 To examine the impact of policy directives and performance feedback on the organization (specifically the coordination) of foot care programs for veterans, as mandated by public law within the Department of Veterans Affairs Health Care System (VA). Study Design: Case study of 10 VA medical centers performing diabetes-related amputations.

 Based on expert consensus, we identified 16 recommended foot care delivery coordination strategies. Structured interview protocols developed for primary care, foot care, and surgical providers, as well as administrators, were adapted from a prior study of surgical departments.

 Although performance measurement results for foot risk screening and referral were high at all study sites over 2 calendar years (average 85%, range 69% to 92%), the number of coordination strategies implemented by any site was relatively low, averaging only 5.4 or 34% (range 1-12 strategies). No facility had systematically collected data to evaluate whether preventive foot care was provided to patients with high-risk foot conditions, or whether these patients had unmet foot care needs.

 Although foot care policies and data feedback resulted in extremely high rates of adherence to foot-related performance measurement, there remained opportunities for improvement in the development of coordinated, technology-supported, data-driven, patient-centered foot care programs.

(Am J Manag Care. 2004;10(part 2):171-180)

The Institute of Medicine has stated that the healthcare system is comprised of multiple interacting systems of care (including emergency, ambulatory, inpatient, imaging, laboratory, and pharmacy), delivered by networks of individuals, teams, and payer systems that "function in such diverse and diffuse management, accountability, and information systems that the overall term health system is today a misnomer." 

 Consequently, the Institute stated that a radical re-engineering of the American healthcare system is needed to deliver high-quality, patient-centered care. The Institute envisioned a central microsystem of care (ie, a unit that actually provides medical care), driven by systems-oriented approaches utilizing information technology to increase the rate of knowledge diffusion to clinicians, in order to standardize the provision of evidence-based medicine and to promote patient-centered care. 

Other conceptual frameworks to implement care for chronic disease have been proposed, including the Chronic Disease Model.

This model, which incorporates evidence-based medicine and informatics, results in productive patient-provider interactions and improved patient outcomes. The Joint Commission on Accreditation of Healthcare Organizations relied on this model when it developed its Disease Management Certification Standards, which include program management, clinical information management, supporting self-management, delivering or facilitating clinical care, and performance measurement.

However, the organizational factors that lead to the development and implementation of successful disease management programs remain poorly understood.

Coordination of care has been proposed as a key organizational factor for improving the quality of chronic care delivery in smaller, more organized microsystems such as intensive care units and surgical care services. 

More recently, we have demonstrated that greater coordination of care by programming, as reported by professionals involved in Department of Veterans Affairs Health Care System (VA) foot care programs, is associated with fewer minor amputations.

In this study we utilize organizational contingency theory as a conceptual model, and site visits as a methodology, for the evaluation of coordination strategies for foot care delivery, an important but often-overlooked aspect of diabetes chronic care disease management. Specifically, we examined foot care programs at 10 sites within the VA to understand the impact of policies and performance measurement feedback on the implementation of specific coordination strategies to support foot care delivery to individuals with high-risk foot conditions. 

Within the VA, law and policy are explicit in their establishment of facility foot care programs with annual reports to Congress. These policies mandate, but do not fund, VA networks and facilities within these networks to establish multidisciplinary foot care teams and a designated facility-level coordinator.

Although the policies do not mandate a specific organizational framework, they direct facility managers to identify veterans at risk for lower-limb complications; provide preventive care; track high-risk foot care across the continuum of outpatient, inpatient and rehabilitative care; and provide education, orthotics, and social support. Furthermore, the VA directive is supported by nationally issued foot care guidelines for risk stratification11 and quarterly and cumulative performance measurements on facility and network foot screening and referral of high-risk patients to foot specialists. 

Lower Extremity Complications in VHA (FY 89- 99) Part II: LFA Rates, Combined Conditions, and Outpatient Utilization.

Because veterans with diabetes account for two thirds of all amputations 

in the VA, we focused on this cohort at 10 VA medical centers performing lower-limb amputations. The 10 medical centers selected each performed more than 30 lower-limb amputations in fiscal year (FY) 1999. Study sites included those with high and low age-adjusted lower-limb amputation rates by geographic area (Healthcare Analysis and Information Group. 

 VA Intranet document. Contact author to gain access). Approval was received from each study site's institutional review board. 

The theoretical basis of the study was organizational contingency theory, which states that an organization's structure, systems, and practice should support its unique mission in the context of its environment.

Building on general organizational theory, Charns and Schaefer 

developed a conceptual framework that described healthcare coordination in 2 major categories: programming and feedback. A study of VA surgical services based on this framework demonstrated that more intense programming and feedback approaches to coordination among surgeons, anesthesiologists, and nurses resulted in lower risk-adjusted postsurgical complications in the VA.

In our study, we assessed coordination by programming and feedback, and the global support resources supporting a study site's foot care program. 

 is best characterized as the use of preestablished plans, policies, procedures, information, and communication systems to standardize the work that needs to be performed through specification of tasks and skill sets necessary to perform them. Such approaches have been noted to be efficient in that they require minimal verbal communication among the individuals performing the task.

An example of coordination by programming would be use of computerized reminders in the electronic medical record for mandated performance measures. Coordination by programming is efficient for routine work, but ineffective for situations of greater uncertainty. 

 involves adjustment of actions based on new information; within organizations, it involves either personal or group responses to facilitate the transfer of that information.

Examples of feedback strategies at the level of clinical care include supervisory coordination and peer interactions such as committees, multidisciplinary rounds, and team meetings. In the surgery study, the organizations with the best clinical outcomes had high levels of both programming and feedback. 

Coordination by programming and coordination by feedback occur within an organizational context of global support resources provided by the healthcare system (rather than the individual clinicians). Contingency theory posits that to implement coordinating mechanisms in the most effective manner (and therefore to achieve high levels of performance), organizational leaders have an imperative to manage resources such as staff, space, and information systems to support the required coordination.

Foot care for people with diabetes covers a broad continuum. Diabetic foot care includes screening examinations to detect high-risk conditions, surveillance of patients with foot risk factors to maintain healthy feet while preventing complications, and providing salvage care for complications including ulcers, infections, and advanced peripheral vascular disease. 

 describes the 3 levels of our foot care framework and conceptual constructs for coordination by programming and by feedback. For example, coordination by programming ensures that multidisciplinary providers have training and a minimum skill set before performing foot exams and wound care. An example of coordination by feedback is structured opportunities for multidisciplinary clinicians to review findings and discuss issues. We used this foot care framework to develop 7 programming strategies (

), and 6 global resources to support them (

), all of which were evaluated in our site interview protocols. 

Structured interview protocols were developed for primary care, foot care, and surgical providers and administrators, based on domains of coordination adapted from the VA National Surgical Quality Improvement Program

 and specific strategies for foot care programs adapted from the literature.

The site visit interview protocols are available on the Diabetes Quality Enhancement Research Initiative Web site (http://www.va.gov/ annarbor-hsrd/queri/queri_projects.htm).

The interview assessed components such as the structure of foot care provided by each service in inpatient and outpatient settings, confidence in care, working relationships, education and training, coordinating mechanisms, patient issues, resources, and recommendations. The interview protocols were designed to evaluate whether each medical center had developed foot care—specific programming policies and procedures; whether each medical center had coordinated feedback approaches to standardize and coordinate the delivery of foot care across the continuum of screening, surveillance, and salvage; and whether interviewees were aware of their facility's foot care performance measurement results and amputation rates. Additionally, because the VA had recently implemented a nationwide computerized patient record system, we specifically addressed whether the organization's informatics framework provided information to assist staff at the time of patient contact, and whether high-risk foot registries had been created. Finally, we designed the interview protocols to evaluate whether the facility had addressed structural issues pertinent to the global administration of an integrated foot care program such as staffing, staff privileges, space, and information systems pertinent to the delivery of foot care. 

Facility-Level Characteristics and External Peer Review Program Performance Rates 

(International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]

The prevalence of veterans with diabetes at each study site was estimated from the VA Patient Treatment File administrative data for FY 2000. Patients with diabetes were defined by having a diagnosis code 

 357.2x, 362.x, 648.0x, 250.xx, or 366.41) listed on at least 2 outpatient visits or 1 inpatient stay, or a prescription for a hyperglycemic medication.

 Individuals were identified as having a high-risk foot based on 

 codes for neuropathy, hammertoes, Charcot foot, peripheral arterial disease, peripheral vascular procedures, deep infections or ulcers, or prior amputation. Because a specific code for the identification of the insensate foot was not available to providers during the study period, individuals with foot risk are underestimated. 

We obtained facility-level performance rates for an annual foot risk stratification examination for FY 2000 and FY 2001 from chart audits performed by an independent contractor (West Virginia Medical Institute) through the VA External Peer Review Program administered by the Department of Veterans Affairs Headquarters Office of Quality and Performance. Because the VA is not an insurance indemnity system requiring premiums, continuous enrollment criteria were established by including veterans with 12 months of continuous enrollment and more than 1 visit annually to a primary care or medical subspecialty clinic. We then constructed a combined score for each facility (the average of a visual, pulse, and sensory examination, and referral of high-risk veterans with any abnormality on screening to a foot care specialist). 

Seven individuals with expertise in foot care policy, clinical care, and/or research served as site reviewers. Each site visit team was comprised of 3 experienced professionals, including 1 with VA headquarters pro- grammatic responsibility for an aspect of foot care, 1 with research expertise, and 1 with clinical foot care responsibility. Site visits occurred between October 2000 and April 2001. We interviewed providers involved in highrisk diabetic foot care, including general, vascular, and orthopedic surgeons; rehabilitation specialists; podiatrists; physical therapists; pedorthists; orthotists; diabetes care specialists; diabetes educators; dermatologists; and infectious disease physicians interested in diabetic foot care. We also interviewed administrators and a convenience sample of primary care providers at each study site. Interviews lasted 50 minutes, and site visits were conducted over 1-2 days. 

After receiving input from other team members, the lead reviewer for each site wrote a qualitative assessment of findings. This report detailed which of the 7 coordination-by-programming and 9 coordination-byfeedback activities a site had implemented (see Tables 2 and 3). Five study sites were referral centers for patients requiring amputations from other facilities; therefore, they had an additional coordination-by-feedback strategy. However, none of the sites had this strategy in place. We also identified whether or not global support resources were adequate for routine activities of a foot care program. Without information on the facility ampu- tation rates, the research team then used a nominal group process to qualitatively rank the 10 sites based on perceived effectiveness of their foot care program. We used Spearman rank correlation to correlate nominal facility-level rankings with the number of coordinating strategies, cross-sectional FY 2000 and FY 2001 foot care performance measurement, and longitudinal change in performance measurement. 

On average, sites had only implemented 5.4 (34%) of the 16 combined coordination-by-programming and coordination-by-feedback strategies (range 1-12 strategies) described in Tables 2 and 3, and 2.2 (37%) of the 6 global support strategies (range 1-4 strategies) noted in Table 4. An examination of coordination by programming (Table 2) indicated that 6 of the 10 sites had formal written policies; however, the extent of these policies varied widely and ranged from organizational administrative memorandums to formal policies for screening in the primary care clinics, consultation referrals, and manuals for house staff or nurses. 

Although all sites indicated that they provided patient education, only 2 sites standardized efforts across care delivery units such as primary care, diabetes clinics, and foot care clinics. In general, sections at each site used different education materials. Only 1 site provided patients with information regarding their personal foot risk level after screening. Seven sites indicated that they had 24-hour patient telephone triage available, but none provided routine written information to patients on whom to contact for foot care emergencies or what conditions would merit provider contact. At these 7 sites, the effectiveness of the triage system had not been evaluated. 

Half of the study sites outsourced custom (molded to fit the patient's foot) footwear. At 1 site, a pedorthist attends the podiatry clinic to coordinate footwear dispensing. Only this 1 site had a formal mechanism (eg, a patient survey, follow-up fittings) to evaluate the quality of footwear dispensed from the VA from either the provider or the patient perspective. 

Foot care coordination by feedback was assessed in terms of multidisciplinary and peer communication, committee structures, and performance feedback. Table 3 shows that 7 of 10 sites indicated that they had multidisciplinary clinics for high-risk patients. In several instances, this feedback resulted from clinics being held at the same time in contiguous areas. In other sites, a specific effort was made to have podiatrists attend diabetes clinics, or to have vascular surgeons integrated into highrisk foot clinics. None of the study sites had ongoing multidisciplinary clinical conferences to discuss foot care for high-risk patients, although multidisciplinary inpatient rounds often were held. Although 5 study centers were referral sites for other VA facilities without surgical services, we did not identify formal coordination-by-feedback strategies to provide systems-level feedback to the referral sites regarding pretransfer care. 

Clinicians at 7 sites were aware of current performance measurement data at their facility. However, the intensity of review and sharing of the data varied. At 1 site, an interviewee noted that "posting of results" would be a more accurate term than reviewing results. On the other hand, several sites actively monitored the process and results of screening. At 1 site, providers reviewed the records of a sample of patients with diabetes to evaluate unmet needs, and at 2 other sites, administrators used random chart reviews only to identify "outliers." 

Ulcer and amputation results from the Office of Policy and Planning, which were posted on the VA Intranet, were reviewed by only 3 sites. These 3 were sites with formal high-risk foot care committees that reported to medical center leaders. Two additional sites tracked amputation rates in their defined service population, while 2 sites tracked the number of amputations over several years but did not express the data as rates. None of the 5 sites that received referrals for amputations were able to determine what percentage of all patients treated, and amputations performed, were patients from referring facilities. 

Only 2 sites reported quality improvement efforts in the area of foot care beyond foot risk screening examinations. One site perceived that its efforts had led to a reduction in amputations, whereas the other noted that consensus had not been reached over implementation issues. None of the sites were able to evaluate the success of their program in terms of real-time outcomes that could be impacted quickly, such as continuity of foot care for high-risk patients, patient self-reports of unmet needs, or patient and provider satisfaction with footwear. 

Global support mechanisms for foot care programs such as registries were available only at 2 sites (see Table 4). Despite the VA's sophisticated computerized patient record system, none of the sites had instituted a comprehensive, integrated electronic registry that would support all components of a foot care program such as tracking of individuals with high-risk feet or provision of preventive care, including education, footwear, or durable medical equipment. One site had a diabetes registry that provided clinicians with the status of veterans' foot examinations, and one site had a registry of veterans with high-risk feet or prior amputations. Departmental records were maintained at several other sites that represented a partial registry for patients screened in primary care, treated for ulcers, or treated with bypass surgery. These data were not available to the entire foot care team. None of the sites were able to provide the site team with data on the number of diabetes-related visits, continuity of foot care or missed appointments, ulcers, or amputations. No site had linked a patient's high-risk status for lower-extremity complications to the electronic medical record, although individual providers could incorporate that information into the problem list. 

, we used nominal group rankings to order our study sites into low-, medium-, and high-performing facilities. We created a summary score for each site of coordination by programming and feedback mechanisms, and global support resources. We also created a summary measure of each study site's foot care—related performance measurement data, as previously described. There were no statistically significant correlations between the reviewers' nominal group rankings or the number of coordination strategies and either the cross-sectional summary External Peer Review Program performance measurement score or the change in score from FY 2000 to FY 2001. 

Prevention of lower-extremity complications is of great importance in the VA veteran user population, because approximately 20% of veteran clinical users have diabetes, and more than 15% of persons with diabetes would be expected to develop lower-extremity complications over the course of their lifetime, with significant morbidity, mortality, and costs.

The Veterans Health Administration has many similarities to a staff-model delivery system and has a nationwide, standardized, electronic medical record system that can be customized at the local level.

Consequently, foot care delivery in the VA provides a unique model in which to evaluate coordination strategies in the implementation of chronic disease programs because of the explicit linkage of policy with ongoing and active performance measurement and feedback, and the high prevalence of the condition. 

A number of studies suggest that multidisciplinary foot care programs based in general diabetes, specialty, or research clinics have improved patient outcomes, 

and we recently reported that summary provider-reported coordination scales correlated with improved outcomes. Therefore, we anticipated that the commonality of the VA foot care programs (microsystems of care) would be in their coordinating activities, especially those that involve VA data and the electronic medical record. 

Consequently, we were surprised to find that the bestperforming facility had implemented only 14 of the 22 coordinating and global-resources (Tables 2-4) strategies that we had identified on the basis of expert consensus, and that 6 of the 10 sites had implemented fewer than one third of those strategies. Of particular concern were the observations that interfacility coordination of foot care between tertiary care and referral centers was not optimal at 5 sites, and that high-risk foot registries had not been established despite the strength of the VA informatics systems and the availability of facility-level reg- istries from VA headquarters. Individual microsystems of care within facilities that provided services for individuals with high-risk feet (eg, vascular surgery) constructed hand-entered databases. Nonetheless, in today's environment of patient safety, informal coordination strategies cannot substitute for formal mechanisms. Although veteran education is a national priority for the Veterans Administration,

foot care education was not systematized within the facilities we studied. Furthermore, we found that self-reports of patient foot care needs were not systematically evaluated at any facility. 

Lower Extremity Complications in VHA (FY 89-99) Part II: LFA Rates, Combined Conditions, and Outpatient Utilization.

Documented adherence to foot risk screening and referral recommendations markedly improved after the introduction of performance measures. For example, in FY 2000 the mean national percentages of veterans with diabetes receiving an annual visual, sensory, and vascular (pulses) foot examination were 93%, 82%, and 78%, respectively, compared with 77%, 38%, and 51% during the baseline FY 1995-FY 1996 period.

Furthermore, 85% of patients with an abnormal screening result were referred to a foot care provider. During this time period, major, minor, and total amputations performed in the Veterans Health Administration each decreased by about 16% despite a 34% increase in the number of veteran clinical users, although these data do not account for out-of-system procedures (Healthcare Analysis and Information Group. 

 VA Intranet document. Contact author to gain access). These findings indicate the high functioning of the VA with respect to the provision of foot care in 2000 and 2001 and are without a contemporaneous benchmark in any other system of care, because the Health Plan Employer Data and Information Set (HEDISÂ®) did not incorporate foot care screening into its measurement set. Our findings are also consistent within the larger context of the systematization of quality in the VA through structure and policy 

and application of health services research.

Nonetheless, our findings indicate that during the study period there remained opportunities to improve foot care coordination strategies despite explicit national policy and the potential availability of informatics support. These findings are consistent with recent literature suggesting that there are more fundamental issues for the existence of a “quality chasmâ€ in healthcare other than the lack of organized systems of care and unavailability of reliable and timely data.

Economic competition and reimbursement have been cited as barriers to achieving efficient medical care. However, the VA is not in a competitive marketplace and subsidizes its largely economically challenged population. Based on the Veterans Equitable Resource Allocation Law, network-level and facilitylevel funding is primarily based on the number and complexity of patients.

However, fixed funding must be allocated across multiple clinical programs in the VA, as well as a variety of mandated preventive and disease- specific performance measures. 

One possible explanation for our findings is that facilities give priority to resource allocation for quality improvement in measured areas of care.

This thesis is supported by the emphasis at all study sites on improving adherence to foot screening and referral, which are mandated performance measures, often through the use of clinical reminders in the electronic medical record; however, less emphasis is placed on quality improvement in unmeasured surveillance and salvage activities (postscreening care). 

In addition, competing needs may prevent the facility from having informatics personnel devote time to developing program-specific products such as foot care registries for identifying and tracking patients at risk. It is possible that the improvement in performance measures for foot screening may have led managers to prioritize allocation of resources to other programs. Alternatively, the line managers responsible for organizing and providing oversight for foot care programs may not understand the purpose of foot care performance measurement, 33 which in this case is to identify a high-risk cohort that can be tracked for foot care delivery. 

Another possibility is that healthcare systems often are allocated insufficient resources for the development of care management programs, resulting in a lack of incentives to encourage physician participation.

Although the VA is largely a staff-model system, certain key individuals in foot care programs (eg, podiatrists, vascular surgeons) may not be full time, and thus have a decreased incentive for unreimbursed involvement in quality improvement activities. These factors may impede implementation of the Institute of Medicine's recommendations for consistent development of effective teams and teamwork based on a sound human resources development strategy. 

We recognize a number of limitations to our findings. Our coordination strategies were adapted from work performed in surgical intensive care units,7 and while the foot care—specific strategies we evaluated have face validity based on a small study from 1 outpatient setting,

there is no evidence of their effectiveness in reducing ulcers and amputations. Our list of strategies may have overlooked other key elements. Furthermore, although the VA directives on foot care recommended general strategies to achieve multidisciplinary programs, specific strategies were not provided to field facilities. 

In addition, we evaluated a small, not randomly selected, sample of VA medical centers. It is possible that other medical centers have achieved the objectives of a coordinated foot care program, but were not included in the study. This limits the generalizability of our findings both to other VA facilities and to other systems of care. Although we attempted to interview front-line providers, their managers, and senior administrators at each site, we acknowledge the possibility of both reviewer and respondent bias. Finally, given the rapid evolution of the VA electronic medical record, and the recent introduction of care coordination as a major Veterans Health Administration initiative, our findings may not be representative of VA foot care programs in 2004. Nonetheless, our findings may have ongoing relevance to the private sector, where neither foot care measures nor an electronic medical record are routinely implemented in the ambulatory care setting. 

In conclusion, our key study finding is that neither explicit policies nor mandated process measures to identify patients at risk for lower extremity complications necessarily resulted in the development of systematized, real time, data-driven, patient-centered foot care for veterans with diabetes who were at high risk of sustaining lower limb complications. 

These observations suggest the importance of additional research to better understand how to improve coordination of care of patients with chronic disease in the outpatient setting. In addition, we propose that organizations that accredit chronic disease programs go beyond policies, performance measures, and patient interviews, and attempt to evaluate coordination of care. Findings from our studies suggest that provider surveys 

and site visits may be synergistic in evaluating the coordination-of-care strategies within a defined healthcare system. Such knowledge could be used internally by healthcare systems to develop and manage action plans to improve coordination, and evaluate and track trends in key elements of disease-specific care, including patient-centered care, even if these results are not able to be used for public comparisons. 

We would like to thank Gerald Hawley, RN, MSN, for his assistance in creating the facility-specific diabetes registries. We also thank the 10 site principal investigators whose efforts were invaluable to the conduct of this research, but whose names must be withheld to protect site anonymity. 

From the Department of Veterans Affairs New Jersey Health Care System, Center for Healthcare Knowledge Management, East Orange, NJ 1(LP); the University of Medicine and Dentistry of New Jersey, New Jersey Medical School, Newark, NJ (LP); the VA Management Decision & Research Center and the Program on Health Policy and Management, Boston University School of Public Health, Boston, Mass (MPC); the VA Medical and Regional Office Center, Department of Veterans Affairs, White River Junction, Vt (JSW); the Department of Community and Family Medicine, Dartmouth Medical School, Hanover, NH (JSW); the Cleveland VA Medical Center, Department of Veterans Affairs, Cleveland, OH (JMR); Health Services and Research and Development (KMB, GER), and Primary and Specialty Medical Care Services (LH), VA Puget Sound Health Care System, Department of Veterans Affairs, Seattle, Wash; and the Departments of Health Services and Epidemiology, University of Washington, Seattle, Wash (GER). 

This research was supported by grant DIS-99037 from the Department of Veterans Affairs, Health Services Research and Development. The views expressed in this article are those of the authors and do not necessarily represent the views of the agencies providing support. 

Address correspondence to: Leonard Pogach, MD, MBA, VA New Jersey Health Care System, Health Services Research Center for Healthcare Knowledge Management, East Orange VAMC, Room 9-160 (111), 385 Tremont Avenue, East Orange, NJ 07018. E-mail: 

Articles

POLICY

Impact of Policies and Performance Measurement on Development of Organizational Coordinating Strategies for Chronic Care Delivery

 To examine prevalence and co-occurrence of diabetes mellitus (DM), hypertension (HT), and elevated low-density lipoprotein cholesterol (dyslipidemia, or DL) in a managed care population.

 The study population included all adults (age > 20 years) who had been members of Kaiser Permanente, Northern California, for at least 4 months on December 31, 2001 (n = 2.1 million). Criteria from the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of Hypertension, the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, and the Northern California Kaiser Permanente Diabetes Registry were applied to computerized databases for an 18-month period to identify HT, DL, and DM, respectively. Because screening for these conditions is incomplete, we applied age- and sex-specific prevalence estimates from the Third National Health and Nutrition Examination Survey to simulate full ascertainment.

 Unadjusted prevalence rates of HT, DL, and DM were 23.8%, 17.6%, and 6.6 %, respectively. More than 50% of persons with either HT or DL also had at least 1 other condition. Of all persons with DM, 74% had HT, 73% had DL, and 56% had both. Under full ascertainment, prevalence increased to 27.6%, 35.6%, and 8.7% for HT, DL, and DM, respectively, and co-occurrence increased further.

 HT, DL, and DM co-occur in most affected individuals. To avoid fragmentation of care, disease management strategies should aim to manage these conditions within the same programs.

(Am J Manag Care. 2004;10(part 2):163-170)

Clinical trials have established that behavioral and pharmacologic therapy for lowering blood pressure, low-density lipoprotein cholesterol (LDL-C) levels, and glycemia in diabetes can markedly lower risk of cardiovascular disease, including both coronary heart disease (CHD) and stroke.

 In the case of diabetes, the benefits of improving glycemic control may predominantly affect rates of microvascular complications rather than cardiovascular disease (CVD), but there is evidence that at least some hypoglycemic regimens also may lower risk of CVD.

 Moreover, lowering LDL-C and blood pressure has proven to be particularly beneficial in diabetes patients, in part because of their greatly elevated baseline risk of CVD.

These 3 conditions co-occur. Hypertension is common in persons with diabetes.

 Associations between hypertension or diabetes and elevated LDL-C are less consistent,

 but other lipid abnormalities—including decreased high-density lipoprotein cholesterol (HDLC), increased plasma triglycerides, and increased levels of small, dense, LDL-C particles—frequently accompany both hypertension and glucose intolerance in diabetes and in the metabolic syndrome.

 Because of the increased risk of CVD, the most recent revision of the National Cholesterol Education Program guidelines (Adult Treatment Panel 3 [ATP 3]) recommends more aggressive management of both hypertension and lipid abnormalities when they co-occur, or in the presence of diabetes.

Population-based approaches to disease and risk factor management have appeared within organized healthcare systems in recent years.

 However, if the extent of co-occurrence of these 3 conditions is high, disease management programs that manage all 3 conditions simultaneously may be more effective and efficient than programs targeted at single conditions. 

Until recently, estimating the prevalence and overlap of hypertension with other conditions in clinical populations has been difficult because of a lack of computerized data for identifying hypertension. Whereas computerized laboratory and pharmacy data have been used for several years to identify diabetes and LDL-C levels in large healthcare systems, outpatient blood pressure readings have been much less widely available. Moreover, unlike the medications used to treat diabetes and LDL-C abnormalities, those used for hypertension are not unique to the condition and therefore do not identify the condition simply by their presence. These data deficiencies may have contributed to a lower level of attention to hypertension in disease management and quality improvement efforts. 

In this study, we used newly available computerized data on outpatient blood pressure levels, along with computerized laboratory results, outpatient diagnoses, and prescriptions, to estimate the prevalence and overlap of these 3 conditions in a very large managed care population. Because screening for diabetes and elevated LDL-C levels is not uniformly practiced by clinicians, these 2 conditions are not fully ascertained in clinical databases. We therefore used data from the Third National Health and Nutrition Examination Survey (NHANES 3) to estimate what prevalence and co-occurrence of the 3 conditions would be if each were fully ascertained. 

This study included the entire adult membership (age 20 years and above) of the Kaiser Permanente (KP) Medical Care Program, Northern California, as of December 31, 2001, who had been continuously enrolled for at least the prior 4 months. Kaiser Permanente is a fully integrated healthcare system that provided comprehensive medical care to just over 3 million members in northern California in 2001. Computerized clinical databases, including outpatient blood pressure measurements, diagnoses, laboratory results, and prescriptions, as well as hospital discharge databases, were searched for the prior 18 months to identify cases and to estimate the 18-month period prevalence of hypertension, dyslipidemia (elevated LDL-C), and diabetes mellitus. The rationale for the 18-month ascertainment window was that outpatient blood pressure measures have only been available for this period. 

The diagnostic criteria for each condition are provided in 

. All criteria were applied to computerized clinical data. For each condition, criteria depended in part on outpatient diagnoses and prescription records. However, hypertension and LDL-C elevations also could be identified without a diagnosis or prescription, based solely on laboratory or outpatient blood pressure results. Persons who met criteria for either of these conditions, but who did not have a compatible diagnosis or an appropriate prescription during the 18-month period, were classified as "undiagnosed" by a clinician. No comparable "undiagnosed" category was specified for diabetes because of the lack of a suitably diagnostic laboratory result. Glycosylated hemoglobin (HbA

) was used only rarely as a screening test. Screening fasting glucose requires repetition to confirm, which would be unlikely unless the diagnosis was strongly suspected (and therefore recorded). For hypertension and LDL-C, we used the risk-level specific blood pressure and LDL-C cut points specified by The Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 6)

 respectively. Hospital discharge diagnoses, outpatient diagnoses recorded at any time during the past 5 years, and laboratory data (serum creatinine values) were used to identify risk factors (eg, CHD, target organ disease) that lead to lower cut points for these 2 conditions. 

Each of these clinical data sources has been shown to be highly accurate in validations against medical record review. The prescription and laboratory result databases are archived directly from electronic medical records and are therefore the official clinical records of each. The accuracy of the inpatient and outpatient diagnosis databases has been documented for several diagnoses.

 Since mid-2000, the optically scanned encounter forms used in all of KP's outpatient clinics have allowed entry of a blood pressure recording in 8 preset categories for both systolic and diastolic pressure. These categories allow distinction of values above and below all key cut points shown in Table 1. In most clinics, blood pressure is initially measured by a medical assistant and entered onto the paper medical record. Clinicians review these entries, may repeat the measurement, and then enter either the recorded value or the value obtained at repeat measurement onto the encounter form. The accuracy of these computerized readings was examined in a study conducted by KP's Department of Quality and Utilization after the first year of blood pressure data entry (with more than 2.8 million blood pressure measurements entered). In 95% of 522 cases reviewed, the blood pressure category in the computerized database matched a blood pressure recorded in the medical chart at the same visit for both systolic and diastolic pressure. Some of the apparent discrepancies almost certainly reflect clinician failure to record the remeasured blood pressure in the paper record as well as true errors on the encounter form. 

Estimating Prevalence and Overlap Using Clinical Data and Under Full Ascertainment 

Age- and sex-specific prevalence of each condition was identified from the clinical data described above. The co-occurrence (joint prevalence) of each pair of conditions and of all 3 conditions was also determined. Venn diagrams were used to allow visual examination of the overlap. 

To examine changes in prevalence and overlap that might occur if 100% of the population had been screened for each condition, we obtained NHANES 3 data from the National Center for Health Statistics, Centers for Disease Control and Prevention.

 Sex- and age-specific (10-year intervals) prevalences of hypertension, elevated LDL-C, and diabetes mellitus were calculated, along with joint prevalence, using the same risk-specific diagnostic criteria used for KP data (Table 1). Direct measurements of LDLC, blood pressure, HbA

, and fasting glucose were available for 100% of the sample. To diagnose diabetes, either a HbA

 value of &#8805;7% or a fasting glucose concentration of &#8805;126 mg/dL was used. These measurements were supplemented with self-report by subjects of current use of antihypertensive, lipid-lowering, and antidiabetic medications. Ascertainment of the comorbid conditions required to identify appropriate risk factor cut points for hypertension and LDL-C was based on self-report in the NHANES sample. These age- and sex-specific prevalence estimates for the NHANES sample were then applied to the age and sex distribution of the KP adult membership. 

compares demographic and clinical characteristics of the KP population and the NHANES sample. The prevalences of diabetes, hypertension, and LDL-C elevations were, as expected, higher in the NHANES cohort because the entire population was evaluated for each condition. The prevalence of most comorbid conditions was quite similar for the KP population and the NHANES sample. 

Frequency of Evaluation for Hypertension and Elevated LDL-C

Not surprisingly, evaluation for both hypertension and elevated LDL-C was much more common at older ages (

) in the KP population. At all ages, evaluation was more frequent for hypertension than for LDL-C. Recorded blood pressure values were more frequent for women than for men, particularly at younger ages, presumably because women are more likely to have outpatient visits than men. A check of utilization data for the 18-month window revealed that 29.6% of men, but only 12.9% of women, in the 20- to 44-year-old age stratum had no outpatient visits during the 18- month window. These proportions decreased to only 6.9% of men and 5.9% of women in the 65+ year age group. Undiagnosed hypertension (ie, elevated values with no outpatient diagnosis of hypertension and no evidence of prescription of antihypertensive medications) was infrequent at all ages in terms of absolute prevalence. As a proportion of all hypertension identified, lack of a diagnosis or pharmacotherapy was more frequent in persons younger than age 45 years. In terms of both absolute prevalence and as a proportion of all LDLC elevations identified, LDL-C elevations were more likely to be undiagnosed at all ages. 

Prevalence and Overlap of the 3 Conditions 

The prevalence of each condition increased markedly with age, as did the clustering or co-occurrence of 2 or all 3 conditions (

). The prevalence of having all 3 conditions showed the strongest positive association with age. Hypertension was noted more frequently in women than men at all ages, whereas the prevalence of diabetes and LDL-C elevations was modestly greater for men. Isolated diabetes mellitus was uncommon at all ages. Even in the youngest group of adults, where the prevalence of hypertension and elevated LDL-C was relatively low, more than two thirds of both men and women with diabetes had at least 1 of the other 2 conditions. This proportion approached 90% for those age 45 to 64 years, and 95% for those age 65 years and above. 

The overlap of the 3 conditions in clinical data for the entire adult population was striking (

). More than half of the patients with either hypertension or elevations of LDL-C also had the other condition, and 3.7% of all adult patients had all 3 conditions. Isolated hypertension was more common than isolated elevation of LDL-C, because ascertainment for blood pressure was greater than that for LDL-C levels. Findings changed somewhat under conditions of full ascertainment, as simulated with NHANES 3 data. Prevalence of elevated LDL-C more than doubled, reflecting the relatively low rates of testing found in the clinical data, and isolated elevations of LDL-C became more common, accounting for 47% of all dyslipidemia. However, identifying more dyslipidemia actually reduced the prevalence of isolated hypertension and diabetes, despite somewhat increased detection of each of these conditions overall. Comparison of the two Venn diagrams suggests that even with full ascertainment, these 3 common conditions overlap in adult populations. 

Whether based on clinically available data (which somewhat underestimate prevalence) or on full ascertainment using NHANES 3 data, diabetes mellitus, hypertension, and elevations of LDL-C co-occur at levels much greater than would be predicted solely on the basis of their prevalence. Explaining these associations is beyond the scope of the present paper, but substantial evidence points to obesity and insulin resistance as factors tightly linked to these conditions.

 From the disease management perspective, 2 important messages emerge. First, identification of any 1 of these conditions should trigger a search for the other 2 conditions. Second, programs aimed at managing any 1 of these conditions should be prepared to evaluate and manage all 3 conditions. A multifactorial program targeting all 3 risk factors in persons with type 2 diabetes has recently been shown to dramatically lower the incidence of cardiovascular disease and other complications of diabetes.

The remarkable overlap of diabetes mellitus with each of the other conditions deserves comment. More than 90% of all women and approximately 88% of all men with diabetes also met criteria for at least 1 of the other 2 conditions; 56% of all patients with diabetes had all 3 conditions. Neither of 2 possible artifactual explanations appear to account for most of this overlap. Lower cut points were used to diagnose both hypertension and dyslipidemia in the presence of diabetes, making the co-occurrence of both conditions with diabetes somewhat more likely. However, when we redefined hypertension and LDL-C elevation in diabetes patients with the same cut points used for nondiabetic patients, 83% of both men and women with diabetes still had at least 1 other condition, and 46% of each sex had both. A second possible explanation is the increased tendency of physicians to screen for and diagnose hypertension and LDL-C in the presence of diabetes (surveillance bias). However, when we used NHANES 3 data to simulate full ascertainment, the overlap was even greater, primarily because much more dyslipidemia is found when everyone is screened. Thus, even if screening for diabetes increases dramatically, we should not expect to uncover many patients with isolated diabetes. 

The overlap of dyslipidemia with both hypertension and diabetes was particularly striking in light of our definition, which focused solely on elevations of LDLC. Reduction of LDL-C remains the primary aim of ATP 3 guidelines

 and also has been the exclusive lipid focus of most disease management programs. However, ATP 3 also extends attention to abnormalities of plasma triglycerides and HDL-C. If these abnormalities (fasting triglycerides of Â¡Ã200 mg/dL, reductions in HDL-C to <40 mg/dL, or both) had been included in our definition, 94 322 additional persons would be identified, and the population prevalence of dyslipidemia would increase from 17.6% to 22.1%. Because these 2 abnormalities are typical of the metabolic syndrome, 

 which is strongly associated with both hypertension and type 2 diabetes, overlap of dyslipidemia with these conditions would increase further if the definition of dyslipidemia were expanded. 

Our study is among the first to use outpatient blood pressure readings as well as clinical diagnoses to identify hypertension in a clinical population. This approach, more typical of epidemiologic than clinical studies, may slightly overestimate both prevalence and underdiagnosis (ie, high readings without a recorded diagnosis) of hypertension by including some patients who also had several normal blood pressure measurements during the 18 months in addition to the required 2 elevated readings. Nevertheless, our findings do suggest the likelihood that small proportions of patients meeting clinical criteria for hypertension remain undiagnosed and untreated. 

The apparently low level of screening for lipids, especially in younger persons, is exaggerated by the methods used in this study. Most clinical guidelines call for screening every 5 years after initial normal values in the absence of diabetes or coronary heart disease. To maintain consistency with our ascertainment approach for hypertension and diabetes, we sought lipid measurements for only an 18-month period. This approach would count as "unscreened" many lower risk individuals who had had normal results within the prior 3.5 years, and for whom screening was indicated during this interval. The extraordinary prevalence of elevated LDL-C under full ascertainment emphasizes the importance of periodic screening. 

Elevations of LDL-C were more likely than hypertension to be undiagnosed at all ages. However, we used the new cut points of the ATP 3 for diagnosing elevated LDL-C. These lower cut points, particularly for persons with diabetes or other CHD risk equivalents (cut point of Â¡Ã100 mg/dL) and for other persons with 2 or more CHD risk factors (cut point of Â¡Ã130 mg/dL), were first promulgated in May 2001, halfway through our study period. Thus, it may overstate the case to label these persons as "undiagnosed." The large impact of changes in these cut points on the numbers of persons eligible for treatment has been documented.

Dissemination of this information into clinical practice would not be expected to be complete in this narrow time frame. Further, for many of these persons, pharmacotherapy is not necessarily indicated,

and clinicians may be somewhat less likely to note a diagnosis when pharmacotherapy is not contemplated. Nevertheless, others have previously found that elevations of LDL-C are more likely to be overlooked and untreated than hypertension or hyperglycemia, even among persons with diabetes.

Prevalence estimates for each condition may be slightly low because we included persons who had been members for as short a time as the last 4 months of the 18-month window. Thus, small numbers of persons with 1 or more conditions may not have been members long enough to generate diagnostic, prescription, or laboratory data to identify their conditions. Recalculation of prevalence estimates in persons who were continuously enrolled for the full 18 months reduced the population size by approximately 15% and increased the prevalence of hypertension from 23.8% to 26.2%, of dyslipidemia from 17.6% to 19.3%, and of diabetes from 6.6% to 7.3%. 

Finally, some differences in prevalence between the KP population and the NHANES sample could be due to differences in the distribution of race/ethnicity of the 2 groups, or to the fact that the entire KP population resides in northern California, whereas NHANES is a national sample. The NHANES sample was 10.6% African American and 8.9% Hispanic/Latino. Complete race/ethnicity information for the KP membership is not available, but a large survey of 30 000 adult members in 1999 suggested that the KP membership age 20 years and above is approximately 64% white, 7% African American, 12% Hispanic/Latino, and 16% Asian/Pacific Islander. 

In conclusion, our data confirm that all 3 of these conditions should be considered and managed together as part of both primary and secondary CVD prevention efforts. As computerized blood pressure data become more available to healthcare systems, improved population approaches to supporting the management of these associated conditions can be implemented.

From the Division of Research, Kaiser Permanente Medical Care Program, Northern California, Oakland, Calif (JVS, TP, AJK, MA, SS); and Pfizer, Inc, New York, NY (JL, AA, DP). 

This research was funded by a grant from Pfizer, Inc. 

Address correspondence to: Joe V. Selby MD MPH, Division of Research, 2000 Broadway, Oakland, CA 94612. 

1. Hypertension Detection and Follow-up Program Cooperative Group.

 Five-year findings of the hypertension detection and followup program. I. Reduction in mortality of persons with high blood pressure, including mild hypertension. 

2. Systolic Hypertension in the Elderly Program Cooperative Research Group.

 Prevention of stroke by antihypertensive drug treatment in older persons with isolated systolic hypertension. 

 The Lipid Research Clinics Coronary Primary Prevention Trial results. I. Reduction in incidence of coronary heart disease. 

Helsinki Heart Study: primaryprevention trial with gemfibrozil in middle-aged men with dyslipidemia. Safety of treatment, changes in risk factors, and incidence of coronary heart disease. 

 Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). 

6. The Diabetes Control and Complications Trial Research Group.

 The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. 

Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). 

 Effect of intensive bloodglucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). 

 Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. 

High Rates of Co-occurrence of Hypertension, Elevated Low-Density Lipoprotein Cholesterol, and Diabetes Mellitus in a Large Managed Care Population

 To review the literature on comorbid depression and diabetes (DM/D) and present a conceptual framework for integrating depression management with diabetes care in a managed care environment.

 Depression affects diabetes management by 1) directly affecting patientsâ€™ health-related quality of life, 2) reducing physical activity levels, 3) limiting adherence to self-care regimens, and 4) impairing patientsâ€™ ability to communicate effectively with clinicians. Small randomized trials suggest that both antidepressant medication and cognitive behavioral therapies (CBTs) or related approaches may improve not only DM/D patientsâ€™ depressive symptoms, but their physical health as well.

 An effective DM/D management strategy should include the following elements: 1) systematic identification of DM/D patients and quality-of-care reviews, 2) proactive patient monitoring between outpatient encounters, 3) intensive efforts to coordinate treatment across providers, 4) increased access to CBT or related therapies addressing patientsâ€™ depressive symptoms and diabetes self care, and 5) an emphasis on promoting physical activity to address both depressive symptoms and physiologic dysregulation. The conceptual framework developed for the care of DM/D patients offers general insights into the management of patients with multiple chronic medical disorders

(Am J Manag Care. 2004;10(part 2):152-162)

Medical care for diabetes is improving in many health systems. The Department of Veterans Affairs,

have reorganized some diabetes services to reflect principles of effective chronic disease management. As a result, they have increased the provision of recommended care processes and improved patients' glycosylated hemoglobin (HbA

) levels–an important intermediate outcome linked to long-term complications such as blindness, limb amputation, and kidney failure.

Nevertheless, nationwide studies report that many diabetes patients continue to have difficulty with self care and experience suboptimal outcomes.

Comorbid medical problems such as depression may be key barriers to achieving treatment gains. Unfortunately, most diabetes management algorithms provide only a limited framework for considering the treatment of comorbid medical conditions. Rather, comorbid conditions usually are addressed under the broad rubric of "tailoring" guidelines to patients' unique needs–a strategy that often fails to conceptually integrate care, prioritize interventions, and identify treatment synergies (ie, treatments that impact more than 1 of the patient&#8219;s comorbid conditions and thus increase treatment value). A more explicit method of identifying priorities for treatment planning could lead to more effective and rational care for patients with comorbid conditions, especially in the context of limited healthcare resources. 

Patients with diabetes and depression (DM/D) comprise a large, costly, and vulnerable subgroup of diabetes patients. The purpose of this paper is to review the literature on comorbid depression and diabetes, and present a conceptual framework for integrating depression management with diabetes care. We discuss the ways in which depression might affect diabetes patients' self-care activities and outcomes, even when health systems offer excellent diabetes-specific treatment. Given the state-of-the-science in DM/D research, an exhaustive quantitative summary of studies addressing specific dimensions of DM/D care (eg, the relative effectiveness of interventions focusing on exercise promotion vs intensive pharmacologic management) is not possible. Nevertheless, we highlight some of the most important literature from relevant fields, examine the treatment implications suggested by the links between these 2 conditions, and suggest ways in which DM/D care may offer general insights into how to conceptualize treatment and manage patients with multiple chronic disorders. 

Like diabetes, major depressive disorder is a common and debilitating illness. Among patients in primary care, 4% to 15% meet criteria for major depression and another 9% to 16% meet criteria for other depressive disorders.

Elderly and poor individuals have particularly high rates of depression, and these groups also are at greatest risk for diabetes and diabetes-related complications. 

Diagnostic and Statistical Manual for Mental Disorders

Depression is twice as common among diabetes patients than in the general population, with 15% to 30% of diabetes patients meeting criteria for depression, depending on whether 

 criteria or high scores on standard depression scales are used to define clinically significant illness.

Evidence is conflicting with regard to whether depression is an independent risk factor for diabetes.

However, depression clearly is more common among diabetes patients–especially those with comorbid conditions.

After an initial episode of depression, patients with diabetes relapse more frequently than other patients.

Depression is a risk factor for hypertension,

and each of these illnesses increases the rate of cardiovascular events among diabetes patients. Although the relationship is complex and may differ for patients with type 1 and type 2 diabetes,

it is clear that depression among diabetes patients is associated with poor glycemic control. 

Patients with DM/D also have higher rates of retinopathy19 and macrovascular complications such as stroke and myocardial infarction (MI) than do nondepressed diabetes patients,

and they report more diabetes- related symptoms.

Patients with major depression respond to antidepressants, depression-specific psychotherapies, or both. Meta-analyses report absolute increases in remission rates of 17% to 39% among patients randomized to receive antidepressants compared with patients receiving placebo.

Antidepressants are as effective among patients with comorbid medical illnesses as they are among patients with major depression alone.

Numerous randomized trials have demonstrated the efficacy of psychotherapies such as cognitive behavioral therapy (CBT) or related approaches (eg, problem- solving therapy) for patients with depression.

Cognitive behavioral therapists work with patients to interrupt self-perpetuating cycles of negative thoughts, low mood, decreased motivation, and inactivity. They also use behavioral techniques to increase pleasurable and productive activities. Depressed patients who receive CBT appear less likely to relapse after treatment is discontinued than patients receiving only antidepressant medication.

In randomized trials, Lustman and colleagues demonstrated that both antidepressant medication and CBT are efficacious for patients with DM/D. In one study, they found that 67% of DM/D patients receiving fluoxetine showed improvement on the Beck Depression Inventory after 8 weeks of treatment compared with 37% of patients receiving placebo.

In a second study, a conservative intent-to-treat analysis showed that 71% of DM/D patients randomized to a 10- week CBT intervention achieved remission, compared with 22% of patients randomized to diabetes education alone (

 < .001); 58% of the CBT patients were in remission at the 6-month follow-up, compared with 26% of controls (

Although there are no large studies indicating that depression treatments for DM/D patients improve medical outcomes, studies by Lustman and colleagues suggest that the treatments may have this important effect. In their CBT study,

these investigators found that patients receiving CBT had substantially better HbA1C levels 6 months after the intervention than did control patients (9.5% vs 10.9%; 

Despite the potential efficacy of depression treatment, DM/D patients are likely to experience significant gaps in their depression care. Providers often fail to detect depression among their patients,

and when depression is identified, depression-specific therapies often are not initiated.

Primary care physicians may be uncomfortable managing depression because they lack treatment expertise or because of time constraints and competing clinical demands. Patients may be unwilling to accept or continue antidepressant treatment because of concerns about stigma or side effects.

Effective CBT therapy often is unavailable because of the lack of adequately trained therapists or limitations on patients' mental health benefits.

Among patients who initiate a course of antidepressant medication, long-term adherence is poor. In a large managed care organization, 28% of patients discontinued their antidepressants within the first month of treatment and 44% discontinued these medications by the third month.

Pharmaceutical claims databases suggest that 30% of patients with private insurance and 70% of patients with Medicaid coverage fill fewer than 4 30-day prescriptions in the 6 months after initiation of antidepressant therapy and thus do not receive an adequate antidepressant trial.

A potential barrier to effective depression management among diabetes patients is the lack of a coherent view of patients' clinical problems that encompasses both disorders. At the system level, this often is reflected by a structural lack of integration between medical and mental health services, with mental health and general medical providers often treating patients in different locations, maintaining separate medical records, and communicating infrequently.

Primary care providers often do not appreciate the importance of aggressive depression management for patients' overall health, and patients often do not draw connections between their depressive symptoms and their ability to manage their diabetes. Despite a good deal of descriptive epidemiology about the confluence of diabetes and depression as well as the availability of effective treatments, there is little consensus regarding the mechanisms linking these 2 conditions or how the care of DM/D patients might be most effectively managed. 

CONCEPTUAL FRAMEWORK FOR THE RELATIONSHIP BETWEEN DIABETES AND DEPRESSION

Based on an extensive review of the literature, we developed a framework for better understanding the impact of depression on diabetes care and treatment outcomes in managed care settings (

). This model specifies 4 main pathways through which depression may affect outcomes among patients with DM/D: 1) by directly impacting their health-related quality of life, 2) by affecting their physical activity levels, 3) by affecting their self efficacy for diabetes management and self care, and 4) by affecting their ability to communicate effectively with healthcare providers. The Figure also suggests the theoretical impact of treatment with antidepressants or behaviorally focused counseling such as CBT. 

First Pathway: Depression Directly Affects DM/D Patients' Quality of Life and Functioning

Depression directly affects the health-related quality of life of patients with many chronic disorders, including diabetes.

Depression is associated with increased mortality rates among patients with heart failure,

February 2004 - Part 2

Impact of Policies and Performance Measurement on Development of Organizational Coordinating Strategies for Chronic Care Delivery

High Rates of Co-occurrence of Hypertension, Elevated Low-Density Lipoprotein Cholesterol, and Diabetes Mellitus in a Large Managed Care Population

Addressing the Needs of Patients With Multiple Chronic Illnesses

Costs and Utilization Associated With Pharmaceutical Adherence in a Diabetic Population

Referral Management and the Care of Patients With Diabetes

Cholesterol Management of Patients With Diabetes in a Primary Care Practice""CBased Research Network

The Intensification of Drug Therapy for Diabetes and Its Complications

Evaluation of Angiotensin-Converting Enzyme Inhibitor Use in Patients With Type 2 Diabetes in a State Managed Care Plan

Improving Diabetes Treatment Quality in Managed Care Organizations