As of 2022, one quarter of U.S. adults have a diagnosed mental health disorder, making access to behavioral health (BH) services critically important. While access to telebehavioral health (TBH; defined as a form of synchronous telehealth-the process of providing BH care from a distance, usually using videoconferencing technology) expanded during the COVID-19 pandemic, it remains limited in rural and frontier areas. Little attention has been paid to spatial associations in TBH access within geographically rural U.S. states. This study examines geospatial access to TBH and its association with social vulnerability variables and rurality in New Mexico (NM), exploring health care resource allocation, geography, rurality, and social vulnerability. Two geospatial data sets were developed to model spatial access to BH specialists and average drive time to reliable cell phone coverage. BH provider data came from the National Provider Inventory directory. A four-step model framework, including global and geographically weighted logistic and linear regression, was used to examine associations between social vulnerability variables, rurality and TBH access vulnerability, and their geographic patterns. Most of the tracts were not classified as "vulnerable," but some showed varying vulnerability levels. Several census tract variables correlated positively with increased telecommunications and BH access vulnerability. Rural NM, especially American Indian or Alaska Native areas, had poorer access to BH services and reliable mobile data. This study highlights spatial disparities in TBH access in rural NM. Improving TBH access and telecommunications infrastructure are crucial for addressing service challenges, particularly for rural areas and American Indian or Alaska Native populations. Federal initiatives are essential to prevent worsening inequalities.

To assess the association between urinary arsenic concentrations and birth outcomes by harmonizing data from three independent birth cohorts. We harmonized and analyzed data from the Navajo Birth Cohort Study (NBCS), the New Hampshire Birth Cohort Study (NHBCS), and the PROTECT Center study based on Puerto Rico. Birth outcomes of interest included birth weight, head circumference, birth length, gestational age at delivery, incidence of preterm birth, and size for gestational age. Urinary arsenic concentrations were used as the primary exposure metric. Harmonization involved aligning variable formats, adjusting for differences in laboratory methods, and excluding incompatible covariates, such as income and race. Harmonization increased the total sample size (N = 3222) across cohorts. However, pooled analyses did not consistently demonstrate increased statistical power. Effect estimates for arsenic exposure were attenuated in some cases, and confidence intervals remained wide or even expanded relative to individual cohort analyses. Differences in biospecimen collection and laboratory assay methods required cohort-specific adjustments. Due to missing arsenic speciation data, the PROTECT cohort was excluded from two exposure models. Variability across cohorts limited the interpretability and precision of pooled estimates despite harmonization efforts. While harmonizing data across multiple cohorts increased the sample size, it did not necessarily enhance statistical power or strengthen observed associations. Differences in data collection, laboratory methods, and available covariates posed significant challenges. These findings underscore the need for caution when interpreting pooled results from heterogeneous sources and highlight the importance of prospective planning for data harmonization in multi-cohort studies.

Rural-urban classification schemes are frequently used in ecological studies of population health. However, the algorithms used to produce these classifications as well as their underlying assumptions may not match their intended use in health research. Here, we focus on the spatial distribution of features of the physical environment that are related to health - such as healthcare - to examine the extent to which eight classification schemes capture the heterogeneous context of rural places. We further explore how well rural-urban classifications distinguish between different types of rural places by comparing rural Tribal reservations with other rural areas in the American southwest. Because health services and infrastructure are often distributed through state and federal programs to underserved populations in rural areas, this approach speaks to the broader political implications in how rural communities are defined and represented. Results indicate that rural-urban classifications do not adequately reflect heterogeneous contexts within and across rural places. We advocate for more appropriate population health models that explain contextual differences in the relationship between health and place.

Community-based research (CBR) in geography is increasingly emphasizing participatory approaches that center the voices of local communities in the research process. This shift seeks to move away from extractive research practices by fostering collaborations built on reciprocity and respect - particularly with Indigenous and marginalized groups. At the core of this approach is co-produced knowledge, wherein communities actively shape research priorities, methodologies, and interpretations. Rather than imposing external frameworks, these collaborations recognize the value of local and Indigenous knowledge systems in informing research and driving meaningful outcomes. In this paper, we review contemporary CBR literature in geography and GIScience and present a case study on environmental health concerns related to mining legacies in the U.S. This research, led by GIScience and geospatial experts in collaboration with a Tribal community, illustrates opportunities to advance CBR theory and practice within these fields. As CBR becomes increasingly integrated into GIScience projects, we critically examine the positionality of GIScience researchers in this transition, the challenges they face, and the lessons learned. The paper closes with a discussion of best practices for CBR. While all research involves some degree of extractivism, we explore how CBR can help ensure that communities derive direct and tangible benefits from participation in GIScience and geography research.

Background Advances in drinking water infrastructure and treatment throughout the 20 th and early 21 st century dramatically improved water reliability and quality in the United States (US) and other parts of the world. However, numerous chemical contaminants from a range of anthropogenic and natural sources continue to pose chronic health concerns, even in countries with established drinking water regulations, such as the US. Objective/Methods In this review, we summarize exposure risk profiles and health effects for seven legacy and emerging drinking water contaminants or contaminant groups: arsenic, disinfection by-products, fracking-related substances, lead, nitrate, per- and polyfluorinated alkyl substances (PFAS) and uranium. We begin with an overview of US public water systems, and US and global drinking water regulation. We end with a summary of cross-cutting challenges that burden US drinking water systems: aging and deteriorated water infrastructure, vulnerabilities for children in school and childcare facilities, climate change, disparities in access to safe and reliable drinking water, uneven enforcement of drinking water standards, inadequate health assessments, large numbers of chemicals within a class, a preponderance of small water systems, and issues facing US Indigenous communities. Results Research and data on US drinking water contamination show that exposure profiles, health risks, and water quality reliability issues vary widely across populations, geographically and by contaminant. Factors include water source, local and regional features, aging water infrastructure, industrial or commercial activities, and social determinants. Understanding the risk profiles of different drinking water contaminants is necessary for anticipating local and general problems, ascertaining the state of drinking water resources, and developing mitigation strategies. Impact statement Drinking water contamination is widespread, even in the US. Exposure risk profiles vary by contaminant. Understanding the risk profiles of different drinking water contaminants is necessary for anticipating local and general public health problems, ascertaining the state of drinking water resources, and developing mitigation strategies.

Worldwide, the COVID-19 pandemic has been influenced by a combination of environmental and sociodemographic drivers. To date, population studies have overwhelmingly focused on the impact of societal factors. In New Mexico, the rate of COVID-19 infection and mortality varied significantly among the state's geographically dispersed, and racially and ethnically diverse populations who are exposed to unique environmental contaminants related to resource extraction industries (e.g. fracking, mining, oil and gas exploration). By looking at local patterns of COVID-19 disease severity, we sought to uncover the spatially varying factors underlying the pandemic. We further explored the compounding role of potential long-term exposures to various environmental contaminants on COVID-19 mortality prior to widespread applications of vaccinations. To illustrate the spatial heterogeneity of these complex associations, we leveraged multiple modeling approaches to account for spatial non-stationarity in model terms. Multiscale geographically weighted regression (MGWR) results indicate that increased potential exposure to fugitive mine waste is significantly associated with COVID-19 mortality in areas of the state where socioeconomically disadvantaged populations were among the hardest hit in the early months of the pandemic. This relationship is paradoxically reversed in global models, which fail to account for spatial relationships between variables. This work contributes both to environmental health sciences and the growing body of literature exploring the implications of spatial nonstationarity in health research.

Personal exposure studies suffer from uncertainty issues, largely stemming from individual behaviour uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modelling that incorporated behaviour classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States to address questions on potential livestock exposure to abandoned uranium mines (AUMs). The study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behaviour subgroups – grazing, travelling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behaviour classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-min-interval for two flocks of sheep and goats during the spring and summer of 2019. Analysis and modelling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviours with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behaviour or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviours reported by livestock owners. These results suggest that the proposed method including behaviour subgroups with probability/uncertainties more closely resembled the observed grazing behaviours. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants.

People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R-2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Unless a toxicant builds up in a deep compartment, intake by the human body must on average balance the amount that is lost. We apply this idea to assess arsenic (As) exposure misclassification in three previously studied populations in rural Bangladesh (n = 11,224), Navajo Nation in the Southwestern United States (n = 619), and northern Chile (n = 630), under varying assumptions about As sources. Relationships between As intake and excretion were simulated by taking into account additional sources, as well as variability in urine dilution inferred from urinary creatinine. The simulations bring As intake closer to As excretion but also indicate that some exposure misclassification remains. In rural Bangladesh, accounting for intake from more than one well and rice improved the alignment of intake and excretion, especially at low exposure. In Navajo Nation, comparing intake and excretion revealed home dust as an important source. Finally, in northern Chile, while food-frequency questionnaires and urinary As speciation indicate fish and shellfish sources, persistent imbalance of intake and excretion suggests imprecise measures of drinking water arsenic as a major cause of exposure misclassification. The mass-balance approach could prove to be useful for evaluating sources of exposure to toxicants in other settings.

Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application.

Primary healthcare (PHC) is a keystone component of population health. However, inequities in public transportation access hinder equitable usage of PHC services by minoritized populations. Using the multimodal enhanced 2-step floating catchment area method and data in 2018 and 2019 for spatial access to PHC providers (n = 1166) and social vulnerability markers through census block (n = 543) and tract data (n = 226), a generalized linear mixed-effect model (GLMEM) was constructed to test the effects of sociodemographic and community area correlates on both car and bus transit spatial access to PHC in the Albuquerque, New Mexico (NM) metropolitan area. Results for bus spatial access to PHC showed lower access for Hispanics (B = − 0.097 ± 0.029 [− 0.154, − 0.040]) and non-Hispanic Whites (B = − 0.106 ± 0.032 [− 0.169, − 0.043]) and a positive association between single-family households and bus spatial access (B = 1.573 ± 0.349 [0.866, 2.261]). Greater disability vulnerability (B = − 0.569 ± 0.173 [− 0.919, − 0.259]) and language vulnerability (B = − 0.569 ± 0.173 [− 0.919, − 0.259]) were associated with decreased bus spatial access. For car spatial access to PHC, greater SES vulnerability (B = − 0.338 ± 0.021 [− 1.568, -0.143]), disability (B = − 0.721 ± .092 [− 0.862, − 0.50 9]), and language vulnerability (B = − 0.686 ± 0.172 [− 1.044, − 0.362]) were associated with less car spatial access. Results indicate a disproportionate burden of low PHC access among disadvantaged population groups who rely heavily on public transportation. These results necessitate targeted interventions to reduce these disparities in access to PHC.

GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy. However, inherent uncertainties in the road datasets used in spatial access estimates remain largely underreported. These uncertainties might result in unrecognized biases within visualization products and decision-making outcomes that strive to improve social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance for car and bus transportation using proprietary, volunteer-information-based, and free (non-volunteer-information-based) street networks. We then interpreted the measured variations through the lens of street data uncertainty and its propagation in a common E2SFCA model of spatial accessibility. Results indicated that travel impedance disagreement propagates through the modeling process to effect agreement of spatial access index (SPAI) estimates among different street sources, with larger uncertainties observed for bus travel than car travel. Higher impedance coefficients (beta), a model parameter, reduced the impact of street-source variations on estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. We also demonstrated that a relative spatial access measure - the spatial access ratio (SPAR) - reduced uncertainties introduced by the choice of street datasets. Careful selection of reliable street sources and model parameters (e.g. higher beta), as well as consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy. When reliable/accurate road network data are not accessible or data quality information is not available, the SPAR is a suitable alternative or supplement to SPAI for visualization and analyses.

Operating with a conceptual workflow for the appropriate processing of high-spatial resolution small unmanned aircraft system (sUAS) data for hydrologic modelling of floodplains during flood events, this research investigated the effects of input data fidelity on hydrologic model generation. A digital surface model (DSM) and co-registered orthophoto mosaic of a stretch of the Drau River in southern Austria was generated. A digital terrain model (DTM) was then approximated from the generated DSM to within a vertical root-mean-square error (RMSE) of 4.65 cm. Horizontal and random metrics of roughness were calculated based on the DSM and then used to determine spatially-varying values of Manning's n coefficient across an observed floodplain. A distributed two-dimensional hydrologic model of a river channel and simulated floodplain was performed using open source hydraulic modelling software. The effectiveness of each roughness metric at multiple pixel resolutions to model significant variations in reported velocity, water surface elevation and depth during a flood event was tested in a controlled sensitivity analysis. Results indicated that accuracy in hydrologic modelling is impacted by image spatial resolution. Research results conclude by emphasizing the importance of context in hydrologic modelling scenarios to effectively meet the needs of end users.

Retrospective data harmonization across multiple research cohorts and studies is frequently done to increase statistical power, provide comparison analysis, and create a richer data source for data mining. However, when combining disparate data sources, harmonization projects face data management and analysis challenges. These include differences in the data dictionaries and variable definitions, privacy concerns surrounding health data representing sensitive populations, and lack of properly defined data models. With the availability of mature open-source web-based database technologies, developing a complete software architecture to overcome the challenges associated with the harmonization process can alleviate many roadblocks. By leveraging state-of-the-art software engineering and database principles, we can ensure data quality and enable cross-center online access and collaboration. This paper outlines a complete software architecture developed and customized using the Django web framework, leveraged to harmonize sensitive data collected from three NIH-support birth cohorts. We describe our framework and show how we successfully overcame challenges faced when harmonizing data from these cohorts. We discuss our efforts in data cleaning, data sharing, data transformation, data visualization, and analytics, while reflecting on what we have learned to date from these harmonized datasets.

The Navajo Nation (NN), a sovereign indigenous tribal nation in the Southwestern United States, is home to 523 abandoned uranium mines (AUMs). Previous health studies have articulated numerous human health hazards associated with AUMs and multiple environmental mechanisms/pathways (e.g., air, water, and soil) for contaminant transport. Despite this evidence, the limited modeling of AUM contamination that exists relies solely on proximity to mines and only considers single rather than combined pathways from which the contamination is a product. In order to better understand the spatial dynamics of contaminant exposure across the NN, we adopted the following established geospatial and computational methods to develop a more sophisticated environmental risk map illustrating the potential for AUM contamination: GIS-based multi-criteria decision analysis (GIS-MCDA), fuzzy logic, and analytic hierarchy process (AHP). Eight criteria layers were selected for the GIS-MCDA model: proximity to AUMs, roadway proximity, drainage proximity, topographic landforms, wind index, topographic wind exposure, vegetation index, and groundwater contamination. Model sensitivity was evaluated using the one-at-a-time method, and statistical validation analysis was conducted using two separate environmental datasets. The sensitivity analysis indicated consistency and reliability of the model. Model results were strongly associated with environmental uranium concentrations. The model classifies 20.2% of the NN as high potential for AUM contamination while 65.7% and 14.1% of the region are at medium and low risk, respectively. This study is entirely a novel application and a crucial first step toward informing future epidemiologic studies and ongoing remediation efforts to reduce human exposure to AUM waste.

While estimates of pulmonary arterial hypertension incidence and prevalence commonly range from 1–3/million and 15–25/million, respectively, clinical experience at our institution suggested much higher rates. We sought to describe the disease burden of pulmonary arterial hypertension in the geographic area served by our Pulmonary Hypertension Clinic and compare it to the REVEAL registry. Our secondary objectives were to document pulmonary arterial hypertension prevalence in minorities underrepresented in REVEAL (Hispanics and Native Americans) and to address the association of pulmonary arterial hypertension with exposure to drugs and moderately increased residential altitude in this population. Retrospective review of pulmonary arterial hypertension clinic patients alive during 2016 identified 154 patients. Hispanic patients made up 35.7% of the cohort, a much greater percentage than REVEAL, p < .001 but smaller than the percentage of Hispanic patients (48.4%) in geographic area served by the clinic. Pulmonary arterial hypertension due to drug exposure was more common and idiopathic pulmonary arterial hypertension was less common than in REVEAL ( p < .001). Overall, pulmonary arterial hypertension incidence was 14 cases per million, greater than the REVEAL registry, odds ratio 6.3 (95% CI: 4.2–9.5), ( p < .001). Annual period prevalence of pulmonary arterial hypertension was 93 cases per million, also greater than the REVEAL, odds ratio = 7.5 (95% CI: 6.4–8.8) and remained greater when the clinic cohort was constrained to patients with hemodynamic severity comparable to REVEAL, odds ratio = 3.8 (95% CI: 3.0–4.6), ( p < .001). There was a strong association between pulmonary arterial hypertension prevalence and residence at altitude > 4000 ft, odds ratio = 26.6 (95% CI: 8.5–83.5), p < .001; however, this was potentially confounded by pulmonary arterial hypertension treatment referral patterns. These findings document a much higher local pulmonary arterial hypertension incidence and prevalence than previously reported in REVEAL. While population ethnicity differed markedly from REVEAL, the disease burden was not driven by these differences. The possible association of moderately increased residential altitude with pulmonary arterial hypertension warrants further evaluation.