scholarly journals Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrhoeal disease in Ifanadiana, rural Madagascar

2021 ◽  
Vol 288 (1946) ◽  
pp. 20202501
Author(s):  
Michelle V. Evans ◽  
Matthew H. Bonds ◽  
Laura F. Cordier ◽  
John M. Drake ◽  
Felana Ihantamalala ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Disease Risk ◽  
Spatial Scales ◽  
Explanatory Power ◽  
Diarrhoeal Disease ◽  
Local Health ◽  
Fine Scale ◽  
Local Public Health ◽  
Precision Health

Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.

scholarly journals Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrheal disease in Ifanadiana, rural Madagascar

2020 ◽  
Author(s):  
Michelle V Evans ◽  
Matthew H Bonds ◽  
Laura F Cordier ◽  
John M Drake ◽  
Felana Ihantamalala ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Diarrheal Disease ◽  
Disease Risk ◽  
Disease Incidence ◽  
Explanatory Power ◽  
Household Wealth ◽  
Health System Strengthening ◽  
Local Health ◽  
Fine Scale ◽  
Precision Health

AbstractDiarrheal disease (DD) is responsible for over 700,000 child deaths annually, the majority in the tropics. Due to its strong environmental signature, DD is amenable to precision health mapping, a technique that leverages spatial relationships between socio-ecological variables and disease to predict hotspots of disease risk. However, precision health mapping tends to rely heavily on data collected at coarse spatial scales over large spatial extents. There is little evidence that such methods produce operationally-relevant predictions at sufficiently fine enough spatio-temporal scales (e.g. village level) to improve local health outcomes. Here, we use two fine-scale health datasets (<5 km) collected from a health system strengthening initiative in Ifanadiana, Madagascar and identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic, and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both an individual and commune-level spatial scale. Specifically, a child’s age, sex, and household wealth were the primary determinants of disease. Climatic variables predicted strong seasonality in DD, with the highest incidence in the colder, drier months of the austral winter, but did not predict spatial patterns in disease. Importantly, our models account for less than half of the total variation in disease incidence, suggesting that the socio-ecological covariates identified as important via global precision health mapping efforts have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.

scholarly journals High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network

2020 ◽  
Vol 12 (4) ◽  
pp. 635 ◽  
Author(s):  
Bart Kranstauber ◽  
Willem Bouten ◽  
Hidde Leijnse ◽  
Berend-Christiaan Wijers ◽  
Liesbeth Verlinden ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Scales ◽  
Regional Scale ◽  
Habitat Associations ◽  
Fine Scale ◽  
Migration Patterns ◽  
Spatial Image ◽  
Weather Radars ◽  
Spatial Coverage ◽  
Radar Imagery

Weather radars provide detailed information on aerial movements of organisms. However, interpreting fine-scale radar imagery remains challenging because of changes in aerial sampling altitude with distance from the radar. Fine-scale radar imagery has primarily been used to assess mass exodus at sunset to study stopover habitat associations. Here, we present a method that enables a more intuitive integration of information across elevation scans projected in a two-dimensional spatial image of fine-scale radar reflectivity. We applied this method on nights of intense bird migration to demonstrate how the spatial distribution of migrants can be explored at finer spatial scales and across multiple radars during the higher flying en-route phase of migration. The resulting reflectivity maps enable explorative analysis of factors influencing their regional and fine-scale distribution. We illustrate the method’s application by generating time-series of composites of up to 20 radars, achieving a nearly complete spatial coverage of a large part of Northwest Europe. These visualizations are highly useful in interpreting regional-scale migration patterns and provide detailed information on bird movements in the landscape and aerial environment.

scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

scholarly journals Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian-Yu Li ◽  
Yan-Ting Chen ◽  
Meng-Zhu Shi ◽  
Jian-Wei Li ◽  
Rui-Bin Xu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Plutella Xylostella ◽  
Agricultural Landscape ◽  
Seasonal Pattern ◽  
Distribution Patterns ◽  
Geostatistical Analysis ◽  
Distribution Model ◽  
Detailed Knowledge ◽  
Fine Scale ◽  
Spatio Temporal

AbstractA detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth, Plutella xylostella, is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of P. xylostella in an agricultural landscape. Therefore, in this study, the spatial distribution of P. xylostella was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The P. xylostella adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for P. xylostella. Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of P. xylostella was not limited to the Brassica vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that Brassica vegetables distribution and growth stage, have dominant impacts on the spatial distribution of P. xylostella in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of P. xylostella in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of P. xylostella.

scholarly journals Reproductive performance links to fine-scale spatial patterns of female grey seal relatedness

2001 ◽  
Vol 268 (1468) ◽  
pp. 711-717 ◽  
Author(s):  
P. P. Pomeroy ◽  
J. Worthington Wilmer ◽  
W. Amos ◽  
S. D. Twiss
Keyword(s):  
Spatial Patterns ◽  
Reproductive Performance ◽  
Fine Scale ◽  
Grey Seal

scholarly journals Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130194 ◽  
Author(s):  
Michael D. Madritch ◽  
Clayton C. Kingdon ◽  
Aditya Singh ◽  
Karen E. Mock ◽  
Richard L. Lindroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Populus Tremuloides ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Spectral Signature ◽  
Spectral Variation ◽  
Fine Scale ◽  
Below Ground ◽  
Landscape Scales ◽  
Spectrometer Data

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

Meningococcal Disease Prevention and Control Strategies for Practice-based Physicians

PEDIATRICS ◽  
1996 ◽  
Vol 97 (3) ◽  
pp. 404-412 ◽  
Author(s):  
◽  
Keyword(s):  
Meningococcal Disease ◽  
Primary Care Physicians ◽  
Disease Risk ◽  
Control Strategies ◽  
The United States ◽  
Invasive Meningococcal Disease ◽  
Appropriate Use ◽  
Local Public Health ◽  
Public Health Authorities ◽  
Control And Prevention

Localized outbreaks of meningococcal disease in the United States and Canada continue to cause serious alarm within communities as a result of the fulminating pattern of the disease, high mortality rate, and high incidence among adolescents. The increasing number of outbreaks since 1991 has raised questions about the management and prevention of further cases during an outbreak. The purpose of this statement is to guide primary-care physicians in their role in infection control and prevention of both sporadic cases and outbreaks of invasive meningococcal disease. This statement provides information on the epidemiology of meningococcal disease, including definitions of sporadic, secondary, and coprimary cases, clusters of cases, and outbreaks. Data are presented on identification of cases, disease risk of contacts, and agents for chemoprophylaxis, and recommendations are given for: (1) risk assessment of contacts, (2) administration of chemoprophylaxis, (3) appropriate use of meningococcal vaccine, (4) appropriate use of the microbiology laboratory, (5) the necessity for timely and appropriate reporting of invasive meningococcal disease to local public health authorities, and (6) information on counseling and public education that may be helpful during an outbreak to minimize public anxiety. An additional section, "Information for Sharing," which uses a question-and-answer format and which may be helpful to parents and community and health care workers during an outbreak, is also provided.

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Distribution Pattern ◽  
Spatial Patterns ◽  
Spillover Effect ◽  
Tertiary Industry ◽  
Innovation Capacity ◽  
Structure Index ◽  
Secondary Industry ◽  
Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

scholarly journals The Effects of Funding Change and Reorganization on Patterns of Emergency Response in a Local Health Agency

2014 ◽  
Vol 129 (6_suppl4) ◽  
pp. 166-172 ◽  
Author(s):  
Russell G. Schuh ◽  
Michelle Basque ◽  
Margaret A. Potter
Keyword(s):  
Emergency Response ◽  
Structural Changes ◽  
Health Agency ◽  
Local Health ◽  
Local Public Health ◽  
Response Data ◽  
Equal Rate ◽  
Response Capacity ◽  
Categorical Response ◽  
Internal Function

Indicators for Stress Adaptation Analytics (ISAAC) is a protocol to measure the emergency response behavior of organizations within local public health systems. We used ISAAC measurements to analyze how funding and structural changes may have affected the emergency response capacity of a local health agency. We developed ISAAC profiles for an agency's consecutive fiscal years 2013 and 2014, during which funding cuts and organizational restructuring had occurred. ISAAC uses descriptive and categorical response data to obtain a function stress score and a weighted contribution score to the agency's total response. In the absence of an emergency, we simulated one by assuming that each function was stressed at an equal rate for each of the two years and then we compared the differences between the two years. The simulations revealed that seemingly minor personnel or budget changes in health departments can mask considerable variation in change at the internal function level.

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