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.”

Geographically Weighted Regression to Measure Spatial Variations in Correlations between Water Pollution versus Land Use in a Coastal Watershed

OCMOcean & Coastal Management, Volume 103, January 2015, Pages 14–24

By Jinliang Huang, Yaling Huang,Robert Gilmore Pontius Jr., and Zhenyu Zhang

“Highlights

  • GWR reveals spatial variation in water pollution-land use linkages.
  • Water pollution is associated more with built-up than with cropland or forest.
  • More built-up is associated with more pollution for less urbanized sub-watersheds.
  • Forest has a stronger negative association with pollution in urban sub-watersheds.
  • Cropland has a weak association with water pollution among 21 sub-watersheds.

“Land use can influence river pollution and such relationships might or might not vary spatially. Conventional global statistics assume one relationship for the entire study extent, and are not designed to consider whether a relationship varies across space. We used geographically weighted regression to consider whether relationships between land use and water pollution vary spatially across a subtropical coastal watershed of Southeast China. Surface water samples of baseflow for seven pollutants were collected twelve times during 2010–2013 from headwater sub-watersheds. We computed 21 univariate regressions, which consisted of three regressions for each of the seven pollutants. Each of the three regressions considered one of three independent variables, i.e. the percent of the sub-watershed that was cropland, built-up, or forest.

Local R2 values and local parameter estimates for GWR cropland models among three types of sub-watershed.

Local R2 values and local parameter estimates for GWR cropland models among three types of sub-watershed.

“Cropland had a local R2 less than 0.2 for most pollutants, while it had a positive association with water pollution in the agricultural sub-watersheds and a negative association with water pollution in the non-agricultural sub-watersheds. Built-up had a positive association with all pollutants consistently across space, while the increase in pollution per increase in built-up density was largest in the sub-watersheds with low built-up density. The local R2 values were stronger with built-up than with cropland and forest. The local R2 values for built-up varied spatially, and the pattern of the spatial variation was not consistent among the seven pollutants. Forest had a negative association with most pollutants across space. Forest had a stronger negative association with water pollution in the urban sub-watersheds than in the agricultural sub-watersheds. This research provides an insight into land-water linkages, which we discuss with respect to other watersheds in the literature.”

A GIS-based Relational Data Model for Multi-dimensional Representation of River Hydrodynamics and Morphodynamics

EMS-S13648152Environmental Modelling & Software, Volume 65, March 2015, Pages 79–93

By Dongsu Kim, Marian Muste, and Venkatesh Merwade

“Highlights

  • Represent river data in a curvilinear coordinate system to support river channel oriented spatial analyses.
  • Represent multidimensional river features through points, lines, polygons, and volumes.
  • Represent simulated gridded data for river channels that can be readily coupled with observed data.
  • Represent spatio-temporal evolution of dynamic river objects using Eulerian or Lagrangian observational frameworks.
  • Efficiently store and retrieve data acquired in-situ along with the ancillary metadata.

“The emerging capabilities of the geo-based information systems to integrate spatial and temporal attributes of in-situ measurements is a long-waited solution to efficiently organize, visualize, and analyze the vast amount of data produced by the new generations of river instruments. This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

“The proposed model, labeled Arc River, is built in close connection with the existing Arc Hydro data model developed for water-related features to ensure the connection of the river characteristics with their floodplains and watersheds. This paper illustrates Arc River data model capabilities in conjunction with Acoustic Doppler Current Profiler measurements to demonstrate that essential river morphodynamics and hydrodynamics aspects can be described using data on the flow and its boundaries.”

HIV and Hepatitis C Mortality in Massachusetts, 2002–2011: Spatial Cluster and Trend Analysis of HIV and HCV Using Multiple Cause of Death

PLOS One, Published Online 11 December 2014

By David J. Meyers, Maria Elena Hood, and Thomas J. Stopka

Background
Infectious diseases, while associated with a much smaller proportion of deaths than they were 50 years ago, still play a significant role in mortality across the state of Massachusetts. Most analysis of infectious disease mortality in the state only take into account the underlying cause of death, rather than contributing causes of death, which may not capture the full extent of mortality trends for infectious diseases such as HIV and the Hepatitis C virus (HCV).

Methods
In this study we sought to evaluate current trends in infectious disease mortality across the state using a multiple cause of death methodology. We performed a mortality trend analysis, identified spatial clusters of disease using a 5-step geoprocessing approach and examined spatial-temporal clustering trends in infectious disease mortality in Massachusetts from 2002–2011, with a focus on HIV/AIDS and HCV.

HCV Mortality rates by census tract, 2002–2011. Crude Mortality Rates were calculated based on the 2010 census population estimates at the census tract level for all-causes of HCV. Rates were classified by quintile. Shapefiles were provided by MassGIS, death data were provided by the Massachusetts Department of Public Health, and population estimates were provided by the US Census Bureau. NAD 1983 Massachusetts State Plain was used for projection. Maps created in ArcGIS 10.2.

HCV Mortality rates by census tract, 2002–2011. Crude Mortality Rates were calculated based on the 2010 census population estimates at the census tract level for all-causes of HCV. Rates were classified by quintile. Shapefiles were provided by MassGIS, death data were provided by the Massachusetts Department of Public Health, and population estimates were provided by the US Census Bureau. NAD 1983 Massachusetts State Plain was used for projection. Maps created in ArcGIS 10.2.

Results
Significant clusters of high infectious disease mortality in space and time throughout the state were detected through both spatial and space time cluster analysis. The most significant clusters occurred in Springfield, Worcester, South Boston, the Merrimack Valley, and New Bedford with other smaller clusters detected across the state. Multiple cause of death mortality rates were much higher than underlying cause mortality alone, and significant disparities existed across race and age groups.

Conclusions
We found that our multi-method analyses, which focused on contributing causes of death, were more robust than analyses that focused on underlying cause of death alone. Our results may be used to inform public health resource allocation for infectious disease prevention and treatment programs, provide novel insight into the current state of infectious disease mortality throughout the state, and benefited from approaches that may more accurately document mortality trends.”

OGC Seeks Comment on New Working Group Focused on Representing Time Series Spatial Observation Data

OGC_Logo_Border_Blue_3DThe OGC seeks comment on the charter for a new OGC Standards Working Group (SWG) being formed to develop an OGC standard – TimeSeriesML 1.0 – for the representation of time series observations data. This work is motivated by requirements in hydrology and meteorology, but the standard will be designed to be useful in any application that involves periodic sampling of spatially referenced data. Time scales could range from the geologic time scales of climate to the nanosecond time scales of radiofrequency spectrum. It is intended that the proposed TimeSeriesML 1.0 standard will be implemented as an application schema of the Geography Markup Language version 3.3 and make use of “OMXML,” the OGC XML implementation of the OGC and ISO Observations and Measurements (O&M) conceptual model (OGC Observations and Measurements v2.0 also published as ISO/DIS 19156).

This SWG will develop a TimeSeriesML 1.0 candidate standard submission, coordinate a public comment period, and process any comments received during this period. The final deliverable of the SWG will be a version of the candidate standard for consideration by the OGC membership for approval as an OGC standard.

The draft charter for the OGC TimeSeriesML Standards Working Group is available at https://portal.opengeospatial.org/files/60856. Send comments on the charter to charter-requests [at] opengeospatial.org.

The 30 day public comment period ends 20 November 2014. After the ad hoc group seeking to form the new SWG has addressed comments received in response to this Request for Comments (RFC) the draft charter will be submitted to the OGC Technical Committee and Planning Committee for their review and likely approval.

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The OGC® is an international consortium of more than 495 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org.

[Source: OGC press release]

Spatio-temporal Analysis of Forest Changes in Contrasting Land Use Regimes of Zanzibar, Tanzania

Applied GeographyApplied Geography, Volume 55, December 2014, Pages 193–202

By Markus Kukkonen and Niina Käyhkö

“Highlights:

  • Deforestation has accelerated in Unguja between 1975 and 2009 and current deforestation rate is 0.46%.
  • There are significant differences in forest changes and drivers of change between community, government and agroforest land use regimes of Zanzibar.
  • Shifting cultivation, urban expansion and spread of permanent agriculture are the main proximate causes of forest clearings.
  • The accelerating deforestation rate sheds negative light on the long-term developments of the forest cover.

“We have estimated forest changes and deforestation trends on the island of Unguja (Zanzibar) over the last three decades based on satellite images, forest cover change trajectory and post-forest land cover analysis. The results show that deforestation has intensified and forest cover change rate has changed from 0.03% to −0.46% between 1975–1996 and 1996–2009. On average 0.88 km2 of forests were lost annually, which makes altogether 29.9 km2 during the 34 year study period. Using three distinctive land use regimes prevailing on the island, we are able to show that in reality the changes and their causes were unique in each region. The community forest land use regime was dominated by shifting cultivation related cyclical changes combined with growing deforestation rates. The deforestation rates were also high in agroforest land use regime, but here forest clearings were associated with urban sprawl. Opposite to these two regimes, the cover increased in government forest areas, due to large tree planting schemes. However, forest clearings increased significantly since 1996 in government areas and currently all regimes are facing decreasing forest cover. Population growth, in-migration, urbanization, tourism and increasing demand of agricultural and forestry products were the main underlying causes behind the deforestation. Although, the long-term developments of the forest cover are dictated by these relatively uncontrollable underlying causes, we suggest few actions to restrain deforestation and its effects. These actions include establishment of protected area network with forest corridors, heeding trees in urban and agricultural land use planning, replanting cleared governmental plantations and extending plantations outside the Island.”

The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System

PLOS_ONEPLoS ONE 9(9): e83848, published online 10 September 2014

By Changjun Bao, Wanwan Liu, Yefei Zhu, Wendong Liu, Jianli Hu, Qi Liang, Yuejia Cheng, Ying Wu, Rongbin Yu, Minghao Zhou, Hongbing Shen, Feng Chen, Fenyang Tang, and Zhihang Peng

Background
Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation.

Methods
Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu.

 Interpolated maps of HFRS by IDW in Jiangsu in 2001, 2004, 2007 and 2010. The incidence of HFRS per 100,000 residents is shown in the map. The incidence of HFRS has a positive relationship with color depth.

Interpolated maps of HFRS by IDW in Jiangsu in 2001, 2004, 2007 and 2010. The incidence of HFRS per 100,000 residents is shown in the map. The incidence of HFRS has a positive relationship with color depth.

Results
HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19) and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25), and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes.

Conclusion
The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis.”