Modeling travel impedance to medical care for children with birth defects using Geographic Information Systems.

BACKGROUND: Children with birth defects may face significant geographic barriers accessing medical care and specialized services. Using a Geographic Information Systems-based approach, one-way travel time and distance to access medical care for children born with spina bifida was estimated. METHODS: Using 2007 road information from the Florida Department of Transportation, we built a topological network of Florida roads. Live-born Florida infants with spina bifida during 1998 to 2007 were identified by the Florida Birth Defects Registry and linked to hospital discharge records. Maternal residence at delivery and hospitalization locations were identified during the first year of life. RESULTS: Of 668 infants with spina bifida, 8.1% (n = 54) could not be linked to inpatient data, resulting in 614 infants. Of those 614 infants, 99.7% (n = 612) of the maternal residential addresses at delivery were successfully geocoded. Infants with spina bifida living in rural areas in Florida experienced travel times almost twice as high compared with those living in urban areas. When aggregated at county levels, one-way network travel times exhibited statistically significant spatial autocorrelation, indicating that families living in some clusters of counties experienced substantially greater travel times compared with families living in other areas of Florida. CONCLUSION: This analysis demonstrates the usefulness of linking birth defects registry and hospital discharge data to examine geographic differences in access to medical care. Geographic Information Systems methods are important in evaluating accessibility and geographic barriers to care and could be used among children with special health care needs, including children with birth defects.

Uncertainty in geospatial health: challenges and opportunities ahead.

PURPOSE: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

An assessment of the non-fatal crash risks associated with substance use during rush and non-rush hour periods in the United States.

BACKGROUND: Understanding how substance use is associated with severe crash injuries may inform emergency care preparedness. OBJECTIVES: This study aims to assess the association of substance use and crash injury severity at all times of the day and during rush (6-9 AM; 3-7 PM) and non-rush-hours. Further, this study assesses the probabilities of occurrence of low acuity, emergent, and critical injuries associated with substance use. METHODS: Crash data were extracted from the 2019 National Emergency Medical Services Information System. The outcome variable was non-fatal crash injury, assessed on an ordinal scale: critical, emergent, low acuity. The predictor variable was the presence of substance use (alcohol or illicit drugs). Age, gender, injured part, revised trauma score, the location of the crash, the road user type, and the geographical region were included as potential confounders. Partially proportional ordinal logistic regression was used to assess the unadjusted and adjusted odds of critical and emergent injuries compared to low acuity injury. RESULTS: Substance use was associated with approximately two-fold adjusted odds of critical and emergent injuries compared to low acuity injury at all times of the day and during the rush and non-rush hours. Although the proportion of substance use was higher during the non-rush hour period, the interaction effect of rush hour and substance use resulted in higher odds of critical and emergent injuries compared to low acuity injury. CONCLUSION: Substance use is associated with increased odds of critical and emergent injury severity. Reducing substance use-related crash injuries may reduce adverse crash injuries.

What is the place for space in epidemiology?

At the heart of spatial epidemiology is the need to describe and understand variation in population health. In this review and introduction to the themed issue on "Spatial Analysis and GIS in Epidemiology," we present theoretical foundations and methodological developments in spatial epidemiology, discuss spatial analytical techniques and their public health applications, and identify novel data sources and applications with the potential to make epidemiology more consequential. Challenges with using georeferenced data are also explored, including dealing with small sample sizes, missingness, generalizability, and geographic scale. Given the increasing availability of spatial data and visualization tools, we have an opportunity to overcome traditionally siloed fields and practice settings to advance knowledge and more appropriately respond to emerging public health crises.

Predicting coliform presence in private wells as a function of well characteristics, parcel size and leachfield soil rating.

Public water systems must be tested frequently for coliform bacteria to determine whether other pathogens may be present, yet no testing or disinfection is required for private wells. In this paper, we identify whether well age, type of well, well depth, parcel size, and soil ratings for a leachfield can predict the probability of detecting coliform bacteria in private wells using a multivariate logistic regression model. Samples from 1163 wells were analyzed for the presence of coliform bacteria between October 2017 and October 2019 across Gaston County, North Carolina, USA. The maximum well age was 30 years, and bored wells (median age = 24 years) were older than drilled wells (median age = 19 years). Bored wells were shallower (mean depth = 18 m) compared to drilled wells (mean depth = 79 m). We found coliform bacteria in 329 samples, including 290 of 1091 drilled wells and 39 of 72 bored wells. The model results showed bored wells were 4.76 times more likely to contain bacteria compared to drilled wells. We found that the likelihood of coliform bacteria significantly increased with well age, suggesting that those constructed before well standards were enforced in 1989 may be at a higher risk. We found no significant association between poorly rated soils for a leachfield, well depth, parcel size and the likelihood of having coliform in wells. These findings can be leveraged to determine areas of concern to encourage well users to take action to reduce their risk of drinking possible pathogens in well water.

Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia.

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level-where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space-time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

Second-Phase Sampling Designs for Non-Stationary Spatial Variables.

In spatial sampling, once initial samples of the primary variable have been collected, it is possible to take additional measurements, an approach known as second-phase sampling. Additional samples are usually collected away from observation locations, or where the kriging variance is maximum. However, the kriging variance (also known as prediction error variance) is independent of data values and computed under the assumption of stationary spatial process, which is often violated in practice. In this paper, we weight the kriging variance with another criterion, giving greater sampling importance to locations exhibiting significant spatial roughness that is computed by a spatial moving average window. Additional samples are allocated using a simulated annealing procedure since the weighted objective function is non-linear. A case study using an exhaustive remote sensing image illustrates the procedure. Combinations of first-phase systematic and nested sampling designs (or patterns) of varying densities are generated, while the location of additional observations is guided in a way which optimizes the proposed objective function. The true pixel value at the new points is extracted, the semivariogram model updated, and the image reconstructed. Second-phase sampling patterns optimizing the proposed criterion lead to predictions closer to the true image than when using the kriging variance as the main criterion. This improvement is stronger when there is a low density of first-phase samples, and decreases however as the initial density increases.

Evaluating the reliability of automated collision notification systems.

The use of an automated collision notification (ACN) device in vehicles can greatly reduce the time between crash occurrence and notification of emergency medical services (EMSs). Most ACN devices rely on cellular technology to report important crash information to the proper authorities. The objective of this study was to examine the ability of the existing western New York cellular analog system to support ACN systems. The first task was to develop a model predicting the probability of successfully completing an emergency ACN call at attenuated levels of received signal strength indicator (RSSI), a measurement of the bond between cell phone and tower. Then, empirical estimates were made of the time necessary for call completion at given levels of the RSSI. The RSSI is sampled at locations throughout Erie County, New York, and this information is used to determine the probability of successful call completion for different locations within the county. This model was then applied to historic data for selected past crashes. Finally, the findings were compared with real-world crash data obtained from the ACN Field Operational Test program, where 750 ACN devices were installed in cars and their performance examined over time. An interpolated map of the sampled RSSI values suggests that cellular coverage in Erie County is adequate to support the automated collision network technology. The models and techniques described here are applicable to other areas and regions of the country.