Sarah Green, BA

 To examine the association between health plan out-of-pocket (OOP) costs for antiepileptic drugs and healthcare utilization (HCU) and overall plan spending among US-based commercial health plan beneficiaries with epilepsy.

 The Truven MarketScan Commercial Claims database for January 1, 2009, to June 30, 2015, was used. Patients 65 years or younger with epilepsy and at least 12 months of continuous enrollment before index (date meeting first epilepsy diagnostic criteria) were included. Analyses were adjusted for age group, gender, beneficiary relationship, insurance plan type, and Charlson Comorbidity Index score. Primary outcomes included proportion of days covered (PDC), HCU, and healthcare spending in 90-day postindex periods. Associations between OOP costs and mean PDC, HCU, and plan healthcare spending per 90-day period were estimated.

 Across 5159 plans, 187,241 beneficiaries met eligibility criteria; 54.3% were female, 41.7% were aged 45 to 65 years, and 62.4% were in preferred provider organization plans. Across postindex 90-day periods, mean (SD) PDC, epilepsy-specific hospitalizations, outpatient visits, and emergency department visits were 0.85 (0.26), 0.02 (0.13), 0.34 (0.47), and 0.05 (0.22), respectively. Median (interquartile range) spending per 90-day period was $1488 ($459-$4705); median epilepsy-specific spending was $139 ($18-$623). Multivariable linear regression without health plan fixed effects revealed that higher OOP spending was associated with a decrease in PDC (coefficient, —0.008; 95% CI, –0.009 to –0.006; 

 <.001) and an increase in overall spending (218.6; 95% CI, 47.9-389.2; 

 = .012). Health plan fixed effects model estimates were similar, except for epilepsy-specific spending, which was significant (120.6; 95% CI, 29.2-211.9; 

 Increases in beneficiaries’ OOP costs led to higher overall spending and lower PDC.

Health plans have increasingly shifted costs of medical care and medications to their beneficiaries, resulting in repercussions for the management of chronic disease.

In beneficiaries with epilepsy, higher out-of-pocket (OOP) spending was associated with significant decreases in the proportion of days covered with an antiepileptic drug and in inpatient hospitalizations, and an increase in epilepsy-related outpatient visits.

Higher OOP costs were associated with increased total healthcare spending.

Greater formulary restriction has significant impact on beneficiaries and their management of epilepsy, with potentially severe consequences for patient outcomes.

Over the past 15 years, many healthcare plans have increased beneficiary cost sharing for prescription drugs as a mechanism to contain healthcare spending, and literature suggests that less generous prescription drug coverage reduces drug expenditures for healthcare plans.

 Some of this decrease simply shifts the drug acquisition costs, once paid by the plan, to the patients through higher out-of-pocket (OOP) costs in the form of increased deductibles, co-payments, or coinsurance. Similarly, individuals in high-deductible plans may have higher OOP spending. Higher cost sharing leads to lower medication utilization and adherence, especially among patients with chronic conditions.

 Studies across a number of chronic diseases (eg, rheumatoid arthritis, multiple sclerosis, hypertension, and hypercholesterolemia) report an association between higher OOP costs and lower drug utilization.

 Reduced utilization of prescription drugs with proven benefits could result in both worse health outcomes and higher overall costs.

Epilepsy is a chronic brain disorder characterized by recurrent seizures. The Institute of Medicine (now the National Academy of Medicine) estimates that 1 in 26 individuals in the United States will have seizures during their lifetime and nearly 1% of the US population lives with active epilepsy.

 Epidemiologically, epilepsy has a bimodal distribution of incident cases occurring early and later in a patient’s life.

 In the aging population, this increased incidence of epilepsy often results from stroke, trauma, and other etiologies.

 Mortality associated with epilepsy is increased at all ages and is more common with poorly controlled seizures.

Simply stated, the goal of epilepsy therapy is 2-fold: no seizures and no adverse effects (AEs). A wide range of antiepileptic drugs (AEDs) exist, and selecting the optimal treatment for an individual patient involves the consideration of many factors, including patient comorbidities and concomitant medications, risk of drug—drug interactions, and individual AED safety and efficacy profiles.

 AEDs have been shown to be effective in controlling seizures in up to two-thirds of patients who receive them.

 One-third of patients may not respond to initial AED monotherapy,

 necessitating modification of AED treatment. Breakthrough seizures may occur with suboptimal AED dosing due to intolerable AEs or titration or from inadequate efficacy. Patients who attain long-term (ie, ≥2 years) seizure freedom with medication may consider treatment cessation; however, this is a challenge,

 with studies reporting seizure recurrence in more than one-fourth of patients after a median of 41 months.

Higher cost sharing in the form of OOP costs may discourage or delay patients’ access to the most appropriate medication that may control their seizures while minimizing AEs. As a consequence, treatment decisions driven by affordability rather than clinical appropriateness may lead to suboptimal treatment (from an efficacy or tolerability viewpoint). This may result in higher medical costs in both the short term (eg, due to injuries or hospitalizations) and the long term (eg, osteoporosis-related complications from long-term use of enzyme-inducing AEDs). To date, studies on the relationship between higher plan cost sharing for AEDs and the utilization of AEDs, overall healthcare utilization (HCU), and plan spending for enrollees with epilepsy have not been reported. The objective of this study was to examine how health plan cost sharing for AEDs relates to AED utilization, HCU, and overall plan spending among US-based commercial health plan beneficiaries with epilepsy.

A retrospective cohort study of individuals with epilepsy was conducted using data from the Truven MarketScan Commercial Claims research database for January 1, 2009, through June 30, 2015, and reporting aligned with STROBE guidelines for cohort studies.

 Individuals 65 years or younger were included in the analysis if they had at least 12 months of continuous enrollment prior to meeting 1 of the following diagnostic conditions: 2 or more inpatient or outpatient claims with an 

International Classification of Diseases, Ninth Revision

) code of 345.xx; 1 or more claim with code 345.xx and 1 or more claim with code 780.39; 1 or more claim with code 345.xx and a pharmacy claim for an AED; and 2 or more claims with a code of 780.39 and a pharmacy claim for an AED. In cases requiring an 

 code and AED pharmacy claim, both had to occur within a 12-month period. The index date was defined as the date on which the first of the diagnostic criteria was met after a period of 12 months of continuous enrollment. Individuals in capitated health maintenance organization (HMO) plans were excluded because claims are not submitted for each service provided; thus, measurement of their HCU and spending was not captured. The follow-up period began at the index date and was divided into 90-day periods until the beneficiary was no longer enrolled or was administratively censored on December 31, 2015. All periods were required to have the full 90 days; any partial period due to censoring was excluded from the analysis. A 90-day period was chosen based on prior studies of the association between cost sharing and healthcare spending that found that outcomes assessed over quarters are sensitive enough to identify changes over time.

 All patient characteristics, cost-sharing measures, and HCU and spending outcomes were assessed for each 90-day period.

All analyses were adjusted for the individual’s age group (0-18, 19-45, or 46-65 years), gender, relationship to beneficiary (employee, spouse, or child/other), insurance plan type (comprehensive, exclusive provider organization, HMO, point of service [POS], preferred provider organization [PPO], POS with capitation, consumer-driven health plan, or high-deductible health plan), and Charlson Comorbidity Index (CCI) score (0, 1, 2, or ≥3),

 which was based on diagnoses in the year preceding the start of each 90-day period. In subgroup analyses, an epilepsy-specific comorbidity index

 was included; however, estimates were unchanged and, thus, data reflect models adjusted for the CCI score.

The objective of the analysis was to estimate how OOP costs for AEDs related to the proportion of days covered (PDC) and health plan spending for individuals with epilepsy. Because drug prices are largely uniform across plans, we focused on price differences introduced by a plan’s generosity. A commonly used method to construct a market-basket index of a set of representative AEDs was employed to calculate OOP costs.

 To construct the market basket of AEDs, all dispensings in a calendar year for a random sample of 100 individuals with at least 1 dispensing for an AED in that year were aggregated. Each AED in this market basket was assigned a weight equal to the relative frequency of dispensings for that AED. For example, if in 2009, lacosamide made up 40% of all AED dispensing claims across the sample of 100 people and phenytoin made up the remaining 60%, then the weights for lacosamide and phenytoin would be 0.4 and 0.6, respectively. The product of the weight and the average OOP cost, which included the patient’s deductible, coinsurance, and co-payment, for the AED were then summed across all AEDs for each plan-year. Thus, if in health plan A, the average OOP cost was $5 for lacosamide and $10 for phenytoin, then the market-basket value would be the weighted average of the 2: ($5 × 0.4) + ($10 × 0.6) = $8. Because prescriptions could range from 30 to 90 days’ supply, a standardized measure of the cost per day based on the days’ supply of AEDs was created (instead of using the number of dispensings).

Primary outcomes were measures of PDC, HCU, and healthcare spending in each 90-day period; the mean and median across all postindex 90-day periods were calculated for each outcome measure. PDC was chosen, as opposed to other commonly used measures of adherence (eg, medication possession ratio), based on a study of AED adherence that found PDC to be a more stable measure.

 PDC has also been used as a quality indicator for treatment of other chronic diseases.

 An algorithm for calculating PDC across short time periods using claims data was employed.

 The algorithm used shorter time periods as the denominator based on the pattern of drug dispensing to allow the PDC to vary, versus calculating the PDC over a set period of time, such as a year. For comparison with prior studies, the annual PDC was calculated according to standard methods.

 Only individuals with at least 1 AED dispensing in each quarter were included in the PDC measure. HCU was calculated as the total number of inpatient admissions, outpatient visits, and emergency department (ED) visits; epilepsy-specific HCU required claims with a primary diagnosis of epilepsy (

 code 345.xx or 780.39). Total outpatient, inpatient, and overall spending, as well as overall epilepsy-specific spending, were calculated as the sum of the deductible, co-pay, coinsurance, amount paid by insurance, and amount paid by coordination of benefits for each measure as identified from the adjudicated claims. Overall plan spending included total spending on all claims for patients with any primary diagnosis, whereas epilepsy-specific spending was defined as total spending on all claims for patients with a primary diagnosis of epilepsy.

Multivariate linear models were used, first with and then without health plan fixed effects, to estimate the association between OOP spending for each plan and PDC and health plan spending in each 90-day period. For each 90-day period, a market-basket measure was used for the year in which the period began. Thus, the period beginning April 1, 2011, was assigned to the market-basket measure calculated for the calendar year 2011. Conducting the analysis over 90-day intervals is consistent with prior studies.

 A stable measure of the market basket was calculated over a 1-year time frame to avoid spikes in the prescribing of a single drug. Consequently, the market-basket distribution of AEDs was more likely to reflect a representative distribution of AEDs. Each model was adjusted for the patient’s age at the start of the period, gender, relationship to beneficiary, plan type, and calendar year. In sensitivity analyses, a categorical measure of OOP costs was used to further examine the linear relationship between healthcare spending and OOP costs. Categories were defined as low, medium, and high according to the tertile of OOP costs in that year.

To test model and study design assumptions, subgroup analyses were conducted. First, the cohort was limited to only those who were newly diagnosed with epilepsy, defined as beneficiaries with no AED dispensing or epilepsy diagnosis for at least 3 years. Second, the top and bottom 1% of spending values were excluded to limit the effect of spending outliers. In both cases, models were fit without health plan fixed effects. Last, 2-part models were used to account for skewed spending. However, because of the low prevalence of some of the outcomes (eg, inpatient hospitalizations), models failed to converge and are not included in the results.

A total of 187,241 beneficiaries across 5159 health plans met the study’s eligibility criteria (

). The mean number of 90-day periods per beneficiary was 10.4, and median OOP costs as measured by the market-basket index were $0.30 per day. There was a slightly higher percentage of females (54.3%), and 41.7% of beneficiaries were between the ages of 45 and 65 years. Most individuals were the primary beneficiary (42.6%), had a CCI score of 0 (72.4%), and were enrolled in a PPO plan (62.4%) (

The mean (SD) PDC over 90-day periods was 0.85 (0.26), and 71.3% of individuals had a 90-day PDC greater than 0.80. The annual PDC was 0.79 (0.23). Analysis of HCU showed that overall and epilepsy-specific inpatient hospitalizations, as well as ED visits, across &#8203;90-day periods were infrequent, whereas the number of outpatient visits overall was higher (

). Mean outpatient spending in a 90-day period made up the largest proportion of overall spending (45.0%). Epilepsy-specific healthcare spending was much lower, accounting for 15.8% of overall health plan spending in a 90-day period (Table 2).

In multivariable linear regression models without health plan fixed effects, higher OOP spending was associated with statistically significant decreases in PDC and inpatient hospitalizations, but an increase in epilepsy-related outpatient visits (

). Similarly, higher OOP spending was associated with an increase in overall spending, both inpatient and outpatient, but not overall epilepsy-specific spending (Table 3). When health plan fixed effects were included in the model, overall plan spending had similar trends and higher coefficients, with the exception of overall epilepsy-specific and total epilepsy-specific outpatient spending. Point estimates for PDC were slightly attenuated from models without health plan fixed effects and were not statistically significant (

= .057) (Table 3). In sensitivity analyses, the direction of results was generally consistent with linear specifications of OOP costs, with the exception of total outpatient costs, which were negatively associated with OOP spending (

Several subgroup analyses were conducted to test the robustness of the estimates. All subgroup analyses were fit models without fixed effects. First, when the cohort was limited to newly diagnosed beneficiaries (n = 15,990), no significant associations were found between OOP spending and PDC or healthcare spending, with the exception of total outpatient spending, which was positively associated with OOP spending (

). Second, when the top and bottom 1% were trimmed from the total spending values, overall plan spending (coefficient, 169.9; 95% CI, 98.6-241.2; 

 <.001) was positively associated with OOP costs, whereas total inpatient spending (coefficient, —72.6; 95% CI, –108.5 to –36.7; 

<.001), overall epilepsy-specific spending (coefficient, —28.7; 95% CI, –39.3 to –18.2; 

<.001), and total epilepsy-specific outpatient spending (coefficient, —7.9; 95% CI, –13.1 to –2.7; 

= .003) were negatively associated with OOP costs (

In this study of more than 180,000 privately insured individuals with epilepsy, higher OOP cost sharing for AEDs was associated with a decrease in PDC and an increase in overall and epilepsy-related healthcare spending. These findings were consistent across several subgroup analyses, including variable definitions and model specification. Shifting costs to patients is one way that insurers can try to reduce spending. However, our results suggest that the unintended consequences of this approach may actually increase health plan spending in the long run. Thus, policies (eg, requirements to fail 1 treatment first) that penalize patients via higher OOP costs for not responding to a particular treatment financially disincentivize patients from taking a prescribed medicine that may be their best treatment. Additionally, they may detour physicians from their initial treatment selection, which was in the best interest of the patient.

The association among OOP costs, HCU, and spending has been previously examined.

 Although higher OOP costs are generally associated with lower utilization and higher spending, these associations are often weaker in patients with a chronic condition receiving ongoing care, reflecting a lower price sensitivity than in patients receiving intermittent medications, such as nonsteroidal anti-inflammatory drugs or antihistamines.

 Thus, although we found a negative association between OOP spending and HCU, the magnitude of those associations may reflect this trade-off. In addition, our estimates reflect the association over a 90-day period, in which small estimates may translate into substantial changes over longer periods of time. For example, these results indicate that a $10 increase in OOP spending is associated with a decrease in PDC of 0.16 per year. Furthermore, although the magnitude of our estimates may appear small relative to previous studies, the high costs associated with the treatment of epilepsy suggest that the impact in absolute terms may still be very high. The Institute of Medicine has estimated the total direct and indirect annual costs associated with epilepsy to be $15.5 billion.

 Thus, if all health plans capped their OOP spending at the median value of $0.30 per day, one would expect to save $140 per person per year among privately insured individuals, translating to an estimated savings of $121 million per year, or 0.8% of the total costs associated with epilepsy (

To our knowledge, this study is the first to examine the association among OOP costs, HCU, and spending in a population of privately insured individuals with epilepsy. Several strengths are worth mentioning. First, the use of a market-basket index to measure OOP spending

 allowed us to standardize the comparisons across plans, thereby avoiding the potential for one plan’s benefit design to bias the comparison. A second strength of the study is the large sample size. Although epilepsy is the fourth most common neurological condition in the United States,

 it is still rare enough that large cohorts of individuals with epilepsy can be difficult to find and expensive to follow.

 The Truven MarketScan database has more than 40 million covered lives, which provided a large enough sampling to identify and follow more than 180,000 individuals with epilepsy, providing the statistical power to test hypotheses related to rare outcomes, such as inpatient hospitalization. A third study strength is the use of a newly published validated algorithm for calculating PDC over shorter time periods, such as the 90-day periods used in the current analysis.

 PDC measured over this shorter period provided a granularity in the measures that would not be possible over the more commonly used longer periods of time (eg, 1 year).

Although this is the first study to examine the effect of OOP costs on HCU and spending for individuals with epilepsy, there were several limitations. First, this was a privately insured population 65 years and younger; therefore, our results may not be generalizable to uninsured individuals, those with Medicaid or Medicare, or individuals with a lower socioeconomic status, for whom the cost-sharing structures may differ greatly. Second, although the use of a 90-day period over which to measure outcomes provided the sensitivity to detect smaller changes over time, some outcomes, such as epilepsy-related hospitalizations, were rare and resulted in zero-inflated counts in each period, which limited the options for model specification. Third, patients varied with respect to the length of time since their initial epilepsy diagnosis, and individuals’ experiences prior to inclusion in the study were not captured. Fourth, we did not look at treatment patterns, persistence, or rate of abandonment, which could also have been factored into our models as HCU measures or as outcomes. In subgroup analyses, the sample was limited to beneficiaries with new diagnoses, but because of the dramatic reduction in sample size and the highly skewed distribution of outcomes, estimates were not stable.

As healthcare costs continue to rise, payers may seek out alternative methods of limiting spending, including transferring some of the cost burden to patients. However, our findings suggest that, for patients with epilepsy, the long-term effect of this decision may be counterproductive, as patients reduce their use of preventive services and medications, which may translate into costlier care later on. Furthermore, given the potentially severe consequences of reducing medication adherence, such as greater seizure frequency that can lead to death, payers should carefully consider the unintended consequences of increased cost sharing for patients with epilepsy.&ensp;

Editorial support was provided by Lynne Isbell, PhD, CMPP, of Evidence Scientific Solutions (Philadelphia, Pennsylvania) and was funded by UCB Pharma.

 Department of Health Care Policy, Harvard Medical School (NRJ, DCG), Boston, MA; Department of Health Services Research, Policy and Practice, Brown University (NRJ), Providence, RI; UCB Pharma (JF, IW), Smyrna, GA; Precision Health Economics (SG), Los Angeles, CA; Department of Neurology, The University of Arizona Health Sciences Center (DML), Tucson, AZ.

 Dr Joyce and Dr Grabowski report a consultancy for Precision Health Economics, which received payment for this project from UCB Pharma. Dr Fishman reports employment with and stock ownership in UCB Pharma. Ms Green reports employment with Precision Health Economics. Dr Labiner reports grants pending for a drug study from MonoSol and grants received for drug studies from Upsher-Smith and UCB Pharma. Dr Wild reports employment with UCB Pharma at the time of the study. 

 Concept and design (NRJ, JF, SG, DML, IW, DCG); acquisition of data (JF, SG); analysis and interpretation of data (NRJ, JF, DML, IW, DCG); drafting of the manuscript (NRJ, JF, DML, IW, DCG); critical revision of the manuscript for important intellectual content (NRJ, JF, DML, IW, DCG); statistical analysis (NRJ, DCG); obtaining funding (JF); administrative, technical, or logistic support (JF, SG); and supervision (DCG).

 Nina R. Joyce, PhD, Brown University School of Public Health, Box G-S121-6, 121 South Main St, Providence, RI 02198. Email: nina_joyce@brown.edu.

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Articles

Increased out-of-pocket costs for antiepileptic drugs were associated with decreased adherence, higher healthcare utilization, and higher spending among US commercial health plan beneficiaries with epilepsy.

Cost Sharing for Antiepileptic Drugs: Medication Utilization and Health Plan Costs

 To determine the lifetime social value of using the guideline-recommended vaccines for children born in the United States in 2009. 

This study utilized an economic model with parameter values sourced from clinical and observational data, as well as the literature. 

The model quantified the health effects of routine vaccination for 14 diseases in terms of quality-adjusted life-years (QALYs) saved. The health effects were then valued by applying an economic value of a QALY. Producers’ profits were estimated using data on vaccine prices, profit margins, and the number of vaccines administrated in the 2009 US birth cohort. The costs of producing the vaccines were subtracted from the value of the health effects to yield the total social value of vaccination. The producers’ and consumers’ shares of this social value were calculated. Sensitivity analyses were conducted to determine how results depend on underlying parameter assumptions. 

Estimates indicated that vaccination of this cohort will save 1.2 million QALYs, relative to no vaccination. Of those health gains, 88% stemmed from reduced mortality and 12% from reduced morbidity. We estimated a social value of $184.1 billion from these gains, of which $3.4 billion accrues to manufacturers as profits, while $180.7 billion accrues to the rest of society. In sensitivity analysis, the total social value ranged from $40 billion to $675 billion, and the manufacturers’ share ranged from 0.3% to 11.5%. 

 Policy makers should account for this social value when considering policies affecting incentives to vaccinate and develop new vaccines.

		By preventing illness and premature deaths, vaccination of children born in the United States in 2009 will generate $184 billion in lifetime social value above the costs of the vaccines. 

		Because saving a child’s life yields many healthy life-years, the large majority (88%) of the health benefits of vaccines is due to avoided premature deaths rather than reduced morbidity (12%). 

		The high social value of vaccines has improved population health and provided economic benefit to multiple stakeholders, including patients, health plans, and vaccine manufacturers, whose profits in this cohort amount to approximately 2% ($3.2 billion) of the total social value.

The innovation of childhood vaccines has resulted in a decline in infectious disease, as well as gains in length and quality of life. Smallpox has been eradicated, poliomyelitis is nearly eliminated, and many other vaccine-preventable diseases have seen declines in incidence.

 Although adverse events (AEs) can occur with vaccines,

 and recent research has focused on their rising costs,

 the postvaccine era has seen life expectancy increase 15 to 25 years compared with the pre-vaccine era, and further gains are expected.

 Evidence suggests a large share of these survival gains is due to the control of infectious disease through vaccination.

When encouraged by public health policies, vaccination also provides a benefit to government and private payers by reducing overall costs and increasing population health. The CDC has cited evidence that common childhood vaccinations save over $5 in direct medical costs and effects on productivity for every $1 spent.

 Maciosek and colleagues found that preventative childhood immunization produced annual net medical savings of $267 per person.

 Vaccination also generates community (herd) immunity by reducing disease incidence and transmission, thus resulting in a healthier population.

Because vaccines have been successful at preventing disease, the public is no longer regularly confronted with many vaccine-preventable diseases, and the health and economic benefits of vaccination may be underappreciated.

 As childhood vaccines have reduced disease prevalence, real and perceived AEs of vaccination have become more salient to parents than the vaccine-targeted diseases.

 Consequently, vaccination rates in many US states have declined in recent years.

 As vaccination rates slip, the risk of new outbreaks increases.

Moreover, although consumers are insulated from the cost of many vaccines, vaccine cost is an important consideration for payers and providers and has been criticized.

 This focus on AEs and costs has obscured vaccines’ overall value to individuals and society. Previous research has yet to show how the total social value of vaccines is divided between innovators who develop these technologies and patients and the broader society who benefit from them. Therefore, in this study, we sought to measure the social value of childhood vaccines in the United States and the distribution of that value to manufacturers versus the rest of society.

The concept of social value of therapies and its distribution between manufacturers and patients has been described in other disease areas. For instance, Grabrowski et al found that statin usage resulted in a social value of $1.25 trillion, of which patients received 76%.

 Yin et al performed a similar analysis on tyrosine kinase inhibitors for the treatment of chronic myeloid leukemia and found a social value of $143 billion—90% of which was retained by patients.

 Recent gains in cancer survival have provided $1.9 trillion of additional social value, with 81% to 95% of that being retained by patients.

 Lastly, HIV/AIDS therapies have generated $1.38 trillion in social value, with 95% accruing to patients.

 Such analyses are net monetary benefit analyses, which is a common economic way of thinking about value which is distinct from cost-effectiveness analysis. The aim is to measure the total value a given health intervention generates for society, and how that value is distributed across patients and manufacturers.

This study applied similar methods to determine the social value of childhood vaccines for a birth cohort in the United States. Consistent with previous research, social value was defined from an economic perspective as the quantity of resources, in monetary terms, that society would be willing to give up in order to retain the health gains attributable to vaccines. Put another way, the overall social value of vaccines equals the aggregate value retained by consumers (above the actual payments for vaccines) plus the value retained by manufacturers (in the form of vaccine profits). We decomposed the social value into the shares accruing to manufacturers versus the rest of society. For infectious diseases, the social value includes not only those vaccinated, but also those not vaccinated who benefit from the reduction in disease incidence.

The study entailed constructing an economic model based on observational and clinical data. The model calculates the social value of the routine pediatric vaccination schedule used in the United States in 2009. We do so by quantifying the health effects of routine vaccination of children born in the United States in 2009. In particular, vaccines to prevent the following 14 diseases were considered: congenital rubella syndrome, diphtheria, haemophilus influenzae type b (Hib), hepatitis A, hepatitis B, measles, mumps, pertussis, pneumococcus-related diseases (including pneumococcal disease, otitis media, pneumonia, and meningitis), polio, rotavirus, rubella, tetanus, and varicella. The influenza vaccine was not included because its changing seasonal nature would have required different methods.

The social value was estimated by applying an economic value to the health effects of vaccines, measured in terms of quality-adjusted life-years (QALYs) saved through vaccination. QALYs take into account both duration and quality of life. A year in perfect health would be measured as 1 QALY, whereas death counts as 0. From the value of the QALYs gained, the costs to produce vaccines were subtracted. This yielded the social value—or in economic terms, the total surplus&mdash;of vaccines, and represents the economic value of the health gains from vaccines minus the resources society spent to produce them. The shares of the total surplus accruing to manufacturers (producer surplus) versus the rest of society (consumer surplus) were also calculated.

It should be noted that vaccine-preventable illnesses impose additional costs on society beyond the utility loss infected individuals experience, including caregiver utility loss and the use of special services for persistent disability. Therefore our estimate of the health value of vaccination should be considered a lower bound.

In addition, one should exercise caution in interpreting the results of this framework for rotavirus, since in industrialized countries like the United States, the costs of rotavirus are mainly hospitalization and caregiver utility loss, as rotavirus mortality and morbidity are lower in the industrialized setting. In contrast, many of the other studied vaccines target illnesses that imposed a high mortality and morbidity burden in the pre-vaccine era.

According to the CDC, 4,130,665 children were born in the United States in 2009.

 The health effects of vaccination in this cohort were estimated by combining data from the literature with life tables from the Human Mortality Database.

 From the literature, we obtained for each disease data on cases of illness prevented, premature deaths avoided, average age of onset, average age at death from the disease, average duration of disease, and utility loss. Specific parameter values and sources are available in the eAppendix (

). The survival benefits of vaccination were net of adverse reactions to vaccination. To obtain the economic value of a QALY, we considered values generated by revealed and stated preference studies.

 A mid-range value of $150,000 was used and varied in sensitivity analysis.

Estimating manufacturers’ profits required 3 types of data: 1) data on vaccine prices, 2) data on manufacturers’ profit margins, and 3) data on the number of vaccines administered in the 2009 US birth cohort. We obtained data on prices (available in the eAppendix) from the CDC Vaccines for Children Program website, which contains archived data on public and private vaccine prices from 2008 to 2015.

We obtained data on vaccine manufacturers’ profit margins from annual reports and financial statements. When measuring manufacturers’ profits, we used the gross profit margin, which represents the sales volume minus production costs. Obtaining a companywide average across the top 5 vaccine manufacturers produced an average gross profit of 75%.

 This is a conservative approach, as gross profits do not subtract out manufacturer research and development (R&D) and marketing expenses. By using the gross profit margin, we can view vaccines’ social value as society’s benefit from vaccination, and society’s investment in R&D as the cost of inventing and developing the vaccines. (Subtracting R&D from profits would negate this framing.) This framing is useful because investments should be undertaken when the benefits (ie, the return on investment) exceeds the cost; social value is an important part of this equation. Moreover, R&D costs include the costs of many failures that the innovator encountered on the way to the given successful product; there is not an established method for measuring R&D costs for vaccines.

To estimate the number of vaccines administered, we required data on vaccine coverage rates, dosage schedules, wastage, and the cohort size. Following previous work, we assumed that 53% of vaccine doses were publicly (vs privately) administered and the wastage rate—the rate at which additional vaccines must be purchased beyond those needed for each vaccinated child because some vaccines will be unused&mdash;was 5%.

 from the CDC. Doses administered between ages 0 and 18 were included, but the costs of any adult booster doses were excluded. Given that any adult booster doses occur many years into the child’s life, whereas the lives saved and illnesses avoided from vaccination are realized mainly in early childhood, the effect of the focus on childhood doses should be minimal.

The 3 analytic steps are described broadly below. Additional detail is provided in the eAppendix. Throughout the analysis, monetary values were inflation-adjusted to 2014 US dollars using the Consumer Price Index,

 and an annual discount rate of 3% was applied.

Step 1: Value Health Effects of Vaccination

The health effects of vaccines were calculated by summing the changes in morbidity and mortality among the 2009 US birth cohort due to vaccination. The mortality effects were calculated as the number of deaths averted from vaccination multiplied by the QALYs the typical child would lose from dying of the given disease (calculated as average life expectancy minus average age at death from the given disease times aged-adjusted utility). The morbidity effects were calculated as the number of cases of illness prevented through vaccination times the typical duration of illness times the disutility from the given illness. The health effects of vaccines in QALYs were then converted to economic terms by valuing each QALY at $150,000.

 This yielded the economic value of the health effects of vaccination.

Step 2: Estimate Manufacturers’ Profits

Vaccine manufacturers’ profits from a given vaccine were estimated by multiplying the vaccine’s price by the number of vaccines sold by the profit margin. Although vaccines typically consist of multiple doses, and vaccination rates vary by dose, in the profit calculations we assumed that all children who received the first dose of a vaccine would also receive all subsequent doses. This assumption overestimates profits because some children will not receive all doses, and manufacturers’ profits will be lower than they would be had these children received all of their doses.

Using the results of the previous 2 steps, we calculated consumer, producer, and total surplus (ie, social value). Consumer surplus was calculated as the value of the health effects of vaccines (from Step 1) minus the cost of the vaccines (from Step 2). Producer surplus equaled the manufacturer profits (from Step 2). Consumer and producer surplus together yielded the total surplus.

We performed analyses to test the sensitivity of the model to the parameters. Specifically, we varied all parameters by 

10%, except for the disease-specific vaccination rates, which were varied by 

 5% to avoid specifying rates over 100%. In addition, we also varied the value of a QALY from $50,000 through $250,000 to reflect the wide range of values in the literature, and varied the discount rate from 0% to 6%. Lastly, we conducted an analysis, which included the parents’ time cost to take the children to receive the vaccines, taking into account that multiple vaccines may be given in the same visit.

The health effects of vaccination are reported in 

. Compared with no vaccination, an estimated 1.2 million QALYs will be saved due to vaccination among children born in the United States in 2009, for a value of $185.2 billion. Because vaccines typically prevent deaths that would have occurred in childhood, these avoided deaths save a large number of QALYs, whereas QALY gains from avoided illness are more modest. Consequently, 88% of the health value of vaccines is due to avoided death compared with 12% due to avoided illness. Among vaccines, diphtheria, tetanus, and pertussis (DTaP/Tdap) will have the largest health value by an order of magnitude, at nearly 800,000 QALYs saved for a value of $119 billion. Other vaccines with large health values are pneumococcus-related diseases (154,000 QALYs, $23 billion); measles, mumps, and rubella (MMR) (135,000 QALYs, $19 billion); and hepatitis B (79,000 QALYs, $12 billion). The smallest health value will be for rotavirus, at 5264 QALYs and $790 million.

 reports estimates of manufacturers’ revenues, costs, gross and net profits by vaccines given to the 2009 US birth cohort. Estimated profits are the lowest for the hepatitis A vaccine ($109 million gross, $36 million net), and the highest for pneumococcus-related diseases ($1.0 billion gross, $350 million net). Across all 14 diseases, vaccines generate an estimated $4.5 billion in revenues, $1.1 billion in costs, $3.4 billion in gross profits, and $1.1 billion in net profits.

The total social value by vaccine and its distribution across manufacturers and consumers are reported in 

. Consumers’ share of value, or surplus, ranges from $10 million for rotavirus to $119 billion for DTaP/Tdap. Vaccines producing the greatest consumer surplus do not necessarily provide the largest profit, or producer surplus, to manufacturers. Total social value ranges from $595 million from rotavirus to $119 billion from DTaP/Tdap, with the full vaccination schedule generating $184 billion in social value (

), or $45,000 per child. Of that total social value, 1.8% accrues to manufacturers, whereas 98.2% accrues to the rest of society. The manufacturers’ share ranges from 0.3% for DTaP/Tdap to 98.3% for rotavirus, and is less than 15% for 7 out of the 9 vaccines.

In sensitivity analyses, we found that the model is most sensitive to the economic value of a QALY, the premature deaths prevented from vaccination, and the discount rate, which together contributed 98.1% of the variance in results (

). In simulations, the total social value ranged from $153 billion to $227 billion, and the manufacturers’ share ranged from 1.3% to 2.6%. In other words, varying parameters by 

10% maintained a social value of at least $37,172 per child, with at least 97% of the value retained as consumer surplus. In addition, varying the value of a QALY from $50,000 to $250,000 led to social value ranging from $61 billion to $308 billion, and varying the discount rate from 0% to 6% led to social value ranging from $447 billion to $107 billion. Subtracting parents’ time cost led to a social value of $182 billion and manufacture share of 1.8%.

Our estimates suggest that routine childhood vaccination produces a large social value. In particular, we found that vaccination will save 1.2 million QALYs among children born in the United States in 2009, relative to a counterfactual of no vaccination. Vaccines treating common diseases, such as DTaP/Tdap, pneumococcus, and MMR, generate a particularly large share of the health benefits.

Childhood illness is often transitory, but many childhood diseases have nontrivial fatality rates, and a death in childhood costs many years of healthy life. Therefore, it is not surprising that the bulk of these health gains (88%) stems from reduced mortality, with the remainder (12%) from reduced morbidity. Valuing each life-year at $150,000 and subtracting the production costs of vaccines, we found that vaccination of the 2009 birth cohort generated $184 billion in social value, or $45,000 per child. Of this, $3.4 billion (2%) accrues to manufacturers in the form of profits, while $180.7 billion (98%) is retained by the rest of society. Sensitivity analysis showed that the large social value and large share retained by society are robust to a wide range of plausible parameters.

The 2% manufacturers’ share of social value we found is smaller than that found in similar analyses of other health interventions. The manufacturers’ share of social value was between 5% to 24% in HIV/AIDS, chronic myeloid leukemia, and heart disease.

 The distribution of gains between manufacturers and the rest of society is determined by the cost of the intervention and the health gains it produces. Health gains will generally be larger in the context of diseases that are prevalent or take life early. Therefore, it is not surprising that childhood vaccines come out favorably in these comparisons, as they are among the most cost-effective health interventions

 and they preserve the health of children, who, in the absence of a devastating childhood illness, typically go on to lead a long, healthy life. To put the health results in context, in other industries, the producers’ share of total social value has ranged from 4% for minivans

The fact that 98% of the social value from existing childhood vaccines goes to children and their families is good news for access to current vaccines; however, it has longer-term implications for innovation. A 2% share of value suggests relatively weak incentives for the development of new and improved vaccines, relative to the incentives to develop cancer drugs, cholesterol-lowering drugs, or broadband Internet. However, there is debate in the literature about the exact manufacturer share of surplus required to optimally incentivize innovation, and therefore, although this study informs the debate, it does not determine optimal vaccine pricing.

Because the model relies on inputs from the literature, it is limited by their availability and quality. When exact parameters were not available, conservative assumptions were made. For example, costs and profits were overestimated by assuming children who receive 1 dose of a vaccine receive all subsequent doses, thus underestimating social value and overestimating manufacturers’ share. In cases in which illness is usually short and mild but can be severe and long-lasting (eg, polio), we assumed a short and mild case in our utility calculations. In calculating profit margins, we assumed the companywide profit margin applied to a given vaccine, although vaccines may comprise a less profitable division of pharmaceutical firms.

 We also used gross margin, which does not take R&D and marketing costs into account, rather than net margin, so that we could consider the social value as the return on society’s investment in developing the vaccine.

Additionally, while the 2009 birth cohort was selected for this analysis due to the availability of data on the health effects of vaccination,

 it has the drawback of including in the analysis a year of shortage of the Hib vaccine.

 However, given the scale of the findings of this study, this consideration likely had minimal impact.

Vaccination of children born in the United States in 2009 will save 1.2 million quality-adjusted life-years, generating $184 billion in social value net of vaccination costs.

Sarah Green, BA

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