This study measures the impact of Medicaid formulary restrictions on the rate at which patients who failed on a drug therapy for schizophrenia return to the same therapy.
We measure the impact of Medicaid formulary restrictions (FRs) on the rate at which patients who previously failed a drug therapy for schizophrenia are returned to that therapy.
We collect drug-level information on FRs in state Medicaid programs and examine claims of noninstitutionalized Medicaid enrollees with schizophrenia.
A difference-in-differences technique is used to compute the change in the probability of adverse outcomes before and after a state adopts an FR. This change is compared with the change in failure probabilities in states with no FRs.
Regardless of FRs, patients tend to resume the same drug after an adverse medical event. In 2005, 69% of inpatient mental health—related admissions resulted in patients resuming the same therapy within 6 months of the event, and 63% of patients resumed the same drug after a mental health–related emergency department admission. In states where FRs limit access to all atypicals, the likelihood of a patient resuming the same atypical after having ceased treatment for at least 30 days increases by 20.1% relative to patients in states without restrictions. Additionally, patients in states that impose FRs on all atypicals are 11.6% more likely to discontinue all treatments.
FR may increase the likelihood that patients will return to failed treatments or cease treatment altogether. Although formularies are designed to reduce drug spending, an unintended consequence may be an increase in the use of other services needed to treat patients with schizophrenia.
Am J Manag Care. 2014;20(3):219-228We measure the impact of Medicaid formulary restrictions (FRs) on the rate at which patients who previously failed on a particular drug therapy for schizophrenia are returned to that same therapy. Our findings suggest that FRs increase the likelihood that patients will return to failed treatments, or totally cease treatment.
Between 1995 and 2005, Medicaid spending on drugs increased an average of 13% per year—an increase due in part to physicians favoring newer, more expensive drugs.1,2 To attempt to control costs, many state Medicaid programs responded by imposing policies that encourage the use of lower-cost drugs.2,3 Prior authorization, for example, is designed to reduce pharmacy spending by requiring physicians to seek approval from the state’s Medicaid program before prescribing certain high-cost drugs to patients. These controls are designed to reduce the demand for expensive drugs when lowercost alternatives exist, which ultimately results in reduced Medicaid spending. But these policies may affect healthcare spending and utilization through other channels, especially if patients do not respond well to the preferred drugs. Physicians match patients to the drug and dosage that is optimal for each patient based on various practice guidelines, recommendations, and formulary practices.4,5 On average, several treatments might be equally effective at the population level, but heterogeneous patients will respond differently to each. Removing a drug from coverage could adversely affect those who would have most benefited from it. If the use of these drugs leads to deteriorating health status, the result may be higher nonpharmaceutical medical spending.6
An important example of this trade-off is the second generation of drugs for the treatment of schizophrenia, called atypical antipsychotics, which account for almost 15% of Medicaid spending.7 For this reason, they are increasingly popular targets of state Medicaid formulary restrictions.8,9 Because a number of patients may respond only to 1 drug, formulary restrictions can place patients with idiosyncratic treatment responses at greater risk of uncontrolled symptoms.10,11 Uncontrolled schizophrenia has dramatic costs for patients (about 10% of whom commit suicide), and for society in terms of increased social service use and crime.12 Although lower-cost alternatives to atypicals do exist, they are problematic. The first generation of schizophrenia drugs resulted in improvement of symptoms but with potentially debilitating side effects.10 Atypical antipsychotics were introduced in the 1990s and have largely replaced first-generation treatments because atypicals control schizophrenia better and have fewer negative side effects. For example, about 30% to 40% of patients relapse with first-generation drugs (relative to 80% without treatment), but with second-generation drugs, relapse rates fall to about 25%.10
Formulary restrictions appear to alter doctors’ prescribing behavior. Previous studies have examined the impact of utilization management programs and found that formulary restrictions reduced the use of atypicals by about 5%.9 After prior authorization of atypicals became a requirement in Maine, the risk of discontinuation rose by 29%.2 Studies in Georgia and New Hampshire found that prior authorization reduced the use of targeted drugs and increased the use of other health services.13,14 Previous studies of the impact of restrictions on treatment have focused on individual states. This study, to the best of our knowledge, is the first nationally representative study of formulary restrictions’ impact on treatment. In this study, we examine how formulary restrictions of atypicals affect the likelihood that a patient who has been diagnosed with schizophrenia, and who has had some marker of an unsuccessful response to a particular drug therapy, is continued on that same drug therapy, changes the type of drug, or discontinues all treatment.
Policy information on the restrictions placed on atypical antipsychotics from 1999 to 2005 was collected from 24 state Medicaid programs via a mailed survey and personal communications with policy directors. States reported whether prior authorization, quantity limits, step therapy (fail-first), and other policies applied to a list of drugs identified by US brand name. To supplement this information, additional formulary information was collected using publicly available documents. Regulation data were collected at the individual drug level. The survey included 10 atypicals. All major atypical antipsychotic agents are addressed in the survey, except for paliperidone (brand: Invega) and olanzapine/fluoxetine combined therapy (brand: Symbax), both of which were not yet commercially available during the study period. States reported if each drug was placed under prior authorization, quantity limits, step therapy, dose restriction, or duplicate therapy restrictions in each year.
Medicaid claims for 24 states (12 states that adopted formulary restrictions and 12 states that did not) for a 5-year period (2001-2005) were used to measure medical and pharmacy use at the individual level. We selected the states shown in based on the size of the Medicaid population; the fraction of enrollees in fee-for-service Medicaid (with complete claims); and whether the state Medicaid program had imposed restrictions on selected classes of medications over the study period. The years included in this study (2001-2005) captured the widespread introduction and use of prior authorization requirements in many state Medicaid programs, particularly for atypical antipsychotics.
The sample consisted of Medicaid enrollees 18 years and older who have schizophrenia in each of the study states. These patients were identified by (1) the presence of an International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code beginning with 295 in any diagnosis position for inpatient, outpatient, or long-term care claims, and (2) a confirmatory schizophrenia diagnosis at least 30 days after the initial diagnosis. Thus, we eliminated individuals who were diagnosed only once without a subsequent diagnosis. This eliminated individuals with a socalled “rule-out” diagnosis: a physician may make an initial diagnosis of suspected schizophrenia that is later ruled out. The requirement of a subsequent “confirmatory” diagnosis excluded “ruled out” cases from the sample. This criterion also imposed a 30-day minimum window of observation for inclusion in the study. In addition, to be included, a patient must have filled at least 1 prescription for one of the drugs included in our sample. In practice, this restriction eliminated very few patients because those with a subsequent confirmatory diagnosis are almost always given a prescription.
The presents a consort diagram for the sample construction: 944,609 patients over the age of 18 years who have had at least 1 schizophrenia diagnosis. Of these, about 77% had a confirmatory diagnosis after 30 days. Of the remaining population, 63%, or 460,991 patients, were prescribed an atypical.
STUDY DESIGNEstimation Approach
To estimate the impact of prior authorization on treatment failure and “replication of failure,” a difference-in-differences method (DD) was used. In the DD method, 2 groups—patients in states with restrictions and patients in states without restrictions— were observed in 2 different time periods: 2001 and 2005. Patients living in states that adopted formulary restrictions after 2001 were, in the terminology of DD models, exposed to “treatment” in 2005, while those in nonformulary states were not exposed in either period. This method first calculates the change in the average likelihood of the outcome variable in the treated group, and then calculates the corresponding change in the nontreated group. The excess change in the treated group, above and beyond the change in the nontreated group, represents the estimated DD effect. For example, in states with formulary restrictions, we calculated the change in the likelihood of a patient resuming the same medication after a 30-day gap before and after the implementation of the restriction. We then compared this with the corresponding difference in likelihood within states that never adopted restrictions. The approach removes the effects of trends due, for example, to general changes in atypical use between 2001 and 2005. Because formulary restrictions were measured at the state level, we clustered the standard errors at the state level. We utilized an alpha of 0.10, although the tables included alternative alpha values.
To examine the replication of failure, all patients currently with schizophrenia who are taking an atypical antipsychotic for their condition were identified. Then, patients were classified as failing on a particular therapy if they either were admitted to a hospital for their disease or had a medication gap greater than 30 days (in a baseline year). Patients who failed treatment were followed over time, and their claims were examined for resumption of treatment with the same drug after at least a 30-day gap. As a robustness check, we examined longer gaps, but faced serious sample size limitations due to the limited number of people observed restarting after longer gaps. Finally, we also examined whether individuals on an atypical discontinued drug therapy altogether, switched from an atypical to a typical (first generation) antipsychotic, or switced from one atypical to another.
We included an indicator variable equal to 1 in the year 2005 and an indicator for states that adopt formulary restrictions. The interaction between the 2 indicators, year 2005 and formulary restrictions, is the difference-in-differences estimate discussed above (labeled DD in the tables).
shows summary statistics measuring atypical antipsychotic usage along with emergency department (ED) and inpatient admissions related to mental health causes, stratified by the number of atypicals under a formulary restriction in a given state-year. The observed population with schizophrenia increased slightly over time, from just fewer than 500,000 in 2001 to about 541,000 in 2005. The number of patients with schizophrenia receiving atypical antipsychotics increased from nearly 195,000 in 2001 to over 320,000 in 2004, and then fell to about 273,000 in 2005.
Each patient, on average, experienced 0.22 inpatient admissions related to mental health issues and 0.06 ED visits per year. Approximately 2.8% of the sample was admitted to the ED and 7.9% was admitted on an inpatient basis for issues related to mental health.
summarizes the replication of failure by year, and the restrictiveness of Medicaid formulary in that year. The variable of interest is the number of times a patient goes off a particular medication for 30 days and then resumes that same medication. For example, a value of 1 indicates that the average patient had one 30-day gap in their medication. Patients in states with formulary restrictions tend to have slightly more replication of failure, measured as stoppages and restarts. For example, in 2005, individuals in states with all atypicals under restriction experienced an average of 1.04 stoppages and subsequent restarts of the same atypical in the same year, while those in states with no restrictions experienced an average of 0.99 stoppages and restarts per year.
Measuring replication of failure using inpatient or ED admission provides a more explicit measure of failure. Repeated failure here signifies that the patient was admitted for a mental health issue while on a particular drug, but then was put back on that atypical within 6 months of the admission. Approximately 65% of inpatient admissions result in the individual resuming the same atypical they were on before the admission, within 6 months. There is no significant difference over time. Among ED visits, 59% result in the same atypicals being resumed within 6 months in both restrictive and unrestrictive states.
, , , and ( and ) show the estimates from difference-in-differences regressions comparing outcomes from 2001 with those from 2005. The first outcome measured in Table 4 is for patients who resumed the same atypicals after experiencing at least a 30-day gap in stock of the drug. The sample is restricted to those patients who filled at least 1 atypical prescription in the given year. States that place some drugs on prior authorization are compared with states with no restrictions (Table 4A). The difference-in-differences coefficient is calculated to be 0.062 (standard error [SE] 0.009): formulary restrictions on some atypicals are associated with a 6.2% increase in the probability that an individual resumes the same atypical after a stoppage, relative to the control group. In the regressions that use the states that have placed all atypicals under a formulary restriction by 2005 as the treatment group (Table 4B), the difference-in-differences coefficient is found to be 0.201 (SE 0.011). This shows a substantial effect of placing all atypicals onto a formulary restriction, as the probability of resuming the same atypical after a stoppage of at least 30 days increased by 20.1% relative to the control group states.
The next outcomes measured were resumptions of the same atypical antipsychotic after an inpatient and ED admission with a mental health diagnosis. The sample was further restricted to patients meeting our inclusion criteria who were also admitted as an inpatient, or who visited the ED in the given year with a mental health condition. When states with no formulary restrictions are compared with those with only some atypicals restricted, the results suggest no statistical impact of these restrictions on resuming the same atypical after an admission. In states that restricted all atypicals, the policy was not shown to have any effect on resuming the same atypical after an ED visit.
Table 5 (A and B) shows difference-in-differences results for the outcome of switching from one atypical to another. The control group includes patients in states with no formulary restrictions, while the 2 treatment groups consist of patients in (1) states that placed some atypicals on a formulary restriction, or (2) states that placed all atypicals on formulary restrictions. Individuals in restrictive states who were admitted as inpatients experienced a reduction of about 4.3% in the number of switches between atypicals relative to those in nonrestrictive states. In this case, a triggering event reduces the likelihood that a patient switches from one atypical to another. Similar effects are observed for ED admissions; the reduction in number of switches following an ED admission is 3.2%.
The results in Table 6 (A and B) examine the relationship between the introduction of formulary restrictions on atypicals and patients stopping the use of atypicals in favor of typical antipsychotics that are not on formulary restrictions. For states that placed all atypicals onto formulary restrictions, the number of patients stopping atypical usage in favor of typical antipsychotics is shown to have increased by less than 3.5%.
Finally, Table 7 shows estimates for when atypical users stopped the use of all mental health drugs. Among those in states that restrict all atypicals, the likelihood of discontinuation increased by 11.6%.
Although formulary restrictions hold the promise of reduced Medicaid outlays for pharmaceuticals, they may also have unintended effects that raise Medicaid costs in other areas. In this study, we examined the impact of formulary restrictions on the treatment of schizophrenia. Schizophrenia is very responsive to psychopharmacologic treatment, but the treatment response is highly heterogeneous across patients, and some patients respond only to a single medication. Moreover, the process of finding the appropriate medication is often one of trial and error. Because of this, treatment failures would be expected to cause a switch to a different medication.
We examined several different treatment failures, such as a patient going off medication, a patient being admitted to the hospital or visiting the ED with a psychological episode, or a patient terminating drug treatment altogether. These rates of replication of failure are relatively large, with 65% of inpatient admissions and 59% of ED admissions resulting in a resumption of the same atypical, regardless of the formulary policies in place. Such rates suggest possible quality-of-care issues, but a deep examination of quality of care lies beyond the scope of this study.
We find that replication of failure increases when states adopt formulary restrictions. These findings are also suggestive of a causal effect. We found that formulary restrictions on atypical antipsychotics increase the likelihood that a patient who has stopped taking medication will end up back on the same drug—an indication that the drug failed, but was restarted. This likelihood increases by 6.2% if the patient resides in a state that has implemented formulary restrictions on some atypicals in its Medicaid program, and by 20.1% if the state of residence’s formulary restrictions cover all atypicals. This suggests that formulary restrictions cause doctors to return patients to treatments for which there is evidence that the treatment is not effective.
Moreover, we found that the likelihood that a patient who was initially on an atypical switches to an older and less expensive medication (a typical/first generation) increases by 3.5% if the state’s formulary restricts all atypicals, again relative to the control group of states without restrictions. This again suggests that state formulary restrictions are pushing doctors to alter their treatment choices in ways that they did not think benefited the patient before the formulary restriction. Finally, we found that when states impose formulary restrictions on atypicals, the likelihood that a patient on atypicals discontinues all treatment with either typicals or atypicals increases by 11.6% if the state restricts all atypicals under its formulary restrictions. These findings indicate that state formulary restrictions have an impact on the likelihood that doctors will try an alternative therapy when markers of failure present themselves, and that doctors are induced by formulary restrictions to switch to alternative therapies even when there are no indicators that the current treatment is ineffective.Author Affiliations: University of Southern California, Leonard D. Schaeffer Center, Los Angeles, CA (DPG); Johnson & Johnson, West Chester, PA (RD); Janssen Pharmaceuticals LLC, Titusville, NJ (JF); University of Southern California, Los Angeles, CA (RMC).
Source of Funding: This research was funded by Janssen Scientific Affairs, LLC, Titusville, NJ.
Author Disclosures: Dr Goldman is a partner of Precision Health Economics. Dr Conrad was an employee of Precision Health Economics at the time of study completion. Mr Fastenau is an employee of Janssen Scientific Affairs, LLC, which funded this study, and a Johnson & Johnson stockholder. Dr Dirani was an employee of Janssen Scientific Affairs, LLC at the time of study completion.
Authorship Information: Concept and design (DPG, RD, JF, RMC); analysis and interpretation of data (DPG, RD, JF, RMC); drafting of the manuscript (DPG, RD, JF, RMC); critical revision of the manuscript for important intellectual content (JF); statistical analysis (RD, RMC); supervision (DPG).
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