasting about 350 ms. These brain states remain unaltered if T2 is presented during this period, although T2 is processed by the brain, suggesting that ongoing networks of brain activity are active for longer than early evoked-potentials and are not overwritten by an upcoming new stimulus. Intracerebral recordings demonstrate that ASG is already present at the level of ventral cochlear nucleus (vCN) and inferior colliculus and is amplified across the hierarchy in bottom-up direction. This study uncovers the extended stability of sensory-evoked brain states and long duration of ASG, and sheds light on generators of ASG and possible interactions between bottom-up and top-down mechanisms. Stimulus-evoked responses of neurons fade rapidly and last usually up to around 100 ms in small brains such as the mice brain. The brain also attenuates the response to the second of a pair of stimuli , a phenomenon that is called sensory gating and is impoverished in schizophrenia patients. Here, first, we demonstrate that sensory gating is present in ventral cochlear nucleus (vCN), the first station of auditory pathway in the CNS, and is hierarchically organized such that sensory gating is amplified as the signal travels from early brain regions to higher order processing areas. Second, we show that brain networks are active for longer than early evoked-potentials highlighting the importance of often-neglected late components of brain response to sensory stimuli.In the auditory system, a single repetition of a tone is enough for the brain to diminish the amplitude of the neural response to the second tone (T2). This adaptive property, called auditory sensory gating (ASG), has been detected in many brain regions in human and animals including auditory cortices , prefronSeveral mechanisms may be responsible for ASG, from local short-term synaptic mechanisms in the auditory cortices  to largeIn which brain region within the auditory network is ASG first detectable? How long after the first tone (T1) is the response to the T2 still gated? What are the differences between ASG following short and long ISIs in local brain areas and global brain states? As yet, we do not have clear responses to these questions. Indeed, most ASG studies used a single ISI of 500 ms and focused their analyses on specific surface EEG components or on early stimulus-evoked responses in local brain areas. For a better understanding of the mechanisms underpinning ASG, three points are important to be considered. First, we must study the dynamics of ASG in human and in awake animal models from short to long ISI as the adaptive mechanisms may be different for different ISI durations. Second, the effects of auditory stimuli and ASG should be examined beyond the initial evoked response, i.e., further during the ISI. Finally, ASG should be studied at large-scale level as auditory stimuli activate a large set of neural networks and we hypothesized that they potentially exert a profound influence across the entire brain by modifying the global brain state.In the present work, we therefore systematically characterized the effects of ASG using a paired-tone paradigm with variable ISIs (125–2000 ms) and recorded brain responses of awake mice at a large-scale level with high-density EEG and intracerebral electrodes in multiple brain areas ranging from as early as ventral cochlear nucleus (vCN) to higher-order brain areas . We also studied the effect of auditory and ASG processing at the level of brain states using clustering analyses of surface voltage topographies and pre-T2 brain activity recorded intracerebrally from multiple brain regions.Twenty-six male C57BL/6J mice  were recorded in this study. We recorded epicranial EEG in seven mice . Intracranial recordings were performed in 19 mice. One to three recording sessions from different brain regions were accomplished in each animal. In a few cases, a similar brain region was recorded twice in two different recording sessions but in slightly different sites. Population analyses are based on the number of recording sites. All experiments were in accordance with the applicable Swiss and European regulations on animal experimentation and were approved by the Ethics Committee on Animal Experimentation of the University of Geneva and the Veterinary Office of Canton of Geneva.The animals were anesthetized in an induction box with a 2.5% isoflurane and were mounted in a stereotaxic frame with continuous delivery of 1–1.5% isoflurane to maintain the anesthesia. Artificial tears ophthalmic gel  was used to avoid corneal drying and the body temperature was monitored through a rectal probe connected to a closed loop heating pad. An incision was made on the skin and the skin was retracted. Subsequently, the positions of epicranial electrodes or intracranial electrode entry points were marked with ink. The skull was covered by a layer of glue . Once the glue was dried, a small patch with a diameter of around 500 μm was removed at each electrode location. A ring-like head-holder was fixed on the occipital and nasal bones using dental cement . The center of the ring was filled with silicon sealant . Animals were returned to their cage (single-housed post-surgery) and antibiotics  and nonsteroidal anti-inflammatory and analgesic medicines  were added to their drinking water. The animals were allowed to recover from the surgery for at least 3 d before the training started.The mice were positioned on a stereotaxic frame equipped to head-fix the animals using the head-posts on their head. The training was continued twice a day for 3 d preceding the recording day. Acoustic stimuli were synthetized digitally using Real-Time Processor Visual Design Studio  and were generated by a RZ6 processor (Tucker-Davis Technologies). Calibration of the setup was performed by measuring the sound pressure level (SPL) emitted by the speaker when driven by the wideband noise. The animals learned to stay still while paired-tone paradigm presented pairs of tones  separated by a variable ISI . The stimuli were noise bursts of 10 ms long including a 3-ms rise and a 3-ms fall and were presented from a frontal speaker  positioned at 20-cm distance from the center of the head. The ISI of 62.5 ms was not analyzed here as it was too short epoch for the most of analysis in the present study. The sound intensity, delivered from our calibrated sound system, was started at 40 dB and was increased on successive sessions of training until it reached 70 dB on the last day of training. This intensity was then used during every recording session. An intertrial interval between 8–12 s separated each pair of tones ensuring no remaining brain activity from the preceding trials at the beginning of each trial.The details of surface recordings were described previously . BrieflyData analysis was performed using the Cartool software  is based on the spatial standard deviation of voltage values at all electrodes and represents the magnitude of activity  at a large-scale level . This mehe brain . The abohe brain . Note thhe brain  that rephe brain . To takehe brain  to identV is the potential at position h and Δh is the distance between electrodes. CSD represents the spatiotemporal profile of extracellular current sinks and sources. To extract spectral characteristics of the LFPs, the signal was epoched containing segments that start 3 s before and end 3 s after each trial. To decompose the epoched LFP traces for frequency analysis, a family of complex morlet wavelets (ω) were used based on the following formula:Before any analysis, intracranially recorded data were low-pass filtered at 300 Hz and downsampled to 1 kHz. The low-pass filtered signal contains LFPs from which iAEPs were drawn. iAEPs were obtained by averaging across trials and grand averages were obtained by averaging across iAEPs of individual recordings. Latencies of iAEPs were calculated using the onset of the initial positive peak of the ICc and the onset of the main negative deflection of the Au1. For the ACC, the timestamp of the initial trough was considered as the latency. We used current source density (CSD) as a guide to the best electrode channel in each recording session. CSD was estimated as the second spatial derivative of the LFP along the depth as described earlier  according Klusta  followedt tests with Bonferroni corrections for multiple comparisons whenever applicable and one-way and two-way repeated measures ANOVAs followed by Tukey’s post hoc tests were used for comparisons among different conditions. For clustering analysis, the grand averaged data were first clustered followed by back-fitting to individual subjects. The results of fitting were then statistically tested using two-way ANOVAs. FDR corrected paired t-tests were used to determine a significant difference between GFP at individual timestamps compared to baseline GFP. Pearson correlation coefficients were calculated as a measure of signal similarity between different conditions. The specific contrasts and the statistical tests that have been employed to examine these contrasts are described wherever applicable.Statistical analyses were performed using MATLAB and Prism software (GraphPad). All statistics that are presented in this study concern group-level statistics based on the averaged data  of individual animals or recording sessions. Paired We first recorded AEPs in awake animals using epicranial electrodes (eAEP) covering the entire dorsal surface of the brain A. By conn = 7 mice; first 120 ms) using a topographic hierarchical clustering algorithm . As shown in Although voltage topographies at individual time points could give insight into dominant brain processes at each time point, these processes often remain active for a period that encompasses several time points. to characterize the sequence of the large-scale brain processes following the auditory stimulation in more details, we clustered D the surlgorithm . The optlgorithm . Since dpost hoc Tukey’s tests for multiple comparisons showed that the ratios were significantly lower than control  = 103.2; p < 0.0001) at all ISIs although the degree of gating was decreasing  with increased ISIs. The ratio of the T1 responses of two randomly selected ISIs was used as control . Note that T2 was still processed even at the shortest ISI (125) where the gating was the strongest, as it is visible from the small trough to peak component after T2 (t test showing that the T2 response amplitudes were significantly larger than zero (t(6) = 4.62, p < 0.01).We then recorded surface EEG while paired auditory stimuli (wideband noise) were presented. The ISIs were randomly varied (125–2000 ms) across trials. As illustrated in EEG traces of an example subject in Although classic method of measuring ASG gives information on the magnitude of gating, it neglects dynamics of simultaneous brain activity in other brain regions. Therefore, to examine the effects of sensory gating on large-scale brain networks, we used the clustering analysis of the surface auditory evoked voltage maps described above. For this analysis, we first took epochs of 500 ms starting from T1 of the grand averages of the responses to T1 of all ISIs together. For the ISI of 500 ms and longer, fitting these clustered maps evoked by T1 to the grand average eAEPs evoked by T2 indicated a similar sequence of topographies , althougImportantly, the clustering and fitting analyses revealed an important characteristic of the brain states: the sequence of brain states were not perturbed by T2 stimulations of short ISIs (125 and 250 ms) that take place within the post-T1 period of the evoked brain states. As shown in n = 12; 6.43 ± 0.20 ms) to the Au1  and the ACC cortices . F = 14.51, p < 10−4; F = 16.84, p < 10−4; and F = 41.43, p < 10−4 for the ICc, Au1, and ACC, respectively) and tone  = 32.29, p < 10−4; F = 18.84, p < 0.002; and F = 86.14, p < 10−4 for the ICc, Au1, and ACC, respectively) and a significant tone × ISI interaction (p < 10−4). Bonferroni post hoc tests corrected for multiple comparisons revealed a significant decrease (p < 0.001) in the amplitude of T2 response at all ISIs except 2000 ms in the ICc and Au1 and 1000 ms in the Au1. In the ACC, there was a significant reduction at all ISIs including 2000 ms . The area between the T1 and T2 response curves is an indication of the magnitude of sensory gating . In the ACC, the ratios were significantly different from the control at all ISIs . Note that confirming surface EEG results, intracranial recordings also showed that the T2 is indeed processed even at the shortest ISI despite being strongly gated. This was statistically confirmed by one-sample t tests of ASG ratios for the shortest ISI that revealed significantly larger ratios than zero at all three regions . This confirms that the T2 was indeed processed across the hierarchy despite being strongly gated. In addition to the decreased magnitude of gating with increasing ISIs, the average T2/T1 ratios  = 40.92; p < 10−4) statistically corroborating the hierarchical enhancement of the ASG.We then conducted intracranial recordings in ICc, Au1, or ACC  to invesy gating D–F; the y gating G. The fi1 ratios G illustrp < 0.001, p < 0.05, and p < 0.01 for the ICc, Au1, and ACC, respectively) supporting the extended brain stability suggested by the clustering analysis of the scalp EEG. There was a non-significant trend for the ISI of 250 ms, but there was no correlation for the ISI of 500 ms  of the brain states are reflected in the activity of local networks in the auditory pathway (ICc and Au1) and ACC? Might there be traces of stability in these signals although T2 stimulus is processed? We therefore further examined the similarity between short-ISI post-T2 iAEPs with time-matched long-ISI iAEPs. We calculated linear dependence between these signals using Pearson correlation coefficient. Coefficient of zero corresponds to absolutely inconsistent signals whereas coefficient of 1 corresponds to identical signals. Note that this measure takes into account signal trajectory across time and ignores scaling in the magnitude of the signal. Morphologically coherent signals across time will be highly correlated even if the amplitudes are markedly different. Therefore, this is an ideal measure to address whether T2 stimulation at different time points abolishes signal similarity or a degree of similarity survives despite T2 stimulation. f 500 ms D,E.Sensory gating is long lasting and the second stimulus is attenuated even after 2000 ms which is far beyond the remaining processes following T1 stimulation which lasts approximately up to 350 ms post-T1 B. The brDespite the controversy about the origins of ASG, we showed that the ASG exists at the level of the ICc consistent with the indirect evidence , suggestWe used variable ISIs and both epicranial and intracranial recordings to investigate large-scale brain networks governing ASG. We demonstrate that the ASG is long lasting, takes place in areas ranging as early as the vCN and ICc to higher order areas of Au1 and ACC and is hierarchically organized. Furthermore, we show that auditory stimulation results in the activation of a sequence of stable brain networks lasting up to ∼350 ms. During this period, the T2  of paired stimuli does not change the architecture of brain topographies evoked by the T1 suggesting that induced networks resist perturbation by incoming stimuli. This was evidenced by a striking similarity between voltage topographies representing active brain networks following T2 in short duration ISIs  and time-matched post-T1 topographies in long duration ISIs  as well as a corresponding similarity between intracranially recorded LFPs. After this period, the brain reaches a baseline activity level and a second auditory stimulus induces similar network as the first stimulus. However, gating is preserved at least up to 2000 ms at the level of the amplitude of the neural responses.Given the clinical relevance of ASG in which the degree of gating of the T2 is used as a pathologic biomarker of schizophrenia , most stWhat is the state of brain networks following auditory sensory stimulation? In other words, how the dipoles across brain regions are configured following the stimulus? The stability of brain states activated by the T1 suggests that the dynamics of the activity of all dipoles result in a reproducible sequence of brain states during which distinct subsets of those dipoles are simultaneously active. Topographic mapping is a reference-free measure that represents global configuration of the underlying neuronal activity  and baseMight the extended brain stability represent a fundamental property of brain function? Might it have a functional role? Our experiments suggest that the extended stability of brain states and their resistance against perturbation may result from two factors: The strength of global brain networks as evidenced by the GFP B and the


What are the prices of top-selling brand-name prescription drugs in the United States, and how have these prices changed in recent years?In this economic evaluation of 49 common top-selling brand-name drugs, 78% of the drugs that have been available since 2012 have seen an increase in insurer and out-of-pocket costs by more than 50%, and 44% have more than doubled in price.Prices of brand-name drugs in the United States are likely to continue to increase, which warrants greater price transparency. High and continually increasing pharmaceutical drug spending is a major health and health policy concern in the United States.To demonstrate trends in prices among popular brand-name prescription drugs.This economic evaluation of drug prices focuses on 49 top-selling brand-name medications in the United States. Pharmacy claims data from January 1, 2012, through December 31, 2017, were obtained from Blue Cross Blue Shield Axis, a database that includes data from more than 35 million individuals with private pharmaceutical insurance. Drugs that exceeded $500 million in US sales or $1 billion in worldwide sales were examined.The median sum of out-of-pocket and insurance costs paid by patients or insurers for common prescriptions, presented annually and monthly, was the primary outcome.P = .81) nor between drugs with or without a Food and Drug Administration–approved therapeutic equivalent . Changes in prices paid were highly correlated with third-party estimates of changes in drug net prices , suggesting that the current rebate system, which incentivizes high list prices and greater reliance on rebates, increases overall costs.In total, 132 brand-name prescription drugs were identified in 2017 that met the inclusion criteria. Of this total, the study focused on 49 top-selling drugs that exceeded 100 000 pharmacy claims. Substantial cost increases among these drugs was near universal, with a 76% median cost increase from January 2012 through December 2017, and almost all drugs (48 [98%]) displaying regular annual or biannual price increases. Of the 36 drugs that have been available since 2012, 28 (78%) have seen an increase in insurer and out-of-pocket costs by more than 50%, and 16 (44%) have more than doubled in price. Insulins  and tumor necrosis factor inhibitors  demonstrated highly correlated price increases, coinciding with some of the largest growth in drug costs. Relative price changes did not differ between drugs that entered the market in the past 3 to 6 years and those that have been on the market longer (number of drugs, 13 vs 36; median, 29% increase from January 2015 through December 2017; The growth of drug spending in the United States associated with government-protected market exclusivity is likely to continue; greater price transparency is warranted. This economic evaluation study uses pharmaceutical insurance claims data to report the consistent rise in prices of commonly used prescription medicines in the United States. As such, the debate surrounding drug costs has expanded beyond the academic and political8 realms and even into popular culture.9In the United States, most of the insured population typically only directly pays through a copayment insurance program, and the full drug costs are generally not realized beyond the purchase of shared benefit plans from private insurers (often supplemented by employers) or payment of taxes that fund public insurers. For those among the 12.1% of uninsured or underinsured adults,10 Information on negotiated rebates is proprietary, with overall estimates of net price at 28% off list prices.11 Manufacturers may set list prices independently of pharmacy benefit managers, and they may do so at any time for any number of reasons, ranging from benign rationale  to allegations of illegal practice.15Data on the costs of drugs remain opaque and generally confusing. List prices are set by pharmaceutical manufacturers and represent the payment shared between the payer  and the insured . However, manufacturers may offer rebates to pharmacy benefit managers, who act on behalf of the payer during annual negotiations in exchange for preferred formulary placement. Rebates vary by product, manufacturer, and pharmacy benefit manager. Rebates are returned retrospectively after the point of sale based on volume purchases and therefore cannot be directly linked to an individual purchase.16 The US Department of Health and Human Services recently concluded the current rebate system harms federal health care programs and their beneficiaries.11Although it is generally recognized that list prices for brand-name drugs have risen, a review of trends in recent years is warranted. One explanation of increasing list prices is that greater rebates are being offered, which sufficiently offset increased prices, although evidence suggests that adopting these practices is associated with even higher costs billed to consumers.We mapped out the drug costs of the top-selling brand-name drugs in the United States over a 6-year period from 2012 through 2107. We combined out-of-pocket and insurer-paid costs for common brand-name pharmacy prescriptions from private insurers, and we compared these costs with third-party estimates of net prices to assess the implications of rebates. The results highlight the extent of unimpeded, growing drug costs in the pharmaceutical market.CHEERS) reporting guideline for economic evaluations.This study was deemed as non–human participant research by the Scripps Institutional Review Board. This study followed the Consolidated Health Economic Evaluation Reporting Standards –compliant access to BCBS Axis data for research purposes.Prescription-level pharmacy claims data from January 1, 2012, through December 31, 2017, were obtained from the Blue Cross Blue Shield (BCBS) Axis,18 All pharmacy claims for each of these drugs were extracted from the database using Food and Drug Administration (FDA) National Drug Code (NDC) identifiers.19 Drugs that are typically administered in a clinical setting or not distributed through a pharmacy are not well represented in pharmacy claims data and were omitted from downstream analyses. The most common prescription of each drug was identified according to a combination of its NDC (in instances of a drug with multiple codes) and its billed quantity dispensed in the prescription .20 These metrics were based on a comparison between quarterly estimates of third-party vendor pharmaceutical unit volumes and product-level net sales reported by manufacturers in the same quarter. Nondrug products  were omitted, and drug approval dates and approved therapeutic equivalents were extracted from https://www.fda.gov/.The study included drugs that reached more than 100 000 total pharmacy claims and were covered under pharmacy insurance for at least 3 years. In cases in which the same drug was found under different trade names, the most common trade name was used. Proprietary estimates of quarterly net prices of these drugs were obtained from SSR Health.Total price paid of each claim, representing the sum of out-of-pocket cost paid by a plan member and cost paid by the insurer, was the primary outcome of interest. In instances in which the billed unit quantity differed from an individual claim and the most commonly billed unit quantity, the costs were normalized to the most commonly billed quantity by calculating the cost per billed unit. For example, a paid amount of $100 for 1 unit and $200 for 2 units for the same drug would both be considered $100 per unit. Median costs for the most common prescriptions were summarized in each calendar month. In general, measures of variation  were small and are not presented in the results.2-S1)/S1, in which S1 was the median cost for the most common prescription on the first date and S2 was for the second date. Relative price changes are presented here as the change in price from January 1, 2012, to December 31, 2017. For drugs that were not on the market in January 2012, the earliest available date was used to find S1, and a 6-year relative price change was also extrapolated by scaling the relative price change to a 72-month period . Likewise, a 3-year relative price change was calculated for all drugs from January 1, 2015, to December 31, 2017, for a more appropriate direct comparison of drugs that had entered the market after January 1, 2012, with those on the market before January 1, 2012. Month-to-month relative price changes were identified by calculating the difference in median costs between consecutive months and then scaling the difference by the preceding month’s median cost using the following formula: (Si-Si-1)/Si-1, in which Si is the median cost for the most common prescription during the i-th month.Relative price changes were found by calculating the difference in median costs between 2 dates and then scaling this difference by the preceding date’s median cost; the following formula was used: . In either case, these differences were normalized by years of data. For example, a drug with a $100 net price per unit in 2012 and $200 in 2017 would have a mean annual increase of 20% each year for 5 years. Pearson correlation between these mean annual net price increases and the mean median price increase within the BCBS data was assessed with linear regression. All analyses were performed in R, version 3.3.2 . Statistical tests were 2-sided using a significance level of P < .05.The association between relative price changes and therapeutic equivalents was assessed using a 2-sided A total of 132 brand-name prescription drugs were identified in 2017 that met the criteria of exceeding $500 million in US sales or $1 billion worldwide. Among these products, 61 drugs had greater than 100 000 pharmacy claims in the BCBS Axis from January 1, 2012, through December 31, 2017, with 55 drugs available and covered under pharmacy insurance for at least 3 years (41 for the entire 6-year observation period) and 49 drugs with net price data available from SSR Health. This study focused on these 49 drugs, and eTable 2 in the Claims for 13 (27%) of the 49 drugs were first found after January 1, 2012, with claims for the remaining 36 drugs occurring throughout the observation period. The median time from FDA approval until the end of the observation period  for all drugs was 11.6 years. Seventeen drugs (35%) had FDA-approved therapeutic equivalents . For reference, the term of a new patent filed in the United States is 20 years from the date of application, and new chemical entity exclusivity rights granted by the FDA last for 5 years regardless of whether the drug is protected under a patent.P = .81). Similarly, the 6-year relative price increase of the 17 drugs with therapeutic equivalents was not different from the 6-year relative price increase of the other 32 drugs without therapeutic equivalents .Median total costs for the most common prescriptions of each of the 49 high-volume brand-name drugs from 2012 through 2017 are presented in the For most of the drugs examined, a steadily increasing cost trend was observed. The month-by-month median cost of Humira A was repP = 2.5 × 10−6). The costs of Humalog and Novolog were similarly most correlated with each other , with Lantus most correlated with Humalog  and also with Novolog .Owing to the general cost-increasing trajectories shared across drugs, a number of nonzero correlations in the monthly relative cost changes were observed between drug pairs . These net price rates were correlated with the paid insurer and out-of-pocket cost rates obtained from BCBS data across all drugs  (P = .02). Net price rates remained correlated with drug cost rates among the 26 drugs .Quarterly estimates of net price per unit were obtained for each drug. Thirty-five drugs (71%) had 24 quarters (6 years) of data from 2012 through 2017. The mean of the annual net price increases across all 49 drugs was 9.0%  . Mean anThis study presents drug cost data from more than 35 million US individuals with private pharmaceutical insurance. These data demonstrate an industry-wide trend of substantial increases in costs for top-selling brand-name prescription drugs from 2012 through 2017. Because most products displayed continual, marked annual increases throughout the observation window, we expect these products to continue along this price escalation course, along with emerging products. Given the median annual cost increase of 9.5%, our results suggest the costs for popular brand-name drugs would double every 7 to 8 years. Competition among brand-name competitors appeared to do little to stymie rising costs. Instead, products that may be prescribed interchangeably, such as Humira and Enbrel or Humalog, Humulin, and Novolog, were highly synchronized in relative cost changes while demonstrating some of the largest cost increases in the industry over the past 6 years. Such seeming coordination coinciding with high price increases is particularly worrisome.4), it is not certain if the trends we observed are not already a function of volume changes or speculation of upcoming volume changes as each drug approaches and surpasses the end of its federally protected exclusivity periods.We did not see any evidence of price changes being associated with the existence of therapeutic equivalents. This finding suggests that prices of brand-name drugs are not largely affected by the presence of generic drugs or perhaps biosimilar products and others that may enter the market in the future. Implementation of the price transparency legislation passed in October 2018 may guide patients to seek lower-priced alternatives to brand-name drugs when available, which may ultimately lead to different price trends in the future compared with the trends we observed. However, this likelihood is unknown. Even if patients and clinicians preferred generic alternatives . This association suggests that the offered supposition that higher list prices and greater reliance on rebates reduce costs may be untrue. Instead, increases in list prices, and thus increases in insurer and out-of-pocket costs paid, may coincide with increases in net prices, which in turn make these drugs more expensive overall. Seemingly biannual price increases should not be considered benign pricing strategies to offset paid against net price discrepancies in the current rebate system. Greater transparency is needed.A limitation of this study was the lack of information on rebates and how they affected net prices. Rebates are issued in bulk and cannot be linked to individual claims. Rebates vary by drug and by payer, with 16% of all private insurer-branded drug spending returned as rebates in 2016.The costs of brand-name drugs have risen substantially in the past 6 years, with regular increases occurring 1 to 2 times per year. With so few exceptions to this norm, costs will likely continue to rise unless bold actions are taken.