This study demonstrates that common pharmacy claims-based measures underestimate the effect of actual adherence on inpatient costs among patients with serious mental illness.
ABSTRACTObjectives: To quantify how adherence mismeasurement affects the estimated impact of adherence on inpatient costs among patients with serious mental illness (SMI).
Study Design: Proportion of days covered (PDC) is a common claims-based measure of medication adherence. Because PDC does not measure medication ingestion, however, it may inaccurately measure adherence. We derived a formula to correct the bias that occurs in adherence-utilization studies resulting from errors in claims-based measures of adherence.
Methods: We conducted a literature review to identify the correlation between gold-standard and claims-based adherence measures. We derived a bias-correction methodology to address claims-based medication adherence measurement error. We then applied this methodology to a case study of patients with SMI who initiated atypical antipsychotics in 2 large claims databases.
Results: Our literature review identified 6 studies of interest. The 4 most relevant ones measured correlations between 0.38 and 0.91. Our preferred estimate implies that the effect of adherence on inpatient spending estimated from claims data would understate the true effect by a factor of 5.3, if there were no other sources of bias. Although our procedure corrects for measurement error, such error also may amplify or mitigate other potential biases. For instance, if adherent patients are healthier than nonadherent ones, measurement error makes the resulting bias worse. On the other hand, if adherent patients are sicker, measurement error mitigates the other bias.
Conclusions: Measurement error due to claims-based adherence measures is worth addressing, alongside other more widely emphasized sources of bias in inference.
Am J Manag Care. 2017;23(5):e156-e163
Using pharmacy claims data on patients with serious mental illness (SMI), this study demonstrates that increased medication adherence correlates with lower inpatient costs. A bias-correction formula was used to show that measurement error in claims-based adherence measures results in the effects of adherence being underestimated by a factor of 5.3.
Nonadherence to medication is a prevalent and burdensome problem among patients with serious mental illness (SMI).1 Estimates of adherence to antipsychotics among patients with schizophrenia-spectrum disorders, for instance, range from 47% to 95%.2 The consequences of poor adherence include suboptimal health outcomes and higher avoidable healthcare costs.3,4 In patients with schizophrenia, medication nonadherence impedes recovery,5-7 increases the risk of hospitalization,6,8-12 and extends the length of in-hospital stays.6,11 Overall, hospitalizations due to medication nonadherence have been estimated to cost more than $100 billion annually in the United States,13 and hospitalization costs due to antipsychotic nonadherence specifically have been estimated at $1.5 billion annually.14
CMS includes medication adherence as a rating measure when determining healthcare quality.15 A common method to indirectly assess adherence is the proportion of days covered (PDC),16 which typically uses prescription claims data and is calculated as the proportion of days in the measurement period, usually 1 year, for which the patient has medication on hand. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) has determined that PDC is one of the preferred methods to calculate medication adherence.17 PDC also is used to measure adherence to antipsychotic medications as part of the Healthcare Effectiveness Data and Information Set quality measures.18
Although PDC is widely used, it suffers from 2 key shortcomings. First, PDC underestimates adherence when patients pay cash for medication or use other coverage options that fail to result in a recorded insurance claim. Second, PDC overstates adherence when patients purchase but do not take a given medication. New technologies, such as electronic pillboxes, smart caps, or ingestible sensors, may provide more accurate adherence measurements, but currently, payers and providers rarely use these technologies to monitor adherence.
Neither the magnitude nor the direction of the bias associated with PDC adherence estimates are widely discussed or incorporated into adherence analyses. This study aimed to quantify how adherence mismeasurement affects the estimated impact of adherence on inpatient costs among patients with SMI.
Our methodology relies on a 3-step approach to estimate the potential impact of measurement error on inpatient spending. We mathematically derived a formula for a bias-correction factor. As this factor depends principally on the link between true and measured adherence, we next conducted a review of the literature to identify studies that measured the relationship between a “gold standard” measure of adherence (eg, Medication Event Monitoring System [MEMS] caps, electronic pill counts) and adherence measured in claims data. Finally, we applied the bias-correction factor to a case study of patients with SMI who initiated therapy with an atypical antipsychotic.
Consider the case where a researcher wants to measure the relationship between patient adherence and inpatient spending. One commonly used approach is an ordinary least squares (OLS) regression, such as the following:
In this case, the dependent variable Yi represents inpatient spending for patient i, PDCi represents patient adherence to atypical antipsychotics, and the vector Wi' contains other patient covariates of interest. The coefficient on adherence, β1, is the primary parameter of interest.
Mismeasurement in PDC biases the estimated effect of adherence on inpatient spending (ie, β1 Ì‚) toward 0. However, the true effect of adherence on inpatient costs can be derived if the relationship between measured adherence in claims data and true adherence is known. As shown in eAppendix 1 (eAppendices available at ajmc.com), one can correct for measurement error bias using the following formulation:
β1 = β1 Ì‚
where β1 is the true effect of adherence on inpatient spending after adjusting for adherence mismeasurement, β1 Ì‚is the OLS estimator from the regression in equation 1, and (1-R2PDCi,Wi )/(ρ2-R2PDCi,Wi) is the correction factor for the mismeasurement in PDCi. The term R2PDCi,Xi is the R-squared value of linear regression of measured adherence (PDCi) on all other patient covariates (Wi ), and ρ2 is the square of the correlation between measured and true adherence.
The true effect of adherence is feasible to estimate, but problematic. First, one must assume that there cannot be any unobserved patient characteristics that affect both medication adherence and inpatient spending. For instance, patients with less severe forms of SMI may be more likely to be adherent to their medication and have lower inpatient costs.19,20 To address this, we derived the bias that would remain in the case where medication adherence was an endogenous variable in eAppendix 1. The formula we derived fully addresses the bias due to measurement error, even when adherence is endogenous, so that researchers will recover the same parameter that would have been estimated if they had had access to correctly measured adherence. To be clear, our approach does not simultaneously address the endogeneity itself; rather, it represents a full solution to the problem of measurement error in adherence due to the use of claims data.
Second, one needs a reliable estimate of the correlation between true medication adherence and claims-based adherence. The following section describes our review of the literature that was used to identify this parameter.
A targeted literature review was conducted in Google Scholar and PubMed to identify estimates of the relationship between gold-standard adherence measures and claims-based adherence measures. The search combined free text and medical subject headings (MeSHs; in PubMed only) that describe various measures of adherence (MEMS caps, direct observation, PDC, medication possession ration, self-report, prescription claims, lab tests, pill count, and physician estimate) and the search term “adherence accuracy.” Searches were conducted without disease specification, with the term “schizophrenia,” and with the term “serious mental illness.” After reviewing the results of this initial search, the search was conducted again with other central nervous system diseases, specifically multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease. A maximum of 50 titles were screened in each search or until titles were no longer generally relevant to the research questions.
Abstracts were screened from relevant titles, which were defined as papers that discussed or compared multiple measures of medication adherence. Titles were not relevant if they indicated an intervention to improve adherence, factors influencing adherence, or outcomes associated with adherence or nonadherence, without language suggesting relevance. Titles that were non-English, nonhuman, and did not focus on schizophrenia or another SMI were also not investigated. Full texts were assessed if their abstracts included adherence measures collected using different methodologies and these data were explicitly compared in the results or conclusions sections. Abstracts and full texts were not identified or screened more than once if they appeared as results from multiple searches or were duplicated in databases. Additional citations were identified through previous literature searches, forward reference searches of each manuscript, and the references used in each manuscript. A complete description of the search terms used, number of full texts, abstracts, and articles reviewed is contained in eAppendix 2.
Empirical Analysis Case Study
We used the Truven Health Analytics MarketScan (MarketScan) Commercial Claims and Encounters Database and the Medicaid Multi-State Database from October 1, 2007 through December 31, 2013 to identify patients with SMI. The commercial database included medical and pharmacy claims for individuals and their dependents who were covered by employer-sponsored private health insurance. The Medicaid database included medical and pharmacy claims of Medicaid beneficiaries from 11 deidentified but geographically dispersed states. We limited the sample to individuals aged 18 or older who had at least 1 inpatient or 2 outpatient claims with a diagnosis code for schizophrenia (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] diagnosis code: 295.x), bipolar disorder (ICD-9-CM diagnosis code: 296.0x—296.1x, 296.4x–296.8x), or major depressive disorder (ICD-9-CM diagnosis code: 296.2x—296.3x, 311.x).
To measure adherence and healthcare utilization among patients initiating therapy, patients were required to have a new prescription for an antipsychotic and to be continuously enrolled for 6 or more months before and 12 or more months after the date they filled the new prescription. We required that a patient have an SMI diagnosis and no antipsychotic prescriptions during the 6-month “clean” period before the medication initiation date. Both atypical (eg, aripiprazole, asenapine, iloperidone, lurasidone, olanzapine, paliperidone, quetiapine, risperidone, ziprasidone) and typical (eg, chlorpromazine, fluphenazine haloperidol, perphenazine) oral antipsychotics were included in the analysis. Patients using clozapine were excluded from the sample, as clozapine is typically used only for patients who do not respond to at least 2 other antipsychotic medications.21 Also excluded were patients missing data on age or patients who received antipsychotics via mail order.
We applied an OLS regression to measure the effect of medication adherence on inpatient cost measured over the 365 days following the initiation of the antipsychotic medication regimen. Costs included payments made by primary payers (ie, commercial insurers or Medicaid), patient out-of-pocket payments, and other third-party payments. All costs were inflated to 2015 US dollars using the Consumer Price Index.22 The primary independent variable of interest was a patient’s PDC, which was calculated as the total days covered by all antipsychotics supplied across all claims in the 365 days after the initial medication fill divided by 365. As this study focused on nonadherence rather than overuse, PDC values were capped at 100%.
To account for compositional differences between the populations of adherent and nonadherent patients, we included patient demographics and health status as explanatory variables in our regression analysis. The Charlson Comorbidity Index23 was used to measure patients’ overall health status.24 Mental health status was measured based on the presence of comorbid SMI conditions (ie, schizophrenia, bipolar disorder, or major depressive disorder), a diagnosis of alcoholism (ICD-9-CM: 265.2, 291.1-291.3, 291.5-291.9, 303.0, 303.9, 305.0, 357.5, 425.5, 535.3, 571.0-571.3, 980.x, V11.3), or drug dependence or abuse (ICD-9-CM: 292.x, 304.x, 305.2—305.9, V65.42).24,25 We also included an indicator for whether the patient was commercially insured or covered by Medicaid and a measure of inpatient spending levels during the 6 months before antipsychotic initiation, as a measure of disease severity. In addition to our baseline specification, we included interaction terms of PDC with indicator terms for specific SMI diagnoses (ie, schizophrenia, bipolar disorder, and major depressive disorder) to estimate the effect of adherence for patients with a specific type of SMI.
All statistical analyses were performed using Stata-MP version 13.0 (StataCorp LP; College Station, Texas).
Estimates of the correlation between gold-standard medication adherence and other adherence measures varied widely in the literature. As shown in Table 1, only 2 studies measured the correlation between electronic monitoring systems and prescription refill records, with correlation values of 0.32 and 0.48, respectively.26,27 Two additional studies calculated the correlation between electronic monitoring systems and non-claims measures.20,28 When we broadened our definition of gold standard to include pill counts, we found 2 additional studies.29,30 Ignoring the relationship between adherence and patient characteristics, these figures indicate that the effect of adherence on inpatient spending could be underestimated by a factor ranging from 1.2 to 9.8, due to measurement error alone.
Empirical Case Study
In our empirical case study, 145,235 patients in the commercially insured population and 86,321 patients in the Medicaid population had an SMI and initiated an antipsychotic medication regimen (Figure 1). Descriptive statistics for patient characteristics are shown in Table 2. Patient age was concentrated between 28 and 55 years, and 65.3% of patients in our sample were women. Of these patients, 17.7% had an alcohol dependence problem and 9.8% suffered from drug abuse. Among patients with an SMI, 16.4%, 47.8%, and 75.6% had schizophrenia, bipolar disorder, or major depressive disorder, respectively. The average patient had a PDC of 48.0%.
Results from the regression analysis indicated that patients with higher adherence had lower levels of inpatient spending (Table 3). In our baseline approach (model 1), a 10-percentage-point increase in PDC was correlated with a change in annual inpatient cost of —$41 (95% confidence interval [CI], –$65 to –$16; P = .001) per patient with an SMI. When allowing for PDC to interact with each of the 3 SMIs of interest (model 2), schizophrenia patients with a 10-percentage-point higher PDC had a higher inpatient cost of $86 (95% CI, —$152 to –$20; P = .011). The corresponding amounts for major depressive disorder and bipolar disorder were $67 (95% CI, —$106 to –$27; P = .001) and $10 (95% CI, —$45 to $26; P = .596), respectively.
When we corrected for adherence mismeasurement using the bias-correction formula above and our preferred estimate for the correlation between claims-based and true adherence, the impact of medication on inpatient spending increased by a factor of 5.3. This estimate is calculated from the correlation from Hansen et al32 and empirically estimated the R2 between the PDC and patient covariates:
When we applied this bias-correction factor to our case study, patients with a 10-percentage-point greater PDC had $217 (95% CI, —$347 to –$87) higher inpatient cost per patient (Figure 2). Even if we applied the most conservative adjustment factor from Bruce et al,28 that would still inflate the relationship by 20%:
(ie, 1.2 =
Patients with better medication adherence had lower inpatient costs, but the magnitude of this relationship is underestimated when adherence is measured using claims-based metrics. Failure to adjust for measurement error in this context understates the impact of adherence on inpatient costs by a factor of 5.3 using our preferred estimate. This study provides a practical strategy for eliminating the bias due specifically to mismeasured adherence, quantitatively demonstrates that this bias is quite substantial, and calls for the development of newer, more accurate measures of adherence. Finally, we show that measurement error has significantly decreased the estimated size of the impact of adherence; thus, medication adherence might be even more important than currently shown.
To our knowledge, this is the first study to propose a concrete adherence mismeasurement adjustment factor. Although the errors-in-variables measurement error bias is well known in the statistical literature, correcting for this bias requires a valid estimate of the correlation between true and measured adherence. By conducting a literature review between gold-standard and measured adherence, researchers and practitioners can now determine the true effect of medication adherence for any outcome of interest.
One key limitation of this approach is that the measurement error adjustment may not, in itself, address other potential forms of bias, like endogeneity bias. Specifically, our approach does not solve problems with the linear regression identification strategy, but—conditional on having a robust identification strategy—does correct for errors-in-variables measurement error bias. Indeed, if the effect of adherence on health care costs is overstated in a regression of health care costs on true adherence, correcting for measurement error could move us farther away from the truth, and vice-versa. Although the measurement error correction accurately and fully accounts for the errors-in-variables measurement error, it does not, in itself, address other possible forms of bias, such as the potential endogeneity of adherence.
For observational claims-based data analyses, such as our case study, there are a number of reasons why identification of the effect of true adherence on healthcare costs could suffer from endogeneity bias, but the sign of this bias is likely unknown. For instance, patients with recently diagnosed schizophrenia may have less insight into their disease than patients who have experienced the condition for longer,32 but these new patients also typically have less severe, earlier-stage forms of the disease.33 Prior research suggests patients with more limited disease insight have lower medication adherence.34 By this logic, patients who are more adherent would have a more serious form of SMI and the effect of adherence on spending would be understated due to this problem of endogeneity of true adherence. Measurement error would exacerbate this problem, but even correcting for it would not remove the endogeneity bias. On the other hand, some studies have found that less severe forms of schizophrenia are associated with higher rates of medication compliance.35,36 This would lead to the opposite scenario, in which measurement error actually mitigates the endogeneity bias. Other studies have found no statistically significant relationship between adherence and baseline disease severity.37 In short, although our case study is observational in nature, both the magnitude and size of the bias from this observational study is unknown.
There are a number of other limitations of this study. First, our model assumed that adherence measured by MEMS caps in the literature represented true adherence. However, MEMS caps only measure the opening of a pill bottle, not actual ingestion of a pill, and thus, adherence measured with MEMS caps is also imperfect.
Second, we assumed that the relationship between adherence and inpatient spending is linear. In practice, a 10-percentage point adherence increase from 0% to 10% PDC may have a larger or smaller impact on inpatient spending then increasing adherence from 90% to 100%. However, measurement error in nonlinear models depends heavily on the model’s functional form and thus cannot be derived in a general case.38,39
Third, the data used in the case study represent a convenience sample from health insurers and large employers, from Medicare patients with a Medicare supplemental plan, and patients with Medicaid coverage in 11 states. Nevertheless, average patient characteristics in our sample (eg, age, sex, substance abuse, adherence) were fairly representative of the national patient population with SMI.40,41 Likewise, the average PDC in our study (48%) was in line with estimates reported in recent systematic reviews of antipsychotic adherence for patients with SMI.2,18,42
Future research should explore the costs and benefits of using more accurate adherence measures to inform patients, providers, payers, and other stakeholders. Med-eMonitor (InforMedix; Rockville, Maryland), for example, stores a patient’s medication and can measure the time and date the patient opens the container.27 Another example is a digital health feedback system that uses an ingestible sensor embedded within a tablet to track adherence through patient ingestion.43 However, the most appropriate adherence measurement collection approach depends on both the accuracy of the technology and other factors, such as the cost of data collection, the burden on patients and providers, and the ability to standardize data collection, among others.
Patients with SMI who had higher levels of medication adherence had lower inpatient costs, but the magnitude of this relationship is underestimated when adherence is measured using claims-based metrics. We derived a bias-correction formula to show that measurement error in claims-based adherence measures results in the effects of adherence being underestimated by a factor of 5.3. In part due to the size of this bias, we believe that measurement error due to claims-based adherence measures is worth addressing, alongside other more widely emphasized sources of bias in inference.
Author Affiliations: Precision Health Economics (JS, ES), Los Angeles, CA; Otsuka Pharmaceutical Development and Commercialization, Inc (FF), Princeton, NJ; ODH, Inc (AH), Princeton, NJ; Yale University (EV), New Haven, CT; University of Southern California (DL), Los Angeles, CA.
Source of Funding: Study funded by Otsuka America Pharmaceutical, Inc.
Author Disclosures: Drs Shafrin and Scherer are employees of Precision Health Economics, which was paid by Otsuka to conduct this research. Ms Forma is an employee of Otsuka America Pharmaceutical, Inc. Dr Hatch is an employee of Otsuka Pharmaceutical and has a patent pending relevant to adherence, but not related directly to manuscript submission. Dr Vytalacil is an employee of Yale University. Dr Lakdawalla holds equity and an executive role in Precision Health Economics. The authors report no other relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (JS, FF, DL); acquisition of data (JS, FF); analysis and interpretation of data (JS, FF, AH, DL, ES); drafting of the manuscript (JS, FF, AH, DL, ES); critical revision of the manuscript for important intellectual content (JS, FF, AH, EV, DL); statistical analysis (JS, EV, ES); provision of patients or study materials (); obtaining funding (FF); administrative, technical, or logistic support (JS, FF); and supervision (JS, FF, DL).
Address Correspondence to: Jason Shafrin, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Suite 500, Los Angeles, CA 90025. E-mail: email@example.com.
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