Socio-environmental Drivers and Suicide in Australia: Bayesian Spatial Analysis

BMC Public HealthBMC Public Health 2014, 14:681, Published 4 July 2014

By Xin Qi, Wenbiao Hu, Kerrie Mengersen, and Shilu Tong

Background
The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods.

Methods
Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE)) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology (BoM). A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs.

Suicide rates across LGAs and unincorporated SLAs, 1996-2000.

Suicide rates across LGAs and unincorporated SLAs, 1996-2000.

Results
In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1[degree sign]C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996-2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001-2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III.

Conclusions
Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.”

The Gravity of Pollination: Integrating At-site Features into Spatial Analysis of Contemporary Pollen Movement

meMolecular Ecology, Accepted July 2014

By Michelle F. DiLeo, Jenna C. Siu, Matthew K. Rhodes, Adriana López-Villalobos, Angela Redwine, Kelly Ksiazek, and Rodney J. Dyer

“Pollen-mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site-specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape-level genetic connectivity in plants have focused on the effects of inter-individual distance using spatial and increasingly ecological separation; yet have not incorporated individual plant characteristics or other at-site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at-site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at-site characteristics provided better models than those that only considered inter-individual spatial distance (e.g., IBD). Models parameterized using conditional genetic covariance (e.g., Population Graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at-site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between-site spatial (IBD) and ecological (Isolation By Resistance) variables. Overall, our results show that including inter-individual and local ecological variation greatly aids in characterizing landscape-level measures of contemporary gene flow.”

Mapping the Potential for Biofuel Production on Marginal Lands: Differences in Definitions, Data and Models across Scales

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 430-459

By Sarah Lewis and Maggi Kelly

“As energy policies mandate increases in bioenergy production, new research supports growing bioenergy feedstocks on marginal lands. Subsequently there has been an increase in published work that uses Geographic Information Systems (GIS) to map the availability of marginal land as a proxy for bioenergy crop potential. However, despite the similarity in stated intent among these works a number of inconsistencies remain across studies that make comparisons and standardization difficult. We reviewed a collection of recent literature that mapped bioenergy potential on marginal lands at varying scales, and found that there is no common working definition of marginal land across all of these works. Specifically, we found considerable differences in mapped results that are driven by dissimilarities in definitions, model framework, data inputs, scale and treatment of uncertainty. Most papers reviewed here employed relatively simple GIS overlays of input criteria, distinct thresholds identifying marginal land, and few details describing accuracy and uncertainty. These differences are likely to be major impediments to integration of studies mapping marginal lands for bioenergy production. We suggest that there is future need for spatial modeling of bioenergy, yet further scholarship is needed to compare across countries and scales to understand the global potential for bioenergy crops.  ”

Spatial Analysis of Cattle and Shoat Population in Ethiopia: Growth Trend, Distribution and Market Access

SPSpringerPlus, 2014, 3:310

By Samson Leta and Frehiwot Mesele

“The livestock subsector has an enormous contribution to Ethiopia’s national economy and livelihoods of many Ethiopians. The subsector contributes about 16.5% of the national Gross Domestic Product (GDP) and 35.6% of the agricultural GDP. It also contributes 15% of export earnings and 30% of agricultural employment. The livestock subsector currently support and sustain livelihoods for 80% of all rural population. The GDP of livestock related activities valued at 59 billion birr. Ethiopian livestock population trends, distribution and marketing vary considerably across space and time due to a variety of reasons. This study was aimed to assess cattle and shoat population growth trend, distribution and their access to market. Regression analysis was used to assess the cattle and shoat population growth trend and Geographic Information Systems (GIS) techniques were used to determine the spatial distribution of cattle and shoats, and their relative access to market.

Proximity to major livestock market center and all weather roads.

Proximity to major livestock market center and all weather roads.

“The data sets used are agricultural census (2001/02) and annual CSA agricultural sample survey (1995/96 to 2012/13). In the past eighteen years, the livestock population namely cattle, sheep and goat grew from 54.5 million to over 103.5 million with average annual increment of 3.4 million. The current average national cattle, sheep and goat population per km2 are estimated to be 71, 33 and 29 respectively (excluding Addis Ababa, Afar and Somali regions). From the total livestock population the country owns about 46% cattle, 43% sheep and 40% goats are reared within 10 km radius from major livestock market centres and all-weather roads. On the other hand, three fourth of the country’s land mass which comprises 15% of the cattle, 20% of the sheep and 21% of goat population is not accessible to market (greater than 30 km from major livestock market centres). It is found that the central highland regions account for the largest share of livestock population and also more accessible to market. Defining the spatial and temporal variations of livestock population is crucial in order to develop a sound and geographically targeted livestock development policy.”

Spatial Distribution and Risk Factors of Influenza in Jiangsu Province, China, based on Geographical Information System

ghGeospatial Health, Volume 8, Number 2, May 2014, Pages 429-435

By Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, and Zhi-Hang Peng

“Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission.

Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2 006 (b), 2009 (c) and 2011 (d).

Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2006 (b), 2009 (c) and 2011 (d).

“A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics. ”

Integrating the Huff Model and Floating Catchment Area Methods to Analyze Spatial Access to Healthcare Services

Transactions in GISTransactions in GIS, Volume 18, Issue 3, pages 436–448, June 2014

By Jun Luo

“Analysis of spatial access to healthcare services is critical for effective health resource planning. Gravity-based spatial access models have been widely used to estimate spatial access to healthcare services. Among them, the floating catchment area (FCA) methods have been proved to be informative and helpful to the designation of Health Professional Shortage Areas (HPSAs). This article integrates the Huff Model with the FCA method to articulate population selection on services. Through the proposed approach, population demand on healthcare services is adjusted by a Huff Model-based selection probability that reflects the impacts of both distance impedance and service site capacity.

Spatial patterns of the census tracts' spatial access to healthcare services for each distance impedance coefficient. Note: β is the distance impedance coefficient

Spatial patterns of the census tracts’ spatial access to healthcare services for each distance impedance coefficient. Note: β is the distance impedance coefficient

“The new approach moderates the over- or under-estimating of population demand that occurred with previous methods. Furthermore, the method uses a continuous distance impedance weight function instead of the arbitrarily defined subzones of previous studies. A case study of spatial access to primary care in Springfield, MO, showed that the proposed method can effectively moderate the population demand on service sites and therefore can generate more reliable spatial access measures.”

A Spatial and Temporal Analysis of Japanese Encephalitis in Mainland China, 1963–1975

PLOS_ONEPLOS ONE, Published Online June 09, 2014

By Xiaolong Li, Xiaoyan Gao, Zhoupeng Ren, Yuxi Cao, Jinfeng Wang, Guodong Liang

“More than a million Japanese encephalitis (JE) cases occurred in mainland China from the 1960s to 1970s without vaccine interventions. The aim of this study is to analyze the spatial and temporal pattern of JE cases reported in mainland China from 1965 to 1973 in the absence of JE vaccination, and to discuss the impacts of climatic and geographical factors on JE during that period. Thus, the data of reported JE cases at provincial level and monthly precipitation and monthly mean temperature from 1963 to 1975 in mainland China were collected. Local Indicators of Spatial Association analysis was performed to identify spatial clusters at the province level. During that period, The epidemic peaked in 1966 and 1971 and the JE incidence reached up to 20.58/100000 and 20.92/100000, respectively. The endemic regions can be divided into three classes including high, medium, and low prevalence regions.

Japanese encephalitis in mainland China during 1963–1975. (A) China experienced a natural JE epidemic period with no vaccine interventions from 1963 to 1975. The bar graph of JE incidence in mainland China from 1951 to 2008 is cited from reference 10. (B) During the JE epidemic in 1971, JE incidence in 11 provinces distributed in the coastal areas of eastern China (red color) was higher than the national average (20.92/100,000). (C) LISA cluster map for JE incidence during 1963–1975 shows the center of cluster in color. High-High indicates a significant (P<0.05) spatial cluster of high JE incidence values; Low-Low represents a spatial cluster of low JE incidence values.

Japanese encephalitis in mainland China during 1963–1975. (A) China experienced a natural JE epidemic period with no vaccine interventions from 1963 to 1975. The bar graph of JE incidence in mainland China from 1951 to 2008 is cited from reference 10. (B) During the JE epidemic in 1971, JE incidence in 11 provinces distributed in the coastal areas of eastern China (red color) was higher than the national average (20.92/100,000). (C) LISA cluster map for JE incidence during 1963–1975 shows the center of cluster in color. High-High indicates a significant (P<0.05) spatial cluster of high JE incidence values; Low-Low represents a spatial cluster of low JE incidence values.

“Through spatial cluster analysis, JE epidemic hot spots were identified; most were located in the Yangtze River Plain which lies in the southeast of China. In addition, JE incidence was shown to vary among eight geomorphic units in China. Also, the JE incidence in the Loess Plateau and the North China Plain was showed to increase with the rise of temperature. Likewise, JE incidence in the Loess Plateau and the Yangtze River Plain was observed a same trend with the increase of rainfall. In conclusion, the JE cases clustered geographically during the epidemic period. Besides, the JE incidence was markedly higher on the plains than plateaus. These results may provide an insight into the epidemiological characteristics of JE in the absence of vaccine interventions and assist health authorities, both in China and potentially in Europe and Americas, in JE prevention and control strategies.”

Spatial Analysis of Factors Associated with HIV Infection among Young People in Uganda, 2011

BMC Public HealthBMC Public Health, 14:555, Published 05 June 2014

By Lucy A Chimoyi and Eustasius Musenge

Background

The HIV epidemic in East Africa is of public health importance with an increasing number of young people getting infected. This study sought to identify spatial clusters and examine the geographical variation of HIV infection at a regional level while accounting for risk factors associated with HIV/AIDS among young people in Uganda.

Methods

A secondary data analysis was conducted on a survey cross-sectional design whose data were obtained from the 2011 Uganda Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) for 7 518 young people aged 15-24 years. The analysis was performed in three stages while incorporating population survey sampling weights. Maximum likelihood-based logistic regression models were used to explore the non-spatially adjusted factors associated with HIV infection. Spatial scan statistic was used to identify geographical clusters of elevated HIV infections which justified modelling using a spatial random effects model by Bayesian-based logistic regression models.

Results

In this study, 309/533 HIV sero-positive female participants were selected with majority residing in the rural areas [386(72%)]. Compared to singles, those currently [Adjusted Odds Ratio (AOR) =3.64; (95%CI; 1.25-10.27)] and previously married [AOR = 5.62; (95%CI: 1.52-20.75)] participants had significantly higher likelihood of HIV infections. Sexually Transmitted Infections [AOR = 2.21; (95%CI: 1.35-3.60)] were more than twice likely associated with HIV infection. One significant (p < 0.05) primary cluster of HIV prevalence around central Uganda emerged from the SaTScan cluster analysis. Spatial analysis disclosed behavioural factors associated with greater odds of HIV infection such as; alcohol use before sexual intercourse [Posterior Odds Ratio (POR) =1.32; 95% (BCI: 1.11-1.63)]. Condom use [POR = 0.54; (95%BCI: 0.41-0.69)] and circumcision [POR = 0.66; (95%BCI: 0.45-0.99)] provided a protective effect against HIV.

uganda

Conclusions

The study revealed associations between high-risk sexual behaviour and HIV infection. Behavioural change interventions should therefore be pertinent to the prevention of HIV. Spatial analysis further revealed a significant HIV cluster towards the Central and Eastern areas of Uganda. We propose that interventions targeting young people should initially focus on these regions and subsequently spread out across Uganda.”

Analyzing Spatial Clustering and the Spatiotemporal Nature and Trends of HIV/AIDS Prevalence using GIS: The Case of Malawi, 1994-2010

bmcidBMC Infectious Diseases 2014, 14:285, Published online 23 May 2014

By Leo C Zulu, Ezekiel Kalipeni, and Eliza Johannes

Background
Although local spatiotemporal analysis can improve understanding of geographic variation of the HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent declines, Malawi’s estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for 2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level.

Methods
Inverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering.

malawi

Results
Analysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district levels. However, prevalence was spatially leveling out within and across ‘sub-epidemics’ while declining significantly after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots” clustered among eleven southern districts/cities while a “coldspot” captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts.

Conclusions
Spatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis management, particularly in rural hotspot districts, as further research is done on drivers at finer scale.”

Read the paper [PDF]

Geospatial Technology Speeds Analysis for US Army

Ready-to-Use Templates Quickly Deliver Critical Information to the Field

Esri has provided a recently revised set of customized templates to the US Army for its Distributed Common Ground System-Army (DCGS-A). These easy-to-use templates come with maps, analytic capabilities, and other visualization tools, as well as a simple information model for creating geospatial products.

The templates will help geospatial engineers, intelligence analysts, and geospatial intelligence (GEOINT) imagery analysts using DCGS-A to rapidly make products to support requests from commanders for operations around the world. DCGS-A is the US Army’s main system for processing and posting data; providing mapping and weather information; and sharing intelligence, surveillance, and reconnaissance (ISR) information with army units.

“Esri technology, such as the templates, gives the army an easy-to-use, technical advantage that helps soldiers optimize GEOINT capability resident in DCGS-A,” said Colonel Ed Riehle, the US Army Training and Doctrine Command’s Capability Manager for Sensor Processing.

“Esri is pleased to have partnered closely with the staff who work with the DCGS-A to deliver these important and innovative templates and tools for the US Army,” said Esri president Jack Dangermond. “We designed them so the analysts can be more productive in what is a very fast-paced operational tempo. We also believe that by adopting these tools, the value of the US Army’s investment in Esri technology will be maximized.”

The templates were customized to match the DCGS-A workflows. Esri staff worked with analysts and specialists at the US Army Intelligence Center of Excellence at Fort Huachuca, Arizona, to refine the template requirements.

These new resources will help analysts do everything from creating sketches of military compounds to making maps that show safe and suitable areas for landing helicopters. The revised templates include

  • Incident analysis to map improvised explosive device (IED) incidents or other significant events.
  • Cross-country mobility analysis to identify key terrain and sketch approach routes.
  • Intervisibility analysis to identify areas of cover and concealment.

The templates will be available on a variety of army websites including the DCGS-A Portal and the Intelligence Knowledge Network as well as in ArcGIS for the Military—Land Operations, an Esri product optimized to provide an interoperable platform to manage, visualize, analyze, and share geospatial information for land-based missions.

Esri will demonstrate ArcGIS for the Military—Land Operations at the GEOINT Symposium April 14–17, in Tampa, Florida, in booth #5036.

To learn more about ArcGIS for the Military—Land Operations templates and other resources, visit solutions.arcgis.com/military/.

[Source: Esri press release]