Computational protocol: Geographic disparities in chronic obstructive pulmonary disease (COPD) hospitalization among Medicare beneficiaries in the United States

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Protocol publication

[…] The preliminary descriptive analysis included crude rates and maps. Statistical analysis began with spatial cluster analysis to explore spatial continuities and discontinuities of COPD hospitalization rates. Spatial continuities (or discontinuities) indicate that COPD hospitalization rates and risk exhibit smooth (or disruptive) variation between adjacent geographic areas. Namely the rate or risk surface of COPD hospitalization has a smooth (or discordant) spatial pattern among neighboring areas. These smooth or discordant patterns could be attributable to a number of as yet undefined factors. A Bayesian hierarchical spatial modeling approach was applied to characterize the geography of COPD hospitalization risk. The goal was to identify which HSAs or regions have significantly higher or lower COPD hospitalization risks compared to the average HSA level risk, and whether HSA local or regional characteristics, or both, may contribute to HSAs’ excessive or reduced COPD hospitalization risks. Regional characteristics are those environmental/contextual conditions that extend over a large area, perhaps encompassing several states (eg, Appalachia) – these may represent widespread spatial processes, such as regionalized poverty. HSA local characteristics represent environmental/contextual conditions that occur in smaller areas (eg, one or a few contiguous counties) – these may represent place-specific conditions, such as localized occupational exposures.State and HSA level COPD hospitalization rate maps ( and ) showed large geographic variations and strong spatial continuities and some discontinuities over the US. Using STIS software (TerraSeer, Inc), the Moran I index was computed to assess the overall spatial autocorrelation of HSA COPD hospitalization rates across the US, while local indicators of spatial association were computed to examine their local spatial dependence and discontinuities. Local spatial cluster analysis classified HSAs into the following five categories: 1) spatial clusters of HSAs with significantly higher rates than surrounding HSAs; 2) spatial clusters of HSAs with significantly lower rates than the surrounding HSAs; 3) spatial outliers of HSAs with significantly higher rates than surrounding HSAs; 4) spatial outliers of HSAs with significantly lower rates than surrounding HSAs; and 5) HSAs that have no significant spatial autocorrelation with surrounding HSAs for COPD hospitalization rates.Bayesian hierarchical spatial modeling is commonly used in disease mapping and small area risk assessment. A Bayesian spatial mixture model was constructed, which extends the classical spatial convolution model by introducing a spatial mixture parameter to balance the spatially smooth and disruptive effects on COPD outcomes. The spatial mixture model was implemented via Markov Chain Monte Carlo methods in WinBUGS. […]

Pipeline specifications

Software tools Spdep, WinBUGS
Applications Drug design, Miscellaneous
Organisms Homo sapiens
Diseases Pulmonary Disease, Chronic Obstructive, Overbite