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资源匮乏地区的心血管健康:对本地治里地区加油站员工开展的一项基于移动应用程序的风险预测研究

Cardiovascular wellness in low-resource settings: A mobile app-based risk prediction study among fuel filling station employees in Puducherry district.

作者信息

Ramadass Divyabharathy, Vasudevan Jyothi, Dsouza Madonna J, Subramanian Baalaji

机构信息

Department of Community Medicine, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidyapeeth University, Puducherry, India.

Department of Family Medicine, Head Quarters Hospital, Cuddalore, Tamil Nadu, India.

出版信息

J Family Med Prim Care. 2024 Dec;13(12):5714-5719. doi: 10.4103/jfmpc.jfmpc_638_24. Epub 2024 Dec 9.

DOI:10.4103/jfmpc.jfmpc_638_24
PMID:39790758
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11709064/
Abstract

BACKGROUND

India is witnessing a significant increase in the prevalence of non-communicable diseases (NCDs), and addressing this requires a comprehensive and multi-faceted approach. The burden of NCDs puts a strain on the healthcare system, requiring an increased focus on preventive measures, early detection, and management of chronic conditions. Adopting a risk-based approach to cardiovascular diseases (CVDs) in resource-poor settings offers several economic and social advantages.

AIMS AND OBJECTIVES

The aim of the study was to assess the prevalence of CVD risk factors among fuel filling station employees in the Puducherry district and the 10-year CVD risk prediction score among the study participants with the World Health Organisation package of essential non-communicable (WHO PEN) app and package for resource-poor settings.

METHODS

A community-based cross-sectional study was conducted among the fuel filling station employees in Puducherry. A universal sampling method was employed. The data were collected using a pilot-tested, predesigned, structured questionnaire and the WHO PEN app was used to estimate the CVD risk score. The data were collected from February 2021 to January 2022 and analysed using Statistical Package for Social Sciences (SPSS) version 20. Frequency distribution along with the Chi-square test was employed to test statistical significance.

RESULTS

Out of 212 subjects, 170 (80.2%) were males, out of which 116 (54.7%) were between 40 and 50 years old. Nearly half the participants (48%) had CVD risk scores ranging from 5 to 20%, with an increased prevalence of CVD risk factors, namely, obesity/overweight (65.5%), physical inactivity (58.5%), hypertension (52%), alcohol consumption (51%) and tobacco consumption in any form (25.5%).

CONCLUSIONS

This study sheds light on the sedentary nature of the occupation and the increased prevalence of CVD risk factors among the study participants. It is also evident that the participants had higher CVD risk scores for developing CVDs in the future. Recommendations: The use of mobile-based apps can be used as a feasible strategy to save scarce resources in delivering primary health care. We also propose that the nature of occupation be taken into account as one of the parameters for risk prediction. Risk prediction assessment should be made mandatory during the annual examination of employees.

摘要

背景

印度非传染性疾病(NCDs)的患病率正在显著上升,应对这一问题需要采取全面且多方面的方法。非传染性疾病的负担给医疗系统带来了压力,这就需要更加关注预防措施、早期检测以及慢性病的管理。在资源匮乏的环境中采用基于风险的方法来应对心血管疾病(CVDs)具有若干经济和社会优势。

目的

本研究的目的是评估本地治里地区加油站员工中心血管疾病风险因素的患病率,以及使用世界卫生组织基本非传染性疾病软件包(WHO PEN)应用程序和资源匮乏环境软件包对研究参与者进行10年心血管疾病风险预测评分。

方法

在本地治里的加油站员工中开展了一项基于社区的横断面研究。采用普查抽样方法。使用经过预试验、预先设计的结构化问卷收集数据,并使用WHO PEN应用程序来估计心血管疾病风险评分。数据收集时间为2021年2月至2022年1月,并使用社会科学统计软件包(SPSS)20版进行分析。采用频率分布和卡方检验来检验统计学显著性。

结果

在212名受试者中,170名(80.2%)为男性,其中116名(54.7%)年龄在40至50岁之间。近一半的参与者(48%)心血管疾病风险评分为5%至20%,心血管疾病风险因素的患病率有所上升,即肥胖/超重(65.5%)、身体活动不足(58.5%)、高血压(52%)、饮酒(51%)以及任何形式的烟草消费(25.5%)。

结论

本研究揭示了该职业久坐不动的性质以及研究参与者中心血管疾病风险因素患病率的上升。同样明显的是,参与者未来患心血管疾病的心血管疾病风险评分更高。建议:使用基于移动设备的应用程序可作为在提供初级卫生保健时节省稀缺资源的可行策略。我们还建议将职业性质作为风险预测的参数之一加以考虑。在员工年度体检期间应强制进行风险预测评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf6/11709064/264333a8cd9e/JFMPC-13-5714-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf6/11709064/32559d533d59/JFMPC-13-5714-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf6/11709064/264333a8cd9e/JFMPC-13-5714-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf6/11709064/32559d533d59/JFMPC-13-5714-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf6/11709064/264333a8cd9e/JFMPC-13-5714-g002.jpg

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