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Related Concept Videos

Life Tables01:22

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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,...
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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...
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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...
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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,...
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Bias in Epidemiological Studies01:29

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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:  
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Censoring Survival Data01:09

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Measurement of Lifespan in Drosophila melanogaster
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Gauging Class and Caste differences in Mortality: The Indian Experience.

Udaya S Mishra1, Basant Kumar Panda2

  • 1Centre for Development Studies, Trivandrum, Kerala, India.

Omega
|October 12, 2022
PubMed
Summary

Mortality rates in India are higher in poorer households and for Scheduled Caste/Tribe communities. The Relative Deprivation Index (RDI) reveals significant class-caste disparities in death prevalence across states.

Keywords:
ClassIndiaNFHScastemortality

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Area of Science:

  • Public Health
  • Sociology
  • Demography

Background:

  • Socioeconomic and class-caste disparities significantly influence health outcomes and mortality.
  • Understanding these differences is crucial for targeted public health interventions.

Purpose of the Study:

  • To analyze class-caste based differences in mortality experience using household prevalence of death.
  • To investigate the relationship between socioeconomic status, social group identity, and mortality risk in India.

Main Methods:

  • Utilized data from 75,432 death cases from the National Family Health Survey-4.
  • Employed the Relative Deprivation Index (RDI) to assess mortality disparities.
  • Analyzed variations in mortality prevalence across different states, social, and economic groups.

Main Results:

  • The overall prevalence of death in India was found to be 11.8%, with significant state-level variations.
  • Poorer households, Scheduled Tribe, and Scheduled Caste households exhibited a uniform disadvantage in mortality across many states.
  • The RDI indicated differential mortality experiences linked to socio-economic identities.

Conclusions:

  • Evidence confirms significant disparities in mortality based on class, caste, and economic status in India.
  • Poorer states and marginalized social/economic classes face a marked disadvantage regarding mortality.
  • Findings underscore the need for policies addressing systemic inequalities to improve public health outcomes.