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Related Concept Videos

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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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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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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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.
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Related Experiment Video

Updated: Jan 8, 2026

Determining Soil-transmitted Helminth Infection Status and Physical Fitness of School-aged Children
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Public Health.

Wei Ye1, Kate Jun2, Ying Liu1

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

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 23, 2025
PubMed
Summary
This summary is machine-generated.

Medicare data effectively predicts dementia and mild cognitive impairment (MCI) risk using chronic conditions and race/ethnicity. This approach aids in identifying individuals needing cognitive evaluation without memory complaints.

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Area of Science:

  • Gerontology
  • Public Health
  • Data Science

Background:

  • Dementia risk factors are established and can identify individuals needing cognitive evaluation.
  • Medicare administrative data offers a unique opportunity to identify at-risk individuals due to their widespread use.
  • The study operationalized 14 Lancet Commission risk factors within Medicare data to assess dementia and mild cognitive impairment (MCI) prediction.

Purpose of the Study:

  • To evaluate the predictability of dementia and MCI using operationalized risk factors from Medicare administrative data.
  • To determine which of the 14 Lancet Commission risk factors can be effectively utilized in Medicare claims data.
  • To assess the accuracy of predicting cognitive impairment using administrative data and demographic information.

Main Methods:

  • Operationalized 10 chronic conditions using diagnosis codes; used COPD as a proxy for smoking.
  • Approximated education levels using geographic information and linked Medicare data with Health and Retirement Study (HRS) survey responses.
  • Developed and validated a predictive model for cognitive impairment using data from 2000-2014, validated with 2016 data, assessing performance via Area Under the Curve (AUC).

Main Results:

  • Age and sex predicted dementia with an AUC of 0.7305; adding chronic conditions improved AUC to 0.7867.
  • Inclusion of race/ethnicity further increased dementia prediction AUC to 0.8300, independent of comorbidities.
  • Individual-level education showed predictive value (AUC 0.8627), but its absence in claims data limits practical use; geographic education measures did not significantly contribute. MCI prediction AUC was 0.7193.

Conclusions:

  • Medicare administrative data can accurately predict dementia and MCI risk.
  • Race/ethnicity is an independent predictor of cognitive risk, potentially reflecting disease control, social, or genetic factors.
  • Integrating social determinants of health data with Medicare claims would enhance cognitive impairment detection capabilities.