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南非资源匮乏学校教师代谢综合征的预测因素:卡齐健康职场健康干预的基线调查结果

Predictors of metabolic syndrome among teachers in under-resourced schools in South Africa: Baseline findings from the KaziHealth workplace health intervention.

作者信息

Joubert Nandi, Adams Larissa, Hattendorf Jan, Degen Jan, Dolley Danielle, Gresse Annelie, Müller Ivan, Nqweniso Siphesihle, Probst-Hensch Nicole, Seelig Harald, Steinmann Peter, Randt Rosa du, Pühse Uwe, Utzinger Jürg, Gerber Markus, Walter Cheryl

机构信息

Swiss Tropical and Public Health Institute, Allschwil, Switzerland.

University of Basel, Basel, Switzerland.

出版信息

PLOS Glob Public Health. 2025 Jun 6;5(6):e0004681. doi: 10.1371/journal.pgph.0004681. eCollection 2025.

DOI:10.1371/journal.pgph.0004681
PMID:40478910
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12143548/
Abstract

Non-communicable diseases (NCDs) account for most of global deaths, with rising prevalence in low-and middle-income countries. Metabolic syndrome (MetS) is a cluster of interrelated NCD risk factors, including obesity, dyslipidaemia, hypertension, and hyperglycaemia, that amplify NCD risk. Four leading modifiable factors driving NCDs are physical inactivity, tobacco and alcohol use, and an unhealthy diet. Teachers in under-resourced schools face significant health challenges, with their wellbeing often overlooked in South Africa. The prevalence and severity of MetS, adherence to physical activity guidelines, and associations with modifiable NCD risk factors were assessed. This study, part of the 'KaziBantu: Healthy Schools for Healthy Communities' project, included 168 teachers (aged 21-72, mean = 47 years) from 8 under-resourced schools in Gqeberha, South Africa. Data collection included MetS markers, device-measured physical activity, tobacco and alcohol use, and total fat, saturated fat, sugar, and sodium intake. Covariates included age, sex, race, education, and household income. MetS was observed in 58% of participants, with central obesity (79%) being the most prevalent component, followed by hypertension (59%). The largest proportion of participants (26%) had 3 MetS components, followed by 4 components (20%), while 12% had all 5 components. Nearly half (44%) of teachers were physically inactive and 53% exceeded total fat intake recommendations. Bayesian multilevel logistic regression revealed key predictors of MetS: age (per year increase; odds ratio[OR]=1.15, 95% credible interval[CrI] [1.07, 1.24]) and high daily total fat intake (OR=3.37, 95%CrI [1.03, 11.96]) were positively associated, while hours spent in moderate-to-vigorous intensity physical activity per week (OR=0.73, 95%CrI [0.55, 0.95]) and higher monthly household income (OR=0.14, 95%CrI [0.02, 0.72]) were protective. Addressing physical inactivity and unhealthy diets through tailored interventions is crucial to reducing MetS prevalence and improving the health of teachers working in low-resourced settings. Effective solutions should empower healthier lifestyles while tackling structural barriers to health equity.

摘要

非传染性疾病(NCDs)占全球死亡人数的大部分,在低收入和中等收入国家的患病率呈上升趋势。代谢综合征(MetS)是一组相互关联的非传染性疾病风险因素,包括肥胖、血脂异常、高血压和高血糖,这些因素会增加非传染性疾病的风险。导致非传染性疾病的四个主要可改变因素是身体活动不足、烟草和酒精使用以及不健康饮食。资源匮乏学校的教师面临重大健康挑战,在南非,他们的健康状况往往被忽视。本研究评估了代谢综合征的患病率和严重程度、对身体活动指南的遵守情况以及与可改变的非传染性疾病风险因素的关联。这项研究是“KaziBantu:健康学校,健康社区”项目的一部分,纳入了来自南非伊丽莎白港8所资源匮乏学校的168名教师(年龄在21 - 72岁之间,平均年龄 = 47岁)。数据收集包括代谢综合征标志物、通过设备测量的身体活动、烟草和酒精使用以及总脂肪、饱和脂肪、糖和钠的摄入量。协变量包括年龄、性别、种族、教育程度和家庭收入。58%的参与者被观察到患有代谢综合征,其中中心性肥胖(79%)是最常见的组成部分,其次是高血压(59%)。最大比例的参与者(26%)有3个代谢综合征组成部分,其次是4个组成部分(20%),而12%的参与者有所有5个组成部分。近一半(44%)的教师身体活动不足,53%的教师总脂肪摄入量超过建议值。贝叶斯多水平逻辑回归揭示了代谢综合征的关键预测因素:年龄(每年增加;优势比[OR]=1.15,95%可信区间[CrI][1.07, 1.24])和每日高总脂肪摄入量(OR=3.37,95%CrI[1.03, 11.96])呈正相关,而每周中等至剧烈强度身体活动的时长(OR=0.73,95%CrI[0.55, 0.95])和较高的月家庭收入(OR=0.14,95%CrI[0.02, 0.72])具有保护作用。通过量身定制的干预措施解决身体活动不足和不健康饮食问题对于降低代谢综合征患病率和改善资源匮乏环境中工作的教师的健康状况至关重要。有效的解决方案应在消除健康公平的结构性障碍的同时,促进更健康的生活方式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af0f/12143548/b1eb38122968/pgph.0004681.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af0f/12143548/81b17e6ceda1/pgph.0004681.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af0f/12143548/b1eb38122968/pgph.0004681.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af0f/12143548/81b17e6ceda1/pgph.0004681.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af0f/12143548/b1eb38122968/pgph.0004681.g002.jpg

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