Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data

Abstract

Background: Coal workers' pneumoconiosis (CWP) is an occupational lung disease due to inhalation of coal dust. We estimated mortality from CWP and other pneumoconioses among Medicare beneficiaries.

Methods: We used the 5% Medicare Limited Claims Data Set, 2011-2014, to identify patients diagnosed with ICD-9-CM 500 (CWP) through 505 (Asbestosis, Pneumoconiosis due to other silica or silicates, Pneumoconiosis due to other inorganic dust, Pneumonopathy due to inhalation of other dust, and Pneumoconiosis, unspecified) codes. We applied binary regression models with spatial random effects to determine the association between CWP and mortality. Our inferences are based on Bayesian spatial hierarchical models, and model fitting was performed using Integrated Nested Laplace Approximation (INLA) algorithm in R/RStudio software.

Results: The median age of the sample was 76 years. In a sample of 8531 Medicare beneficiaries, 2568 died. Medicare beneficiaries with CWP had 25% higher odds of death (adjusted OR: 1.25, 95% CI: 1.07, 1.46) than those with other types of pneumoconiosis. The number of comorbid conditions elevated the odds of death by 10% (adjusted OR: 1.10, 95% CI: 1.09, 1.10).

Conclusion: CWP increases the likelihood of death among Medicare beneficiaries. Healthcare professionals should make concerted efforts to monitor patients with CWP to prevent premature mortality.

Keywords: Bayesian analysis; comorbidity; lung disease; occupational hazard; spatial models.