School Neighborhood Disadvantage as a Predictor of Long-Term Sick Leave Among Teachers: Prospective Cohort Study

病假 置信区间 人口学 住所 泊松回归 医学 弱势群体 比率 前瞻性队列研究 危险系数 老年学 环境卫生 人口 经济 社会学 外科 内科学 经济增长 物理疗法
作者
Mikko Virtanen,Mika Kivimäki,Jaana Pentti,Tuula Oksanen,Kirsi Ahola,Anne Linna,Anne Kouvonen,Paula Salo,Jussi Vahtera
出处
期刊:American Journal of Epidemiology [Oxford University Press]
卷期号:171 (7): 785-792 被引量:34
标识
DOI:10.1093/aje/kwp459
摘要

This ongoing prospective study examined characteristics of school neighborhood and neighborhood of residence as predictors of sick leave among school teachers. School neighborhood income data for 226 lower-level comprehensive schools in 10 towns in Finland were derived from Statistics Finland and were linked to register-based data on 3,063 teachers with no long-term sick leave at study entry. Outcome was medically certified (>9 days) sick leave spells during a mean follow-up of 4.3 years from data collection in 2000–2001. A multilevel, cross-classified Poisson regression model, adjusted for age, type of teaching job, length and type of job contract, school size, baseline health status, and income level of the teacher's residential area, showed a rate ratio of 1.30 (95% confidence interval: 1.03, 1.63) for sick leave among female teachers working in schools located in low-income neighborhoods compared with those working in high-income neighborhoods. A low income level of the teacher's residential area was also independently associated with sick leave among female teachers (rate ratio = 1.50, 95% confidence interval: 1.18, 1.91). Exposure to both low-income school neighborhoods and low-income residential neighborhoods was associated with the greatest risk of sick leave (rate ratio = 1.71, 95% confidence interval: 1.27, 2.30). This study indicates that working and living in a socioeconomically disadvantaged neighborhood is associated with increased risk of sick leave among female teachers.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
果果完成签到,获得积分20
1秒前
华仔应助一起去看海采纳,获得10
2秒前
乐乐应助郭子仪采纳,获得10
2秒前
HAOHAO发布了新的文献求助10
3秒前
隐形的雁完成签到,获得积分10
6秒前
只与你完成签到 ,获得积分10
7秒前
8秒前
传奇3应助怡然的扬采纳,获得10
9秒前
9秒前
一起去看海完成签到,获得积分20
9秒前
9秒前
ccm应助清脆琳采纳,获得10
9秒前
NexusExplorer应助果果采纳,获得10
10秒前
13秒前
xmhxpz发布了新的文献求助10
14秒前
DSFSD完成签到,获得积分10
17秒前
17秒前
进口小宵完成签到,获得积分10
19秒前
优秀藏鸟完成签到 ,获得积分10
21秒前
22秒前
泷生发布了新的文献求助10
22秒前
22秒前
23秒前
不配.应助MADAO采纳,获得200
23秒前
24秒前
三月完成签到,获得积分20
25秒前
cizzz发布了新的文献求助10
28秒前
果果发布了新的文献求助10
29秒前
29秒前
29秒前
Criminology34应助nadeem采纳,获得10
31秒前
英俊的铭应助Tom47采纳,获得10
31秒前
33秒前
王小茗发布了新的文献求助10
34秒前
暗中讨饭完成签到,获得积分10
35秒前
Vincent发布了新的文献求助10
36秒前
科研通AI6应助长大水果采纳,获得10
36秒前
37秒前
等待冰之完成签到 ,获得积分10
37秒前
37秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1621
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 800
King Tyrant 600
Essential Guides for Early Career Teachers: Mental Well-being and Self-care 500
A Guide to Genetic Counseling, 3rd Edition 500
Laryngeal Mask Anesthesia: Principles and Practice. 2nd ed 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5563539
求助须知:如何正确求助?哪些是违规求助? 4648430
关于积分的说明 14684815
捐赠科研通 4590392
什么是DOI,文献DOI怎么找? 2518479
邀请新用户注册赠送积分活动 1491143
关于科研通互助平台的介绍 1462432