Tracy A. Lieu, MD, MPH

A 21-gene test that predicts recurrence risk among women with hormone receptor positive (HR+), localized breast cancer was nationally recommended in 2007, but we know little about its subsequent impact. We evaluated: a) patient characteristics associated with test use, b) correlations between Recurrence Score (RS) and chemotherapy, and c) whether test introduction was associated with a reduction in chemotherapy use.

The Kaiser Permanente Northern California tumor registry and electronic health records from 2005 to 2012 were used to identify HR+, human epidermal growth factor receptor 2 negative, node-negative cancers. Analyses used logistic regression with propensity score matching and 2-level logistic regression.

Of the 7004 patients who met guidelines for testing, 22% were tested and 26% had chemotherapy. Test use was more likely in younger women (for ages 40-49 years vs 50-64 years: odds ratio [OR], 1.22; 95% CI, 1.04-1.44), in women with tumors sized 1.0 to 2.0 cm versus >2 cm (OR, 1.20; 95% CI, 1.03-1.40), and in women from higher-income neighborhoods (for each $10,000 increase in area median income: OR, 1.05; 95% CI, 1.03-1.07). Among patients with low RS, 8% had chemotherapy versus 72% among patients with high RS (

 <.01). In propensity score-matched analyses, testing was associated with an absolute reduction of 6.2% in the proportion of women receiving chemotherapy (95% CI, 2.9%-9.5%); the 2-level model showed a similar but nonsignificant (

The 21-gene test is used in a minority of eligible patients in this integrated plan. Its use appears to be associated with a modest decrease in overall chemotherapy use.

	In this retrospective cohort study, we evaluated the use of a multigene test that predicts recurrence risk in localized breast cancer, and associated trends in chemotherapy use in eligible patients.

		The multigene test was used in fewer than 1 in 3 eligible patients, with use plateauing in 2010 through 2012.

		Recurrence risk scores on the test were highly, but not perfectly, correlated with the use of chemotherapy. 

		Introduction of the test was associated with a modest decrease in overall chemotherapy use.

Genetic testing of tumor cells has the potential to revolutionize the care of patients with breast cancer and to accelerate the benefits of personalized medicine.

 Several studies have observed that these tests have been incorporated into clinical practice and seem to influence chemotherapy decisions.

 Recent US studies of claims data and the National Cancer Institute’s Surveillance Epidemiology and End Results (SEER) data found that only 20% to 30% of eligible women were being tested, with the claims data study observing that reimbursement by insurers has increased slowly.

 It is unknown whether acceptance and use of this test have been more complete in integrated, fully capitated systems where the costs of care are covered and decisions are less likely to be affected by financial incentives for or against test or chemotherapy use. These tests are potentially costly, and may be marketed directly to patients, as well as physicians.

 As genetic testing in cancer grows more common, healthcare systems need to systematically evaluate how consistently such tests are being used, and how much incremental benefit they add to baseline clinical practices.

Breast cancer genetic testing provides a useful paradigm for evaluating the impact of such tests at a population level. Decisions about the use of adjuvant chemotherapy, for women with early-stage breast tumors that are estrogen or progesterone receptor positive (ER/PR+), can be especially difficult, as most will never experience a recurrence, even without adjuvant chemotherapy. Currently, several gene-expression profiling tests are being marketed to clinicians and patients as tools to enhance the accuracy of predicting recurrence risk and the likelihood of realizing benefit from adjuvant chemotherapy. The 21-gene Oncotype DX (Genomic Health, Inc, Redwood City, California) breast cancer assay has been validated in clinical trials to predict risk of distant recurrence in patients with early-stage, node-negative, ER/PR+, human epidermal growth factor receptor 2 negative (HER2—) cancers.

 Guidelines for incorporating Oncotype DX testing into treatment decisions were published in 2007

; however, little is known about how this test and other genomic tests are being incorporated into real-world oncology practice and how they affect patterns of care.

This study’s overall objective was to assess how the 21-gene test is being used among patients with early-stage breast cancer in a large integrated health delivery system. Among patients who met current guidelines for use of this test, our aims were to: a) compare the demographic and clinical characteristics of patients who had the test with those who did not; b) describe chemotherapy use among women with test results that indicate low, intermediate, and high risk of recurrence; and c) evaluate whether the introduction of the test was associated with a change in chemotherapy use.

Kaiser Permanente Northern California (KPNC) is a nonprofit integrated healthcare delivery system that currently provides care to more than 3.9 million members. Within KPNC, essentially all primary and specialty care, and the vast majority of emergency and hospital care, is delivered by providers working within a single care system for patients of a single health plan.

Identification of Breast Cancers and Cancer Characteristics

The KPNC tumor registry—a contributor to the SEER program of cancer registries&mdash;was used to identify all female KPNC members diagnosed with invasive, nonmetastatic, incident breast cancer between September 1, 2005 (when significant use of Oncotype DX began at KPNC), and June 30, 2012. A member’s first, newly diagnosed breast cancer during this period was included. The tumor registry includes patient age, sex, diagnosis date, tumor size, node involvement, ER/PR status, stage, and initial chemotherapy treatment. HER2 status was determined using the results of Immunohistochemistry and Fluorescence In Situ Hybridization tests.

In 2007, both the National Comprehensive Cancer Network (NCCN) and the American Society of Clinical Oncology included Oncotype DX testing in their guidelines.

 Following NCCN guidelines, we selected women for whom Oncotype DX was to be considered: those with ER/PR+, HER2—, stage I and stage II breast cancers having primary tumors ≥0.51 cm with either no lymph node involvement or only ≤2 mm axillary node micro-metastases.

 The Oncotype DX assay analyzes the expression of 21 genes to provide a Recurrence Score (RS) corresponding to the risk of distant recurrence at 10 years among tamoxifen-treated patients not treated with chemotherapy.

 The RS is classified into 3 categories based on likelihood of distant recurrence: low risk (RS <18), intermediate risk (RS 18-30), and high risk (RS ≥31). Low RS has been shown to predict little benefit from chemotherapy, whereas high RS predicts greater benefit.

 The routine approach in this medical group was to follow NCCN recommendations; thus, other genetic tests for breast cancer were not commonly used.

Due to the timing of SEER registry reporting requirements, there is some underascertainment of the chemotherapy treatment status in the tumor registry. As part of an ongoing prospective study of newly diagnosed cases of breast cancer,

 a subset of cases were reviewed to validate the chemotherapy treatment status. Of 7004 eligible cancers, 62% were reviewed. Of 1071 patients validated to have used chemotherapy, 86.18% were correctly classified as such in the registry, and of 3240 patients validated not to have used chemotherapy, 99.97% were correctly classified in the registry. The chemotherapy status used was the validated one, if it existed; otherwise, the registry status was used.

Patients were assigned to a census block group (defined by the US 2010 Census) based on their home address at the time of cancer diagnosis. Block group income and education were based on the 2006 to 2010 American Community Survey.

 We used data from the year before the cancer diagnosis to create a modified Deyo version of the Charlson comorbidity index.

 From administrative databases, we extracted (from the year before the cancer diagnosis) the following additional variables for each patient for use in the propensity score matching: primary medical center used for care, number of clinic visits and hospital days, and their associated cost.

To identify the predictors of receiving 21-gene testing, we used logistic regression, in which receipt of the test was the dependent variable. Independent variables were calendar year of cancer diagnosis (as a categorical variable), age group (5 categories: aged <40, 40 to <50, 50 to <65, 65 to <75, and ≥75 years), race/ethnicity (Asian, black, white Hispanic, white non-Hispanic, and other/unknown), tumor size (3 categories: 0.5 cm to ≤1.0 cm, >1.0 cm to ≤2.0 cm, and >2.0 cm), comorbidity (3 categories: 0, 1-2, and ≥3 comorbidities), census block group median income, and proportion of adults in the block group with less than a high school degree.

To assess the potential impact of 21-gene testing on receipt of chemotherapy, we used 2 different analytic approaches. The first approach directly evaluated whether women who received the test were more or less likely to receive chemotherapy compared with women who were not tested. We ran a logistic regression in which the dependent variable was whether the woman was tested, and the independent variables were the same as those listed above, plus the following (to increase further the similarity of the matched cohorts): primary medical center for care, patient’s age-squared (for potential nonlinear age effects), costs of clinic and hospital services, as well as the number of clinic visits and hospital days in the year before cancer diagnosis. Model calibration and discrimination were good (C statistic = 0.81; Hosmer-Lemeshow goodness of fit test: 

= .32, with nonsignificance reflecting adequate fit). The resulting predicted probability of receiving the test was the patient’s propensity score.

We selected patients who received the 21-gene test and matched them 1-to-1 to patients who were not tested. Matching was performed using the Mahalanobis metric matching within calipers, defined as one-fourth of the standard deviation of the logit of the propensity score.

 Using this methodology, 93% (n = 1462) of tested women were matched to a nontested woman, and after matching, there were no significant differences between the cohorts with regard to the variables used in the propensity score calculation. However, women in the matched cohorts were younger and had fewer comorbidities than the general pool of women from whom they were drawn. This reflects the fact that among the entire cohort of patients with cancer, tested women tended to be different from nontested women.

Using the propensity-matched samples, we calculated the percent of women in each sample receiving chemotherapy, and the corresponding ratio of the odds of receiving chemotherapy among women tested, to the odds of receiving chemotherapy among matched women not tested. As a sensitivity analysis, the matched analysis was repeated among the subset of patients with validated chemotherapy use.

The second analytical approach was to treat the overall percent of women who received testing as a predictive variable for receiving chemotherapy. This approach is similar to “ecological” regression, wherein aggregates are used either in place of, or in addition to, individual-level predictors.

 Unlike an interrupted time-series analysis in which differences before and after some change in practice are assessed, this approach does not require us to arbitrarily define time periods as “before” or “after” the introduction of the test, given that testing was phased in over time. We started with an analytic data set with 1 record per woman. For each calendar year and age group, we calculated the percent of women who received the test, and added this variable to the analytic data set. Thus, a woman diagnosed with cancer in 2011 who was aged 50 to <65 years, had a variable added to her record that reflected the percent of women in her age group in 2011 who were tested. Using these patient-level records, we ran a logistic regression in which receipt of chemotherapy was the dependent variable, and the independent variable of interest was the percent of women in the strata who were tested. The woman’s own test status is not included in the model. Other independent variables included in the model were the same as those used in the model identifying predictors of testing. The results of this model were then used to predict the percent of women receiving chemotherapy, assuming 1 of 2 scenarios: 1) 0% of women in her age group and year were tested, and 2) 30% were tested (which was about the maximum percent of women who received the 21-gene test in any given year).

The first approach has been called an “individual-level analysis” and the second approach a “2-level analysis.”

 Each of these approaches has distinct advantages and disadvantages. The individual-level analysis has the advantage of directly measuring the relationship between the individual’s use of the test and their use of chemotherapy, but does not account for certain aspects associated with self-selection of testing. For example, women with a strong predilection for or against chemotherapy may choose not to be tested—a problem similar to “confounding by indication.” The 2-level “ecologic” analysis factors out some of the potential unmeasured confounders related to which women choose to be tested.

 However, that approach is prone to bias if there are other unmeasured factors that may have increased or decreased use of chemotherapy at the same time as the increase in the use of testing.

We identified 7004 women diagnosed with cancers meeting our inclusion criteria, who met guidelines for 21-gene testing (

). The majority of women were under 65 years of age (57%) and 71% were non-Hispanic white. Three-fourths of cancers were stage I, and 70% of tumors were >1.0 cm in size. Overall, 21-gene testing was performed for 22% of women. In adjusted analyses, compared with women aged 50 to <65 years, women aged 40 to <50 years were more likely to be tested (OR, 1.22; 95% CI, 1.04-1.44), whereas women aged 65 to <75 years and ≥75 years were less likely to be tested (OR, 0.42; 95% CI, 0.36 to 0.49; and OR, 0.04; 95% CI, 0.02-0.06, respectively) (

). Compared with women with tumors >2.0 cm, women with tumors 0.5 cm to ≤1.0 cm were less likely to be tested (OR, 0.51; 95% CI, 0.42-0.61), whereas women with tumors >1.0 to ≤2.0 cm were more likely to be tested (OR, 1.20; 95% CI, 1.03-1.40). Each $10,000 increase in block group median income was associated with increased odds of testing (OR, 1.05; 95% CI, 1.03-1.07).

Among women with 21-gene testing, 52%, 39%, and 9% had low-risk, intermediate-risk, and high-risk RS values, respectively (

). Among women who had the test, a slightly higher percentage (26%) received chemotherapy compared with those who did not (22%; 

 <.01). Among women with low-, intermediate-, and high-risk RS, 8%, 40%, and 72%, respectively, received chemotherapy.

Between 2005 and 2012, the percent of eligible women receiving 21-gene testing rose from 8% to more than 25%, while the percent of women receiving chemotherapy decreased modestly from 26% to 22% (

). Younger women were much more likely to receive chemotherapy, and the 2 age groups with the most women receiving chemotherapy had the most pronounced downward trends in chemotherapy use from 2005 to 2012 (from 59% to 47% for those aged 40 to <50 years, and from 35% to 24% for those aged 50 to <65 years) (

In analyses with individual-level propensity score matching (n = 2924), receipt of the 21-gene testing was associated with decreased odds of chemotherapy (OR, 0.74; 95% CI, 0.63-0.87), corresponding to a reduction in the percent of women receiving chemotherapy from 32.7% to 26.5%, or an absolute reduction of 6.2% (95% CI, 2.9%-9.5%). When including only women with validated chemotherapy treatment status, 21-gene testing was associated with lower odds of chemotherapy (OR, 0.64; 95% CI, 0.53-0.78) than in the primary analysis, corresponding to an absolute reduction of 9.5% (95% CI, 5.3%-13.6%). Among this matched cohort, women who had received Oncotype DX testing and went on to receive chemotherapy were older, and were more likely to have stage I cancer and smaller tumors, than women who had not received Oncotype DX testing and went on to receive chemotherapy.

In the 2-level multivariable (ecological) analysis, each 10% increase in the absolute percent of women tested was associated with a 0.92 decreased odds of chemotherapy, but this result was not statistically significant (95% CI, 0.83-1.03; 

 = .14). The point estimates of this model imply that 26% of women in the study would have received chemotherapy in the absence of any testing, while 23% would receive chemotherapy if 30% of women were tested.

This is among the first large investigations of the use and impact of the 21-gene test for breast cancer recurrence risk in a managed care population. In this integrated system, we found that although rates of use increased over time, only 20% to 25% of patients meeting guidelines received testing. In adjusted analyses, use of testing was differential by age, tumor size, and neighborhood-level income. Patterns of chemotherapy use were generally consistent with test results, with those having a low RS far less likely to have chemotherapy than those with a high RS. When used, the test was associated with a modest reduction in overall chemotherapy use.

The rates of testing we observed were similar to other reports from US populations,

 including those of a large, for-profit oncology network.

 One difference from past studies is that we found no racial/ethnic differences in use of the test. For example, Guth et al found that women treated at municipal hospitals—who were more likely to be of low income and nonwhite&mdash;were less likely to have the 21-gene test than socioeconomically similar women seen in tertiary care settings (3% vs 30%).

 The fact that the test was fully covered by insurance in our setting removed patient-level financial barriers and may have mitigated racial/ethnic variation in test use. However, patients living in higher-income areas were more likely to have the test than those living in lower-income areas.

The other clinical and demographic correlates of nonuse of 21-gene testing that we identified, such as smaller tumor size and older age, have also been observed in other settings.

 For these subgroups, clinicians and/or their patients may have decided that chemotherapy was not indicated, so that testing would not change treatment decisions. Clinicians may also feel that standard clinicopathologic prognostic factors, existing decision tools (eg, Adjuvant!),

 and/or a patient’s health status or preferences are more important in treatment decision making than 21-gene test results.

 However, it has been reported that, when obtained, the 21-gene results can change pre-testing treatment decisions in about 30% to 50% of cases.

 A survey of KPNC oncologists, completed in spring 2015, suggests that among those patients who have the 21-gene test, the results cause changes in chemotherapy decisions in approximately 40%—and that these changes are equally divided among changes away from having chemotherapy and changes toward having chemotherapy (Lieu et al [unpublished data from a survey of 85 KPNC oncologists via mail and e-mail, as part of the overall project that produced this paper]).

This 21-gene test and other multigene tests have been promoted as likely to be cost-effective since test costs are expected to be offset by decreases in chemotherapy use among women with low recurrence risk, and, hence, low predicted benefits of chemotherapy.

 In our matched analysis, we found that testing was associated with a 6% to 10% reduction in chemotherapy use. A recent meta-analysis estimated a somewhat higher percent reduction of 12%.

 Our “ecologic” analysis indicated that an increase in testing in the KPNC setting, from 0% to 30%, resulted in approximately a 3% absolute decrease in the percent of women getting chemotherapy. The 2-level analysis is a more indirect (and conservative) approach, with fewer observations due to the summarized nature of the analysis, and, therefore, has less power. Nevertheless, the direction of the result was the same as that of the propensity score-matched analysis. Regardless of approach, our estimated reductions in chemotherapy were far lower than the 31% reduction in chemotherapy observed in a smaller study of a younger cohort in Ireland.

 The lower reduction in chemotherapy we observed relative to other studies was most likely due to our patient population being older and having a lower baseline percent of women receiving chemotherapy, as well as a lower percentage of eligible women being tested, compared with the study from Ireland.

Our results should not be construed as suggesting that the 21-gene test is being underused, as the study only evaluated chemotherapy in relation to current levels of testing. Our results cannot be extrapolated to project whether, or by how much, chemotherapy use would decrease if the test were used for a higher percentage of patients. The current level of testing may reflect clinicians’ judgments that the nontested patients would not benefit from testing because the decision about chemotherapy is already clear in their cases.

Chemotherapy treatment decisions in our study were not always in accord with the RS. Among women with low RS, 8% received chemotherapy anyway, and 28% of those with high RS did not receive chemotherapy. Similar discordant use patterns were noted in a recent meta-analysis.

 Our data did not enable us to determine the reasons for these conflicting choices; however, based on other reports,

 it seems that discordant use patterns are not unexpected since multigene testing is only 1 factor in complex chemotherapy decisions, and test results are not an absolute mandate for or against chemotherapy. For instance, doctors may sometimes use the test to discourage a low-risk (based on tumor size, histology, grade) or unhealthy patient who wants chemotherapy from having it, or to encourage treatment in a high-risk, healthy patient who does not want it. In the latter situation, a patient may continue to refuse chemotherapy even after receiving a high-risk test result. Genomic testing may increase anxiety and impair decision making or results may be poorly understood.

This study has many strengths, including its fully enumerated managed care population, inclusion of only those cases with clinical indications for 21-gene testing, and ability to relate test results to chemotherapy use. Although these findings from an integrated health plan population in California may not be representative of all practice settings, the testing and treatment patterns we found were remarkably similar to those reported from other settings.

 This suggests that clinical norms, randomized controlled trial evidence, and national recommendations may be more important to physicians’ ordering behavior than the costs of the test or who is covering those costs. That chemotherapy is sometimes discordant from therapy suggested by the RS indicates that patient factors may be as, or more important than the healthcare structure. These hypotheses will need to be tested explicitly in future research across diverse healthcare systems and populations.

Overall, this study suggests that in a large integrated healthcare system, the 21-gene test is used in a minority of eligible patients, but when used, it is leading to clinically appropriate patterns of chemotherapy use. Optimizing the benefit and efficiency of this and other genomic tests for cancer patients will require additional research on the factors that drive test use and subsequent decisions about chemotherapy. 

Articles

A multigene test for breast cancer recurrence risk was used in a minority of eligible patients, yet was associated with a decrease in chemotherapy use.

Breast Cancer Multigene Testing Trends and Impact on Chemotherapy Use

To examine how enrollment in highdeductible health plans (HDHPs) affects use of well-child visits relative to traditional plans, when preventive care is exempt from the deductible.

We selected children aged <18 years enrolled in a large Massachusetts health plan through employers offering only 1 type of plan. Children were in traditional plans for a 12-month baseline period between 2001 and 2004, then were either switched by a decision of the parent’s employer to an HDHP or kept in the traditional plan (controls) for a 12-month follow-up period. Preventive and other office visits were exempt from the deductible and subject to copayments, as in traditional plans. The primary outcome was whether the child received well-child visits recommended for the 12-month period. Using generalized linear mixed models, we compared the change in receipt of recommended well-child visits between baseline and follow-up for the HDHP group relative to controls.

We identified 1598 children who were switched to HDHPs and 10,093 controls. Between baseline and follow-up, the mean proportion of recommended well-child visits received by HDHP children decreased slightly from 0.846 to 0.841, and from 0.861 to 0.855 for controls. In adjusted models, there was no significant difference in the change in probability that recommended well-child visits were received by HDHP children compared with controls (P = .69).

Receipt of recommended well-child visits did not change for children switching from traditional plans to HDHPs that exempt preventive care from the deductible.

There is concern that increased costs in high-deductible health plans and confusion over which services are subject to the deductible will cause enrollees to underuse important services such as pediatric preventive care.

Receipt of recommended well-child visits did not significantly change for children switching to high-deductible plans compared with those remaining in traditional plans when preventive care was exempt from the deductible.

Use of well-child visits appears to be preserved in high-deductible plans when preventive care is exempt from the deductible.

Rising healthcare costs have led to greater cost-sharing requirements in health insurance plans. Among the newer cost-sharing strategies are high-deductible health plans (HDHPs). Such plans have lower premiums than traditional plans and attempt to control costs by requiring that enrollees assume more responsibility for healthcare costs through annual deductibles that often exceed $1000 per individual and $2000 per family. Adoption of  HDHPs has risen among employers and employees, including those with children.

 In 2007, 18% of privately insured children were enrolled in HDHPs.

Prevalence of HDHPs is likely to rise, as such plans have been part of health reform proposals to make lower-cost coverage available. Proponents of HDHPs believe that increased responsibility for healthcare costs will lead enrollees to reduce unnecessary care, use more cost-effective services, and increase preventive care use.

 Others worry that out-of-pocket costs in HDHPs may lead to underuse of needed care. More than 20 years ago, the RAND Health Insurance Experiment showed that adults and children used fewer services of all types, including preventive care, with increased cost-sharing and deductibles.

 Some recent data in adults show that use of preventive services in HDHPs is similar to use in traditional plans.

 However, other data suggest a reduction in preventive services such as screening colonoscopy.

 Current research on the effects of HDHPs on children’s use of healthcare is limited, although the response to cost-sharing may differ between children and adults.

Many HDHPs exempt pediatric preventive services such as well-child visits and immunizations from the deductible, although anywhere from 8% to 53% of enrollees in HDHPs have plans where preventive care is subject to the deductible.

 Exempting important services such as preventive care from high levels of cost-sharing, a strategy known as value-based insurance design, has been suggested as a means of preserving their use in the presence of increased cost-sharing.

 However, the complexity of HDHPs may cause confusion for enrollees over which services are or are not subject to the deductible, making them more likely to forego services they mistakenly believe are subject to the deductible.

 Enrollees in HDHPs that exempt preventive visits from the deductible also may be dissuaded from seeking them because of unexpected deductible costs for laboratory or other services that arise during preventive visits.

Our study sought to examine whether children in HDHPs that exempt preventive services from the deductible were as likely to receive recommended well-child visits as children in traditional plans.

This study was designed as a pre—post comparison between groups, comparing children who were switched into HDHPs with those who were kept in traditional health plans. The study used health plan enrollment and claims data from Harvard Pilgrim Health Care, a nonprofit New England health plan with more than 1 million members who receive care in a variety of organizational settings. This study focused on employer-sponsored plans in Massachusetts.

In March 2002, Harvard Pilgrim began offering HDHPs with family deductibles ranging from $1000 to $4000 per year. The benefit structure was similar across HDHPs studied. Services subject to the deductible included emergency department visits, diagnostic tests, hospitalizations, and therapeutic procedures (eg, physical therapy). Office visits (including well-child visits) were subject to a $20 copayment and were excluded from the deductible (ie, families paid only the copayment and not the full cost, even if the deductible limit had not been reached). Prescription drugs also were exempt from the deductible and subject to copayments. Preventive services such as immunizations, routine hemoglobin and lead levels, and tuberculosis screening were covered at no cost. A Health Reimbursement Arrangement (an account for out-of-pocket healthcare expenses) was available but offered by few employers.

In contrast, traditional HMO plans in Harvard Pilgrim lacked deductibles and had copayments for emergency department visits and office visits (including well-child visits), full coverage for preventive care and diagnostic tests, and limited cost-sharing for hospitalizations. Office visits and prescription drugs had copayments that were similar to those in the HDHPs, with most office visit copayments between $10 and $20.

This study was approved by the institutional review board of Harvard Pilgrim Health Care.

As part of a larger study of families in HDHPs,

 we identified families with at least 1 child aged 18 years or younger who were enrolled together in the same health plan account in a traditional Harvard Pilgrim HMO for a 12-month baseline period anytime between April 2001 and June 2004, and then were switched to an HDHP for a follow-up period of at least 12 months. Families were excluded if (1) a family member was >65 years of age or (2) a family member did not have continuous enrollment through the same employer for the 24-month period. We identified a control group of families with at least 1 child aged 18 years or younger who remained in a traditional Harvard Pilgrim HMO plan for at least 24 months. To reduce selection effects, we limited our study to families who were insured through employers offering only 1 type of health plan; that is, they did not offer a choice between Harvard Pilgrim and other health plans, or a choice between different types of Harvard Pilgrim plans (89% of families in this population). In this way, the choice to switch to an HDHP (or to remain in a traditional HMO) was made at the employer level, not at the employee level. National data suggest that enrollees without a choice of plans constitute half of the population in HDHPs.

We selected all eligible families that were switched by the employer to an HDHP. For each HDHP family, we selected 8 control families whose employer continued to offer only a traditional HMO plan; control families were matched only on the basis of contemporaneous enrollment periods and were otherwise randomly selected. The date the family switched to an HDHP was assigned as the index date separating the baseline and follow-up periods. Controls were assigned the same index date as their matched HDHP family. Some control families were subsequently excluded if a family member no longer met the age or continuous enrollment criteria for the 24-month period around the assigned index date. Children younger than age 18 years on the index date were selected from this population for the study sample.

The primary outcome of interest was whether recommended well-child visits were received by a child in a 12-month period (baseline or follow-up) based on an algorithm adapted from other studies.

 Well-child visits were identified from claims data as having either (1) one of the following 

 codes: 99381, 99382, 99383-99385, 99391, 99392, 99393-99395, or 99432; or (2) one of the following 

International Classification of Diseases, Ninth Revision 

) codes: V20.2, V70.0, V70.3, V70.5, V70.6, V70.8, or V70.9.23 Based on the child’s age and recommendations of the American Academy of Pediatrics,

we determined the minimum number of well-child visits that should be received over the prior 12 months (

). In calculating the minimum number of visits recommended, we allowed a 1-month grace period for receiving visits through the 24-month visit.

 However, a child receiving a visit more than 1 month late could still end up receiving the recommended number of visits by the end of the 12-month period. For the yearly visits after the 24-month visit, we allowed the child to have a visit anytime over a 12-month period.

 As American Academy of Pediatrics recommendations do not include visits at age 7 or 9 years, we did not require a well-child visit for children aged 7.00 to 9.99 years. Based on the child’s age at the end of the baseline and follow-up periods, we calculated the number of well-child visits received and the number recommended for that 12-month period.

The primary predictor variable was whether the child was switched to an HDHP or remained in a traditional plan. Other covariates included clinical and neighborhood-level socioeconomic variables. We used geocoded addresses from enrollment files to obtain data on each family’s census block group from the 2000 United States Census. We defined a family’s neighborhood (census block group) as high poverty if 10% or more of residents had incomes below the federal poverty level,

 as low education if 25% or more of residents aged 25 years or older lacked a high school degree,

 and as predominantly black if 66% or more of residents were black.

Using Harvard Pilgrim data, the parent’s employer was categorized as small (<50 employees) or large (>50 employees). We used the Johns Hopkins Adjusted Clinical Group (ACG) System to measure morbidity for each child using age, sex, and 

 diagnosis codes from claims from the 12-month baseline period.

 This system assigns a morbidity weight standardized across a reference population of adults and children that is scaled around an average morbidity of 1.0, with higher scores indicating sicker patients.

 We identified chronic conditions during the baseline period using the Chronic Condition Checklist created by researchers at the Johns Hopkins School of Public Health. The Checklist uses 

codes from claims data to identify conditions expected to last more than 12 months and have a substantive impact on future health or functional status.

For bivariate analyses, we used χ2 tests to compare baseline child characteristics between the HDHP group and controls, and Fisher’s exact test in cases where frequencies were small. In unadjusted and adjusted analyses, we modeled the probability of receiving recommended well-child visits using logistic regression. Generalized linear mixed models were used to account for multiple recommended visits per child, for multiple children per family, and for subjects being measured in both baseline and follow-up periods. The model included study group (HDHP or control), study period (baseline or follow-up), and an interaction term between study group and study period. The interaction term reflects the difference between groups in the baseline—to–follow-up difference, or the difference in differences, and is the key element of interest. For adjusted analyses, covariates in the model were chosen a priori and included age; sex; number of children in the family; whether the child’s neighborhood was high poverty, low education, or predominantly black; morbidity weight; presence of a chronic condition; office visit copayment; and index year. To make the findings more interpretable, we used this model to calculate the predicted probabilities of receiving recommended well-child visits in the baseline and follow-up periods for children in the HDHP and control groups. To illustrate the range of predicted probabilities, we calculated predicted probabilities for children of different ages. Other covariates were held at their mean or modal values. We then used the model-derived predicted probabilities for the HDHP and control groups for the baseline and follow-up periods to calculate the difference between the HDHP and control groups in change from baseline to follow-up [(predicted probability HDHP

)]. If children were aged 7.00-9.99 years at the end of the baseline or follow-up periods, they were not included for that study period, as there is no recommended visit for 7- and 9-year-olds.

We identified 1598 children who were switched to HDHPs through 126 employers and 10,093 control children who remained in traditional plans through 945 employers. Employers ranged in size from fewer than 9 employees to more than 1000. 

 shows the characteristics of children in the HDHP and control groups in the baseline year when both groups were in traditional plans. Although few children lived in neighborhoods that were low education or predominantly black, children in the control group were significantly more likely than those in the HDHP group to be from such neighborhoods, a finding consistent with other data that HDHP enrollees are more likely to have higher education and less likely to be black.

 Children in the control group were also more likely to have above-average morbidity, but there was no significant difference in the percentage with a chronic condition. Children in the HDHP group were significantly more likely than control children to have parents who worked for small employers. There were no significant differences between the HDHP and control groups in other measured characteristics. In unadjusted analyses for children in the HDHP group, the mean proportion of recommended well-child visits received declined from 0.846 in the baseline year to 0.841 in the follow-up year (data not shown). For control children, the mean proportion of recommended well-child visits received declined from 0.861 to 0.855. Compared with controls, the change in proportion of recommended well-child visits received from baseline to follow-up for HDHP children was minor (only 0.001 higher) and not significant (P = .968 from the model without covariates).

In adjusted analyses, we did not see a significant change in the receipt of recommended well-child visits from baseline to follow-up for children switching to an HDHP compared with children staying in a traditional plan (P = .69 for the interaction term) (

). Of note, receipt of recommended well-child visits was significantly associated with younger age, living in a neighborhood that was not high poverty or low education, and having a chronic condition. Compared with changes from baseline to follow-up for controls, the predicted probability of receiving recommended well-child visits after switching to an HDHP was only slightly smaller (

). This finding was similar across age groups, ranging from 0.001 less for children younger than age 3 years to 0.008 less for children aged 13-18 years.

This study found that use of recommended well-child visits did not significantly change for children switching to an HDHP compared with those remaining in traditional plans. In these HDHPs, where well-child visits were not subject to the deductible, differences in receipt of recommended well-child visits compared with traditional plans were minimal.

Our study confirmed in a pediatric population the findings from studies in adult populations that show no change in preventive care use in HDHPs that exempt such services from the deductible.

 This is reassuring given other data indicating that preventive care and office visits may decrease in HDHPs even when these visits are exempt from the deductible, perhaps due to confusion about which services are subject to the deductible or attempts to avoid other associated costs where the deductible might apply.

 We do not know whether families in our study incurred unexpected deductible costs during well-child visits from services such as laboratory tests that were subject to the deductible. These indirect effects of deductible policies merit further research.

Our findings do not necessarily imply that concerns about underuse of services such as well-child visits are unfounded in those HDHPs that do not exempt preventive care from the deductible.

 Regulations for federally qualified HDHPs with tax-exempt health savings accounts specify that preventive services are permitted but not required to be exempt from the deductible.

 Up to 53% of employees in HDHPs have a deductible that applies to all services, including preventive care.

Data from the RAND Health Insurance Experiment  showed that increased cost-sharing decreases use of highly effective services such as preventive care as well as less effective services for both adults and children.

When cost-sharing is applied differentially so that specific important services have little or no cost-sharing, use of these services has been shown to be preserved.

 Our study supports this idea in that well-child visits were maintained in HDHPs when they were exempt from the deductible. However, our study was not able to examine use of well-child visits in HDHPs where they were subject to the deductible. Other studies have found that use of adult preventive services in HDHPs is maintained for services exempt from the deductible but is reduced for preventive services that are subject to the deductible.

The theory that HDHPs might increase use of preventive services does not seem to be supported in the case of well-child visits.

 Although those who choose to enroll in HDHPs may be more likely to be actively involved healthcare consumers who engage in healthy behaviors,

 we found no evidence to indicate that HDHPs foster this active involvement.

Because this study examined enrollees in a single health plan, our conclusions may not generalize to HDHPs offered by other insurers in other regions with different benefit policies. In particular, our study did not include HDHPs with health savings accounts. However, most HDHP enrollees nationally do not have such accounts to pay for out-of-pocket costs.

 In addition, the HDHPs we studied exempted not just preventive care but all office visits from the deductible; policies that exempt only specific preventive services from the deductible may have a different effect. Because health plan enrollment is not random, the lack of a randomized design may have biased our results. This bias was mitigated by use of a strong, quasiexperimental design and our deliberate focus on a population without member-level choice of health plan. However, employer-level selection effects may still exist.

 We did not have individual-level data on income, education, and race/ethnicity for our subjects. However, we were able to use census block group data as a proxy for family socioeconomic variables, which can provide reasonable estimations of individual-level socioeconomic measures.

 Based on these data, our population appeared to have relatively high socioeconomic status, as would be expected in a commercially insured population. Our results may not generalize to more socioeconomically vulnerable children in HDHPs for whom underuse of services due to increased cost sharing or misunderstanding about plan design may be more problematic.

Our results may allay fears that enrollment in HDHPs will lead to reductions in children’s use of important preventive services, at least in a situation where such services are exempt from the deductible. This may be reassuring to families, employers, insurers, and policy makers who seek lower-cost health insurance options, but it also suggests the need to carefully consider whether important preventive services are part of the deductible when designing and adopting HDHPs. Value-based insurance design, the idea of decreasing cost-sharing for high-value services rather than applying deductibles and cost-sharing broadly as blunt instruments, may have particular relevance for HDHPs.

 Because of the potential for confusion about nuanced benefit designs in HDHPs, plan descriptions and decision support systems will need to provide clear, readily available information about which services are subject to the deductible. Because of their lower cost, enrollment in HDHPs is likely to grow as part of state and national efforts to expand insurance coverage.

 Their design and effects on healthcare utilization should be carefully monitored.

In HDHPs where preventive care is exempt from the deductible, there does not appear to be a reduction in use of recommended well-child visits. Further research should assess the effects of HDHP enrollment on children’s use of important preventive and other services, especially when these services are subject to the deductible.

We would like to thank Christopher Forrest, MD, PhD, and Linda Dunbar, PhD, for making the Chronic Condition Checklist available and assisting with its use.

When preventive care was exempt from the deductible, well-child visits did not change for children switching from traditional plans to high-deductible plans.


Tracy A. Lieu, MD, MPH

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