Allison B. Rosen, MD, ScD

 To determine whether trends in psychological distress exist in the United States and whether trends in healthcare expenditures and outpatient visits were associated with psychological distress.

 Sequential cross-sectional study of nationally representative data.

We examined data from the National Health Interview Survey (NHIS) from 1997 to 2004 linked to 2 years of subsequent Medical Expenditure Panel Survey (MEPS) data. Psychological distress was measured in the NHIS using the K6, a 6-item scale of the Kessler Psychological Distress Scale, which we classified as no/low, mild-moderate, or severe. We examined subsequent annualized total, outpatient, and office-based expenditures, and outpatient and office-based

Psychological distress remained stable from 1997 to 2004. There were upward trends in overall healthcare expenditures (P <.001) and outpatient expenditures (P <.001), but not outpatient visits. Overall healthcare expenditures, outpatient expenditures, and outpatient visits significantly increased as psychological distress increased from no/low to mild-moderate to severe. The interaction between psychological distress strata and year was not significant for expenditures or for visits.

The upward trend in total and outpatient healthcare expenditures in the United States appears unrelated to psychological distress, although healthcare expenditures are consistently higher among those with greater psychological distress. Future work will explore the impact of treatment on costs and stability of the nation’s mental health over time.

	The upward trend in total and outpatient healthcare expenditures in the United States appears to be unrelated to psychological distress.

		Our findings do not reflect a trend toward an improving value of healthcare as it relates to mental health symptoms.

		Future work will seek to determine the impact of mental health treatment on costs and stability of the nation’s mental health over time.

	A critical first step in assessing the value of healthcare in the United States as it relates to mental health is determining whether trends in mental health symptoms have had an impact on changes in healthcare expenditures over time. It is well recognized that healthcare costs have been increasing,

 with an average growth rate of 5.4% in healthcare expenditures between 1993 and 2000.

 Mental health spending has increased as part of total spending, though at a lower rate.

 Spending on prescription drugs to treat mental health and substance abuse conditions increased markedly between 1996 and 2000.

 Mental health conditions—including depression,

have been linked to higher healthcare costs. These higher costs are not attributable solely to higher mental health costs. While some reports suggest that the prevalence of mental health conditions in the United States is stable,

 there is also evidence that mental distress has increased over time.

	Given the increased use of prescription medications to treat depression, anxiety, psychotic illnesses, and other mental health conditions, one might expect that while the prevalence of mental health conditions might not change, the overall burden of mental health symptoms might decline over time. Therefore, since healthcare costs are known to be increasing, a trend toward improving value of healthcare as it relates to mental health would be reflected by a relationship between mental health symptoms and expenditures; that is, either the proportion of those with elevated mental health symptoms would decrease over time or the spending for those with a greater burden of mental health symptoms would drop over time relative to those individuals with fewer symptoms.

	Quantifying how mental health plays a role in changing healthcare costs is not easily done. Consideration of the impact of diagnosed mental health conditions over time may be influenced by changes in screening and in recognition, as well as shifts in nosology. Examining only diagnosed or self-reported conditions also misses the impact of mental health conditions that are undiagnosed or unreported. Furthermore, how mental health may be related to healthcare expenditures is not solely determined on the basis of having a mental health condition, but on the burden of mental health-related symptoms; that is, severity of symptoms may bea key determinant for related expenditures, which has been shown, for example, in depression

 Because healthcare expenditures and utilization in those with known mental health conditions tend to be higher, overall costs must be evaluated.

	We sought to examine trends in overall mental health and subsequent healthcare expenditures in the United States by using linked data from 2 nationally representative surveys of noninstitutionalized individuals, the National Health Interview Survey (NHIS) and the Medical Expenditure Panel Survey (MEPS). These linked surveys are well suited to address trends in mental health and expenditures. Starting in 1997, a 6-item scale of the Kessler Psychological Distress Scale (K6), a shortened form of a previously developed 10-question psychological distress scale, was introduced into NHIS, which provides a measure of psychological distress that has been validated with respect to mental health diagnoses.

 Starting in 1996, MEPS used the NHIS sampling frame to track national health expenditures. Using these linked data sets, we sought to examine whether there has been a change over time in psychological distress in the United States and whether adjusting for factors known to be associated with mental disorders would impact a trend in psychological distress if it were observed. We also sought to examine whether there were trends in healthcare expenditures and outpatient visits associated with the level of psychological distress, with the expectation that good value in healthcare related to mental health symptoms would be reflected by either a decrease over time in the population burden of psychological distress or a reduction over time in subsequent healthcare costs and expenditures

	among those with greater psychological distress.

	We performed a sequential cross-sectional study wherein we examined 8 consecutive years of data. Data are from NHIS and MEPS, both of which provide nationally representative data. NHIS data are collected on a new sample each year, and MEPS uses a subset of this sample in the 2 subsequent years. While the MEPS data are a subset of the NHIS sample, MEPS still uses a survey sample design such that the weighted values are nationally representative, which minimizes selection bias that might occur with selection of the MEPS subsample. 

 We obtained NHIS data from 1997 through 2004, which provided information on psychological distress (described below), and the 2 subsequent years of MEPS data for each of these samples to obtain subsequent utilization and expenditure data. We limited our study to those persons 18 years and older, resulting in weighted data representing 25,618,369 adults. The study was exempted from institutional review board approval as the data were publicly available and participants could not be identified from the data.

	Psychological distress was measured using the K6, a 6-item scale of the Kessler Psychological Distress Scale. It was developed for use in general-purpose health surveys because it is short, has strong psychometric properties, and can discriminate Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) cases from noncases with consistency across sociodemographic subsamples.

 The K6 was demonstrated to detect more than 90% of DSM-IV diagnoses as determined by the Structured Clinical Interview for DSM-IV criteria.

 The K6 asks how often during the past 30 days the respondent felt nervous, hopeless, restless, or fidgety; so depressed that nothing could cheer them up; or that everything was an effort; or worthless. Responses are on a 4-point scale (none of the time, a little of the time, most of the time, or all of the time). The sum of the response codes for the 6 items gives a score with a range of 0 to 24, with higher scores indicating greater psychological distress. We used cut-points previously reported in the literature to stratify K6 scores into no/low psychological distress (0-6), mild-moderate psychological distress (7-12), and severe distress (13-24).

 Of note, the threshold K6 score of less than 12 versus 13 or greater was found to be optimal for discerning a serious mental illness diagnosis, with a total classification accuracy of 0.92

; this definition has been used in other reports as well.

	To better characterize the population and to account for potential confounders in the relationship between expenditures and psychological distress over time, we obtained a number of variables from the baseline (ie, NHIS) data. These characteristics included age, sex, marital status, race/ethnicity, yearly income, insurance status, medical comorbidity, and psychiatric diagnoses. Our choice of potential confounders was primarily based on prior literature regarding affectivedisorders. Age, race, and income were related to affective disorders in the National Comorbidity Survey,

 and sex and marital status were related to affective disorders in the National Comorbidity Survey Replication.

 The relationship between greater medical comorbidity and depression is well recognized.

	Age was divided into decades (18-29, 30-39, 40-49, 50- 59, 60-69, 70-79, 80 ). Race/ethnicity was coded as white, black, nonblack Hispanic, and other. Marital status was married, single, separated, divorced, widowed, or unknown. We categorized yearly income relative to poverty status: less than 200% Federal Poverty Level (FPL), 200%-400% FPL, more than 400% FPL, and unknown. Health insurance status was divided into private, public only (ie, not covered by a commercial insurer and covered by a federal, state, or local program), or none. We determined medical comorbidity using the Charlson Comorbidity Index applied to the International Classification of Diseases, Ninth Revision (ICD-9) codes for reported medical conditions.

 We wanted to account for the presence of psychiatric conditions, which were broadly captured (either present or absent) as affective disorders; psychoses, schizophrenia, and related disorders; and anxiety, somatoform, dissociative, and personality disorders (see the eAppendix at www.ajmc.com for the ICD-9 codes for each of these categories).

	We examined (1) total expenditures (which include inpatient, outpatient, office-based, emergency department, home health, prescriptions, dental, and other expenditures), (2) outpatient and office-based expenditures, and (3) outpatient and office-based visits. The total expenditures category is composed of all health services expenditures associated with office-based visits, hospital outpatient visits, emergency department visits, inpatient hospital stays, dental visits, home healthcare, prescription medicines, vision aids, and other medical supplies and equipment. Note that we will combine outpatient and office-based categories and herein refer to these simply as outpatient. As we had 2 years of follow-up data, we chose to handle these as the per person average of the 2 years. Expenditures were annualized and expressed in 2006 dollars.

	We initially examined how demographic and clinical characteristics, total expenditures, outpatient expenditures, and outpatient visits differed across the 3 strata of psychological distress (K6 scores 0-6, 7-12, or >12). To do this, we used analysis of variance for normally distributed continuous values, the Mann-Whitney U test for non—normally distributed continuous values, and c2 tests for categorical data.

	We then examined our data for trends over time using bivariate linear regression models where each characteristic was treated as the dependent variable and the year was handled as the independent variable. Similarly, to determine whether psychological distress changed over time, we examined for a linear trend in mean K6 by year in a simple regression model with K6 as the dependent variable and year as the independent variable. To assess whether other factors influenced the trend in psychological distress, we added to the model covariates (age, sex, marital status, race/ethnicity, yearly income, insurance status, medical comorbidity) that have been observed to be associated with various mental health conditions. In addition, we created a polytomous model with the 3 K6 groups as the dependent variable and time as independent variable while adjusting for these other factors.

	For expenditures and utilization, we performed 3 sets of  regression analyses, 1 set for each: (1) total expenditures, (2) outpatient expenditures, and (3) outpatient visits. To test trends, we first examined a simple model examining whether there was a significant relationship between expenditures and strata of psychological distress (which was expected, with greater expenditures for those with greater psychological distress) and between expenditures and year. We then tested the interaction of strata of psychological distress and year in a model that also included these as main effects. Lastly, we ran a complete model with the year by psychological distress interaction fully adjusted for potential confounders. Because we did not wish to exclude those with zero expenditures over the 2-year study period, we used fully adjusted 2-part models25 to estimate the mean value of total and outpatient expenditures by psychological distress strata. In part 1 of the 2-part model, we calculated predicted probabilities of incurring any expenditure using logistic regression. In part 2 of the 2-part models, we transformed the non-zero expenditure data using the natural log and accounted for heteroscedasticity by including Duan’s smearing estimator in the calculation of the retransformed expected values. Results from part 1 (predicted probability of incurring any expenditures vs none) and part 2 (predicted expenditure conditional on having any expenditures) were multiplied to produce each person’s expected expenditures.

	As the data were from a survey sample, all analyses were adjusted for this design using generalized estimating equations. 

 We used SAS version 9.2 (SAS Institute Inc, Cary, NC) for data management and descriptive statistics and Stata SE version 10 (StataCorp, College Station, TX) for the modeling. We used a 2-sided alpha of .05.

	The characteristics of the noninstitutionalized US population are shown by year in 

. Some demographic characteristics have shifted over time. Insurance status also has shifted, such that the proportion with private insurance declined while publicly funded insurance and lack of insurance increased. Of note, the prevalence of some categories of diagnosed mental health conditions (specifically, affective disorders as well as anxiety, somatoform, dissociative, and personality disorders) trended upward over time.

, we show characteristics of the population across the strata of psychological distress. In our sample, 86.2% of the population was classified as having no/low psychological distress, 9.7% had mild-moderate psychological distress, and 4.1% had severe psychological distress. Every characteristic we examined differed significantly across the strata of psychological distress. Those with higher levels of distress were more likely to be older, female, unmarried, minority, poorer, uninsured, sicker both medically and psychiatrically, less educated, and living in a rural area; these characteristics are consistent with previous reports.28 All of these factors were therefore selected as covariates in our models adjusting for factors associated with psychological distress. Note that the frequencies for the K6 strata in the subset population of those with linked NHIS and MEPS data were similar to those in the NHIS population who were not in the linked MEPS data: in the NHIS-MEPS data, there were 86.2% in the no/low distress group (K6 score 0-6), 9.7% in the mild-moderate group (K6 score 7-12), and 4.1% in the severe group (K6 score >12) compared with 87.9% in the no/low group, 8.7% in the mild-moderate group, and 3.4% in the severe group among the NHIS-only population.

	Time was not significantly related to psychological distress in any of our analyses. It remained stable over the period from 1997 to 2004 (

). In addition, the mean psychological distress score was flat over this time period. This indicates that despite an increase in diagnosed mental health conditions over time, the symptom burden did not appear to increase.

 shows how expenditures and utilization changed over time within each stratum of psychological distress (unadjusted values are shown). Note that we present mean values, as these could be weighted using the survey sample. As expected, the fully adjusted model for total per person overall healthcare expenditures showed a significant upward trend over time (0.05, 95% confidence interval [CI] 0.04, 0.06; P <.001). Outpatient expenditures also trended upward in the fully adjusted model (0.04, 95% CI 0.03, 0.05; P <.001). However, we did not observe any change over time in the number of outpatient visits (in the fully adjusted model, 1.01, 95% CI 0.99, 1.02; P = .26).

	There was a gradient in total per person expenditures across strata of psychological distress. The fully adjusted mean annualized per person total healthcare expenditure (in 2006 US dollars) was $4820 (95% CI $3830, $4986) for those in the group with no/low distress. Compared with the no/lowdistress group, these mean expenditures were significantly higher among the group with mild-moderate distress ($8549, 95% CI $5645, $9261; P <.001) and the group with severe distress ($11,954, 95% CI $7704, $13,646; P <.001). The mean total expenditures for the groups with mild-moderate distress and severe distress also differed significantly (P <.0001).

	Our findings for outpatient expenditures were similar to our findings for total expenditures. The mean annualized outpatient expenditures in 2006 US dollars were $1396 (95% CI $1354, $1434) for the group with no/low distress, $2090 (95% CI $1924, $2263) for the group with mild-moderate distress, and $2766 (95% CI $2445, $3105) for the group with severe distress. These expenditures differed significantly between each group (P <.0001).

	For outpatient visits, the strata of psychological distress also differed. With the no/low-distress group as the referent, the incremental relative risk for an office-based or outpatient visit for those in the group with mild-moderate distress was 1.27 (95% CI 1.19, 1.37; P <.001) and that for the group with severe distress was 1.54 (95% CI 1.37, 1.72; P <.001). Of note, the groups with mild-moderate and severe distress also differed significantly (P = .003).

	We examined the interaction of psychological distress strata and year for total expenditures, outpatient expenditures, and outpatient visits to test whether changes in psychological distress over time were related to expenditures and utilization. Given that we found that psychological distress scores were relatively stable over time, it was not surprising to find that the interaction between psychological distress strata and year was not significant for total expenditures, outpatient expenditures, or office-based and outpatient visits.

	Despite the marked increase in total and outpatient expenditures between 1997 and 2004, the relationship between psychological distress and subsequent expenditures and outpatient visits remained stable over time. Higher psychological distress was associated with greater subsequent healthcare expenditures and more outpatient visits even after adjustment for several factors associated with higher psychological distress, most notably mental health conditions. It is notable that psychological distress remained stable over nearly a decade, which suggests that the mental health of Americans has neither worsened nor improved. Our work is concordant with thefindings of Kessler and colleagues, who compared data from the National Comorbidity Survey conducted from 1990 to 1992 with data from the National Comorbidity Survey Replication conducted from 2001 to 2003, and did not find an increase in the prevalence of mental disorders.

 However, data from Behavioral Risk Factor Surveillance System surveys from 1993 to 2001 indicated that frequent mental distress, defined as half the days in the preceding month as being days in which respondents reported their mental health was not good, increased over this time period.

	Numerous studies have demonstrated the relationship between mental health conditions and higher expenditures. In a study of primary care patients, those with depression or anxiety had higher outpatient costs than those without depression or anxiety, even after adjusting for comorbid medical conditions.

 In a cross-sectional study using 1994 NHIS data, those with self-reported depressive syndromes had greater inpatient and outpatient costs.

 Observational studies in primary care practices also reported a higher number of primary care visits, outpatient charges, and total charges for those with a depression diagnosis compared with those without one,

 and higher medical charges for those with more symptoms of depression and with a physician diagnosis of depression.

 In a study of more than 1000 primary care patients, Olfson and Gameroff found that those with generalized anxiety disorder had significantly higher median medical care charges than those who did not.

 Bryant-Comstock and colleagues reported that individuals with bipolar disorder had yearly healthcare costs that were quadruple those for individuals without bipolar disorder; however, only slightly less than a quarter of the bipolar patient’s costs were mental health—related costs.

	A large study of an insurance plan’s claims data concluded that bipolar disorder was the most expensive mental health condition,with the 3% of patients with bipolar disorder accounting for 12.4% of total costs to the plan.

 In a study using Medicare and Medicaid data from New Hampshire that compared schizophrenia with depression, dementia, and other medical conditions, Bartels and colleagues found that overall and outpatient expenditures for patients with schizophrenia were generally higher than expenditures for other groups, with the only exception being the comparison with patients who had early-onset dementia.

Using 2001-2002 MEPS data limited to those with a schizophrenia diagnosis, patients with schizophrenia had mean yearly expenses of $3726

 compared with $3302 for the general population

 (both amounts in 2002 US dollars). Substance Abuse and Mental Health Services Administration reports have shown that the rate of increase in mental healthcare spending is lower than the overall increase in healthcare spending,

 but there has been a marked increase in prescription drug costs for mental health medications.

 This can be explained by shifts over this time in how mental health conditions are treated as well as the availability of treatments. Our work showed a lack of an interaction between year and psychological distress strata with respect to overall and outpatient expenditures and outpatient visits (ie, strata of psychological distress were not related to changes in these utilization measures over time). In the context of previous work, this suggests the value of overall healthcare as it relates to mental health symptoms may be neutral.

	It is essential to consider the role of treatment when assessing value in care, but that was beyond the scope of this study. However, some previous research on depression sheds some light on this issue. A small study tested whether treatment of depression with antidepressants would change service use by patients with high medical expenditures.

 They found that for the 20 participating patients, medical service use costs fell from $13.28 per day to $6.75 per day when the patients were treated for depression, not including cost of depression treatment (with that included, the daily cost was $12.55). A larger study found that among 385 patients started on antidepressants, those who had received at least the minimum recommended therapy for 3 months had lower total medical costs over 6 months compared with those who received less than adequate treatment ($1872vs $2622).

 The investigators in that study found that the difference between the 2 groups was due to lower non—mental health-related costs. In another study that enrolled individuals with high rates of healthcare utilization, those who received treatment for depression gained 47.7 depression-free days over the course of a year.

 The authors concluded that treating this population represented good value; the incremental cost per depression-free day was $51.84. In total, however, the evidence supporting a “cost offset” (eg, reduction in medical costs) from depression treatment is controversial, as several studies suggest that depression treatment does not lead to cost reductions for patients with other diseases.

	We must acknowledge the limitations of our work. First, we had no serial measurement of psychological distress over time; we examined the effect of a single, dynamic state on an annualized assessment of the 2 subsequent years of healthcare expenditures and outpatient visits. Despite this, there were clear differences in the expenditures associated with strata of psychological distress from year to year, meaning that the relationship between psychological distress and subsequent expenditures is relatively stable. Second, we did not account for the impact of treatment, as that was beyond the scope of what we were able to address; however, we intend to investigate this issue further. Third, as with all observational studies, we may not have accounted for unmeasured confounding factors, though we were able to adjust for many known factors. Finally, the population included in this analysis was a subsample of the NHIS participants; therefore, selection bias may be a concern. However, the survey methodology of MEPS mitigates this bias in general. Furthermore, the frequency of psychological distress in our sample was similar to that in non-MEPS NHIS participants, which also suggests this bias was not a concern.

	In summary, we found that the upward trend in total and outpatient healthcare expenditures in the United States appears to be unrelated to psychological distress, although healthcare expenditures are consistently higher among those with greater psychological distress. Furthermore, we did not observe a change in psychological distress in the United States from 1997 to 2004. Our findings do not reflect a trend toward an improving value of healthcare as it relates to mental health symptoms, as we found neither a decrease over time in the population burden of psychological distress nor a reduction over time in subsequent healthcare costs and expenditures among those with greater psychological distress. In future work, we will seek to determine the impact of mental health treatment on costs and stability of the nation’s mental health over time.

 From Center on Systems, Outcomes, and Quality in Chronic Disease & Rehabilitation (PAP), Providence VA Medical Center and Alpert Medical School of Brown University, Providence, RI; Department of Population Health Sciences (JMH, WPW), School of Medicine and Public Health University of Wisconsin, Madison, WI; Department of Quantitative Health Sciences (ABR), University of Massachusetts Medical School, Worcester, MA; and National Bureau of Economic Research (ABR), Cambridge, MA.

Articles

The upward trend in total and outpatient healthcare expenditures in the United States appears to be unrelated to psychological distress.

Psychological Distress and Trends in Healthcare Expenditures and Outpatient Healthcare

To investigate the extent to which preferred drug lists and tiered formularies reflect evidence of value, as measured in published cost-utility analyses (CUAs). 

Using 1998-2001 data from a large registry of cost effectiveness analyses, we examined the 2004 Florida Medicaid preferred drug list and the 2004 Harvard Pilgrim Pharmacy Program 3-tier formulary, and compared cost-utility ratios (standardized to 2002 US dollars) of drugs with preferred and nonpreferred status. 

Few drugs on the formularies had any cost-utility data available. Of those that did, median cost-utility ratios were somewhat higher (less favorable) for Florida's preferred drugs compared with the nonpreferred drugs ($25 465 vs $13 085; 

= .09). Ratios did not differ for drugs on tiers 1 and 2 of the Harvard Pilgrim formulary, although they were higher for tier 3 and for excluded drugs ($18 309, $18 846, $52 119, and $22 580, respectively; 

= .01). Among therapies reported to be cost-saving or to have cost-utility ratios below $50 000, 77% had favored status in Florida Medicaid and 73% in Harvard Pilgrim. Among dominated drug interventions (reported to be more costly and less effective than alternatives), 95% had favored status in Florida Medicaid and 56% in Harvard Pilgrim. 

This study underscores the paucity of published cost-utility data available to formulary committees. Some discrepancies prevail between the value of drugs, as reflected in published cost-utility ratios, and the formulary placement policies of 2 large health plans. 

Formularies with tiered copayment structures or preferred drug lists have become increasingly popular, as plans seek to provide financial inducements for enrollees to select the least expensive drugs while avoiding the restrictions of entirely closed formulary systems.

Mounting evidence suggests that incentive-based formularies are associated with lower costs and smaller increases in drug utilization and expenditures compared with control groups.

But researchers also have found that they are associated with undesirable effects: patients suddenly faced with higher copayments are more likely to switch medications or to discontinue medications entirely.

Moreover, studies have found that cost sharing may be followed by reductions in the use of "essential" drugs, higher rates of serious adverse events, and increased use of emergency department visits and hospital days.

In theory, plan formulary placement decisions should be guided by information about a drug's overall value or cost effectiveness. Ideally, drugs with favorable cost effectiveness would receive preferential formulary status (ie, physicians would have incentives to prescribe these drugs rather than nonpreferred drugs). Instead, observers complain that plans often base patient contributions on drugs' acquisition costs and, in fact, do not consider value in formulary placement decisions.

However, there has been little empirical evidence on whether formulary policies actually reflect evidence of value in terms of cost effectiveness. The objective of this study was to examine whether the formulary drug-placement decisions of 2 large healthcare providers reflect the findings of published cost-utility analyses (CUAs). We acknowledge at the outset that this is an exploratory exercise whose purposes are to investigate the type and quality of cost-utility data available to formulary decision makers, and to further discussions about the need to move the healthcare system toward value-based benefit design. 

To measure a drug's value, we focused on CUA, a special type of cost-effectiveness analysis in which health effects are measured in terms of quality-adjusted life years (QALYs) gained. Leaders in the field have recommended this outcome as the standard for cost-effectiveness research.

Though not without limitations, CUAs are appealing as a measure of overall value for several reasons: they capture in a single measure gains from both prolongation and quality of life, they incorporate the value or preferences people place on different health outcomes, and they provide a common metric for comparing analyses of diverse interventions and conditions.

Data on the cost effectiveness or value of drugs were derived from the Harvard Center for Risk Analysis Cost-Effectiveness Registry, a database of 539 original CUAs published in the public health and medical literature from 1976 through 2001. Cost-utility analyses were included if they were published in a MEDLINE-referenced journal and provided an original cost-utility estimate. Review papers and methodological papers were excluded. More detail about the registry is provided elsewhere.

Briefly, 2 readers independently collected data on each article and then convened for a consensus review to resolve discrepancies. Data were collected on a wide range of items related to the methods used to estimate and report costs, health effects, preference weights, modeling assumptions, and study limitations. We also included a subjective assessment of overall study quality on a Likert-type scale from 1 (low) to 7 (high), which reflects readers' evaluation of the rigor, reasonableness, and usefulness of each analysis. Data on the cost-utility ratios associated with each intervention also were collected and standardized to 2002 US dollars.

Drug Data in the Cost-utility Analysis Registry. 

For this study we focused on CUAs in the registry pertaining to pharmaceutical interventions (378 CUAs reporting 898 cost-utility ratios). To exclude potentially outmoded analyses, we further restricted the sample to studies published from 1998 through 2001, leaving a final sample of 112 studies and 266 cost-utility ratios (the number of ratios exceeds the number of studies because some analyses contain more than 1 ratio). The complete list of references is available from the authors. 

We examined publicly available formularies of 2 healthcare providers, the Florida Medicaid preferred drug list and the Harvard Pilgrim Healthcare Pharmacy Program (HPHC) in Boston. These plans were chosen arbitrarily, based on the availability of formulary information, although we did set out to select plans representing different geographic regions and types of payers. 

The Florida Medicaid program lists prescription products on a preferred drug list selected by the pharmaceutical and therapeutics committee as "efficacious, safe, and cost effective choices when prescribing for Medicaid patients."

Our analysis used the March 22, 2004, updated list, which contained 1749 drugs. 

The HPHC categorizes medications into 1 of 3 tiers. Patients make the lowest copayment for drugs on tier 1, which consists of generic drugs approved by the US Food and Drug Administration (FDA). Tier 2 drugs are brand-name drugs granted preferred status (and a moderate copayment) by HPHC based on reviews of their relative safety, effectiveness, and cost.

Tier 3 contains nonpreferred drugs for which patients make the highest copayment. The actual copayment amount varies, depending on which benefit design is selected by the beneficiaries' employer group. Some prescription medications are excluded from coverage, such as Avage (tazarotene) for skin conditions and the weight-loss treatments Meridia (sibutramine) and Xenical (orlistat). 

We examined the relationship between the placement of drugs on formularies and evidence of value, as reflected in published cost-utility ratios. Specifically, we investigated whether the drugs on Florida Medicaid's preferred lists had lower (ie, more favorable) cost-utility ratios than those with nonpreferred status. Similarly, we investigated whether drugs on HPHC's lower tiers (reflecting lower copayments) had more favorable cost-utility ratios than those on higher tiers. 

We compared the median cost-utility ratios between preferred and nonpreferred drugs in the Florida Medicaid program, and among drugs with different tier classifications on the HPHC formulary. We used the Wilcoxon rank sum and Kruskal-Wallis nonparametric tests because of heteroskedasticity and heavily skewed ratio distributions.

We also sought to find examples of drugs with good overall cost effectiveness or value that were 

on preferred lists or favorable tiers, and conversely whether preferred lists or favorable tiers contained drugs with unfavorable cost effectiveness. These "discordant" pairs were then reviewed to explore possible reasons for discrepancies. Note that we excluded from our analysis combination therapies (drug A + drug B) for which the plan pays only partially (either drug A or B). For example, a CUA might analyze etoposide plus cisplatin versus gemcitabine in patients with metastatic non-small-cell lung cancer. In the Harvard Pilgrim Health Plan, etoposide is covered, while cisplatin is not. 

The 266 cost-utility ratios in our sample (representing 102 drugs) covered a wide range of disease categories, most commonly infectious diseases (31%), malignancies (17%), musculoskeletal and rheumatologic diseases (13%), and cardiovascular disease (13%) (

). The majority (59%) were drugs for chronic diseases (treatment course of 18 months or longer). In terms of sponsorship, 33% came from studies funded by the pharmaceutical industry and 25% from government-funded studies; in 34%, the funding source could not be determined from the article. 

Of the 266 cost-utility ratios pertaining to these 102 drugs, 150 (56%) were in the 0-$50 000 per QALY range (a range often cited as representing good value for money);

43 (16%) were more than $100 000 per QALY; and 25 (9%) each were cost saving or dominated (ie, meaning the cost of the drug in question was higher and its health effects lower than its comparator) (

Of 1749 drugs on the Florida Medicaid preferred list, 102 drugs (5.8%) had any recently published data from CUAs. Median cost-utility ratios for drugs on Florida Medicaid's preferred list ($25 465) were higher than those on the nonpreferred list ($13 085) (

). Median ratios for drugs on tiers 1, 2, and 3 of the HPHC plan were $18 309, $18 846, and $52 119, respectively, and $22 580 for excluded drugs (

= .01 by the Kruskal-Wallis test, meaning that at least 1 tier had either a higher or a lower median cost-utility ratio than the other tiers). 

show the distributions of cost-utility ratios according to preferred/nonpreferred status in the Florida Medicaid plan. The figure shows, for example, that for drugs on the preferred list, 29 cost-utility ratios (17% of the total number of cost-utility ratios for drugs on the preferred list) were more than $100 000 and an additional 20 were dominated; in contrast, for nonpreferred drugs, 2 ratios were cost saving and 32 were between 0 and $50 000. 

shows an analogous distribution for cost-utility ratios of drugs on the various HPHC tiers. In some instances, drugs reported to have good value received unfavorable tier placement (eg, for drugs on tier 3, 1 cost-utility result was cost saving and QALY improving, and 19 ratios were less than $50 000), and drugs with poor value received favorable tier placement (5 ratios for tier 1 were more than $100 000 per QALY and 7 ratios for tier 1 drugs reflected dominated interventions). 

Where cost-utility ratios were available for Florida Medicaid, 64% of preferred drugs (111 of 173 ratios) and 72% (34 of 47) of nonpreferred drugs had ratios below $50 000, whereas for HPHC, those percentages were 71% (115 of 163) and 58% (42 of 73), respectively (where 

The quality of CUAs varied slightly across formulary placement categories. For example, the quality score was 4.3 for CUAs of drugs on the Florida Medicaid preferred drug list vs 4.5 for CUAs of drugs on the nonpreferred list (

= .049). For the HPHC formulary, the quality score was 4.4 for CUAs of drugs on tiers 1 and 2 and 5.4 for CUAs of drugs on tier 3 (

provides selected examples of drugs with favorable cost-utility ratios that did not receive preferential status, as well as drugs with unfavorable cost-utility ratios that did receive preferential status. For example, interferon beta for multiple sclerosis and riluzole for amyotrophic lateral sclerosis received favorable status on both plans, despite high (less favorable) reported cost-utility ratios. In contrast, donepezil for Alzheimer's disease did not receive favorable tier placement, despite a favorable cost-utility ratio. 

To our knowledge, this is the first study to examine whether tiered formularies and preferred drug lists reflect evidence of value, as measured in published CUAs. Our findings underscore the challenges inherent in this exercise: only 6% of drugs on the Medicaid preferred drug list had corresponding data from recently published CUAs, for example. 

Where CUA data were available, they suggested that formulary decisions frequently reflect published evidence of value in that drugs with favorable formulary placement reflect products with "reasonable" cost-utility ratios. (Note that if we had used $100 000 per QALY as the measure of societal willingness to pay, the consistency between formulary placement and evidence of value would have been greater.) 

The results also suggest some room for improvement. There were numerous examples of reportedly cost-effective drugs excluded from preferred lists or favorable formulary tiers, as well as examples of drugs with unfavorable cost-effectiveness profiles that were included on preferred drug lists and favored formulary tiers. The data hint that HPHC's formulary may be more in-line with value (as measured by CUA) than that of Florida Medicaid. Pressure to cover drugs in public programs also could help explain why median cost-utility ratios in our sample were higher for drugs on Florida's preferred list than for those on HPHC's, as well as the fact that Florida Medicaid covered expensive cancer therapies that were dominated by other treatments. We emphasize, however, that more work is needed on this question and that we have not provided new evidence that anyone is hurt by a specific formulary decision. 

In general, we emphasize that ours is a descriptive and exploratory exercise and that one should exercise caution about inferring causality between the cost-utility literature and formulary placement decisions. It is difficult to obtain a clear reading from publicly available CUAs about the actual choices facing the formulary managers, or about the trust they place in the data. Researchers have documented the fact that methods used for conducting and reporting cost-utility analyses are highly variable.

 Moreover, the decisions of a pharmacy and therapeutics committee are complex and reflect a multitude of factors, including the size and makeup of the pharmacy and therapeutics committee and the incentives facing the director of the pharmacy.

A key problem for our study is that published CUAs may not include an appropriate comparator therapy. The extent to which drugs have treatment alternatives is a key factor in determining actual formulary placement. Some products are expensive, but because there is little or no alternative they are likely to receive favorable formulary placement. On the other hand, some drugs have many treatment alternatives or close substitutes, and as a result are assigned to a higher tier or receive nonpreferred status. In the HPHC formulary, for example, all of the drugs assigned to tier 2 or 3 had treatment alternatives; in contrast, of the 21 unique drugs assigned to tier 1, 10 were generics and 9 of the remaining 11 were arguably the drug of choice for at least 1 indication. 

However, published CUAs may exclude the relevant comparator. They may compare a new drug with placebo or with an inferior alternative, rather than to the next-best intervention, thus exaggerating the value of the drug in question. Another problem is that study authors may fail to present results of CUAs in all relevant population subgroups. Drugs may be cost effective for some indications and not others, but CUAs are not always clear about distinctions. 

A measure of the degree to which the comparator drug and target population differ can be seen in the case of interferon ?-1b for multiple sclerosis, where cost-utility ratios varied from $44 000 per QALY for patients with secondary progressive multiple sclerosis (where the treatment comparator was no treatment) to $1.9 million per QALY for interferon compared with "best practice" in ambulatory patients with secondary progressive multiple sclerosis. 

There are other problems with trying to match published CUAs to formulary decisions. One is that cost effectiveness studies often are not available at the time of FDA approval, and published CUAs of a drug tend to lag behind published clinical studies.

Another is that CUAs typically do not incorporate or anticipate generic drugs as comparator therapies, which can dramatically change cost-utility ratios. Some observers have noted that CUAs may better help with decisions about whether a new class of drugs are cost effective rather than which drugs within a class are appropriate.

Furthermore, even if the ratios do reflect a reasonable measure of societal value, it is possible that they do not reflect value from the perspective of health plans. Importantly, we lack data on the actual drug prices paid by the plans, which could be considerably lower than the drug prices used in the CUAs, and hence improve the drug's cost effectiveness. Plans–and pharmacy benefits managers, who frequently negotiate sizable discounts–may indeed find it cost effective to give preferential status to discounted drugs, even if published CUAs suggest that, based on average wholesale prices, the drug in question is not cost effective. 

Even if the prices used in the CUAs are accurate, strict rankings of cost-utility ratios may not reflect how plans wish to allocate resources. Results of CUAs, conducted from a societal perspective, may not be seen as relevant for health plan managers who worry about short-term financial shortfalls, total plan costs, and drug budget "silos."

It is difficult to determine the impact of the CUA "perspective" in our sample, because many studies are not clear about their perspective. In addition, there is inconsistency in the manner in which analysts use the term 

: some state a societal perspective, but only include direct healthcare costs and nominal nonhealth costs; others include productivity costs. 

Empirical evidence suggests that public preferences for allocating society's resources may not be consistent with a goal of maximizing QALYs subject to a budget constraint. Instead people want to give hope to and avoid discrimination against patients with chronic illness or disability even when treatments are not cost effective.

These inclinations may explain some of the discrepancies we found (eg, preferred formulary placement of drugs with high cost-utility ratios, such as drugs for multiple sclerosis and amyotrophic lateral sclerosis). 

There may be pressure to cover certain therapies even if they have unfavorable cost-effectiveness ratios. This may be a particular concern for Medicaid programs, which may be more sensitive to public pressure than private plans. 

Despite these concerns, we believe that our analyses provide important information and hope that they further policy discussions about the need to move the healthcare system toward value-based benefit design. Many reimbursement authorities abroad have used CUAs (and other forms of economic evaluation) to inform formulary decisions. In the United States, initiatives such as the US Preventive Services Task Force have begun to consider cost effectiveness in their deliberations. Moreover, although health plans may not adopt a societal perspective, they do compete for enrollees and it is not unreasonable to believe that CUAs approximate value in many and even most cases. For these reasons, we believe more consideration of data from CUAs would represent a positive step forward for US formularies. 

Despite limitations, CUA remains an important technique for measuring value. The number of CUAs has been growing and their quality may be improving.

 Some payers have found that CUAs can be an important part of a sequenced assessment of value that begins with the application of evidence-based medicine. In this characterization, consideration of clinically important benefits and harms, as well as the subpopulations for which products have specific advantages, forms the basis for assessments of medication value. Cost-utility analyses can be a useful extension of that assessment when they focus on the specific clinical situations for which the medication is most appropriate. 

Some formulary committees are beginning to incorporate evidence of value explicitly into their deliberations and in some cases are requesting or requiring that drug firms formally submit evidence of value to their formulary committees.

Our study suggests that these efforts should go further. 

Our data suggest a need for formulary policies to offer more targeted guidance for drugs. Formularies tend to be blunt instruments, broadly covering the use of a drug. Yet whether a drug is cost effective depends on the specific characteristics of the recipients (eg, age, clinical condition). Ideally, formulary policy will be defined accordingly. For example, guidelines for lipid-lowering drugs reflect patient-specific characteristics such as prior infarction. In practice, however, it is a challenge to incorporate heterogeneity in potential benefit among patients.

More and better data will be needed if we are to truly move toward value-based formulary decision making. Congress and other federal officials also might help by addressing gaps in the evidence base, for example, by funding more "pragmatic" clinical trials to aid decision makers,

and by funding cost-effectiveness analyses.

In the future it will be important to update these analyses and expand them to include other health plans. As more studies provide information on drug value, our next challenge is to incorporate value into formularies to increase the use of effective and cost-effective medications. 

From the Department of Health Policy and Management and Center for Risk Analysis, Harvard School of Public Health, Boston, Mass (PJN, PJL, DG, MCW); the Department of Health Policy and Administration, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC (PJL); the Department of Health Systems Management, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel (DG); Merck & Co, Inc, West Point, Pa (MB, ST, EM); and the Division of General Medicine, Department of Health Management and Policy, University of Michigan Schools of Medicine and Public Health, and Center for Practice Management and Outcomes Research, Ann Arbor Veterans Affairs Medical Center, Ann Arbor, Mich (ABR). 

This project was supported by grant R01 HS10919 from the Agency for Healthcare Quality and Research and an unrestricted grant from Merck and Co. 

Allison B. Rosen, MD, ScD

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