Regan Casey, Fehily Caitlin, Campbell Elizabeth, Bowman Jenny, Faulkner Jack, Oldmeadow Christopher, Bartlem Kate
School of Psychological Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia.
Hunter New England Population Health, Locked Bag 10, Wallsend, NSW 2287, Australia.
Prev Med Rep. 2022 Jun 27;28:101870. doi: 10.1016/j.pmedr.2022.101870. eCollection 2022 Aug.
This study identified clusters of chronic disease risks and explored associations between clusters and demographic characteristics and mental health conditions, among people accessing community mental health services. Data from a cross-sectional telephone survey of Australian mental health consumers (n = 567) were analysed. Clusters were identified based on tobacco smoking (53.5%), harmful chronic alcohol consumption (20.1%), harmful acute alcohol consumption (43.5%), inadequate fruit and vegetable intake (66.0%), inadequate physical activity (75.5%), inadequate strength activity (81.8%), and high body mass index (BMI) (67.9%), using latent class analysis. Multinomial logistic regression examined associations between cluster membership and participant characteristics. Three groups were identified: Cluster 1 (19.05%) had < 0.5 probabilities for most risks; Cluster 2 (34.04%) had high probabilities of all risks, particularly tobacco smoking and both types of harmful alcohol consumption; and Cluster 3 (46.91%) had high probabilities of both inadequate physical and strength activity, inadequate fruit and vegetable intake, and high BMI. Compared to Cluster 1 membership, participants with higher education were less likely to be in either Cluster 2 or 3, females or those over 55 were more likely to be in Cluster 3, those with a substance use disorder were more likely to be in Cluster 2, and those with a personality disorder were less likely to be in Cluster 3. The clustering patterns reinforce the importance of addressing multiple chronic disease risks for people with a mental health condition Preventive care interventions targeting clusters of risks may help reduce the burden of chronic disease among this high-risk population.
本研究在使用社区心理健康服务的人群中,识别了慢性病风险群组,并探讨了这些群组与人口统计学特征及心理健康状况之间的关联。分析了对澳大利亚心理健康服务使用者进行的横断面电话调查(n = 567)的数据。使用潜在类别分析,基于吸烟(53.5%)、有害慢性酒精消费(20.1%)、有害急性酒精消费(43.5%)、果蔬摄入不足(66.0%)、身体活动不足(75.5%)、力量活动不足(81.8%)以及高体重指数(BMI)(67.9%)确定了群组。多项逻辑回归分析检验了群组归属与参与者特征之间的关联。确定了三组:第1组(19.05%)大多数风险的概率<0.5;第2组(34.04%)所有风险的概率都很高,尤其是吸烟以及两种类型的有害酒精消费;第3组(46.91%)身体和力量活动不足、果蔬摄入不足以及BMI高的概率都很高。与第1组相比,受过高等教育的参与者进入第2组或第3组的可能性较小,女性或55岁以上的人进入第3组的可能性更大,患有物质使用障碍的人进入第2组的可能性更大,而患有精神障碍的人进入第3组的可能性较小。这些群组模式强化了针对有心理健康状况的人群应对多种慢性病风险的重要性。针对风险群组的预防性护理干预措施可能有助于减轻这一高危人群的慢性病负担。