Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)



Research Advisor

Leslie M. McKeon, PhD, RN, NEA


Patricia D. Cunningham, DNSc, PMHNP/CNSBC, FNP-BC Mary S. Dietrich, PhD, MS Veronica F. Engle, PhD, RN, GNP, FAAN Margaret T. Hartig, PhD, RN, FNP-BC


Cardiometabolic Screening, Chronic Care Model, Community Mental Health, Quality, Severe Mental Illness


Background: Persons with a severe mental illness (SMI) prematurely lose up to 25 years of life when compared to the general population. This patient population has increased morbidity and mortality due to higher than normal rates of obesity, hypertension, diabetes, and cardiovascular disease. Treatment of SMI often includes the use of atypical antipsychotic (AA) medication which has been associated with the development of cardiometabolic illnesses. In response to the higher rates of co-morbid, chronic physical illness, monitoring guidelines for cardiometabolic illness have been published. Despite these guidelines, screening rates for cardiometabolic illness in this population remain low. Neither community mental health nor primary care systems address the physical health concerns of persons with a severe mental illness, thus widening the quality gap for this at risk, vulnerable population. The Chronic Care Model provides a systems framework for addressing the wide range of health needs for chronically ill populations and has successfully been used in improving the quality of care for persons with chronic physical health conditions. Few published studies have used the Chronic Care Model as a framework to guide improving the quality of care for persons with a SMI.

Objective: The purpose of this study was to better understand how the delivery system design of a community mental health center affects quality outcomes for persons with a SMI treated on an AA medication that are at high risk for developing cardiometabolic illness.

Methods: This cross-sectional study used baseline patient health data of persons with a SMI to analyze cardiometabolic screening rates, based on the American Diabetes Association (ADA), American Psychiatric Association (APA), Association of Clinical Endocrinologists, and North American Association for the Study of Obesity second generation antipsychotic monitoring guideline. The guideline included history of cardiovascular disease and biologic monitoring at baseline, 12 weeks, and both baseline and 12 weeks. This retrospective study used existing data from an electronic health record. A member of the clinic data team electronically extracted study demographic variables. All other study variables were manually extracted by the study investigator. The theoretical basis for this study was supported by the Care Model, an adapted version of the Chronic Care Model.

Results: The study sample consisted of 190 patients. The mean patient age was 37.13 years with a SD ± 11.7 years and a range of 19 - 70 years. The majority of patients were men (58.4%) and most patients were single (90.5%). More than one-half of the patients (53.7%) represented a minority race, though most patients were not Hispanic (95.3%). Most patients were not currently employed (88.9%) and nearly one-half of the patients lived below the federal poverty guidelines (47.4%). Ninety percent of the patients were enrolled in the Medicare or State Medical Assistance program. More patients in the study were diagnosed with a mood disorder (72.1%) than a thought disorder (27.9%). Most patients (61.6%) did not schedule their baseline or followup visit, but rather “walked” into the clinic without prior notice. The average number of visits during the initial treatment phase was 3.7 ± 1.4 and more than one-third of patients had the same provider at baseline and follow-up (36.3%).

No patients received all recommended screening measures per the ADA and APA monitoring guideline. Biological measures (excluding history of cardiovascular disease) were evaluated for ten patients at baseline, three patients at follow-up and one patient at both baseline and follow-up. At baseline, rates for each screening measure were as follows: weight or BMI (64.2%), blood pressure (62.1%), fasting plasma glucose or hemoglobin A1c (27.9%), fasting lipid profile (8.4%) and family or personal history of cardiovascular disease (34.7%). At followup, rates of each cardiometabolic screening measure were as follows: weight or BMI (63.2%), blood pressure (61.6%), fasting plasma glucose or hemoglobin A1c (13.2%), fasting lipid profile (9.5%). Summaries of the unadjusted (r) and adjusted (beta) associations between combined delivery system design candidate variables and each of the quality outcome variables at baseline revealed associations between being a current smoker (r = .15, p = .041), having a clinic primary care provider (r = .21, p = .003), being a walk-in at baseline (r = .14, p - .048), and the number of screening measures. At follow-up, no statistically significant associations were observed.

Conclusion: Data suggest that the delivery system design of a community mental health center inadequately addresses screening for cardiometabolic symptoms of persons with SMI. Findings show that adherence to the full panel of ADA and APA recommended cardiometabolic screening measures for persons treated on an AA medication is abysmal. Even rates of common screening measures, such as blood pressure, are poor. The Care Model was a useful theoretical framework to guide the study. Results of the study indicate that SMI patients may interact with the health care system differently than patients with chronic medical conditions. It is feasible that the high rate of unscheduled visits, or “walk-in” visits and number of different providers caring for patients during the initial treatment phase contributes to poor quality care. Subsequent recommendations include developing an intervention study to evaluate quality outcomes using a) an integrated care delivery design specifically for SMI patients and b) expanding the Care Model components to include the health system organization, decision support, self-management support, and clinical information systems. It is critically important that care delivery systems for persons with SMI be integrated for optimal health outcomes.