» Articles » PMID: 25457593

Spatial Prevalence and Associations Among Respiratory Diseases in Maine

Overview
Publisher Elsevier
Specialty Public Health
Date 2014 Dec 3
PMID 25457593
Citations 6
Authors
Affiliations
Soon will be listed here.
Abstract

Chronic respiratory diseases rank among the leading global disease burdens. Maine's respiratory disease prevalence exceeds the US average, despite limited urbanization/industrialization. To provide insight into potential etiologic factors among this unique, rural population, we analyzed the spatial distributions of, and potential associations among asthma, COPD, pneumonia, and URI adult outpatient data (n=47,099) from all outpatient transactions (n=5,052,900) in 2009 for Maine hospitals and affiliate clinics, using spatial scan statistic, geographic weighted regression (GWR), and a Delaunay graph algorithm. Non-random high prevalence regions were identified, the majority of which (84% of the population underlying all regions) exhibited clusters for all four respiratory diseases. GWR provided further evidence of spatial correlation (R(2)=0.991) between the communicable and noncommunicable diseases under investigation, suggesting spatial interdependence in risk. Sensitivity analyses of known respiratory disease risks did not fully explain our results. Prospective epidemiology studies are needed to clarify all contributors to risk.

Citing Articles

Spatiotemporal clusters of acute respiratory infections associated with socioeconomic, meteorological, and air pollution factors in South Punjab, Pakistan.

Fatima M, Butt I, MohammadEbrahimi S, Kiani B, Gruebner O BMC Public Health. 2025; 25(1):469.

PMID: 39910401 PMC: 11800423. DOI: 10.1186/s12889-025-21741-4.


Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Howard S, Odoi A PeerJ. 2024; 12:e17771.

PMID: 39104363 PMC: 11299531. DOI: 10.7717/peerj.17771.


A Bayesian spatio-temporal analysis of neighborhood pediatric asthma emergency department visit disparities.

Bozigar M, Lawson A, Pearce J, King K, Svendsen E Health Place. 2020; 66:102426.

PMID: 33011491 PMC: 8591955. DOI: 10.1016/j.healthplace.2020.102426.


A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma.

Bozigar M, Lawson A, Pearce J, King K, Svendsen E Int J Health Geogr. 2020; 19(1):9.

PMID: 32188481 PMC: 7081565. DOI: 10.1186/s12942-020-00203-7.


Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma.

Niyonsenga T, Coffee N, Del Fante P, Hoj S, Daniel M BMC Health Serv Res. 2018; 18(1):897.

PMID: 30477507 PMC: 6260571. DOI: 10.1186/s12913-018-3714-5.