Related Experiment Videos
Bayesian Spatial Conditional Logistic Regression Modelling of Overweight/Obesity Prevalence and Determinants Among
Killian Asampana Asosega1,2, Eric N Aidoo3, Atinuke Olusola Adebanji4
1Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, knust.edu.gh.
Global Health, Epidemiology and Genomics
|July 29, 2026
Summary
Overweight/obesity (OW/OB) is a significant public health issue for women of reproductive age in Ghana. Higher risk is linked to education, age, marriage, urban living, and more children, necessitating targeted regional interventions.
Area of Science:
- Public Health
- Epidemiology
- Spatial Analysis
Background:
- Overweight/obesity (OW/OB) presents a critical public health challenge for reproductive-aged women in low- and middle-income countries (LMICs), including Ghana.
- Rising maternal average BMI and fragile healthcare systems in sub-Saharan Africa (SSA) countries like Ghana necessitate urgent surveillance and intervention strategies.
Purpose of the Study:
- Identify risk factors for OW/OB in Ghanaian women of reproductive age.
- Analyze spatial distribution of OW/OB risk, accounting for spatial autocorrelation.
- Investigate the confounding effect of modern contraceptive use (MCU) on OW/OB risk.
Main Methods:
- Utilized cross-sectional data from the 2022 Ghana Demographic and Health Survey (GDHS) of 7054 women (aged 15-49).
- Employed a Bayesian spatial conditional logistic regression model to identify significant risk factors.
- Used MCU as a stratification variable to control for potential metabolic or behavioral influences.
Main Results:
- 36.9% of women were overweight/obese (22.5% overweight, 14.4% obese).
- High OW/OB rates (>50%) were observed in Ashanti and Greater Accra regions; lowest (<20%) in North-East region.
- Significant risk factors included higher education, increased age, marriage, urban residency, and higher parity. Regional disparities showed higher burden in middle/southern Ghana compared to northern Ghana.
Conclusions:
- Spatial heterogeneity in OW/OB risk supports targeted regional preventive strategies, especially in high-risk hotspots like Ashanti and Ahafo regions.
- Interventions can help reduce the public health burden of maternal OW/OB in Ghana.
- Addressing maternal OW/OB contributes to ensuring quality health for women (SDG 3).
Related Concept Videos
01:25Statistical 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:
01:29Bias 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:
01:14Parametric 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...
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...
01:27Confounding 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 phenomenon...
01:21Causality 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...
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.