Preliminary Prevalence and Determinants of Non-Communicable Diseases among Aeta Communities in Pampanga
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ABC's for Global Health Foundation Inc., City of San Fernando, Pampanga, Philippines
Popul. Med. 2026;8(Supplement Supplement 1):A1942
ABSTRACT
BACKGROUND:
Indigenous communities, such as the Aeta of Pampanga, are often excluded from national health surveillance despite undergoing rapid lifestyle and cultural transitions that increase the risk of non-communicable diseases (NCDs). This study aims to determine the prevalence of metabolic syndrome (MetS), hypertension (HTN), and diabetes mellitus (DM) while identifying associated sociodemographic and biophysical risk factors among adult Aetas.
METHODS:
As part of an ongoing research project, a cross-sectional survey was conducted among an initial cohort of 172 adult Aeta participants. Frequencies and percentages were determined to describe the sample's sociodemographic and biophysical profiles. Associations were evaluated between predictors and outcomes. Binary logistic regression was utilized to identify independent predictors and report odds ratios (OR) for each NCD.
RESULTS:
Preliminary findings reveal significant associations between lifestyle factors and metabolic outcomes. Metabolic syndrome (R^2=0.328) is primarily driven by BMI (OR=1.275, p<0.001) and age (OR=1.039, p=0.02), while being a non-drinker serves as a protective factor (OR=0.097, p=0.013). Hypertension (R^2=0.173) is significantly associated with being a former smoker (OR=3.66, p=0.035) and specific lipid markers, including Total Cholesterol (p=0.003) and LDL (p=0.048). Diabetes mellitus (R^2=0.101) is strongly predicted by Fasting Blood Sugar (p<0.001), with current/binge drinking significantly increasing risk (OR=5.583, p=0.042). Notably, while MetS correlates significantly with both HTN (r=0.312, p<.001) and DM (r=0.234, p=0.002), the association between HTN and DM is weak and non-significant (r=0.130, p=.091).
CONCLUSIONS:
These preliminary results highlight a substantial NCD burden within Aeta communities, driven by a combination of biophysical markers and behavioral shifts. As data collection is currently ongoing, these findings provide an initial baseline for local health offices to develop culturally sensitive, evidence-based interventions aimed at mitigating chronic disease risks in indigenous populations.