scholarly journals An Analysis of Spatial Clustering of Stroke Types, In-hospital Mortality, and Reported Risk Factors in Alberta, Canada, Using Geographic Information Systems

2015 ◽  
Vol 42 (5) ◽  
pp. 299-309 ◽  
Author(s):  
Susan van Rheenen ◽  
Timothy W.J. Watson ◽  
Shelley Alexander ◽  
Michael D. Hill
Keyword(s):  
Risk Factors ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Hospital Mortality ◽  
Hot Spots ◽  
Regional Differences ◽  
Stroke Risk ◽  
Stroke Care ◽  
Geographic Information ◽  
Ischemic Attack

ABSTRACTBackgroundDespite advances in the quality and delivery of stroke care, regional disparities in stroke incidence and outcome persist. Spatial analysis using geographic information systems (GIS) can assist in identifying high-risk populations and regional differences in efficacy of stroke care. The aim of this study was to identify and locate geographic clusters of high or low rates of stroke, risk factors, and in-hospital mortality across a provincial health care network in Alberta, Canada.MethodsThis study employed a spatial epidemiological approach using population-based hospital administrative data. Getis-Ord Gi* and Spatial Scan statistics were used to identify and locate statistically significant “hot” and “cold” spots of stroke occurrence by type, risk factors, and in-hospital mortality.ResultsMarked regional variations were found. East central Alberta was a significant hot spot for ischemic stroke (relative risk [RR] 1.43, p<0.001), transient ischemic attack (RR 2.25, p<0.05), and in-hospital mortality (RR 1.50, p<0.05). Hot spots of intracerebral hemorrhage (RR 1.80, p<0.05) and subarachnoid hemorrhage (RR 1.64, p<0.05) were identified in a major urban centre. Unexpectedly, stroke risk factor hot spots (RR 2.58, p<0.001) were not spatially associated (did not overlap) with hot spots of ischemic stroke, transient ischemic attack, or in-hospital mortality.ConclusionsIntegration of health care administrative data sets with geographic information systems contributes valuable information by identifying the existence and location of regional disparities in the spatial distribution of stroke occurrence and outcomes. Findings from this study raise important questions regarding why regional differences exist and how disparities might be mitigated.

scholarly journals 181. Demographic and Geographic Epidemiology of Staphylococcus aureus Bacteremia using Geographic Information Systems Mapping and Spatial Statistics

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S199-S199
Author(s):  
Julia Marshall ◽  
Vance G Fowler ◽  
Felicia Ruffin ◽  
Paul Lantos ◽  
Christopher Timmins
Keyword(s):  
Risk Factors ◽  
Staphylococcus Aureus ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Clinical Characteristics ◽  
Geographic Information ◽  
Research Support ◽  
Material Support ◽  
Staphylococcus Aureus Bacteremia

Abstract Background Risk factors for community-associated Staphylococcus aureus bacteremia (SAB) are incompletely understood. We used Geographic Information Systems (GIS) and spatial statistics to analyze demographic and geographic epidemiology of SAB in the community. Methods We used the S. aureus Bacteremia Group Prospective Cohort Study (SABG-PCS) at Duke University Medical Center to obtain demographic and clinical data. We used the American Community Survey and U.S. Census to supply neighborhood variables. Secular trends in demographic and clinical characteristics of SAB patients prospectively enrolled between 1995 and 2015 (n = 2478) were determined using linear regressions. To characterize spatial patterns in Methicillin-resistant S. aureus (MRSA) bacteremia compared to Methicillin-susceptible S. aureus (MSSA) bacteremia, we used GIS mapping and selected a subgroup of patients (n = 667) living in and around Durham County, North Carolina. We then created generalized additive models (GAMs) using this subgroup to detect geographic heterogeneities in probabilities of MRSA infections compared to MSSA infections. Results We found evidence of changing demographic and clinical characteristics of SAB patients over the 21-year period. The proportion of infections acquired in the community increased significantly (p &lt; 0.0001). However, we did not detect spatial heterogeneities of MRSA infections in Durham County. Patient location of residence was not significantly associated with antimicrobial-resistant infections. Patient age and year of hospital admission were the only statistically significant covariates in our spatial models. Conclusion We utilized a novel method to analyze SAB in the community using GIS and spatial statistics. Future research should prioritize community transmission of S. aureus to identify robust risk factors for infection. Disclosures Vance G. Fowler, Jr., MD, MHS, Achaogen (Consultant)Advanced Liquid Logics (Grant/Research Support)Affinergy (Consultant, Grant/Research Support)Affinium (Consultant)Akagera (Consultant)Allergan (Grant/Research Support)Amphliphi Biosciences (Consultant)Aridis (Consultant)Armata (Consultant)Basilea (Consultant, Grant/Research Support)Bayer (Consultant)C3J (Consultant)Cerexa (Consultant, Other Financial or Material Support, Educational fees)Contrafect (Consultant, Grant/Research Support)Debiopharm (Consultant, Other Financial or Material Support, Educational fees)Destiny (Consultant)Durata (Consultant, Other Financial or Material Support, educational fees)Genentech (Consultant, Grant/Research Support)Green Cross (Other Financial or Material Support, Educational fees)Integrated Biotherapeutics (Consultant)Janssen (Consultant, Grant/Research Support)Karius (Grant/Research Support)Locus (Grant/Research Support)Medical Biosurfaces (Grant/Research Support)Medicines Co. (Consultant)MedImmune (Consultant, Grant/Research Support)Merck (Grant/Research Support)NIH (Grant/Research Support)Novadigm (Consultant)Novartis (Consultant, Grant/Research Support)Pfizer (Grant/Research Support)Regeneron (Consultant, Grant/Research Support)sepsis diagnostics (Other Financial or Material Support, Pending patent for host gene expression signature diagnostic for sepsis.)Tetraphase (Consultant)Theravance (Consultant, Grant/Research Support, Other Financial or Material Support, Educational fees)Trius (Consultant)UpToDate (Other Financial or Material Support, Royalties)Valanbio (Consultant, Other Financial or Material Support, Stock options)xBiotech (Consultant)

scholarly journals 2395. Analysis of Countywide Clostridium difficile Infection using Descriptive Statistics and Geographic Information Systems Mapping

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S827-S827
Author(s):  
Jeanne Li ◽  
Kevin Mwenda ◽  
Leslie Stanfield ◽  
Richard Beswick
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Clostridium Difficile ◽  
Clostridium Difficile Infection ◽  
Hot Spots ◽  
The United States ◽  
Geographic Information ◽  
Descriptive Statistics ◽  
Santa Barbara ◽  
Santa Barbara County

Abstract Background Clostridium difficile infection (CDI) is now the most common pathogen causing nosocomial infectious diarrhea in the United States, and more than 500,000 people are estimated to have either healthcare-associated (HA) or community acquired (CA) CDI. The epidemiology of CDI is incompletely understood with more than 50% of all CDI cases occurring in the outpatient community and growing at a pace that is greater than HA-CDI. Methods Patients with CDI within Santa Barbara County, California were identified via three types of tests: Clostridium difficile PCR, gastrointestinal panel by PCR, and enzyme immunoassay (EIA) via local laboratory. Basic patient characteristics were analyzed using descriptive statistics. Changes with CA-CDI incidence were examined on a quarterly basis to identify and compare quarterly trends in CA-CDI incidence. Geographic Information Systems (GIS) mapping was utilized to provide better spatial understanding of disease distribution across communities. Results Over 2,000 unique patients with CDI were identified between January 1, 2013 and January 31, 2019. Median age of these patients was 64 years (interquartile range: 45 – 78) and 60% were female. Hot spots of CDI within Santa Barbara County were localized to three major cities: Santa Barbara, Goleta, and Lompoc. Our results show that based on seasonal quarterly data CDI occurred most frequently in winter months. Conclusion In conclusion, CDI hot spots occurred most frequently during winter months and could possibly be associated with increased antibiotic treatment during flu season. Using the results from this study, we believe that by utilizing spatial and seasonal trends associated with CDI, physicians may be able to identify, diagnose and treat patients with CDI more promptly in Santa Barbara County. Disclosures All authors: No reported disclosures.

scholarly journals Analysis and Assessment of Lithuanian Road Accidents by AHP Method

2018 ◽  
Vol 13 (3) ◽  
Author(s):  
Marius Jakimavičius
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Analytical Hierarchy Process ◽  
Spatial Data ◽  
Hot Spots ◽  
Information Technologies ◽  
Geographic Information ◽  
Road Accidents ◽  
Hierarchy Process ◽  
Process Method

Lithuanian road accidents were evaluated based on the geographic information systems and multi-criteria method of Analytical Hierarchy Process This paper presents the methodology for selecting and ranking high accident concentration sections on the roads of national significance. Methodology involves the following process phases: 1) preparation of spatial data of the road accidents; 2) estimation of road sections with a high accident rate; 3) calculation of spatial statistics for estimation of accident points and hot spots; 4) selecting indicators for multi-criteria assessment; 5) calculation by Analytical Hierarchy Process method and ranking the selected high accident concentration sections. Assessment of spatial clustering of accidents and hot spots was carried out following geo-information technologies and using Getis-Ord Gi  statistics and point density functions. This geospatial criterion was integrated into multicriteria assessment for ranking the high accident concentration sections by using the Analytical Hierarchy Process method. Presented method is useful for various agencies in order to improve their planning and management strategies for better traffic conditions as well as to reduce the number of accidents. The result of the research presents selection methodology of dangerous accident section and ranking of the tenth the most dangerous sections involving geographic information systems and Analytical Hierarchy Process method.

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