Related Experiment Video
Updated: Jun 28, 2026
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
[Mortality Data System: a case study on municipalities with data underreporting].
Paulo Germano de Frias1, Pricila Melissa Honorato Pereira, Carla Lourenço Tavares de Andrade
1Instituto Materno Infantil Professor Fernando Figueira, Recife, Brasil.
Cadernos De Saude Publica
|October 25, 2008
Summary
Infant mortality estimation in Brazil faced significant challenges due to underreporting. Active data searches revealed substantial unrecorded infant deaths, highlighting critical system flaws.
Area of Science:
- Public Health
- Demography
- Health Information Systems
Context:
- Brazilian municipalities with severe underreporting of deaths.
- Infant mortality estimation in the year 2000.
Purpose:
- To estimate infant mortality in areas with significant underreporting.
- To analyze information sources and implementation challenges of the Mortality Data System (SIM).
Summary:
- An active search identified 344 unrecorded infant deaths (66% of total).
- Low reporting from official sources (notary offices, healthcare facilities) indicates issues with death reporting, certification, and data flow.
- The Community Health Agents Program/Family Health Program proved crucial for data collection.
Impact:
- Highlights critical deficiencies in the Mortality Data System (SIM) implementation.
- Underscores the need for active data searches in programs like the Community Health Agents Program in underreporting areas.
- Identifies insufficient training and high staff turnover as key barriers to effective system implementation.
Related Concept Videos
01:20Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:22Life Tables
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,...
01:09Censoring Survival Data
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 reasons...
01:24Kaplan-Meier Approach
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,...
01:22Applications of Life Tables
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...
01:15Assumptions 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.