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

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Dimensions of Health and Illness01:21

Dimensions of Health and Illness

The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...

You might also read

Related Articles

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

Sort by
Same author

Approaches to Approximating National Public Health Data.

American journal of public health·2026
Same author

Changes in PM2.5 exposure due to residential relocation and mortality among U.S. Veterans.

American journal of epidemiology·2026
Same author

Defining Prenatal Care Surveillance Metrics Using Electronic Health Record Data.

JAMA health forum·2026
Same author

Transforming the Patient-Centered Medical Home: Reimagining Primary Care Through AI.

Journal of general internal medicine·2026
Same author

Disentangling the Multifactorial Influences on Diabetes Risk Among Rural Communities: Food Environment, Diet Quality, and Dietary Chemical Exposures.

Diabetes/metabolism research and reviews·2026
Same author

"The Agenda of the People": A Multisector Partnership for COVID-19 Mitigation in New York City.

American journal of public health·2026

Related Experiment Video

Updated: Jun 8, 2026

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Within-City Average Life Expectancy "Gaps": A Useful Health Equity Metric.

Ben R Spoer1, Isabel S Nelson2, Matthew Lee3

  • 1Department of Population Health, Division of Epidemiology, New York University Grossman School of Medicine, New York, NY, USA. Benjamin.Spoer2@nyulangone.org.

Journal of Urban Health : Bulletin of the New York Academy of Medicine
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

Significant life expectancy gaps exist within US cities, averaging 11.8 years. These health disparities correlate strongly with racial segregation and poverty, highlighting critical areas for health equity initiatives.

More Related Videos

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans
10:08

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans

Published on: May 18, 2022

Long-Term Culture and Monitoring of Isolated Caenorhabditis elegans on Solid Media in Multi-Well Devices
09:32

Long-Term Culture and Monitoring of Isolated Caenorhabditis elegans on Solid Media in Multi-Well Devices

Published on: December 9, 2022

Related Experiment Videos

Last Updated: Jun 8, 2026

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans
10:08

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans

Published on: May 18, 2022

Long-Term Culture and Monitoring of Isolated Caenorhabditis elegans on Solid Media in Multi-Well Devices
09:32

Long-Term Culture and Monitoring of Isolated Caenorhabditis elegans on Solid Media in Multi-Well Devices

Published on: December 9, 2022

Area of Science:

  • Public Health
  • Urban Health
  • Health Equity Research

Background:

  • Significant disparities in life expectancy are observed across neighborhoods within US cities.
  • Understanding the scale and correlates of these within-city life expectancy gaps is crucial for addressing health inequities.

Purpose of the Study:

  • To characterize the magnitude of life expectancy gaps within 948 US cities.
  • To examine the correlation between these within-city life expectancy gaps and various social and environmental factors.

Main Methods:

  • Life expectancy estimates were obtained from the US Life Expectancy Estimation Program.
  • Within-city life expectancy gaps were calculated by comparing the highest and lowest tract-level estimates in each city.
  • Spearman's correlation coefficient was used to assess relationships with social and environmental characteristics.

Main Results:

  • The average life expectancy gap across US cities was 11.8 years.
  • Larger life expectancy gaps were observed in cities with lower overall life expectancy.
  • The strongest correlations for life expectancy gaps were with racialized residential segregation, child poverty rates, and household income.

Conclusions:

  • Substantial between-neighborhood differences in life expectancy are a widespread issue in US cities.
  • Life expectancy gaps represent a significant and actionable target for developing effective health equity goals.