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2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2137-2141, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018429

RESUMO

Ultrasound images have an inherently low lateral resolution due to the size of transducers that are used in standard clinical scanners. This makes for low resolution images, as well as imprecise lateral displacement estimation. In speckle tracking, the well known discipline of estimating displacement by tracking pixel movement, lateral interpolation is often used to get subsample accurate displacement estimation. Standard methods for interpolation are known as inverse distance weighting methods, of which the well known cubic interpolation method is a part. Kriging interpolation, however, is a stochastic approach that uses statistical data to calculate interpolated data points as opposed to the purely mathematical methods of more traditional interpolators. This analysis tests the efficacy of one variety of Kriging interpolation, called Simple Kriging, on ultrasound data. Simple Kriging is tested on its accuracy to interpolate a sparse ultrasound image frame, as well as its usefulness in interpolating the correlation map to estimate subsample displacement. The applied bias of the estimation using Simple Kriging is also tested by interpolating the autocorrelation map where displacement is zero. Simple Kriging is an alternative interpolation scheme that could be used with image data and its accuracy is comparable to the accuracy of using the cubic interpolation.


Assuntos
Testes Diagnósticos de Rotina , Análise Espacial , Ultrassonografia
3.
Environ Monit Assess ; 192(11): 719, 2020 Oct 21.
Artigo em Inglês | MEDLINE | ID: mdl-33083907

RESUMO

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.


Assuntos
Poluição do Ar , Monitoramento Ambiental , Poluição do Ar/análise , Malásia , Cadeias de Markov , Análise Espacial
4.
PLoS One ; 15(10): e0240624, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33045016

RESUMO

BACKGROUND: There is increasing demand for post-acute care services, which is amplified by the COVID-19 pandemic. AIMS: We studied the pattern of spatial association between post-acute care services and acute care facilities and evaluated how geographic variability could influence their use. METHODS: We compiled data on CMS-certified acute care and critical access hospitals and post-acute health care services (nursing homes, home health care services, inpatient rehabilitation facilities, long-term care hospitals, and hospice facilities). We used the colocation quotient (CLQ) to measure the magnitude and direction of association (clustering or segregation) between post-acute care providers and hospitals. This metric allows pairwise comparison of categorical data; a value <1 indicates spatial segregation and a value >1 spatial clustering. Unity marks the lack of spatial dependence (random distribution). RESULTS: With the exception of nursing homes (CLQ 1.26), all other types of post-acute care providers are spatially segregated from rural critical access hospitals. Long-term care facilities ranked first (had the lowest global CLQ, 0.06), hospice facilities ranked last (had the highest global CLQ estimate, 0.54). Instead, post-acute care services either clustered with (inpatient rehabilitation 2.76, long-term care 2.10, nursing homes 1.37) or were only weakly segregated (home health care 0.86) from acute care hospitals. Home health care (1.44), hospice services (1.46), and nursing homes (1.08) spatially clustered with the same category of services. Results were robust in the sensitivity analysis and we provided illustrative examples of local variation for the states of MA and IA. CONCLUSION: Post-acute care services are isolated from critical access hospitals, and have a clustering pattern with the same category services and acute care hospitals. Such misdistribution of resources may result in both underuse and a substitution effect on the type of post-acute care between rural and urban areas and undermine public health during increasing demand, such as the COVID-19 pandemic.


Assuntos
Infecções por Coronavirus/patologia , Cuidados Críticos/estatística & dados numéricos , Pneumonia Viral/patologia , Análise Espacial , Cuidados Semi-Intensivos/estatística & dados numéricos , Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/virologia , Hospitais/estatística & dados numéricos , Humanos , Casas de Saúde/estatística & dados numéricos , Pandemias , Pneumonia Viral/virologia , Estados Unidos
5.
Cien Saude Colet ; 25(suppl 2): 4141-4150, 2020 Oct.
Artigo em Português, Inglês | MEDLINE | ID: mdl-33027350

RESUMO

The aim of this study was to analyze the Severe Acute Respiratory Syndrome (SARS) pattern in Pernambuco before and during a COVID-19 pandemic. Ecological study conducted from January to June, 2015 to 2019 and from January 1 to June 15, 2020. The detection rates by municipality and by Regional Health of residence were calculated. The spatial area of SARS was estimated through the risk ratio. Before the pandemic, there were 5,617 cases of SARS, 187 cases/month and 23.8 cases/100 thousand inhabitants, while during the pandemic there were 15,100 cases, 2,516 cases/month and 320.3 cases/100 thousand inhabitants, which represents a 13-fold increase in detection. The following expanded (p < 0,001): the occurrence in elderly people, the collection of samples and the identification of SARS etiological agent with predominance of SARS by COVID-19. Most municipalities experienced a 20-fold higher detection than expected, suggesting a process of virus spread to the hinterlands. The excess risk associate with lower IDHM, the condition of the municipality being the headquarters of the Regional Health and the presence of a highway in the municipality. The change in the pattern of occurrence of SRAG, combined with Spatial analysis may contribute to action planning at different levels of management.


Assuntos
Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Síndrome Respiratória Aguda Grave/epidemiologia , Adolescente , Adulto , Betacoronavirus , Brasil/epidemiologia , Criança , Monitoramento Epidemiológico , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Pandemias , Vigilância em Saúde Pública , Fatores de Risco , Análise Espacial , Adulto Jovem
6.
Infect Dis Poverty ; 9(1): 124, 2020 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-32867851

RESUMO

BACKGROUND: Coronavirus disease 2019 (COVID-19) was confirmed in Brazil in February 2020. Since then, the disease has spread throughout the country, reaching the poorest areas. This study analyzes the relationship between COVID-19 and the population's living conditions. We aimed to identify social determinants related to the incidence, mortality, and case fatality rate of COVID-19 in Brazil, in 2020. METHODS: This is an ecological study evaluating the relationship between COVID-19 incidence, mortality, and case fatality rates and 49 social indicators of human development and social vulnerability. For the analysis, bivariate spatial correlation and multivariate and spatial regression models (spatial lag model and spatial error models) were used, considering a 95% confidence interval and a significance level of 5%. RESULTS: A total of 44.8% of municipalities registered confirmed cases of COVID-19 and 14.7% had deaths. We observed that 56.2% of municipalities with confirmed cases had very low human development (COVID-19 incidence rate: 59.00/100 000; mortality rate: 36.75/1 000 000), and 52.8% had very high vulnerability (COVID-19 incidence rate: 41.68/100 000; mortality rate: 27.46/1 000 000). The regression model showed 17 indicators associated with transmission of COVID-19 in Brazil. CONCLUSIONS: Although COVID-19 first arrived in the most developed and least vulnerable municipalities in Brazil, it has already reached locations that are farther from large urban centers, whose populations are exposed to a context of intense social vulnerability. Based on these findings, it is necessary to adopt measures that take local social aspects into account in order to contain the pandemic.


Assuntos
Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Determinantes Sociais da Saúde , Adolescente , Brasil/epidemiologia , Criança , Pré-Escolar , Intervalos de Confiança , Infecções por Coronavirus/mortalidade , Educação , Emprego , Humanos , Incidência , Renda , Longevidade , Análise Multivariada , Pandemias , Pneumonia Viral/mortalidade , Pobreza , Análise de Regressão , Saneamento , Esgotos , Condições Sociais , Análise Espacial , Abastecimento de Água/normas , Adulto Jovem
7.
Cien Saude Colet ; 25(9): 3385-3392, 2020 Sep.
Artigo em Inglês, Português | MEDLINE | ID: mdl-32876242

RESUMO

In the current scenario of the COVID-19 pandemic, Brazilian states and municipalities have adopted social distancing measures as a strategy to reduce the number of cases and control the disease. These measures affect populations and territories differently. This study aims to analyze the trend of social distancing in this pandemic and its relationship with the context of living conditions in Salvador, Bahia, Brazil. An ecological study with spatial distribution was conducted. The municipality's Social Distancing Index and the Living Conditions Index were calculated. Global and Local Moran Indices were employed to assess the degree of spatial dependence and autocorrelation. Fluctuations were observed in the social distancing levels during the analyzed period, with higher distancing percentages in neighborhoods with more favorable living conditions. The analysis and interpretation of COVID-19 containment measures, such as social distancing, should consider the profile of local vulnerability of each territory for the correct dimensioning of pandemic mitigation strategies from the perspective of developing social actions enabling greater adherence of the most impoverished populations.


Assuntos
Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Condições Sociais , Isolamento Social , Brasil/epidemiologia , Cidades , Infecções por Coronavirus/prevenção & controle , Humanos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Análise Espacial , Populações Vulneráveis
8.
Cien Saude Colet ; 25(9): 3377-3384, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32876254

RESUMO

At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.


Assuntos
Infecções por Coronavirus/epidemiologia , Surtos de Doenças , Pneumonia Viral/epidemiologia , Brasil/epidemiologia , Cidades , Humanos , Pandemias , Saúde Pública , Análise Espacial
9.
Zhong Nan Da Xue Xue Bao Yi Xue Ban ; 45(5): 582-590, 2020 May 28.
Artigo em Inglês, Chinês | MEDLINE | ID: mdl-32879111

RESUMO

OBJECTIVES: To analyze the regional epidemic features of coronavirus disease 2019 (COVID-19) in Henan Province, China. METHODS: According to the data of COVID-19 patients and the resident population at the end of 2018 in Henan Province, statistical description and analysis of epidemiological characteristics of COVID-19 in Henan Province were conducted, including the time distribution, population distribution, and regional distribution. RESULTS: The cumulative incidence of COVID-19 in Henan Province was 1.32/100 000, the cure rate was 98.03%, and the fatality rate was 1.73% by March 9, 2020. The incidence curve showed that the epidemic peak reached from January 24 to January 28. The high-incidence area was Xinyang, with a standardized cumulative incidence rate of 4.36/100 000. There were 580 female COVID-19 patients (45.60%), 688 males (54.09%) in Henan Province. The incidence of males was 1.41/100 000, while the incidence of females was 1.23/100 000. The age with the highest incidence of COVID-19 in Henan Province was 20-69 years old (88.68%). The incidence rate was highest in men aged 30-39 (2.51/ 100 000), while the lowest rate in women aged 0-9 (0.16/100 000). There were 1 225 local patients (96.31%), and the rural patients (45.73%) were slightly higher than the urban patients (44.02%) in Henan Province. A total of 63.60% patients had traveled or lived in Hubei or contacted with people who came from Hubei to Henan. The proportion of patients whose family members suffered from COVID-19 was 32.70%. Global spatial autocorrelation analysis suggested that there was a statistically significant positive correlation in the spatial distribution of COVID-19 patients in Henan Province (Moran's I=0.248, Z=2.955, P<0.01). CONCLUSIONS: There are differences in the morbidity and mortality of COVID-19 patients in different areas of Henan Province, with epidemic peak reaching from January 24 to January 28. Henan is dominated by local patients, male patients, and patients with contact history in Hubei. The space appears to be moderately clustered.


Assuntos
Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Adulto , Idoso , Betacoronavirus , Criança , Pré-Escolar , China/epidemiologia , Feminino , Humanos , Incidência , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Pandemias , Análise Espacial , Adulto Jovem
10.
BMC Public Health ; 20(1): 1362, 2020 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-32891120

RESUMO

BACKGROUND: An estimate of 2-3 million children under 5 die in the world annually due to vaccine-preventable disease. In Ethiopia, incomplete immunization accounts for nearly 16% of under-five mortality, and there is spatial variation for vaccination of children in Ethiopia. Spatial variation of vaccination can create hotspot of under vaccination and delay control and elimination of vaccine preventable disease. Thus, this study aims to assess the spatial distribution of incomplete immunization among children in Ethiopia from the three consecutive Ethiopia demographic and health survey data. METHOD: A cross-sectional study was employed from Ethiopia demographic and health survey (2005, 2011and 2016) data. In total, 7901mothers who have children aged (12-35) months were included in this study. ArcGIS 10.5 Software was used for global and local statistics analysis and mapping. In addition, a Bernoulli model was used to analyze the purely spatial cluster detection of incomplete immunization. GWR version 4 Software was used to model spatial relationships. RESULT: The proportion of incomplete immunization was 74.6% in 2005, 71.4% in 2011, and 55.1% in 2016. The spatial distribution of incomplete immunization was clustered in all the study periods (2005, 2011, and 2016) with global Moran's I of 0.3629, 1.0700, and 0.8796 respectively. Getis-Ord analysis pointed out high-risk regions for incomplete immunization: In 2005, hot spot (high risk) regions were detected in Kefa, Gamogofa, KembataTemibaro, and Hadya zones of SNNPR region, Jimma zone of Oromiya region. Similarly, Kefa, Gamogofa, Kembatatemibaro, Dawuro, and Hadya zones of SNNPR region; Jimma and West Arsi zones of Oromiya region were hot spot regions. In 2016, Afder, Gode, Korahe, Warder Zones of Somali region were hot spot regions. Geographically weighted regression identified different significant variables; being not educated and poor wealth index were the two common for incomplete immunization in different parts of the country in all the three surveys. CONCLUSION: Incomplete immunization was reduced overtime across the study periods. The spatial distribution of incomplete immunization was clustered and High-risk areas were identified in all the study periods. Predictors of incomplete immunization were identified in the three consecutive surveys.


Assuntos
Vacinação/estatística & dados numéricos , Adulto , Pré-Escolar , Estudos Transversais , Demografia , Escolaridade , Etiópia , Feminino , Inquéritos Epidemiológicos , Humanos , Lactente , Masculino , Fatores de Risco , Classe Social , Análise Espacial , Regressão Espacial , Inquéritos e Questionários , Cobertura Vacinal
11.
Rev Soc Bras Med Trop ; 53: e20200027, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32997047

RESUMO

INTRODUCTION: In this study, we aim to compare spatial statistic models to estimate the spatial distribution of Zika and Chikungunya infections in the city of Recife, Brazil. We also aim to establish the relationship between the diseases and the analyzed geographical conditions. METHODS: The models were defined by combining three categories: type of spatial unit, calculation of the dependent variable format, and estimation methods (Geographical Weighted Regression [GWR] and Ordinary Least Square [OLS]). We identified the most accurate model to estimate the spatial distribution of the diseases. After selecting the model that provided best results, the relationship between the geographical conditions and the incidence of the diseases was analyzed. RESULTS: It was observed that the matrix of 100 meters (as the spatial unit) showed the highest efficiency to estimate the diseases. The best results were observed in the models that utilized the kernel density estimation (as the calculation of the dependent variable). In all models, the GWR method showed the best results. By considering the OLS coefficient values, it was observed that all geographical conditions are related to the incidence of Zika and Chikungunya, while the GWR coefficient values showed where this relationship was more noticeable. CONCLUSIONS: The model that utilized the combination of the matrix of 100 meters, kernel density estimation (as the calculation of the dependent variable) and GWR method showed the highest efficiency in estimating the spatial distribution of the diseases. The coefficient values showed that all analyzed geographical conditions are related to the illnesses' incidence.


Assuntos
Febre de Chikungunya , Infecção por Zika virus , Zika virus , Brasil , Humanos , Análise de Regressão , Análise Espacial
12.
Nat Commun ; 11(1): 4551, 2020 09 11.
Artigo em Inglês | MEDLINE | ID: mdl-32917870

RESUMO

Circular RNAs (circRNAs) have recently gained substantial attention in the cancer research field where most, including the putative oncogene ciRS-7 (CDR1as), have been proposed to function as competitive endogenous RNAs (ceRNAs) by sponging specific microRNAs. Here, we report the first spatially resolved cellular expression patterns of ciRS-7 in colon cancer and show that ciRS-7 is completely absent in the cancer cells, but highly expressed in stromal cells within the tumor microenvironment. Additionally, our data suggest that this generally apply to classical oncogene-driven adenocarcinomas, but not to other cancers, including malignant melanoma. Moreover, we find that correlations between circRNA and mRNA expression, which are commonly interpreted as evidence of a ceRNA function, can be explained by different cancer-to-stromal cell ratios among the studied tumor specimens. Together, these results have wide implications for future circRNA studies and highlight the importance of spatially resolving expression patterns of circRNAs proposed to function as ceRNAs.


Assuntos
Neoplasias do Colo/genética , Regulação Neoplásica da Expressão Gênica , MicroRNAs/metabolismo , RNA Circular/metabolismo , RNA Longo não Codificante/metabolismo , Microambiente Tumoral/genética , Idoso , Neoplasias do Colo/patologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Oncogenes/genética , Estudos Prospectivos , RNA Circular/genética , RNA Longo não Codificante/genética , Análise Espacial
13.
Int J Health Geogr ; 19(1): 36, 2020 09 14.
Artigo em Inglês | MEDLINE | ID: mdl-32928236

RESUMO

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) pandemic, has infected millions of people and caused hundreds of thousands of deaths. While COVID-19 has overwhelmed healthcare resources (e.g., healthcare personnel, testing resources, hospital beds, and ventilators) in a number of countries, limited research has been conducted to understand spatial accessibility of such resources. This study fills this gap by rapidly measuring the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. METHOD: The rapid measurement is achieved by resolving computational intensity of an enhanced two-step floating catchment area (E2SFCA) method through a parallel computing strategy based on cyberGIS (cyber geographic information science and systems). The E2SFCA has two major steps. First, it calculates a bed-to-population ratio for each hospital location. Second, it sums these ratios for residential locations where hospital locations overlap. RESULTS: The comparison of the spatial accessibility measures for COVID-19 patients to those of population at risk identifies which geographic areas need additional healthcare resources to improve access. The results also help delineate the areas that may face a COVID-19-induced shortage of healthcare resources. The Chicagoland, particularly the southern Chicago, shows an additional need for resources. This study also identified vulnerable population residing in the areas with low spatial accessibility in Chicago. CONCLUSION: Rapidly measuring spatial accessibility of healthcare resources provides an improved understanding of how well the healthcare infrastructure is equipped to save people's lives during the COVID-19 pandemic. The findings are relevant for policymakers and public health practitioners to allocate existing healthcare resources or distribute new resources for maximum access to health services.


Assuntos
Área Programática de Saúde/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Recursos em Saúde/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Betacoronavirus , Acesso aos Serviços de Saúde/organização & administração , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Illinois , Unidades de Terapia Intensiva/estatística & dados numéricos , Pandemias , Fatores Socioeconômicos , Análise Espacial , Ventiladores Mecânicos/provisão & distribução
14.
BMJ Open ; 10(9): e039749, 2020 09 29.
Artigo em Inglês | MEDLINE | ID: mdl-32994257

RESUMO

OBJECTIVES: The growth of COVID-19 infections in England raises questions about system vulnerability. Several factors that vary across geographies, such as age, existing disease prevalence, medical resource availability and deprivation, can trigger adverse effects on the National Health System during a pandemic. In this paper, we present data on these factors and combine them to create an index to show which areas are more exposed. This technique can help policy makers to moderate the impact of similar pandemics. DESIGN: We combine several sources of data, which describe specific risk factors linked with the outbreak of a respiratory pathogen, that could leave local areas vulnerable to the harmful consequences of large-scale outbreaks of contagious diseases. We combine these measures to generate an index of community-level vulnerability. SETTING: 91 Clinical Commissioning Groups (CCGs) in England. MAIN OUTCOME MEASURES: We merge 15 measures spatially to generate an index of community-level vulnerability. These measures cover prevalence rates of high-risk diseases; proxies for the at-risk population density; availability of staff and quality of healthcare facilities. RESULTS: We find that 80% of CCGs that score in the highest quartile of vulnerability are located in the North of England (24 out of 30). Here, vulnerability stems from a faster rate of population ageing and from the widespread presence of underlying at-risk diseases. These same areas, especially the North-East Coast areas of Lancashire, also appear vulnerable to adverse shocks to healthcare supply due to tighter labour markets for healthcare personnel. Importantly, our index correlates with a measure of social deprivation, indicating that these communities suffer from long-standing lack of economic opportunities and are characterised by low public and private resource endowments. CONCLUSIONS: Evidence-based policy is crucial to mitigate the health impact of pandemics such as COVID-19. While current attention focuses on curbing rates of contagion, we introduce a vulnerability index combining data that can help policy makers identify the most vulnerable communities. We find that this index is positively correlated with COVID-19 deaths and it can thus be used to guide targeted capacity building. These results suggest that a stronger focus on deprived and vulnerable communities is needed to tackle future threats from emerging and re-emerging infectious disease.


Assuntos
Controle de Doenças Transmissíveis , Infecções por Coronavirus , Transmissão de Doença Infecciosa/prevenção & controle , Recursos em Saúde/provisão & distribução , Acesso aos Serviços de Saúde/normas , Pandemias , Pneumonia Viral , Betacoronavirus , Controle de Doenças Transmissíveis/métodos , Controle de Doenças Transmissíveis/organização & administração , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Inglaterra/epidemiologia , Disparidades nos Níveis de Saúde , Humanos , Determinação de Necessidades de Cuidados de Saúde , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Prevalência , Saúde Pública/métodos , Saúde Pública/tendências , Melhoria de Qualidade/organização & administração , Fatores de Risco , Análise Espacial
15.
Nat Commun ; 11(1): 4174, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873789

RESUMO

Renewable energy production is necessary to halt climate change and reverse associated biodiversity losses. However, generating the required technologies and infrastructure will drive an increase in the production of many metals, creating new mining threats for biodiversity. Here, we map mining areas and assess their spatial coincidence with biodiversity conservation sites and priorities. Mining potentially influences 50 million km2 of Earth's land surface, with 8% coinciding with Protected Areas, 7% with Key Biodiversity Areas, and 16% with Remaining Wilderness. Most mining areas (82%) target materials needed for renewable energy production, and areas that overlap with Protected Areas and Remaining Wilderness contain a greater density of mines (our indicator of threat severity) compared to the overlapping mining areas that target other materials. Mining threats to biodiversity will increase as more mines target materials for renewable energy production and, without strategic planning, these new threats to biodiversity may surpass those averted by climate change mitigation.


Assuntos
Biodiversidade , Mudança Climática , Conservação dos Recursos Naturais/estatística & dados numéricos , Mineração/estatística & dados numéricos , Energia Renovável/efeitos adversos , Análise Espacial
16.
PLoS One ; 15(8): e0237527, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32810170

RESUMO

Endemic and restricted-range species are considered to be particularly vulnerable to the effects of environmental change, which makes assessing likely climate change effects on geographic distributions of such species important to the development of integrated conservation strategies. Here, we determined distributional patterns for an endemic species of Dianthus (Dianthus polylepis) in the Irano-Turanian region using a maximum-entropy algorithm. In total, 70 occurrence points and 19 climatic variables were used to estimate the potential distributional area under current conditions and two future representative concentration pathway (RCP2.6 and RCP8.5) scenarios under seven general circulation models for 2050. Mean diurnal range, iso-thermality, minimum temperature of coldest quarter, and annual precipitation were major factors that appeared to structure the distribution of the species. Most current potential suitable areas were located in montane regions. Model transfers to future-climate scenarios displayed upward shifts in elevation and northward shifts geographically for the species. Our results can be used to define high-priority areas in the Irano-Turanian region for conservation management plans for this species and can offer a template for analyses of other endangered and threatened species in the region.


Assuntos
Mudança Climática , Dianthus/fisiologia , Altitude , Caryophyllaceae/classificação , Caryophyllaceae/fisiologia , Conservação dos Recursos Naturais , Demografia , Dianthus/classificação , Ecossistema , Espécies em Perigo de Extinção , Geografia , Irã (Geográfico) , Análise Espacial , Turcomenistão
17.
Infect Dis Poverty ; 9(1): 112, 2020 Aug 12.
Artigo em Inglês | MEDLINE | ID: mdl-32787916

RESUMO

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) had spread worldwide. Although the world has intensively focused on the epidemic center during this period of time, it is imperative to emphasize that more attention should also be paid to some impoverished areas in China since they are more vulnerable to disease outbreak due to their weak health service capacities. Therefore, this study took Liangshan Yi Autonomous Prefecture as an example to analyze the COVID-19 epidemic in the impoverished area, evaluate the control effect and explore future control strategies. METHODS: In this study, we collected information including age, gender, nationality, occupation, and address of all COVID-19 cases reported from 25 January 2020 to 23 April 2020 in Liangshan Prefecture from the Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), which were used under license and not publicly available. Additionally, we retrieved other information of cases through epidemiological investigation reports reviewing. Data were analyzed using the software Excel 2010 and SPSS 17.0. The geographic distribution of cases was mapped using ArcGIS10.2. RESULTS: By 23 April 2020, a total of 13 COVID-19 cases and two asymptomatic SARS-CoV-2 carriers were reported in Liangshan, in three family clusters. Among the cases, eight cases had a history of sojourning in Hubei Province (61.54%), of which six were related to Wuhan. Cases aged under 44 years accounted for 61.54%, with no child case. The delay of patients' hospital visiting, and the low degree of cooperation in epidemiological investigation are problems. CONCLUSIONS: During the study period, Liangshan was well under control. This was mainly contributed to strict preventive strategies aimed at local culture, inter-sectoral coordination and highly degree of public cooperation. Besides, some possible environmentally and culturally preventive factors (e.g., rapid air flow and family concept) would affect disease prevention and control. In the next step, the health education about COVID-19 should be strengthened and carried out according to the special culture of ethnic minorities to enhance public awareness of timely medical treatment.


Assuntos
Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Áreas de Pobreza , Adulto , Distribuição por Idade , Idoso , Portador Sadio/epidemiologia , Portador Sadio/transmissão , Portador Sadio/virologia , China/epidemiologia , Análise por Conglomerados , Busca de Comunicante , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Quarentena , Análise Espacial , Fatores de Tempo , Adulto Jovem
18.
PLoS One ; 15(8): e0237661, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32817708

RESUMO

BACKGROUND: Globally, India is home to every third child affected by stunting. While numerous studies have examined the correlates of childhood stunting (CS) in India, most of these studies have focused on examining the role of proximal factors, and the role of contextual factors is much less studied. This study presents a comprehensive picture of both proximal and contextual determinants of CS in India, expanding the current evidence base. The present study is guided by the WHO conceptual framework, which outlines the context, causes, and consequences of CS. DATA AND METHODS: The study used exploratory spatial data analysis tools to analyse the spatial pattern and correlates of CS, using data from the fourth round (2015-16) of the National Family Health Survey (NFHS-4) and the 2011 Census of India. RESULTS: The study findings reiterate that CS continues to be high in India, with several hot spot states and districts, and that children from the central and eastern region of the nation, namely, Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh are particularly vulnerable. Our analysis has identified six risk factors-maternal short stature, large household size, closely spaced births, prevalence of hypertension among women, household poverty, open defecation, and extreme temperature-and four protective factors-female education, access to improved drinking water, dietary diversity among children, and iron and folic acid (IFA) supplementation during pregnancy. CONCLUSIONS: The study highlights the need for investing in pre-conception care, addressing both demand- and supply-side barriers to increase the coverage of nutrition-specific interventions, implementing programmes to promote the intake of healthy foods from an early age, providing contraceptive counselling and services to unmarried and married adolescents and young women and men, and universalizing quality primary and secondary education that is inclusive and equitable to avert the burden of childhood stunting in India.


Assuntos
Transtornos do Crescimento/epidemiologia , Terapia Nutricional , Análise Espacial , Adolescente , Adulto , Pré-Escolar , Feminino , Transtornos do Crescimento/dietoterapia , Transtornos do Crescimento/patologia , Inquéritos Epidemiológicos , Humanos , Índia/epidemiologia , Lactente , Recém-Nascido , Masculino , Estado Nutricional , Fatores de Risco , Fatores Socioeconômicos , Adulto Jovem
19.
Int J Health Geogr ; 19(1): 32, 2020 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-32791994

RESUMO

BACKGROUND: As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale. METHODS: By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations. RESULTS: A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure. CONCLUSIONS: The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.


Assuntos
Ambiente Construído , Doenças Transmissíveis Emergentes/epidemiologia , Infecções por Coronavirus/epidemiologia , Meio Ambiente , Pneumonia Viral/epidemiologia , Fatores Socioeconômicos , Análise Espacial , Teorema de Bayes , Betacoronavirus , Estudos Transversais , Mapeamento Geográfico , Alemanha/epidemiologia , Humanos , Incidência , Aprendizado de Máquina , Pandemias , Fatores de Risco
20.
Ann Epidemiol ; 51: 7-13, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32827672

RESUMO

PURPOSE: The population and spatial characteristics of COVID-19 infections are poorly understood, but there is increasing evidence that in addition to individual clinical factors, demographic, socioeconomic, and racial characteristics play an important role. METHODS: We analyzed positive COVID-19 testing results counts within New York City ZIP Code Tabulation Areas with Bayesian hierarchical Poisson spatial models using integrated nested Laplace approximations. RESULTS: Spatial clustering accounted for approximately 32% of the variation in the data. There was a nearly five-fold increase in the risk of a positive COVID-19 test (incidence density ratio = 4.8, 95% credible interval 2.4, 9.7) associated with the proportion of black/African American residents. Increases in the proportion of residents older than 65 years, housing density, and the proportion of residents with heart disease were each associated with an approximate doubling of risk. In a multivariable model including estimates for age, chronic obstructive pulmonary disease, heart disease, housing density, and black/African American race, the only variables that remained associated with positive COVID-19 testing with a probability greater than chance were the proportion of black/African American residents and proportion of older persons. CONCLUSIONS: Areas with large proportions of black/African American residents are at markedly higher risk that is not fully explained by characteristics of the environment and pre-existing conditions in the population.


Assuntos
Afro-Americanos/estatística & dados numéricos , Infecções por Coronavirus/etnologia , Disparidades nos Níveis de Saúde , Disparidades em Assistência à Saúde/estatística & dados numéricos , Habitação , Pneumonia Viral/etnologia , Características de Residência , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Cidade de Nova Iorque/epidemiologia , Pandemias , Fatores Socioeconômicos , Análise Espacial
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