Chronic Disease Epidemiological Analysis with Sociodemographic and Behavioral Factors

A cross-sectional analysis of nationally representative survey data examines associations between sociodemographic factors and hypertension, diabetes, and cardiovascular disease. The study, authored by Chidimma Evangeline Oliobi, used survey-weighted logistic regression and multiple imputation to handle missing data. Model discrimination was good, with AUC scores of 0.80 for hypertension, 0.77 for diabetes, and 0.83 for CVD.

Use Cases

• Modeling age and gender interaction effects on disease risk based on the significant age×gender interactions described.
• Analyzing the association between sociodemographic factors like income and cardiometabolic outcomes as mentioned in the results.
• Assessing model discrimination and calibration for chronic disease prediction using the reported AUC and calibration metrics.
• Investigating non-linear relationships between age and disease prevalence using the restricted cubic spline methodology described.

Strengths

• Analysis is based on nationally representative survey data, suggesting a structured sampling approach.
• Model performance is quantified with specific AUC scores: 0.80 for hypertension, 0.77 for diabetes, and 0.83 for CVD.
• The study addresses missing data using a specific method: multiple imputation with five imputations.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small in scale at 275.1 KB, indicating limited raw data or a summary document.

Provenance

Source

Chidimma Evangeline Oliobi

Collection Method

Cross-sectional analysis of nationally representative survey data using survey-weighted multivariable logistic regression.

Freshness

Last updated 2026-05-22 06:10:41; freshness should be verified.

Geography

Nationally representative, but specific country is not stated.