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相关概念视频

Primary Healthcare Services01:30

Primary Healthcare Services

1.9K
Primary care promotes wellness and prevents disease. This care includes health promotion, education, protection (such as immunizations), early disease screening, and environmental considerations. Settings providing this type of healthcare include physician offices, public health clinics, school nursing, and community health nursing.
In 1978, international leaders convened in Alma-Ata, Kazakhstan, for what would be a pivotal event in global health. The Alma-Ata Declaration was the first to call...
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Levels of Health Promotion and Illness Prevention01:26

Levels of Health Promotion and Illness Prevention

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Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
In primary prevention, actions taken before disease onset prevent the disease from...
14.1K
Preventive Healthcare Services01:30

Preventive Healthcare Services

1.8K
Preventive healthcare services keep people healthy via frequent check-ups, screening, and counseling. They primarily aid in disease prevention rather than treating an acute or chronic illness. Preventive treatment also keeps individuals productive and energetic, allowing them to work well into their retirement years. Examples of preventive care services include:
1.8K
Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
439
Healthcare Agencies II01:17

Healthcare Agencies II

1000
There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
1000
Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

3.9K
At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
3.9K

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相关实验视频

Updated: Jan 8, 2026

Determining Soil-transmitted Helminth Infection Status and Physical Fitness of School-aged Children
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公共卫生 公共卫生

Yi-Han Hu1, Harris Jamal1, Xinlei Deng2

  • 1Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA.

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PubMed
概括
此摘要是机器生成的。

环境因素,如邻里特征和空气污染,除了个别风险因素之外,并不能显著改善中风预测. 然而,当临床数据有限时,将人口统计数据与环境数据结合起来是有希望的.

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科学领域:

  • 流行病学 流行病学
  • 环境健康 环境健康
  • 机器学习在医学中的应用

背景情况:

  • 脑卒中是血管痴呆的主要原因.
  • 传统的中风预测模型侧重于个体风险因素.
  • 环境暴露可能会影响脑血管健康,需要新的预测方法.

研究的目的:

  • 开发和比较用于中风风险预测的机器学习模型.
  • 评估环境风险模型 (Env-RiskModel) 与标准风险因素模型 (Standard-RiskModel) 的预测性能.

主要方法:

  • 利用了来自研究中风的地理和种族差异原因 (REGARDS) 的数据 (n=24,251).
  • 开发了五种随机生存森林模型,包括人口统计,社区特征 (绿地,社会脆弱性,灾害风险) 和环境空气污染 (臭氧).
  • 使用C指数和时间依赖的接收器操作特征 (AUROC) 曲线下的区域进行模型性能比较.

主要成果:

  • 在12.1年的时间里,6.0%的人患上了中风.
  • 纳入环境因素的模型显示,与仅仅人口统计数据相比,环境因素的改善幅度微乎其微.
  • 环境风险模型 (C指数=66.1%) 和标准风险模型 (C指数=65.7%) 显示出可比的预测性能.

结论:

  • 环境因素并没有显著提高超出已确定的个人风险因素中风预测.
  • 环境暴露与人口统计数据相结合,在临床数据稀缺的情况下,为中风预测提供了可行的替代方案.
  • 需要进一步的研究来探索额外的环境变量,以评估中风风险.