Reciprocal Relationships between Diabetes and Depression
Reciprocal Relationships between Diabetes and Depression
批准号:
7024491
负责人:
PATRICK J O'CONNOR
金额:
$30.36万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-03-01 至 2008-02-29
中文摘要
描述(由申请人提供):该项目的目的是更好地了解糖尿病和抑郁症之间错综复杂的相互关系,以便为160亿患有这两种慢性疾病的美国人提供更有效的临床护理。这个为期四年的项目有三个具体目标(1)了解成人糖尿病如何影响临床显著性抑郁症的发病和管理,(2)了解临床显著性抑郁症如何影响成人糖尿病的管理、进展和结局,以及(3)比较抑郁症对糖尿病和糖尿病对抑郁症影响的相对强度。该项目由明尼苏达州的卫生伙伴医疗集团(Health Partners Medical Group)实施,利用了已经在17.5万名明确定义的初级保健人群中建立的复杂慢性病监测系统。由1000名成年糖尿病患者组成的偶发队列和由3000名糖尿病患者组成的流行队列的年龄、性别和临床/预约日期均与非糖尿病研究对象相匹配。这些队列在4年的时间内进行跟踪,以确定时间关系。线性回归、逻辑回归和结构方程模型被用来检验七个特定的假设,这将促进我们对如何改善为患有这两种疾病的患者提供护理的理解。该研究的优势包括:(a)一个经验丰富的研究团队,在糖尿病、抑郁症、质量改进和卫生服务研究方面有可靠的研究记录;(b)特征明确的研究人群;(c)预先开发出有效的方法来识别糖尿病和抑郁症,并使用自动化数据测量合并症、药物治疗、药物依从性和护理利用,以及(d)先前成功地使用一套类似的策略来提高糖尿病的护理质量。该项目将提供重要的缺失信息,以改善对全世界数百万患有糖尿病和抑郁症的患者的护理。
英文摘要
DESCRIPTION (provided by applicant): The objective of this project is to provide a better understanding of the intricate and complex reciprocal relationship between diabetes and depression, in order to provide more effective clinical care to the 1,600,000 million Americans who suffer from both these chronic conditions. This four-year project has three specific aims (1) To understand how diabetes mellitus in adults affects the onset and management of clinically significant depression, (2) To understand how clinically significant depression affects the management, progression, and outcomes of diabetes mellitus in adults, and (3) To compare the relative strength of influence of depression on diabetes and diabetes on depression. The project is conducted at Health Partners Medical Group in Minnesota, and takes advantage of sophisticated chronic disease surveillance systems already in place in a well-defined primary care population of 175,000 adults. An incident cohort of 1,000 adults with diabetes and a prevalence cohort of 3,000 patients with diabetes are each age-, gender-, and clinic/appointment date matched to a study subject without diabetes. These cohorts are tracked over a 4-year period to enable specification of temporal relationships. Linear regression, logistic regression, and structural equation modeling are used to test seven specific hypotheses, which will advance our understanding of how to improve the care provided to patients with both conditions. Strengths of the study include the following: (a) an experienced research team with a proven research record in diabetes, depression, quality improvement, and health services research; (b) a well-characterized study population; (c) prior development of validated methods to identify diabetes and depression, and measures of comorbidity, pharmaceutical treatment, medication adherence, and utilization of care using automated data, and (d) prior success improving diabetes quality of care using a similar set of strategies. This project will provide important missing information needed to improve the care of millions of people worldwide who suffer the devastating consequences of comorbid diabetes and depression.
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