Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

69
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...
69
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

107
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
107
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

310
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
310
Introduction to Epidemiology01:26

Introduction to Epidemiology

655
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
655
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

498
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
498
Causality in Epidemiology01:21

Causality in Epidemiology

323
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
323

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Deep Learning Approach for Pixel-Level Material Classification via Hyperspectral Imaging.

Journal of imaging·2026
Same author

Investigating Digital Health Information Literacy Effects on Public Hospitals' Nursing Personnel Combating the COVID-19 Pandemic.

Creative nursing·2026
Same author

Multi-Area, Multi-Service and Multi-Tier Edge-Cloud Continuum Planning.

Sensors (Basel, Switzerland)·2025
Same author

Wood Waste Valorization and Classification Approaches: A systematic review.

Open research Europe·2025
Same author

The Music-Related Quality of Life Measure (MuRQoL): A Scoping Review of Its Validation and Application.

Audiology research·2025
Same author

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jun 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review.

Giorgos Dritsakis1, Ioannis Gallos1, Maria-Elisavet Psomiadi2

  • 1Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.

Online Journal of Public Health Informatics
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

Data analytics tools for noncommunicable diseases (NCDs) policy making exist but see limited use. This review highlights their potential and suggests improvements for public health policy development.

Keywords:
data analyticsdecision supportdescriptivedigital toolsimplementationnoncommunicable diseasespolicy makingpredictivepublic health

More Related Videos

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

719
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.2K

Related Experiment Videos

Last Updated: Jun 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

719
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.2K

Area of Science:

  • Public Health
  • Health Informatics
  • Health Policy

Background:

  • Growing need for evidence-based strategies in public health policy.
  • Leveraging big data and artificial intelligence (AI) for policy optimization.
  • Focus on noncommunicable diseases (NCDs) policy development.

Purpose of the Study:

  • To review data analytics tools for NCD policy making.
  • To assess the implementation of these tools.
  • To identify gaps and opportunities for improving policy support.

Main Methods:

  • Scoping review of PubMed and IEEE databases.
  • Search limited to the last 10 years.
  • Analysis of 9 articles detailing 7 data analytics tools.

Main Results:

  • Tools included descriptive and predictive analytics; prescriptive analytics pilots were absent.
  • Cancer was the least studied condition among piloted tools.
  • Limited real-world implementation and use by policy makers observed.

Conclusions:

  • Data analytics for NCD policy making is underutilized despite available tools.
  • Significant potential exists for data analytics to inform public health policy.
  • Recommendations provided for developing future digital policy support tools.