scholarly journals Phenylketonuria incidence in China between 2013 and 2017 based on data from the Chinese newborn screening information system: a descriptive study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e031474 ◽  
Author(s):  
Liangcheng Xiang ◽  
Jing Tao ◽  
Kui Deng ◽  
Xiaohong Li ◽  
Qi Li ◽  
...  
Keyword(s):  
Information System ◽  
Newborn Screening ◽  
Spatial Autocorrelation ◽  
Spatial Clustering ◽  
Mild Disease ◽  
System A ◽  
High Incidence ◽  
Global And Local ◽  
Global Moran’S I ◽  
Local Spatial Autocorrelation

ObjectiveThis study examines the incidence and spatial clustering of phenylketonuria (PKU) in China between 2013 and 2017.MethodsData from the Chinese Newborn Screening Information System were analysed to assess PKU incidence with 95% CIs by province, region and disease severity. Spatial clustering of PKU cases was analysed using global and local spatial autocorrelation analysis in the geographic information system.ResultsThe database contained 4925 neonates with confirmed PKU during the study period, corresponding to an incidence of 6.28 (95% CI: 6.11 to 6.46) per 100 000 neonates screened. Incidence was highest in the provinces of Gansu, Ningxia and Qinghai, where it ranged from 19.00 to 28.63 per 100 000 neonates screened. Overall incidence was higher in the northern part of the country, where classical disease predominated, than in the southern part, where mild disease predominated. PKU cases clustered spatially (global Moran’s I=0.3603,Z=5.3097, p<0.001), and local spatial autocorrelation identified four northern provinces as high–high clusters (Gansu, Qinghai, Ningxia and Shanxi).ConclusionsChina shows an intermediate PKU incidence among countries, and incidence differs substantially among Chinese provinces and between northern and southern regions. Our results suggest the need to focus efforts on screening, diagnosing and treating PKU in high-incidence provinces.

scholarly journals Spatiotemporal Analysis of Serogroup C Meningococcal Meningitis Spread in Niger and Nigeria and Implications for Epidemic Response

2019 ◽  
Vol 220 (Supplement_4) ◽  
pp. S244-S252 ◽  
Author(s):  
Laura V Cooper ◽  
Olivier Ronveaux ◽  
Katya Fernandez ◽  
Clement Lingani ◽  
Kadade Goumbi ◽  
...  
Keyword(s):  
Spatial Clustering ◽  
Spatiotemporal Analysis ◽  
Meningococcal Meningitis ◽  
High Coverage ◽  
Alternative Strategies ◽  
High Incidence ◽  
Epidemic Response ◽  
Vaccination Campaigns ◽  
Global And Local ◽  
The Impact

Abstract Background After the re-emergence of serogroup C meningococcal meningitis (MM) in Nigeria and Niger, we aimed to re-evaluate the vaccination policy used to respond to outbreaks of MM in the African meningitis belt by investigating alternative strategies using a lower incidence threshold and information about neighboring districts. Methods We used data on suspected and laboratory-confirmed cases in Niger and Nigeria from 2013 to 2017. We calculated global and local Moran’s I-statistics to identify spatial clustering of districts with high MM incidence. We used a Pinner model to estimate the impact of vaccination campaigns occurring between 2015 and 2017 and to evaluate the impact of 3 alternative district-level vaccination strategies, compared with that currently used. Results We found significant clustering of high incidence districts in every year, with local clusters around Tambuwal, Nigeria in 2013 and 2014, Niamey, Niger in 2016, and in Sokoto and Zamfara States in Nigeria in 2017. We estimate that the vaccination campaigns implemented in 2015, 2016, and 2017 prevented 6% of MM cases. Using the current strategy but with high coverage (85%) and timely distribution (4 weeks), these campaigns could have prevented 10% of cases. This strategy required the fewest doses of vaccine to prevent a case. None of the alternative strategies we evaluated were more efficient, but they would have prevented the occurrence of more cases overall. Conclusions Although we observed significant spatial clustering in MM in Nigeria and Niger between 2013 and 2017, there is no strong evidence to support a change in methods for epidemic response in terms of lowering the intervention threshold or targeting neighboring districts for reactive vaccination.

scholarly journals Spatial Clustering of ILI in Yunnan Province, China, Based on a Geographical Information System

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Xia Xiao ◽  
Chunrui Luo ◽  
Xiaoxiao Song ◽  
Wei Liu ◽  
Le Cai ◽  
...  
Keyword(s):  
Information System ◽  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Yunnan Province ◽  
Spatial Clustering ◽  
Sentinel Surveillance ◽  
Geographical Information ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Cross Border

This research explored the spatial pattern of ILI in one poorer and numerous cross-border-mobility-populations in China. A spatial autocorrelation analysis, "Local" and "Global", "Moran" I, carried out in Yunnan province for 5-year sentinel surveillance data. Four counties shown high susceptible to ILI, which maybe result from poorer surrounding districts or be neighboring with Vietnam or/and Laos.

scholarly journals Changes in the spatial pattern of net earnings: Evidence from Serbia

2015 ◽  
Vol 65 (3) ◽  
pp. 351-365 ◽  
Author(s):  
Uglješa Stankov ◽  
Vanja Dragićević
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Clustering ◽  
Hot Spot ◽  
Spatial Segregation ◽  
Cold Spot ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
The North ◽  
Spatial Changes ◽  
Global And Local

Spatial autocorrelation analysis is an important method that can reveal the structure and patterns of economic spatial variables. It can be used to identify not only global spatial patterns in the country, but also characteristic locations at micro levels. In this research, we used spatial autocorrelation methodologies, including Global Moran’s I and Local Getis—Ord Gi statistics to identify the intensity of the spatial clustering of municipalities in Serbia by the level of average monthly net earnings from 2001 to 2010. We identified and mapped local clusters (hot and cold spots) by the level of average monthly net earnings for the same period. The results show that overall spatial segregation between municipalities with high and low average monthly net earnings was predominantly increasing during the investigated period. Local statistics illustrated that overall spatial segregation followed a broad north—south divide, with a concentration of municipalities with high net earnings in the north of Serbia, and low net earnings in the south. Closer inspection showed that at the beginning of the study period, there were three statistically significant hot spots in the north. As time passed, only one highly clustered hot spot remained — the Belgrade region. One cold spot retained a relatively stable position in the country’s southeast. This research shows that spatial changes of net earnings can be successfully studied with respect to statistically significant global and local spatial associations in the variables using spatial autocorrelation analysis.

scholarly journals La evolución espacial de los subcentros de empleo en Ciudad Juárez, Chihuahua (1994-2004): un análisis con indicadores de autocorrelación espacial global y local / The Spatial Evolution of Employment Subcenters in Ciudad Juárez, Chihuahua (1994-2004): An Analysis Using Global and Local Spatial Autocorrelation Indicators

2015 ◽  
Vol 30 (2) ◽  
pp. 433 ◽  
Author(s):  
César M. Fuentes ◽  
Vladimir Hernández
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Central Business District ◽  
High Density ◽  
Ciudad Juarez ◽  
Urban Structure ◽  
Threshold Method ◽  
Total Employment ◽  
Global And Local ◽  
Local Spatial Autocorrelation

El objetivo del artículo es identificar los subcentros de empleo total mediante el uso de indicadores de autocorrelación espacial global y local en Ciudad Juárez, Chihuahua, en el periodo 1994-2004. Esta metodología usa matrices de pesos espaciales e incorpora la noción de unidades vecinas y no está limitada al criterio de contigüidad del método de doble umbral. La variable usada fue la densidad bruta de empleo total (manufactura, comercio y servicio) en los años 1994 y 2004 a nivel de AGEB, obtenida de los Censos Económicos (INEGI, 1994 y 2004). Mediante el uso de dos indicadores de autocorrelación espacial, en específico el I de Moran y los indicadores locales de asociación espacial (LISA por sus siglas en inglés), fue posible identificar varios centros y subcentros de empleo total. Los resultados muestran la presencia de dependencia y heterogeneidad espacial que se manifiestan en la forma de agrupamientos de alta densidad de empleo (alto-alto) tanto en el distrito central de negocios (DCN) como en el subcentro de empleo mixto localizado en el corredor industrial de la avenida Rafael Pérez Serna. Asimismo, existen varios subcentros de empleo manufacturero aislados de alta densidad (alto-alto) localizados sobre las principales vialidades dirigidas a puertos internacionales. En este contexto, se puede concluir que la distribución del empleo fuera del DCN, producto de economías de aglomeración, implica la presencia de una estructura urbana policéntrica.Abstract:The objective of this article is to identify total employment subcenters through the use of global and local spatial autocorrelation indicators in Ciudad Juárez, Chihuahua, during the period from 1994-2004. This methodology uses spatial weights matrices, includes the notion of neighboring units and is not restricted to the contiguity criterion of the double threshold method. The variable used was the gross density of total employment (manufacturing, trade and service) in 1994 and 2004 at the ageb level, obtained from the Economic Census (INEGI, 1994 and 2004). Two spatial autocorrelation indicators, specifically Moran’s I and local indicators of spatial association (LISA) were used to identify several centers and sub-centers of total employment. The results show the presence of dependence and spatial heterogeneity expressed in the form of groups of high density employment (high-high) in both the central business district (CBD) and the mixed employment subcenter located in the industrial corridor of Avenida Rafael Pérez Serna. Likewise, there are several isolated high density manufacturing employment subcenters (high-high) located on the main roads leading to international ports. In this context, one can conclude that employment distribution outside the CBD, resulting from agglomeration economies, implies the presence of a polycentric urban structure.

Global and Local Spatial Autocorrelation in Predictive Clustering Trees

2011 ◽  
pp. 307-322 ◽  
Author(s):  
Daniela Stojanova ◽  
Michelangelo Ceci ◽  
Annalisa Appice ◽  
Donato Malerba ◽  
Sašo Džeroski
Keyword(s):  
Spatial Autocorrelation ◽  
Global And Local ◽  
Predictive Clustering Trees ◽  
Local Spatial Autocorrelation

scholarly journals Spatial analysis of AIDS in the state of Maranhão: an ecological study 2011-2018

2022 ◽  
Vol 75 (1) ◽  
Author(s):  
Livia Cristina Sousa ◽  
Tereza Cristina Silva ◽  
Thaís Furtado Ferreira ◽  
Arlene de Jesus Mendes Caldas
Keyword(s):  
Information System ◽  
Spatial Analysis ◽  
High Risk ◽  
Spatial Autocorrelation ◽  
Ecological Study ◽  
Health Management ◽  
Temporal Distribution ◽  
Risk Areas ◽  
Spatio Temporal ◽  
Global And Local

ABSTRACT Objective: Analyze the spatio-temporal distribution of AIDS cases in Maranhão. Methods: Ecological study of AIDS cases in the Notifiable Diseases Information System, 2011-2018. Gross and adjusted incidences were calculated using the Baysean method; then, the Moran Global and Local Indices to observe the existence of spatial autocorrelation of the cases and for the delimitation of high and low risk clusters. Results: 6,349 cases were reported, which were distributed heterogeneously. There was an advance of cases to new areas and persistence in old areas, such as in the capital São Luís and its surroundings. The dissemination did not occur at random, with positive spatial autocorrelation, with evidence of the formation of clusters in the municipalities of São Luís, São José de Ribamar and Paço do Lumiar. Conclusion: High-risk areas have been identified and should be considered a priority for investment in health, management, and organization of health services.

scholarly journals Laws of Geography

2018 ◽  
Author(s):  
Michael Leitner ◽  
Philip Glasner ◽  
Ourania Kounadi
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Spatial Autocorrelation ◽  
Statistical Methods ◽  
Spatial Interpolation ◽  
Main Part ◽  
Crime Analysis ◽  
Geospatial Technology ◽  
Cold Spots ◽  
Global And Local

The most prominent law in geography is Tobler’s first law (TFL) of geography, which states that “everything is related to everything else, but near things are more related than distant things.” No other law in geography has received more attention than TFL. It is important because many spatial statistical methods have been developed since its publication and, especially since the advent of geographic information system (GIS) and geospatial technology, have been conceptually based on it. These methods include global and local indicators of spatial autocorrelation (SA), spatial and spatial-temporal hotspots and cold spots, and spatial interpolation. All of these are highly relevant to spatial crime analysis, modeling, and mapping and will be discussed in the main part of this text.

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