Advances in spatial epidemiology and geographic information systems

Kirby, Russell S.; Delmelle, Eric; Eberth, Jan M.

doi:10.1016/j.annepidem.2016.12.001

Cited by 251 publications

(245 citation statements)

References 83 publications

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“…For instance, the “driving distance” variable used in this study was a limited measure of “distance.” A five‐mile trip in the city—where public transit, well maintained public infrastructure, and mobility assistance are readily available—is quite different from a five‐mile trip in the country. Spatial epidemiology and GIS methods offer excellent tools to explore these differences (e.g., Kirby, Delmelle, & Eberth, ).…”

Section: Discussionmentioning

confidence: 99%

Rural mental health service utilization in a Texas telepsychology clinic

Tarlow

McCord

et al. 2019

J Clin Psychol

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Objective: To determine if service utilization behaviors varied with the remoteness of clients served by a telepsychology clinic in a predominantly rural health professional shortage area (HPSA) in Texas. Methods: Archival data from 290 low-income clients wereanalyzed to test associations between service utilization behaviors and clients' distance from the nearby access points where they traveled to receive counseling services from remotely located counselors.Results: Distance to access points did not predict differences in service utilization behaviors. However, clients in rural counties traveled farther to receive services than clients in metropolitan areas. Some utilization behaviors varied by demographic variables and depressive symptom severity. Conclusions:A "hub and spoke" model of telehealth service delivery is an acceptable model for improving access to mental health care services in rural and underserved communities, even for clients who live relatively far from access points. Telehealth providers should consider geographic, socioeconomic, transportation, and health-related barriers to care experienced by clients when implementing services and policies. K E Y W O R D S dropout, rural mental health, telehealth, telepsychology, utilization ORCID Kevin R. Tarlow

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Section: Discussionmentioning

confidence: 99%

Rural mental health service utilization in a Texas telepsychology clinic

Tarlow

McCord

et al. 2019

J Clin Psychol

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“…Geographic research has suggested that issues including spatial autocorrelation and spatially varying relationships can bias the estimates of model parameters in regression analyses when geographically referenced data are used [39,40,41]. Therefore, alternative spatial regression models (i.e., lag or error) could be used to address the spatial autocorrelation problem if statistically significant Moran’s I is being flagged.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, alternative spatial regression models (i.e., lag or error) could be used to address the spatial autocorrelation problem if statistically significant Moran’s I is being flagged. Likewise, geographically weighted regression (GWR) is often employed to generate local regression models that address inconsistent relationships between dependent and independent variables across a study area [41,42]. Therefore, in order to investigate whether ovarian incidence rates were related to TRI emissions from pulp and paper plants, we used ordinary least squares (OLS) regression first for both the state- and county-level data.…”

Section: Methodsmentioning

confidence: 99%

Ovarian Cancer Incidence in the U.S. and Toxic Emissions from Pulp and Paper Plants: A Geospatial Analysis

Hanchette

Zhang

Schwartz

2018

IJERPH

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Ovarian cancer is the fifth leading cause of female cancer mortality in the U.S. and accounts for five percent of all cancer deaths among women. No environmental risk factors for ovarian cancer have been confirmed. We previously reported that ovarian cancer incidence rates at the state level were significantly correlated with the extent of pulp and paper manufacturing. We evaluated that association using county-level data and advanced geospatial methods. Specifically, we investigated the relationship of spatial patterns of ovarian cancer incidence rates with toxic emissions from pulp and paper facilities using data from the Environmental Protection Agency’s Toxic Release Inventory (TRI). Geospatial analysis identified clusters of counties with high ovarian cancer incidence rates in south-central Iowa, Wisconsin, New York, Pennsylvania, Alabama, and Georgia. A bivariate local indicator of spatial autocorrelation (LISA) analysis confirmed that counties with high ovarian cancer rates were associated with counties with large numbers of pulp and paper mills. Regression analysis of state level data indicated a positive correlation between ovarian cancer and water pollutant emissions. A similar relationship was identified from the analysis of county-level data. These data support a possible role of water-borne pollutants from pulp and paper mills in the etiology of ovarian cancer.

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“…Characterization and localization of the patient's residence allows not only to identify clusters, but also to cross-link them with the existing primary and secondary care network, in order to understand their relationship with accessibility to care. [5,16]…”

Section: Spatial Autocorrelation Statistics Serving Community Psychiatrymentioning

confidence: 99%

No man is an Island: spatial clustering and access to primary care as possible targets for the development of new community mental health approaches.

Nascimento

Lourenço

Coelho

et al. 2020

Preprint

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Purpose: to understand if patients seen at Centro Hospitalar Psiquiátrico de Lisboa (CHPL) live in geographical clusters or randomly throughout the city, as well as determine their access to the psychiatric hospital and primary care facilities (PCF). Methods: spatial autocorrelation statistics regarding all patients observed at CHPL in 2017, at the census subsection level, considering a queen criterion of contiguity, regarding not only their overall number but also main diagnosis, and admission to the psychiatric ward - voluntary or compulsory. Distance to the hospital and to the closest PCF was measured (for each patient and the variables cited above), and the mean values were compared. Finally, the total number of patients around each PCF was counted, considering specified radius sizes of 656 and 1000m. Results: All 5161 patients (509 psychiatric admissions) were geolocated, and statistical significance regarding patient clustering was found for the total number (p-0.0001) and specific group of disorders, namely Schizophrenia and related disorders (p-0.007) and depressive disorders (p-0.0002). Patients who were admitted in a psychiatric ward live farther away from the hospital (p-0.002), with the compulsory admissions (versus voluntary ones) living even farther (p-0.004). Furthermore, defining a radius of 1000m for each PCF allowed the identification of two PCF with more than 1000 patients, and two others with more than 800. Conclusions: as patients seem to live in geographical clusters (and considering PCFs with the highest number of them), possible locations for the development of programs regarding mental health treatment and prevention can now be identified.

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Advances in spatial epidemiology and geographic information systems

Cited by 251 publications

References 83 publications

Rural mental health service utilization in a Texas telepsychology clinic

Rural mental health service utilization in a Texas telepsychology clinic

Ovarian Cancer Incidence in the U.S. and Toxic Emissions from Pulp and Paper Plants: A Geospatial Analysis

No man is an Island: spatial clustering and access to primary care as possible targets for the development of new community mental health approaches.

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