Association between spatial accessibility to fire protection services and unintentional residential fire injuries or deaths: a cross-sectional study in Dallas, Texas.

OBJECTIVES: In addition to neighbourhood characteristics encompassing racial composition, socioeconomic status and housing environments, it was unclear if location characteristics relating to the accessibility of fire protection services were risk factors for unintentional residential fire-related injuries in urban areas. This study was aimed to measure spatial accessibility to fire protection services at the census block group level, and examine whether it is associated with unintentional residential fire-related injuries. DESIGN: A cross-sectional study. SETTING: Unintentional residential fire incidents between 2012 and 2015 in Dallas City, Texas, USA. MAIN OUTCOME MEASURES: Using multiple logistic regression, the study analysed association between unintentional residential fire incidents in the city and risk factors, including spatial accessibility to fire protection services, measured by the two-step floating catchment area method. RESULTS: Compared with incidents without any injuries, fire incidents involving injuries were significantly more likely to include unconfined fire, fire originating in a cooking area, a bedroom for less than five people or a common family area, census block groups with >75% African American residents, >25% elderly single-person households and positive population growth rates <25% or >25%. Incidents involving injuries were significantly less likely to occur in areas with high spatial accessibility, as measured by spatial accessibility score. CONCLUSIONS: In addition to fire characteristics and neighbourhood demographics, spatial accessibility to fire protection services was significantly associated with unintentional residential fire-related injuries. The findings can be used to help select locations of additional fire stations.

Are Publicly Funded Health Databases Geographically Detailed and Timely Enough to Support Patient-Centered Outcomes Research?

Emerging health care research paradigms such as comparative effectiveness research (CER), patient-centered outcome research (PCOR), and precision medicine (PM) share one ultimate goal: constructing evidence to provide the right treatment to the right patient at the right time. We argue that to succeed at this goal, it is crucial to have both timely access to individual-level data and fine geographic granularity in the data. Existing data will continue to be an important resource for observational studies as new data sources are developed. We examined widely used publicly funded health databases and population-based survey systems and found four ways they could be improved to better support the new research paradigms: (1) finer and more consistent geographic granularity, (2) more complete geographic coverage of the US population, (3) shorter time from data collection to data release, and (4) improved environments for restricted data access. We believe that existing data sources, if utilized optimally, and newly developed data infrastructures will both play a key role in expanding our insight into what treatments, at what time, work for each patient.

A Closer Look at the Bivariate Association between Ambient Air Pollution and Allergic Diseases: The Role of Spatial Analysis.

Although previous ecological studies investigating the association between air pollution and allergic diseases accounted for temporal or seasonal relationships, few studies address spatial non-stationarity or autocorrelation explicitly. Our objective was to examine bivariate correlation between outdoor air pollutants and the prevalence of allergic diseases, highlighting the limitation of a non-spatial correlation measure, and suggesting an alternative to address spatial autocorrelation. The 5-year prevalence data (2011⁻2015) of allergic rhinitis, atopic dermatitis, and asthma were integrated with the measures of four major air pollutants (SO2, NO2, CO, and PM10) for each of the 423 sub-districts of Seoul. Lee's L statistics, which captures how much bivariate associations are spatially clustered, was calculated and compared with Pearson's correlation coefficient for each pair of the air pollutants and allergic diseases. A series of maps showing spatiotemporal patterns of allergic diseases at the sub-district level reveals a substantial degree of spatial heterogeneity. A high spatial autocorrelation was observed for all pollutants and diseases, leading to significant dissimilarities between the two bivariate association measures. The local L statistics identifies the areas where a specific air pollutant is considered to be contributing to a type of allergic disease. This study suggests that a bivariate correlation measure between air pollutants and allergic diseases should capture spatially-clustered phenomenon of the association, and detect the local instability in their relationships. It highlights the role of spatial analysis in investigating the contribution of the local-level spatiotemporal dynamics of air pollution to trends and the distribution of allergic diseases.

GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy.

PURPOSE: The role of PM10 in the development of allergic diseases remains controversial among epidemiological studies, partly due to the inability to control for spatial variations in large-scale risk factors. This study aims to investigate spatial correspondence between the level of PM10 and allergic diseases at the sub-district level in Seoul, Korea, in order to evaluate whether the impact of PM10 is observable and spatially varies across the subdistricts. METHODS: PM10 measurements at 25 monitoring stations in the city were interpolated to 424 sub-districts where annual inpatient and outpatient count data for 3 types of allergic diseases (atopic dermatitis, asthma, and allergic rhinitis) were collected. We estimated multiple ordinary least square regression models to examine the association of the PM10 level with each of the allergic diseases, controlling for various sub-district level covariates. Geographically weighted regression (GWR) models were conducted to evaluate how the impact of PM10 varies across the sub-districts. RESULTS: PM10 was found to be a significant predictor of atopic dermatitis patient count (P<0.01), with greater association when spatially interpolated at the sub-district level. No significant effect of PM10 was observed on allergic rhinitis and asthma when socioeconomic factors were controlled for. GWR models revealed spatial variation of PM10 effects on atopic dermatitis across the sub-districts in Seoul. The relationship of PM10 levels to atopic dermatitis patient counts is found to be significant only in the Gangbuk region (P<0.01), along with other covariates including average land value, poverty rate, level of education and apartment rate (P<0.01). CONCLUSIONS: Our findings imply that PM10 effects on allergic diseases might not be consistent throughout Seoul. GIS-based spatial modeling techniques could play a role in evaluating spatial variation of air pollution impacts on allergic diseases at the sub-district level, which could provide valuable guidelines for environmental and public health policymakers.