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Impact of the Affordable Care Act's Physician Payments Sunshine Act on branded statin prescribing

Abstract Objective To evaluate the impact of the Affordable Care Act's Physician Payments Sunshine Act (PPSA), which mandates disclosure of industry payments to physicians, on physician prescribing of branded statins. Data Sources Administrative claims data from 2011 to 2015 from three large national commercial insurers were provided by the Health Care Cost Institute. Study Design We adopted a difference‐in‐differences and event study design, leveraging the control group of physicians in two states, MA and VT, which implemented state laws on disclosure of industry payments prior to the national PPSA. To further address potential confounding caused by differences in prescribing patterns across states, our analytical sample includes physicians practicing in border counties between the treatment (NH, NY, and RI) and control (MA and VT) states. Data Collection We restricted our sample to physicians who had at least 50 new‐fill prescription claims for statins during the five‐year study period, with at least one new‐fill prescription claim each year. Principal Findings We found that the PPSA led to a 7% (p < 0.001) reduction in monthly new prescriptions of brand‐name statin over the study period, with little change in generic prescribing. The reduction in branded prescriptions was concentrated among physicians with the highest tercile of drug spending pre‐PPSA, with a decrease of 15% (p < 0.001) in new branded statin prescriptions. The decline was most prominent after mandated reporting of industry payments began before the payment data was published. Conclusions The PPSA may have achieved its intended effect of reducing branded prescriptions at least in the short run, particularly among physicians most likely to have engaged in excessive or low‐value prescribing of branded drugs.

• Prior state-level legislations mandating disclosure of industry payments to physicians have led to decreases in prescribing of branded drugs, with mixed findings on generic drugs.

What this study adds
• The nationwide Physician Payments Sunshine Act (PPSA) led to decreases in branded statin prescribing, with little change in generic statin prescribing.
• The reduction in branded prescriptions was concentrated among physicians with the highest tercile of drug spending pre-PPSA.
• There was little change in risk compositions of patients receiving any statin drugs before and after the PPSA.

| INTRODUCTION
Prescription medications comprise an estimated 17% of total health care spending in the United States. 1 The growth of prescription drug spending consistently outpaces the growth of aggregate health spending. Among contributing factors to the high prescription drug spending in the U.S. are rapidly increasing prices of brand-name drugs and physicians' prescribing brand-name drugs when low-cost alternatives (such as generic equivalents) are available. 2 Physicians' prescription of generic versus brand-name drugs can be determined by a number of factors, including physicians' beliefs and information about generic equivalence, 3,4 the state generic substitution policy, 5 patients' moral hazard behavior, 6 and the payments (to physicians) from the pharmaceutical industry. 7 Industry payments play an important role in determining physician drug prescription. In the U.S., the total dollar value of industry payments exceeded $43 billion between 2013 and 2018, and more than 80% of physicians reported having financial relationships with the industry. 8,9 These payments exist in various forms including fees for professional services (such as consulting and speaking bureaus), gifts, and meals. [8][9][10][11] Payments made to physicians, especially small payments such as meals and gifts, can be an important channel for the industry to build connections with physicians and deliver new information about drugs to physicians. 12 However, pharmaceutical companies may also exert undue influence on physicians through gifts and other financial exchanges, which may distort physician prescribing away from the best interests of patients and lead to suboptimal medical decision making including low-value drug prescriptions. 10,13 Several studies suggest that industry payments may have led to biased advice of physicians in the form of over-prescription of brand-name drugs. 7,14,15 To address the potential conflict of interest faced by physicians related to the industry payments, the Center for Medicare and

Medicaid Services (CMS) implemented the Physician Payments
Sunshine Act (PPSA) as part of the Affordable Care Act. 16  The PPSA represents the first systematic nationwide effort to publicly report the financial interactions between pharmaceutical companies and physicians. 17 In this study, we conducted one of the first evaluations of the impact of the PPSA on prescribing of branded drugs. We used 2011-2015 Health Care Cost Institute data and employed a difference-in-differences and event study design to study changes in statin prescriptions. Statins are a class of oral medications that lower cholesterol levels. They are widely prescribed for the prevention of adverse cardiovascular outcomes (such as myocardial infarction and stroke). 18 We focused on the statin drug class because it is not only widely prescribed, but also includes a mixture of branded and generic drugs, and has been associated with high levels of industry payments to providers. 11,19 Findings of the study are expected to inform the effectiveness of PPSA in curbing excessive prescribing of expensive brand-name drugs and rapid growth in drug spending.

| Study design and sample
We employed a difference-in-differences (DD) study design that leverages control group physicians in the two states, Massachusetts (MA) and Vermont (VT), which previously implemented state-specific legislations mandating public disclosure of payments from pharmaceutical companies to physicians prior to the PPSA. The key assumption underlying the validity of the DD design is that outcomes in the treated and control groups would have followed parallel trends in the absence of the treatment, and the baseline level differences in outcomes do not threaten the validity of the design so long as they remained constant (during the pre-treatment period and in the absence of the treatment). 20 The choice of the control group relied on the assumption that the PPSA would not have affected drug prescribing in these two states to the same extent as in states without such disclosure legislation pre-PPSA. This was supported by existing literature showing that the 2008 MA public disclosure law decreased average drug prescriptions, especially for brand-name drugs. 21 Our treatment group consisted of physicians in three neighboring states of MA and VT: New York (NY), Rhode Island (RI), and New Hampshire (NH). We excluded Connecticut from our study as it experienced an unusually large increase in enrollees of commercial insurance during our study period as a result of the Affordable Care Act relative to all other states in the U.S., 22 which renders it less comparable to the control group.
To ensure further comparability between treatment and control groups, following prior literature, 21 we restricted our study sample to those physicians who practiced in the border counties between treatment and control states and remained in the same state throughout the study period ( Figure S1 shows the map highlighting the border counties in our study sample). We further accounted for potential time-varying confounding by dividing the border counties into three regions, with each region consisting of counties in both treated and control states, and controlling for region-year-month fixed effects in our DD specification (described further below). The regions were assigned based on borderlines between treated and control states, as the adjacent MA and VT states border NY on the west (Region 1), RI on the south (Region 2), and NH on the east (Region 3). Table S1 shows socioeconomic characteristics and health care resources of treated and control counties by region. They were broadly similar between treated and control countries in the same region.
We additionally required that physicians in our sample have at least 50 new-fill prescription claims for statins during the five-year study period, with at least one new-fill prescription claim in each year.
We focused on new-fill as opposed to refilling prescriptions, as the former represents a deliberate prescribing decision on the part of the physician (or patient), the quantity of which was likely to change more immediately in response to the PPSA. . It also lists the total amount of industry payments associated with each branded drug during the study period. Not surprisingly, the largest payments were associated with branded statins without generic equivalents (e.g., Crestor, Vytorin, and Livalo). The table indicates a generic entry for three statin-branded drugs (Caduet, Lescol, and Lipitor) that went off patent during the study period. Including these drugs in our sample could potentially introduce confounding from patent expiration and generic entry. We, therefore, excluded these three drugs from our main sample. For sensitivity analysis (described below), we included all statin-branded drugs in Table S2. There was also no change in generics substitution policies in our sample states during the study period. These policies are shown in Table S3.

| Measures and statistical analyses
Our DD design examined the differential change in monthly new branded-statin prescriptions after PPSA between physicians in treated and control counties. We estimated the following primary specification: where i indexes physicians, t indexes months (t {1,2, … 0.60}) and r indexes regions (r {West, South, East}). y is the outcome of interest, that is, logged number of monthly new-fill prescription claims for branded statins. Following prior literature, 21 we added one to the raw number of claims before applying the logarithm transformation to handle the large number of zero claims (alternative Poisson count models yielded similar results). It is important to note that each physician included in our study sample contributes exactly 60 months, conditioning on meeting the inclusion criteria (≥50 new-fill claims, and at least one new-fill claim in each year of the study period) regardless of whether she had any relevant drug claim in that month. This is because any given month with zero claims is relevant information that needs to be incorporated in model estimation.
We further after the PPSA came into effect, that is, after CMS began collecting payment data from drug companies. The two post-treatment periods defined above allowed us to distinguish between these two periods and assess these potential staggered responses.
We controlled for η, a vector of physician fixed effects, and region- year-month fixed effects. The region-by-month fixed effects additionally control for all time-specific factors common between treated and control counties within a region, and which may differ across regions. γ and δ are the coefficients of the time-varying controls and region-by-month fixed effects, respectively. We also controlled for an extensive set of time- show results with and without these controls. The error term ε is clustered at the physician level.
We conducted a few additional analyses to ensure the robustness of our study design and results. First, we plotted trends in branded statin prescriptions by state and by drug to rule out alternative causes for changes in branded prescriptions, such as insurer-imposed state or drug-specific changes in formularies or prior authorization requirements. Second, the validity of our DD design relies on the assumption of parallel trends, that is, there were no differential changes in prescriptions between treated and control group physicians prior to PPSA. We tested this assumption using an event study regression that was similar to Equation (1) but replaced the two DD interactions with interactions between the treatment indicator and an indicator for each year-month in our study period. Third, to further ensure that the results are not sensitive to our study sample restricting to only physicians practicing in border counties, we included all physicians in the five states (MA, VT, NY, RI, and NH) and re-estimated the DD model with propensity score matching. Specifically, we estimated the propensity score using logistic regression at the physician level with receiving treatment (being in NY, RI, or NH) as the dependent variable and the pre-treatment period monthly number of new branded and generic statin prescriptions as the independent variables. We used the nearest-neighbor matching procedure to select physicians in the control group that were observably similar to those in the treated group. 23,24 We used the DD design with the border-county sample as our preferred study design because of known limitations with the propensity score matching approach used in DD analysis as it can fail to account for certain confounding factors. 23 We also conducted a number of supplementary analyses to better   Figure S2). Similar patterns were also observed for each drug or drug group ( Figure S3). These patterns provide further support that the decline in branded statin prescriptions in the treated relative to the control group observed in Figure 1 was not unique to a single state or a drug, and may be attributed to broader changes brought about by the PPSA.   (Table S3). Propensity-score matched DD estimates including all statin prescribing physicians from the five states yielded very similar results (Table S4). Table 2

| Event study
The results from the event study appeared to lend support to the parallel trends assumption underlying the DD design ( Figure 2).
There was no evidence of an obvious trend during our pretreatment period preceding the PPSA proposed rule. Consistent with the DD regression results in Table 2, branded statins experienced the largest decline after payment data reporting began in 2013. By August 2014, the month before payment data was published, prescriptions were 10.9% (p < 0.001) lower compared to those in November 2011 among the treated group relative to the control. Table S7 shows DD results with aggregated risk characteristics of patients receiving any statins as the dependent variables. The DD coefficients were almost all small and insignificant, suggesting little change in the risk composition of patients receiving statins before and after the PPSA.

| DISCUSSION
In this study, we examined the impact of the first national policy disclosing payments from the industry to providers on physician F I G U R E 1 Number of branded statin prescriptions overtime. Monthly number of total new-fill branded statins prescription claims from all physicians in the treated and control groups. The treated group includes physicians practicing in counties of NH, NY, and RI that border MA or VT. The control group includes physicians practicing in counties of MA and VT that border NH, NY, or RI. such as an enhanced awareness of the need to curb unnecessary prescribing and contain drug spending. 21,26 We first rule out a patient response as the main driver of the observed changes in prescribing. Our event study results revealed little change in branded statin prescriptions after the online disclosure of the payment data. This suggests that patients were not responding F I G U R E 2 Monthly adjusted changes in branded statin prescriptions among the treated relative to the control group. Percentage changes in the monthly number of new-fill prescriptions from treated relative to control physicians and their 95% CIs are presented. Converted using the formula (exp[β] À 1) Â 100%, where β is an estimated coefficient (or its confidence interval bounds) on the interaction between the indicator for the treated group and the indicator for a specific year-month in the event study regression. The treated group includes physicians practicing in counties of NH, NY, and RI that border MA or VT. The control group includes physicians practicing in counties of MA and VT that border NH, NY, or RI. The omitted month is November 2011, the month before the release of the PPSA proposed rule. The event study regression additionally controlled for physician fixed effects, region-by-year-month fixed effects, and all patient and county characteristics in Table 1. [Color figure can be viewed at wileyonlinelibrary.com] to the publicized payment information. Decreased access to information on newly marketed drugs is also less likely as a mechanism, since excluding new branded drugs has little impact on our estimates. Since providers are likely to be less familiar with the newest drugs, reduced access to information would be expected to have the greatest impact on prescribing those medications, but this effect was not observed.
We cannot rule out the mechanism involving reduced exposure to industry payments as we could not observe the industry payment to each physician before the PPSA. It is also possible that physicians reduced branded statin prescribing out of concerns for maintaining reputation or prescribing efficiency. Even before it was officially implemented, the PPSA in the form of a proposed rule may be seen as providing signaling information regarding the prevalence and severity of the influence of the pharmaceutical industry in distorting physician prescribing. The legislation also provided more information to physicians regarding the direct linkage between their prescribing behavior and rising health care costs. 16,30 Physicians (and their organizations) may have responded to this signal by engaging in enhanced selfmonitoring and more conservative prescribing.
The fact that the largest reductions in branded drugs occurred among physicians in the highest tercile of drug spending pre-PPSA is also potentially consistent with this mechanism: physicians with a high level of pre-PPSA drug spending ("heavy prescribers") may have been more likely to engage in excessive or low-value prescribing, or be perceived as such. It is noteworthy that they also had the largest average Finally, the fact that we found little change in risk composition of patients receiving any statins before and after the PPSA, along with the finding that the PPSA decreased branded statin prescribing but did not change generic statin prescribing, means we could not rule out that the legislation has potentially led to underuse of statin drugs. The reason is that if physicians stopped prescribing statins only to patients who were healthier and may benefit less from the drugs, we would have expected to see a higher risk among patients receiving statins after the PPSA. However, it is still possible that physicians chose to stop prescribing statins to patients who differed in risk profiles not captured in our analysis.
Our study has several limitations. First, to the extent that the PPSA exerted any further impact on physician prescribing in MA and VT beyond the prior state laws, our results may underestimate the true impact of the PPSA. Second, prescription claims data does not capture prescriptions that patients never fill or they purchase out of pocket. However, this limitation is not expected to vary differently over time across the geographic regions examined in this study. Using prescription claims is standard in pharmacoepidemiology studies and is considered more accurate than relying on electronic prescriptions. Third, we were unable to explain drug prescriptions for patients covered by other commercial insurers not included in HCCI data, Medicare fee-for-service, or Medicaid program due to data limitations. Finally, our estimates may not be generalizable to physicians practicing in states outside of the five included in our analyses.
Overall, we found that PPSA achieved its intended effect of reducing branded prescriptions at least in the short run. Our findings support extending the disclosure policy to non-physician providers with prescribing authority who may also be subject to industry influence. 18,31 One caveat is the law's potential impact on patient outcomes. It is possible that the PPSA may prompt physicians to prescribe overly conservatively and stop prescribing to patients altogether who could otherwise benefit from taking the drugs. An assessment of the long-run impact of the PPSA on patient outcomes and hence the value of prescribing is an important topic for future research.