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
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
YUAN完成签到,获得积分10
刚刚
李健的粉丝团团长应助ww采纳,获得10
刚刚
1秒前
1秒前
古哥发布了新的文献求助10
1秒前
YeMa发布了新的文献求助10
2秒前
3秒前
lsw1516完成签到,获得积分10
3秒前
英吉利25发布了新的文献求助10
3秒前
hhh完成签到,获得积分10
4秒前
小井完成签到,获得积分10
5秒前
虚幻的枫发布了新的文献求助10
5秒前
善良板栗发布了新的文献求助10
5秒前
ADAMWS发布了新的文献求助10
5秒前
迷路的糜完成签到,获得积分10
6秒前
小白t73完成签到 ,获得积分10
6秒前
七月流火给夏侯觅风的求助进行了留言
6秒前
李健应助alkaidt采纳,获得10
6秒前
羊羊羊完成签到 ,获得积分10
6秒前
6秒前
小何发布了新的文献求助20
7秒前
漂亮的傲白完成签到,获得积分10
7秒前
7秒前
嘻嘻哈哈完成签到,获得积分0
7秒前
8秒前
8秒前
书虫发布了新的文献求助10
9秒前
9秒前
11秒前
甜甜果汁发布了新的文献求助20
11秒前
11秒前
lsw1516发布了新的文献求助10
12秒前
fairy发布了新的文献求助10
12秒前
无花果应助Dio4C采纳,获得10
12秒前
大个应助Naturewoman采纳,获得10
12秒前
13秒前
支初晴完成签到 ,获得积分10
13秒前
EASA发布了新的文献求助10
13秒前
13秒前
14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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Rheumatoid arthritis drugs market analysis North America, Europe, Asia, Rest of world (ROW)-US, UK, Germany, France, China-size and Forecast 2024-2028 500
17α-Methyltestosterone Immersion Induces Sex Reversal in Female Mandarin Fish (Siniperca Chuatsi) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6364898
求助须知:如何正确求助?哪些是违规求助? 8178864
关于积分的说明 17239318
捐赠科研通 5419951
什么是DOI,文献DOI怎么找? 2867816
邀请新用户注册赠送积分活动 1844885
关于科研通互助平台的介绍 1692343