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一项基于人群的美国成年人抑郁症状患者健康服务缺陷的横断面研究。

A population-based cross-sectional study of health service deficits among U.S. adults with depressive symptoms.

机构信息

Essentia Institute of Rural Health, Duluth, MN 55803, USA.

出版信息

BMC Health Serv Res. 2013 May 1;13:160. doi: 10.1186/1472-6963-13-160.

DOI:10.1186/1472-6963-13-160
PMID:23634983
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3662567/
Abstract

BACKGROUND

Depression is a psychiatric condition that affects approximately one in five U.S. adults in their lifetime. No study that we know of has examined depressive symptoms and health service deficits in rural compared with non-rural populations. Four factors constitute the variable health service deficits: did not have health insurance, did not have a healthcare provider, deferred medical care because of cost and did not have a routine medical exam, all within the last 12 months. The aim of this study was to ascertain the prevalence of health service deficits in rural versus non-rural adults with depressive symptoms. Examining depressive symptoms by health service deficits is important because it allows us to approximate those with the condition who might not be receiving care for it. By analyzing national, population-based data, this study sought to fill in some important epidemiological gaps regarding depressive symptoms and health service deficits.

METHODS

For this analysis the population of interest was U.S. adults identified as currently having depressive symptoms using the PHQ-8 criteria. Behavior Risk Factor Surveillance Survey 2006 data were used in this analysis. Health service deficits was the primary dependent variable. Multivariate logistic regression analysis was performed to examine health service deficits experienced by adults with depression controlling for socioeconomic status, race and ethnicity and geographic locale (rural or non-rural).

RESULTS

Logistic regression analysis yielded that U.S. adults currently having depressive symptoms who were of low socioeconomic status, Hispanic ethnicity, or living in a rural locale were more likely to have at least one health service deficit.

CONCLUSION

Analyzing data collected by a large surveillance system such as BRFSS, allows for an analysis incorporating an array of covariates not available from clinically-based data such as electronic health records. By identifying clinically depressed U.S. adults who also have at least one health service deficit, we were able to ascertain those most likely not receiving care for this debilitating condition. We believe community pharmacists are well suited to assist in connecting depressed, vulnerable populations with appropriate and needed care. This care would be best provided by an inter-professional team led by a primary care provider.

摘要

背景

抑郁症是一种精神疾病,大约有五分之一的美国成年人一生中会受到影响。据我们所知,没有研究比较过农村和非农村人群的抑郁症状和卫生服务缺陷。构成卫生服务缺陷的四个因素是:在过去 12 个月内,没有医疗保险、没有医疗服务提供者、因费用而推迟医疗保健、没有常规体检。本研究的目的是确定农村和非农村有抑郁症状的成年人的卫生服务缺陷的流行率。通过卫生服务缺陷来检查抑郁症状很重要,因为它可以让我们大致了解那些可能没有得到治疗的患者。通过分析全国性的、基于人群的数据,本研究试图填补关于抑郁症状和卫生服务缺陷的一些重要的流行病学空白。

方法

本分析的目标人群是使用 PHQ-8 标准确定的目前有抑郁症状的美国成年人。本分析使用了行为风险因素监测调查 2006 年的数据。卫生服务缺陷是主要的因变量。使用多变量逻辑回归分析,控制社会经济地位、种族和民族以及地理位置(农村或非农村),检查有抑郁症状的成年人所经历的卫生服务缺陷。

结果

逻辑回归分析表明,社会经济地位低、西班牙裔或居住在农村地区的目前有抑郁症状的美国成年人更有可能至少有一种卫生服务缺陷。

结论

通过分析 BRFSS 等大型监测系统收集的数据,可以进行分析,纳入从电子健康记录等基于临床的数据中无法获得的一系列协变量。通过确定也至少有一种卫生服务缺陷的有临床抑郁的美国成年人,我们能够确定那些最有可能没有得到这种使人衰弱的疾病的治疗的人。我们认为社区药剂师非常适合帮助将脆弱的抑郁人群与适当和必要的护理联系起来。这种护理最好由一个由初级保健提供者领导的跨专业团队提供。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/16d43a9e7e09/1472-6963-13-160-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/30c30c6c7756/1472-6963-13-160-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/dab7e45b2b54/1472-6963-13-160-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/231b2e060bbf/1472-6963-13-160-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/16d43a9e7e09/1472-6963-13-160-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/30c30c6c7756/1472-6963-13-160-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/dab7e45b2b54/1472-6963-13-160-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/231b2e060bbf/1472-6963-13-160-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0abe/3662567/16d43a9e7e09/1472-6963-13-160-4.jpg

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