Suppr超能文献

中国西部精神分裂症患者药物不依从与对他人暴力风险。

Medication Nonadherence and Risk of Violence to Others Among Patients With Schizophrenia in Western China.

机构信息

Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.

Sichuan Mental Health Center, Third Hospital of Mianyang, Mianyang, China.

出版信息

JAMA Netw Open. 2023 Apr 3;6(4):e235891. doi: 10.1001/jamanetworkopen.2023.5891.

Abstract

IMPORTANCE

Reducing violence to others in community-based patients with schizophrenia has important implications for public health. Increasing medication adherence is often used to reduce the risk of violence, yet little is known about the association between medication nonadherence and violence to others in this population.

OBJECTIVE

To examine the association between medication nonadherence and violence to others among community-based patients with schizophrenia.

DESIGN, SETTING, AND PARTICIPANTS: This large, naturalistic, prospective cohort study was performed in western China from May 1, 2006, to December 31, 2018. The data set was from the integrated management information platform for severe mental disorders. As of December 31, 2018, 292 667 patients with schizophrenia were registered in the platform. During follow-up, patients could enter or leave the cohort at any time. Maximum follow-up was 12.8 years, with a mean (SD) of 4.2 (2.3) years. Data analysis was conducted from July 1, 2021, to September 30, 2022.

EXPOSURES

Medication nonadherence.

MAIN OUTCOMES AND MEASURES

Violence to others throughout the follow-up period was the outcome, including minor nuisances, violating the Law of the People's Republic of China on Penalties for Administration of Public Security (APS law), and violating criminal law. Information about these behaviors was provided by the public security department. Directed acyclic graphs were used to identify and control confounders. Propensity score matching and generalized linear mixed-effects models were used for analysis.

RESULTS

The final study sample included 207 569 patients with schizophrenia. The mean (SD) age was 51.3 (14.5) years, and 107 271 (51.7%) were women; 27 698 (13.3%) perpetrated violence to others, including 22 312 of 142 394 with medication nonadherence (15.7%) and 5386 of 65 175 with adherence (8.3%). In 112 710 propensity score-matched cases, risks of minor nuisances (odds ratio [OR], 1.82 [95% CI, 1.75-1.90]; P < .001), violating APS law (OR, 1.91 [95% CI, 1.78-2.05]; P < .001), and violating criminal law (OR, 1.50 [95% CI, 1.33-1.71]; P < .001) were higher in patients with nonadherence. However, the risk did not increase with higher medication nonadherence. There were differences in risk of violating APS law between urban and rural areas.

CONCLUSIONS AND RELEVANCE

Medication nonadherence was associated with a higher risk of violence to others among community-based patients with schizophrenia, but the risk did not increase as medication nonadherence increased.

摘要

重要性

减少社区精神分裂症患者对他人的暴力行为对公共卫生具有重要意义。增加药物依从性通常用于降低暴力风险,但在该人群中,药物不依从与对他人的暴力行为之间的关联知之甚少。

目的

研究社区精神分裂症患者药物不依从与对他人暴力行为之间的关系。

设计、地点和参与者:这是一项大型、自然主义、前瞻性队列研究,于 2006 年 5 月 1 日至 2018 年 12 月 31 日在中国西部进行。该数据集来自严重精神障碍综合管理信息平台。截至 2018 年 12 月 31 日,共有 292667 名精神分裂症患者在该平台上登记。在随访期间,患者可以随时进入或离开队列。最大随访时间为 12.8 年,平均(SD)为 4.2(2.3)年。数据分析于 2021 年 7 月 1 日至 2022 年 9 月 30 日进行。

暴露

药物不依从。

主要结果和措施

整个随访期间发生的暴力行为是结局,包括轻微滋扰、违反《中华人民共和国治安管理处罚法》(APS 法)和违反刑法。这些行为的信息由公安部门提供。有向无环图用于识别和控制混杂因素。采用倾向评分匹配和广义线性混合效应模型进行分析。

结果

最终的研究样本包括 207569 名精神分裂症患者。平均(SD)年龄为 51.3(14.5)岁,107271 名(51.7%)为女性;27698 人(13.3%)实施了暴力行为,其中 22312 人(15.7%)药物不依从,65175 人(8.3%)药物依从。在 112710 例匹配后的倾向评分病例中,轻微滋扰的风险(比值比[OR],1.82 [95%置信区间[CI],1.75-1.90];P<0.001)、违反 APS 法(OR,1.91 [95% CI,1.78-2.05];P<0.001)和违反刑法(OR,1.50 [95% CI,1.33-1.71];P<0.001)的风险在不依从者中更高。然而,随着药物不依从性的增加,风险并没有增加。城乡地区违反 APS 法的风险存在差异。

结论和相关性

药物不依从与社区精神分裂症患者对他人暴力行为的风险增加有关,但随着药物不依从性的增加,风险并未增加。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b3/10077101/585ffa3353e1/jamanetwopen-e235891-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验