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Major depression is one of the major mental conditions that have major impacts on quality of life and cost of health care. Over 50% of patients with major depression develop role impairment (Waraich et al., 2004). Much effort has been devoted to creating mental health interventions that lead to reduced costs to health care systems and society, while providing at least equal benefit. A body of evidence seems to suggest that managed care approaches such as collaborative care, stepped care and case management offer improved outcomes in term of prevention and treatment of major depression.
In this series of articles, we will review the potential impact of health information technologies such as computerized decision-support systems, and depression screening through reviewing electronic medical records, to the management of major depression.
In the first article, we review the incidence, and prevalence of major depression in different population. This is the first step toward estimating the extent and the cost of the disease and identifying areas that health information technology can help with.
The incidence of major depression has been estimated in the following studies (Kruijshaar et al., 2005):
- Lundby Study: 2.0 per 1000 person per year
- Baltimore Epidemiologic Catchment Area (ECA) follow-up: 4.5 per 1000 person per year
The lifetime risk of major depression is estimated to be 30% for women and 20% for men (Patten and Lee, 2004; Patten and Lee, 2005).
According to Patten (2006), the major risk factors for developing major depression are:
· Genetic factors: Studies of twins indicate that genetic factors account for 70% chance of developing depression. The risk of developing a mood disorder is 27%. If you have one parent who developed major depression and 50% if both of parents have.
· Gender: Women are more likely to develop depression than men. The rates for both genders before puberty are similar.
· Age: The age of onset between 25 and 30 years.
· Marital status: Married and never-married persons have lower rates of depression than those divorced, separated, and widowed. For example, in the Epidemiologic Catchment Area conducted by the National Institute of Mental Health (NIMH), divorced and separated individuals had over a twofold increase compared to those married and never married. Divorce and separation also increased the likelihood of the first depressive episode.
· Socio-economic status: Socio-economic status is not a strong risk factor. However, worseningsocio-economic circumstances is associated with depression.
· Life events: Events such as death of a close relative, assault, serious marital problems, and divorce/breakup predicted the incidence of major depression with the odds ratios of greater than 10.
· General medical illness: Cancer, Cardiovascular Disease, AIDS, Respiratory Disorders, Cancer, and several neurologic conditions.
In summary, although the causes of major depressive disorder are not well understood, it is possible to identify people at elevated risk for major depression. Preventive measures can be used to lower the risk for first episodes.
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Patten SB, Lee RC: Refining estimates of major depression incidence and episode duration in Canada using a Monte Carlo Markov model. Med Decis Making 2004, 24:351-358.
• Patten SB, Lee RC: Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys. Popul Hlth Metr 2005, 3:11.
• Kruijshaar ME, Barendregt J, Vos T, de Graaf R, Spijker J, Andrews G: Lifetime prevalence estimates of major depression: An indirect estimation method and a quantification of recall bias. Eur J Epidemiol 2005, 20:103-111.
• Patten SB: A major depression prognosis calculator based on episode duration. Clin Pract Epidemiol Mental Hlth 2006, 2:13.
• Waraich PS, Goldner EM, Somers JM, Hsu L: Prevalence and incidence studies of mood disorders: a systematic review of the literature. Can J Psychiatry 2004, 49:124-138.