Relationships between obesity management and depression management in a university-based family medicine center

Abstract

Obesity and depression make a significant contribution to the morbidity and mortality of populations in the United States and globally. Depression and obesity are consistently linked in a bidirectional relationship with each other in quantitative studies (Green, Bazata, Fox, & Grandy, 2012; Harrison, Miller, Schmitt, & Touchet, 2010; Pan et al., 2012). When both diseases occur, a detrimental synergistic effect on overall health and treatment response is often seen (Ma & Xiao, 2010). Both obesity and depression, occurring independently or concurrently, are consistently linked to increased healthcare costs placing an increased burden on the healthcare system (Simon et al., 2011).

The prevalence of overweight and obesity has steadily increased over the past several decades. As of 2010, 63.9% of the U.S. population was overweight, with 27.6% was obese (McGregor et al., 2010). As a consequence of the increasing prevalence of obesity, diseases and disabilities associated with excess weight such as coronary heart disease, stroke, diabetes, hypertension, hyperlipidemia, musculoskeletal disorders, certain cancers, and depression have become more prevalent (Jerrell, McIntyre, & Tripathi, 2010; Keyser & Pincus, 2010; Vgontzas et al., 2009). Because obesity is associated with increased morbidity and mortality, obesity diagnosis and treatment should be a priority in primary care. Despite clear recommendations to assess all adults for overweight and obesity with BMI (Gustafson et al., 2006), primary care providers significantly underdiag-nose and undertreat excess weight (Ding, Lehrer, Rosen-quist, & Audrain-McGovern, 2009; Watkins et al., 2006). The baseline data for Healthy People 2020 document that only 48.7% of primary care physicians regularly assess body mass index in their adult patients in 2008 (Healthy-People.gov, 2013).

The prevalence of depression in the United States has been reported to be over 8% (Li, Ford, Strine, & Mokdad, 2008). The estimated lifetime prevalence for major depressive disorder is 13.2% (O'Connor et al., 2009). Depression is estimated to be the number one cause of disability in the United States within the next two decades (Whooley, 2012). The U.S. Preventive Services Task Force recommends screening adults for depression in clinical practices that have systems in place to assure accurate diagnosis, effective treatment, and follow-up (U.S. Preventive Services Task Force [USPSTF], 2009). Despite the significant impact of depression on patient outcomes, data show that only 2.2% of primary care office visits screened adults for depression in 2007 (Prins et al., 2010).

Expanding what is known about obesity and depression management in primary care centers in Appalachia is consistent with the most recent priorities set forth by the National Institutes of Health and Healthy People 2020, which puts emphasis on understanding relationships between psychosocial and behavioral determinants of health and chronic illnesses in groups that experience health disparities (Pancoska et al., 2009). Understanding what influences the health and function of Appalachian adults is essential so that both healthcare providers and policymakers can appropriately meet patient needs. It is important that obesity and depression be studied within Appalachian culture because culture is a contributor to obesity and depression (Everson, Maty, Lynch, & Kaplan, 2002; Pancoska et al., 2009). The purpose of this study is to describe and examine relationships among sociodemographics, obesity, and depression management in Appalachian adults receiving care at a university-based family practice clinic.

In West Virginia, the prevalence of depression has been reported to be higher than the national average at 14.8% (Winkelmayer et al., 2005). There may be even more adults in Appalachia who are suffering with untreated depression. Lack of screening for depression in primary care sites likely contributes to the prevalence of untreated depression. Additionally, adults in Appalachia have been reported to engage less often in help-seeking behaviors that may contribute to diminished encounters with the healthcare system (Huttlinger et al., 2004). Postponing health care could lead to problem-focused visits where the priority is treatment of acute problems, leaving Appalachian adults without appropriate preventive screenings. In addition, studies of rural adults indicate that screening practices may be biased toward young women and screening patterns tend to be informal rather than evidence-based reliable and valid depression screening tools (Melnyk et al., 2006).

Methods

This study was conducted using a cross-sectional, quantitative, nonexperimental descriptive, and predictive design. Data were collected from a random sample of 240 adults drawn from a population who lived in a federally designated Appalachian county and who attended primary care visits at a university family medicine clinic within a 12-month period. Adults who were homeless, pregnant, or diagnosed with dementia or debilitating psychiatric illness were excluded from the study. Data were collected from outpatient electronic medical records (EMRs). The study was approved by the West Virginia University institutional review board. The study was guided by two research questions.

Results

Discussion

In a majority of healthcare settings, particularly in Appalachia, providers will frequently see patients with obesity and depression. The results of this study indicate that there is insufficient treatment of obesity and depression. The undertreatment of obesity and depression is consistent with findings from other studies (Harrison, Miller, Schmitt, & Touchet, 2010; Romera et al., 2013) that suggest there may be gender bias in the diagnosis and treatment planning of depression in this specific population. Harrison et al. (2010) also reported undertreatment of depression and a gender bias against treatment for males (Harrison et al., 2010). Female gender, younger adult age, more severe degree of obesity, living in the Midwest, and being seen by a cardiologist have been identified as predictors of obesity diagnosis (Bleich, Pickett-Blakely, & Cooper, 2011). Further studies are needed to explore why the gender bias exists and to identify barriers to treatment of obesity and depression in primary care settings.

Given the obesity epidemic in the United States, it is imperative that providers document variables such as current weight, BMI, and obesity status in order to identify the disease process and initiate consistent, timely, and effective treatment. Recent literature has reported that increased documentation of obesity can lead to increased management of obesity, but not necessarily in overweight persons (Bordowitz, Morland, & Reich, 2007). Other reports have concluded that increased documentation of BMI resulted in increased management for both obesity and overweight status (Waring, Roberts, Parker, & Eaton, 2009). In this study, it is significant that older adults were less likely to be diagnosed with obesity. National trends suggest significant increases in obesity in older adults by 2050 (Fakhouri, Ogden, Carroll, Kit, & Flegal, 2012). Although body fat can be protective in older adults, morbid obesity contributes to functional impairment and poorer chronic illness outcomes, particularly with diabetes and heart disease, two areas of disparity in the Appalachian population.

It is clinically meaningful that only two of our 240 participants were screened for depression using a reliable and valid age-appropriate depression screening tool. USPSTF recommends screening adults for depression in clinics that have resources to assure accurate diagnosis, effective treatment, and follow-up (USPSTF, 2009). It is possible that the screening tool was not easily accessible in the EMR for provider use. Future work is needed on best practices for implementing national recommendations regarding depression management within the EMR.

In addition, the gender analysis of depression screening indicates that there may be a gender bias in diagnosing and in treatment methods for depression. These findings are concurrent with reports from the WHO indicating gender bias in the diagnosis and treatment of depression (WHO, 2013). This leaves an unfortunate and clinically significant gender gap in the diagnosis and treatment planning of men suffering from depression. It is possible that providers are more apt to diagnose women— who typically may share feelings more during a visit than men—with depression and start treatment earlier in this population. Although not statistically significant, the difference between the number of men and women getting referred for counseling and receiving pharmaceutical management for depression is clinically relevant. Finally, while the sociodemographics available to be collected in this study from the EMR did not result in explanation for obesity and depression diagnosis or management, enhanced reliability and consistency of documentation of income would have allowed for accurate determination of economic status that may be predictive of obesity or depression.

References