 Patients  prescribed medications with US Food and Drug Administration—issued black box  warnings (BBWs) warrant additional vigilance by prescribers because these drugs  can cause serious adverse drug events. Seniors are at greater risk for adverse  drug events due to increased medication burden and greater health  vulnerability.

 To improve our  understanding of the prescribing and patient-monitoring practices of physicians  prescribing medications with a BBW to patients age ≥65 years in an ambulatory  care setting.

 A retrospective  cohort study of administrative pharmacy and medical claims identified 58,190  patients age ≥65 years in the Horizon Blue Cross Blue Shield of New Jersey  beneficiary population with ≥1 claim for≥1 of the 8 targeted medications between  January 1, 2005, and December 31, 2005. Medications included carbamazepine,  amiodarone, ketoconazole, loop diuretics, methotrexate, cyclosporine, metformin  and combinations, and cilostazol. Patients were followed 12 months from the  index prescription date to evaluate prescriber compliance with BBWs using  operationalized definitions of compliance.

 Patients prescribed  drugs with a drug-laboratory warning had lower rates of prescriber BBW  compliance (0.7%-24.9%) than patients prescribed drugs with a drug-disease  warning (84.7%-90.2%).

Administrative  claims analysis identified low rates of prescriber compliance with BBWs in  managing patients age ≥65 years. Claims analysis may be a cost-effective  strategy to monitor prescriber compliance with BBWs in older patients at higher  risk.

(Am J Manag Care.  2009;15(11):e103-e108)

A retrospective cohort study of administrative pharmacy and medical claims  was done to evaluate prescriber compliance with black box warnings (BBWs) in  patients age ≥65 years, using operationalized definitions of compliance.

Elderly patients prescribed drugs with a drug-laboratory BBW had lower rates  of prescriber BBW compliance than patients prescribed drugs with a drug-disease  warning.

Until electronic health records are universally adopted, administrative  claims analysis could be a good alternative for monitoring prescriber compliance  with BBWs in adults age ≥65 years.

The US Food and Drug Administration (FDA) requires that certain  prescription medications be accompanied by a black box warning (BBW). A BBW is  the strongest  warning imposed by the FDA and indicates that clinical or animal  toxicity studies have demonstrated that use of the medication is associated with  serious  risk or hazard that could lead to death or serious injury.

 A BBW communicates medication-specific risks to the healthcare provider, often  emphasizing the need for close monitoring of patients started on these  medications and outlining specific clinical management practices to be followed  to reduce risk.

Prescriber compliance with BBWs has been shown to be  variable and dependent on factors such as the type of warning, patient age, and  sex.

 Types of BBWs include those that require laboratory testing  either before therapy initiation, during treatment, or both (ie, drug-laboratory  warning); avoiding prescribing in the presence of another specified medication  (ie, drug-drug warning); avoiding prescribing in the presence of another  specified health condition (ie, drug-disease warning); or knowing the risks  associated with prescribing the medication for a specific population. Previous  research demonstrated that prescriber BBW compliance tends to be lower for  medications with a drug-laboratory warning and for medications prescribed to  females and to patients at least 75 years old.

Given  limited evidence demonstrating an increased risk of BBW prescribing violations  in older adults, the goal of this research was to improve our understanding of  the prescribing and patient-monitoring practices of physicians who prescribe  medications with a BBW to patients age ≥65 years in an ambulatory care  setting.

This research was designed  as a retrospective cohort study of the Horizon Blue Cross Blue Shield of New  Jersey beneficiary population age ≥65 years. Horizon is a large health plan in  the Northeast with approximately 2 million members. Its lines of business  include both commercial and Medicare participants. Horizon eligibility and  administrative medical and pharmacy claims records were analyzed to evaluate the  management of patients who were prescribed medications with a BBW.  

The study period was divided into 3 segments. The index period  ranged from January 1, 2005, to December 31, 2005, and was used to identify the  first prescription for 1 of the 8 medications with a BBW targeted in this study  (ie, the index prescription). The baseline period comprised the 6 months  immediately preceding the index prescription date and was used to determine  patient prior drug utilization for the medications of interest and prior  healthcare utilization. The follow-up period covered the 12 months immediately  following the index prescription date and was used to evaluate prescriber  compliance with BBWs. Patients were eligible for inclusion in the study if they  were at least 65 years old at the beginning of the follow-up period and  continuously eligible for pharmacy benefits from July 1, 2004, through December  31, 2006.

Study medications were selected systematically from the  universe of Horizon administrative pharmacy claims for patients age ≥65 years in  2006. Eight medications or groups of medications were selected based on those  with the highest claim volume, clinical relevance to an older population based  on the clinical judgment of study authors, and practicability of effectively  evaluating prescriber BBW compliance using administrative pharmacy and medical  claims records. The 8 medications and groups of medications selected for  analysis included carbamazepine, amiodarone, ketoconazole, loop diuretics,  methotrexate, cyclosporine, metformin and combinations, and cilostazol (

), which all had a BBW release date prior to the start of the  study period.

The study population comprised the 58,190 adults age  ≥65 years with at least 1 claim for 1 or more of the 8 targeted medications  between January 1, 2005, and December 31, 2005. The population was divided into  new users and existing users of the target medication for tracking purposes  because some of the management recommendations differ for new and existing users  of the medications. New users had no claim for a medication of interest within  the study baseline period (ie, the 6 months immediately preceding the index  prescription date in 2005). Existing users had at least 1 claim for a medication  of interest within the baseline period. This research was conducted in  compliance with applicable laws governing the use and disclosure of protected  health information, including the privacy regulation issued pursuant to the  Health Insurance Portability and Accountability Act of 1996.

Defining Black Box  Warning Types and Prescriber Compliance

The BBWs for the 8  study medications were categorized as either drug-disease (ie, metformin and  combinations, and cilostazol) or drug-laboratory (ie, carbamazepine, amiodarone,  ketoconazole, loop diuretics, methotrexate, and cyclosporine) based on the  content of the BBW stipulating either avoidance of prescribing the medication in  the presence of another specified health condition or recommendation of  laboratory testing in conjunction with treatment (eg, before therapy initiation,  during treatment, or both).

Operationalized definitions of BBW  prescribing compliance were derived for all 8 study medications. Definitions  were derived from the FDA's BBW recommendations and/or recommendations available  in the drug insert for managing patients treated with the drugs, and were  modified slightly to enable tracking compliance using administrative medical  claims records as the basis for evaluation (Table 1). Diagnosis codes defined by  the International Classification of Diseases, Ninth Revision, Clinical  Modification were used to identify specific health conditions in medical claims  records. The American Medical Association's Current Procedural Terminology codes  were used to identify the presence of laboratory tests. Operationalized  definitions of BBW-compliant patient management for each medication appear in  Table 1.

In describing physician compliance with BBW-specific  clinical management practices, the number and percentage of patients who were  managed in compliance with the BBW were quantified for each medication by using  the operationalized definitions of  compliance.

Rates of BBW-compliant  patient management were examined using our operationalized definitions (Table  1). We observed very low rates of physician compliance with the BBW for some  medications (

). The lowest rates  were observed in patients prescribed drugs with a drug-laboratory warning. Rates  ranged from 0.7% (cyclosporine) to 24.9% (loop diuretics). Rates of  BBW-compliant management were much higher for drugs with a drug-disease warning.  Rates ranged from 84.7% for metformin and combinations to 90.2% for  cilostazol.

This study uncovered high  rates of prescribing medications regardless of the presence of a BBW and  substantial variation in the level of BBW compliance by type of BBW (ie,  drug-disease vs drug-laboratory) in the clinical management of Horizon  participants age ≥65 years. Compliance with BBWs associated with a drug-disease  interaction (ie, metformin and cilostazol) was high. Compliance was much lower  for medications with a drug-laboratory BBW. 

Our results are  consistent with previous research that documented good compliance with  drug-disease BBWs.

 Both of these studies also reported lower rates  of prescriber compliance with drug-laboratory BBWs than with drug-disease  BBWs.

 However, the compliance rates for drug-laboratory BBWs  observed in our study were notably lower than those previously  reported.

Some of these differences can be attributed to the research  methods specific to each study and to the fact that the BBW for a single  medication can vary by indication and age of the patient. Wagner et al studied  individuals from birth to age >85 years using administrative pharmacy claims  to evaluate BBW prescribing.

 The population in the study by Lasser  et al included individuals age ≥18 years and used electronic health  records.

 Our study focused on adults age ≥65 years and used  administrative pharmacy and medical claims.

There are several  limitations to this study. First, our medical claims may have been incomplete  for several reasons. The claims were limited to 3 diagnosis fields, and we could  not determine the presence of a specific diagnosis of interest if it were not  captured in 1 of these 3 fields. Another reason for incomplete medical claims is  that approximately 40% of our sample was enrolled in a plan with a capitated  laboratory benefit. In a capitated laboratory benefit, health plans reimburse  laboratories participating in their preferred provider network for a predefined  and limited number of laboratory tests. Additional laboratory services over the  limit are not billed to the health plan; therefore, claims for those services  are not captured in the administrative claims database. A third possible  explanation for incomplete medical records is that a portion of the Horizon  study sample also was concurrently enrolled in Medicare Part B. We were not able  to identify patients with Medicare Part B coverage given the information  available to us. As a result, we could neither exclude them from the analysis  nor estimate the proportion of our study sample affected by the incomplete data.  Furthermore, we were unaware of the participants' complete medical coverage  eligibility and assumed the pharmacy coverage (which spanned July 1, 2004,  through December 31, 2006) was equivalent to the medical coverage. Another  limitation to this analysis was that administrative claims document the  procedures and laboratory tests that were performed, not necessarily the  procedures and tests that were ordered. Convenience, cost, and other factors may  contribute to a patient's decision not to adhere to provider instructions.  Finally, the high rates of noncompliant management observed in this study may be  related to a provider's evaluation of a patient's prognosis and a weighed  assessment of necessary medical care versus ethical treatment under the  circumstances.

Prescriber compliance with BBWs is an important issue  in the clinical management of older adults because of the potential risk of  adverse drug events in this already higher risk population. In general, clinical  management of adults age ≥65 years is challenging because older adults are more  likely to be in poorer health and use more prescription and over-the-counter  medications than younger adults.

 Temporal trends have indicated  that the mean number of medications used by older adults is  increasing

; a concomitant increase in the number of adverse drug  events can be anticipated.

 For these reasons, compliance with BBWs  is a critical component of appropriate patient management, particularly in  adults age ≥65 years, and a major patient safety  issue.

Administrative medical and pharmacy claims data could provide  a valuable resource for health plans to conduct periodic surveillance of  provider compliance with BBWs. The surveillance goal is timely and targeted  educational intervention linked to continuous improvement in patient safety and  quality of care. Although we found that administrative claims tend to  overestimate noncompliance with drug-laboratory BBWs, estimates of compliance  with drug-disease BBWs are more in line with other published estimates. Our  findings also are consistent with those of other studies reporting that provider  compliance with BBWs is suboptimal.

 Medical chart review could be  considered the gold standard for monitoring BBW compliance because patient  charts include detailed documentation of prescriber behavior. However, medical  chart review is both laborious and costly. Electronic health records are an  ideal data source for this type of activity, but many private practices and  clinics have not yet adopted the technology. Until electronic health records are  universally adopted, administrative claims analysis could be a good alternative  for monitoring prescriber compliance with BBWs in adults age ≥65 years. Chart  review could be performed as needed on a case-by-case basis based on the outcome  of the claims analysis to more thoroughly investigate any outcome.  

Administrative claims analysis  identified low rates of prescriber compliance with BBWs in managing patients age  ≥65 years. Until electronic health records are more widely available, we propose  that billing data may be an acceptable preliminary resource to evaluate  prescriber compliance with BBWs among patients using high-risk medications.

From CVS Caremark (JAR), Hunt  Valley, MD; CVS Caremark (CC, MCM), Scottsdale, AZ; CVS Caremark (JEB),  Northbrook, IL; and Horizon Blue Cross Blue Shield (AM, SAJ), Newark, NJ. Dr  Berger is now with Health Intelligence Partners, Chicago, IL. Funding Source:  None reported.

Dr Berger reports owning stock  in CVS Caremark. The other authors (JAR, CC, MCM, AM, SAJ) report no  relationship or financial interest with any entity that would pose a conflict of  interest with the subject matter of this article.

Articles

WEB EXCLUSIVE


Elderly patients prescribed drugs with a drug-laboratory black box warning  (BBW) had lower rates of prescriber BBW compliance than patients prescribed  drugs with a drug-disease warning.


Prescriber Compliance With Black Box Warnings in Older Adult Patients

 To examine how the influence of cost-sharing on adherence to antihypertensive drugs varies across adherence levels.

Cross-sectional study using medical and pharmacy claims and benefits data on 83,893 commercially insured patients with hypertension from the 2000-2001 Medstat MarketScan Database.

We measured drug adherence using the medication possession ratio (MPR) for antihypertensive drugs over 9 months. Drug cost-sharing was measured as either copayments or coinsurance. Other patient characteristics included age, sex, comorbidity, health plan type, and county-level sociodemographics. We compared adherence for different cost-sharing categories with a bivariate test of equal medians and simultaneous quantile regressions predicting different percentiles of drug adherence.

Median MPR was high (>80%) across all cost-sharing categories. Among the poorest adherers, the regression-adjusted MPR was 8 to 9 points lower among patients with the highest drug cost-sharing compared with patients with the lowest cost-sharing (copayment $5 or less). The effects of cost-sharing were smaller at the median (2-3 points lower) and nonsignificant at higher levels of adherence. Other significant factors influencing adherence at low adherence levels were drug class and comorbidity.

Cost-sharing had a substantial negative association with adherence among low adherers and little association at higher adherence levels. At a clinical level, physicians should closely monitor adherence to antihypertensive drugs, particularly for patients with multiple comorbidities and those taking multiple drugs. At a health system level, current benefit designs should encourage adherence while limiting the cost burden of drugs for patients with multiple chronic conditions taking multiple drugs.

A cross-sectional study of a large sample of working-age adults was done to examine how cost-sharing affects adherence to antihypertensive drugs.

Cost-sharing had the most significant and negative relationship with adherence for low adherers; it did not have a significant relationship with adherence for high adherers.

Other predictors of worse adherence for low adherers were drug class and greater comorbidity. 

Rising costs for prescription drugs are a particular concern for poor adherers.

As a result of the growing prevalence of chronic diseases due to the aging population and better detection and identification of individuals with such diseases, and the development of new drugs that are highly effective, the healthcare system relies heavily on prescription drugs to manage chronic conditions and reduce morbidity and mortality. Adherence to some drugs can be low, and concerns exist over whether patients are receiving the full benefit from their drugs. Among factors influencing patients’ adherence to drugs, one of the strongest relationships exists between higher out-of-pocket payments for drugs and less drug utilization, including lower adherence to drug prescriptions

 with modest price effects for drugs to treat asymptomatic conditions including hypertension.

It is unknown, however, whether patients with low drug utilization are more likely to be responsive to drug prices compared with patients with higher utilization of drugs. Demand for prescription drugs may have heterogeneity in price responsiveness; for example, patients who experience adverse effects or do not feel the drug is working well for them may have more price-elastic demand for the drug than other patients.

Using claims data from employer-sponsored health plans for a large sample of working-age adults diagnosed with hypertension, we applied an innovative quantile regression approach to examine how drug cost-sharing and other patient and health plan characteristics affect adherence to antihypertensive drugs across the distribution of adherence rather than looking at mean adherence, which provides a more limited picture. We focused on hypertension because it is one of the most prevalent chronic conditions in the United States, can be treated effectively with prescription drugs, and is similar to other asymptomatic conditions for which long-term management is critical to reduce the risk of morbidity and mortality. Moreover, when comorbid with other conditions such as diabetes, hypertension leads to development of diseases and complications including renal and diabetic eye disease.

 The direct costs of care attributable to hypertension are estimated to be $569 per year per capita, and the total costs (including the cost of comorbid conditions) are estimated to be $4073 per year per capita, or $110.3 billion per year for the United States.

 Therefore, adherence to drugs and overall management of hypertension have significant cost implications.

This study used data from the 2000-2001 Medstat MarketScan Database. Claims data were analyzed for 83,893 patients age 18 to 64 years who were diagnosed with hypertension (

International Classification of Diseases, Ninth Revision 

] codes 401-405) on any medical claim during 2000, were continuously enrolled in their health plan for the entire 2-year study period, and had at least 1 drug claim for an antihypertensive drug during an index period (January 2000-June 2000). Benefits information was available only from large firms, covering approximately 59% of patients. 

Patient-level variables were measured in 2000 and obtained from enrollment and benefits information linked to medical and pharmacy claims data. County of residence was used to link to US Census data on household income, race, and education. This study was approved by the UCLA Institutional Review Board (approval #G06-09-089-02).

Antihypertensive drugs included all drugs identified in 7 classes of drugs: angiotensin-converting enzyme (ACE) inhibitors, beta blockers, angiotensin II receptor blockers, thiazide diuretics, calcium channel blockers, alpha-1 blockers, and sympathetic blockers. Users of antihypertensive drugs were identified by any prescription for an antihypertensive drug occurring within a 6-month period at the beginning of 2000, which was considered to be the index prescription. Immediately following the last days supply of the index prescription, refill information was used to calculate the medication possession ratio (MPR) over a 9-month period by drug class. (An earlier study using administrative data measured adherence to antihypertensive drugs over 250 days and concluded that this measure of adherence was valid as it correlated well with drug effects.

) The days supplied for all refills within a given class was summed and divided by 270 days to calculate the MPR, prorating the last refill when necessary. The MPR represents the amount of time for which a patient had his/her drug supply, and it could vary from 0% to more than 100% if patients had overlapping prescriptions. The MPR values were truncated for 2% of patients with an MPR above 120%.

A single MPR was calculated if patients switched from one drug class to another during the study period. If patients filled prescriptions for drugs in multiple classes simultaneously, then a separate MPR was calculated for each drug class, and the mean of all classes was calculated. 

Patient cost-sharing for drugs was categorized as ≤$5 copayment per prescription (the reference group), $6 to $12 copayment, ≥$15 copayment, 10% coinsurance, or 20% coinsurance. Separate groups were created for copayment and coinsurance groups because the amount paid under coinsurance depends on the type of drug filled, and patients are more price responsive under coinsurance than under copayments.

 As 64% of study patients filled prescriptions for brand-name rather than generic drugs, and cost-sharing between brand-name and generic drugs was highly correlated (Pearson correlation coefficient = 0.44), only cost-sharing for brand-name drugs was used in analyses. Analyses originally included separate indicators of cost-sharing for generic and brand-name drugs and number of drug plan tiers, but a high degree of collinearity was found among these measures, so generic cost-sharing and number of drug plan tiers were dropped from analyses.

Comorbidity was measured using morbidity groupings from 

 codes in medical claims called aggregated diagnosis groups (ADGs)

 using dummy codes for 32 ADGs (not mutually exclusive), count of major ADGs (conditions with greater severity) ranging from 0 to 8, and dummy variables for congestive heart  failure, diabetes, ischemic heart disease, hyperlipidemia, and depression—comorbidities associated with cardiovascular disease.

Dummy categories were created for each of the 7 classes of antihypertensive drugs with thiazide diuretics as the reference category.

Additional covariates included patient cost-sharing for outpatient visits, type of health plan, receipt of preventive care, age, sex, employee working status, county-level median household income, percentage of nonwhite residents, and percentage of residents with less than a high school education.

A nonparametric test of equal medians was used to test for bivariate differences in the median MPR by patient and health plan characteristics. Simultaneous quantile regression then was used to estimate MPR. Quantile regression allows for differential effects of cost-sharing on the conditional median and other conditional quantiles of adherence. In quantile regression we regressed specific quantiles (or percentiles) of the dependent variable on patient characteristics.

 Predicting the conditional quantile in quantile regression is different from taking the unconditional distribution and dividing up the dependent variable to run separate regressions; the latter procedure is problematic because it artificially reduces the variation in the dependent variable.

 = drug adherence, to model the qth quantile, we defined the function

 is any quantile ranging between 0 and 1.0.

The regression coefficients were chosen to minimize the function:

The estimated quantile of the distribution of d, conditional on the values of the predictor variables, was:

where Q(.) denotes the predicted quantile.

Five simultaneous quantile regression models were estimated setting q equal to 0.1, 0.25, 0.5, 0.75, and 0.9 (representing the 10th, 25th, 50th or median, 75th, and 90th percentiles of adherence). Coefficients are presented along with standard errors bootstrapped using the normal method with 500 repetitions. The coefficient in quantile regression represents how the specific quantile (

) changes with a unit change in the predictor, so the coefficients represent the difference in points of the MPR. Wald tests of the differences between coefficients in the median regression model versus the 0.10 and 0.90 quantile model estimates were conducted. A conservative significance level of <.01 was used in all analyses because of the large number of significance tests conducted. All analyses were conducted with Stata version 9.2 (Stata-Corp LP, College Station, TX).

Discussion of the bivariate and regression results presented below focus on the impact of the main covariates of interest (cost-sharing, drug class, comorbidity) on adherence.

Characteristics of the sample are shown in 

. Most of the patients were older than 45 years of age and fairly diverse in terms of their sociodemographic characteristics and health plan type. Drug copayments were more common than coinsurance, and cost-sharing requirements were quite high. For example, only 13% of study patients had copayments of $5 or less and 25% of patients had coinsurance rates of 20%. Comorbidities were common among this sample of patients with hypertension. More than one-third of patients had hyperlipidemia, and almost one-fifth had diabetes.

Higher drug cost-sharing in the form of higher copayments and coinsurance was associated with lower median adherence. Among patients with copayments, the lowest drug copayment group ($5 or less) experienced the highest median adherence, and there was little difference between patients who paid $6 to $12 versus patients who paid $15 or more. Patients with 10% or 20% drug coinsurance also had lower median adherence levels compared with patients who had copayments of $5 or less. Similarly, medical cost-sharing had a negative relationship with median adherence.

By drug class, median adherence was highest for patients on ACE inhibitors and beta blockers, and lowest for patients taking sympathetic blockers and thiazide diuretics, 2 drug classes with the strongest side effects. Comorbidity appeared to be negatively associated with adherence.

), the significant relationship between higher cost-sharing and lower adherence was observed in the median and lower percentiles of adherence, seen by comparing coefficients by cost-sharing category down the columns within each percentile of adherence. The coefficients represent the change in adherence compared with the lowest cost-sharing group (copayment of $5 or less). The regression constants shown in the second-to-last row of the table (“Constant”) represent the percentile of adherence when all covariates are equal to zero, and the coefficients are additive in order to calculate the percentile for a particular group. For example, the 10th percentile of adherence for patients with a copayment of $5 or less who were taking thiazide diuretics and had no comorbidities, with all other covariates set to zero, was 8.63%.

In column 2 (the 10th percentile of adherence), patients with higher drug cost-sharing (copayments greater than $5 or drug coinsurance rates of 10% and 20%) had an adherence measure of 8 to 10 points less compared than that for patients with copayments of $5 or less. At the median (the 50th percentile of adherence), the association with costsharing was more modest, with differences of 2 to 3 points in MPR across the higher costsharing groups. At the 90th percentile of adherence, higher drug copayments and drug coinsurance no longer had a significant negative association with adherence, and the $6 to $12 copayment group actually had a significantly higher MPR than the group with the lowest copayment. Overall, the relationship with cost-sharing appeared to be consistently reduced between the 10th percentile and the higher percentiles, which is apparent when comparing coefficients across the rows for each cost-sharing category.

Although there were significant differences in adherence between the lowest cost-sharing group and higher cost-sharing groups, there did not appear to be much of a gradient in adherence between higher cost-sharing groups.

MANAGERIAL

Cost-sharing for antihypertensive drugs had the most significant and negative relationship with adherence for low adherers.

Cost-Sharing and Adherence to Antihypertensives for Low and High Adherers

To identify factors associated with delayed radiotherapy (RT) in older women with early-stage breast cancer.

We studied 541 women age ≥65 years diagnosed with early-stage breast cancer in 1990-1994 at 5 integrated healthcare delivery systems and treated with breast-conserving surgery and RT, but not chemotherapy. We examined whether demographic, tumor, or treatment characteristics were associated with RT delays of >8 weeks postsurgery using χ2 tests and multivariable logistic regression.

Seventy-six women (14%) had delayed RT, with a median delay of 14 weeks. Even though they had insurance and access to care, nonwhite and Hispanic women were much more likely than white women to have delayed RT (odds ratio = 3.3; 95% confidence interval = 1.7, 10) in multivariable analyses that controlled for demographic and clinical variables.

Timely RT should be facilitated through physician and patient education, navigation, and notification programs to improve quality of care. Queues for RT appointments should be evaluated on an ongoing basis to ensure adequate access. Future research should examine modifiable barriers to RT timeliness and whether delays impact long-term outcomes.

This study identified factors associated with delayed radiotherapy (RT) for women age ≥65 years with breast cancer in 5 integrated healthcare delivery systems.

Delayed RT was experienced by 14% of the study subjects, even though they had insurance and access to care.

Women receiving delayed care were more likely to be nonwhite or Hispanic.

Queues for RT appointments should be evaluated on an ongoing basis to ensure adequate access.

Breast cancer, diagnosed in 180,000 women in the United States in 2008,

 usually is treated with mastectomy or breast-conserving surgery (BCS) with or without radiotherapy (RT).

 Treatment characteristics vary by race/ethnicity, socioeconomic status, geographic region, healthcare access, and insurance status.

Quality of care, including timing and completeness of RT and chemotherapy, can affect recurrence and survival rates.

 The start of RT is time sensitive, yet many factors can affect whether a patient experiences a delayed start. If she has positive nodes, she may be more likely to have longer discussions about treatment options because more options are available.

 As breast cancer treatment requires extensive coordination of care across multiple providers and specialties, patients may experience difficulties navigating the healthcare system.

 Typical treatment starts with surgery and lymph node assessment, followed by recovery time after surgery, subsequent RT to the whole breast for 5 to 6 weeks, and a 1-week RT boost to the tumor bed.

 Positive lymph nodes are an indication for chemotherapy to be part of the treatment.

There is conflicting evidence regarding the optimal timing of RT following BCS.

 One study suggests that RT delay does not negatively affect breast cancer—related health outcomes,

 A systematic review concluded that RT delay was associated with higher rates of local recurrence.

 Delay also may be associated with psychological distress and anxiety, which can negatively impact the care process and outcomes.

This study assesses patient-level predictors of delayed RT in an insured population of older women with early-stage breast cancer treated in integrated healthcare delivery systems, a group that has not been studied for timeliness in receiving RT.

This study is nested in the Breast Cancer Treatment Effectiveness in Older Women Study cohort.

 We included 541 women age ≥65 years diagnosed with unilateral, early-stage breast cancer (American Joint Commission on Cancer stage I or II22) enrolled in 5 integrated delivery systems in the Cancer Research Network, a  collaborative group of research programs, enrolled populations, and data systems of 14 health systems nationwide.

 Women diagnosed from 1990 through 1994 were followed for 10 years. All subjects in this nested cohort received BCS with a full course of whole breast RT, but no chemotherapy. Subjects from the original cohort receiving BCS without RT (23%) or mastectomy (53%) were excluded.

 We restricted the sample to women not receiving chemotherapy to eliminate variations in RT timing related to chemotherapy sequencing. We used automated administrative databases, medical record review, and tumor registries to ascertain age, race/ethnicity, Charlson Comorbidity Index score,

 lymph node status, tumor size, histology, estrogen receptor (ER) or progesterone receptor (PR) status, and any use of tamoxifen. To calculate the Charlson Comorbidity Index score, information on comorbid conditions was collected from medical records for all healthcare services provided in the year before diagnosis. This index includes 18 conditions weighted to predict mortality. We excluded cancer diagnoses from the index and categorized the remaining score as 0, 1, or 2+. Because others have shown that an 8-week delay can impact health outcomes, we defined RT delay as starting RT more than 8 weeks following BCS, based on medical record data.

All research was conducted following approval from local institutional review boards and in accordance with the ethical standards of those boards.

We calculated the percentage of women with delayed RT and the percentage with timely RT by demographic and clinical characteristics. We used 

 tests and fit multivariable logistic regression models to identify factors associated with delayed RT, controlling for integrated delivery system site.

The sample included 541 women, of whom 76 (14%) had a delay of more than 8 weeks (

). Among women with RT delayed more than 8 weeks, the median delay was 14 weeks, and 21 subjects (4%) had delays of more than 12 weeks. More than two-thirds of the sample was younger than age 75 years, and few (3.3%) scored more than 1 on the Charlson Comorbidity Index. Almost 7% were ER−/PR−, and 13% of subjects had positive nodes (

), women of nonwhite race or Hispanic ethnicity had significantly higher odds of delayed RT (odds ratio [OR] = 3.3, 95% confidence interval [CI] = 1.7, 10). We had limited precision in general, as indicated by some wide CIs (eg, tumor size >2 cm [OR = 1.9, 95% CI = 0.9, 4.1] and ER−/PR− status [OR = 1.8, 95% CI = 0.7, 4.9]).

In this fully insured population of older women with breast cancer who had access to integrated delivery systems, about 1 in 7 women experienced delayed RT following BCS, a rate similar to that reported in other studies.

 Surprisingly, in this population, the proportion with RT delays of more than 8 weeks was about the same as that found in a fee-for-service Medicare population (14% vs 16%, respectively).

 One might expect better coordinated, and therefore less delayed, care in integrated healthcare delivery systems, yet delays of more than 8 weeks may be due to healthcare access issues such as transportation problems, language concordance or communication issues, or limited patient knowledge about the importance of timely RT—all barriers that are not mediated by insurance status. Alternatively, delays may be caused by poor care coordination by clinicians and the healthcare system or patient-related barriers, concerns, or anxiety regarding decision making and treatment

The only predictor of delayed RT was nonwhite or Hispanic race/ethnicity, consistent with previous research.11,18 Indeed, delayed RT, which may represent uncoordinated care (among other possible factors), was not affected significantly by other included clinical or demographic factors. Whether these delays in turn affect mortality for patients in integrated delivery systems could not be shown because of limited statistical power.

All eligible women were included in our study population, and we had no loss to follow-up in the treatment period. This design advantage substantially limited the potential for selection bias. Another strength of this study is that all subjects in the cohort had equal access to complete treatment by having access to care in an integrated healthcare delivery system. In addition to sociodemographic information (beyond age), we had important information about the tumors themselves that helped explain treatment receipt and reduced confounding in our analyses. Nonetheless, we did not have information on characteristics such as patient educational attainment, income, transportation availability, radiation oncologist availability, language barriers, or prefer ences for care; doctor-patient communication; or healthcare system programs such as care coordination—all of which may be greater barriers to or facilitators of care for older women diagnosed with breast cancer.

 These characteristics may directly or indirectly influence care-seeking behavior and care receipt among nonwhite and Hispanic women, who are more likely to be of lower socioeconomic status.

 All subjects had Medicare coverage in these healthcare systems with the same benefits for RT and Medicare paying for RT in inpatient, outpatient, and freestanding clinics. Outpatient RT requires a set copayment, which was usually lower than fee-for-service copayments, thereby reducing barriers to care even more. It is unlikely that coverage levels affected timeliness of RT.

We have shown that access to healthcare, as reflected by a patient’s membership in an integrated healthcare delivery system, does not in itself ensure timely therapy. Rates of RT delay were similar between integrated delivery systems and the fee-for-service sector11 and appear to be affected by race/ethnicity. Healthcare delivery systems and providers should consider targeting care coordination efforts to traditionally underserved patients to improve timeliness of RT.

: From the Department of Public Health (HTG), Weill Cornell Medical College, New York, NY; the Department of Geriatrics (SST, RAS) and the Department of Epidemiology (TLL), Boston University Medical Center, Boston, MA; Group Health Research Institute (DSMB), Group Health Cooperative, Seattle, WA; Meyers Primary Care Institute (TSF), Worcester, MA; HealthPartners Research Foundation (FW), Bloomington, MN; Department of Epidemiology & Public Health (MUY), Yale University, New Haven, CT; Henry Ford Health System (MUY), Detroit, MI; Research & Evaluation (VPQ), Kaiser Permanente Southern California, Pasadena, CA; and the Department of Social Services and Health Policy (AMG), Wake Forest University, Winston-Salem, NC.

: Funded by the National Cancer Institute (R01 CA093772; Dr Silliman, principal investigator) and in part by the American Cancer Society (MRSGT-04-002-01-CPHPS; Dr Gold, principal investigator).

: The authors (HTG, SST, DSMB, TSF, FW, MUY, TLL, VPQ, AMG, RAS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (HTG, SST, DSMB, TSF, FW, MUY, TLL, RAS); acquisition of data (SST, DSMB, TSF, FW, MUY, VPQ, AMG, RAS); analysis and interpretation of data (HTG, SST, DSMB, TLL, VPQ, AMG, RAS); drafting of the manuscript (HTG, DSMB, VPQ); critical revision of the manuscript for important intellectual content (HTG, SST, DSMB, TSF, FW, MUY, TLL, AMG, RAS); statistical analysis (HTG, SST); provision of study materials or patients (DSMB, MUY, VPQ, RAS); obtaining funding (HTG, MUY, VPQ, AMG, RAS); administrative, technical, or logistic support (DSMB, RAS); and supervision (DSMB, FW).

: Heather Taffet Gold, PhD, Department of Public Health, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065-6304. E-mail: heg2001@med.cornell.edu.

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. The effect of less than definitive care on breast carcinoma recurrence and mortality. 

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ONCOLOGY FOCUS

Among older women in 5 integrated delivery systems, 14% experienced delayed radiotherapy for breast cancer, even though they had insurance and access to care.


Delayed Radiotherapy for Breast Cancer Patients in Integrated Delivery Systems

 To help health professionals understand and evaluate the concept of efficiency and its measurement in practice.

We reviewed conceptual and practical analyses of healthcare efficiency and its measurement and describe our findings.

We searched the following 3 sources: the MEDLINE and EconLit databases for articles published from 1990 to 2008 using the keywords efficiency, inefficiency, productivity, and economic profiling; seminal economic studies of efficiency identified in MEDLINE or EconLit or in economics reference materials; and the “gray literature” on efficiency measures developed by private organizations.

An essential element of efficiency is that healthcare outputs are as large as possible given their inputs. An efficiency measure specifies outputs and inputs appropriate to the goals of the assessment. Participants in the healthcare system have differing perspectives about the goals of efficiency assessments, relevant outputs and inputs, and validity of measures.

The broad meaning and the value of healthcare efficiency seem uncontroversial, yet any particular application may be confronted with conflicting perspectives and with practical challenges.

Efficiency measures are being used with the goal of improving the use of healthcare resources.

There are important differences in perspective about the meaning of efficiency and the appropriateness of measures.

The discipline of economics offers a practical framework for defining and measuring efficiency, especially among healthcare providers.

In implementing and assessing efficiency measures, the perspective of the evaluator must be understood, and healthcare inputs and outputs must be identified.

If there is substantial variation in quality in a particular context, a measure that does not account for quality could lead to unintended consequences.

Consider a physician whose medical group contracts with several managed care health plans. These plans have asked the group to produce standard measures of clinical quality, and the physician and her colleagues have performed well, even earning bonus payments. But now the physician learns that her practice is also to be evaluated for its “efficiency.” A health plan intends to assess physicians for efficiency in treating acute back pain to tier payments. In addition, the chief executive of the physician’s medical group has announced a program to monitor efficiency within the group. Our physician is left to wonder “Will these assessments accurately reflect meaningful aspects of performance?”

Situations like these are common, and the objective of this research brief is to help health professionals understand and evaluate the concept of efficiency and its measurement in practice. In particular, we describe the potential usefulness of efficiency measures for managerial decision making, emphasize the importance of perspective in defining and evaluating measures, and explore some practical considerations for measurement.

A definition of efficiency and some potential measures

We extensively reviewed efficiency concepts and existing healthcare measures based on searches of the following 3 sources: (1) the ME DLINE and EconLit databases for articles published from 1990 to 2008 using the keywords efficiency, inefficiency, productivity, and economic profiling; (2) seminal economic studies of efficiency identified in MEDLINE or EconLit or in economics reference materials; and (3) the “gray literature” on efficiency measures developed by private organizations. This literature review and our analytic framework are described in an evidence report for the Agency for Healthcare Research and Quality

; a companion article systematically reviews the efficiency measures that we found.

Stakeholders in US healthcare are increasingly seeking to measure and improve efficiency. This article describes an efficiency concept and some practical measurement challenges.

November 2009

Prescriber Compliance With Black Box Warnings in Older Adult Patients

Cost-Sharing and Adherence to Antihypertensives for Low and High Adherers

Delayed Radiotherapy for Breast Cancer Patients in Integrated Delivery Systems

Efficiency and Its Measurement: What Practitioners Need to Know

The Personal Financial Burden of Cancer for the Working-Aged Population

A Payer's Perspective on the Cost Effectiveness of Gastric Bypass Surgery

Effectiveness of Care Coordination and Health Counseling in Advancing Illness

Randomized Trial of an Electronic Asthma Monitoring System Among New York City Children

Cancer Care Disparities: Research Regarding Timeliness and Potential Coordination

Correlates of Prostate-Specific Antigen Testing in a Large Multiethnic Cohort