Pre-pregnancy Weight and Racial-Ethnic Disparities in Pregnancy-Associated Conditions in the State of Georgia: A Population-Based Study.

INTRODUCTION: We investigate racial-ethnic disparities in pre-pregnancy obesity and pregnancy weight gain, which are known to increase the risk of pregnancy-associated conditions. METHODS: We used 4-year (2017-2020) combined Georgia Pregnancy Risk Assessment Monitoring System data (N = 3208) to investigate racial-ethnic disparities in the incidence of gestational hypertension (GHT), gestational diabetes mellitus (GDM), and postpartum depression (PPD) and their associated risk with pre-pregnancy overweight/obesity after controlling for demographic and other confounders using regression modeling. The geographic distributions of hypertension and PPD rates at the county level were compared to the patterns of racial-ethnic populations and hospitals. RESULTS: The PPD rates were higher among Asian (17.6), Hispanic (14.4), and Black (14.3); GDM was highest among Asian (16.0) mothers; and GHT was the highest among Black (11.7) followed by White mothers (9.0). Pre-pregnancy overweight and obese conditions increased the odds of hypertension in Black (2 ½ times) and White (> 3 ½ times) mothers. Premature birth increased the odds of hypertension (2-3 times) in all mothers. Pre-pregnancy weight also increased the odds of GDM (3-7 times) in these racial groups. Premature birth increases the odds twice as likely for PPD in Hispanic and White mothers. The convergence of high PPD and hypertension rates with high proportions of racial and ethnic minorities, and lack of hospital presence, indicates areas where healthcare interventions are required. CONCLUSIONS: These findings underscore the importance of promoting a healthy pre-pregnancy weight to reduce the burden of maternal morbidity and pregnancy outcomes in general. More comprehensive prenatal monitoring using technological interventions for self-care has a great promise of being effective in maintaining a healthy pregnancy.

Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model.

Accidental releases of untreated sewage into the environment, known as sewage spills, may cause adverse gastrointestinal stress to exposed populations, especially in young, elderly, or immune-compromised individuals. In addition to human pathogens, untreated sewage contains high levels of micropollutants, organic matter, nitrogen, and phosphorus, potentially resulting in aquatic ecosystem impacts such as algal blooms, depleted oxygen, and fish kills in spill-impacted waterways. Our Geographic Information System (GIS) model, Spill Footprint Exposure Risk (SFER) integrates fine-scale elevation data (1/3 arc-second) with flowpath tracing methods to estimate the expected overland pathways of sewage spills and the locations where they are likely to pool. The SFER model can be integrated with secondary measures tailored to the unique needs of decision-makers so they can assess spatially potential exposure risk. To illustrate avenues to assess risk, we developed risk measures for land and population health. The land risk of sewage spills is calculated for subwatershed regions by computing the proportion of the subwatershed's area that is affected by one modeled footprint. The population health risk is assessed by computing the estimated number of individuals who are within the modeled footprint using fine-scale (90 square meters) population estimates data from LandScan USA. In the results, with a focus on the Atlanta metropolitan region, potential strategies to combine these risk measures with the SFER model are outlined to identify specific areas for intervention.

Aligning Policy, Place and Public Value: Planning Age Friendly Cities in Municipal Organizations.

KEY POINTS: Age-friendly planning should not fall to local departments of senior services only.Planning coordination of age-friendly policy results in more diverse outcomes.Mapping is a tool helping policy makers visualize alternative opportunities.Maps give stakeholders the ability to track and monitor progress.This approach is easily replicable for cities implementing age-friendly programs.

Cascading effects of COVID-19 on population mobility and air quality: An exploration including place characteristics using geovisualization.

This study hypothesizes that public health responses to coronavirus disease 2019 (COVID-19), including a mandated restriction of activity (commonly called a 'lockdown') resulted in reduced transportation activities and changes in air quality in Texas, USA. This presented a natural experiment where population mobility and air quality before and after the lockdown could be compared. Changes in mobility were measured by SafeGraph mobility data (from opt-in smart phone applications that transmit location data) and air quality changes were based on NO2 concentrations measured by the European Space Agency's Sentinel-5 Precursor satellite (from the TROPOspheric Monitoring Instrument). The changes in population mobility and NO2 concentration between mid-March 2020 (lockdown initiated) and the end of 2020, as compared to the same time window in 2019, were the basis of exploring the lockdown hypothesis. Additionally, numerous socio-economic (place based) indicators were hypothesized to follow public health vulnerability assumptions based on COVID- 19 incidence patterns. This hypothesis was subjected to geovisualization techniques in order to find potential patterns and insights into the complex combinations of these place-based data. Our results suggest that simultaneously visualizing COVID-19, mobility, air quality and socio-economic data yields insights in underlying spatial processes related to public health policy decisions. The hypothesis that the lockdown resulted in reduced mobility and NO2 concentrations was found partially correct - this trend was observed in highly urbanized areas, but not in less populated areas. Data related public health vulnerability assumptions (e.g. a region's age, poverty, education, etc.) were agreed with in part, but disagreed with in part.

Evaluation of Heuristics for the P-Median Problem: Scale and Spatial Demand Distribution.

The objective of the p-median problem is to identify p source locations and map them to n destinations while minimizing the average distance between destinations and corresponding sources. Several heuristic algorithms have been developed to solve this general class of facility location problems. In this study, we add to the current literature in two ways: (1) we present a thorough evaluation of existing classic heuristics and (2) we investigate the effect of spatial distribution of destination locations, and the number of sources and destinations on the performance of these algorithms for varying problem sizes using synthetic and real datasets. The performance of these algorithms is evaluated using the objective function value, time taken to achieve the solution, and the stability of the solution. The sensitivity of existing algorithms to the spatial distribution of destinations and scale of the problem with respect to the three metrics is analyzed in the paper. The utility of the study is demonstrated by evaluating these algorithms to select the locations of ad-hoc clinics that need to be set up for resource distribution during a bio-emergency. We demonstrate that interchange algorithms achieve good quality solutions with respect to both the execution time and cost function values, and they are more stable for clustered distributions.

Evaluating spatial patterns of seasonal ozone exposure and incidence of respiratory emergency room visits in Dallas-Fort Worth.

BACKGROUND: In urban environments, environmental air pollution poses significant risks to respiratory health. Moreover, the seasonal spatial variability of the air pollutant ozone, and respiratory illness within Dallas-Fort Worth (DFW) is not well understood. We examine the relationships between spatial patterns of long-term ozone exposure and respiratory illness to better understand impacts on health outcomes. We propose that this study will establish an enhanced understanding of the spatio-temporal characteristics of ozone concentrations and respiratory emergency room visits (ERV) incidence. METHODS: Air pollution data (ozone) and ERV incidence data from DFW was used to evaluate the relationships between exposures and outcomes using three steps: (1) develop a geostatistical model to produce quarterly maps of ozone exposure for the DFW area; (2) use spatial analysis techniques to identify clusters of zip codes with high or low values of ozone exposure and respiratory ERV incidence; and (3) use concentration-response curves to evaluate the relationships between respiratory ERV incidence and ozone exposure. RESULTS: Respiratory ERV incidence was highest in quarters 1 and 4, while ozone exposure was highest in quarters 2 and 3. Extensive statistically significant spatial clusters of ozone regions were identified. Although the maps revealed that there was no regional association between the spatial patterns of high respiratory ERV incidence and ozone exposure, the concentration-response analysis suggests that lower levels of ozone exposure may still contribute to adverse respiratory outcomes.

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus.

BACKGROUND: This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data. METHODS: Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. RESULTS: Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Using GIS to detect cluster and spatial disparity in maternal health indicators: a need for social health interventions.

Maternal mortality remains a serious global health concern. Although global efforts have produced some encouraging results in some World Health Organization's health regions, disparities persist within many countries. Additionally, in many developing countries, inadequate documentation of various health events including maternal mortality and morbidity, make it difficult to determine the true extent of the problem. Maternal health indicators are therefore proxies used in estimating health status in developing countries. Using geospatial and geovisualization techniques, this study examines district level disparities in two maternal health indicators in Ghana antenatal care (ANC) visits and skilled birth attendance (SBA). The results reveal districts with complete lack of access to higher health care professionals and others with underutilization of antenatal services. The findings provide important input for targeting location-specific public health and maternal health interventions.

Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping.

BACKGROUND: Maps of disease rates produced without careful consideration of the underlying population distribution may be unreliable due to the well-known small numbers problem. Smoothing methods such as Kernel Density Estimation (KDE) are employed to control the population basis of spatial support used to calculate each disease rate. The degree of smoothing is controlled by a user-defined parameter (bandwidth or threshold) which influences the resolution of the disease map and the reliability of the computed rates. Methods for automatically selecting a smoothing parameter such as normal scale, plug-in, and smoothed cross validation bandwidth selectors have been proposed for use with non-spatial data, but their relative utilities remain unknown. This study assesses the relative performance of these methods in terms of resolution and reliability for disease mapping. RESULTS: Using a simulated dataset of heart disease mortality among males aged 35 years and older in Texas, we assess methods for automatically selecting a smoothing parameter. Our results show that while all parameter choices accurately estimate the overall state rates, they vary in terms of the degree of spatial resolution. Further, parameter choices resulting in desirable characteristics for one sub group of the population (e.g., a specific age-group) may not necessarily be appropriate for other groups. CONCLUSION: We show that the appropriate threshold value depends on the characteristics of the data, and that bandwidth selector algorithms can be used to guide such decisions about mapping parameters. An unguided choice may produce maps that distort the balance of resolution and statistical reliability.

Challenges of Designing and Implementing High Consequence Infectious Disease Response.

Ebola is a high consequence infectious disease-a disease with the potential to cause outbreaks, epidemics, or pandemics with deadly possibilities, highly infectious, pathogenic, and virulent. Ebola's first reported cases in the United States in September 2014 led to the development of preparedness capabilities for the mitigation of possible rapid outbreaks, with the Centers for Disease Control and Prevention (CDC) providing guidelines to assist public health officials in infectious disease response planning. These guidelines include broad goals for state and local agencies and detailed information concerning the types of resources needed at health care facilities. However, the spatial configuration of populations and existing health care facilities is neglected. An incomplete understanding of the demand landscape may result in an inefficient and inequitable allocation of resources to populations. Hence, this paper examines challenges in implementing CDC's guidance for Ebola preparedness and mitigation in the context of geospatial allocation of health resources and discusses possible strategies for addressing such challenges. (Disaster Med Public Health Preparedness. 2018;12:563-566).

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters.

BACKGROUND: The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. METHODS: We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. RESULTS: LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). CONCLUSIONS: The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Quantifying Access Disparities in Response Plans.

Effective response planning and preparedness are critical to the health and well-being of communities in the face of biological emergencies. Response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable subpopulations, resulting in access disparities during emergency response. For a response plan to be effective, sufficient mitigation resources must be made accessible to target populations within short, federally-mandated time frames. A major challenge in response plan design is to establish a balance between the allocation of available resources and the provision of equal access to PODs for all individuals in a given geographic region. Limitations on the availability, granularity, and currency of data to identify vulnerable populations further complicate the planning process. To address these challenges and limitations, data driven methods to quantify vulnerabilities in the context of response plans have been developed and are explored in this article.

RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning.

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or GIS expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer (RE-PLAN) framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this article, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tool are demonstrated.

The impact of data suppression on local mortality rates: the case of CDC WONDER.

CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) is the nation's primary data repository for health statistics. Before WONDER data are released to the public, data cells with fewer than 10 case counts are suppressed. We showed that maps produced from suppressed data have predictable geographic biases that can be removed by applying population data in the system and an algorithm that uses regional rates to estimate missing data. By using CDC WONDER heart disease mortality data, we demonstrated that effects of suppression could be largely overcome.

Mapping late testers for HIV in Texas.

Understanding the spatial patterns of late testing for HIV infection is critically important for designing and evaluating intervention strategies to reduce the social and economic burdens of HIV/AIDS. Traditional mapping methods that rely on frequency counts or rates in predefined areal units are known to be problematic due to issues of small numbers and visual biases. Additionally, confidentiality requirements associated with health data further restrict the ability to produce cartographic representations at fine geographic scales. While kernel density estimation methods produce stable and geographically detailed patterns of the late testing burden, the resulting pattern depends critically on the definition of the at-risk population. Using three definitions of at risk groups, we examine the cartographic representation of HIV late testers in Texas and show that the resulting spatial patterns and the interpretation of disease burdens are different based on the choice of the at-risk population. Disease mappers should exercise considerable caution in selecting the denominator population for mapping.

A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement.

In the presence of naturally occurring and man-made public health threats, the feasibility of regional bio-emergency contingency plans plays a crucial role in the mitigation of such emergencies. While the analysis of in-place response scenarios provides a measure of quality for a given plan, it involves human judgment to identify improvements in plans that are otherwise likely to fail. Since resource constraints and government mandates limit the availability of service provided in case of an emergency, computational techniques can determine optimal locations for providing emergency response assuming that the uniform distribution of demand across homogeneous resources will yield and optimal service outcome. This paper presents an algorithm that recursively partitions the geographic space into sub-regions while equally distributing the population across the partitions. For this method, we have proven the existence of an upper bound on the deviation from the optimal population size for sub-regions.