To assess whether Medicare formularies restrict access to drugs receiving new FDA black box warnings for which safer drug alternatives are available.

A retrospective analysis using Medicare Prescription Drug Plan Formulary files to determine formulary changes for drugs receiving FDA black box warnings between 2007 and 2013.

We identified all FDA-approved medications available in tablet or capsule formulation that received a black box warning between 2007 and 2013 related to death and/or cardiovascular risk. We then determined formulary coverage of these drugs pre-black box warning, 1 year after, and 2 years after. For each formulary, we identified formulary restrictiveness, defined as: unrestrictive coverage (no prior authorization or step therapy), restrictive coverage (prior authorization or step therapy required), or no coverage.

Nine drugs with at least 1 FDA-approved safer drug alternative received 10 new black box warnings for death and/or cardiovascular risk between 2007 and 2013. In response to FDA black box warnings, overall formulary restrictiveness increased for 40% (n = 4) of drugs at 1 year, and for 50% (n = 5) at 2 years. However, for the majority of drugs (n = 7),  most formularies remained unrestrictive 2 years after a new black box warning.

Medicare formularies became more restrictive for half of the drugs that recently received new FDA black box warnings for death and/or cardiovascular risk and for which safer drug alternatives are available. However, a substantial proportion of formularies remained unrestrictive, suggesting inconsistent responses to new safety information to curtail the use of these medications.

The impact of FDA black box warnings on formulary restrictiveness is not well known. This study investigated whether Medicare formulary coverage changed after oral drugs with safe available drug alternatives received new FDA black box warnings related to death and/or cardiovascular risk. 

Some formularies either dropped coverage or began requiring prior authorization to restrict the prescribing of unsafe drugs. 

A substantial proportion of formularies made no changes to restrict coverage. 

There are opportunities to improve consistency in formulary responses to FDA black box warnings to curtail utilization of drugs with known safety risks and for which safer drug alternatives are available.

The FDA requires that drugs with serious or life-threatening risks have a boxed warning (commonly referred to as a “black box warning” [BBW]) on their label. The BBW is one of the FDA’s strongest actions short of drug recall or withdrawal, and is intended to inform patients and prescribers about serious safety concerns that must be considered in assessing the risks and benefits of a drug or that the FDA allows use of the drug only with restrictions to ensure safe use. However, BBWs have not been consistently associated with reduced prescribing

 for several possible reasons. First, prescribers may be unaware that the FDA had issued the warnings.

Second, prescribers may think that, despite BBWs, the drugs have a superior benefit/risk ratio to alternative therapies or that the safety concern is not as severe as suggested, and opt to continue prescribing while using strategies to mitigate risk, such as closer patient monitoring.

 Third, BBWs may apply only to limited patient populations, and prescribers may not believe the risk applies more generally.

Another possibility is that BBWs are not taken into account within insurance plan drug formularies. Formularies limit prescribing of unsafe drugs through 2 strategies: formulary exclusion (ie, no coverage) or utilization management (eg, prior authorization or step therapy). One study found no change to state Medicaid plans’ prior authorization requirements following the 2005 BBW for atypical antipsychotic use among the elderly,

 and another found that only 2 state Medicaid programs restricted use after the 2007 BBW for rosiglitazone.

 To better understand the potential role that insurance plan drug formularies may play in restricting the use of potentially unsafe drugs that have safer available alternatives, we characterized changes to Medicare formularies for oral drugs that received new BBWs between 2007 and 2013 for death and/or cardiovascular risk. As these are among the most severe adverse events, we hypothesized that they would have the greatest likelihood of resulting in formulary changes to restrict use. Because each BBW is specific to the drug’s indications and risk, we focused on a limited sample to provide an illustrative summary of formulary changes.

We conducted a retrospective analysis of Medicare formularies using data from the CMS Prescription Drug Plan Formulary Files. We obtained data on both standalone prescription drug plans and Medicare Advantage prescription drug plan formularies for Medicare Part D, as of each June and December from 2006 through 2015.

We used the FDA’s MedWatch website to identify all FDA-approved tablet or capsule formulation medications used in an outpatient setting that received BBWs between 2007 and 2013 related to death and/or cardiovascular risk. MedWatch is the agency’s safety information and adverse event reporting program. We examined all BBWs and first excluded those for drugs used in-hospital only or not relevant to Medicare beneficiaries. We also excluded BBWs for combination drugs, since the primary drug would already be included in our sample. Next, we excluded BBWs for cancer, HIV, or immunosuppressants as these are protected classes treating diseases that require specialized pathways of care. We also excluded warnings for a narrow subset of patients and warnings related to drug-drug interactions. Lastly, we limited our study to drugs with at least 1 drug alternative without a BBW approved for use for the same primary condition, based on the US Pharmacopeial Convention Category & Class versions 5.0 and 6.0. When a drug had multiple BBWs, each warning was considered a unique event.

Our main outcome was formulary restrictiveness based on formulary exclusion, step therapy, or prior authorization. For each drug’s lowest available dose, we classified all Medicare formularies as unrestrictive, restrictive, or no coverage at 3 time points: immediately preceding, at least 1 year after, and at least 2 years after the BBW. An unrestrictive formulary covered the drug without prior authorization or step therapy requirements. A restrictive formulary covered the drug but required either prior authorization and/or step therapy. A formulary that did not explicitly provide coverage did not include the drug on its formulary, which means patients are responsible for the entire drug cost except in very rare circumstances.

We used descriptive statistics to characterize the proportion of formularies that were unrestrictive, restrictive, and did not provide coverage for each drug at all 3 time points. We used χ

 or Fischer’s Exact tests to examine differences in formulary restrictiveness for each drug, comparing restrictiveness pre-BBW to restrictiveness at both 1 year and 2 years afterward. All statistical tests were 2-tailed with a type 1 error rate of 0.025 to account for multiple comparisons for each drug.

Nine medications in a tablet or capsule formulation received a total of 10 new BBWs related to death and/or cardiovascular risk between 2007 and 2013 and had at least 1 drug alternative without a BBW. Four of the 9 drugs (44%) had existing BBWs before the FDA issued BBWs between 2007 and 2013. Seven (70%) of the 10 BBWs were related to death and 5 (50%) to cardiovascular risk (2 related to both cardiovascular risk and death). Two BBWs, those for propylthiouracil and ketoconazole, specifically advised against use of the given medication except in very unique circumstances.

Two years after each of the 10 new BBWs, for 5 (50%) drugs, Medicare formularies became more restrictive, on average, by removing coverage or adding prior authorization requirements. There was no overall increase in step therapy requirements. For 1 (10%) drug, formularies became less restrictive on average and for 4 (40%) drugs there was no overall change in average formulary restrictiveness. When restricting to the 4 drugs with prior BBWs, results were overall similar: Medicare formularies became more restrictive for 2 (50%) drugs at both 1 and 2 years.

Rosiglitazone is approved to improve glycemic control for type 2 diabetes. In August 2007, thiazolidinediones, a medication class that includes rosiglitazone and pioglitazone, received a BBW for congestive heart failure. Prior to this BBW, 78% (n = 255) of formularies provided unrestrictive coverage, 19% (n = 62) provided restrictive coverage, and 3% (n = 10) provided no coverage. There was no significant change in formulary restrictiveness at 1 or 2 years (Figure).

Prior to its strengthened February 2011 BBW for a significantly increased risk of myocardial infarction, 57% (n = 148) of formularies provided unrestrictive coverage, 36% (n = 94) provided restrictive coverage, and 7% (n = 18) provided no coverage. At 1 year, rates were 32%, 36%, and 32%, respectively (

 <.001) (Figure), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were due to fewer formularies providing coverage and covering formularies requiring prior authorization (Table).

Pioglitazone is approved to improve glycemic control for type 2 diabetes. Prior to its August 2007 BBW for congestive heart failure, 67% (n = 220) of formularies provided unrestrictive coverage, 19% (n = 62) provided restrictive coverage, and 14% (n = 45) provided no coverage. At 1 year, rates were 73%, 27%, and 0%, respectively (

<.001) (Figure), suggesting that formularies had become significantly less restrictive. Changes in restrictiveness were predominantly due to more formularies providing coverage.

Zyban is approved for smoking cessation. Prior to its July 2009 BBW for serious neuropsychiatric events, including suicide, 24% (n = 75) of formularies provided unrestrictive coverage, 5% (n = 11) provided restrictive coverage, and 73% (n = 232) provided no coverage. There was no significant change in formulary restrictiveness at 1 year (Figure). At 2 years, rates were 19%, 0%, and 80%, respectively (

 = .006), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to fewer formularies providing coverage.

Varenicline is approved for smoking cessation. Prior to its July 2009 BBW for serious neuropsychiatric events including suicide, 60% (n = 191) of formularies provided unrestrictive coverage, 21% (n = 68) provided restrictive coverage, and 19% (n = 59) provided no coverage. At 1 year, rates were 51%, 49%, and 0%, respectively (

 <.001) (Figure). Changes in restrictiveness were predominantly due to more formularies requiring prior authorization (Table).

Quinine is approved for malaria treatment, although it was widely used for nocturnal leg cramps. Prior to its November 2009 BBW explaining that use for nocturnal leg cramps could result in life-threatening hematological reactions, 60% (n = 159) of formularies provided unrestrictive coverage, 18% (n = 56) provided restrictive coverage, and 32% (n = 103) provided no coverage. There was no significant change in formulary restrictiveness at 1 year (Figure). At 2 years, rates were 41%, 27%, and 31%, respectively (

 = .012), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to more formularies requiring prior authorization (Table).

Propylthiouracil is approved for treatment of hyperthyroidism. Prior to its April 2010 BBW for potentially fatal liver failure, stating propylthiouracil was only indicated in patients intolerant of methimazole, 100% (n = 292) of formularies provided unrestrictive coverage. There was no significant change in formulary restrictiveness at 1 or 2 years (Figure).

Dronedarone is approved for treatment of paroxysmal or persistent atrial fibrillation or atrial flutter but included a BBW contraindicating its use in patients with advanced heart failure. Prior to its additional December 2011 BBW contraindicating its use in patients with permanent atrial fibrillation because of cardiovascular risk, 82% (n = 199) of formularies provided unrestrictive coverage, 9% (n = 22) provided restrictive coverage, and 9% (n = 23) provided no coverage. At 1 year, rates were 72%, 10%, and 18%, respectively (

 <.001) (Figure), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to fewer formularies providing coverage. At 2 years, rates were 80%, 8%, and 12%, respectively (

 = .63), suggesting that changes in formulary restrictiveness had reversed, as more formularies began covering the drug again.

Dantrolene is approved for treatment of chronic spasticity. In July 2012, its prior BBW on fatal and nonfatal hepatotoxicity was updated to note that a higher proportion of hepatic events had fatal outcomes in elderly patients, although the majority of these cases were complicated by confounding factors. Prior to the addition to this BBW, 88% (n = 242) of formularies provided unrestrictive coverage, 0% (n = 11) provided restrictive coverage, and 12% (n = 34) provided no coverage. There was no significant change in formulary restrictiveness at 1 or 2 years (Figure).

Ketoconazole is approved as an antifungal therapy. Previous BBWs had been issued for QT prolongation and hepatotoxicity. Prior to the July 2013 BBW stating that the drug should only be used when other effective antifungal therapy was not available or tolerated, 100% (n = 314) of formularies provided unrestrictive coverage. At 1 year, 84% of formularies provided unrestrictive coverage, 16% provided restrictive coverage, and 1% provided no coverage (

 <.001) (Figure), suggesting that formularies had become significantly more restrictive. Changes in restrictiveness were predominantly due to more formularies requiring prior authorization (Table).

After 10 new BBWs were issued by FDA between 2007 and 2013 for death and/or cardiovascular risk among 9 tablet or capsule drugs with safer available drug alternatives, we observed that Medicare formularies became somewhat, but not universally, more restrictive at 1 and 2 years afterward. Formularies sometimes removed coverage and/or implemented prior authorization requirements to deter the use of drugs with new safety warnings. However, a substantial number of formularies continued to provide unrestrictive coverage for these drugs after the FDA’s strong safety warning. Providing unrestrictive coverage when other effective but safer drug alternatives are available suggests that formularies are not taking advantage of existing utilization management strategies to promote the use of safer available alternative medications, thus unnecessarily exposing some Medicare beneficiaries to risky medications. There were no consistent patterns in the drugs for which BBWs were not associated with increased formulary restrictiveness.

CMS instructs its contracting Part D sponsor formularies to consider drug safety and efficacy when making decisions about which drugs to include on formularies.

 Further, although changes are generally constrained once the coverage year begins, CMS specifically allows formularies to add utilization management tools at any time after a new FDA BBW and allows these unsafe drugs to be removed without meeting advance notice requirements.

First, our conclusions are limited to BBWs related to death and/or cardiovascular risk. Second, our analysis excluded drugs in a non-tablet or capsule formulation and those without safer available drug alternatives. Third, we did not examine changes in Medicare formulary tier, which is a strategy most likely to be used to impose cost-sharing requirements and limit the use of expensive, as opposed to unsafe, therapies. Fourth, our data may not be generalizable to formularies of insurers other than Medicare. Nevertheless, our findings provide important insights into the role of formulary policy because Medicare beneficiaries represent the largest proportion of insured patients using prescription medications. Finally, we did not examine the impact of formulary changes at the plan level or on prescriptions dispensed. Ultimately, linking formulary restrictiveness after BBWs to patient outcomes would help inform future formulary management decisions.

Medicare formularies increased restrictions to some drugs with new FDA BBWs for death and/or cardiovascular risk. Although this indicates important action, a substantial portion of formularies provided unrestrictive drug coverage 1 and 2 years afterward. Increasing the use of formulary exclusion or restrictiveness for drugs that receive BBWs could help curtail prescriptions of unsafe medications when safer drug alternatives exist.

Articles

WEB EXCLUSIVE

Medicare formularies were inconsistent in increasing restrictiveness to drugs that received FDA black box warnings for death and/or cardiovascular risk with safer available drug alternatives.

Association Between FDA Black Box Warnings and Medicare Formulary Coverage Changes

 Underutilization of smoking cessation treatments contributes to high rates of smoking in socioeconomically disadvantaged populations. Guided by a conceptual framework, the present study explored how healthcare provider factors, social environment characteristics, and cessation beliefs influence treatment utilization among low-income smokers and whether these associations vary by age.

Analyses were conducted on baseline data from 2406 participants enrolled in a randomized controlled trial that evaluated the effectiveness of a proactive outreach cessation intervention among a sample of younger (18-34 years) and older (35-64 years) smokers enrolled in public healthcare assistance programs.

 Multivariable logistic regression models predicted past year cessation treatment utilization (CTU) among younger and older smokers. Independent variables included measures of healthcare provider barriers, social environment characteristics, and cessation beliefs.

Younger smokers were less likely to have CTU than older smokers (27.2% vs 36.2%; 

<.001). In both groups, number of cigarettes per day, more problems accessing healthcare, receiving medication-related cessation advice, and readiness to quit were positively associated with CTU (

 <.05). Among younger smokers, living with another smoker was associated with lower odds of CTU while receipt of cessation advice was associated (

 In this sample of low-income smokers, interest in quitting was high but treatment utilization was low. Increasing utilization of cessation treatments via interventions that target issues specific to low-income smokers, including healthcare provider access and advice, the home environment, and motivation to quit, is an important step toward reducing smoking rates in this population.

Underutilization of cessation treatments contributes to high rates of smoking in socioeconomically disadvantaged populations. A conceptual framework was developed to assess the influence of healthcare provider factors, social environment characteristics, and beliefs about cessation treatment among younger (18-34 years) and older (35-64 years) low-income smokers. Regression models using variables from our conceptual framework predicted baseline cessation treatment utilization (CTU) among younger and older smokers enrolled in a proactive outreach tobacco treatment randomized controlled trial. Results indicate that cessation advice from healthcare providers, characteristics of the home environment, and cessation beliefs predict treatment use and that these predictors are consistent across age groups. 

Younger and older socioeconomically disadvantaged smokers share common predictors of CTU. 

Difficulty accessing healthcare, receipt of medication-related cessation advice, and readiness to quit are positively associated with CTU. 

Bolstering rates of physician-delivered cessation treatment may reduce socioeconomic disparities in smoking prevalence.

Among US adults younger than 65 years, 30% of the medically uninsured and Medicaid enrollees are current cigarette smokers compared with 15% of adults with private health insurance.

 Smokers with lower incomes are less likely to use evidence-based smoking cessation treatments, like pharmacotherapy and counseling, than smokers with higher incomes.

 In an analysis of the 2010 National Health Interview Survey, among smokers who were attempting to quit, only 29.9% of Medicaid enrollees used a cessation aid compared with 37.1% of individuals with private health insurance.

 The low rate of smoking cessation treatment utilization (CTU) among low-income smokers likely contributes to the socioeconomic disparity in smoking prevalence among US adults.

Socioeconomically disadvantaged smokers experience barriers to CTU at the healthcare provider and psychosocial levels. The present study organizes these barriers to CTU in a conceptual framework informed by elements of Social Cognitive Theory (SCT),

 the Transtheoretical (Stages of Change) Model,

 and the Biopsychosocial Model of Perceived Discrimination (

 Drawing from SCT, the framework emphasizes the influence of healthcare provider and psychosocial factors on CTU. The Stages of Change Model highlights the roles that precontemplation, contemplation, and action play in enacting behavior change. The Biopsychosocial Model of Perceived Discrimination elucidates how perceptions of healthcare provider bias influence CTU.

Healthcare providers often lack the time or motivation to discuss smoking cessation treatments with their patients.

 Competing treatment demands may play an especially important role with socioeconomically disadvantaged patients.

 A lack of knowledge regarding insurance coverage for cessation treatment in this population and skepticism about treatment effectiveness

 likely contribute to underadministration of treatment. Furthermore, healthcare providers’ lack of competence to discuss smoking cessation treatments in a culturally sensitive manner can contribute to low CTU rates.

Social environment characteristics also influence smokers’ propensity to utilize cessation treatment. Indeed, smokers who live with another smoker are less likely to utilize cessation treatments than those not living with a smoker.

 It is hypothesized that other characteristics of smokers’ social environments, including perceived social support and social norms, also influence CTU. Other data show that psychosocial factors pertaining to cessation beliefs, including readiness to quit and self-efficacy for quitting, influence smokers’ CTU.

Smokers who believe that they will stop smoking in the next year are more likely to utilize treatment than those who do not believe they will quit in the next year.

Although the conceptual framework asserts that socioeconomically disadvantaged smokers share many common barriers to CTU, it is hypothesized that the influence of these barriers may vary as a function of age. Younger smokers engage in frequent quit attempts,

 but they use cessation treatments at lower rates than older smokers.

 This age disparity in CTU could be due to age-related differences in nicotine dependence, use of healthcare resources, social environment, and cessation-related beliefs.

As such, examining the factors that influence CTU separately for younger and older smokers may inform more effective cessation interventions for these smokers. The primary aim of the present study was to examine the predictors of CTU among smokers enrolled in publicly subsidized healthcare programs, with a focus on healthcare provider and psychosocial barriers. A secondary objective was to determine whether the predictors of CTU differ by age. Analyses are intended to inform future interventions designed to increase CTU in socioeconomically disadvantaged populations and to elucidate how healthcare provider factors influence the propensity with which these smokers utilize cessation treatment.

We used baseline data from a randomized controlled trial evaluating the effectiveness of a proactive care tobacco cessation outreach intervention in a sample of adult smokers enrolled in Minnesota Health Care Programs (MHCP).

 MHCP is a state-funded health insurance plan for low-income Minnesota residents comprising 2 publicly subsidized healthcare programs: Medicaid and MinnesotaCare. The study population sample was stratified by age group (18-24, 25-34, and 35-64 years), gender, and healthcare program (Medicaid and MinnesotaCare). Institutional review board approval for the study was obtained from the University of Minnesota and the Minnesota Department of Human Services.

Eligibility criteria included: 1) a valid home address, 2) proficiency in English, and 3) current cigarette smoking (having smoked a cigarette in the past 30 days, even as little as 1 puff). Baseline surveys were mailed to 21,181 prospective participants aged 18 to 64 years who were MHCP clients. A total of 9362 baseline surveys were returned. Of these, 6826 individuals did not meet study inclusion criteria and 130 declined to participate. The remaining smokers (n = 2406) were enrolled in the study and randomized to proactive outreach or usual care.

Conceptual Framework: Predictors of Smoking Cessation Treatment Utilization

To conform to the proposed conceptual framework, distinct blocks of variables relating to healthcare provider factors and psychosocial characteristics were formed on the basis of their underlying constructs. Blocks assessing demographics and smoking history were also formed to enable block adjustment for known predictors of CTU. The demographic factors assessed were insurance type, gender, race/ethnicity, education, employment status, and income. In terms of smoking history, the California Tobacco Survey

 and the CDC Behavioral Risk Factor Surveillance System

 assessed lifetime duration of smoking, time until first cigarette upon waking, and quit attempts in the past year.

 A composite variable measuring healthcare access was created by summing 5 items pertaining to cost of care, transportation, and ease of access. Each item was assessed on a 3-point scale, with higher scores indicating greater healthcare provider barriers. Participants also indicated whether they had a regular doctor.

 Healthcare Effectiveness Data and Information Set tobacco performance measures

 were used to assess participants’ past year healthcare experiences, including the receipt of advice to quit, to use cessation medications, to use ways (besides products) to help with quitting, and the receipt of any cessation-related care.

Healthcare provider bias/cultural competence.

 A composite variable measuring healthcare provider bias was created by taking the mean of 3 items from the Physician Bias and Interpersonal Cultural Competence Measures Scale.

 Each item was assessed on a 5-point scale, with higher values indicating greater physician bias.

Psychosocial Barriers: Social Environment

A composite variable measuring perceived social support for cessation was created by taking the mean of 2 support-related variables.

 Another composite variable measuring overall social support was created by taking the mean of 6 general social support-related variables, pertaining to issues like help with housework, monetary assistance, and emotional support. Both of these variables were assessed on a 5-point scale, with higher scores indicating greater support.

 Participants reported the proportion of their close friends and family who smoke.

Participants reported whether they lived with a child younger than 18 years, whether they lived with another smoker, and smoking rules within their home.

 Self-efficacy for quitting was measured on a scale from 1 to 10, with higher values indicating greater confidence in quitting.

 Readiness to quit on a scale from 1 to 10 was assessed using the Contemplation Ladder (CL), with higher values indicating greater readiness to quit.

 Items assessed past-year use of nicotine replacement therapy (NRT) products, prescription cessation medications, and behavioral counseling.

The 2 younger age strata (18-24 and 25-34 years) were merged into a single group (n = 1320) due to similarities with respect to the independent variables of interest and to increase the power of the regression analyses; the older age group (35-64 years; n = 1086) was not altered. The younger and older groups were compared across baseline demographics, smoking history, healthcare provider barriers, social environment characteristics, and cessation beliefs using 

Separately for the 2 age groups, we fitted a logistic regression model for past-year smoking CTU (ie, NRT, prescription medication, and/or behavioral counseling) at baseline using variables from the conceptual model as predictors. To evaluate the constructs presented in the conceptual model, we examined the contribution of each block of predictors using a likelihood ratio test comparing the fitted model with a model where a given block of predictors was dropped. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) are also reported for all individual predictors in the regression models.

In the regression models performed for the younger and older age groups, there were 252 (19%) and 249 (23%) missing observations, respectively. Due to this high percentage of missing data, an imputation procedure was performed and logistic regression models were run on the resulting imputed datasets, with results aggregated using standard methods for multiple imputation. In general, the estimates and associated CIs obtained with the imputed datasets were consistent in terms of direction, magnitude, and statistical significance compared with those obtained using the original dataset. One exception was the “presence of another smoker in the home” estimate obtained for the younger age group, which was no longer statistically significant in the regression model run on the imputed datasets, although similar to the original model in direction and magnitude.

Comparison of Younger and Older Age Groups

Younger smokers reported lower levels of nicotine dependence than older smokers and had lower rates of past year CTU (27.2% vs 36.2%; 

). The younger and older age groups differed on measures of healthcare access, receipt of cessation advice from their healthcare provider, and perceptions of physician bias. The groups also differed on social support, social norms for smoking, and home environment (

Predictors of Past Year Treatment Utilization by Age Group

Evaluating the conceptual model in the younger age group, access to healthcare, physician cessation advice, and readiness to quit were significantly associated with CTU (all 

With respect to individual predictors, participants who received any form of cessation care from their healthcare provider had higher odds of CTU than those who had not received cessation care (aOR, 1.87; 95% CI, 1.19-2.94; 

 = .007). Participants whose healthcare provider had discussed smoking cessation medications had higher odds of CTU than those who indicated that their healthcare provider had not discussed these medications (aOR, 3.90; 95% CI, 2.46-6.20; 

 <.001). Surprisingly, difficulty of healthcare access was associated with higher odds of CTU (aOR, 1.12; 95% CI, 1.04-1.20; 

 = .004). Participants who live with another smoker had lower odds of CTU than those who do not live with another smoker (aOR, 0.69; 95% CI, 0.49-0.97; 

 = .033). Higher CL scores were associated with higher odds of CTU (aOR, 1.27; 95% CI, 1.17-1.37; 

Evaluating the conceptual model in the older age group, access to healthcare and physician cessation advice were significantly associated with CTU (all 

 <.05). Readiness to quit was also significantly associated with CTU (

With respect to the individual predictors, participants whose healthcare provider had discussed smoking cessation medications had higher odds of CTU than those who indicated that their healthcare provider had not discussed these medications (aOR, 4.24; 95% CI, 2.70-6.67; 

 <.001). Higher CL scores were associated with higher odds of CTU (aOR, 1.38; 95% CI, 1.27-1.50; 

 <.001). Unexpectedly, higher scores on difficulty of healthcare access were associated with higher odds of CTU (aOR, 1.11; 95% CI, 1.03-1.19; 

Although many socioeconomically disadvantaged smokers in our sample were interested in quitting smoking and reported a recent quit attempt, the majority had not used NRT products, prescription cessation medications, or behavioral counseling in the past year. Older smokers were more likely to have utilized cessation treatments in the past year than younger smokers; this result is consistent with past findings.

A conceptual framework was developed to identify potential predictors of CTU (Figure). This framework describes the influence of healthcare provider and psychosocial barriers on the likelihood of utilizing cessation treatment among socioeconomically disadvantaged smokers. In evaluating the framework, it was found that access to healthcare, receipt of cessation advice from one’s healthcare provider, and readiness to quit were significantly associated with CTU in both age groups. In the younger group, the presence of another smoker in the home was significantly associated with CTU.

We had posited that predictors of CTU would vary by age; however, the results for the 2 age groups are largely consistent with respect to aORs and CIs. This suggests that socioeconomically disadvantaged smokers, regardless of age, experience many common barriers to treatment access and utilization, particularly with respect to healthcare provider access and advice, as well as cessation beliefs.

Among socioeconomically disadvantaged smokers, knowledge regarding the availability and effectiveness of cessation treatments, like NRT and counseling, are positively associated with their use.

 As such, breaking down barriers to healthcare access and cessation advice is critical for bolstering CTU. In the present study, this is evidenced by the pronounced effect of physician-delivered cessation advice on CTU. In the younger age group, receipt of cessation-related care from a healthcare provider was positively associated with CTU. Furthermore, in both age groups, smokers whose healthcare providers discussed the use of cessation medications had approximately 4 times higher odds of CTU than smokers whose healthcare providers did not discuss these medications.

A conceptual framework was used to examine the influence of healthcare provider and psychosocial barriers on treatment utilization among younger and older socioeconomically disadvantaged smokers.

Treatment Barriers Among Younger and Older Socioeconomically Disadvantaged Smokers

 This paper investigates back-transfer: the transfer of patients near the end of their acute hospitalization to a local community hospital for the completion of their medical care. We seek to describe factors contributing to back-transfer, with the goal of elucidating the current use of back-transfer and barriers to its more widespread adoption for rural healthcare regionalization.

 This was a retrospective study of adults hospitalized in Iowa between 2005 and 2013 to identify back-transferred patients. Demographic, geographic, rurality, procedural, and disease information was compared among cases and control groups using univariate analysis and multivariable logistic regression. 

Over the 9-year period, 172,544 back-transfer eligible patients were admitted to 1 of 5 large Iowa hospitals, of which 287 (0.2%) were back-transferred. Back-transferred patients were more likely than their non—back-transferred counterparts to be older, male, and white; to live in large rural areas; and to have public insurance. As inpatients, they had longer median lengths of stay (15 vs 5 days; 

 <.001), more medical comorbidities, and were more likely to have a cardiac catheterization procedure than the control group.

Back-transfer is a very rare event. While demographic and medical differences between back-transferred patients and controls may partially explain the infrequency, other systematic barriers must exist to limit back-transfer. These barriers likely include legal, financial, logistical, and patient care concerns. Despite the rarity with which it is employed, back-transfer is a promising strategy that could better utilize health resources, especially in rural America. 

Our study found that back-transfer of patients from large hospitals to small hospitals during an acute care episode is a rare event. Demographic and medical differences may partially explain the infrequency, but systematic (legal, financial, logistical, and patient care) barriers must exist to limit back-transfer. 

Back-transport could maximize availability of specialty services, support rural health systems, and move patients closer to home and family. 

Widespread adoption of back-transfer through changes in policy and clinical practice patterns may be a way to better utilize healthcare resources.

The concepts of regionalization and centralization are central to managed care. Here, we define regionalization as an “active process by which patients are appropriately matched to appropriate resources.”

 This can be compared with centralization, which is a more unplanned process in which patients are transferred to larger medical centers for a variety of medical, financial, legal, and personal reasons. In rural states, regionalization and centralization processes occur concurrently. Many small hospitals are closing and, thus, care is becoming centralized to fewer hospitals. Simultaneously, small local hospitals are unable to provide comprehensive services, so patients are often transferred for specific types of problems, such as stroke care and cardiac catheterization.

Although transfer to regional care centers is lifesaving for many rural residents, variability exists in patient preferences for transfer. Liu et al reported that 56% of rural patients who lived near a federally designated critical access hospital (CAH) would have preferred to have care locally, but were transferred or referred by their local provider because the care they required was unavailable at their local CAH.

 Treatment at a regional or tertiary hospital can be a burden for rural patients.

 Tertiary care hospitals are often far from home, and family members have increased expenses and stressors related to visiting ill loved ones.

With tertiary care centers near capacity and smaller community hospitals sitting nearly empty,

 some have wondered how regionalization can better utilize existing healthcare resources in financially strained health systems at both ends of the hospital-size spectrum. One proposed strategy to better align healthcare resources with utilization is the concept of “back-transfer”—transferring patients near the end of their acute hospitalization to a lower-acuity hospital for the completion of their care.

 This is part of healthcare regionalization in that patient needs are matched to hospital capabilities, but in this case, those needs can be met at a less-specialized hospital.

Back-transfer has been described previously for select patient populations. In Europe and Canada, the practice is used for cardiac patients after uncomplicated angiography and percutaneous coronary intervention (PCI) in specialty centers.

 Matteau et al suggest immediate back-transfer would be safe for up to two-thirds of ST-elevation myocardial infarction patients after PCI.

 In the United States, back-transfer (or “back-transport”) is used for the continued care of neonates after treatment at a tertiary neonatal intensive care unit.

 However, for adult populations in the United States, this concept has not been widely adopted and application of this transfer practice has not been studied.

In this study, we describe a cohort of patients transferred from larger hospitals to smaller hospitals for ongoing acute care over a 9-year period in a largely rural midwestern state. We sought to identify factors contributing to back-transfer, with the goal of elucidating the current use of back-transfer. We also discuss systematic barriers and potential benefits of its more widespread adoption.

This was an observational unmatched case-control study of all adults (18 years or older) hospitalized in Iowa between 2005 and 2013. Patients were identified from administrative claims data in the Iowa Hospital Association inpatient data set. Patient records were linked across inter-hospital transfer using a probabilistic linkage algorithm incorporating date of birth, sex, patient zip code, county of residence, visit date, and Social Security number through sequential matching. Records were limited to those eligible for back-transfer. Eligibility was defined as at least a 3-day inpatient hospitalization, residence at least 32 km from a tertiary center and within 32 km of a nontertiary hospital. The University of Iowa institutional review board approved the project under waiver of informed consent. This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Rurality was defined based on zip code of residence according to Rural-Urban Commuting Area (RUCA) definitions.

 Geographic distances were calculated by the Google Maps Application Programming Interface

 as the driving distance between each hospital and the centroid of the zip code of residence. Diagnosis categories were derived from 

International Classification of Diseases, 9th Edition, Clinical Modification

) codes categorized using the Clinical Classifications Software (CCS) available from the Agency for Healthcare Research and Quality Health Care Utilization Project (HCUP).

 Comorbidities were defined using Elixhauser methodology.

 Surgical procedures were defined as any invasive or noninvasive procedure typically performed in a surgical suite, identified using the Surgery Flag Software available from HCUP.18 Cardiac catheterization was defined by these 

 procedure codes: 3722, 3723, 8850, 8851, 8852, 8853, 8854, 8855, 8856, 8857, 8858, 3606, 3607, 3609, and 0066. CAHs were defined by federal designation at the time of transfer.

Back-transfer was defined as a transfer to a smaller, less-specialized hospital as part of an acute care hospitalization. An algorithm using both emergency department (ED) volume and state trauma-level designation was used to identify back-transfers. First, hospitals were rank-ordered by annual ED census. Then, hospital trauma level was examined, and higher-level hospitals (eg, state trauma level II hospitals) ranked below lower-level hospitals (eg, state trauma level III hospitals) were moved on the list so the trauma level designation was preserved. Back-transfers were patients who were transferred from a larger hospital to a smaller one, requiring an interval of at least 5 steps down the rank-ordered list. The 5-step threshold was selected after careful examination by 2 independent investigators. Hospitals within 5 steps were felt to be indistinguishable in medical capabilities. For the purposes of this manuscript, the “large” hospital is defined as having a higher annual ED volume and/or trauma designation, and the “small” hospital as having lower annual ED volume and/or trauma designation. Once back-transfers were identified, the investigators observed that all back-transfers originated from 5 large Iowa hospitals. The data set was further limited to only those 5 hospitals. Demographics, diagnoses, procedures, insurance status, rurality of patient residence, and geographic variables were collected.

Univariate analyses were performed using the t test, c2 test, or Wilcoxon rank-sum test, as appropriate. For the analysis, primary diagnosis is a mutually exclusive category, as an individual can have only 1 primary diagnosis. On the other hand, individuals can have multiple comorbidities. Thus, for the primary diagnosis, group-wise analyses were performed, and for the comorbidities, individual comparisons were made. A multivariable logistic regression explanatory model was developed to identify factors independently associated with back-transfer. Candidate variables for inclusion were selected if univariate analysis suggested a relationship with the dependent variable (back-transfer; 

 <.20). Final model variables were selected based on Bayesian Information Criterion to achieve a parsimonious final model. Interactions and co-linearity were tested in the model, but none were significant. Statistical analysis was conducted using SAS version 9.4 (SAS Institute; Cary, North Carolina) and Stata version 13.1 (StataCorp; College Station, Texas).

Over the 9-year period, 172,544 patients meeting inclusion criteria were admitted, of which 287 (0.2%) patients were back-transferred (

). All back-transfers were initiated by 5 large hospitals and accepted by 39 smaller Iowa hospitals, of which 16 were CAHs. There was no significant change in hospital admission volume or in back-transfers over the period of this study.

. Back-transferred patients were more likely than their nontransferred counterparts to be male, older, and white; to live in large rural areas; and to have public insurance.

Length of stay (LOS) differed by back-transfer status. Back-transferred patients were in the hospital longer than the control patients (median of 15 days vs 5 days; 

 <.001), and the median admission LOS prior to back-transfer was 8 days (

). Total LOS for back-transferred patients was negatively related to rurality (measured by RUCA). As rurality increased, LOS decreased, with a median LOS of 17 days estimated among urban residents compared with 10 days for residents of small rural areas (

 = .009). This trend was not found in the control group, which had a median LOS of 5 days regardless of rurality of residence. Those transferred to CAHs had shorter median posttransfer LOS than those transferred to non-CAHs (4.7 days vs 8.7 days; 

In most cases, patients were not hospitalized for acute care at the nearest hospital to their home (Table 2). Patients who were back-transferred were initially admitted to hospitals much farther from home (143 km vs 87 km; 

 <.0001). After back-transfer, patients were much closer to home (median = 10 km) and 53% were sent to the hospital nearest their home. Only 8% (n = 22) of back-transferred patients were transferred to CAHs.

 shows the disease characteristics of the study population. Controls and back-transferred patients had similar prevalence of CCS level 1 diagnostic codes. The most common primary disease categories in both patient groups were neoplastic, cardiovascular, digestive disease, and injury/poisoning. Significant differences between the 2 groups were present (

 <.0001), with the greatest differences in obstetric and musculoskeletal diseases. Back-transferred patients were more likely to have each of the comorbidities evaluated. Finally, univariate modeling showed surgical rates were similar between the 2 groups, but back-transferred patients were more likely to have a cardiac catheterization procedure (

Multivariable modeling revealed several strong predictors of back-transfer (

). The strongest associations were with cardiac catheterization (odds ratio [OR], 2.90; 95% CI, 1.88-4.49), having Medicaid or other state/federal-issued insurance (OR, 1.61; 95% CI, 1.11-2.33), male gender (OR, 1.57; 95% CI, 1.23-2.00), aged 51 to 65 years (OR, 1.53; 95% CI, 1.08-2.18), or older than 80 years (OR, 1.67; 95% CI 1.00-2.77). A weak association was also observed with hospital volume (OR, 0.995; 95% CI, 0.994-0.997). Several of the primary diagnoses were significant predictors of back-transfer. These were compared using the most common primary diagnosis code (CCS 7, cardiovascular disease) as a referent group. Significant disease associations with back-transfer were identified for CCS 4 (blood and blood-forming organ diseases: OR, 2.48; 95% CI, 1.04-5.91), CCS 6 (nervous system and sense organ diseases: OR, 1.95; 95% CI, 1.05-3.65), CCS 9 (digestive system diseases: OR, 1.91; 95% CI, 1.22-2.98), and CCS 13 (musculoskeletal system and connective tissue diseases: OR, 0.41; 95% CI, 0.20-0.85). Rural residence, white race, admission year, Medicare, and age between 66 and 80 years were neither positively or negatively associated with back-transfer in the multivariable model.

Back-transfer is a healthcare regionalization technique not previously reported for US adults. This study reports back-transfer using a large statewide claims database. The current study showed differences between back-transferred patients and those who completed their inpatient stay at one of the 5 large hospitals included in the study. Back-transferred patients were more likely than their non—back-transferred counterparts to be male, older, and white; to live in large rural areas; and to have public insurance. Total LOS was also longer for back-transferred patients. Medically, back-transferred patients had chronic diseases with multiple comorbidities and were more likely to have a cardiac catheterization. These differences appear to indicate that back-transferred patients are more medically complex.

It is possible that some of these findings are interconnected in that rural residents tend to be older and on public insurance,

 but not all of these differences can be attributed to the rural-urban dichotomy. Nationwide analyses from the National Center for Health Statistics has shown no significant difference between patients admitted to rural and urban hospitals for LOS or number of diagnoses.19 Further investigation is warranted to better understand the reason for these demographic and medical differences. We hypothesize that back-transferred patients may request a transfer closer to home because they have a good understanding of their chronic health conditions and anticipate a longer LOS. Furthermore, hospitals in larger rural areas may be more willing to accept back-transfers than hospitals in smaller rural areas because of greater service availability, and providers may select these hospitals for patients who have common chronic conditions.

Obviously, not all patients will be candidates for back-transfer. High-risk obstetric patients, those with rare diseases, or those in whom a fluctuating clinical course can be anticipated, to name a few examples, will require ongoing specialist care throughout an acute hospitalization. However, for many patients suffering from more routine diseases with a more predictable clinical course (eg, following cardiac catheterization), back-transfer may be an appropriate option.

Despite the rarity with which it is employed, back-transfer is a promising strategy that could better utilize health resources, so why is it so rare? We have identified several systematic barriers to widespread adoption of back-transfer in the United States. These barriers can be separated into 4 categories: policy, finance, logistics, and patient care concerns.

No law directly forbids back-transfer of patients as long as the transfer meets the legal requirements set forth by CMS in the Emergency Medical Treatment and Active Labor Act.

Furthermore, the “Stark Laws” apply to transfers in that large hospitals cannot incentivize patients to transfer, encourage back-transfer to specific hospitals, nor enter into contracts with small hospitals to take back-transfers (although this may change with accountable care organizations’ [ACOs’] structured hospital networks) (personal communication with Joseph Clamon, JD, January 25, 2016).

 The current payment system for hospitals de-incentivizes back-transfer in both direct and indirect ways. Directly, hospitals that are paid on a diagnosis-related group (DRG) model stand to lose revenue by the “early” transfer of patients prior to the full DRG payment period (personal communication with A. Showers, BBA, January 8, 2016). Depending on the payment structure of the accepting hospital (Inpatient Prospective Payment System versus cost-based reimbursement at a CAH), the reimbursement from a back-transferred patient for a short acute stay may not cover the cost of caring for the patient.

 Indirectly, the sending hospital risks financial losses through disruption of referrals and early readmission. Although many tertiary care hospitals have transfer agreements that require sending hospitals to “take back” their transfers after stabilization, these clauses are rarely used. Using this mechanism to force smaller hospitals into taking a back-transfer may result in fewer transfers from that facility in the future, thus reducing revenue for the tertiary hospital in the long term (personal communication with Joseph Clamon, JD, January 25, 2016). Finally, if a patient is back-transferred, but their health deteriorates at the smaller hospital, they may be returned for further care. This would count as a readmission and likely not be reimbursed, whereas if the patient were kept at the large hospital and experienced complications, the entire care episode would be covered under the initial admission.

 Overall, current payment models do not support back-transfer and risk of financial loss likely plays a significant role in the low frequency of back-transfer.

 The process of transferring a patient is complex: identification of an accepting hospital and physician, organizing transport, and sharing medical records all require effort. Therefore, an elective back-transfer often comes up against significant inertia within the healthcare system. It can be difficult to find a willing hospital and physician, a patient may only want to go to certain hospitals,

 differences in electronic health record systems complicate sharing of health records,

 and most back-transferred patients require ambulance transfer, which is often not covered by insurance (personal communication with Peggy O’Neill, RN, MSN, ACM, January 21, 2016). Each of these obstacles is a potential place where the transfer could derail, and failures result in wasted time for healthcare providers. Thus, the simplest solution is often to keep the patient until they are ready for discharge; consequently, the tertiary hospital’s ability to provide specialty care is diluted by provision of routine care for stable inpatients.

 Both physicians and patients may have concerns about the ability of a small hospital to care for back-transferred patients. Providers at tertiary care hospitals often feel uncomfortable sending their patients back for continued acute management. They do not know if the patient will receive appropriate care (ie, they may not know the capabilities of the small hospital, especially CAHs). Also, the receiving hospital or physician may feel uncomfortable taking a complex patient. Furthermore, loss of continuity and treatment by multiple providers is known to worsen outcomes for patients with multiple chronic conditions or cancer, and those who have received a surgical intervention.

 Finally, patients themselves may have concerns regarding the quality of care at smaller local hospitals. In some cases, patients may have sought care electively at tertiary centers because they perceive higher quality and more comprehensive care.

 These patients may not want to be back-transferred to their local hospital.

The barriers above create challenges to increased adoption of back-transfer. However, application of this strategy may optimize use of healthcare resources, especially in rural America. Back-transfer could be nurtured as a regionalization tool through creation of a tiered system in which all hospitals are included on a registry of their capabilities,

 similar to the National Trauma Registry System. This would help providers identify appropriate back-transfer destinations and alleviate concerns about patient care. Furthermore, as healthcare reform occurs in the United States, managed care through ACOs may help network hospitals join functional “hub-and-spoke” systems, with the tertiary care center serving as the “hub” point for specialty care and patient care plan development, and smaller hospitals serving as “spokes,” managing routine management and delivering specialist-directed care. ACOs with this organization could create stable transfer agreements among several institutions based on objective criteria for transfer, or for management of specific conditions, thus avoiding problems with patient selection bias.21 Within an ACO or managed care system, shared electronic medical records could further facilitate information transfer. These organizational changes would reduce the workload associated with arranging a transfer. Clearly, these types of systemic changes will be difficult to execute without state and federal support.

The results of this study must be considered in light of several limitations. First, back-transfer was a very rare event, and with our strict selection criteria, the final studied population was small. This limits the ability to make comparisons between the relatively large control group and small number of cases, and to measure variability from year to year. Second, the use of ICD-9-CM codes does not allow evaluation of disease severity or other circumstances that may have influenced providers’ decision to back-transfer patients. Third, travel distances were computed based on zip code centroids because specific address information was not available in the data. Although this method is imprecise, approximating distance is still important to appreciate a view of healthcare access for rural residents. Fourth, this dataset does not give insight into why patients were admitted or the situation of discharge from the 5 large hospitals included in this study (eg, transfer for higher level of care vs patient preference to bypass local hospitals). Fifth, because we are looking at patient-level predictors, we have not examined business relationships among hospitals or the nonprofit versus for-profit status of the hospitals in this analysis. Finally, this study used claims data from a single Midwestern state and may not be generalizable to the United States as a whole. Future work should make an effort to control for these limitations.

Despite the rarity with which it is employed, back-transfer is a promising strategy that could better utilize health resources. Appropriate use of back-transfer could simultaneously maximize the availability of specialty services, support rural health systems, and move patients closer to their homes and families. There are several systematic barriers to widespread adoption of back-transfer in the United States, including policy, finance, logistics, and patient care concerns. Further work is needed to better understand the prevalence and outcomes of back-transfer in practice and the barriers impeding its expansion.&ensp;

The authors would like to thank Ann Showers, BBA; Joseph Clamon, JD; and Peggy O’Neill, RN, MSN, ACM, for sharing their expertise regarding the barriers to back-transfer.  

University of Iowa Carver College of Medicine, (LFN), Iowa City, IA; Department of Emergency Medicine (KKH, AA, NMM), and Division of Critical Care, Department of Anesthesia (NMM), University of Iowa Carver College of Medicine, Iowa City, IA; Department of Health Management and Policy, University of Iowa College of Public Health (DMS), Iowa City, IA.

The University of Iowa Department of Emergency Medicine funded this work. Dr. Mohr is additionally funded by grants from the Emergency Medicine Foundation and the Rural Telehealth Research Center, funded by the Federal Office of Rural Health Policy (Health Resources and Services Administration) grant number 1 U1C RH29074-01.

The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (LFN, DMS, AA, NMM); acquisition of data (LFN, KKH); analysis and interpretation of data (LFN, KKH, NMM); drafting of the manuscript (LFN, KKH, DMS, AA, NMM); critical revision of the manuscript for important intellectual content (KKH, DMS, AA, NMM); statistical analysis (KKH, NMM); obtaining funding (NMM); administrative, technical, or logistic support (AA); and supervision (NMM).

Nicholas M. Mohr, MD, MS, University of Iowa Carver College of Medicine, 200 Hawkins Dr, 1008 RCP, Iowa City, IA 52242. E-mail: Nicholas-mohr@uiowa.edu. 

1. Carr B, Martinez R. Executive summary—2010 consensus conference. 

2. Liu JJ, Bellamy GR, McCormick M. Patient bypass behavior and critical access hospitals: implications for patient retention. 

 2007;23(1):17-24. doi: 10.1111/j.1748-0361.2006.00063.x.

The authors investigated back-transfer: the transfer of patients near the end of an acute hospitalization to a local community hospital for completion of their medical care.

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