Related Experiment Video
Updated: Mar 1, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.9K
Child mortality inequalities across Rwanda districts: a geoadditive continuous-time survival analysis
François Niragire1, Thomas N O Achia, Alexandre Lyambabaje
1Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali. fniragire@ur.ac.rw.
Geospatial Health
|May 31, 2017
Summary
Child mortality in Rwanda varies significantly by district, with higher risks in Musanze and Nyabihu. Maternal health factors and economic status are key determinants, guiding targeted child survival programs.
Area of Science:
- * Global Health
- * Demography
- * Epidemiology
Background:
- * Child survival programs are most effective when addressing significant and area-specific factors.
- * Understanding spatial variations in child mortality is crucial for targeted interventions.
Purpose of the Study:
- * To assess the key determinants of child mortality at the district level in Rwanda.
- * To analyze the spatial variation of child mortality across Rwandan districts.
Main Methods:
- * Analysis of data from the 2010 Rwanda Demographic and Health Survey (8817 live births).
- * Application of a full Bayesian geo-additive continuous-time hazard model.
- * Identification of factors associated with child death hazards.
Main Results:
- * Substantial district-level spatial variation in childhood mortality observed.
- * Higher death hazards identified in Musanze and Nyabihu districts.
- * Lower death rates linked to medium/high economic status, four antenatal care visits, facility delivery, prolonged breastfeeding, younger maternal age (<31 years), and longer birth intervals.
Conclusions:
- * Child mortality reduction programs in Rwanda must consider district-specific spatial characteristics and maternal factors.
- * Strengthening access to and utilization of maternal and child healthcare services is essential.
- * Reducing the economic status gap among households is critical for improving child survival.
Related Concept Videos
01:15Assumptions of Survival Analysis
468
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.
468
01:20Comparing the Survival Analysis of Two or More Groups
674
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...
674
01:14Parametric Survival Analysis: Weibull and Exponential Methods
1.2K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.2K
01:18Introduction To Survival Analysis
894
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...
894
01:21Cancer Survival Analysis
805
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...
805
01:22Applications of Life Tables
393
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...
393