Michael S. Broder, MD

To assess impact on health outcomes and healthcare expenditures of adopting a 21-gene assay for women with early-stage, minimally node-positive, estrogen receptor—positive (N (1-3)/ER ) HER2-negative breast cancer.

We adapted a deterministic decision-analytic model to estimate costs and quality-of-life outcomes associated with chemotherapy, adverse events, supportive care, recurrence, and second primary cancers for usual care compared with care determined by the 21-gene assay recurrence score, where 71% and 54% of women, respectively, were treated with adjuvant chemotherapy. Model input data were based on national statistics, published literature, physician surveys, and Medicare Part B prices.

Annual numbers of events were multiplied by quality-adjusted life-years (QALYs) lost and costs to estimate net health and economic impacts of each strategy. Analyses were from a managed care payer perspective for the US population.

 Patients receiving the assay were predicted to gain 0.127 QALY and save $4359 annually from avoiding chemotherapy, adverse events, supportive care, and secondary primary tumors. For a 2-million member plan, net gains were 4.44 QALYs/year and savings were $13,476/year. Cost savings were greater for the Medicare population. Although overall results were sensitive only to reduced impact of testing and chemotherapy costs, they were still highly cost-effective (incremental cost-effectiveness ratio <$20,000/QALY).

 Use of a 21-gene assay in patients with early-stage N (1-3)/ER HER2-negative breast cancer may improve health outcomes and add no incremental cost, thereby providing valuable insight for health plans, the Centers for Medicare & Medicaid Services, and clinicians regarding coverage policies and treatment decisions.

	By providing better predictions of recurrence risk, a 21-gene assay in patients with earlystage N (1-3)/ER HER2-negative breast cancer has the potential to significantly improve US healthcare quality at no additional payer cost.

		Our model predicted that the substantial savings in chemotherapy-related costs would outweigh the cost of the test itself and that patients would experience substantial quality-oflife gains associated with reductions in chemotherapy.

		These results provide insight for health plans, the Centers for Medicare & Medicaid Services, and clinicians when establishing coverage policies and making treatment decisions.

	Large clinical trials have demonstrated significant benefits of combining chemotherapy with hormonal therapy in the adjuvant treatment of women with node-negative, estrogen receptor— positive (N−/ER ) breast cancer.

 Hormonal treatment with tamoxifen alone after surgery in this population has been shown to reduce the 10-year likelihood of distant recurrence to 15%.

 Depending on disease and tumor characteristics, adding chemotherapy can further reduce recurrence risk in some N−/ER patients, although others derive no added benefit from adjuvant chemotherapy.

 DX Recurrence Score reverse transcriptase polymerase chain reaction assay can reliably predict individual recurrence risks among N−/ER patients based on gene expression in the tumor tissue.

 The assay has been incorporated into National Comprehensive Cancer Network (NCCN) and American Society of Clinical Oncology (ASCO) treatment guidelines for N−/ER patients with HER2-negative tumors

 and has allowed oncologists to move from population-based to individualized estimates of recurrence risk, reducing unnecessary chemotherapy treatment among patients with low predicted recurrence risk.

	In node-positive, estrogen receptor—positive (N (1-3)/ER ) breast cancer, however, NCCN and ASCO guidelines recommend treatment with both hormonal therapy and chemotherapy for most HER2- negative cancers, although recent research suggests that up to 40% of these patients may remain disease-free without chemotherapy.

In a recent analysis of the TransATAC study, results of the 21-gene assay were significantly associated with time to distant recurrence and provided significant prognostic value beyond that provided by Adjuvant! Online in both N− and N (1-3)/ER cancers.

 In addition, an analysis of the SWOG-8814 clinical trial confirmed the lack of benefit of chemotherapy in women with N (1-3)/ER disease who had low recurrence scores (<18).

	Better predictions of recurrence risk could help oncologists individualize treatment recommendations. Eliminating chemotherapy for patients unlikely to benefit from it might both decrease costs and improve quality of life.

 We built a decision-analytic model to predict cost-effectiveness and cost savings of using the 21-gene assay Onco

 DX in women with earlystage N (1-3)(N1a-N1c)/ER HER2-negative breast cancer; the cost-effectiveness and cost savings were compared with the cost of treating this early-stage breast cancer according to current US guidelines.

	We adapted a deterministic decision-analytic model (

) to estimate the cost-effectiveness of adopting the 21-gene assay in treatment decisions for women with N (1-3)/ ER HER2-negative early-stage breast cancer, comparing outcomes for 2 scenarios: usual care, in which chemotherapy treatment decisions reflected historical standards based on US NCCN guidelines; and genomic assay testing, in which decisions were modified based on assay results. The model followed women from being disease free to living with nonprogressed and progressed disease, and then to death either from disease or other causes. Model inputs were obtained from published literature, national statistics, randomized clinical trials, Medicare part B prices, and surveys of breast cancer patients and medical oncologists.

	Patients in the model faced risks of being diagnosed with N (1-3)/ER HER2-negative breast cancer with varying recurrence risks; being tested with the 21-gene assay; receiving chemotherapy; experiencing adverse events, second primary cancers, or distal recurrence; and dying within a 30-year time horizon (

). Outcomes of interest were incremental quality-adjusted life-years (QALYs, per patient and total plan), costs (per patient, per member per month, and total plan), and incremental cost-effectiveness associated with the use of the assay in adjuvant chemotherapy treatment decisions. Patients in both usual care and testing strategies either received chemotherapy followed by hormonal therapy or hormonal therapy alone. The single difference between usual care and assay testing strategies was that fewer patients in the testing group received chemotherapy, which in turn affected costs and quality-of-life decrements associated with chemotherapy and related adverse events, supportive care, and second primary cancers.

	We evaluated the model for a hypothetical cohort of 2 million total health plan members with an age distributionrepresentative of the US population, estimating results for all women with N (1-3)/ER HER2-negative breast cancer. We also evaluated the model for a subset of those 65 years and older. We did not address assay use among patients with N- or HER2 tumors. Analyses were from a payer (managed care) perspective, and annual risks were based on average risks over a 30-year time horizon. Models were developed and analyzed using Microsoft Office Excel 2003 (Redmond, WA).

	Table 1 shows parameter estimates used in the deterministic decision-analytic model. We used 2004-2005 SEER*STAT data to estimate an age-adjusted incidence of early-stage N (1-3)/ER breast cancer of 9.74 diagnoses per 100,000 population and a 90% prevalence of HER2-negative tumors, using an age distribution representative of the US population and a mean age of diagnosis of 62 years.

 We considered women to be N if they had 1-3 positive nodes based on evidence that most N patients receiving the assay had <3 positive nodes.

 Diagnosed patients in usual care were assumed to be treated according to national guidelines that recommended adjuvant chemotherapy treatment for all N (1-3)/ ER patients.

 Because some patients may refuse chemotherapy and oncologists may not recommend chemotherapy in all cases (eg, among N1a patients with only 1 positive node), we assumed that 71% of women in usual care would receive chemotherapy treatment.

	In the testing strategy, we assumed that 20% of diagnosed patients would be tested with the assay. Among these patients, we estimated a 24% relative reduction in chemotherapy use associated with assay results based on data from a survey of oncologists who used the assay in this patient population.

 Compared with the 71% uptake in usual care, 54% [(1.0 — 0.24 x 71%)] of tested women would receive chemotherapy treatment. A post hoc analysis of the same survey showed a 31% relative reduction in chemotherapy for patients 65 years and older; we thus assumed a 49% chemotherapy rate [(1.0 – 0.31) x 71%)] among a subset of tested women in this

We assumed that the decrease in chemotherapy associated with assay testing occurred only in patients with low recurrent score,

so disease recurrence rates did not differ between strategies. Age-specific all-cause mortality was estimated based on national statistics, and for age- and disease-specific mortality we used SEER estimates of overall agespecific breast cancer mortality.

 These estimates were used to calculate total QALYs lost with each strategy.

	We categorized chemotherapy-related adverse events into minor events, major events, fatal events, and second primary cancers. Minor adverse events were defined as grade 

 (mild or moderate), where applicable, of the following: chemotherapy- induced nausea and vomiting; neutropenia; thrombocytopenia; diarrhea; alopecia; cardiovascular-functional (defined as any cardiovascular event that was asymptomatic, transient, or responded to treatment); phlebitis; infection; hemorrhagic cystitis; fever; and weight gain or loss. When these adverse events were grade 

 (severe, life threatening, or fatal), they were considered major, as were neurosensory or neuromotor toxicity, arthralgia, myalgia, granulocytopenia, hypersensitivity reaction, thromboembolic event, ovarian failure, cognitive dysfunction, congestive heart failure, and febrile neutropenia. Any such minor or major events that caused death were considered separately as fatal events.

	The incidence of minor adverse events was based on published reports of key trials of anthracycline therapy and antiemetic regimens.

We estimated an 85% minor adverse event rate to account for the higher rates of chemotherapy-induced nausea and vomiting seen in patients receiving some newer regimens and the almost-universal alopecia occurrence seen in patients receiving other regimens.

We estimated the probability of major adverse events based on published literature indicating that risk of these events among N breast cancer patients ranges between 20% and 35%,

and we assumed an average incidence of 30%.

	We estimated a 2.7% probability of second primary cancer based on the assumption of a 20-year survival time among diseased patients without recurrence or second primary cancer and a 13.6/10,000 person-years absolute excess risk of second primary nonbreast cancer after treatment (20 x 13.6/10,000 = 2.72%).

 Use patterns of chemotherapyrelated supportive care (including treatment with granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor, erythropoietin, and antiemetics) were based on a 2007 survey of medical oncologists in private oncology practices.

 The numbers of annual disease diagnoses, Onco

 DX tests, and adverse events associated with each strategy were estimated as the products of event incidence and plan population estimates for each age group. The total annual numbers of events were multiplied by their associated QALYs lost and costs (

) to estimate the net health and economic impact of each strategy. All QALY and cost estimates were discounted at 3% per year.

Estimates of QALYs lost associated with each episode of chemotherapy treatment were based on an analysis that evaluated the survival benefit required by a cohort of patients with early-stage breast cancer before they would be willing to have chemotherapy

 and on an economic analysis of targeted chemotherapy use among women with early-stage breast cancer.

 We applied 0.5 QALY lost over the course of a lifetime for each patient who received chemotherapy, assuming that this decrement encompassed the health-related quality-of-life effects associated with all chemotherapy-related supportive care and adverse events except

	for chemotherapy-related second primary cancers.

	We computed QALYs lost associated with secondary primary tumors and disease recurrence using a decision-analytic modeling approach, in which we incorporated disease-specific mortality, health-related quality-of-life weights, and mortality from other causes.

 On the basis of assumptions from previous economic analyses of chemotherapy use in breast cancer,

 we weighted the years of survival with a utility of 0.9 for women without recurrence or other tumor and 0.7 for those with either event, adjusted at a 3% annual discount rate and assuming a 2-year average survival time after diagnosis with recurrence or second tumor.

 We applied the resulting 9.1 QALY decrement to recurrent breast cancer as well as to second primary nonbreast cancers based on assuming that this average value encapsulates the highly varying survival rates associated with such cancers. We did not assume quality-oflife or survival decrements associated with assay testing.

 DX testing and direct medical costs associated with chemotherapy-related drugs and administration, adverse events, and supportive care in the treatment of primary and recurrent breast cancer and  second primary cancers. The assay cost was based on a US unit price of $3975, effective July 1, 2009. To determine per episode chemotherapy drug and administration costs, we considered treatment patterns reported by oncologists for common injectable and oral chemotherapy regimens

 and each regimen’s 2008 Medicare Part B average sales price (ASP). Assuming that 95% of recommended doses would be received

and adding 6% for over-ASP costs, we estimated a final per unit chemotherapy cost of $13,360 ([$13,267 x 95%] x [1 6%]; Table 2).

	Chemotherapy-related adverse event and supportive care costs per treatment episode ($8047 and $4283, respectively) were estimated based on per event costs from a recent economic analysis of adjuvant chemotherapy

 and on 2008 Medicare Part B ASP and were weighted by event incidence. 

 We estimated costs of distant recurrence based on assumptions from published cost-effectiveness and costof- illness analyses.

 We defined distant recurrence costs as direct medical costs of chemotherapy drugs and administration, all visits, laboratory tests and imaging, hospitalizations, and end-of-life care, for an estimated total cost of $40,162. All costs were adjusted to 2009 dollars using the Consumer Price Index.

	One-way sensitivity analyses were conducted to evaluate the impact of key model assumptions on outcomes. We varied the proportion of patients tested from 10% to 60%

	and the reduction in chemotherapy treatment associated with use of the assay from 15% to 40%,

as well as all costs associated with adjuvant chemotherapy treatment (±25% of base case estimate). We also conducted an analysis for a Medicare population, for whom we applied a model start age of 72 years to represent the mean age of diagnosis and a 31% reduction in chemotherapy associated with assay testing; all other estimates (eg, disease incidence) for this scenario remained consistent with those in the base case.

 shows the results of the base case analysis evaluating QALYs and cost outcomes for the assay testing strategy compared with the status quo in a 2-million member plan, of whom 175 (2,000,000 x [9.74/100,000] x 90% = 175) patients would be diagnosed with early-stage N (1-3)/ER HER2-negative breast cancer and 35 (175 x 20%) would be tested annually. The model predicted that each of these women receiving the assay would, over the course of 1 year, gain 0.085 QALY from avoiding chemotherapy and 0.042 QALY from the reducntion in chemotherapy-related secondary primary tumors, for a net annual gain of 0.127 QALY per tested patient.

	The testing strategy would also result in annual cost savings of $4359 per patient tested, including $2267 in avoided chemotherapy, $727 in avoided supportive care, and $1365 in avoided adverse events. With the cost of the assay included, testing would result in annual total net savings of $384 per patient tested. For a 2-million member plan and compared with the status quo of treating 71% of eligible patients, use of the assay in treatment decisions was predicted to result in a total of 4.44 QALYs gained and $13,476 saved each year, with savings of $0.0006 per member per month or $6 per eligible N (1-3)/ER HER2-negative member per month.

	Overall, the assay testing strategy was predicted to increase QALYs and to be cost neutral with respect to cost saving. These results were not sensitive to changes in input estimates of the proportion of patients tested, to a greater reduction in chemotherapy associated with assay testing, or to higher costs associated with adjuvant chemotherapy treatment. In addition, QALY gains and cost savings were greatest when the rates of testing were increased, when there was a greater reduction

	in chemotherapy associated with assay testing, and when chemotherapy-related costs were increased.

	For example, when we varied the proportion of patients tested between 10% and 60%, the predicted total plan QALYs gained ranged between 2.22 and 13.33 and total plan savings ranged between $6738 and $40,427, but there was no change in per patient QALY gains or costs saved. Increasing the impact of testing to a 40% reduction in chemotherapy increased net annual QALYs gained to 7.44 and cost savings to more than $116,000. Use of the assay was associated with any cost accrual rather than savings only when the reduction in chemotherapy associated with assay use was reduced to 15% and when chemotherapy-related costs were 25% lower than those in the base case. However, in both cases there were still QALY gains and the incremental cost-effectiveness ratios (ICERs) were low at $15,578 and $5567 per QALY, respectively.

	When testing was limited to women 65 years and older (eg, to represent the Medicare population) and there was a 31% reduction in chemotherapy associated with assay testing, net QALY gains remained almost the same at 4.40 but cost savings more than quadrupled to $61,267.

	Interventions to improve health are often evaluated with regard to their cost and effectiveness, and most interventions require increased expenditures in exchange for improved health.

 The cost per year of life gained for the last 4 decades of medical spending in the United States was $30,000 to $85,000,

and a frequently cited threshold value for costeffectiveness is $50,000 per QALY.

 The incremental costeffectiveness of the Onco

 DX assay for use in early-stage N (1-3)/ER   HER2-negative breast cancer appears to fall well below this level and indeed improves health while being either cost neutral or cost saving. That is, a health plan using the assay for patients with 1 to 3 positive nodes would experience improved quality outcomes among its members at essentially zero incremental cost.

	Opportunities to improve health at no incremental cost are rare. A systematic review of the cost-effectiveness literature found that fewer than 1 in 5 interventions were cost saving,

 and another analysis found that only 4 of 18 widely recommended quality measures saved money.

 Our results suggest that the 21-gene assay, on the other hand, not only provides marginal cost savings to payers but also offers vital quality improvement to patients, who could avoid toxicities such as nausea and vomiting, ovarian failure, new primary tumors, or cognitive dysfunction.

Articles

A decision-analytic model was used to estimate cost-effectiveness of adopting a 21-gene assay in treatment decisions for women with early-stage N (1-3)/ER  HER2-negative breast cancer.

Cost-Effectiveness of 21-Gene Assay in Node-Positive, Early-Stage Breast Cancer

To investigate differences in demographics, physician specialty, and medication use between patients who achieve high versus low ratios of controller to total asthma medications.

We used a Health Insurance Portability and Accountability Act—compliant claims database to identify patients aged 5 to 56 years with persistent asthma during a premeasurement year and a measurement year. Based on values in the measurement year, the ratio of controller to total asthma medications ratio was defined using the following formula: (Units of Controllers) / (Units of Controllers + Relievers). Descriptive analysis and multivariate logistic regression models were used to examine patients with high and low ratios.

The final study group comprised 38,538 patients with persistent asthma; 28,496 (73.9%) had high ratios. Specialty of usual-care physician differed (P <.001), with more high-ratio patients than low-ratio patients having an allergist or pulmonologist. Patients who received combination inhaled corticosteroid—long-acting β-agonist therapy (odds ratio [OR], 2.4) or leukotriene receptor antagonist therapy (OR, 3.5) were more likely to be in the high-ratio group compared with those dispensed a single inhaled corticosteroid. High-group and low-group assignment could be calculated by partial-year data: assignment based on 1 quarter of data was concordant with assignment based on full-year ratio in 91% of cases (Pearson product moment correlation coefficient, 0.864; κ statistic, 0.761), and assignment based on 2 quarters of data was concordant with full-year results in 94% of cases (Pearson product moment correlation coefficient, 0.928; κ statistic, 0.843).

A high ratio of controller to total asthma medications is associated with greater controller adherence and with more controller fills. The ratio can be calculated using 1 or 2 quarters of pharmacy claims data, at a time when intervention may reduce asthma-related exacerbations. Interventions that may improve the ratio include changing from single inhaled corticosteroid therapy and from asthma specialist care.

The ratio of controller to total asthma medications may be a useful tool for improving quality of care.

Patients with greater adherence to controller therapy are more likely to have high ratios.

Higher ratios are seen in patients treated with medications other than single inhaled corticosteroids.

Ratios may be calculated using less than a full year of data.

The Healthcare Effectiveness Data and Information Set (HEDIS), developed by the National Committee for Quality Assurance,

 reports quality measures for various health conditions. The asthma measure assesses the proportion of patients who are prescribed a controller medication in a given year, and it has become a focus of quality improvement programs nationally.

 The current measure may not be ideal for directing improvement efforts because patients with “appropriate” care (those with >1 controller medication prescribed) may actually have an increased risk of requiring emergency hospital care.

A revision to the measure that would address this issue has been proposed.

 This proposed measure, the ratio of controller to total (controller plus rescue) asthma medications, has been shown to be a better indicator of the need for emergency hospital care than the HEDIS measure.

 A higher ratio also is significantly related to improved asthmarelated quality of life, better disease control, and reduced symptoms.

 In addition to validating its association with relevant outcomes, studies

 of the ratio have demonstrated its performance among various plans and regions, tested different inclusion criteria, and examined the ideal cutoff point for high versus low ratios. However, little information is available regarding which patient populations perform well on this measure or what individual patient characteristics indicate poor performance. The type and number of controller and reliever medications that comprise the ratio have not been studied extensively. Having a better understanding of this ratio measure would improve plans’ (and physicians’) ability to identify patients at risk of having poorly controlled asthma and might help plans, through targeted intervention, improve their compliance with the measure.

The primary objectives of this study were to investigate differences between patients who achieve high versus low ratios of controller to total asthma medications, to identify patient characteristics that are associated with high ratios, and to compare the type and number of controller and reliever medications used by patients with high versus low ratios.

We conducted a cohort study to examine the characteristics of patients with high and low ratios of controller to total asthma medications. We used the PharMetrics database, a Health Insurance Portability and Accountability Act—compliant administrative claims database (

). This database contains adjudicated pharmacy and medical claims submitted by providers, healthcare facilities, and pharmacies. The study was exempt from human subjects review.

We included patients who had persistent asthma as defined by the current HEDIS measure during a premeasurement year (October 1, 2005, to September 30, 2006) and a measurement year (October 1, 2006, to September 30, 2007) who were aged 5 to 56 years during the measurement year and who were continuously enrolled during those 2 years. Patients with emphysema or chronic obstructive pulmonary disease (COPD) (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] codes 492, 518.2, 491.2, 493.2, 496, and 506.4) were excluded.

The main outcome measure was the ratio of controller to total asthma medications, as described by Schatz et al.

 Patients with a ratio of at least 0.5 were classified as high-ratio patients, and those with a ratio of less than 0.5 were classified as low-ratio patients. Based on values in the measurement year, the ratio was defined using the following formula: (Units of Controllers)/(Units of Controllers + Relievers). Controller medications included cromolyn sodium, leukotriene modifiers, nedocromil, inhaled corticosteroids (including combination inhaled corticosteroid—long-acting β-agonists), methylxanthines, and omalizumab. Reliever medications included inhaled short-acting β-agonists. Long-acting β-agonists as individual drugs or as part of combination therapy were not counted in the numerator or the denominator for the ratio calculation.

To count medication units for oral medications, 1 U was considered equivalent to 1 dispensing event (for a 30-day supply). If a patient received a 90-day supply, it was considered 3 U. For inhalers, 1 U was 1 canister. For injected medications, 1 U was 1 claim, but if a subsequent claim had a service date within 21 days, it was ignored (eg, claims on days 1 and 15 counted as 1 U).

 We used HEDIS definitions to count dispensing events.

 For injected medications (omalizumab), each claim was counted as a dispensing event. The number of canisters dispensed in each claim was determined by a ratio of quantity to package size. For example, a claim of triamcinolone acetonide inhalation aerosol, 75 mcg/actuation (20-g package size), with a quantity of 20 was interpreted as 1 canister dispensed. Any claim with a ratio of quantity to package size of less than 1 was counted as 1 canister. For claims with a ratio of quantity to package size of at least 1, we rounded the number to a whole number of canisters. For example, claims of aerosol solution, 75 mcg/actuation (20-g package size), with quantities of 35 and 25 were counted as 2 canisters (35/20 = 1.75 [rounded to 2]) and 1 canister (25/20 = 1.25 [rounded to 1]), respectively. If a claim was for more than 12 canisters, it was truncated to 12 canisters.

We evaluated patient characteristics in the premeasurement year and in the measurement year. Characteristics evaluated in the measurement year included demographics (age, sex, and geographic region of care), specialty of usual-care physician, and controller or reliever use. To determine the specialties of the patients’ usual physicians, we reviewed office visit claims for evidence of evaluation and management services (defined as those with 

codes for office or other outpatient services, office consultations, and preventive medicine services). We identified the specialty of the physician with whom each patient had the most evaluation and management visits during the measurement year and considered that the patient’s usual-care physician.

 We reported extent of controller and reliever use in the measurement year and calculated controller medication possession ratios (MPRs), defined as the sum of the “days of supply” divided by the total number of days in the measurement year (365 days). If the days of supply exceeded 365, the total number of covered days was truncated to 365 days.

We assessed asthma control in the premeasurement year with variables that reflect impairment and risk using 2 key concepts described by the National Asthma Education and Prevention Program.

 To assess impairment, we measured whether patients had 6 or more short-acting β-agonist canisters dispensed in the premeasurement year. Such use has been shown to be a marker of impairment in previous investigations.

 To assess risk, we measured whether patients had the following: (1) at least 2 oral corticosteroid dispensing events, (2) any asthma-related (with primary diagnosis of asthma [ICD-9-CM code 493]) hospitalization, or (3) any asthma-related emergency department (ED) visits.

 We assessed impairment and risk in the premeasurement year rather than in the measurement year to understand the effect of baseline control on the ratio.

To describe patient clinical characteristics and chronic disease burden, we used claims in the premeasurement year and in the measurement year to capture more completely any relevant diagnoses. We evaluated clinical characteristics using individual and grouped diagnoses. We used Clinical Classifications Software, developed by the Agency for Healthcare Research and Quality (

http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

), to identify patients with mental disorders and disorders of the gastrointestinal system, conditions that may affect medication adherence and asthma severity.

 Individual diagnoses included reflux (ICD-9-CM codes 530.11 and 530.81), sinusitis (codes 461.x and 473.x), rhinitis (codes 477.0, 477.8, 477.9, and 472.0), acute upper respiratory tract infection (code 465.x), and nasal polyposis (code 471.x). To account for the burden of chronic health conditions, we applied the method by Hwang et al

 to assess the number of chronic health conditions each patient had.

We conducted a descriptive analysis to compare demographic and clinical characteristics, specialty of usual-care physician, and medication use between the high-ratio and low-ratio groups. In addition to high-ratio and low-ratio groups, we further divided patients into 4 groups based on their actual ratios (<0.25, 0.25 to <0.5, 0.5 to <0.75, and >0.75) and compared their characteristics. We studied quartiles for a more precise evaluation. We conducted bivariate analyses comparing selected characteristics between the high-ratio and low-ratio groups. Characteristics that were significantly associated with the ratio were included in the multivariate models as independent variables. We compared ratios calculated using the first quarter, the first 2 quarters, and the first 3 quarters of data with full-year ratios using Pearson product moment correlation coefficient and κ statistic.

We used 2 multivariate logistic regression models to estimate the association between certain characteristics and a high ratio. The first model excluded patients with a zero ratio (no controller use). It also excluded patients treated with only theophylline or cromolyn sodium, as these drugs are not commonly used as single agents in current practice (<1% of patients in the study). The second model included patients with no controller use. We reported adjusted odds ratios (ORs) and their 95% confidence intervals (CIs). All tests were 2-sided with a significance level of .05. All data transformations and statistical analyses were performed using SAS version 9.1

We identified 150,903 patients with persistent asthma in the premeasurement year and 156,649 in the measurement year. After excluding those who did not meet the age criteria, those who were not continuously enrolled, those who had evidence of emphysema or COPD, and those who did not use controllers or relievers in the measurement year, 38,538 patients remained (

Of the final study cohort, 28,496 (73.9%) were classified as having high (>0.5) ratios. The mean age did not differ between the groups with high versus low ratios. Age group, sex, and geographic region of care differed (

 <.001), with a higher proportion of high-ratio patients aged 5 to 9 years (14.1% vs 8.7%), female (55.2% vs 50.5%), and from the South (18.3% vs 13.6%). Specialty of usual-care physician in the measurement year differed (

 <.001), with a higher proportion of high-ratio than low-ratio patients having an allergist (7.6% vs 3.9%) or a pulmonologist (2.9% vs 2.2%) (

The mean MPR in the measurement year was 0.603 for high-ratio patients and 0.203 for low-ratio patients (Table 1). High-ratio patients used a mean of 10.3 U of controllers; low-ratio patients used a mean of 3.1 U (

 <.001). Controller medication use differed between groups (

 <.001): 7.7% of high-ratio patients used 0 to 3 U (lowest quartile) compared with 67.9% of low-ratio patients, and 35.3% of high-ratio patients used at least 12 U (highest quartile) compared with 4.1% of low-ratio patients. There were significant differences in reliever use during the measurement year (

 <.001): the mean number of reliever units used was 2.4 in the high-ratio group compared with 10.1 in the low-ratio group. Almost 30% of high-ratio patients had no reliever fills in the measurement year (0% of low-ratio patients had no reliever fills, as by definition having no reliever use produces a ratio of 1.0). The highest quartile of reliever use included those with 7 or more canisters used. Just over 8% of high-ratio patients and 63.3% of low-ratio patients were in this group.

There was a statistically significant difference between the groups in asthma control measures during the premeasurement year. High-ratio patients had less impairment, with only 16.4% filling 6 or more short-acting β-agonist canisters compared with 63.3% of low-ratio patients (

). High-ratio patients had lower risk than low-ratio patients in each of the areas measured (

 <.001 for each category). High-ratio patients had more total chronic health conditions (4.0 vs 3.6, 

 <.001) and more of the individual comorbidities assessed, except for mental disorders, which were more common among low-ratio patients (

). When we further classified patients into 4 ratio groups (<0.25, 0.25 to <0.5, 0.5 to <0.75, and >0.75), the results for medication use, asthma control, and comorbidities were similar (data not shown).

High-group and low-group assignment could be calculated using partial-year data. High-ratio vs low-ratio group assignment based on 1 quarter of data was concordant with group assignment based on full-year ratio in 91% of cases. Adding a second quarter of data increased concordance to 94%. Pearson product moment correlation coefficient of partial-year to full-year ratio (using a continuous [0-1.0] variable for the ratio) was 0.864 for 1 quarter and 0.928 for 2 quarters. κ statistic, calculated using the categories of high ratio and low ratio, was 0.761 for 1 quarter and 0.843 for 2 quarters (

The 2 multivariate models produced similar results. The model that excluded patients without controller use allowed us to estimate the effect of specific patterns of medication use on the ratio. In multivariate analysis of controller users, older age and male sex significantly lowered the odds of being in the high-ratio group, with an OR of 0.54 (95% CI, 0.48-0.61) among patients aged 10 to 17 years compared with patients aged 5 to 9 years and an OR of 0.69 (95% CI, 0.61-0.77) among patients aged 18 to 56 years compared with patients aged 5 to 9 years. Geographic region of care was associated with ratio group, with an OR of 1.14 (95% CI, 1.01-1.30) for the Northeast compared with the West and an OR of 1.18 (95% CI, 1.03-1.34) for the South compared with the West. Compared with patients having primary care physicians (internists and family physicians), patients having allergists as usual-care physicians had an OR of 1.35 (95% CI, 1.17-1.55) for being in the high-ratio group, and patients having pulmonologists had an OR of 1.26 (95% CI, 1.04-1.52) (

There was a statistically significant association between the type of controller used in the measurement year and the ratio group. Patients whose only controller was a combination inhaled corticosteroid—long-acting β-agonist product had 2.36 (95% CI, 2.15-2.58) times the odds of being in the high-ratio group compared with patients who used only single inhaled corticosteroids (Table 5). Similarly, patients with a leukotriene receptor antagonist as their only controller had an OR of 3.49 (95% CI, 3.15-3.87) for being in the high-ratio group compared with patients who used a single inhaled corticosteroid controller. Users of multiple controllers had an OR of 7.45 (95% CI, 6.80-8.17) for being in the high-ratio group.

Asthma control was associated with outcomes. Patients who filled 6 or more short-acting β-agonist canisters in the premeasurement year, a measure of impairment, had significantly lower odds (OR, 0.11; 95% CI, 0.10-0.12) of having high ratios than patients who filled fewer than 6 canisters (Table 5). Having any asthma-related hospitalization, any asthma-related ED visit, or having filled at least 2 oral corticosteroid prescriptions in the premeasurement year were all significantly associated with lower odds of having a high ratio. Patients with a mental disorder had lower odds of being in the high-ratio group (OR, 0.75; 95% CI, 0.70-0.81), whereas patients with rhinitis had higher odds (OR, 1.22; 95% CI, 1.14-1.31). Each additional chronic health condition increased the odds of being in the high-ratio group (OR, 1.04; 95% CI, 1.02-1.05).

Limitations and problems with the current HEDIS asthma measure have led to consideration of some modifications. One potential modification is the use of a ratio of controller to total asthma medications as the primary quality measure. Using a ratio that is associated with outcomes may result in improvements in the quality of asthma care, and the measure identifies a subpopulation that may benefit from attempts at improvements in care. In a study

 using 3 separate databases, a high ratio was consistently associated with improved outcomes. The proportion of patients with a ratio of at least 0.5 varied from 70% to 75% across the 3 databases. High ratios are also associated with improved patient-reported outcomes,

 but little has been published about specific differences between high-ratio and low-ratio patients or about the stability of the measure over time.

In the present study, 73.9% of patients had high ratios, similar to other published studies

; the mean age and the slight female preponderance were also similar to prior work. In an unadjusted analysis, children younger than 10 years, female patients, and those with allergists and pulmonologists as their usual-care physician were more likely to have high ratios, and these results persisted after adjustment for baseline characteristics. Other variables that had a statistically significant association with the high-ratio group in the multivariate analyses included lack of markers of impairment or risk, use of controllers other than single inhaled corticosteroids, absence of mental disorders, presence of rhinitis or acute upper respiratory tract infection diagnosis, and more chronic health conditions.

In multivariate analyses, the strongest variable associated with ratio group was the type of controller therapy, with single inhaled corticosteroids being associated with lower ratios than combination inhaled corticosteroid—long-acting β-agonist, leukotriene receptor antagonist, and multiple controllers. Patients are more adherent with combination inhaled corticosteroid–long-acting β-agonist inhalers than with concurrent treatment using separate inhaled corticosteroid and long-acting β-agonist inhalers.

 In addition, combination therapy is generally more effective than similar doses of inhaled corticosteroid monotherapy in relieving symptoms and improving lung function,

 so patients might be more consistent in their use of these therapies. Patients receiving long-acting β-agonist therapy may use less short-acting β-agonist, which may increase the ratio for combination products compared with single inhaled corticosteroid. However, if the reduced use of short-acting β-agonist reflects decreased symptoms, this is not an artificial increase. Adherence to leukotriene receptor antagonist therapy is also greater than adherence to inhaled corticosteroid monotherapy.

 Patients treated with multiple controllers were many times more likely to have high ratios, possibly as a result of the calculation used to determine the ratio. To be categorized as using multiple controllers, a patient needed at least 2 controller fills (eg, to identify >2 types of controllers), whereas some users of single therapies had only 1 fill, and a greater number of controller fills results in a higher ratio.

Ratios also increase with increasing controller adherence. This relationship between greater adherence as measured by the MPR and higher ratios may explain some of the associations we observed. Higher ratios were seen in patients who had allergists or pulmonologists as their usual-care physician. Provider knowledge and communication skills influence adherence to treatment, and specialist providers may be better at communicating complex regimens to their patients with asthma.

 Women had higher ratios, and some evidence suggests they may have better adherence than men.

 The presence of a mental disorder was associated with a lower ratio, and some mental disorders, including depression, decrease adherence to therapy.

 Many other factors associated with poor adherence were not captured in our study, such as patients’ education, socioeconomic status, disease understanding, and belief in treatment efficacy.

Some variables may mediate their effect on ratio group through their association with asthma control. Increasing numbers of chronic health conditions, as well as rhinitis and upper respiratory tract infections, all may identify patients with greater risk. These factors may also lead to more office visits and greater opportunities for prescribing controller medications. We used utilization-based measures to adjust our results for disease severity, but there were no clinical measures of severity in our database. This was a limitation of our analysis.

The goal of quality measures and tools for targeted interventions is to improve patient outcomes. Quality measures are provider oriented and aim to improve patient outcomes by improving provider adherence to treatment guidelines (eg, controllers for asthma). This is done by using quality measures as incentives for approaches such as public reporting and pay for performance. In contrast, population management (tools for targeted interventions) is patient oriented and aims to identify high-risk patients who need targeted interventions to improve outcomes. The ratio, which may be calculated from partial-year data, can be used as a quality measure or tool for population management to improve asthma outcomes. A plan interested in intervening for patients with low ratios could use data from 2 quarters or even 1 quarter to identify patients at risk for having a low ratio (if the quantities dispensed are in the 30-day to 60-day range). Interventions such as education, outreach, or medication counseling put in place after a partial-year

calculation of the ratio might not only increase the ratio but also (given the demonstrated link between the ratio and outcomes) reduce emergency hospital care. We found that patients who used combination inhalers versus single inhaled corticosteroid controllers and leukotriene receptor antagonist versus single inhaled corticosteroid were more likely to have high ratios. Using this information, interventions to improve patients’ ratios could be developed. For example, patients with poor control who are receiving single inhaled corticosteroids could switch to combination inhalers, and patients with poor adherence to single inhaled corticosteroids could switch to leukotriene receptor antagonists.22 Patients who received specialty care were more likely to have high ratios; therefore, it may be beneficial to refer patients with evidence of prior impairment or risk to asthma specialists. Studies to evaluate the effectiveness of these interventions should be considered.

The ratio of controller to total asthma medications may be useful as a measure of the quality of asthma care. The measure can be easily calculated using existing administrative data, and patients with higher ratios have improved asthma outcomes. Higher ratios are seen among patients with greater adherence to therapy and among patients treated with medications other than single inhaled corticosteroids. Ratios are stable; interventions focused on patients with low ratios based on 1 or 2 quarters of data may reduce asthma-related exacerbations. The ratio of controller to total asthma medications may be useful as a quality measure and as a tool for targeted intervention to improve asthma outcomes.

From the Partnership for Health Analytic Research (MSB, EC); Beverly Hills, CA; AstraZeneca, LP (BG, DM), Wilmington, DE; and Kaiser Permanente Medical Center (MS), San Diego, CA.

This study was funded by AstraZeneca, LP.

Drs Broder and Chang report receiving payment from AstraZeneca, LP for their involvement in conducting the study and preparing the manuscript. Drs Gutierrez and Meddis are employees of AstraZeneca, LP, the company that provided funding for the study. Dr Schatz reports receiving investigator-initiated grants from Aerocrine, Genentech, GlaxoSmithKline, and Merck.

Concept and design (MSB, BG, EC, DM, MS); acquisition of data (BG); analysis and interpretation of data (MSB, BG, EC, MS); drafting of the manuscript (MSB, BG, DM); critical revision of the manuscript for important intellectual content (MSB, BG, DM, MS); statistical analysis (EC); and obtaining funding (DM).

David Meddis, PhD, AstraZeneca, LP, 1800 Concord Pike, Wilmington, DE 19850. E-mail: 

1. National Committee for Quality Assurance. HEDIS 2008: Healthcare Effectiveness Data and Information Set. Vol 2, Technical Specifications. Washington, DC: National Committee for Quality Assurance (NCQA);2007:126-128. 2. Gelfand EW, Colice GL, Fromer L, Bunn WB III, Davies TJ. Use of the Health Plan Employer Data and Information Set for measuring and improving the quality of asthma care. Ann Allergy Asthma Immunol. 2006;97(3):298-305. 3. Berger WE, Legorreta AP, Blaiss MS, et al. The utility of the Health Plan Employer Data and Information Set (HEDIS) asthma measure to predict asthma-related outcomes. Ann Allergy Asthma Immunol. 2004;93(6):538-545. 4. Fuhlbrigge A, Carey VJ, Adams RJ, et al. Evaluation of asthma prescription measures and health system performance based on emergency department utilization. Med Care. 2004;42(5):465-471. 5. Schatz M, Zeiger RS, Vollmer WM, et al. The controller-to-total asthma medication ratio is associated with patient-centered as well as utilization outcomes. Chest. 2006;130(1):43-50. 6. Schatz M, Nakahiro R, Crawford W, Mendoza G, Mosen D, Stibolt TB. Asthma quality-of-care markers using administrative data. Chest. 2005;128(4):1968-1973.7. Schatz M, Stempel D; American College of Allergy, Asthma and Immunology Task Force; American Academy of Allergy, Asthma and Immunology Task Force. Asthma quality-of-care measures using administrative data: relationships to subsequent exacerbations in multiple databases. Ann Allergy Asthma Immunol. 2008;101(3):235-239. 8. OÃƒÂ¢Ã¯Â¿Â½Ã¯Â¿Â½Malley AS, Pham HH, Schrag D, Wu B, Bach PB. Potentially avoidable hospitalizations for COPD and pneumonia: the role of physician and practice characteristics. Med Care. 2007;45(6):562-570. 9. National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services. Expert Panel Report 3 (EPR3): guidelines for the diagnosis and management of asthma. Revised August 28, 2007. http://www.nhlbi.nih.gov/guidelines/ asthma/asthgdln.htm. Accessed February 17, 2009. 10. Schatz M, Zeiger RS, Vollmer WM, et al. Validation of a ÃƒÅ½Ã‚Â²-agonist long-term asthma control scale derived from computerized pharmacy data. J Allergy Clin Immunol. 2006;117(5):995-1000. 11. Schatz M, Cook EF, Nakahiro R, Petitti D. Inhaled corticosteroids and allergy specialty care reduce emergency hospital use for asthma. J Allergy Clin Immunol. 2003;111(3):503-508. 12. Schatz M, Nakahiro R, Jones CH, Roth RM, Joshua A, Petitti D. Asthma population management: development and validation of a practical 3-level risk stratification scheme. Am J Manag Care. 2004;10(1):25-32. 13. Cowen ME, Dusseau DJ, Toth BG, Guisinger C, Zodet MW, Shyr Y. Casemix adjustment of managed care claims data using the clinical classification for health policy research method. Med Care. 1998;36(7):1108-1113. 14. Hwang W, Weller W, Ireys H, Anderson G. Out-of-pocket medical spending for care of chronic conditions. Health Aff (Millwood). 2001;20(6):267-278. 15. Marceau C, LemiÃƒÆ’Ã‚Â¨re C, Berbiche D, Perreault S, Blais L. Persistence, adherence, and effectiveness of combination therapy among adult patients with asthma. J Allergy Clin Immunol. 2006;118(3):574-581. 16. Greenstone IR, Ni Chroinin MN, Masse V, et al. Combination of inhaled long-acting ÃƒÅ½Ã‚Â²2-agonists and inhaled steroids versus higher dose of inhaled steroids in children and adults with persistent asthma. Cochrane Database Syst Rev. 2005;4:CD005533. 17. Jones C, Santanello NC, Boccuzzi SJ, Wogen J, Strub P, Nelsen LM. Adherence to prescribed treatment for asthma: evidence from pharmacy benefits data. J Asthma. 2003;40(1):93-101. 18. World Health Organization. Adherence to Long-Term Therapies: Evidence for Action. Geneva, Switzerland: World Health Organization; 2003. 19. DiMatteo MR. Variations in patientsÃƒÂ¢Ã¯Â¿Â½Ã¯Â¿Â½ adherence to medical recommendations: a quantitative review of 50 years of research. Med Care. 2004;42(3):200-209. 20. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient compliance. Arch Intern Med. 2000;160(14):2101-2107. 21. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487-497. 22. Tan H, Sarawate C, Singer J, et al. Impact of asthma controller medications on clinical, economic, and patient-reported outcomes. Mayo Clin Proc. 2009;84(8):675-684.

The ratio of controller to total asthma medications can be calculated using 1 or 2 quarters of data; high versus low ratios differentiate patient characteristics.

Michael S. Broder, MD

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