Key result
Modeling hypertension risk factors using a continuous blood pressure scale with gamma regression yielded better model accuracy (AIC 32,777.70) than using a binary scale with logistic regression (AIC 37,556.11).
Why the study?
Risk factors for hypertension are commonly modeled with blood pressure on a binary scale, leading to information loss and reduced statistical efficiency.
Does modeling blood pressure on a continuous scale using gamma regression improve model accuracy compared to a binary scale using logistic regression in adult Ugandans?
Cross-Sectional (n=3,987)
Does modeling blood pressure on a continuous scale using gamma regression improve model accuracy compared to a binary scale using logistic regression in adult Ugandans?
Modeling hypertension risk factors using continuous blood pressure scales with gamma regression provides better statistical accuracy than binary logistic regression.
Binary BP modeling risks information loss in hypertension studies; leaves open whether continuous scales improve risk factor detection in LMIC surveys.
Background: Risk factors for hypertension are commonly identified with blood pressure measured and modeled on a binary rather than continuous scale leading to information loss and reduced statistical efficiency.Methods: We analyzed data from a nationally representative survey of adult Ugandans (18-69 years), focusing on non-communicable disease (NCD) risk factors.We report the accuracy of modeling risk factors for hypertension with blood pressure measured on a binary scale using binary logistic regression versus a continuous scale using gamma regression.We built a binary logistic regression model and two gamma regression models: one for the average systolic blood pressure (SBP) and another for the average diastolic blood pressure (DBP).Model accuracy was compared using Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC).Results: Of the 3,987 participants, the median age was 33 years (interquartile range: 25-45).Gamma regression model for the average SBP identified age, sex, region, marital status, income, and body mass index (BMI) as risk factors for hypertension (deviance = 212,196; AIC = 32,777.70;BIC = 179,331).Gamma regression model for the average DBP showed age, region, marital status, and BMI as risk factors for hypertension (deviance = 230,745; AIC = 35,438.24;BIC = 197,880).The binary logistic regression model indicated age, marital status, and BMI as risk factors for hypertension (deviance = 14,973,578; AIC = 37,556.11;BIC = 14,940,713).Conclusion: Risk factors for hypertension are more accurately modeled when blood pressure is on a continuous than binary scale.These differences have important public health and clinical significance in treating hypertension.We recommend using gamma regression for modeling the risk factors for hypertension.
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Appeli et al. (2024) conducted a cross-sectional in Hypertension (n=3,987). Continuous blood pressure scale modeling (Gamma regression) vs. Binary blood pressure scale modeling (Logistic regression) was evaluated on Model accuracy (Akaike Information Criterion, Bayesian Information Criterion, and deviance). Modeling hypertension risk factors using a continuous blood pressure scale with gamma regression yielded better model accuracy (AIC 32,777.70) than using a binary scale with logistic regression (AIC 37,556.11).
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