Related Experiment Video

Updated: Jun 25, 2026

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
19:57

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

Published on: March 30, 2014

Demographic characteristics and survival with AIDS: health disparities in Chicago, 1993-2001

Girma Woldemichael1, Demian Christiansen, Sandra Thomas

  • 1Epidemiology Program, Department of Public Health, DePaul Center, Rm 2136, 333 S State St, Chicago, IL 60604, USA. woldemichael_girma@cdph.org

American Journal of Public Health
|February 17, 2009
PubMed
Abstract

More Related Videos

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model23:56

Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model

Published on: October 31, 2010

Related Concept Videos

Immunodeficiency Diseases01:25

Immunodeficiency Diseases

Immunodeficiency disorders are conditions in which the immune system's ability to fight infectious disease and cancer is compromised or entirely absent. The immune system comprises a complex network of cells, tissues, and organs that work together to protect the body from potentially harmful invaders. When this system is deficient or not functioning properly, it leaves the body susceptible to infections, diseases, or other complications.
There are three main causes of immunodeficiency disorders...
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...
Sexually Transmitted Infections01:26

Sexually Transmitted Infections

Sexually transmitted infections (STIs) are diseases transmitted primarily through unsafe sexual interactions. Bacteria, viruses, or parasites cause them and can result in severe health complications if untreated.ChlamydiaThe bacterium Chlamydia trachomatis is responsible for the disease Chlamydia, the most common STI in the United States. This peculiar pathogen requires human cells to reproduce, residing intracellularly. The initial infection often goes unnoticed because it typically does not...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Amebiasis01:28

Amebiasis

Entamoeba histolytica, a protozoan parasite, is responsible for intestinal and extraintestinal amebiasis. Though a significant proportion of infections remain asymptomatic, approximately 50 million individuals annually are estimated to present with clinical disease, resulting in up to 100,000 deaths globally. The disease burden is disproportionately high in regions with lower socioeconomic status, such as parts of India, Africa, Mexico, and Latin America.Etiology and TransmissionThe infective...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.