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

Background: 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. Methods: 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 R 2 (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. Results: 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 R 2 = 0.99) and showed good expected predictive performance (LOOIC = – 461.1). Conclusion: 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. Plain Language Summary: Why was this study done? Chronic Obstructive Pulmonary Disease (COPD) is a serious lung condition that makes breathing difficult. In Colombia, many people with COPD are not properly diagnosed, especially in rural areas where healthcare services are limited. Previous studies estimating how many people have COPD in Colombia may not be accurate because they did not consider these differences in healthcare access between regions. What did the researchers do and find? We studied COPD rates across all 33 departments (states) in Colombia using advanced statistical methods. We looked at factors such as how often spirometry tests are performed, poverty levels, and access to modern cooking stoves. We estimated that 2.22% of Colombians have COPD (95% credible interval [CrI]: 2.21– 2.23%), but this varies widely between regions—from 0.81% to 3.50%. Areas with better access to spirometry, a breathing test used to confirm COPD, showed higher diagnosis rates, which suggests that many cases are missed where testing capacity is limited. What do these results mean? These results indicate that diagnosis-based COPD prevalence during 2020– 2023 is lower than historical spirometry-based estimates, likely reflecting under-ascertainment in places where spirometry access is limited. None of Colombia’s 33 departments reaches the National Administrative Benchmark of 1105 spirometry tests per 100,000 adults aged ≥ 40 years, and only Bogotá approaches half of this target. This information can guide health authorities to expand lung function testing and chronic respiratory care in underserved regions. The same modeling strategy could be applied to other chronic diseases that are underdiagnosed in low-resource settings. Keywords: chronic obstructive pulmonary disease, Bayesian analysis, spirometry, health equity, epidemiology, ecological, Colombia