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: To perform a meta-analysis of randomized controlled trials (RCTs) and systematic reviews evaluating the efficacy of self-monitoring of blood glucose (SMBG) levels among patients with diabetes mellitus (DM).

: Meta-analysis of RCTs among patients with DM not taking insulin comparing patients with SMBG versus those without SMBG and reporting results as change in glycosylated hemoglobin (A1C) values. 

: Prior systematic reviews and a PubMed search were used to identify studies. Data were extracted by trained physician reviewers working in duplicate. Trials were classified according to duration of the intervention, and random-effects meta-analysis was used to pool results. 

: Three trials of SMBG of 3 monthsâ€™ duration were too heterogeneous to pool. Nine other trials were identified. Five trials of SMBG of 6 monthsâ€™ duration yielded a pooled effect estimate of a decrease in mean A1C values of -0.21% (95% confidence interval [CI], -0.38% to -0.04%). Four trials that reported outcomes of 1 year or longer yielded a pooled effect estimate of a decrease in mean A1C values of -0.16% (95% CI, -0.38% to 0.05%). Three trials reported hypogly- cemic outcomes, which were increased in the patients using SMBG, although this mostly involved asymptomatic or mild episodes.

: At most, SMBG produces a statistically significant but clinically modest effect in controlling blood glucose levels in patients with DM not taking insulin. It is of questionable value in helping meet target values of glucose control.

Self-monitoring of blood glucose (SMBG) levels is proven effective at helping control glucose levels in patients with diabetes mellitus (DM) taking insulin. The usefulness of SMBG in patients with DM not taking insulin is unclear.

Our meta-analysis of 9 randomized controlled trials of SMBG found a statistically significant improvement in glycosylated hemoglobin outcomes at 6 months of -0.21%. Results at 3 months or 12 months were not significant.

At best, SMBG is an intervention of modest efficacy in patients with DM not taking insulin.

According to the World Health Organization,

 at least 180 million people worldwide have diabetes mellitus (DM). The National Diabetes Information Clearinghouse estimates that DM costs $132 billion in the United States every year.2 Given these estimates along with the projection that the worldwide incidence of DM will double in the next 20 years,

 research into better management of this chronic disease is important.

Glucose control along with blood pressure and lipid control is a main goal of DM management. In addition to pharmaceutical agents, an advocated method to achieve better glucose control is self-monitoring of blood glucose (SMBG) levels. For patients with DM taking insulin, SMBG has generally been accepted as necessary to help monitor the effect of insulin on daily blood glucose levels, with adjustments in dosage made in response to SMBG values. However, evidence supporting the use of SMBG for patients with DM not requiring insulin is not as clear. Self-monitoring of blood glucose levels is postulated to have a beneficial effect on glucose levels in patients not taking insulin by promoting dietary and lifestyle changes that a patient may make as a response to feedback provided by blood glucose results.

Prior trials and systematic reviews have reached conflicting results, with 6 trials

and 3 reviews11-13 reporting benefits and 3 trials

reporting no benefits. With an estimated cost of $466 million for Medicare alone, establishing the efficacy or lack of efficacy of SMBG represents an important goal for providers and payers interested in optimizing management of this condition. Because new larger and better-quality trials of SMBG continue to appear, we undertook a metaanalysis to provide the best current estimate of the efficacy of SMBG in patients with type 2 DM not taking insulin.

We first identified prior systematic reviews and meta-analyses on this topic. We identified 6 reviews.

We judged the search strategy and inclusion and exclusion criteria of the review by Welschen et al,

 which included studies published from 1996 to September 2004, to be comprehensive and acceptable as the basis to begin our review. We updated this review by searching PubMed from the end date of the prior search to July 2007. We searched PubMed for the following terms: randomized controlled trial AND diabetes mellitus, as well as type 2 AND blood glucose self-monitoring. In addition to our PubMed search and screening of references from prior reviews, we performed reference mining of retrieved articles and received articles from experts. During this process, another review

 became available, and we reference mined this review as well.

Data were independently abstracted by 2 general internists (AT, MR) trained in critical reading of the literature, with consensus resolution. The following data were abstracted from included trials: design; randomization and appropriateness; blinding and appropriateness; withdrawals and dropouts described; sample size enrolled and followed up; characteristics of the population, including percentage of women and race/ethnicity; mean, median, and range of ages; body mass index and duration of DM; reported comorbidities; sample size, intervention, and exposure data for each arm of the study (intervention and exposure data included components of the intervention, total number of visits, frequency of SMBG, number of days per week monitored, duration of treatment, and cotherapies); outcomes measured; intervals in which the outcomes were measured; and adverse events.

The mean (SD) A1C level was recorded by treatment arm for each reported follow-up point. For trials that reported a mean outcome but no standard deviation, we estimated the standard deviation by taking the weighted mean standard deviation across all other trials that reported standard deviations for the A1C level.

We synthesized data among the articles that were determined to be clinically eligible. Duration of follow-up and frequency of SMBG were reviewed across studies to see if they were comparable. 

Since the outcome of interest was the same across all trials, a mean difference was calculated for each time point that reported statistical data. The mean difference is the difference between the follow-up mean A1C level for the SMBG group and the follow-up mean A1C level for the control group. A negative mean difference indicates that the SMBG group has a lower mean A1C score than the control group. For our main analysis, we did not control for the baseline mean A1C level for each group (a difference of differences estimate) because there is evidence that this approach is susceptible to bias.

 We present results controlling for the baseline as a sensitivity analysis.

A pooled estimate was calculated by follow-up duration in the following categories: 3 to 6 months, 6 to 11 months, and 12 months or longer. The pooled estimate was calculated using the DerSimonian and Laird

 random-effects model. In addition, we calculated a pooled estimate stratified by highquality and low-quality trials.

 were performed to individually examine the effect of treatment frequency, quality score, and mean baseline A1C level on the mean difference. For trials with more than 1 follow-up duration, the long-term estimates were used.

Test of heterogeneity was performed using the 

values close to 100% represent very high degrees of heterogeneity. Publication bias was examined using Begg rank correlation

 All analyses were conducted in STATA 9 (Stata Corp LP, College Station, Texas).

In total, we examined 55 titles. Seventeen titles were identified from prior systematic reviews. The electronic update search identified 25 titles. An additional 12 titles were identified through reference mining. One was identified by a content expert (JEW). Of the titles identified through our electronic literature search, 14 were rejected as irrelevant to the project. This left 41 from all sources. Eleven articles were excluded at abstract review.

In total, we reviewed 30 articles. Initial screening of the articles resulted in 16 RCTs that measured the effect of SMBG with at least 3 months of follow-up. Seven were excluded, 1 because the trial presented duplicate data, 2 because they did not report A1C levels as an outcome, 1 because the trial included an unknown number of patients taking insulin, and the other 3 because the trials compared a control group of patients with SMBG versus an intervention group of patients with SMBG plus other components (

The 9 RCTs ranged in size from 29 to 988 subjects. All patients had type 2 DM, with mean durations of 3 to 13 years. All trials but 1 included only patients treated without insulin. 

The mean ages of patients ranged from 50 to 66 years. Almost all trials included counseling and education with SMBG in the intervention group, but other components of the intervention varied (

). All trials measured A1C level as an outcome; 5 trials assessed this at 6 months, and 4 trials assessed this at 1 year or later. Three other trials assessed this at 3 months but were too heterogeneous to pool. The quality of trials varied; most trials scored positively on less than half of the criteria on the Delphi list.

 (quality criteria are available in an online table [eAppendix Table; available at 

We grouped trials based on the duration of the intervention. The individual and pooled results are shown in 

 that reported A1C outcomes at 3 months. The trials reported variable results. We did not pool the results of these 3 trials because their results were too heterogeneous, with an 

 statistic of 67%. We identified 5 trials

that reported outcomes at 6 months. One trial

 reported a statistically significant improvement in A1C level, although a second trial 5 yielded a statistically significant result after adjusting for baseline differences. The random-effects pooled effect estimate of these 5 trials was a change in mean A1C values of -0.21% (95% confidence interval [CI], -0.38% to -0.04%). The 

 that reported outcomes at 1 year or longer. No study reported a statistically significant difference between groups in the mean A1C values, although one study 7 reported statistically significant benefits after adjusting for baseline differences in A1C values. The randomeffects pooled effect estimate of these 4 trials was a change in mean A1C values of -0.16% (95% CI, -0.38% to 0.05%). The 

We performed several additional analyses. We compared studies scoring 5 or more Delphi items positively (which we called high quality) with those scoring fewer than 5 items positively (low quality). The pooled results showed no statistically significant differences between high-quality and low-quality studies.

We repeated our primary analysis using as the outcome the difference in A1C levels between groups adjusted for baseline A1C levels (whether to do such adjusting in the results of an RCT is controversial). When analyzed this way, there was much greater heterogeneity between studies, with I2 statistics of 49% and 73% for studies with 6-month and 12-month outcomes, respectively. However, despite this, our primary pooled results were remarkably similar, with a modest effect on A1C levels at 6 months of -0.23% (95% CI, -0.48% to 0.02%) (compared with a pooled result of 0.21% in the main analysis) and a nonsignificant effect at 12 months of -0.26% (95% CI, -1.00% to 0.48%). In the difference of differences analysis, high-quality studies reported lower estimates of effect than low-quality studies, an observation seen in other conditions.

Meta-regression analysis on baseline A1C levels showed differential effectiveness (

 = .06), with higher baseline A1C levels being associated with lesser efficacy of SMBG. Each 1% increase in A1C level was associated with a 0.19% decrease in efficacy of SMBG. Therefore, indirect evidence suggests that SMBG results in a smaller percentage of A1C level change for patients with higher baseline A1C values.

We attempted to identify other components of the intervention or characteristics of the patients associated with greater effectiveness. The trials did not have sufficient dissimilarity in intervention components to permit a meta-regression analysis (Table 1). Almost all studies included SMBG and counseling and education, rendering an assessment of the effect of one without the other impossible, and other intervention components were too sparsely distributed to support meta-regression analysis. Meta-regression analysis using the quality assessment (as a continuous variable or dichotomous variable at a threshold value of 5) also did not demonstrate differences between results.

The funnel plot did not support the existence of publication bias. Neither Begg correlation rank nor Egger asymmetry test yielded evidence of publication bias.

The principal finding of our meta-analysis was a modest but statistically significant improvement in A1C level at 6 months in patients with type 2 DM not requiring insulin when SMBG and education were added to management. The 3-month trials showed variability in results, leaving little to conclude. Similar though slightly smaller reductions in A1C level were found when pooling the 12-month trials. Although the pooled effect estimate at 12 months had a 95% CI that just crossed the null value, the effect estimate was not statistically different from the pooled effect estimate at 6 months.

The efficacy of SMBG has been studied previously. In one such review published by Welschen et al,

 6 RCTs were evaluated and were found to show an overall statistically significant effect of SMBG in decreasing A1C levels by 0.39% (95% CI, -0.56% to -0.21%). All but 1 of the trials in the review were also included in our meta-analysis (the study

 excluded from our analysis compared SMBG with urine testing of glucose levels). In a systematic review published for Agency for Healthcare Research and Quality, Balk et al

 included 5 RCTs and some nonrandomized cohort studies and found results similar to our meta-analysis, specifically a trend toward statistically significant but clinically modest reductions in A1C levels. In contrast, a recent cost-effectiveness analysis estimated that SMBG in a mixed population of patients with type 2 DM resulted in improved quality of life and a cost-effectiveness ratio of less than $10,000 per quality- adjusted life-year.

 However, this analysis was based on data from an observational cohort and did not include more recent RCT data reporting limited effectiveness of SMBG in primary care populations.

Two additional factors suggest that SMBG may be of little importance as a glucose control intervention in patients with type 2 DM not receiving insulin. First, our meta-regression findings suggest that the efficacy of SMBG may be lower as the baseline A1C level is higher. This means that SMBG may be least effective for the patients who need it most. Second, in the study

 that most closely approximates the use of SMBG in a  community-based primary care practice, there was no effect found.

To help interpret the significance of the effect of SMBG on A1C levels demonstrated in our review, it needs to be put in the context of other interventions to control blood glucose levels. In an exhaustive review,

 investigators concluded that many oral DM medications as monotherapy (thiazolidinediones, second-generation sulfonylureas, metformin, and repaglinide) had reductions in A1C levels of about 1.0%. This is about 5 times the effect produced by SMBG in our pooled estimate. Lower A1C reductions have been seen for other classes of hypoglycemic agents (0.5%-0.8% for alpha-glucosidase inhibitors, 0.8% for dipeptidyl peptidase IV inhibitors, and 0.3%-0.45% for amylin analogues), but these are still greater than our findings for SMBG. More comparable may be DM education. Norris et al

 performed a meta-analysis on the effect of DM self-management education on A1C level and found an overall 0.26% reduction in A1C levels a few months after the intervention. Some of the trials included in the meta-analysis involved SMBG along with education, which renders the individual effect of either intervention difficult to separate. Still, SMBG is closer to DM self-management in terms of the effect size than it is to most pharmaceuticals.

Our study has several limitations. An important limitation common to systematic reviews is the quality of the original studies. The sensitivity analysis of our main result did not yield any suggestion that the quality of the trials influenced our findings in a significant way. Another limitation is the heterogeneity of the studies. While there were some differences in the populations being assessed, the most important heterogeneity in this meta-analysis was the differing intervention components added to SMBG and the difference in the recommendations for frequency of SMBG, provider interaction or algorithm to adjust medications, and intensity of education. In addition, because education and counseling were invariably included with SMBG in the various intervention groups, the effect of SMBG alone was impossible to distinguish. Although our literature search procedures were extensive and included all articles identified in prior reviews plus additional articles, publication bias is a limitation. Our formal tests for publication bias did not indicate the presence of possible publication bias, but such tests do not exclude the possibility that such bias exists.

In conclusion, our analysis showed a statistically significant but clinically modest overall reduction in A1C levels when using SMBG in patients with type 2 DM not taking insulin. The results of our meta-regression analysis and the findings by Farmer and colleagues

 further limit the likelihood that SMBG is a particularly useful intervention. Patients, providers, and health plans will need to look elsewhere for interventions that have the kind of effects needed to bring A1C levels down to target values.

: From the Division of Internal Medicine (AT, MR, BM, PGS), and the Division of Endocrinology, Diabetes and Metabolism (JEW), Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, and the RAND Corporation (MJS, AZ, PGS), Santa Monica, CA. 

: This work was funded in part by the Department of Veterans Affairs Health Services Research and Development Evidence Synthesis Activity Pilot Program Project ESP 05-226. The views expressed in this article are those of the authors and do not necessarily represent the views of the US Department of Veterans Affairs.

: The authors (AT, MR, BM, MJS, AZ, PGS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. Dr Weinreb has received lecture fees from Sanofi-Aventis and has attended meetings and conferences on behalf of Novartis.

: Concept and design (PGS); acquisition of data (AT, MR, BM, MJS, AZ); analysis and interpretation of data (AT, MR, JEW, MJS, AZ, PGS); drafting of the manuscript (AT, MR, BM, PGS); critical revision of the manuscript for important intellectual content (AT, JEW, PGS); statistical analysis (AT, MJS, AZ); obtaining funding (PGS); administrative, technical, or logistic support (BM); and supervision (PGS). 

: Ali Towfigh, MD, Division of Internal Medicine, Veterans Affairs Greater Los Angeles Healthcare System, 11301 Wilshire Blvd, Mailcode PACC, Los Angeles, CA 90073. E-mail: 

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REVIEW ARTICLE

A meta-analysis of 9 RCTs found little benefit in self-monitoring of blood glucose levels on A1C outcomes in patients with diabetes mellitus not taking insulin.

Self-monitoring of Blood Glucose Levels in Patients With Type 2 Diabetes Mellitus Not Taking Insulin: A Meta-analysis

: To examine the use of financial incentives related to performance on quality measures reported by oncologists and surgeons associated with a population-based cohort of patients with breast cancer in Los Angeles County, California, and to explore the physician and practice characteristics associated with the use of these incentives among breast cancer care providers. 

: Physician self-reported financial arrangements from a survey of 348 medical oncologists, radiation oncologists, and surgeons caring for patients with breast cancer in Los Angeles County (response rate, 76%). Physicians were asked whether they were subject to financial incentives for quality (ie, patient satisfaction surveys and adherence to practice guidelines). We examined the prevalence and correlates of incentives and  performed multivariate logistic regression analyses to assess predictors of incentives, controlling for other covariates. 

: Twenty percent of respondents reported incentives based on patient satisfaction, and 15% reported incentives based on guideline adherence. The use of incentives for quality in this cohort of oncologists and surgeons was modest and was primarily associated with staff- or group-model health maintenance organization (HMO) settings. In other settings, important predictors were partial physician ownership interest, large practice size, and capitation.

: Most cancer care providers in Los Angeles County outside of staff- or group-model HMOs are not subject to explicit financial incentives based on quality-of-care measures. Those who are, seem more likely to be associated with large practice settings. New approaches are needed to direct financial incentives for quality toward specialists outside of staff- or group-model HMOs if pay-for-performance programs are to succeed in influencing care.

The use of financial incentives based on quality-of-care measures among surgeons and medical and radiation oncologists caring for patients with breast cancer in Los Angeles County, California, was modest, with 20% reporting incentives based on patient satisfaction and 15% reporting incentives based on guideline adherence, similar to rates among primary care physicians a decade ago.

Most cancer care providers outside of staff- or group-model health maintenance organizations (HMOs) were not subject to financial incentives based on quality-ofcare measures.

New approaches to direct financial incentives for quality toward specialists outside of staff- or group-model HMOs are needed if pay-for-performance programs are to succeed.

Healthcare quality is of concern nationally, and healthcare structural arrangements have been evolving rapidly to respond to increasing financial pressures and demands to enhance quality. These changes have been shown to affect primary care delivery,

 yet little is known about how these new organizational and financial arrangements affect the delivery of specialty care, particularly in the context of cancer.

It has long been recognized that financial incentives can have a powerful influence on physician and patient behavior. Associations have been found between implicit financial incentives (ie, provider compensation type) and productivity,

 propensity to use particular treatment options such as breast-conserving surgery7 and growth factors,

 continuity, compliance, patient satisfaction,

 and the use of evidence-based medicine practices.

 Explicit incentives for quality (ie, direct financial incentives tied to performance on quality measures or &#8220;pay for performance&#8221;) have been viewed as a mechanism to stimulate improvements in healthcare quality and patient outcomes by better aligning financial incentives with the delivery of higher-quality care. Although the effect of explicit financial incentives on primary care physicians has been the subject of several studies,

 understanding of these financial arrangements among specialists remains limited. Even less is known about the financial incentives used among specialists who provide cancer care in today&#8217;s managed care&#8211;penetrated environment.

Breast cancer serves as an excellent condition to use as a model for the study of structural characteristics and how they influence quality of care in general and cancer care more specifically. Breast cancer is a complex expensive disease with multiple treatments delivered by multiple providers over time, making patients vulnerable to problems with quality such as poor continuity and coordination of care.

Practice guidelines for breast cancer care issued by the National Comprehensive Cancer Network have been in place since the 1990s. However, a 2002 review

 of the patterns-of-care literature found notable variations in axillary lymph node dissection, hormone receptor status documentation, radiation therapy after breast-conserving surgery, and the use of tamoxifen citrate. The National Initiative for Cancer Care Quality,

 an in-depth study of quality of care for patients with breast or colorectal cancer in 5 states across the United States from 1998 to 2002, revealed substantial opportunities for improvement. Adherence was less than 85% for half of the quality measures, and substantial variation was found across metropolitan areas. Significant proportions of eligible patients did not complete an indicated course of tamoxifen

 and had no documented discussion about postmastectomy reconstruction.

 conducted in 2000 in Los Angeles County, California, 60% of black women, 55% of Spanish-speaking Hispanic women, and 34% of white women did not receive desired help for symptoms. Concerns about financial incentives motivating overuse of certain cancer treatments such as growth factors have also been expressed.

 and evidence from previous interventions,

it is reasonable to expect that financial incentives for quality have the potential to influence physician behavior, aligning financial incentives with desired outcomes. At the time that we conducted the present study, we were unaware of any specific pay-for-performance systems in oncology, but with the substantial energy and enthusiasm mounting behind pay for performance as a way to motivate and reinforce quality in general medicine, as well as the rising concerns regarding quality in oncology, we set out to assess the state of uptake among oncologists and surgeons treating patients with breast cancer.

We developed and conducted a physician survey to describe the structure of breast cancer care in Los Angeles County

 and to evaluate the effect of structure on the quality of care that patients with breast cancer receive.

 In this article, we present findings regarding the prevalence and types of financial incentives tied to performance on quality measures reported by oncologists and surgeons associated with a population-based cohort of patients with breast cancer in Los Angeles County. To assess the determinants of financial incentives tied to performance on quality measures, we explore the physician and practice characteristics associated with the use of these incentives among breast cancer care providers. Based on the literature and cognitive interviews conducted to inform the development of the survey, we hypothesized that explicit incentives for quality, although available and potentially useful for motivating and reinforcing cancer quality assessment and improvement efforts, would be rarely used except for large group-model health maintenance organizations (HMOs) or large multispecialty medical group practices.

We conducted a cross-sectional study of the structure and organization of care associated with the physicians identified among a population-based sample of women with incident breast cancer participating in the Los Angeles Women&#8217;s Health Study, which included a population-based sample of women 50 years and older identified as having a new diagnosis of breast cancer in Los Angeles County in 2000 by the Los Angeles County Cancer Surveillance Program Rapid Case Ascertainment system. A computerassisted telephone interview (CATI) was administered to 1224 women who agreed to participate. The detailed CATI queried women about healthcare providers who fulfilled various roles in their care, including delivering, recommending, or discussing the possible use of breast cancer treatments. We targeted all Los Angeles County medical oncologists, radiation oncologists, and surgeons reported by their patients in the CATI.

We developed items to query physicians about financial reimbursement arrangement and incentives. These were based on the literature

and on cognitive interviews with cancer care specialists.

Physicians were asked to report the proportions of patients in their main practice setting who were primarily covered by each of the following: Medicare fee for service (FFS), Medicare managed care, MediCal FFS, MediCal managed care, private traditional FFS health insurance, private managed care health insurance, other health coverage, and no coverage (uninsured).

We asked physicians to indicate the proportions of clinical compensation that are paid to them in the following ways: salary that is fixed for the year or contract period; salary that may vary during the year or contract period according to factors such as productivity, performance review, and net income of the practice, FFS or discounted FFS, capitated reimbursements, and other.

Physicians were asked whether in the previous 12 months their pay was affected in any way as a result of scoring well on the following: (1) patient satisfaction surveys or (2) other specific measures of quality of care such as adherence to practice guidelines or protocols.

We accounted for the importance in Southern California of network-model managed care involvement. To do so, we derived new practice setting categories by dividing  single-specialty medical groups, multispecialty medical groups, and solo practices into high and low managed care, using as a cut point the median reported percentage (36%) of network-model managed care patients.

 gives results describing characteristics of the physician sample by specialty type and overall. Overall, 20% of respondents reported being subject to incentives for quality based on the results of patient satisfaction surveys and 15% based on the results of other quality measures such as adherence to practice guidelines. Twenty-two percent reported being subject to either, and 13% reported being subject to both types of incentives. These rates did not differ significantly by specialty type.

 gives the results of multivariate analyses. In the multivariate analyses, 2 sets of logistic regression analyses were performed for each of the 3 dependent variables, namely, incentives based on the results of (1) patient satisfaction surveys, (2) other quality measures such as adherence to practice guidelines, and (3) both incentive types. We predicted the use of financial incentives for quality as a function of ownership interest, practice setting type, practice size, FFS reimbursement type, and acceptance of capitation among all physicians surveyed.

In logistic regression analyses predicting incentives based on patient satisfaction (Table 3), group-model HMO practice type was far more likely to be associated with reported use of incentives for quality compared with solo practice type (OR, 33.20; 

 <.001), controlling for ownership interest, practice size, and reimbursement type. Other significant predictors were partial ownership interest compared with full ownership (

 = .04) and large practice size compared with small practice size (

 = .005). Physician sex and specialty type were not significantly associated with the use of incentives for quality and were excluded from final models.

Model 1 in Table 3, including all physicians, illustrates that the use of incentives based on patient satisfaction was overwhelmingly associated with staff- or group-model HMO practice setting type. Model 2 (n = 284) restricted the analysis to physicians not associated with group-model HMOs using the same covariates. In model 2, significant predictors of incentives based on patient satisfaction were partial ownership interest versus full ownership (OR, 4.94; 

 = .002) and any reimbursement in the form of capitation versus none (OR, 3.26; 

Results of the 2 models predicting incentives based on other quality measures such as adherence to quality measures demonstrate that significant predictors of incentives based on quality measures other than patient satisfaction were staff- or group-model HMO practice setting type (OR, 8.58; 

 = .04) and large practice size (OR, 4.41; 

 = .006) (Table 3). Reimbursement predominantly in the form of FFS was negatively associated with the use of such incentives (OR, 0.23; 

 = .03). Ownership interest was not signnificant. In the model excluding staff- or group-model HMO physicians, only large practice size was a significant predictor (OR, 4.78; 

Table 3 also gives results of analyses that specify as dependent variables the use of financial incentives based on both types of quality measures. The results of model 1 were consistent with the results in which the dependent variable was incentives based on quality measures other than patient satisfaction, except that ownership interest was not significant. Excluding staff- or group-model HMO physicians, no independent variables were significant in model 2. 

Most cancer care providers in Los Angeles County outside of staff- or group-model HMOs are not subject to financial incentives based on quality-of-care measures, and those who are seem more likely to be associated with large practice settings. New approaches will be needed to direct financial incentives for quality toward specialists outside of staff- or group-model HMOs if pay-for-performance programs are to succeed in improving quality. However, further research is needed to determine whether the use of financial incentives can indeed influence cancer care and outcomes and what, if any, associations exist between implicit financial incentives (eg, reimbursement based on FFS, capitation, or salary), explicit financial incentives linked to performance on quality measures, and actual outcomes of patients with cancer.

: Department of Medicine (DMT, KLK) and Jonsson Comprehensive Cancer Center (DER-A, PAG), University of California, Los Angeles; Amgen Inc, Thousand Oaks, CA (JLM); and RAND, Santa Monica, CA (JLA).

: This work was supported by a grant from the California Breast Cancer Research Program (#7PB-0126). Dr Tisnado received additional grant support from a Department of Defense Breast Cancer Research Postdoctoral Program (#DAMS17-03-1-0328) and the UCLA Center for Health Improvement of Minority Elderly/Resource Centers for Minority Aging Research (NIH/NIA #P30AG021684). Dr Rose-Ash received grant support from a UCLA Cancer Education and Career Development National Cancer Institute (#R25 CA 087949).

: The authors (DMT, DER-A, JLM, JAL, PAG, KLK) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. This study was presented in part at the annual sessions of the 24th AcademyHealth Research Meeting; June 3-5, 2007; Orlando, FL; and at the California Breast Cancer Research Program; September 7-9, 2007; Los Angeles, CA.

: Concept and design (DMT, DER-A, JLM, PAG); acquisition of data (DMT, DER-A); analysis and interpretation of data (DMT, DER-A, JLM, PAG); drafting of the manuscript (DMT); critical revision of the manuscript for important intellectual content (DMT, DER-A, JLM, PAG); statistical analysis (DMT, DER-A); provision of study materials or patients (DMT); obtaining funding (DMT); and administrative, technical, or logistic support (DMT).

Address correspondence to: Diana M. Tisnado, PhD, Department of Medicine, University of California, Los Angeles, 911 Broxton Plz, Los Angeles, CA 90095-1736. E-mail: 

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POLICY

We present findings about the prevalence and predictors of financial incentives for quality reported by oncologists and surgeons in Los Angeles County, California.

Financial Incentives for Quality in Breast Cancer Care

: To develop a definition of high-risk asthma that more precisely identifies patients needing case management than the 2006 Healthcare Effectiveness Data and Information Set (HEDIS) definition.

: Two-year claims-based study, with cross-sectional phone survey data, for a sample of 769 youths (age 11-17 years) with asthma.

Methods: The 2006 HEDIS measure defines high-risk asthma as meeting any of the following criteria: =1 emergency department (ED) visits, =1 hospitalizations for asthma, =4 asthma medication prescriptions, or =4 ambulatory visits for asthma with =2 prescriptions for asthma medication in 1 year. We created a revised definition =1 ED visits or =1 hospitalizations for asthma or =1 oral steroid prescriptions for asthma) and identified patients with high-risk asthma in year 1 according to each definition. We compared the 2 groups on demographic and clinical characteristics, and healthcare utilization and costs in years 1 and 2.

: The revised definition identified 29% of the sample as having high-risk asthma, whereas the 2006 definition identified 67%. Compared with the 2006 definition, the revised definition identified patients with significantly greater asthma-related physical health problems and higher medical costs in year 1. In year 2, youths classified as high risk by the revised definition made more ED visits and were more likely to use oral steroids than those classified as high risk by the 2006 definition. Conclusion: The revised high-risk asthma definition identifies half as many individuals and is better able to identify patients with poorly controlled asthma in the subsequent year.

This study developed and tested a definition of high-risk asthma based on a single year of patient utilization data that, compared with the 2006 HEDIS definition, more precisely identified patients needing case management.

The 2006 HEDIS definition categorized 67% of youths with asthma as high risk, whereas the revised definition categorized just 29% of youths as high risk.

Compared with the 2006 HEDIS definition, the revised definition identified youths with significantly greater asthma-related physical health problems and higher medical costs in the first year, and youths who made more emergency department visits and were more likely to use oral steroids in the second year.

Asthma is the most common chronic illness of childhood and adolescence. In many  individuals, it persists into adulthood, causing functional impairment and resulting in considerable use of health services.

 Randomized controlled trials have shown asthma disease management programs to be effective,

 and some organized healthcare systems have integrated disease management systems into their process of care. The resources directed toward disease management can be optimized by targeting youths with the highest risk of adverse consequences. A method for identifying these youths based solely on administrative data would be valuable for health plans.

The Healthcare Effectiveness Data and Information Set (HEDIS) definition of high-risk asthma is one method used to identify patients through administrative data. 

 shows the 2006 HEDIS definition and the revised definition proposed in this study. There is evidence to suggest the 2006 HEDIS definition resulted in high levels of misclassification.

A prior study found the definition to be overly broad because 2 criteria (&#8220;4 or more prescriptions for asthma medication&#8221; and &#8220;4 or more ambulatory visits for asthma plus 2 or more prescriptions for asthma medication&#8221;) resulted in including large numbers of young people and may represent patients adhering to and doing well with care.

The HEDIS definition of high-risk asthma was amended in 2007 to reduce the amount of misclassification. The 2007 definition requires patients to meet at least 1 of the utilization criteria in a 12-month period for 2 consecutive 12-month periods and the criteria met in each period do not need to be the same ones. Although the extended time period increases the fidelity of the HEDIS definition, the 24-month period limits the definition&#8217;s utility as a case-finding tool. A definition of high-risk asthma that requires only 12 months of utilization data, instead of 24 months, would enable health plans to identify patients needing disease management in a time frame that would be amenable to preventive interventions. 

In this article we describe development of a revised definition that identifies patients with asthma who are at risk for adverse outcomes, using 12 months of utilization data. The revised definition we proposed is similar to the 2006 definition in that only 12 months of utilization data are required, but the criteria are different. We removed 2 above-mentioned criteria (&#8220;4 or more prescriptions for asthma medication&#8221; and &#8220;4 or more ambulatory visits for asthma plus 2 or more prescriptions for asthma medication&#8221;), hypothesizing that youths who met only these criteria were not as a group at high risk for adverse outcomes. The criterion of 1 or more oral steroid prescriptions for asthma was added to capture youths requiring treatment for significant exacerbations. In this study we tested the revised definition against the 2006 HEDIS definition to determine whether one is more suitable than the other for identifying high-risk patients. Compared with the 2006 HEDIS definition, we hypothesized the revised definition would capture fewer of the youths with well-controlled persistent asthma and fewer of the youths with intermittent asthma.

&#8226; At least 1 emergency department (ED) or urgent care visit in the past year with an asthma diagnosis and at least 1 asthma prescription during that year.

&#8226; At least 1 visit in the past year with an asthma diagnosis and another in the past 18 months, and at least 1 asthma prescription.

&#8226; At least 4 prescriptions for asthma medication in the past year.

These criteria were developed to identify youths with active asthma and screen out patients with very mild asthma (such as mild exercise-induced asthma) and youths with spurious asthma diagnoses (ie, wheezing secondary to acute respiratory infection). All youths meeting inclusion criteria were invited to participate in the STAR study. Participants in the STAR study with cost and utilization data were included in this analysis. All participants gave informed consent. The study protocols were reviewed and approved by the institutional review board of GHC.

High-risk status according to the 2006 HEDIS and revised definitions was determined based on individual utilization in year 1 according to the criteria shown in the Figure. We examined the number of youths identified by individual criteria in order to compare the relative contributions of each criterion. Descriptive statistics were used to evaluate the revised definition of high-risk asthma, first by comparing the characteristics of high-risk youths with those of low-risk youths identified by each definition, and second by comparing the characteristics of the high-risk youths identified by each definition. 

Differences between the high-risk and low-risk groups within each definition were evaluated by t tests for groups with unequal variance and Pearson &#967;2 tests. Differences between the high-risk groups identified by each definition also were compared by 

A revised HEDIS definition of high-risk asthma more precisely identifies patients with functional impairment and higher healthcare utilization who might benefit from case management.

July 2008

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