Infant feeding survival and Markov transition probabilities among children under age 6 months in Uganda

Lumbwe Chola1, Lars T Fadnes, Ingunn M S Engebretsen

  • 1Centre for International Health, University of Bergen, Bergen, Norway. lumbwechola@hotmail.com

Insights

Exclusive breastfeeding is better sustained with interventions. A study in Uganda found that peer counseling significantly reduced the risk of stopping exclusive breastfeeding, highlighting the benefits of targeted infant feeding support.

Area of Science:

  • Public Health
  • Biostatistics
  • Maternal and Child Health

Background:

  • Infant feeding studies often use single-event models, neglecting the dynamic transitions between feeding states.
  • Understanding infant feeding duration requires analyzing multiple feeding events and transitions.

Purpose of the Study:

  • To analyze the determinants of infant feeding duration using both single- and multiple-event Cox regression models.
  • To compare Cox models with parametric survival models for estimating feeding-state transition probabilities.

Main Methods:

  • Utilized data from a community randomized trial in Uganda (2005-2008) promoting exclusive breastfeeding (EBF).
  • Employed single- and multiple-event Cox regression and parametric survival models to analyze feeding transitions.
  • Peer counselors provided antenatal and postnatal support to intervention mothers.

Main Results:

  • Children in the intervention group had a significantly lower risk of ceasing exclusive/predominant breastfeeding (hazard ratio = 0.33).
  • Children in rural areas also showed a reduced risk of EBF/PBF cessation (hazard ratio = 0.79).
  • Parametric models demonstrated a better fit than the Cox model based on the Akaike Information Criterion.

Conclusions:

  • The study highlights the effectiveness of interventions in sustaining exclusive breastfeeding.
  • A multi-event analytical approach provides a more comprehensive understanding of infant feeding dynamics.
  • Findings can inform policy-making for improved infant feeding practices and support programs.

Related Concept Videos

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.
Life Tables01:22

Life 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,...
Uniform Distribution01:19

Uniform Distribution

The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.Two essential properties of this distribution are The area under the rectangular shape equals 1. There is a correspondence between the probability of an event and the area under the curve.Further, the mean and standard deviation of the uniform distribution can be calculated when the lower and upper cut-offs, denoted as a and b,...
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 until a...
Development of the Oral Microbiota01:28

Development of the Oral Microbiota

The establishment of the oral microbiome begins before birth, challenging the long-held belief that the fetal oral cavity is sterile. The presence of oral microbes such as Streptococcus and Fusobacterium in amniotic fluid suggests that microbial exposure may occur in utero, potentially through translocation from the maternal oral or gastrointestinal tract. This early colonization primes the neonatal immune system and sets the stage for subsequent microbial succession. Maternal health,...
Censoring Survival Data01:09

Censoring 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...