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相关概念视频

Primary Healthcare Services01:30

Primary Healthcare Services

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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...
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Preventive Healthcare Services01:30

Preventive Healthcare Services

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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:
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Principles of Disease Surveillance01:26

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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...
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Healthcare Agencies II01:17

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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,...
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Methods Of Healthcare Delivery System01:26

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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...
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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

Wei Ye1, Kate Jun2, Ying Liu1

  • 1University of Southern California, Los Angeles, CA, USA.

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概括
此摘要是机器生成的。

医疗保险数据有效预测痴呆症和轻度认知障碍 (MCI) 风险,使用慢性疾病和种族/种族. 这种方法有助于识别需要认知评估的个人,而没有记忆问题.

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科学领域:

  • 老年学是一门学科.
  • 公共卫生 公共卫生
  • 数据科学数据科学数据科学

背景情况:

  • 建立了痴呆症风险因素,可以识别需要认知评估的个体.
  • 医疗保险行政数据提供了一个独特的机会,可以识别因其广泛使用而处于风险中的个人.
  • 该研究在医疗保险数据中运行了"柳叶刀委员会"的14个风险因素,以评估痴呆和轻度认知障碍 (MCI) 的预测.

研究的目的:

  • 为了评估痴呆和MCI的可预测性,使用Medicare行政数据的操作化风险因素.
  • 为了确定"柳叶刀"委员会的14个风险因素中的哪一个可以有效地用于医疗保险索赔数据.
  • 用行政数据和人口统计信息评估预测认知障碍的准确性.

主要方法:

  • 使用诊断代码运行了10种慢性疾病;使用COPD作为吸烟的代理.
  • 使用地理信息和链接医疗保险数据与健康和退休研究 (HRS) 调查答案的近似教育水平.
  • 使用2000-2014年的数据开发和验证了认知障碍的预测模型,与2016年的数据进行验证,通过曲线下的区域 (AUC) 评估性能.

主要成果:

  • 年龄和性别预测痴呆症的AUC为0.7305;添加慢性疾病改善了AUC至0.7867.
  • 包括种族/族裔进一步增加了痴呆症预测AUC,独立于并发症,达到0.8300.
  • 个人层面的教育显示了预测价值 (AUC 0.8627),但它在索赔数据中缺少限制了实际使用;地理教育措施没有显著贡献. 在MCI预测中,AUC为0.7193.3.

结论:

  • 医疗保险的行政数据可以准确预测痴呆症和MCI风险.
  • 种族/种族是认知风险的独立预测因素,可能反映疾病控制,社会或遗传因素.
  • 将健康数据的社会决定因素与医疗保险索赔整合起来,将提高认知障碍检测能力.