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精神和躯体疾病与违背医嘱出院的相关性:1988-2006 年全国医院出院调查。

Associations of mental, and medical illnesses with against medical advice discharges: the National Hospital Discharge Survey, 1988-2006.

机构信息

Division of Health Policy and Administration, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612, USA.

出版信息

Adm Policy Ment Health. 2013 Mar;40(2):124-32. doi: 10.1007/s10488-011-0382-8.

Abstract

This study examined the association of mental and medical illnesses with the odds for leaving against medical advice (AMA) in a national sample of adult patients who left general hospitals between 1988 and 2006. Leaving AMA was first examined as a function of year and mental illness. Multiple logistic regression analysis was then used to adjust for patient and hospital characteristics when associating mental and major medical diagnoses with AMA discharges. The results indicated that leaving AMA was most strongly associated with mental health problems. However, the impact of mental illness was attenuated after adjusting for medical illnesses, patient and hospital characteristics. The strongest predictors of AMA discharge included being self-pay, having Medicaid insurance, being young and male, and the regional location of the hospital (Northeast). When substance abuse conditions were excluded from the mental illness discharge diagnoses, mental illness had lower odds for leaving AMA. The results may be of value to clinicians, and hospital administrators in helping to profile and target patients at risk for treatment-compliance problems. Prospective primary data collection that would include patient, physician, and hospital variables is recommended.

摘要

这项研究考察了精神和身体疾病与 1988 年至 2006 年间离开综合医院的成年患者离开医疗建议(AMA)的可能性之间的关联。首先,将离开 AMA 作为年份和精神疾病的函数进行了检查。然后,当将精神和主要医学诊断与 AMA 出院相关联时,使用多变量逻辑回归分析来调整患者和医院特征。结果表明,离开 AMA 与心理健康问题的关联最强。但是,在调整了医疗疾病,患者和医院特征后,精神疾病的影响就减弱了。AMA 出院的最强预测因素包括自费,拥有医疗补助保险,年轻和男性以及医院的地理位置(东北地区)。当将物质滥用状况从精神疾病出院诊断中排除时,精神疾病离开 AMA 的可能性较小。这些结果对于临床医生和医院管理人员帮助确定和针对有治疗依从性问题风险的患者可能具有价值。建议进行包括患者,医生和医院变量的前瞻性原始数据收集。

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