Riad Dirani, PhD

To examine the impact of Medicaid prior authorization for atypical antipsychotics on the prevalence of schizophrenia among the prison population.

We collected drug-level information on prior authorization restrictions from Medicaid programs in 30 states to determine which states had prior authorization requirements before 2004. We linked the regulatory data to a survey of prison inmates conducted in 2004.

We used a sample of 16,844 inmates from a nationally representative survey and analyzed the data using cross-sectional regression. To capture the impact of prior authorization, we estimated 2 models: the first included an indicator variable for states requiring prior authorization, and a second model used per capita atypical usage.

Evidence indicated that prior authorization restrictions on atypical antipsychotics are associated with an increase in the odds of a schizophrenic resident being imprisoned in a state. State-level prior authorization requirements for atypical antipsychotics are associated with a 2.7% increase in the likelihood that an imprisoned inmate displays psychotic symptoms, and a 1.25 increase in the likelihood that an inmate was previously diagnosed with schizophrenia by a physician. Higher state-level atypical prescriptions per capita are also associated with lower likelihood of psychotic symptoms and of prior schizophrenia diagnosis among prisoners.

Prior authorization requirements for atypical antipsychotics, which are designed to reduce healthcare costs, are associated with greater prevalence of mental illness within the criminal justice system.This association raises important questions about whether increased costs to the criminal justice system might mitigate or offset prescription drug savings created by prior authorization requirements.

We examined the impact of prior authorization formularies on the likelihood that patients with schizophrenia will be arrested and incarcerated.

These findings suggest that prior authorization requirements for atypical antipsychotics, which are designed to reduce healthcare costs, can result in increased expenditures for the criminal justice system. Given the high cost of incarceration, these increased costs to the criminal justice system could easily offset any savings created by prior authorization requirements.

		Prior authorization requirements for atypical antipsychotics are associated with a 22% increase in the likelihood of imprisonment compared with the likelihood in a state without such a requirement.  

		Prior authorization requirements were found to be associated with an increased likelihood that individuals with schizophrenia will be incarcerated for nonviolent drug offenses.

		As the total costs in the United States that are associated with severe psychiatric disorders in jails are very high, new policies on how to treat incarcerated individuals with schizophrenia, particularly nonviolent offenders, are warranted.

Increasingly, public and private healthcare plans are imposing prior authorization requirements to manage drug spending. Prior authorization plans establish a preferred drugs list (PDL) and require authorization before covering drugs not on the PDL. Such programs are designed to reduce costs by steering utilization toward lower-cost medications, but these policies may also lead to poorer drug adherence.

 Poor adherence to or discontinuation of antipsychotic drugs among patients with schizophrenia is likely to cause acute psychotic episodes

 and often results in contact with law enforcement officers due to threatening behaviors brought on by active symptoms, leading to arrest and incarceration.

While schizophrenia affects only about 1% of the US population,

 it is difficult and expensive to treat and can have a devastating impact if not well controlled.

 The effects of losing continuous effective exposure to medication due to nonadherence are immediate and evident. A study on the temporal relationship between medication nonadherence and hospitalization risk for individuals with schizophrenia found that individuals in the first 10 days following a missed prescription refill had a greater than 50% increase in the risk of mental health hospitalization and a 77% increase in the risk of schizophrenia-specific hospitalization.

As a result of their behavior, mentally ill people are more likely to be arrested than other people who are stopped by police, and more likely to be subsequently convicted and incarcerated.

 Enforcement officials may not be sufficiently trained to recognize mentally ill patients, or they may feel obliged to bring the schizophrenic offender to the criminal justice system when the healthcare system is unwilling or unable to accept a violent patient. Prison systems are often poorly equipped to treat the mentally ill, and, when released these prisoners are more likely to recidivate,

which promotes the vicious cycle of mentally ill criminals entering the criminal justice system, suffering further mental health deterioration in prison, and upon release ending up with a significant likelihood of re-arrest due to poorly controlled mental illness.

Evidence also shows that the mentally ill are more costly to incarcerate than those without mental illness and that other types of rehabilitation for the mentally ill may reduce crime at costs lower than those associated with incarceration.

 Over the past several decades, pharmaceutical advancements in the treatment of schizophrenia have been shown to reduce the likelihood of episodes that start the sequence of events leading to incarceration of patients. The second-generation antipsychotics known as atypicals were introduced in the 1990s. These have largely replaced older antipsychotics, because they are associated with lower rates of relapse. About 30% to 40% of patients relapsed with first-generation drugs (relative to 80% without treatment); for second-generation drugs, relapse rates fell to about 25% to 29%.

 Furthermore, many patients respond to only 1 drug, so that increased treatment options increase the likelihood of successful treatment.

 The lower rates of relapse seen with atypicals may lead to fewer incidents with law enforcement and reduced incarceration of schizophrenia patients.

The evidence suggests that atypicals improve outcomes for certain patients with schizophrenia. The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study found that perphenazine (Trilafon), an older typical, worked about as well as several newer atypicals for the average patient in the trial.

 Subsequent research on the CATIE data clarified that a subset of patients in CATIE strictly benefited from atypical usage, and that providing typicals to all patients would have reduced overall health.

 Taken together, this literature suggests that atypicals represent a valuable treatment option for some patients with schizophrenia.

In this study, we examine the association between Medicaid policy and 2 key outcomes-—namely, utilization of antipsychotics and imprisonment of patients with schizophrenia. We compared the prevalence of patients in prison among states that did or did not institute prior authorization programs for atypical antipsychotics. We also compared prevalence across states that have different levels of atypical antipsychotic utilization. Because of the association between prior authorization regulations and increased risk of discontinuing atypical treatment, and because of the potential link between untreated schizophrenia and incarceration, we hypothesized that prior authorization rules will increase the likelihood that a schizophrenia patient is incarcerated.

Ideally, we would like to have measured the percentage of each state’s population with schizophrenia that is incarcerated, and then examined how this percentage is associated with state prior authorization policy. In practice, we were able to obtain only the percentage of the incarcerated population who were diagnosed as having schizophrenia in each state. If it is assumed that Medicaid pharmacy regulations have no impact on the overall prevalence of schizophrenia in a state, and that they have negligible impact on the rate at which nonschizophrenics are imprisoned, then the impact of pharmacy regulations on the prevalence of schizophrenia in prisons will be comparable with the impact of these regulations on the rate of imprisonment of patients with schizophrenia.

A simplified summary of the underlying process illustrates how restrictive pharmacy policies in Medicaid might lead to a higher prevalence of schizophrenia in prison:

Stage 1: A large majority of patients with schizophrenia are covered by Medicaid.

Stage 2: The state’s restrictive prior authorization policy leads to discontinued, reduced, or inappropriate medication.

Stage 3: Suboptimal medication leads to uncontrolled symptoms.

Stage 4: Active symptoms or deteriorated economic conditions that result from uncontrolled symptoms lead to encounters with law enforcement and arrest.

Stage 5: Repeated or serious arrests lead to imprisonment.

Our hypothesis is that Medicaid prior authorization regulations limit access to atypicals, and that this increases the likelihood that a patient will progress down the list from stage 2 to stage 5.

Our analysis made use of the 2004 Survey of Inmates in State Correctional Facilities (SISCF) and the 2004 Survey of Inmates in Federal Correctional Facilities (SIFCF) conducted for the Bureau of Justice Statistics by the Bureau of the Census; these surveys have been used in previous research.

 They provide nationally representative data on inmates held in state and federal prisons obtained through personal interviews of more than 18,000 inmates in about 300 prisons. These data contain individual-level information on inmates’ mental health conditions and various personal characteristics. A benefit of the SISCF data is that they also contain state indicators, which allow us to match the data to our original survey of antipsychotic coverage.

Using a mailed survey that was conducted in 2009, we collected information from 30 state Medicaid programs on their utilization review policies for atypical antipsychotics over the period 1999 to 2008.

 This survey asked whether prior authorization policies applied to a list of drugs identified by US brand name. To supplement the survey information, we examined Medicaid pharmacy program websites for relevant documents and contacted Medicaid program personnel. This allowed us to identify the 4 states (AK, CA, MA, and NY) that had prior authorization for all atypicals before 2003, along with 24 states that we can determine had no prior authorization before the 2004 SISCF survey. For the remaining states, we are unable to determine the prior authorization policy in 2003, because information on the timing of prior authorization implementation is unclear or unavailable from the mailed survey or websites. (See 

State-level utilization data were obtained from the State Drug Utilization Data Files available from CMS. These data, which include the number of prescriptions filled for each drug by each state Medicaid program by calendar quarter, were obtained for 2003 atypical antipsychotics. State-level Medicaid enrollments as of June of each year were obtained from the Kaiser Family Foundation’s State Health Facts database.

We used prevalence of serious psychological distress (SPD) estimated from the 2003-2004 National Surveys on Drug Use and Health, produced by the Substance Abuse and Mental Health Services Administration (SAMHSA), as a proxy for the prevalence of schizophrenia in the general population. Serious psychological distress was measured using the K6 screening instrument for nonspecific psychological distress. In 2003-2004, SPD was noted in about 10% of the population 18 years or older. If we can assume that the percentage of schizophrenia in SPD does not vary by status of prior authorization or level of atypical utilization, then such an approximation does not affect interpretation of our results.

We estimated the probability that an inmate is screened as positive for psychotic symptoms, and whether an inmate reports a prior schizophrenia diagnosis, using a linear probability model. In addition to an indicator variable that equals 1 if the state in which the inmate is incarcerated has a prior authorization rule for atypical antipsychotics, we included the prevalence of schizophrenia in the general population in the state and several individual inmate characteristics found in 

As documented in Table 1, the probability that a prisoner was previously diagnosed with schizophrenia is relatively low, and this suggests the linear probability modeling approach. Logistic regression is known to perform poorly in binary dependent variable models where positive outcomes are statistically rare; in contrast, linear probability model performance is invariant to the mean of the dependent variable.

 In any event, however, the association between prior authorization (or atypical usage) and the prevalence of mental illness within prisons is robust and statistically significant across linear probability, logistic regression, and probit models. Therefore, our results are not primarily dependent on functional form. All our modeling results are presented for the logistic regression model in 

The outcome variable is an indicator of whether an inmate has screened positive for psychotic symptoms. We utilize 2 questions from the survey. In the first, the survey asked inmates whether during the past year they had seen or heard things that other people said were not there, felt that other people were able to read or control their mind, or felt that someone other than the corrections staff had been spying on or plotting against the inmate. Inmates who answered yes to any of these questions were screened as positive for psychotic symptoms in our analysis. This question measures the presence of psychotic symptoms. Note that it is broader than schizophrenia. Our second measure focuses on a much narrower definition, based on a question that asks the inmate if they have ever been diagnosed by a healthcare provider with schizophrenia or a psychotic disorder. This definition will undercount those inmates who are unwilling or unable to identify themselves as schizophrenic but has the advantage of not resulting from self-diagnosis. As shown in Table 1, 2840 inmates screened positive using the more inclusive definition while 775 reported a prior diagnosis. Significantly, all those with a prior diagnosis also tested positive for the presence of psychotic symptoms.

Articles

This study examined the impact of prior authorization formularies on the likelihood that patients with schizophrenia will be arrested and incarcerated.

Medicaid Prior Authorization Policies and Imprisonment Among Patients With Schizophrenia

 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.

We 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.

		In 2005, 69% of all schizophrenia patients’ mental health—related admissions resulted in patients resuming the same schizophrenia treatment within 6 months

		Patients in states that impose FRs on all atypical antipsychotics are 20.1% more likely to resume use of the same atypical after having ceased treatment for at least 30 days and 11.6% more likely to discontinue all treatments relative to patients in states without restrictions.

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.

 To attempt to control costs, many state Medicaid programs responded by imposing policies that encourage the use of lower-cost drugs.

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.

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.

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.

 For this reason, they are increasingly popular targets of state Medicaid formulary restrictions.

 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.

 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.

 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.

 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%.

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%.

 After prior authorization of atypicals became a requirement in Maine, the risk of discontinuation rose by 29%.

 Studies in Georgia and New Hampshire found that prior authorization reduced the use of targeted drugs and increased the use of other health services.

 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

) 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.

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.

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&mdash; 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.

) 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.

 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).

This research was funded by Janssen Scientific Affairs, LLC, Titusville, NJ.

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.

 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).

Dana P. Goldman, PhD, 3335 South Figueroa St, Unit A, Los Angeles, CA 90089-7273. E-mail: dpgoldma@ healthpolicy.usc.edu.1. Consumer Health Sourcebook. National health expenditures by type of service and source of funds: calendar years 1960 to 2009. http://www .chsourcebook.com/docs/nhe_aggregate_2009.pdf. Published 2009.

2. Soumerai SB, Zhang F, Ross-Degnan D, et al. Use of atypical antipsychotic drugs for schizophrenia in Maine Medicaid following a policy change. Health Aff (Millwood). 2008;27(3):w185-w195.

3. Mello MM, Studdert DM, Brennan TA. The pharmaceutical industry versus Medicaid: limits on state initiatives to control prescription-drug costs. N Engl J Med. 2004;350(6):608-613.

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.

Riad Dirani, PhD

Articles

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