Associations between residence at birth and mental health disorders: a spatial analysis of retrospective cohort data

BMC Public Health
BMC Public Health, (2015) 15:688

By Kate Hoffman, Ann Aschengrau, Thomas F. Webster, Scott M. Bartell, and Verónica M. Vieira

Background: Mental healthdisorders impact approximately one in four US adults. While their causes are likely multifactorial, prior research has linked the risk of certain mental health disorders to prenatal and early childhood environmental exposures, motivating a spatial analysis to determine whether risk varies by birth location.

Methods: We investigated the spatial associations between residence at birth and odds of depression, bipolar disorder, and post-traumatic stress disorder (PTSD) in a retrospective cohort (Cape Cod, Massachusetts, 1969–1983) using generalized additive models to simultaneously smooth location and adjust for confounders. Birth location served as a surrogate for prenatal exposure to the combination of social and environmental factors related to the development of mental illness. We predicted crude and adjusted odds ratios (aOR) for each outcome across the study area. The results were mapped to identify areas of increased risk.

Geographic distribution of PTSD vs. no reported mental illness from analyses using the optimal span size for each model [unadjusted (a) and adjusted for sex, year of birth, family history of mental health diagnosis, father’s occupation, mother’s educational attainment, maternal smoking during pregnancy, and pre/postnatal PCE exposure (b)]. Black contour bands indicate areas of statistically significant increased or decreased odds of outcomes.The scale includes most, but not all, observed odds ratios.

Geographic distribution of PTSD vs. no reported mental illness from analyses using the optimal span size for each model [unadjusted (a) and adjusted for sex, year of birth, family history of mental health diagnosis, father’s occupation, mother’s educational attainment, maternal smoking during pregnancy, and pre/postnatal PCE exposure (b)]. Black contour bands indicate areas of statistically significant increased or decreased odds of outcomes.The scale includes most, but not all, observed odds ratios.

Results: We observed spatial variation in the crude odds ratios of depression that was still present even after accounting for spatial confounding due to geographic differences in the distribution of known risk factors (aOR range: 0.61–3.07, P = 0.03). Similar geographic patterns were seen for the crude odds of PTSD; however, these patterns were no longer present in the adjusted analysis (aOR range: 0.49–1.36, P = 0.79), with family history of mental illness most notably influencing the geographic patterns. Analyses of the odds of bipolar disorder did not show any meaningful spatial variation (aOR range: 0.58–1.17, P = 0.82).

Conclusion: Spatial associations exist between residence at birth and odds of PTSD and depression, but much of this variation can be explained by the geographic distributions of available risk factors. However, these risk factors did not account for all the variation observed with depression, suggesting that other social and environmental factors within our study area need further investigation.”

A Spatio-temporal Analysis of Crime at Washington, DC Metro Rail: Stations’ Crime-generating and Crime-attracting Characteristics as Transportation Nodes and Places

logoCrime Science, Published Online 16 July 2015

By Yasemin Irvin-Erickson and Nancy La Vigne

“Transit stations are acknowledged as particularly criminogenic settings. Transit stations may serve as crime “generators,” breeding crime because they bring together large volumes of people at particular geographies and times. They may also serve as crime “attractors,” providing well-known opportunities for crimes. This paper explores the node and place characteristics that can transform Washington DC, Metro stations to generators and attractors of different crimes at different times of the day. The crime-generating and crime-attracting characteristics of stations are modeled with Negative Binomial Regression analysis. To reflect the temporal trends in crime, crime counts are stratified into three temporal groups: peak hours, off-peak day hours, and off-peak night hours.

Robbery density at peak, non-peak day, and non-peak night hours

Robbery density at peak, non-peak day, and non-peak night hours

“The findings from this study not only suggest that stations assume different nodal and place-based crime-generating and crime-attracting characteristics, but also these roles vary for different crimes and different times. The level of activity and accessibility of a station, the level of crime at a station, and the connectedness of a station to other stations are consistent indicators of high crime rate ratios. Different characteristics of a station—such as being a remote station or belonging to a high or low socioeconomic status block group—are significant correlates for particular crime outcomes such as disorderly conduct, robbery, and larceny. ”

Metadata Topic Harmonization and Semantic Search for Linked-Data-Driven Geoportals: A Case Study Using ArcGIS Online

By Yingjie Hu, Krzysztof Janowicz, Sathya Prasad, and Song Gao

Transactions in GIS, Volume 19, Issue 3, June 2015, Pages 398–416

“Geoportals provide integrated access to geospatial resources, and enable both authorities and the general public to contribute and share data and services. An essential goal of geoportals is to facilitate the discovery of the available resources. Such a process relies heavily on the quality of metadata. While multiple metadata standards have been established, data contributers may adopt different standards when sharing their data via the same geoportal. This is especially the case for user-generated content where various terms and topics can be introduced to describe similar datasets. While this heterogeneity provides a wealth of perspectives, it also complicates resource discovery. With the fast development of the Semantic Web technologies, there is a rise of Linked-Data-driven portals. Although these novel portals open up new ways to organize metadata and retrieve resources, they lack effective semantic search methods.

Comparing estimated relevance scores with human judgments: (a) Without the interaction variable; and (b) With the interaction variable

Comparing estimated relevance scores with human judgments: (a) Without the interaction variable; and (b) With the interaction variable

“This article addresses the two challenges discussed above, namely the topic heterogeneity brought by multiple metadata standards and the lack of established semantic search in Linked-Data-driven geoportals. To harmonize the metadata topics, we employ a natural language processing method, namely Labeled Latent Dirichlet Allocation (LLDA), and train it using standardized metadata from Data.gov. With respect to semantic search, we construct thematic and geographic matching features from the textual metadata descriptions, and train a regression model via a human participants experiment. We evaluate our methods by examining their performances in addressing the two issues. Finally, we implement a semantics-enabled and Linked-Data-driven prototypical geoportal using a sample dataset from Esri’s ArcGIS Online.”

Exploring the Future of Cloud-based GIS in Public Gardens

Hosted by Esri and the American Public Gardens Association
July 20 – 22 , 2015
San Diego Convention Center, San Diego, CA

May 4 FINAL DRAFT APGA Esri 2015 Registration Brochure

Many public gardens are already using GIS to help manage their grounds and collections. Now, new cloud-based GIS tools promise to transform our garden’s collection maps into story-telling tools and apps that can help us engage with visitors, teach science literacy, and advance plant conservation worldwide. We need your voice at the table on how to best move these ideas forward!

Please join us for the APGA-Esri 2015 GIS Symposium on July 20 – 22, 2015, a special track within the 2015 Esri International User Conference in San Diego, CA. This symposium allows for a full immersion in the GIS experience, coupled with two break-away days–a “community conversation”–where we discuss alternatives and work together to design a path forward for the future of cloud-based GIS in public gardens.

2015 Esri User Conference: Sessions For Scientists

The Esri International User Conference has many presentations and events with hundreds of topics covered. To help you find your way to the science related sessions and events, we have prepared this focused agenda.


Key Events: Tuesday, July 21

Science Symposium Reception
5:30 PM – 6:30 PM, Bayfront Hilton, Elevation Room, 30th Floor

Federal GIS Users Reception
6:30 PM – 9:30 PM, San Diego Convention Center, Promenade Patio and Vela Terrace

Planner Codes — Presentation Types

DT Demo Theater
IFS Industry Focus Session
LT Lightning Talk
PP Moderated Paper Session
PS Preconference Seminar
SIG Special Interest Group
SP Special presentation
TW Technical Workshop

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A Promising Tool to Assess Long Term Public Health Effects of Natural Disasters

PLOS_ONECombining Routine Health Survey Data and Geographic Information Systems to Assess Stunting after the 2001 Earthquake in Peru

PLOS | One, Published Online 19 June 2015

By Henny Rydberg, Gaetano Marrone, Susanne Strömdahl, and Johan von Schreeb

Background: Research on long-term health effects of earthquakes is scarce, especially in low- and middle-income countries, which are disproportionately affected by disasters. To date, progress in this area has been hampered by the lack of tools to accurately measure these effects. Here, we explored whether long-term public health effects of earthquakes can be assessed using a combination of readily available data sources on public health and geographic distribution of seismic activity.

ShakeMap image of the 2001 southern Peru earthquake.

ShakeMap image of the 2001 southern Peru earthquake.

Methods: We used childhood stunting as a proxy for public health effects. Data on stunting were attained from Demographic and Health Surveys. Earthquake data were obtained from U.S. Geological Survey’s ShakeMaps, geographic information system-based maps that divide earthquake affected areas into different shaking intensity zones. We combined these two data sources to categorize the surveyed children into different earthquake exposure groups, based on how much their area of residence was affected by the earthquake. We assessed the feasibility of the approach using a real earthquake case – an 8.4 magnitude earthquake that hit southern Peru in 2001.

 GIS-based map of the areas affected by the 2001 southern Peru earthquake.

GIS-based map of the areas affected by the 2001 southern Peru earthquake.

Results and conclusions: Our results indicate that the combination of health survey data and disaster data may offer a readily accessible and accurate method for determining the long-term public health consequences of a natural disaster. Our work allowed us to make pre- and post- earthquake comparisons of stunting, an important indicator of the well-being of a society, as well as comparisons between populations with different levels of exposure to the earthquake. Furthermore, the detailed GIS based data provided a precise and objective definition of earthquake exposure. Our approach should be considered in future public health and disaster research exploring the long-term effects of earthquakes and potentially other natural disasters.”

Spatial analysis of the effect of the 2010 heat wave on stroke mortality in Nanjing, China

Scientific Reports 5, Published 02 June 2015

By Kai Chen, Lei Huang, Lian Zhou, Zongwei Ma, Jun Bi, and Tiantian Li

“To examine the spatial variation of stroke mortality risk during heat wave, we collected 418 stroke mortality cases with permanent addresses for a severe heat wave (July 28–August 15, 2010) and 624 cases for the reference period (July 29–August 16, 2009 and July 27–August 14, 2011) in Nanjing, China. Generalized additive models were used to explore the association between location and stroke mortality risk during the heat wave while controlling individual-level risk factors. Heat wave vulnerability was then applied to explain the possible spatial variations of heat-wave-related mortality risk.

(1) Using reference period 1 (A2); (2) Using reference period 2 (A3). Maximum of daytime land surface temperatures (Terra/MODIS, 1 km resolution) in each period (19 days) was used as the temperature exposure indicator. White areas indicate that land surface temperatures were not available due to cloud cover. Maps were generated using ArcGIS (version 10.0; ESRI, Redlands, CA).

(1) Using reference period 1 (A2); (2) Using reference period 2 (A3). Maximum of daytime land surface temperatures (Terra/MODIS, 1 km resolution) in each period (19 days) was used as the temperature exposure indicator. White areas indicate that land surface temperatures were not available due to cloud cover. Maps were generated using ArcGIS (version 10.0; ESRI, Redlands, CA).

“The overall risk ratio (95% confidence intervals) of stroke mortality due to the heat wave in Nanjing was 1.34 (1.21 to 1.47). Geolocation was found to be significantly associated with the heat-wave-related stroke mortality risk. Using alternative reference periods generated similar results. A district-level risk assessment revealed similar spatial patterns. The highest stroke mortality risk observed in Luhe district was due to the combination of high heat exposure and high vulnerability. Our findings provide evidence that stroke mortality risk is higher in rural areas during heat waves and that these areas require future interventions to reduce vulnerability.”