Karen L. Margolis, MD, MPH

Despite the significant prevalence of elevated blood pressure (BP) and body mass index (BMI) in children, few studies have assessed their combined impact on healthcare costs. This study estimates healthcare costs related to BP and BMI in children and adolescents.

Prospective dynamic cohort study of 71,617 children aged 3 to 17 years with 208,800 child years of enrollment in integrated health systems in Colorado or Minnesota between January 1, 2007, and December 31, 2011.

Generalized linear models were used to calculate standardized annual estimates of total, inpatient, outpatient, and pharmacy costs, outpatient utilization, and receipt of diagnostic and evaluation tests associated with BP status and BMI status.

Total annual costs were significantly lower in children with normal BP ($736, SE = $15) and prehypertension ($945, SE = $10) than children with hypertension ($1972, SE = $74) (

 <.001, each comparison), adjusting for BMI. Total annual cost for children below the 85th percentile of BMI ($822, SE = $8) was significantly lower than for children between the 85th and 95th percentiles ($954, SE = $45) and for children at or above the 95th percentile ($937, SE = $13) (

This study shows strong associations of prehypertension and hypertension, independent of BMI, with healthcare costs in children. Although BMI status was also statistically significantly associated with costs, the major influence on cost in this large cohort of children and adolescents was BP status. Costs related to elevated BMI may be systematically overestimated in studies that do not adjust for BP status.

Prehypertension and hypertension have strong effects on healthcare costs in children, independent of body mass index (BMI).

This study examines the combined impact of blood pressure (BP) and BMI on healthcare costs.

Although BMI status was associated with costs, the major influence on cost in this large cohort of children and adolescents was BP status.

Costs related to elevated BMI may be systematically overestimated in studies that do not adjust for BP status.

The potential long-term adverse outcomes associated with prehypertension (preHT) and hypertension (HT) in children and adolescents are well recognized.

 Blood pressure (BP) in youth correlates with BP in early adulthood and may be associated with elevated cardiovascular (CV) risk later in life.

 Moreover, elevated BP is associated with elevated body mass index (BMI) in children and adolescents,

Because of the acknowledged increased CV risk associated with childhood preHT or HT, it seems reasonable to suggest that healthcare costs would also increase with preHT or HT as the result of increased testing and evaluation, increased follow-up, or increased CV-related comorbidities. However, information on the cost related to elevated BP in children is scarce.

 Studies of obese children suggest modestly elevated costs of care, but none of these prior studies consider the fact that elevated BMI is significantly related to elevated BP.

Studies assessing the relation between BMI and healthcare costs, but failing to account for BP levels or other clinical conditions associated with elevated BMI, may overestimate the independent impact of BMI.

 In this project, we model the association of elevated BP and costs, while taking into account detailed data on BMI and other comorbid conditions (including depression and other mental health conditions). Using this broader approach permits a more accurate assessment of healthcare costs in children and adolescents with elevated BP than data currently available in the literature.

  Electronic health records (EHRs) were used to identify a dynamic, longitudinal cohort of children and adolescents who were enrolled in 2 large integrated health maintenance organizations in Colorado and Minnesota. Children and adolescents aged 3 to 17 years were included in the study if they were continuously enrolled in the health plans for 1 or more years from January 1, 2007, to December 31, 2010; had measured height, weight, and BP; and had comprehensive insurance coverage including coverage for pharmaceuticals. Only BP measurements obtained in primary care outpatient clinics were included in this study; BP measurements obtained in the emergency department (ED) or urgent care centers were excluded.

Both health plans used an EpiCare (Verona, Wisconsin) EHR. Demographics, including age, gender, and race/ethnicity, were derived from administrative eligibility data. BMI and presence of preHT and HT were ascertained from EHR vital signs data. Age and gender-specific BMI percentiles were calculated for each visit in which both height and weight were measured, and children and adolescents were assigned their maximum BMI percentile during the study period. Age, gender, and height-specific BP percentiles were calculated for both systolic BP and diastolic BP measures obtained in outpatient settings (including emergency departments and urgent care clinics)

Children who had 3 or more BP measurements taken during the study period were included. Normal BP was defined as having all systolic BP and diastolic BP measurements < 90th percentile; preHT was defined as having 1 or more BP measurements ≥90th percentile or ≥120/80 mm Hg; HT was defined as either 3 consecutive BP measurements ≥95th percentile or 1 or more BP measurements ≥99th percentile. Children were also identified as having HT if they received a diagnosis of essential hypertension from their healthcare provider during the study period (

International Classification of Diseases, Ninth Revision, Clinical 

 [ICD-9-CM] codes 401.xx). To focus this study on essential hypertension, children diagnosed with secondary hypertension (ICD-9-CM codes 405.xx) or other cardiovascular conditions were excluded. Normal BMI was defined as <85th percentile, overweight as ≥85th to <95th percentile, and obesity as ≥95th percentile.

EHRs accurately measure utilization, but do not directly measure resource use or costs. Therefore, we applied standard pricing methodologies to estimate annual inpatient, outpatient, and pharmacy costs.

 Inpatient admissions were priced using nationally representative 2010 payment rates based on Diagnostic Related Groups. Costs for outpatient services including physician, outpatient hospital, and clinic services; costs for all other outpatient services such as nursing services, laboratory, and radiology were priced using the nationally representative 2010 payment rates per relative value unit. Costs for phar- maceuticals, including prescriptions for antihypertensive and other medications, were priced at 68% of their average wholesale prices in 2010.

We examined utilization of outpatient services to determine whether preHT and HT might be driving utilization and costs through increased diagnostic testing and evaluation. We used procedure codes to estimate the annual number of preventive, office, or outpatient medical visits and the number of outpatient psychiatry and psychology visits. We also identified the likelihood of ED services or specialist consultations. In addition, we identified the likelihood of receiving diagnostic procedures including urinalysis, laboratory testing, chest x-ray, electrocardiogram, renal ultrasound, and echocardiogram.

We measured medical comorbidities using the Chronic Illness and Disability Payment System (CDPS).

 CDPS is a combined diagnostic and pharmacy-based risk adjustment model commonly used to measure illness burden and to adjust capitated payments to health plans. The CDPS algorithm assigns ICD-9-CM diagnosis codes to 58 diagnostic categories including selected mental health conditions, and national drug codes to 15 pharmaceutical categories, and applies severity weights developed from a national database of medical claims. The resulting CDPS score provides a measure of illness burden based on expected future healthcare expenditure conditional on each person’s diagnostic and pharmaceutical profile.

We analyzed inpatient, outpatient, pharmacy, and total costs using generalized linear models. Generalized linear models are commonly used to estimate healthcare costs when the dependent variable is non-negative and when its distribution is noticeably skewed and kurtotic (with a heavy right-hand tail).

 Inpatient and pharmacy costs were estimated using 2-part models: logistic regression was used to estimate the probability of any use of a service, and a generalized linear model based on a gamma family with a log link function was used to estimate costs conditional on receiving at least 1 service. Total and outpatient costs was estimated using a single generalized linear model. The number of preventive, office, or outpatient visits and the number of outpatient psychiatry and psychology visits were estimated using negative binomial regression, and the probability of receipt of each of the remaining measures of outpatient utilization were estimating using logistic regression. We selected these specific distributions based on standard tests for assessing alternative generalized linear and transformed models.

 We assessed goodness of fit using a modified Hosmer-Lemeshow test and a Pregibon’s link test.

The primary predictor variables of interest were indicator variables for BMI ≥85th to <95th percentile, BMI ≥ 95th percentile, preHT, and HT. Analyses of BMI were adjusted for HT, and analyses of HT were adjusted for BMI. Additional control covariates included age (specified as a set of indicator variables with 1 for each year), gender, race/ethnicity, site, and year. We conducted analyses with and without controlling for comorbidity using the CDPS score. Controlling for comorbidity may reduce confounding of the cost estimates if increased illness burden is correlated with BP or BMI status. However, it may be inappropriate to adjust for comorbidity if an increased illness burden is the result of BP or BMI status. Therefore, we conducted 2 sets of analysis to provide a range of estimates and to demonstrate the sensitivity of the estimates to medical comorbidities.

We calculated cost estimates associated with BMI, preHT, and HT that were adjusted for age, sex, race/ ethnicity, CDPS score, and either BMI or BP status. For example, the standardized costs of children with pediatric HT were calculated as the mean predicted values across all children assuming they all had pediatric HT. Standard errors were calculated using the nonparametric bootstrap, and P values were computed using the percentile method from the empirical distributions of the results from 1000 replicates.

All analyses were conducted in STATA, version 12.

This project was reviewed in advance, approved, and monitored by the HealthPartners Institute for Education and Research Institutional Review Board.

The study sample included 71,617 children aged 3 to 17 years followed for 35.0 (SD = 13.7) months and providing 208,800 child years of data. Study sample characteristics are shown in 

. The mean age was 10.3 (SD = 4.4) years, and 51% were female. The majority (57%) were non-Latino white, 14% were Latino, 10% were African American, and 4% were Asian. Native American, Hawaiian / Pacific Islander, or multiple races were 1% or less, and 14% were of unknown race/ethnicity. BMI percentile was normal in 64%, 18% were overweight, and 18% were obese. BP percentile was normal in 55%, 42% were prehypertensive, and 3% had hypertension. Among children identified with hypertension, 83% were identified using only BP measures. Among the 17% with an ICD-9-CM code for essential hypertension, 35% had BP measurements consistent with the diagnosis, while 65% did not have 3 consecutive BP measurements ≥95th percentile, and 31% filled prescriptions for antihypertensive medications. Among the 83% identified by BP only, 2% filled prescriptions for antihypertensive medications.

 shows the mean standardized annual costs among children by BMI and BP status without controlling for additional morbidity. Estimates of costs related to BMI were adjusted for HT, and estimates of costs related to HT were adjusted for BMI. There were no statistically significant interactions between BP and BMI status. As noted in 

, costs for children with preHT ($945, SE = $10) were significantly higher than for children with normal BP ($736, SE = $15), and annual costs for children with HT ($1972, SE = $74) were significantly greater than for children with preHT or normal BP (P <.001 each). Among children with hypertension, there was no difference in total costs between children who received a diagnosis of hypertension compared to children who were identified only by BP measures in the EMR review (P = .814). The largest cost component for all 3 BP groups was outpatient care, followed by pharmacy and inpatient care.

There were smaller cost differentials related to BMI. Total annual cost for children below the 85th percentile of BMI ($822, SE = $8) was significantly lower than for children between the 85th and 95th percentiles ($954, SE = $454) and for children at or above the 95th percentile ($937, SE = $13;) (P <.001 each). Total annual cost for children between the 85th and 95th percentiles of BMI was not statistically significantly different from the total cost for children above the 95th percentile (P = .754).

 shows that the standardized cost differentials between categories narrowed when controlling for additional comorbidity using the CDPS score. The differential in total costs between HT and normotensive children declined by $383 (SE = $90) to $852 (SE = $53), and the cost differential between preHT and normotensive children declined by $67 (SE = $18) to $141 (SE = $9) (P <.001 each) when adjusting for additional comorbidity using CDPS. The differentials in total cost for children above the 95th percentile of BMI and children between the 85th and 95th percentiles compared to children below the 85th percentile declined by $67 (SE = $19, P <.001) and $64 (SE = $48, P = .192), respectively, to $48 (SE = $11, P <.001) and $68 (SE = $15, P <.001) when adjusting for additional comorbidity using CDPS.

 shows estimates of outpatient utilization associated with preHT and HT. There were statistically significant increases in all measures of utilization except for laboratory testing. Utilization was at least 50% higher in HT than normal BP children in all categories, except for laboratory testing (P <.001 each), with the large percentage increases observed for renal ultrasound, echocardiogram, and chest x-ray. However, these procedures were performed at a relatively low rate, and all diagnostic and evaluation procedures with the exception of urinalysis were conducted in fewer than 10% of children with HT. We reviewed the types of specialist consultations, but could not identify any specific pattern to differentiate visits among the BP categories.

The present study shows the strong incremental effects of both preHT and HT, independent of BMI and comorbidity, on healthcare costs in children. Although BMI status also was significantly associated with cost, the major influence on cost in this large cohort of children was BP status. It should be noted that the percent of children with preHT was higher than previously reported.

Because the definition for preHT requires only a single BP measurement, the requirement of 3 separate clinic BP measurements for inclusion in this study increased the number with preHT.

An important factor in this analysis is that hypertension was identified primarily from review of EHRs, with only 17% recognized by the clinic physicians. The lack of recognition of HT by providers using computerized medical record systems has been reported previously,

as has the extent of the underrecognition.

However, there was no significant difference in cost associated with the EHR method, as opposed to physician recognition and diagnosis. Thus, there may be something inherent in the presence of hypertension or prehypertension that leads to increased outpatient and emergency department visits, specialist consultations, increased diagnostic and evaluation procedures, and their associated costs; or medical issues resulting in more frequent clinic visits with greater number of BP measurements resulting in a larger number of diagnoses of preHT or HT. This may account for the significantly greater use of cardiac studies and chest x-rays in the subjects with hypertension.

Elevated BP in adults has been shown to be associated with substantial increased costs in 3 national data sets.

 Studies of adults have further shown that both obesity and hypertension are independent determinants of costs.

In contrast, little attention has been devoted to the costs or to strategies to address elevated BP in children or adolescents. Given the strong relationship between elevated BP and healthcare costs, a case could be made to health plans and payers to consider implementing strategies to manage healthcare utilization of children with elevated BP. These strategies might include improved primary care, case management, health behavior interventions, family education, or other interventions.

In contrast to prior studies, costs of healthcare services in this study were not significantly different between overweight (BMI ≥85th percentile to < 95th percentile) and obese (BMI ≥95th percentile) children controlling for BP status. Studies assessing healthcare utilization and cost related to overweight or obesity previously have not been able to adjust for BP status due to lack of BP data. By including both BMI and BP status in the analyses, this study has been able to extend previous findings by showing that the effect of BMI is lower than previously reported and is confounded by the associated impact of preHT and HT.

These findings are relevant to ongoing clinical and public policy discussions. First, they suggest that greater attention should be paid to elevated BP in children and adolescents as a driver of healthcare costs. Second, elevated BP has a significant effect on cost independent from elevated BMI and other comorbid conditions. Third, studies that do not account for the impact of BP level on utilization may overestimate the effect of BMI on utilization and costs.

There are limitations that should be considered in the interpretation of these data. First, despite the large, racially and ethnically diverse study population, these results may not be generalizable to costs of care in non—insurance-driven healthcare systems. However, it is currently challenging to conduct this type of study and capture detailed clinical and cost data outside the highly integrated healthcare systems included in this study. Second, it is possible that some allopathic care was obtained outside the insurance system, although prior studies suggest that this is well under 5% of total allopathic care. Third, although we adjusted for comorbidity using CDPS, there may be some residual differences in comorbidities by BP status. Finally, the results from this study do not imply causality between hypertension and the increased cost of patient care. However, the data clearly show an association between BP and healthcare costs in youth.

Department of Family and Preventive Medicine, University of California, San Diego (TPG); HealthPartners Institute for Education and Research, Minneapolis, MN (PJO, EOK, NES, KFA, KLM); Department of Pediatrics, University of Minnesota, Minneapolis (ARS); Institute for Health Research, Kaiser Permanente Colorado, Denver (DJM).

Funding was provided by the National Institutes of Health grant R01HL093345 from the National Heart, Lung, and Blood Institute.

 The authors 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 (TPG, PJO, ARS, EOK, KLM); acquisition of data (PJO, KLM); analysis and interpretation of data (TPG, PJO, ARS, EOK, DJM, NES, KFA, EDP, KLM); drafting of the manuscript (TPG, PJO, ARS); critical revision of the manuscript for important intellectual content (TPG, PJO, ARS, EOK, DJM, NES, KFA, EDP, KLM); statistical analysis (TPG); provision of study materials or patients (PJO); obtaining funding (PJO, ARS, NES); administrative, technical, or logistic support (TPG); supervision (TPG, POC).

Todd Gilmer, PhD, Department of Family and Preventive Medicine, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92092-0622. E-mail: tgilmer@ucsd.edu.

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Articles

This study demonstrates a major influence of prehypertension and hypertension on healthcare costs in a large cohort of children, independent of body mass index.

Impact of Hypertension on Healthcare Costs Among Children

: To describe the types of practice system tools that medical groups use for depression care, and to compare these with the tools used for care of other chronic conditions. 

: We surveyed the medical directors of 41 medical groups in Minnesota with sufficient numbers of patients with depression to be included in public reporting of performance measures for depression. The survey asked about the presence of practice systems used for depression and other chronic conditions. 

: The response rate to the survey was 100%. Group sizes ranged from 7 to 2000 physicians and were evenly divided between metropolitan and nonmetropolitan areas. About 60% of the groups were multispecialty. Medical groups were most likely to have information support, decision support, and performance tools (such as a registry, flow sheets or check lists, written standards of care, reminder systems, and performance feedback to physicians) to support management of diabetes (ranging from 65% with a registry to 95% with written standards of care). In general, the fewest of these system tools were in place for management of depression (ranging from 12% with a registry to 58% with written standards of care). One exception was the use of patient self-assessment tools, which was most common for depression (51%). 

: Our findings suggest that a lack of practice system tools may be one element that is hampering improvement in depression care. Further study is needed to demonstrate that implementing and maintaining these changes will improve depression care in diverse medical practices.

(Am J Manag Care. 2007;13(part 1):305-311)

Depression care is a high priority for quality improvement, but no studies have examined whether practice tools and systems to support depression care are present in medical practices. 

This study surveyed the medical directors of 41 medical groups in Minnesota regarding the types of practice tools that were used to support the care of diabetes, cardiovascular heart disease, depression, and asthma. 

Compared with practice system tools for diabetes and cardiovascular heart disease, medical groups uncommonly use such tools for depression care (eg, patient registries, reminder systems, performance feedback to physicians, case managers, or self-management support).

Depression is a common, disabling, and costly condition that is often chronic or recurrent.

 It is also a condition with a large gap between evidence-based care and the actual care most patients with depression receive.

 For these reasons, depression has been identified by the Institute of Medicine as one of the priority areas for transforming healthcare quality.

 The central problem for this transformation appears to be how to implement evidence-based care processes in primary care settings that are usually overburdened and underresourced.

Many randomized controlled trials have shown that system changes improve the care of depressed patients in the primary care setting. These changes include initial assessment of depression diagnostic criteria and severity using standard instruments, tracking and monitoring treatment effectiveness, stepped care for treatment intensification as needed, and relapse prevention.

 Another key role in these trials is a care manager (ideally in the primary care setting) who provides patients with education, support, and care coordination, and facilitates psychiatric consultation and mental health therapy for patients who are not improving. Screening for case-finding could be added to the above list once the rest of the system is in place, as was recommended by the US Preventive Services Task Force (USPSTF).

Compared with other chronic conditions such as diabetes and coronary artery disease, depression care is improving more slowly. Although the present Health Plan Employer Data and Information Set (HEDIS) benchmarks may not be ideal for measuring depression care, they provide a national standard for comparison. Mean HEDIS rates for medication adherence for 3 and 6 months and optimal practitioner contacts in 2005 (61%, 45%, and 21%) have changed little since 2001 (57%, 40%, and 20%, respectively) for patients with commercial insurance.

 Mean HEDIS rates for these measures are even lower and have improved less for Medicare and Medicaid patients. While the HEDIS measures for depression care show higher rates of 3- and 6-month medication adherence in Minnesota compared with the nation as a whole, the numbers are not showing improvement in Minnesota. For example, 6-month medication adherence in Minnesota (for all types of insurance) was 49% in 2002, 51% in 2003, and 49% in 2004.

One reason for the lack of improvement for depression care could be that the types of office systems and practice tools that have been found important to support the care of other chronic conditions have not been adapted to the system and role changes needed for better depression care. Therefore, we studied that possibility through surveys of the medical directors of 41 medical groups in Minnesota. Because small medical groups often lack resources for developing and implementing systems like electronic medical records (EMRs), we were particularly interested in whether there was any relationship between medical group size and the presence of those systems.

We recruited all 41 medical groups in the state with sufficient size and numbers of patients with depression to be included in the annual public reporting of performance measures for depression by Minnesota Community Measurement.

 This organization is sponsored by all the health plans in the state, and it has gradually evolved into a preeminent public accountability system. Then we obtained completed surveys from all 41 medical directors, asking about the presence in their medical groups of a wide variety of practice systems used for depression and other chronic conditions and consistent with the Chronic Care Model of Wagner et al.

 The survey, called PPC, for Physician Practice Connections, was developed by an expert panel led by the National Committee on Quality Assurance (NCQA), of which one of us (LIS) was a member. The surveys were mailed initially in June 2005, and nonrespondents received an e-mail reminder followed by telephone calls, if necessary. The response rate was 100%.

Given the lack of valid and reliable tools for assessing clinical practice systems, NCQA developed the PPC self-report survey tool to measure the presence and function of chronic disease practice systems for use in various pay-for-performance demonstrations. The development process built on an extensive literature review, input from experts and key stakeholders, and multiple pretesting efforts with physician practices.

 The PPC addresses 5 domains that are consistent with the Chronic Care Model's elements: healthcare organization, delivery system redesign, clinical information systems, decision support, and self-management support. Each of the 5 domains was assessed with multiple survey items. Individual items most relevant to depression care were selected from the PPC for the analysis conducted in this report. 

As a reference point for comparison with other medical groups nationally, we also used the methods of Casalino et al to quantify practice systems (called care management processes or CMPs by Casalino) among these groups in relation to their findings for the rest of the country.

 The 4 types of practice systems assessed by the Casalino method were case management, feedback to physicians, use of a disease registry, and adoption of clinical guidelines that are present in charts, in reminder systems, or in order-entry systems. In this method, each of the 4 practice systems was awarded 1 point each for use for 4 conditions (diabetes, asthma, congestive heart failure, and depression), with the exception of not including feedback to physicians for depression (15 points). One additional point was awarded for the use of programs to teach patient self-management support skills for  chronic illnesses in general, for a total of up to 16 points. We used items on the PPC to create a similar score, substituting cardiovascular disease for congestive heart failure items, because we assessed cardiovascular disease, but did not assess congestive heart failure, in our survey.

For categorical items, associations between medical group size and medical group attributes/systems were assessed with contingency tables and Pearson and Mantel-Haenszel χ

 statistics. The Wilcoxon rank sum test was used to test for difference in continuous items by medical group size. Weighted least squares analysis of categorical outcomes was used to test whether systems were equally present across different chronic conditions.

The characteristics of the 41 medical groups, stratified by the number of physicians they contain, are shown in 

. Group size was strongly correlated with the number of practice sites within the medical group, but smaller groups had more patient visits per week per physician. No significant differences were noted in the number of nurses or advanced care providers per physician, but apropos of our interest in depression care, larger group size was strongly associated with the presence of psychiatrists and behavioral health clinicians in the group. About two thirds of the groups were multispecialty, and this proportion increased as group size increased. Roughly half of the medical groups were located in nonmetropolitan areas. Most of the small and medium-sized medical groups were physician-owned, whereas the largest groups were primarily owned by hospitals, health plans, or other nonprofit corporations. Smaller groups had more patients with commercial insurance and fewer patients with Medicare coverage.

Just over half of the groups used an EMR system, either alone or supplemented by paper medical records, and most EMR systems had been in place for 5 years or less (

). As group size increased, fewer relied solely on paper medical records. Medical groups were most able to search electronic data for diagnosis codes (65%), with somewhat fewer able to search problem lists (45%), and prescription medications (40%).

Finally, the mean number of CMPs present in the medical groups in this study was 7.1 (SD 3.2) out of a perfect score of 16.0. No differences were noted in scores by medical group size (

Among these medical groups, large differences were noted for the presence of practice system tools to support depression care compared with other chronic conditions. In contrast to diabetes and cardiovascular disease, common chronic care management tools such as disease registries, flow sheets, physician and patient reminders, performance feedback, and selfmanagement support are uncommon for depression. Although larger medical groups were more likely to include psychiatrists and behavioral health clinicians and to have an EMR, there were no differences in the presence of practice systems for chronic disease care by group size as measured by the Casolino CMP score.

Although approximately half of the groups used patient self-assessment tools for depression symptoms (primarily the Patient Health Questionnaire-9), it may be difficult for this information to be used to improve patient care unless other systems are in place for tracking and monitoring treatment effectiveness. Tracking and monitoring systems such as registries, flow sheets, reminders, pre- and postvisit planning and follow-up are essential for maintaining adherence to treatment plans, appropriate treatment intensification for nonresponders, and relapse prevention. Few groups had nonphysician staff support for depression care, and only the largest groups included psychiatrists or other behavioral health clinicians within their own staff to facilitate collaborative care–2 other key components of depression interventions demonstrated to be effective in randomized trials.

The absence of such practice tools suggests one reason why depression care has failed to improve in primary care practice despite a strong evidence base for what is needed for effective therapy. There is good reason to believe that the higher and improving performance rates for diabetes and heart disease care in the United States are largely attributable to such systems. 

 We can only speculate as to the reasons for there being fewer system tools for depression care, despite a national emphasis on the need for improving the quality of depression care. These reasons might include financial disincentives, time pressures on physicians, negative views of physicians toward depression, lack of physician training in depression treatment, and a practice culture that resists change.

No other studies have documented the presence of various specific systems for depression care among a broad range of medical practices uninvolved in a specific clinical trial in the literature. Both Oxman et al and Unutzer's IMPACT trial have identified some of the systems needed to improve depression care.

 Meredith reported on the Chronic Care Model system changes implemented by those teams participating in a quality improvement collaborative, and several randomized trials have demonstrated the feasibility of practice implementation of proven depression systems.

 However, information about the absolute frequency of specific systems and their frequency relative to systems for other chronic conditions has been missing heretofore. It will be important now to also learn whether many practices have separate systems for each chronic condition or whether they find it more efficient and feasible to use common systems across conditions. Pincus and colleagues pointed out that “a challenge for policy-makers is how to link depression care with the management of other chronic conditions.”

Several limitations to the interpretation of our data deserve mention. Our results are derived from data from only 1 state. However, we did have a 100% response rate among eligible medical groups, so these groups are at least representative of this locale. Moreover, this region has high rates of performance on most measures of care quality relative to the rest of the country, so if depression systems are infrequent here, they may be even lower elsewhere.

 Our analysis of CMPs, using the methods of Casalino et al, also confirms this suggestion, because the national average for physician organizations with at least 20 physicians was 5.1 versus 7.1 out of a possible total score of 16 among our 41 medical groups.

 Like the Casalino study, we used a survey of medical directors rather than direct measures of the systems, so there may be some discrepancy between their reports and reality. In our earlier assessment of the validity of those reports from 32 lead physicians, we found a mean positive predictive value of their reports regarding 7 types of practice systems of 82% compared to an on-site audit (range for the 7 was 55%-100%).

Randomized controlled trials suggest that changes in systems, roles, and relationships are the key elements for improving depression care.

 These changes can be described in similar terms to the care process elements that have been shown to be important for best care of other chronic conditions such as diabetes. Our findings suggest that a lack of practice system tools may be one element that is hampering improvement in depression care. Further study is needed to facilitate the adoption of practice system tools for depression care and to demonstrate that implementing and maintaining these changes will improve depression care in diverse medical practices.

: From HealthPartners Research Foundation, Minneapolis, Minn.

: Funding for this manuscript was provided by RWJ grant #051647: Depression in Primary Care-Linking Clinical and System Strategies Initiative.

: Karen L. Margolis, MD, MPH, HealthPartners Research Foundation, PO Box 1524, MS 21111R, Minneapolis, MN 55440. E-mail: 

: The authors (KLM, LIS, SEA, RRW) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter discussed in this manuscript.

: Concept and design (KLM, LIS, RRW); acquisition of data (KLM, LIS, RRW); analysis and interpretation of data (KLM, LIS, SEA, RRW); drafting of the manuscript (KLM); critical revision of the manuscript for important intellectual content (LIS, SEA, RRW); statistical analysis (SEA, RRW); provision of study materials or patients (RRW); and obtaining funding (RRW). 

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Use of Practice System Tools by Medical Groups for Depression

How Acceptable Are Financial Incentives and Written Feedback for Improving Hypertension Control? Perspectives from Physicians, Clinic Administrators, and Patients