追踪失踪死亡:印度科济科德市COVID-19死亡率影响的探索性研究
Shilka Abraham1, Soumitra Ghosh2
1Master of Public Health Candidate, School of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, Maharashtra, India.
Indian journal of public health
|July 2, 2024
概括
印度的COVID-19死亡人数明显不足,大约一半的死亡人数直接或间接与大流行病有关. 这凸显了需要进行大规模研究,以准确评估真正的死亡率影响.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 人口统计学 人口统计学
背景情况:
- 印度COVID-19的确切死亡率影响仍然是持续辩论的主题.
- 准确的评估对于了解流行病的真正负担至关重要.
研究的目的:
- 调查记录和实际死亡人数之间的差异.
- 为了量化COVID-19死亡率错误分类和间接死亡.
- 为了确定印度的总体流行病相关死亡率影响.
主要方法:
- 利用民事登记数据,葬礼/火葬记录和家庭调查.
- 使用基于世卫组织的仪器进行口头尸检.
- 数据收集时间为2021年8月至11月.
主要成果:
- 与2019年相比,在2020-2021年期间观察到死亡人数大幅增加.
- 发现非机构死亡报告不足 (5.5%) 和可信的COVID-19死亡错误分类.
- 估计约有48%的样本死亡直接或间接归因于大流行.
结论:
- 大流行病相关死亡人数的低估可能在印度各州普遍存在.
- 政策制定者应该启动大规模研究,以获得可靠的州和国家死亡率估计.
更多相关视频
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
2.2K
03:53Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
Published on: November 10, 2023
1.1K
相关概念视频
Cancer Survival Analysis
342
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...
342
Kaplan-Meier Approach
127
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,...
127
Kubler Ross's Stages of Dying
69
Elisabeth Kübler-Ross significantly advanced psychology's understanding of the process of dying with her influential book, On Death and Dying (1969). She focused on studying terminally ill individuals and outlined five stages commonly experienced when coping with death: denial, anger, bargaining, depression, and acceptance.
In denial, individuals reject the reality of their condition, often thinking, "This isn't true; I feel fine," as a way to protect themselves from...
In denial, individuals reject the reality of their condition, often thinking, "This isn't true; I feel fine," as a way to protect themselves from...
69
Factors Affecting Illness
4.2K
When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness,...
For instance, risk factors are connected to illness,...
4.2K
Comparing the Survival Analysis of Two or More Groups
176
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...
176
Introduction To Survival Analysis
214
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...
The primary goal of survival analysis is to estimate survival time—the time...
214
