Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Relative Risk

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

You might also read

Related Articles

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

Sort by

Same author

An Exploration of Machine Learning Methods in Human Biomonitoring.

International journal of environmental research and public health·2026

Same author

Development and Validation of a Canadian Prediction Equation for Incident CKD Using Population-Based, Administrative Data.

Canadian journal of kidney health and disease·2026

Same author

Permanent cognitive or physical impairment after transfer from long-term care to acute care: a retrospective cohort study.

Age and ageing·2026

Same author

Exploring the effects of Cheddar cheese intake on vitamin K status and lipid profiles in overweight middle-aged adults.

Food & function·2026

Same author

Association between potentially inappropriate prescribing and adverse patient outcomes using codified STOPP-START and Beers criteria in large, routinely collected population health datasets: a retrospective cohort study.

Journal of clinical epidemiology·2026

Same author

Assessing obesity beyond body mass index: Integrating physiological and functional indicators of impairment in national health surveillance.

Health reports·2026

Same journal

Comparative social costs of six early years disadvantages: a birth cohort microsimulation study.

Journal of epidemiology and community health·2026

Same journal

Sociodemographic inequalities in non-small cell lung cancer (NSCLC) novel treatments: a national population-based cohort study.

Journal of epidemiology and community health·2026

Same journal

Secular trends in primary care utilisation for mental health problems: a Norwegian register-based population-wide study.

Journal of epidemiology and community health·2026

Same journal

Guide to recent advances in difference-in-differences methodology for population health studies.

Journal of epidemiology and community health·2026

Same journal

From early to fewer first births: ADHD and family formation among young adults.

Journal of epidemiology and community health·2026

Same journal

10-year mortality among first-time mothers involved in family court care proceedings in England: cohort study using linked administrative hospital, mortality and family court records.

Journal of epidemiology and community health·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 19, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Predictive risk algorithms in a population setting: an overview.

Douglas G Manuel¹, Laura C Rosella, Deirdre Hennessy

¹Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. dmanuel@ohri.ca

Journal of Epidemiology and Community Health

|August 4, 2012

Summary

This summary is machine-generated.

Predictive risk algorithms, widely used in clinical settings, offer significant potential for improving population health planning and decision-making. Their application in public health is growing, leveraging routinely collected data for better health outcomes.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: May 19, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Epidemiology
Health Informatics
Biostatistics

Background:

Risk algorithms have transformed clinical decision-making.
Their potential in population health settings is currently underdeveloped.
This highlights a gap in leveraging these tools for public health.

Purpose of the Study:

To elucidate the role of predictive risk algorithms in population health.
To describe their application and development for health planning.
To provide a guide for assessing these algorithms in a public health context.

Main Methods:

Description of predictive risk algorithms and their clinical use.
Exploration of population-level applications and strengths for health planning.
Brief overview of algorithm development and assessment strategies.

Main Results:

Multivariable risk algorithms are the most accurate method for estimating absolute and baseline risk.
Routinely collected data are increasingly available and suitable for developing health planning algorithms.
These algorithms have a proven track record in clinical medicine.

Conclusions:

Risk algorithms are essential for population health planning, providing accurate risk estimates.
The development and application of these algorithms using routinely collected data are key.
Further development and adoption of risk algorithms can enhance population health decision-making.