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 经济增长 内科学 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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
1秒前
ding应助科研通管家采纳,获得10
1秒前
1秒前
华仔应助科研通管家采纳,获得10
1秒前
科研通AI5应助科研通管家采纳,获得10
1秒前
jmy完成签到,获得积分10
1秒前
Leif应助科研通管家采纳,获得10
1秒前
Orange应助科研通管家采纳,获得10
1秒前
小二郎应助科研通管家采纳,获得10
1秒前
英俊的铭应助科研通管家采纳,获得10
1秒前
SciGPT应助科研通管家采纳,获得10
1秒前
无花果应助科研通管家采纳,获得10
1秒前
积极的板栗完成签到 ,获得积分10
1秒前
咯咚完成签到 ,获得积分10
1秒前
ding应助科研通管家采纳,获得10
1秒前
CodeCraft应助科研通管家采纳,获得10
1秒前
今后应助科研通管家采纳,获得10
1秒前
研友_VZG7GZ应助科研通管家采纳,获得10
1秒前
完美世界应助科研通管家采纳,获得10
1秒前
科研通AI5应助科研通管家采纳,获得10
1秒前
maox1aoxin应助科研通管家采纳,获得30
1秒前
科研通AI5应助科研通管家采纳,获得10
1秒前
科研通AI5应助科研通管家采纳,获得10
1秒前
2秒前
领导范儿应助科研通管家采纳,获得10
2秒前
今后应助科研通管家采纳,获得10
2秒前
QXS发布了新的文献求助10
2秒前
2秒前
2秒前
2秒前
Liekkas发布了新的文献求助10
2秒前
可爱的函函应助bdvdsrwteges采纳,获得10
4秒前
木木雨发布了新的文献求助10
5秒前
鬲木发布了新的文献求助10
5秒前
mao12wang发布了新的文献求助10
5秒前
L坨坨完成签到 ,获得积分10
5秒前
耿强发布了新的文献求助10
5秒前
jmy发布了新的文献求助10
6秒前
科研小黑子完成签到,获得积分20
6秒前
6秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527699
求助须知:如何正确求助?哪些是违规求助? 3107752
关于积分的说明 9286499
捐赠科研通 2805513
什么是DOI,文献DOI怎么找? 1539954
邀请新用户注册赠送积分活动 716878
科研通“疑难数据库(出版商)”最低求助积分说明 709759