Te Pou o te Whakaaro Nui, Auckland, New Zealand.
Department of Psychological Medicine, University of Otago, Christchurch, New Zealand.
Int J Ment Health Nurs. 2019 Feb;28(1):288-296. doi: 10.1111/inm.12532. Epub 2018 Aug 18.
Rates of seclusion vary across New Zealand's publicly funded district health board (DHB) adult mental health inpatient services as indicated by national data. Anecdotally, this variation has been attributed to a range of factors directly relating to the people admitted to acute inpatient services. This study examined the extent to which variation in seclusion rates could be explained by the sociodemographic and clinical differences between populations admitted into adult mental health inpatient services. Retrospective data were obtained from the Programme for the Integration of Mental Health Data (PRIMHD). A logistic regression model was fitted to these data, with seclusion (yes/no) as the dependent variable and DHB groups as the independent variable. The DHBs were classified into four groups based on their seclusion rates. The model adjusted for ethnicity, age, number of bed nights, total Health of the Nation Outcome Scales (HoNOS) scores, and compulsory treatment status. Odds ratios remained virtually unchanged after adjustment for sociodemographic and clinical factors. People admitted to DHB Group 4 (highest secluding DHBs) were 11 times more likely to be secluded than people in Group 1 (lowest secluding DHBs), adjusted OR = 11.1, 95% CI [7.5,16.4], P < 0.001. Results indicate DHB variation in seclusion rates cannot be attributed to the sociodemographic and clinical factors of people admitted into DHB adult mental health inpatient services. Instead, this variation may be explained by differences in service delivery models and practice approaches. A model of system improvements aimed at reducing seclusion is discussed.
新西兰公共资助的地区卫生局(DHB)成人精神科住院服务的隔离率因国家数据而异。据推测,这种差异归因于与急性住院服务入院者直接相关的一系列因素。本研究检查了隔离率的变化在多大程度上可以用成人精神科住院服务入院人群的社会人口统计学和临床差异来解释。回顾性数据来自心理健康数据综合计划(PRIMHD)。将这些数据拟合到逻辑回归模型中,将隔离(是/否)作为因变量,DHB 组作为自变量。根据其隔离率将 DHB 分为四组。该模型调整了种族、年龄、床位夜数、国民健康结果量表(HoNOS)总分和强制治疗状态。调整社会人口统计学和临床因素后,比值比几乎保持不变。与 DHB 组 1(隔离率最低的 DHB)相比,被分配到 DHB 组 4(隔离率最高的 DHB)的人被隔离的可能性高 11 倍,调整后的比值比(OR)= 11.1,95%置信区间(CI)[7.5,16.4],P<0.001。结果表明,DHB 隔离率的变化不能归因于 DHB 成人精神科住院服务入院者的社会人口统计学和临床因素。相反,这种差异可能是由于服务提供模式和实践方法的不同造成的。讨论了一种旨在减少隔离的系统改进模型。