Toward a model for predicting depression among veterans
Major depression, identified as a leading cause of disability in the United States, is often first diagnosed by primary care providers. This disability is associated with increased morbidity, mortality, and a lower quality of life. With approximately one in every three veteran diagnosed with depression and the rate of suicide increasing in the United States military (Department of Veterans Affairs, 2009), the Patient Health Questionnaire-2 items (PHQ-2) can be instrumental in identification and monitoring of depressive symptoms. The purposes of this retrospective study were to determine the prevalence of depression, as measured by the federally mandated PHQ-2 in the VA, and to create a model for predicting depression based upon the results and associated variables. A combination of Donabedian's Quality of Life model and Shaver's Biopsychosocial View of Human Health model comprised the conceptual underpinnings for the predictive correlational design employing one year of retrospective data for veterans receiving outpatient primary care at the selected federal agency to with a goal of identifying the presence or absence of depression (dependent variable) and biological, social and environmental factors (independent variables). A sample size calculator was used to calculate the number of charts necessary for a representative sample (n = 300) and charts were randomly selected. Based on inclusion criteria, the final sample size was 140 veterans. Although, the process of developing a theoretical model was not supported with the research findings, there are strong clinical indications for this veteran population. These results for depression, 18.7%, were lower than those reported in the literature. Limitations of the study were the use of a convenience sample and lack of sufficient documentation within the electronic medical records. With the primary care setting often being the first interaction for seeking healthcare, implications for nursing practice include a need to develop more robust strategies to improve recognition of depression. This strategy would include the consideration of the patient's biological, social and monitor biological, social and environmental factors.