Under-5 Malaria and Fever Morbidities as Correlates of Anaemia in Niger: A Heteroscedasticity-Consistent Ordered

Thonaeng Charity Molelekoa1, Abayomi Samuel Oyekale1

  • 1Department of Agricultural Economics and Extension, North-West University Mafikeng Campus, Mmabatho 2735, South Africa.

Insights

Anaemia affects over 73% of children under five in Niger, with malaria and febrile infections significantly increasing risk. Interventions must address regional disparities and improve socioeconomic factors for prevention.

Area of Science:

  • Public Health
  • Pediatrics
  • Infectious Diseases

Background:

  • Anaemia is a critical global health issue, particularly for children under five in Africa, aligning with Sustainable Development Goal 3.
  • The interplay between febrile illnesses like malaria and anaemia prevalence in young children necessitates targeted policy interventions.

Purpose of the Study:

  • To analyze the impact of malaria, other febrile infections, and demographic factors on anaemia prevalence in Nigerien children under five.
  • To identify key risk and protective factors influencing anaemia in this vulnerable population.

Main Methods:

  • Utilized data from the 2021 Niger Malaria Indicator Survey (MIS) under-5 children's module.
  • Employed a heteroscedasticity-consistent ordered probit regression model for data analysis.

Main Results:

  • A high prevalence of anaemia (73.73%) was observed in children under five.
  • Malaria (14.00%) and other febrile infections (33.87%) were significant risk factors, with highest anaemia rates in Tillaberi and Dosso regions.
  • Male gender, multiple births, and higher birth order increased anaemia risk, while wealth, age, urban residence, and media access reduced it.

Conclusions:

  • Anaemia poses a substantial public health challenge for Niger's under-five population.
  • Comprehensive strategies addressing regional, gender, and socioeconomic disparities are crucial.
  • Preventing malaria and febrile illnesses, alongside improving household economic status and promoting health education, are key interventions.

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
299
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:  
151
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

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...
141
Patterns of Fever01:26

Patterns of Fever

Before understanding the types and patterns of fever, it is essential to know its phases.
2.4K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
348
Causality in Epidemiology01:21

Causality in Epidemiology

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...
289