یک تحلیل شبکه در ایالات متحده در مورد پیوستگی علائم افسردگی
Abstract
1- Introduction
2- Methods
3- Results
4- Discussion
5- Conclusions
References
Abstract
Background
Many chronic diseases increase the risk of depressive symptoms, but few studies have examined whether these diseases also affect the composition of symptoms a person is likely to experience. As the risk and progression of depression may vary between chronic diseases, we used network analysis to examine how depression symptoms are connected before and after the diagnosis of diabetes, heart disease, stroke, and cancer.
Methods
Participants (N = 7779) were from the longitudinal survey of the Health and Retirement Study. Participants were eligible if they had information on depression symptoms two and/or four years before and after the diagnosis of either diabetes, heart disease, cancer or stroke. We formed a control group with no chronic disease that was matched on age, sex and ethnic background to those with a disease. We constructed depression symptom networks and compared the overall connectivity of those networks, and depression symptom sum scores, for before and after the diagnosis of each disease.
Results
Depression symptom sum scores increased with the diagnosis of each disease. The connectivity of depression symptoms remained unchanged for all the diseases, except for stroke, for which the connectivity decreased with the diagnosis. Limitations Comorbidity with other chronic diseases was not controlled for as we focused on the onset of specific diseases.
Conclusions
Our results suggest that although the mean level of depression symptoms increases after the diagnosis of chronic disease, with most chronic diseases, these changes are not reflected in the network structure of depression symptoms.
Introduction
Depression is a heterogeneous psychiatric disorder that is often associated with other diseases (Rush and Rush, 2007). These associations also appear to be bidirectional: people with depression have an increased risk of several chronic conditions, such as diabetes, cardiovascular disease, cancer, and stroke (Utzschneider et al., 2007, Schane et al., Ali et al., 2006, Williams et al., 2004, Anderson et al., 2001, Moussavi et al., 2007), but chronic diseases may also contribute to onset of depression (Ali et al., 2006, Anderson et al., 2001, Hackett and Anderson, 2005), as the psychological adjustment to chronic illnesses can be highly challenging (de Ridder et al., 2008). In addition, studies suggest that the co-occurrence of chronic disease and depression may incremental worsen health compared to having either disease alone (Moussavi et al., 2007), and such co-occurrence has been associated with increased mortality (Williams et al., 2004, Pinquart and Duberstein, 2010). Depression involves several symptoms, including e.g. low mood, sleep disturbance, loneliness, lack of initiative, and anhedonia. However, little is known whether the pattern of symptoms might vary depending on the status of physical disease. The severity of depression has been measured by counting how many depression symptoms are present for a given individual (Diagnostic and Statistical 2013), but it has been argued that such an aggregate measure might not adequately portray the complexity of depression (Fried and Nesse, 2015). An alternative approach is to examine the network structure and dynamics of specific depression symptoms, that is, how the symptoms are connected. A denser, more tightly knit network of symptoms, for example, may indicate a higher risk for developing depression (Cramer et al., 2016).