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.

—–

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.”

An Exploratory Spatial Analysis of Geographical Inequalities of Birth Intervals among Young Women in the Democratic Republic of Congo (DRC): A Cross-sectional Study

BMCPCBMC Pregnancy and Childbirth, 14:271, Published Online 13 August 2014

By Tobias F Chirwa, Jocelyn N Mantempa, Felly K Lukumu, Joseph D Kandala, and Ngianga-Bakwin Kandala

Background
The length of time between two successive live births (birth interval), is associated with child survival in the developing world. Short birth intervals (<24 months) contribute to infant and child mortality risks. Contraceptive use contributes to a reduction in short birth intervals, but evidence is lacking in the DRC. We aimed to investigate the proportion of short birth intervals at the provincial level among young women in the DRC.

Methods
Data from the Demographic and Health Survey undertaken in the DRC in 2007 were analyzed. Logistic regression and Bayesian geo-additive models were used to explain provincial inequalities in short birth intervals among women of reproductive age and young women. Posterior odds ratio (OR) and 95% credible region (CR) were estimated via Markov chain Monte Carlo (MCMC) techniques. Posterior spatial effects and the associated posterior probability maps were produced at the provincial-level to highlight provinces with a significant higher risk of short birth interval.

Results
The overall proportion of short birth intervals among all women of reproductive age (15-49 years) and young women (15-24 years) were 30.2% and 38.7% respectively. In multivariate Bayesian geo-additive regression analyses, among the whole sample of women, living in rural areas [OR = 1.07, 95% CR: (0.97, 1.17)], exclusive breastfeeding [1.08 (1.00, 1.17)] and women with primary education [1.06 (1.00, 1.16)], were consistently associated with a higher risk of short birth intervals. For the young women, none of the factors considered were associated with the risk of short birth interval except a marginal effect from the lack of education. There was a spatial variation in the proportion of women reporting short birth intervals and among all women of reproductive age across provinces, with Nord-Kivu [1.12 (1.02, 1.24)], Sud Kivu [1.17 (1.05, 1.29)] and Kasai Occidental [1.18 (1.06, 1.32)] reporting a higher risk of short birth intervals. For young women, the higher risk provinces were Nord-Kivu [1.22 (1.00, 1.54)] and Sud Kivu [1.34 (1.14, 1.63)].

Results show a clear East-south gradient; specifically, Kasai Occidental, Sud-Kivu and Nord Kivu wer e significantly associated with a higher likelihood of short birth intervals, while Kinshasa, Bas Congo and Bandundu provinces were associated with a lower risk of short birth int ervals.

Results show a clear East-south gradient; specifically, Kasai Occidental, Sud-Kivu and Nord Kivu were significantly associated with a higher likelihood of short birth intervals, while
Kinshasa, Bas Congo and Bandundu provinces were associated with a lower risk of short birth intervals.

Conclusions
This study suggests distinct geographic patterns in the proportion of short birth intervals among Congolese women, as well as the potential role of demographic and geographic location factors driving the ongoing higher youth fertility, higher childhood and maternal mortality in the DRC. ”

Adoption of Cropping Sequences in Northeast Montana: A Spatio-temporal Analysis

AEEBy John A. Long, Rick L. Lawrence, Perry R. Miller, Lucy A. Marshall, and Mark C. Greenwood

Agriculture, Ecosystems & Environment, Volume 197, 01 December 2014, Pages 77–87, Published Online 07 August 2014

“Highlights

  • Study was a spatio-temporal analysis of management practices in northeast Montana.
  • We examined cereal–pulse sequences and strip-cropping conversions during 2001–2012.
  • Both practices were spatially clustered in the region.
  • Neither practice was strongly associated with spread due to diffusion of innovation.
  • Both practices were strongly associated with the availability of water.

“Producers make the decision to adopt a particular agricultural practice within a range of social, economic, environmental, and agronomic constraints. The semiarid regions of the US northern Great Plains are dominated by dryland farming practices and the traditional practice has been to rotate small-grain cereals with summer fallow; however, producers are moving away from this practice. The area of fallow in northeastern Montana decreased by one-third and the area of pulse crops increased nearly six-fold during 2001–2012. We previously identified two key practices that are indicative of regionally changing agricultural practices: (1) the broad-scale adoption of cereal–pulse sequences, and (2) the conversion from continuous strip-cropping to block managed cereal-based sequences. Here, we examined the adoption of these two practices from a spatio-temporal perspective to determine if the observed patterns were consistent with those expected from a priori processes: random occurrence, spread and adoption of the practices due to social interaction as described in innovation diffusion theory, or adoption based on environmental factors. Our results suggest that the adoption and spread of both practices were likely constrained by the suitability of the physical environment. Available water, in particular, exerts a fundamental control on the decision whether or not to adopt either practice. We also found evidence for the expansion of these practices due, in part, to social factors, particularly during the early period of adoption. We conclude that producers made the decision whether or not to adopt these practices primarily as a function of environmental suitability and, to a lesser extent, within the context of social interactions.”

SDM Toolbox: A Python-based GIS Toolkit for Landscape Genetic, Biogeographic, and Species Distribution Model Analyses

MEE_CoverMethods in Ecology and Evolution 2014, 5, 694–700

By Jason L. Brown

“1. Species distribution models (SDMs) are broadly used in ecological and evolutionary studies. Almost all SDM methods require extensive data preparation in a geographic information system (GIS) prior to model building. Often, this step is cumbersome and, if not properly done, can lead to poorly parameterized models or in some cases, if too difficult, prevents the realization of SDMs. Further, for many studies, the creation of SDMs is not the final result and the post-modelling processing can be equally arduous as other steps.

Illustrative overview of SDMtoolbox. Basic Tools. SDMtoolbox contains 19 basic tools for converting and batch processing shapefile and raster data.

Illustrative overview of SDMtoolbox. Basic Tools. SDMtoolbox contains 19 basic tools for converting and batch processing shapefile and raster data.

2. SDMtoolbox is designed to facilitate many complicated pre- and post-processing steps commonly required for species distribution modelling and other geospatial analyses. SDMtoolbox consists of 59 Python script-based GIS tools developed and compiled into a single interface.

3. A large set of the tools were created to complement SDMs generated inMaxent or to improve the predictive performance of SDMs created inMaxent. However, SDMtoolbox is not limited to analyses of Maxent models, andmany tools are also available for additional analyses or general geospatial processing: for example, assessing landscape connectivity of haplotype networks (using least-cost corridors or least-cost paths); correcting SDM over-prediction; quantifying distributional changes between current and future SDMs; or for calculating several biodiversity metrics, such as corrected weighted endemism.

4. SDMtoolbox is a free comprehensive python-based toolbox for macroecology, landscape genetic and evolutionary studies to be used in ArcGIS 10.1 (or higher) with the Spatial Analyst extension. The toolkit simplifies many GIS analyses required for species distribution modelling and other analyses, alleviating the need for repetitive and time-consuming climate data pre-processing and post-SDManalyses.”

A Spatial Analysis to Study Access to Emergency Obstetric Transport Services under the Public Private “Janani Express Yojana” Program in Two Districts of Madhya Pradesh, India

rhReproductive Health 2014, 11:57 (22 July 2014)

By Yogesh Sabde, Ayesha De Costa, and Vishal Diwan

Background
The government in Madhya Pradesh (MP), India in 2006, launched “Janani Express Yojana” (JE), a decentralized, 24X7, free emergency transport service for all pregnant women under a public-private partnership. JE supports India’s large conditional cash transfer program, the “Janani Suraksha Yojana” (JSY) in the province and transports on average 60,000 parturients to hospital every month. The model is a relatively low cost one that potentially could be adopted in other parts of India and South Asia. This paper describes the uptake, time taken and geographic equity in access to the service to transport women to a facility in two districts of MP.

“Methods
This was a facility based cross sectional study. We interviewed parturients (n = 468) who delivered during a five day study period at facilities with >10 deliveries/month (n = 61) in two study districts. The women were asked details of transportation used to arrive at the facility, time taken and their residential addresses. These details were plotted onto a Geographic Information System (GIS) to estimate travelled distances and identify statistically significant clusters of mothers (hot spots) reporting delays >2 hours.

In district 2, forests covered 52.4% of the total district area (Figure 8). Most of the hot spot mothers in dis trict 2 acted differently in that they travelled longer distances through the forest areas to ac cess the CEmOC located in the district head quarter. The majority of women will not require to de liver in a Comprehensive EmOC facility, but the alternative to not delivering in a CEmO C facility in this setting is nearly equivalent to delivering in a dysfunctional facility, as none of the other facilities provide complete Basic EmOC which is life saving.

In district 2, forests covered 52.4% of the total district area. Most of the hot spot mothers in district 2 acted differently in that they travelled longer distances through the forest areas to access the CEmOC located in the district head quarter. The majority of women will not require to deliver in a Comprehensive EmOC facility, but the alternative to not delivering in a CEmOC facility in this setting is nearly equivalent to delivering in a dysfunctional facility, as none of the other facilities provide complete Basic EmOC which is life saving.

“Results
JE vehicles were well dispersed across the districts and used by 236 (50.03%) mothers of which 111(47.03%) took >2 hours to reach a facility. Inability of JE vehicle to reach a mother in time was the main reason for delays. There was no correlation between the duration of delay and distance travelled. Maps of the travel paths and travel duration of the women are presented. The study identified hot spots of mothers with delays >2 hours and explored the possible reasons for longer delays.

Conclusions
The JE service was accessible in all parts of the districts. Relatively high utilization rates of JE indicate that it ably supported JSY program to draw more women f or institutional deliveries. However, half of the JE users experienced long (>2 hour) delays. The delayed mothers clustered in difficult terrains of the districts. Additional support particularly for the identified hot spots, enhanced monitoring by state agencies and GIS tools can facilitate better effectiveness of the JE program. ”

A Flexible Spatial Framework for Modeling Spread of Pathogens in Animals with Biosurveillance and Disease Control Applications

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 638-661

By Montiago LaBute, Benjamin McMahon, Mac Brown, Carrie Manore, and Jeanne Fair

“Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial “patches”. Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-and-mouth disease and highly pathogenic avian influenza.

Inter-county level spread of FMD. Green dots indicate where there are susceptible populations of cattle, hogs and/or sheep according to the 2007 USDA NASS agricultural census data. Blue dots indicate where there are 10 or greater asymptomatic animals, red dots indicate where there are one or more symptomatic animals. Black crosses indicate counties which either had no initial susceptible populations or that are depopulated of susceptibles by mitigative measures, i.e., quarantine, culling and/or vaccination.

Inter-county level spread of FMD. Green dots indicate where there are susceptible populations of cattle, hogs and/or sheep according to the 2007 USDA NASS agricultural census data. Blue dots indicate where there are 10 or greater asymptomatic animals, red dots indicate where there are one or more symptomatic animals. Black crosses indicate counties which either had no initial susceptible populations or that are depopulated of susceptibles by mitigative measures, i.e., quarantine, culling and/or vaccination.

“Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols. ”

Mapping Sleeping Bees within Their Nest: Spatial and Temporal Analysis of Worker Honey Bee Sleep

PLOS_ONEPLOS One, Published 16 July 2014

Barrett Anthony Klein, Martin Stiegler, Arno Klein, and Jürgen Tautz

“Patterns of behavior within societies have long been visualized and interpreted using maps. Mapping the occurrence of sleep across individuals within a society could offer clues as to functional aspects of sleep. In spite of this, a detailed spatial analysis of sleep has never been conducted on an invertebrate society. We introduce the concept of mapping sleep across an insect society, and provide an empirical example, mapping sleep patterns within colonies of European honey bees (Apis mellifera L.). Honey bees face variables such as temperature and position of resources within their colony’s nest that may impact their sleep.

Infrared images revealing thermal activity across beehives. (A) Sequence of colony-scale changes across the entrance side of Colony 1. In clockwise order from the upper left corner, 1700, 0400, 0900 and 1500 h, respectively. Entrance/exit is in the lower left corner of the hive, leading out tube at left of each image. Brood comb is most easily seen as the glowing warm area at 0400 h. (B) Observation hive containing Colony 2, with filter-covered lamp at upper right, and bees visibly exiting hive tunnel at lower right. (C) Exposed nest composed of parallel sheets of comb, set up by Dirk Ahrens-Lagast to induce bees to construct a more natural nest architecture; not used in study. B.A.K. took all images with FLIR thermal cameras on non-experiment days under different ambient temperature conditions. Temperature scale values (°C) were adjusted for thermal camera settings.

Infrared image revealing thermal activity across beehives. Exposed nest composed of parallel sheets of comb, set up by Dirk Ahrens-Lagast to induce bees to construct a more natural nest architecture; not used in study. B.A.K. took all images with FLIR thermal cameras on non-experiment days under different ambient temperature conditions. Temperature scale values (°C) were adjusted for thermal camera settings.

“We mapped sleep behavior and temperature of worker bees and produced maps of their nest’s comb contents as the colony grew and contents changed. By following marked bees, we discovered that individuals slept in many locations, but bees of different worker castes slept in different areas of the nest relative to position of the brood and surrounding temperature. Older worker bees generally slept outside cells, closer to the perimeter of the nest, in colder regions, and away from uncapped brood. Younger worker bees generally slept inside cells and closer to the center of the nest, and spent more time asleep than awake when surrounded by uncapped brood. The average surface temperature of sleeping foragers was lower than the surface temperature of their surroundings, offering a possible indicator of sleep for this caste. We propose mechanisms that could generate caste-dependent sleep patterns and discuss functional significance of these patterns.”