Beyond Reported Rates: Detection-Adjusted COPD Prevalence and Underdiagnosis Patterns in Colombia

Chronic obstructive pulmonary disease (COPD) is widely underdiagnosed in Colombia, especially in rural departments with limited access to spirometry. We conducted a department-level ecological study using aggregated administrative data from 2020-2023 to generate diagnosis-based COPD prevalence estimates that explicitly account for regional disparities in diagnostic capacity and socioeconomic conditions.

We assembled department-year data from the Individual Registry of Health Services Delivery, national mortality statistics, and the National Quality of Life Survey. A Bayesian generalized additive model with a Gamma family and log link was fitted to a Composite Bias-Correction Multiplier that captured under-ascertainment as a function of spirometry utilization, COPD lethality, outpatient contact rates, multidimensional poverty, household fuel type, and age structure. Posterior estimates of this multiplier were applied to diagnosis-based COPD prevalence in adults aged ≥40 years to obtain detection-adjusted departmental and national estimates. Model performance was summarized using the Bayesian R² (proportion of variability in the multiplier explained by the covariates) and the leave-one-out information criterion (LOOIC) as a measure of expected predictive fit.

The model estimated a population-weighted national COPD prevalence of 2.22% (95% credible interval [CrI], 2.21-2.23). Detection-adjusted departmental prevalence ranged from 0.81% in Vichada to 3.50% in Caldas, whereas diagnosis-based prevalence ranged from 0.27% to 2.22%. Spirometry utilization correlated strongly with diagnosis-based prevalence (r = 0.85, p < 0.001), and departments with higher COPD lethality and greater multidimensional poverty required larger adjustment multipliers. The model explained most of the variability in the Composite Bias-Correction Multiplier (Bayesian R² = 0.99) and showed good expected predictive performance (LOOIC = -461.1).

COPD prevalence in Colombia shows marked regional heterogeneity driven by demographic risk and uneven diagnostic capacity. Detection-adjusted estimates indicate that the highest burden lies in Andean departments such as Caldas, Boyacá, and Risaralda, while remote Amazon and Orinoco territories experience substantial underdiagnosis. These findings support targeted expansion of spirometry and chronic respiratory care in underserved regions and illustrate how accounting for detection bias can improve chronic disease surveillance in low- and middle-income settings.