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Primary Healthcare Services01:30

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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.
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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.
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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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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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There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
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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:
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Salud Pública

Sayaka Kuwayama1, Wassim Tarraf2, Kevin A Gonzalez1

  • 1University of California, San Diego, La Jolla, CA, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje automático identificó predictores clave de p-tau-181 fosforilado (p-tau-181) plasmático alto en adultos hispanos/latinos. La diabetes, el empleo y la aculturación fueron factores significativos, ofreciendo objetivos para las intervenciones contra la enfermedad de Alzheimer.

Palabras clave:
Salud PúblicaEnfermedad de AlzheimerBiomarcadoresProteínas tauAprendizaje automáticoAdultos hispanos/latinosDiabetesAculturaciónEmpleo

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Área de la Ciencia:

  • Neurología
  • Biomarcadores
  • Aprendizaje automático

Sus antecedentes:

  • La fosfo-tau-181 plasmática (p-tau-181) es un biomarcador confiable en sangre para la enfermedad de Alzheimer (EA), que refleja la patología tau.
  • La p-tau-181 elevada se correlaciona con la deposición de amiloide, la progresión de la EA y el deterioro cognitivo, lo que respalda su uso en el diagnóstico y el seguimiento.
  • Comprender los predictores de p-tau-181 es crucial para la utilidad clínica, la comprensión de los mecanismos de la enfermedad y las intervenciones dirigidas, especialmente en adultos hispanos/latinos de EE. UU. afectados de manera desproporcionada por la EA.

Objetivo del estudio:

  • Identificar predictores de niveles altos de p-tau-181 en una población profundamente caracterizada de adultos hispanos/latinos de mediana edad y mayores con base en EE. UU.
  • Aprovechar modelos de aprendizaje automático para analizar factores de riesgo del curso de vida para el cambio neuropatológico de la EA.
  • Informar estrategias de intervención dirigidas para modificar los procesos patológicos relacionados con la EA en este grupo demográfico.

Principales métodos:

  • Se utilizaron datos del Hispanic Community Health Study/Study of Latinos y su estudio complementario Study of Latinos-Investigation of Neurocognitive Aging (N=5,175).
  • Se emplearon modelos de aprendizaje automático Random Forest para predecir niveles altos de p-tau-181 (medida binaria: >90.º percentil vs. ≤90.º percentil).
  • Se evaluaron 33 posibles predictores basales, incluidos factores de riesgo del curso de vida, utilizando técnicas de permutación para la importancia de los factores.

Principales resultados:

  • El modelo de aprendizaje automático demostró un rendimiento discriminatorio moderado (AUC=0.73) en la predicción de niveles altos de p-tau-181.
  • Los principales predictores identificados incluyeron diabetes, estado de empleo y la subescala de idioma de la Escala Corta de Aculturación para Hispanos.
  • Estos hallazgos resaltan la influencia de los determinantes cardiometabólicos y sociales en los niveles de p-tau-181.

Conclusiones:

  • Múltiples factores del curso de vida predicen los niveles de p-tau-181 en adultos hispanos/latinos de mediana edad y mayores.
  • El riesgo cardiometabólico (p. ej., diabetes) y los determinantes sociales (p. ej., empleo, aculturación) son áreas clave para la intervención.
  • La investigación futura validará estos modelos y ampliará los hallazgos a otros biomarcadores de la enfermedad de Alzheimer.