Related Experiment Video
Updated: Feb 13, 2026
07:12
Profiling Maternal Behavior Responses During Whole-Brain Imaging
Published on: January 24, 2025
1.5K
Epidemiological profile of maternal mortality
Ana Claudia Sierra Martins1, Lélia Souza Silva1
1Centro Universitário Estácio de Juiz de Fora, Graduate in Nursing. Juiz de Fora, Minas Gerais, Brazil.
Revista Brasileira De Enfermagem
|March 22, 2018
Summary
Maternal mortality in Juiz de Fora, Brazil, shows high Cesarean section rates and inadequate prenatal care, contributing to preventable deaths. Addressing these factors is crucial for improving maternal health outcomes.
Area of Science:
- Obstetrics and Gynecology
- Public Health
- Epidemiology
Background:
- Maternal mortality remains a significant public health concern globally.
- Understanding the specific epidemiological profile of maternal deaths is essential for targeted interventions.
Purpose of the Study:
- To determine the epidemiological characteristics of maternal mortality in Juiz de Fora, Minas Gerais.
- To analyze trends and identify risk factors associated with maternal deaths in the region.
Main Methods:
- A retrospective analysis of confidential maternal mortality investigations.
- Data collected for deaths occurring between January 1, 2005, and December 31, 2015.
Main Results:
- Eighty-five maternal deaths were identified in women aged 20-36 years.
- Inadequate prenatal care (less than six visits) was noted in 34.0% of cases.
- High Cesarean section rates (38.8%) and suboptimal obstetric treatment (32.9%) were observed.
- Leading causes of death included hypovolemic shock (14.10%) and uterine hypotony (7.0%).
Conclusions:
- High Cesarean section rates and poor prenatal care adherence are potential contributors to maternal mortality.
- Interventions focusing on improving prenatal care access and optimizing delivery practices are warranted.
Related Concept Videos
01:26Introduction to Epidemiology
1.9K
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
1.9K
01:21Causality in Epidemiology
1.7K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.7K
01:20Study Designs in Epidemiology
1.0K
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
1.0K
01:27Confounding in Epidemiological Studies
858
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
858
01:29Bias in Epidemiological Studies
1.4K
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:
1.4K
01:25Statistical Methods for Analyzing Epidemiological Data
991
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:
991