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灾难幸存者心理健康服务的使用:诱发因素。

Use of mental health services among disaster survivors: predisposing factors.

作者信息

den Ouden Dirk-Jan, van der Velden Peter G, Grievink Linda, Morren Mattijn, Dirkzwager Anja J E, Yzermans C Joris

机构信息

Netherlands Institute for Health Services Research (NIVEL), Utrecht, The Netherlands.

出版信息

BMC Public Health. 2007 Jul 24;7:173. doi: 10.1186/1471-2458-7-173.

DOI:10.1186/1471-2458-7-173
PMID:17650339
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1949813/
Abstract

BACKGROUND

Given the high prevalence of mental health problems after disasters it is important to study health services utilization. This study examines predictors for mental health services (MHS) utilization among survivors of a man-made disaster in the Netherlands (May 2000).

METHODS

Electronic records of survivors (n = 339; over 18 years and older) registered in a mental health service (MHS) were linked with general practice based electronic medical records (EMRs) of survivors and data obtained in surveys. EMR data were available from 16 months pre-disaster until 3 years post-disaster. Symptoms and diagnoses in the EMRs were coded according to the International Classification of Primary Care (ICPC). Surveys were carried out 2-3 weeks and 18 months post-disaster, and included validated questionnaires on psychological distress, post-traumatic stress reactions and social functioning. Demographic and disaster-related variables were available. Predisposing factors for MHS utilization 0-18 months and 18-36 months post-disaster were examined using multiple logistic regression models.

RESULTS

In multiple logistic models, adjusting for demographic and disaster related variables, MHS utilization was predicted by demographic variables (young age, immigrant, public health insurance, unemployment), disaster-related exposure (relocation and injuries), self-reported psychological problems and pre- and post-disaster physician diagnosed health problems (chronic diseases, musculoskeletal problems). After controlling for all health variables, disaster intrusions and avoidance reactions (OR:2.86; CI:1.48-5.53), hostility (OR:2.04; CI:1.28-3.25), pre-disaster chronic diseases (OR:1.82; CI:1.25-2.65), injuries as a result of the disaster (OR:1.80;CI:1.13-2.86), social functioning problems (OR:1.61;CI:1.05-2.44) and younger age (OR:0.98;CI:0.96-0.99) predicted MHS utilization within 18 months post-disaster. Furthermore, disaster intrusions and avoidance reactions (OR:2.29;CI:1.04-5.07) and hostility (OR:3.77;CI:1.51-9.40) predicted MHS utilization following 18 months post-disaster.

CONCLUSION

This study showed that several demographic and disaster-related variables and self-reported and physician diagnosed health problems predicted post-disaster MHS-use. The most important factors to predict post-disaster MHS utilization were disaster intrusions and avoidance reactions and symptoms of hostility (which can be identified as symptoms of PTSD) and pre-disaster chronic diseases.

摘要

背景

鉴于灾难后心理健康问题的高发生率,研究卫生服务利用情况很重要。本研究调查了荷兰一次人为灾难(2000年5月)幸存者中心理健康服务(MHS)利用的预测因素。

方法

在心理健康服务机构登记的幸存者(n = 339;18岁及以上)的电子记录与幸存者基于全科医疗的电子病历(EMR)以及调查中获得的数据相链接。EMR数据可获取灾难前16个月至灾后3年的信息。EMR中的症状和诊断依据国际初级保健分类(ICPC)进行编码。在灾难后2 - 3周和18个月进行调查,调查内容包括经过验证的关于心理困扰、创伤后应激反应和社会功能的问卷。可获取人口统计学和与灾难相关的变量。使用多元逻辑回归模型研究灾难后0 - 18个月和18 - 36个月MHS利用的 predisposing因素。

结果

在多元逻辑模型中,对人口统计学和与灾难相关的变量进行调整后,MHS利用可由以下因素预测:人口统计学变量(年轻、移民、公共医疗保险、失业)、与灾难相关的暴露(重新安置和受伤)、自我报告的心理问题以及灾难前后医生诊断的健康问题(慢性病、肌肉骨骼问题)。在控制所有健康变量后,灾难侵扰和回避反应(OR:2.86;CI:1.48 - 5.53)、敌意(OR:2.04;CI:1.28 - 3.25)、灾难前慢性病(OR:1.82;CI:1.25 - 2.65)、灾难导致的受伤(OR:1.80;CI:1.13 - 2.86)、社会功能问题(OR:1.61;CI:1.05 - 2.44)以及较年轻的年龄(OR:0.98;CI:0.96 - .99)预测了灾后18个月内的MHS利用情况。此外,灾难侵扰和回避反应(OR:2.29;CI:1.04 - 5.07)以及敌意(OR:3.77;CI:1.51 - 9.40)预测了灾后18个月后的MHS利用情况。

结论

本研究表明,若干人口统计学和与灾难相关的变量以及自我报告和医生诊断的健康问题可预测灾后MHS的使用情况。预测灾后MHS利用的最重要因素是灾难侵扰和回避反应、敌意症状(可被识别为创伤后应激障碍的症状)以及灾难前慢性病。

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Am J Public Health. 2007 Apr;97 Suppl 1(Suppl 1):S136-41. doi: 10.2105/AJPH.2006.104943. Epub 2007 Apr 5.
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The importance of estimating selection bias on prevalence estimates shortly after a disaster.灾难后不久对患病率估计值中的选择偏倚进行评估的重要性。
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