相关实验视频

Updated: Jul 13, 2025

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA
12:02

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

12.5K

检查明尼苏达州2020-2021年期间关键工作人员的过度死亡率:职业分析

Harshada Karnik1, Elizabeth Wrigley-Field1, Zachary Levin1

  • 1Harshada Karnik, Zachary Levin, and Jonathon P. Leider are with the Health Policy and Management Division, University of Minnesota School of Public Health, Minneapolis. Elizabeth Wrigley-Field is with the Department of Sociology and Minnesota Population Center, University of Minnesota. Yea-Hung Chen is with the Department of Epidemiology and Biostatistics, University of California, San Francisco. Erik W. Zabel is with the Center for Occupational Health and Safety, Minnesota Department of Health, St. Paul. Marizen Ramirez was with the Division of Environmental Health Sciences, University of Minnesota School of Public Health, Twin Cities when the study was conducted.

American journal of public health
|October 11, 2023
PubMed
概括

民用关键工作人员面临更高的COVID-19死亡率,根据职业和种族存在显著差异. 有色人种的工人经历了不成比例地高的过度死亡,突出了针对性安全协议的迫切需要.

更多相关视频

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K
Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice06:00

Author Spotlight: Deciphering the Long-Term Effects of Low-Level Blast Exposures in Mice

Published on: May 24, 2024

952

相关概念视频

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...
202
Statistical Methods for Analyzing Epidemiological Data01:25

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:
385
Steps in Outbreak Investigation01:18

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:
135
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
252
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...
359
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.
149