Medicine. Spatial turn in health research

A brief review of methods for determining time-activity patterns in California

Air pollution exposure has been found to be linked with numerous adverse human health effects. Because both air pollution concentrations and the location of human individuals change spatiotemporally, understanding the time-activity patterns (TAPs) is of utmost importance for the mitigation of adverse exposures and to improve the accuracy of air pollution and health analyses. "Time-activity patterns" outlined here broadly refer to the spatiotemporal positions of individuals. In this review paper, we briefly review past efforts on collecting individual TAP information for air pollution and health studies, with a specific focus on California efforts. We also critically summarize emerging technologies and approaches for collecting TAP data. Specifically, we critically reviewed five types of emerging TAP data sources, including call detail record, social media location data, Google Location History, iPhone Significant Location, and crowd-sourced location data. This review provides a comprehensive summary and critique of different methods to collect TAP information and offers recommendations for use in retrospective air pollution and health studies.Implications: In this review paper, we provide a comprehensive overview of approaches for collecting time-activity pattern (TAP) data from individuals, a crucial component in understanding human behavior and its implications across various fields such as urban planning, environmental science, and, particularly, public health in relation to air pollution exposures.Furthermore, our paper introduces and critically evaluates several emerging methods for TAP data collection. These novel approaches, including but not limited to Google Location History, iPhone Significant Locations, and crowdsourced smartphone location data, offer unprecedented granularity in tracking human activities. By showcasing these methodologies and their often not well-recognized weaknesses, we highlight both the potential and limitations of these tools to advance our understanding of human behavior patterns, especially in terms of how individuals interact with their environments. This discussion not only showcases the originality of our work but also sets the stage for future research directions that can benefit from these innovative data collection strategies.

The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Does geographic availability of alcohol influence drinking in adolescents? A systematic review of literature associations

BACKGROUND

The role of alcohol geographic availability in influencing adolescent drinking has been debated. However, clear literature consensus has not been reached.

OBJECTIVE

To provide a systematic review of the associations between geographic availability of alcohol outlets measured through different methodologies and drinking outcomes in adolescents.

METHODS

We conducted a systematic search (PubMed/SCOPUS/Web of Science) for articles exploring associations between alcohol availability and adolescent drinking before 2023. Original articles written in English that evaluated adolescent populations (10-19 years old), included at least one quantitative alcohol consumption outcome and its relationship with geographic availability of alcohol, and declared no conflicts of interest were selected for the review. A quality assessment of the selected articles was made using the Newcastle-Ottawa Scale and descriptive analyses were carried out to summarize results.

RESULTS

Thirty-one articles were reviewed (19 cross-sectional and 12 longitudinal studies), which included a total of 507336 participants. Alcohol availability was positively related to drinking prevalence and risky patterns in 53.3% and 60.5% of associations, respectively. Individual-level covariates, the type of alcohol outlets measured and the different methodological approaches to measure outlet density were related to differences in the direction and magnitude of these associations.

CONCLUSION

Just over half of the studies in this review demonstrate a positive association between alcohol availability and adolescent alcohol consumption with no negative associations reported. The review highlights the mix of methodological approaches that are used, which made it difficult to conduct joint analyses. Additional research is needed to explore the appropriateness, effectiveness and reliability of these methods within various contexts.

Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study

BACKGROUND

The Joint United Nations Program on HIV/AIDS (UNAIDS) has set the "95-95-95" targets to ensure that 95% of all people living with HIV will know their HIV status, 95% of all people living with HIV will receive sustained antiretroviral therapy (ART), and 95% of all people receiving ART will achieve viral suppression (<1000 copies/mL). However, few countries have currently achieved these targets, posing challenges to the realization of the UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. The Chinese government has implemented corresponding policies for HIV/AIDS prevention and control; however, it still faces the challenge of a large number of HIV/AIDS cases. Existing research predominantly focuses on the study of a particular region or population in China, and there is relatively limited research on the macro-level analysis of the spatiotemporal distribution of HIV/AIDS across China and its association with socioeconomic factors.

OBJECTIVE

This study seeks to identify the impact of these factors on the spatiotemporal distribution of HIV/AIDS incidence in China, aiming to provide scientific recommendations for future policy development.

METHODS

This study employed ArcGIS 10.2 (Esri) for spatial analysis, encompassing measures such as the imbalance index, geographical concentration index, spatial autocorrelation analysis (Moran I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence in 31 provinces of China from 2009 to 2019. Geographical Detector was used for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on 9 selected socioeconomic indicators to further investigate the influence of socioeconomic factors on HIV/AIDS incidence in China.

RESULTS

The spatiotemporal distribution analysis of HIV/AIDS incidence in China from 2009 to 2019 revealed distinct patterns. The spatial distribution type of HIV/AIDS incidence in China was random in 2009-2010. However, from 2011 to 2019, the distribution pattern evolved toward a clustered arrangement, with the degree of clustering increasing each year. Notably, from 2012 onwards, there was a significant and rapid growth in the aggregation of cold and hot spot clusters of HIV/AIDS incidence in China, stabilizing only by the year 2016. An analysis of the impact of socioeconomic factors on HIV/AIDS incidence in China highlighted the "urbanization rate" and "urban basic medical insurance fund expenditure" as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, among social factors, indicators related to medical resources exerted a crucial influence on HIV/AIDS incidence.

CONCLUSIONS

From 2009 to 2019, HIV/AIDS incidence in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic.

Perceiving noise in daily life: How real-time sound characteristics affect personal momentary noise annoyance in various activity microenvironments and times of day

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments.

The utility of Zip4 codes in spatial epidemiological analysis

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

Analysis of spatial pattern and influencing factors of private clinics in the main urban area of Guiyang in China from 2021 to 2022 based on multi-source data

BACKGROUND

Private clinics are important places for residents to obtain daily medical care. However, previous researches mainly focused on public medical institutions but ignored the issue of systematic allocation of social medical resources such as clinics. It is critical to understand the private clinics distribution to analyze the rational allocation of medical resources and the spatial difference.

METHODS

Based on the field survey, land census, population density, and economic data from Guiyang, this study analyzes the spatial pattern of private clinics in the main urban area of Guiyang and the influencing factors by using spatial analysis methods such as kernel density, standard deviation ellipses, and geo-detector.

RESULTS

The private clinics in the main urban area of Guiyang are characterized by "inner dense, outer sparse dense," showing an overall spatial clustering feature of "four cores and two belts with many points" and "dense inside and sparse outside." Different types of private clinics have distinct spatial distribution characteristics and agglomeration forms. The growth of private clinics is closely linked to the population growth of mountainous cities. The most important factors influencing the spatial pattern of private clinics are residential land factors, followed by traffic factors and population density. The impact of economic, natural, and spatial factors is minimal. When using a geo-detector, the results of multi-factor interaction differ from those of single factors, and factor interactions have greater explanatory power than single factors in clinic distribution.

CONCLUSION

This study investigates the geographic distribution and influencing variables of private clinics in typical mountain cities and identifies the causes of the current disparity in the distribution of healthcare resources. It is necessary to gradually develop the primary healthcare system in mountainous cities with legislation, counterpart support, and social resources. While ensuring equal access to health care for low-income people and mobile populations, various medical needs of community members should be fully considered and implemented as soon as possible.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties

INTRODUCTION

This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.

METHODS

This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022.

RESULTS

The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local.

CONCLUSIONS

The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

Geography of Disparity: Connecting COVID-19 Vulnerability and Social Determinants of Health in Colorado

The COVID-19 pandemic has drawn greater attention to social determinants of health and associated health inequities, which disproportionately affect vulnerable populations and places in the U.S. In this study, we explored geographic patterns of local-level COVID-19 vulnerability and associations with social and health determinants across Colorado. To conceptualize social and health determinants and how together they generate risk and exposure, we integrated the concepts of social vulnerability and syndemic to situate COVID-19 vulnerability within a broader hazards of place framework. Using geospatial statistics and GIS, we estimated census tract-level rates of COVID-19, which are not yet available in Colorado, and mapped areas of high and low incidence risk. We also developed composite indices that characterized social and health vulnerabilities to measure multivariate associations with COVID-19 rates. The findings revealed hotspots of persistent risk in mountain communities since the pandemic emerged in Colorado, as well as clusters of risk in the Urban Front Range's central and southern counties, and across many parts of eastern Colorado. Vulnerability analyses indicate that COVID-19 rates were associated with mental health and chronic conditions along with social determinants that represent inequities in education, income, healthcare access, and race/ethnicity (minority percent of population), which may have disproportionately exposed some communities more than others to infection and severe health outcomes. Overall, the findings provide geographic health information about COVID-19 and vulnerability context, which may better inform local decision-making for interventions and policies that support equity of social determinants of health.Supplemental data for this article is available online at https://doi.org/10.1080/08964289.2021.2021382 .

Development of a Community-Driven Mosquito Surveillance Program for Vectors of La Crosse Virus to Educate, Inform, and Empower a Community

The fields of entomology, geospatial science, and science communication are understaffed in many areas, resulting in poor community awareness and heightened risks of vector-borne diseases. This is especially true in East Tennessee, where La Crosse encephalitis (LACE) causes pediatric illness each year. In response to these problems, we created a community engagement program that includes a yearlong academy for secondary STEM educators in the 6-12 grade classroom. The objectives of this program were to support inquiry-driven classroom learning to foster student interest in STEM fields, produce community-driven mosquito surveillance, and enhance community awareness of LACE. We trained educators in medical entomology, geospatial science, and science communication, and they incorporated those skills into lesson plans for a mosquito oviposition experiment that tested hypotheses developed in the classroom. Here, we share results from the first two years of the MEGA:BITESS academy, tailored for our community by having students ask questions directly related to Aedes mosquito oviposition biology and La Crosse encephalitis. In year one, we recruited 17 educators to participate in the project, and 15 of those educators returned in year two. All participating educators completed the academy, conducted the oviposition experiment, and informed over 400 students about a variety of careers and disciplines for their students. Here, we present a community-based program that helps to address the problems associated with long-term mosquito surveillance, health and science education and communication, career opportunities, and the community needs of Appalachia, as well as the initial data on the effectiveness of two years of an educator-targeted professional-development program.

Investigating Health Context: Using Geospatial Big Data Ecosystem (Preprint)

Background

Objective

Methods

Results

Conclusions

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

Tobacco Retail Environment and Smoking: A Systematic Review of Geographic Exposure Measures and Implications for Future Studies

INTRODUCTION

To review the geographic exposure measures used to characterize the tobacco environment in terms of density of tobacco outlets and proximity to tobacco outlets, and its association with smoking-related outcomes.

METHODS

We used PubMed and Google Scholar to find articles published until December 2019. The search was restricted to studies that (1) measured the density of and/or proximity to tobacco outlets and (2) included associations with smoking outcomes. The extraction was coordinated by several observers. We gathered data on the place of exposure, methodological approaches, and smoking outcomes.

RESULTS

Forty articles were eligible out of 3002 screened papers. Different density and proximity measures were described. 47.4% density calculations were based on simple counts (number of outlets within an area). Kernel density estimations and other measures weighted by the size of the area (outlets per square kilometer), population, and road length were identified. 81.3% of the articles which assessed proximity to tobacco outlets used length distances estimated through the street network. Higher density values were mostly associated with higher smoking prevalence (76.2%), greater tobacco use and smoking initiation (64.3%), and lower cessation outcomes (84.6%). Proximity measures were not associated with any smoking outcome except with cessation (62.5%).

CONCLUSION

Associations between the density of tobacco outlets and smoking outcomes were found regardless of the exposure measure applied. Further research is warranted to better understand how proximity to tobacco outlets may influence the smoking outcomes. This systematic review discusses methodological gaps in the literature and provides insights for future studies exploring the tobacco environment.

IMPLICATIONS

Our findings pose some methodological lessons to improve the exposure measures on the tobacco outlet environment. Solving these methodological gaps is crucial to understand the influence of the tobacco environment on the smoking outcomes. Activity spaces should be considered in further analyses because individuals are exposed to tobacco beyond their residence or school neighborhood. Further studies in this research area demand density estimations weighted by the size of the area, population, or road length, or measured using Kernel density estimations. Proximity calculations should be measured through the street network and should consider travel times apart from the length distance.

Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county

Objectives

An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data.

Methods

We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types.

Results

A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26–35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race.

Conclusions

Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.

State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures

Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.

Why geographic data science is not a science

"Data Science" has taken many disciplines by storm. And for a good reason: New forms and unseen quantities of data enter nearly every scientific field, substantially changing the ways how scientists do science, and potentially allowing them to answer old questions or to pose them in novel ways. The recent success of Data Science is also reflected in corresponding study programs and curricula and the emergence of specialized branches, such as Geographic Data Science (GDS). Some researchers, therefore, claim that Data Science and GDS should be treated as autonomous scientific disciplines, while others fear that it sells nothing but old wine in new bottles. In an attempt to sober the discussion, we investigate GDS and Data Science from the perspective of meta-science. We provide arguments why today's GDS and Data Science should be seen as an interdisciplinary community of practice of data-driven scientists, rather than a scientific discipline. We also discuss what is missing for GDS and Data Science to become genuine scientific disciplines.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

Current State of Geospatial Methodologic Approaches in Canadian Population Oncology Research

Geospatial analyses are increasingly used in population oncology. We provide a first review of geospatial analysis in Canadian population oncology research, compare to international peers, and identify future directions. Geospatial-focused peer-reviewed publications from 1992-2020 were compiled using PubMed, MEDLINE, Web of Science, and Google Scholar. Abstracts were screened for data derived from a Canadian cancer registry and use of geographic information systems. Studies were classified by geospatial methodology, geospatial unit, location, cancer site, and study year. Common limitations were documented from article discussion sections. Our search identified 71 publications using data from all provincial and national cancer registries. Thirty-nine percent (N = 28) were published in the most recent 5-year period (2016-2020). Geospatial methodologies included exposure assessment (32.4%), identifying spatial associations (21.1%), proximity analysis (16.9%), cluster detection (15.5%), and descriptive mapping (14.1%). Common limitations included confounding, ecologic fallacy, not accounting for residential mobility, and small case/population sizes. Geospatial analyses are increasingly used in Canadian population oncology; however, efforts are concentrated among a few provinces and common cancer sites, and data are over a decade old. Limitations were similar to those documented internationally, and more work is needed to address them. Organized efforts are needed to identify common challenges, develop leading practices, and identify shared priorities.

A statistical framework for measuring the temporal stability of human mobility patterns

Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper, we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of our methods with data that comprise the GPS locations of 185 individuals over the course of 18 months. Our empirical results suggest that GPS monitoring should be performed over periods of time that are significantly longer than what has been previously suggested. Furthermore, we argue that GPS study designs should take into account demographic groups.

Why Public Health Needs GIS: A Methodological Overview

The short paper provides an overview on how geographic issues have become increasingly relevant to public health research and policy, particularly through the lens of geographic information systems (GIS). It covers six themes with an emphasis on methodological issues. (1) Our health-related behavior varies across geographic settings, so should public health policy. (2) Facilities (supply) and patients (demand) in a health care market interact with each other across geopolitical borders, and measures of health care accessibility need to capture that. (3) Our health outcome is the result of joint effects of individual attributes and neighborhood characteristics, and an adequate definition of neighborhood is critical for assessing neighborhood effect. (4) Disease rates in areas of small population are unreliable, and one effective way to mitigate the problem is to construct a larger, internally-homogenous and comparable area unit. (5) Defining a scientific geographic unit for health care market is critical for researchers, practitioners, and policy makers to evaluate health care delivery, and GIS enables us to define the unit (e.g., primary care service areas, hospital service areas, and cancer service areas) automatically, efficiently and optimally. (6) Aside from various optimization objectives around "efficiency", it is as important to plan the location and allocation of health care resources toward maximum equality in health care access. Case studies are cited to illustrate each theme.

The Use of Geographic Information Systems for Spatial Ecological Risk Assessments: An Example from the Athabasca Oil Sands Area in Canada

There is an acknowledged need in ecotoxicology for methods that integrate spatial analyses in risk assessment. This has resulted in the emergence of landscape ecotoxicology, a subdiscipline of ecotoxicology. However, landscape ecotoxicology has yet to become common practice in risk assessment due to the underdevelopment of techniques and a lack of standardized methods. In the present study, we demonstrate how geographic information systems (GISs) can serve as a standardized platform to integrate data, assess spatial patterns of ecotoxicological data for multiple species, and assess relationships between chemical mixture exposures and effects on biota for landscape ecotoxicological risks assessment. We use data collected under the Joint Oil Sands Monitoring Program in the Athabasca Oil Sands Region in Alberta, Canada. This dataset is composed of concentrations of contaminants including metals and polycyclic aromatic compounds, and health endpoints measured in 1100 biological samples, including tree swallows, amphibians, gull and tern eggs, plants, and mammals. We present 3 examples using a GIS as a platform and geospatial analysis to: 1) integrate data and assess spatial patterns of contaminant exposure in the region, 2) assess spatial patterns of exposures to complex mixtures, and 3) examine patterns of exposures and responses across the landscape. We summarize the methods used in the present study into a workflow for ease of use. The GIS methods allow researchers to identify hot spots of contamination, use georeferenced monitoring data to derive quantitative exposure-response relationships, and assess complex exposures with more realism. Environ Toxicol Chem 2019;38:2797-2810. © 2019 SETAC.

Population allocation at the housing unit level: estimates around underground natural gas storage wells in PA, OH, NY, WV, MI, and CA

BACKGROUND

Spatially accurate population data are critical for determining health impacts from many known risk factors. However, the utility of the increasing spatial resolution of disease mapping and environmental exposures is limited by the lack of receptor population data at similar sub-census block spatial scales.

METHODS

Here we apply an innovative method (Population Allocation by Occupied Domicile Estimation - ABODE) to disaggregate U.S. Census populations by allocating an average person per household to geospatially-identified residential housing units (RHU). We considered two possible sources of RHU location data: address point locations and building footprint centroids. We compared the performance of ABODE with the common proportional population allocation (PPA) method for estimating the nighttime residential populations within 200 m radii and setback areas (100 - 300 ft) around active underground natural gas storage (UGS) wells (n = 9834) in six U.S. states.

RESULTS

Address location data generally outperformed building footprint data in predicting total counts of census residential housing units, with correlations ranging from 0.67 to 0.81 at the census block level. Using residentially-sited addresses only, ABODE estimated upwards of 20,000 physical households with between 48,126 and 53,250 people living within 200 m of active UGS wells - likely encompassing the size of a proposed UGS Wellhead Safety Zone. Across the 9834 active wells assessed, ABODE estimated between 5074 and 10,198 more people living in these areas compare to PPA, and the difference was significant at the individual well level (p = < 0.0001). By either population estimation method, OH exhibits a substantial degree of hyperlocal land use conflict between populations and UGS wells - more so than other states assessed. In some rare cases, population estimates differed by more than 100 people for the small 200 m² well-areas. ABODE's explicit accounting of physical households confirmed over 50% of PPA predictions as false positives indicated by non-zero predictions in areas absent physical RHUs.

CONCLUSIONS

Compared to PPA - in allocating identical population data at sub-census block spatial scales -ABODE provides a more precise population at risk (PAR) estimate with higher confidence estimates of populations at greatest risk. 65% of UGS wells occupy residential urban and suburban areas indicating the unique land use conflicts presented by UGS systems that likely continue to experience population encroachment. Overall, ABODE confirms tens of thousands of homes and residents are likely located within the proposed UGS Wellhead Safety Zone - and in some cases within state's oil and gas well surface setback distances - of active UGS wells.

Mapping Variation in Breast Cancer Screening: Where to Intervene?

Small geographic areas with lower mammography screening participation rates may reflect gaps in screening efforts. Our objective was to use spatial analyses to understand disparities in mammography screening use and to identify factors to increase its uptake in areas that need it in Lyon metropolitan area, France. Data for screened women between the ages of 50 and 74 were analyzed. Census blocks of screened and non screened women were extracted from the mammography screening programme 2015-2016 dataset. We used spatial regression models, within a generalized additive framework to determine clusters of census blocks with significantly higher prevalence of non-participation of mammography screening. Smoothed risk maps were crude and adjusted on the following covariates: deprivation index and opportunistic screening. Among 178,002 women aged 50 to 74, 49.9% received mammography screening. As hypothesized, women living in highly deprived census blocks had lower participation rates compared to less deprived blocks, 45.2% vs. 51.4% p < 0.001. Spatial analyses identified four clusters, one located in an urban area and three in suburban areas. Moreover, depending on the location of the cluster, the influence came from different variables. Knowing the impact of site-specific risk factors seems to be important for implementing an appropriate prevention intervention.

Revisiting the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM_2.5) and mortality statistics are increasingly used to examine the health effects of PM_2.5. Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM_2.5-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM_2.5concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM_2.5-mortality for the years of 2000, 2005, 2010 and 2015 using three PM_2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM_2.5-mortality from the 10 km CTM-derived PM_2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM_2.5 datasets. The estimated PM_2.5-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM_2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM_2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM_2.5 concentration is the primary factor that leads to the PM_2.5-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM_2.5-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM_2.5-attributable mortality with advancements in PM_2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.

The Landscape of US Lung Cancer Screening Services

BACKGROUND

Low adoption of lung cancer screening is potentially caused by inadequate access to a comprehensive lung cancer screening registry (LCSR), currently a requirement for reimbursement by the Centers for Medicare and Medicaid Services. However, variations in LCSR facilities have not been extensively studied.

METHODS

We applied a hierarchical clustering method to a comprehensive database integrating state-level LCSR facility density, defined as the number of facilities per 100,000 at-risk persons, lung cancer outcomes including mortality and stage-specific incidence, and socioeconomic and behavioral factors.

RESULTS

We found three distinct clusters of LCSR facilities roughly corresponding to the northern (cluster 1), southeastern (cluster 2), and southwestern (cluster 3) states. The southeastern states had the lowest total number of facilities (67 ± 44 in cluster 2, 74 ± 69 in cluster 1, 80 ± 100 in cluster 3), the slowest increase in facilities (23 ± 20 in cluster 2, 26 ± 28 in cluster 1, 27 ± 32 in cluster 3) between 2016 and 2018, and the highest lung cancer burden and current smokers. They ranked second in terms of facility density (2.9 ± 1.0 in cluster 3, 3.8 ± 1.3 in cluster 2, 6.3 ± 2.8 in cluster 1) and increase in facility density (1.1 ± 0.3 in cluster 3, 1.3 ± 0.7 in cluster 2, 2.5 ± 2.5 in cluster 1).

CONCLUSIONS

We found substantial state-level variability in LCSR facilities tied to lung cancer burden, socioeconomic characteristics, and behavioral characteristics. Given the known risk factors of lung cancer, correcting a suboptimal distribution of screening programs will likely lead to improved lung cancer outcomes.

Mapping and Analysing Potential Sources and Transmission Routes of Antimicrobial Resistant Organisms in the Environment using Geographic Information Systems-An Exploratory Study

Antimicrobial resistance (AMR) is one of the leading threats to human health worldwide. The identification of potential sources of antimicrobial resistant organisms (AROs) and their transmission routes in the environment is important for improving our understanding of AMR and to inform and improve policy and monitoring systems, as well as the identification of suitable sampling locations and potential intervention points. This exploratory study uses geographic information systems (GIS) to analyse the spatial distribution of likely ARO sources and transmission routes in four local authority areas (LAAs) in Ireland. A review of relevant spatial data in each LAA, grouped into themes, and categorised into sources and transmission routes, was undertaken. A range of GIS techniques was used to extract, organise, and collate the spatial data into final products in the form of thematic maps for visual and spatial analysis. The results highlight the location of 'clusters' at increased risk of harbouring AMR in each LAA. They also demonstrate the relevance of aquatic transmission routes for ARO mobility and risk of human exposure. The integration of a GIS approach with expert knowledge of AMR is shown to be a useful tool to gain insights into the spatial dimension of AMR and to guide sampling campaigns and intervention points.

Detecting the priority areas for health workforce allocation with LISA functions: an empirical analysis for China

Background

Health workforce misdistribution leads to severe inequity and low-efficiency in health services in the developing countries. Targeting at China, this research aims to reveal, visualize and compare the geographical distribution patterns of different subtypes of urban and rural health workforce and identify the priority regions for health workforce planning and allocation policies designing.

Methods

The health workforce density (workforce-to-population ratio) is adopted to represent the accessibility to health workforce in each geographical unit. Besides a descriptive geography of health workforce as a whole, the local indicators of spatial association (LISA) are used to explore the spatial clusters of different subtypes of health workforce, which are visualized by geographical tools.

Results

Results reveal that regional disparities and spatial clusters exist in China’s health workforce distribution, with different types of workforce exhibiting relatively different spatial distribution characteristics. Besides, huge urban-rural disparities are found in the distribution of health workforce in China. Unexpectedly but intriguingly, most of the high-high and high-low cluster area of urban health workforce are concentrated in the western China (Xinjiang, Xizang etc.), indicating the relative abundant stock of urban health workforce in these units, while the low-low and low-high cluster area of different types of urban health workforce are mainly distributed in middle China. Regarding the rural health workforce, there is an obvious and similar low-low and low-high clustering pattern in western provinces (Sichuan, Yunnan) for the licensed doctors, pharmacists, technologists, which play a critical role in health services delivery.

Conclusions

Different types of health workforce displayed distinct spatial distribution patterns, while the misdistribution of rural health workforce imposed more challenges to the Chinese health sector due to its poorer stock and more disadvantaged positions of backward regions (i.e., low-low and low-high cluster area). Subtype-specific and region-oriented health workforce planning and allocation policies are suggested to be made, aiming at the urban and rural health workforce respectively, by prioritizing the identified low-low and low-high cluster areas.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3737-y) contains supplementary material, which is available to authorized users.

Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions

This research presents a pilot study to develop and compare methods of geographic imputation for estimating the location of missing activity space data collected using geographic ecological momentary assessment (GEMA). As a demonstration, we use data from a previously published analysis of the effect of neighborhood disadvantage, captured at the U.S. Census Bureau tract level, on momentary psychological stress among a sample of 137 urban adolescents. We investigate the impact of listwise deletion on model results and test two geographic imputation techniques adapted for activity space data from hot deck and centroid imputation approaches. Our results indicate that listwise deletion can bias estimates of place effects on health, and that these impacts are mitigated by the use of geographic imputation, particularly regarding inflation of the standard errors. These geographic imputation techniques may be extended in future research by incorporating approaches from the non-spatial imputation literature as well as from conventional geographic imputation and spatial interpolation research that focus on non-activity space data.

Beyond reasonable drought: hotspots reveal a link between the 'Big Dry' and cryptosporidiosis in Australia's Murray Darling Basin

There is little evidence on how the health impacts of drought vary spatially and temporally. With a focus on waterborne cryptosporidiosis, we identify spatio-temporal hotspots and by using interrupted time series analysis, examine the impact of Australia's Big Dry (2001-2009) in these disease clusters in the Murray Darling Drainage Basin. Analyses revealed a statistically significant hotspot in the north of the Australian Capital Territory (ACT) and a hotspot in the north-eastern end of the basin in Queensland. After controlling for long-term trend and seasonality in cryptosporidiosis, interrupted time series analysis of reported cases in these hotspots indicated a statistically significant link with the Big Dry. In both areas, the end of the Big Dry was associated with a lower risk of reported cryptosporidiosis; in the ACT, the estimated relative risk (RR) was 0.16 (95% confidence interval: 0.07; 0.33), and in Queensland the RR was 0.42 (95% confidence interval: 0.19; 0.42). Although these data do not establish a causal association, this research highlights the potential for drought-related health risks.

Wearable sensors for multifactorial personal exposure measurements - A ranking study

Individuals are simultaneously exposed to multiple environmental stressors during their daily life. Studies of adverse health effects and their etiology as well as recommendations for a healthier life style demand for an assessment of multifactorial personal exposure, according to the exposome concept. A challenge is to record exposure while people are moving in heterogeneous urban environments. Therefore wearable sensor technologies are becoming a promising way to measure personal exposure continuously: indoors, outdoors and even on the move. So far, studies which test the accuracy and usability of wearable sensors for multiple stressors are lacking. Performance evaluations are important and should take place beforehand, especially to ensure the success of citizens-oriented studies. For the first time we rigorously examined the accuracy and application suitability of wearable sensors for acoustic noise, heat (temp), particle number counts (PNC) and geo-location (GPS) in different environments. We present an extensive device inter-comparison and a ranking of the sensors based on performance measures, Taylor diagrams, Bland-Altman plots, and ease-of-use aspects. The sensors showed moderate to high correlations with precision reference devices (r = 0.4-0.99). Differences between errors outdoors and indoors suggest that environmental conditions have impact upon the accuracy of the sensors. Reaction time, recording interval, and sensor ventilation are features that play a crucial role for both ease-of-use and accuracy. We conclude with a final performance () ranking: (GPS) >  (noise) >  (temp) >  (PNC). The results are relevant for future epidemiological studies of multifactorial exposure of individuals and their health and should guide the selection of wearables when persons are involved that are technically untaught. Inferences from multifactorial data are based on the performance of all sensors and the weakest chain links are PNC and temp sensors for which our article recommends urgent improvements.

Applications of Space Technologies to Global Health: Scoping Review

BACKGROUND

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations' Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

OBJECTIVE

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

METHODS

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs' Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

RESULTS

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

CONCLUSIONS

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Do long-term care services match population needs? A spatial analysis of nursing homes in Chile

Chile is experiencing a period of rapid aging, which increases the need of long-term care services in the country. Nursing homes have been the traditional alternative to deal with the increase of elderly population in the country, with services supplied by a mix of for-profit and nonprofit providers. Additionally, population exhibits a high degree of geographical concentration. The study aims to identify the determinants of the geographical location of nursing homes in Chile at municipality level. The analysis takes into account the different location criteria for different types of nursing homes as well as potential spatial effects. The paper uses spatial analysis tools to identify clusters of nursing homes and population characteristics and to estimate the determinants of nursing homes availability and coverage in the country. The analysis–based on spatial global and local tests, and spatial autoregressive models- show the existence of clusters of nursing homes as well as clusters of municipalities according to elderly population, income, poverty, population density, and public health insurance coverage. Residuals from ordinary least squares regressions were spatially autocorrelated, showing the need of using spatial models. Estimations show that availability and coverage of nursing homes are positively related with municipality income, and that for-profit and nonprofit facilities respond differently to different factors. A negative coefficient was found for poverty in nonprofit nursing homes, raising doubts about the effectiveness of giving public subsidies to incentive the installation of facilities in areas with high needs and low income.

Geographic Information Science and the Analysis of Place and Health

The representation of place is a key theoretical advancement that Geographic Information Science can offer to improve the understanding of environmental determinants of health, but developing robust computational representations of place requires a substantial departure from conventional notions of geographic representation in Geographic Information Systems (GIS). Unlike conventional GIS representations based on either objects or locations, we suggest place representation should incorporate dynamic subjective, experiential, and relational aspects of place, as the influence of place on health behavior concerns not only the features that can be objectively observed at a particular location but also the environmental perceptions of the individual, as molded by biological, social, and experiential characteristics. In addition, assessments of environmental exposures on health outcomes should focus on individuals' time-activity patterns and microenvironment profiles, which form a potentially unique personalized exposure environment for each individual. Addressing these representational challenges via collaborative research has the potential to advance both Geographic Information Science and health research.

New Directions in Cancer Control and Population Sciences

Cancer control science has been evolving since it first became a focus for cancer research and program activities a century ago. The evolution of the field has responded to historical megatrends along the way that keep it relevant to the cancer-related needs of society. This commentary describes some of the key trends and developments now influencing cancer control and population sciences that reflect societal change and new tools and concepts in modern biomedical science. New directions include the impact of climate change, health care delivery research, the growth of population health science, data science, precision medicine, data sharing, implementation science, and new technologies, including social media and new geospatial methodologies. Cancer Epidemiol Biomarkers Prev; 26(8); 1165-9. ©2017 AACR.

Geospatial approaches to cancer control and population sciences at the United States cancer centers

PURPOSE

Geospatial, contextual, and multilevel research is integral to cancer prevention and control. NCI-designated Cancer Centers are at the forefront of cancer research; therefore, this paper sought to review the geospatial, contextual, and multilevel research at these cancer centers.

METHODS

Investigators used PubMed and Web of Science to compile geospatial publications from 1971 to February 2016 with cancer center-affiliated authors. Relevant abstracts were pulled and classified by six geospatial approaches, eight geospatial scales, and eight cancer sites.

RESULTS

The searches identified 802 geospatial, contextual, and multilevel publications with authors affiliated at 60 of the 68 NCI-designated Cancer Centers. Over 90% were published after 2000. Five cancer centers accounted for approximately 50% of total publications, and 30 cancer centers accounted for over 85% of total publications. Publications covered all geospatial approaches and scales to varying degrees, and 90% dealt with cancer.

CONCLUSIONS

The NCI-designated Cancer Center network is increasingly pursuing geospatial, contextual, and multilevel cancer research, although many cancer centers still conduct limited to no research in this area. Expanding geospatial efforts to research programs across all cancer centers will further enrich cancer prevention and control. Similar reviews may benefit other domestic and international cancer research institutions.

Using Individual GPS Trajectories to Explore Foodscape Exposure: A Case Study in Beijing Metropolitan Area

With the growing interest in studying the characteristics of people’s access to the food environment and its influence upon individual health, there has been a focus on assessing individual food exposure based on GPS trajectories. However, existing studies have largely focused on the overall activity space using short-period trajectories, which ignores the complexity of human movements and the heterogeneity of the spaces that are experienced by the individual over daily life schedules. In this study, we propose a novel framework to extract the exposure areas consisting of the localized activity spaces around daily life centers and non-motorized commuting routes from long-term GPS trajectories. The newly proposed framework is individual-specific and can incorporate the internal heterogeneity of individual activities (spatial extent, stay duration, and timing) in different places as well as the dynamics of the context. A pilot study of the GeoLife dataset suggests that there are significant variations in the magnitude as well as the composition of the food environment in different parts of the individual exposure area, and residential environment is not representative of the overall foodscape exposure.

Spatial access to food: Retiring the food desert metaphor

The food desert metaphor has been widely used over the past few decades as a way to identify regions as being at risk for having little or no access to healthy food. While the simplicity of the metaphor is attractive, this article argues that its usefulness to researchers interested in understanding the relationship between the geography of healthy food opportunities and dietary behaviours is limited. More nuanced approaches to incorporating geography into food access studies, like including transportation, economic factors, and time use, in addition to considering other dimensions of accessibility, are warranted.

Spatial methods for evaluating critical care and trauma transport: A scoping review

PURPOSE

The objective of this scoping review is to inform future applications of spatial research regarding transportation of critically ill patients. We hypothesized that this review would reveal gaps and limitations in the current research regarding use of spatial methods for critical care and trauma transport research.

MATERIALS AND METHODS

Four online databases, Ovid Medline, PubMed, Embase and Scopus, were searched. Studies were selected if they used geospatial methods to analyze a patient transports dataset. 12 studies were included in this review.

RESULTS

Majority of the studies employed spatial methods only to calculate travel time or distance even though methods and tools for more complex spatial analyses are widely available. Half of the studies were found to focus on hospital bypass, 2 studies focused on transportation (air or ground) mode selection, 2 studies compared predicted versus actual travel times, and 2 studies used spatial modeling to understand spatial variation in travel times.

CONCLUSIONS

There is a gap between the availability of spatial tools and their usage for analyzing and improving medical transportation. The adoption of geospatially guided transport decisions can meaningfully impact healthcare expenditures, especially in healthcare systems looking to strategically control expenditures with minimum impact on patient outcomes.

Toward dynamic urban environmental exposure assessments in mental health research

It is increasingly recognized that mental disorders are affected by both personal characteristics and environmental exposures. The built, natural, and social environments can either contribute to or buffer against metal disorders. Environmental exposure assessments related to mental health typically rely on neighborhoods within which people currently live. In this article, I call into question such neighborhood-based exposure assessments at one point in time, because human life unfolds over space and across time. To circumvent inappropriate exposure assessments and to better grasp the etiologies of mental disease, I argue that people are exposed to multiple health-supporting and harmful exposures not only during their daily lives, but also over the course of their lives. This article aims to lay a theoretical foundation elucidating the impact of dynamic environmental exposures on mental health outcomes. I examine, first, the possibilities and challenges for mental health research to integrate people's environmental exposures along their daily paths and, second, how exposures over people's residential history might affect mental health later in life. To push the borders of scientific inquiries, I stress that only such mobility-based approaches facilitate an exploration of exposure duration, exposure sequences, and exposure accumulation.

Spatial Context and Health Inequity: Reconfiguring Race, Place, and Poverty

Intimate connections among race, place, and poverty are increasingly featured in the health disparities literature. However, few models exist that can guide our understanding of these interconnections. We build on the Chicago School of Sociology's contributions in urban research and one of its contemporary elaborations, often described as the "neighborhood effects approach," to propose a three-axis model of health inequity. This model, in alignment with Chicago School theory, postulates a dynamic and adaptive relationship between spatial context and health inequity. Compositional axes of race and poverty form the foundation of the model. These compositional axes then intersect with a third axis of place to compose the built and social environment planes. We develop this model to provide conceptual guidance for clinical, policy, and public health researchers who aim to examine how these three features, taken together, have important implications for urban health.