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Related Concept Videos

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:
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...
Relative Risk01:12

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...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Hazard Ratio01:12

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...

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Related Experiment Video

Updated: Jun 24, 2026

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

Black-white differences in avoidable mortality in the USA, 1980-2005.

J Macinko1, I T Elo

  • 1New York University, Department of Nutrition, Food Studies, and Public Health, New York, NY 10012, USA. james.macinko@nyu.edu

Journal of Epidemiology and Community Health
|April 15, 2009
PubMed
Summary

Black-white disparities in avoidable mortality (AM) persist, particularly from medical care and policy-sensitive causes. Addressing these preventable deaths offers significant potential to narrow racial gaps in mortality.

Related Experiment Videos

Last Updated: Jun 24, 2026

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

Area of Science:

  • Public Health
  • Epidemiology
  • Health Disparities

Background:

  • Avoidable Mortality (AM) encompasses deaths preventable with timely medical care or influenced by public policy and behavior.
  • Significant black-white disparities in mortality exist, necessitating analysis of contributing factors.

Purpose of the Study:

  • To analyze racial disparities in avoidable mortality between black and white populations.
  • To quantify the contribution of different cause-of-death categories to these disparities.

Main Methods:

  • Analysis of mortality under age 65 from 1980-2005, categorized by amenability to medical care, policy/behavior influence, ischemic heart disease, HIV/AIDS, and other causes.
  • Calculation of age-standardized death rates (ASDRs) for race and sex groups.
  • Use of negative binomial regression to model relative risks of death.

Main Results:

  • In 2005, mortality amenable to medical care was the largest contributor to absolute black-white mortality disparity (30% for men, 42% for women).
  • Policy/behavior-sensitive mortality contributed 20% to disparities in men and 4% in women.
  • While absolute disparities decreased for most conditions, relative disparities (rate ratios) remained largely unchanged, except for a substantial increase in HIV/AIDS risks for black individuals.

Conclusions:

  • Substantial potential exists to reduce black-white mortality differences by addressing causes amenable to medical care.
  • Policy and behavior interventions can further narrow these disparities, particularly for men.