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1.
PLoS One ; 16(3): e0247794, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33647044

RESUMO

BACKGROUND: Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country. METHODS: This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). FINDINGS: The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. DISCUSSION: Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.


Assuntos
/epidemiologia , Enfermeiras e Enfermeiros/estatística & dados numéricos , Brasil/epidemiologia , /mortalidade , Cidades/epidemiologia , Demografia , Feminino , Humanos , Incidência , Masculino , Fatores de Risco , Fatores Socioeconômicos , Análise Espacial , Regressão Espacial
2.
Artigo em Inglês | MEDLINE | ID: mdl-33671707

RESUMO

When a public health emergency occurs, a potential sanitation threat will directly change local residents' behavior patterns, especially in high-density urban areas. Their behavior pattern is typically transformed from demand-oriented to security-oriented. This is directly manifested as a differentiation in the population distribution. This study based on a typical area of high-density urban area in central Tianjin, China. We used Baidu heat map (BHM) data to calculate full-day and daytime/nighttime state population aggregation and employed a geographically weighted regression (GWR) model and Moran's I to analyze pre-epidemic/epidemic population aggregation patterns and pre-epidemic/epidemic population flow features. We found that during the COVID-19 epidemic, the population distribution of the study area tended to be homogenous clearly and the density decreased obviously. Compared with the pre-epidemic period: residents' demand for indoor activities increased (average correlation coefficient of the floor area ratio increased by 40.060%); traffic demand decreased (average correlation coefficient of the distance to a main road decreased by 272%); the intensity of the day-and-night population flow declined significantly (its extreme difference decreased by 53.608%); and the large-living-circle pattern of population distribution transformed to multiple small-living circles. This study identified different space utilization mechanisms during the pre-epidemic and epidemic periods. It conducted the minimum living security state of an epidemic-affected city to maintain the operation of a healthy city in the future.


Assuntos
Regressão Espacial , População Urbana , China/epidemiologia , Cidades , Demografia , Humanos
3.
Sci Total Environ ; 761: 144257, 2021 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-33352341

RESUMO

Investigating the spatial distribution characteristics of the coronavirus disease 2019 (COVID-19) and exploring the influence of environmental factors that drive it is the basis for formulating rational and efficient prevention and control countermeasures. Therefore, this study aims to analyze the spatial distribution characteristics of COVID-19 pandemic in Beijing and its relationship with the environmental factors. Based on the incidences of new local COVID-19 cases in Beijing from June 11 to July 5, the spatial clustering characteristics of the COVID-19 pandemic in Beijing was investigated using spatial autocorrelation analysis. The relation between COVID-19 cases and environmental factors was assessed using the Spearman correlation analysis. Finally, geographically weighted regression (GWR) was applied to explore the influence of environmental factors on the spatial distribution of COVID-19 cases. The results showed that the development of COVID-19 pandemic in Beijing from June 11 to July 5 could be divided into two stages. The first stage was the outward expansion from June 11 to June 21, and the second stage (from June 22 to July 5) was the growth of the transmission in areas with existing previous cases. In addition, there was a ring of low value clusters around the Xinfadi market. This area was the key area for prevention and control. Population density and distance to Xinfadi market were the most critical factors that explained the pandemic development. The findings of this study can provide useful information for the global fighting against COVID-19.


Assuntos
Pandemias , Pequim/epidemiologia , Humanos , Análise Espacial , Regressão Espacial
4.
Environ Monit Assess ; 193(1): 15, 2020 Dec 28.
Artigo em Inglês | MEDLINE | ID: mdl-33372250

RESUMO

While numerous studies have explored the spatial patterns and underlying causes of PM2.5 at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM2.5 factors. In order to enrich this research field, we collected PM2.5 monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM2.5 at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM2.5. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM2.5 in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM2.5 governance. (2) The effects of various factors of socio-economic activities on the concentration of PM2.5 have significant spatial heterogeneity among Chinese cities. (3) There is an inverted "U" curve between economic growth and PM2.5. When the per capita income reaches 47,000 yuan, the PM2.5 emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM2.5 governance measures.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Pequim , China , Cidades , Monitoramento Ambiental , Material Particulado/análise , Regressão Espacial
5.
Artigo em Inglês | MEDLINE | ID: mdl-33327395

RESUMO

The environment has direct and indirect effects on mental health. Previous studies acknowledge that the poor design of communities and social environments leads to increased psychological distress, but methodological issues make it difficult to draw clear conclusions. Recent public health, leisure and recreation studies have tried to determine the relationship between recreation opportunities and mental health. However, previous studies have heavily focused on individual contexts rather than national or regional levels; this is a major limitation. It is difficult to reflect the characteristics of community environments effectively with such limited studies, because social environments and infrastructure should be analyzed using a spatial perspective that goes beyond an individual's behavioral patterns. Other limitations include lack of socioeconomic context and appropriate data to represent the characteristics of a local community and its environment. To date, very few studies have tested the spatial relationships between mental health and recreation opportunities on a national level, while controlling for a variety of competing explanations (e.g., the social determinants of mental health). To address these gaps, this study used multi-level spatial data combined with various sources to: (1) identify variables that contribute to spatial disparities of mental health; (2) examine how selected variables influence spatial mental health disparities using a generalized linear model (GLM); (3) specify the spatial variation of the relationships between recreation opportunities and mental health in the continental U.S. using geographically weighted regression (GWR). The findings suggest that multiple factors associated with poor mental health days, particularly walkable access to local parks, showed the strongest explanatory power in both the GLM and GWR models. In addition, negative relationships were found with educational attainment, racial/ethnic dynamics, and lower levels of urbanization, while positive relationships were found with poverty rate and unemployment in the GLM. Finally, the GWR model detected differences in the strength and direction of associations for 3109 counties. These results may address the gaps in previous studies that focused on individual-level scales and did not include a spatial context.


Assuntos
Saúde Mental , Recreação , Meio Ambiente , Humanos , Saúde Mental/estatística & dados numéricos , Modelos Estatísticos , Recreação/psicologia , Fatores Socioeconômicos , Regressão Espacial , Desemprego
6.
BMJ Open ; 10(11): e043560, 2020 11 03.
Artigo em Inglês | MEDLINE | ID: mdl-33148769

RESUMO

OBJECTIVE: To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally. DESIGN: Publicly available register-based ecological study. SETTING: Two hundred and nine countries/territories in the world. PARTICIPANTS: Aggregated data including 10 445 656 confirmed COVID-19 cases. PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website. RESULTS: The average of country/territory-specific COVID-19 CFR is about 2%-3% worldwide and higher than previously reported at 0.7%-1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR. CONCLUSION: The association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.


Assuntos
Doenças Cardiovasculares/mortalidade , Controle de Doenças Transmissíveis/estatística & dados numéricos , Infecções por Coronavirus/mortalidade , Diabetes Mellitus/epidemiologia , Produto Interno Bruto/estatística & dados numéricos , Pneumonia Viral/mortalidade , Densidade Demográfica , Regressão Espacial , Distribuição por Idade , Betacoronavirus , Técnicas de Laboratório Clínico/estatística & dados numéricos , Infecções por Coronavirus/diagnóstico , Política de Saúde , Indicadores Básicos de Saúde , Humanos , Expectativa de Vida , Mortalidade , Pandemias , Prevalência , Fumar/epidemiologia , Análise Espacial
7.
BMC Public Health ; 20(1): 1444, 2020 Sep 23.
Artigo em Inglês | MEDLINE | ID: mdl-32977789

RESUMO

BACKGROUND: Skilled birth attendant (SBA) delivery is vital for the health of mothers and newborns, as most maternal and newborn deaths occur at the time of childbirth or immediately after birth. This problem becomes worsen in Ethiopia in which only 28% of women give birth with the help of SBA. Therefore, this study aimed to explore the spatial variations of SBA delivery and its associated factors in Ethiopia. METHODS: A secondary analysis was carried out using the 2016 Ethiopian Demographic and Health Survey. A total weighted sample of 11,023 women who had a live birth in the 5 years preceding the survey was included in the analysis. Arc-GIS software was used to explore the spatial distribution of SBA and a Bernoulli model was fitted using SaTScan software to identify significant clusters of non-SBA delivery. The Geographic Weighted Regression (GWR) was employed in modeling spatial relationships. Moreover, a multilevel binary logistic regression model was fitted to identify factors associated with SBA delivery. RESULTS: In this study, SBA delivery had spatial variations across the country. The SaTScan spatial analysis identified the primary clusters' spatial window in southeastern Oromia and almost the entire Somalia. The GWR analysis identified different predictors of non- SBA delivery across regions of Ethiopia. In the multilevel analysis, mothers having primary and above educational status, health insurance coverage, and mothers from households with higher wealth status had higher odds of SBA delivery. Being multi and grand multiparous, perception of distance from the health facility as big problem, rural residence, women residing in communities with medium and higher poverty level, and women residing in communities with higher childcare burden had lower odds of SBA delivery. CONCLUSION: Skilled birth attendant delivery had spatial variations across the country. Areas with non-skilled birth attendant delivery and mothers who had no formal education, not health insured, mothers from poor households and communities, Primiparous women, mothers from remote areas, and mothers from communities with higher childcare burden could get special attention in terms of allocation of resources including skilled human power, and improved access to health facilities.


Assuntos
Parto Obstétrico/estatística & dados numéricos , Adolescente , Adulto , Etiópia , Feminino , Humanos , Recém-Nascido , Pessoa de Meia-Idade , Análise Multinível , Gravidez , Fatores Socioeconômicos , Regressão Espacial , Adulto Jovem
8.
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
9.
PLoS One ; 15(9): e0238547, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32946497

RESUMO

Based on 0.01°×0.01° grid data of PM2.5 annual concentration and statistical yearbook data for 11 cities in Hebei Province from 2000 to 2015, the temporal and spatial distribution characteristics of PM2.5 in the study area are analysed, the level of intensive land use in the area is evaluated, and decoupling theory and spatial regression are used to discuss the relationship between PM2.5 concentration and intensive land use and the influence of intensive land use variables on PM2.5 in Hebei Province. The results show that 1. In terms of time, the concentration of PM2.5 in Hebei Province showed an overall upward trend from 2000 to 2015, with the highest in winter and the lowest in summer. The daily variations show double peaks at 8:00-10:00 and 21:00-0:00 and a single valley at 16:00-18:00. 2. In terms of space, the concentration of PM2.5 in Hebei Province is high in the southeast and low in the northwest, and the pollution spillover initially decreases and then increases. 3. In the past 16 years, the level of intensive land use in Hebei Province has increased annually, but blind expansion still exists. 4. Decoupling theory and the spatial lag model show that land use intensity, land input level and land use structure are positively correlated with PM2.5 concentration, land output benefit is negatively correlated with PM2.5 concentration, and PM2.5 concentration and land intensive use level have not yet been decoupled; thus, the relationship is not harmonious. This research can provide a scientific basis for reducing air pollution and promoting the development of urban land resources for intensive and sustainable development.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Recursos Naturais , Material Particulado/análise , China , Cidades , Monitoramento Ambiental , Estações do Ano , Regressão Espacial , Urbanização
10.
J Infect Public Health ; 13(10): 1438-1445, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32773211

RESUMO

OBJECTIVE: This study retrospectively examined the health and social determinants of the COVID-19 outbreak in 175 countries from a spatial epidemiological approach. METHODS: We used spatial analysis to examine the cross-national determinants of confirmed cases of COVID-19 based on the World Health Organization official COVID-19 data and the World Bank Indicators of Interest to the COVID-19 outbreak. All models controlled for COVID-19 government measures. RESULTS: The percentage of the population age between 15-64 years (Age15-64), percentage smokers (SmokTot.), and out-of-pocket expenditure (OOPExp) significantly explained global variation in the current COVID-19 outbreak in 175 countries. The percentage population age group 15-64 and out of pocket expenditure were positively associated with COVID-19. Conversely, the percentage of the total population who smoke was inversely associated with COVID-19 at the global level. CONCLUSIONS: This study is timely and could serve as a potential geospatial guide to developing public health and epidemiological surveillance programs for the outbreak in multiple countries. Removal of catastrophic medical expenditure, smoking cessation, and observing public health guidelines will not only reduce illness related to COVID-19 but also prevent unecessary deaths.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Adolescente , Adulto , Fatores Etários , Betacoronavirus , Bases de Dados Factuais , Gastos em Saúde/estatística & dados numéricos , Humanos , Pessoa de Meia-Idade , Modelos Estatísticos , Estudos Retrospectivos , Fumar/epidemiologia , Regressão Espacial , Adulto Jovem
11.
Sci Total Environ ; 738: 140195, 2020 Oct 10.
Artigo em Inglês | MEDLINE | ID: mdl-32806350

RESUMO

INTRODUCTION: The relative risk (RR) of long-term exposure to PM2.5 in lung cancer mortality (LCM) may vary spatially in China. However, previous studies applying global regression have been unable to capture such variation. We aimed to employ a geographically weighted Poisson regression (GWPR) to estimate the RRs of LCM among the elderly (≥65 years) related to long-term exposure to PM2.5 and the LCM attributable to PM2.5 at the county level in China. METHODS: We obtained annual LCM in the elderly between 2013 and 2015 from the National Death Surveillance. We linked annual mean concentrations of PM2.5 between 2000 and 2004 with LCM using GWPR model at 148 counties across mainland China, adjusting for smoking and socioeconomic covariates. We used county-specific GWPR models to estimate annual average LCM in the elderly between 2013 and 2015 attributable to PM2.5 exposure between 2000 and 2004. RESULTS: The magnitude of the association between long-term exposure to PM2.5 and LCM varied with county. The median of county-specific RRs of LCM among elderly men and women was 1.52 (range: 0.90, 2.40) and 1.49 (range: 0.88, 2.56) for each 10 µg/m3 increment in PM2.5, respectively. The RRs were positively significant (P < 0.05) at 95% (140/148) of counties among both elderly men and women. Higher RRs of PM2.5 among elderly men were located at Southwest and South China, and higher RRs among elderly women were located at Northwest, Southwest, and South China. There were 99,967 and 54,457 lung cancer deaths among elderly men and women that could be attributed to PM2.5, with the attributable fractions of 31.4% and 33.8%, respectively. CONCLUSIONS: The relative importance of long-term exposure to PM2.5 in LCM differed by county. The results could help the government design tailored and efficient interventions. More stringent PM2.5 control is urgently needed to reduce LCM in China.


Assuntos
Neoplasias Pulmonares , Idoso , China , Feminino , Humanos , Masculino , Material Particulado , Fumar , Regressão Espacial
12.
PLoS One ; 15(7): e0235858, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32645068

RESUMO

The characteristics of urban spatial structure and the objective evaluation of the development level of urban economy have always been the concern of urban researchers, However, the spatial relationship between urban spatial structure and urban economic development level is often deliberately ignored. Through the point of interest (POI), the identification framework is constructed, the spatial structure of the city is identified and evaluated, and the Geographically Weighted Regression analysis is carried out with the distribution of unit GDP (Gross Domestic Product) in this study. The research shows that Kunming and Guiyang are polycentric spatial structures and Kunming's structure is more significant. Kunming's economic level is generally higher than Guiyang, but the unit area cannot be compared. The city center will promote the development of the central area in this city, and the more urban centers are distributed within the geographical and spatial range, the greater contribution would have to economic development. In addition, the results of this study will have a positive impact on urban planning and construction, and will also provide a new perspective for the study of cities and related disciplines.


Assuntos
Planejamento de Cidades , Desenvolvimento Econômico , Reforma Urbana , China , Cidades , Planejamento de Cidades/economia , Planejamento de Cidades/métodos , Humanos , Regressão Espacial , Reforma Urbana/economia , Reforma Urbana/métodos , Urbanização
13.
Ying Yong Sheng Tai Xue Bao ; 31(3): 987-998, 2020 Mar.
Artigo em Chinês | MEDLINE | ID: mdl-32537996

RESUMO

Ecological land is essential to sustainable development of urban agglomeration. Based on the results of remote sensing image interpretation, we analyzed the spatial-temporal evolution of ecological land in 32 research units of ecological land in Wuhan urban agglomeration in 2000-2005, 2005-2010 and 2010-2015, using the land use transition matrix, exploratory regression analysis, the ordinary least squares (OLS) model, and geographically weighted regression (GWR) model. Then, the best regression model was selected after perfecting the traditional index system of influencing factors by data of the location and quantitative information of companies, enterprises and life services, etc., and conducting exploratory regression analysis. Finally, we analyzed the influencing factors and spatial differentiation rules of different research periods with GWR model. The results showed that, from 2000 to 2015, the amount of transition from ecological land use to non-ecological land use in the urban agglomeration showed an inverted U-shaped change pattern, and the space showing the expanding trend from point to surface. Land use patterns of 8.4% area had changed in the urban agglomeration, among which the conversion of cultivated land, forest land, grassland, water body and unused land to non-ecological land accounted for 41.9% of the total area. The spatial pattern gradually expanded from the central urban area of Wuhan to the periphery of the municipal sub-center and county-level towns. The total number of passing models in the three stages of exploratory regression analysis was 326. The GWR and OLS regression were used for comparative analysis of all models. The adjusted R2 in the three stages of selected models were 0.83, 0.91 and 0.76, respectively. The former improved by 0.02, 0.03 and 0.02, and the AICc decreased by 2.88, 3.42 and 0.83, respectively. The results of GWR model showed substantially spatial differentiation of influencing factors of ecological land evolution in Wuhan urban agglomeration, and that the influence patterns was dominated by gradual transition in different directions in space, with other patterns such as "V" distribution. The effects of spatial factors were significant. The potential information of spatial data enhanced the interpretation of ecological land evolution within the urban agglomeration.


Assuntos
Monitoramento Ambiental , Regressão Espacial , China , Cidades , Florestas , Análise dos Mínimos Quadrados
14.
Artigo em Inglês | MEDLINE | ID: mdl-32422948

RESUMO

Social and economic factors relate to the prevention and control of infectious diseases. The purpose of this paper was to assess the distribution of COVID-19 morbidity rate in association with social and economic factors and discuss the implications for urban development that help to control infectious diseases. This study was a cross-sectional study. In this study, social and economic factors were classified into three dimensions: built environment, economic activities, and public service status. The method applied in this study was the spatial regression analysis. In the 13 districts in Wuhan, the spatial regression analysis was applied. The results showed that: 1) increasing population density, construction land area proportion, value-added of tertiary industry per unit of land area, total retail sales of consumer goods per unit of land area, public green space density, aged population density were associated with an increased COVID-19 morbidity rate due to the positive characteristics of estimated coefficients of these variables. 2) increasing average building scale, GDP per unit of land area, and hospital density were associated with a decreased COVID-19 morbidity rate due to the negative characteristics of estimated coefficients of these variables. It was concluded that it is possible to control infectious diseases, such as COVID-19, by adjusting social and economic factors. We should guide urban development to improve human health.


Assuntos
Ambiente Construído , Infecções por Coronavirus/epidemiologia , Coronavirus , Desenvolvimento Econômico , Pandemias , Pneumonia Viral/epidemiologia , Densidade Demográfica , Reforma Urbana , Betacoronavirus , China/epidemiologia , Conservação dos Recursos Naturais , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/transmissão , Estudos Transversais , Meio Ambiente , Humanos , Indústrias , Morbidade , Pneumonia Viral/diagnóstico , Pneumonia Viral/transmissão , Planejamento Social , Regressão Espacial
15.
J Environ Manage ; 268: 110646, 2020 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-32389899

RESUMO

Groundwater nitrate contamination has been the main water quality problem threatening the sustainable utilization of water resources in Jeju Island, South Korea. The spatially varying distribution of nitrate levels associated with complex environmental and anthropogenic factors has been a major challenge restricting improved groundwater management. In this study, we applied ordinary least squares (OLS) regression and geographically weighted regression (GWR) models to determine the relationships between the NO3-N concentration and various parameters (topography, hydrology and land use) across the island. A comparison between the OLS regression and GWR prediction models showed that the GWR models outperformed the OLS regression models, with a higher R2 and a lower corrected Akaike Information Criterion (AICc) value than the OLS regression models. Interestingly, the GWR model was able to provide undiscovered information that was not revealed in the OLS regression models. For example, the GWR model found that orchards (OR) and urban (UR) variables significantly contributed to nitrate enrichment in the certain parts of the island, whereas these variables were ignored as a statistically insignificant factor in the OLS regression model. Our study highlighted that GWR models are a useful tool for investigating spatially varying relationships between groundwater quality and environmental factors; therefore, it can be applied to establish advanced groundwater management plans by reflecting the spatial heterogeneity associated with environmental and anthropogenic conditions.


Assuntos
Água Subterrânea , Regressão Espacial , Monitoramento Ambiental , Análise dos Mínimos Quadrados , República da Coreia , Qualidade da Água
16.
PLoS One ; 15(5): e0233790, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32470020

RESUMO

BACKGROUND: Birth interval duration is an important and modifiable risk factor for adverse child and maternal health outcomes. Understanding the spatial distribution of short birth interval, an inter-birth interval of less than 33 months, and its predictors are vital to prioritize and facilitate targeted interventions. However, the spatial variation of short birth interval and its underlying factors have not been investigated in Ethiopia. OBJECTIVE: This study aimed to assess the predictors of short birth interval hot spots in Ethiopia. METHODS: The study used data from the 2016 Ethiopia Demographic and Health Survey and included 8,448 women in the analysis. The spatial variation of short birth interval was first examined using hot spot analysis (Local Getis-Ord Gi* statistic). Ordinary least squares regression was used to identify factors explaining the geographic variation of short birth interval. Geographically weighted regression was used to explore the spatial variability of relationships between short birth interval and selected predictors. RESULTS: Statistically significant hot spots of short birth interval were found in Somali Region, Oromia Region, Southern Nations, Nationalities, and Peoples' Region and some parts of Afar Region. Women with no education or with primary education, having a husband with higher education (above secondary education), and coming from a household with a poorer wealth quintile or middle wealth quintile were predictors of the spatial variation of short birth interval. The predictive strength of these factors varied across the study area. The geographically weighted regression model explained about 64% of the variation in short birth interval occurrence. CONCLUSION: Residing in a geographic area where a high proportion of women had either no education or only primary education, had a husband with higher education, or were from a household in the poorer or middle wealth quintile increased the risk of experiencing short birth interval. Our detailed maps of short birth interval hot spots and its predictors will assist decision makers in implementing precision public health.


Assuntos
Intervalo entre Nascimentos/estatística & dados numéricos , Mapeamento Geográfico , Regressão Espacial , Etiópia/epidemiologia , Feminino , Inquéritos Epidemiológicos , Humanos , Fatores Socioeconômicos
17.
Sci Total Environ ; 728: 138884, 2020 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-32335404

RESUMO

During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.


Assuntos
Infecções por Coronavirus/epidemiologia , Sistemas de Informação Geográfica , Pneumonia Viral/epidemiologia , Betacoronavirus , Demografia , Meio Ambiente , Humanos , Incidência , Pandemias , Fatores Socioeconômicos , Análise Espacial , Regressão Espacial , Estados Unidos/epidemiologia
18.
BMC Public Health ; 20(1): 479, 2020 Apr 10.
Artigo em Inglês | MEDLINE | ID: mdl-32276607

RESUMO

BACKGROUND: Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. METHODS: In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. RESULTS: The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. CONCLUSIONS: The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.


Assuntos
Algoritmos , Epidemias , Doença de Mão, Pé e Boca/transmissão , Modelos Biológicos , China/epidemiologia , Doença de Mão, Pé e Boca/epidemiologia , Humanos , Incidência , Reprodutibilidade dos Testes , Regressão Espacial , Análise Espaço-Temporal
19.
PLoS Negl Trop Dis ; 14(4): e0008179, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32255797

RESUMO

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings.


Assuntos
Surtos de Doenças , Doença da Floresta de Kyasanur/epidemiologia , Zoonoses/epidemiologia , Distribuição Animal , Animais , Biodiversidade , Suscetibilidade a Doenças , Florestas , Humanos , Índia/epidemiologia , Densidade Demográfica , Fatores de Risco , Regressão Espacial
20.
Environ Pollut ; 262: 114257, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32146364

RESUMO

PM2.5 pollution is caused by multiple factors and determining how these factors affect PM2.5 pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM2.5 and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO2 and NO2 concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM2.5 and the different factors varied with location. SO2 emission positively affected PM2.5, and the impact was the strongest in the Beijing-Tianjin-Hebei (BTH) region. The impact of NO2 was generally smaller than that of SO2 and could be important in coastal areas. The impact of meteorological factors on PM2.5 was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM2.5 in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM2.5 in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.


Assuntos
Poluentes Atmosféricos/análise , Poluição do Ar/análise , Meteorologia , Pequim , China , Cidades , Monitoramento Ambiental , Material Particulado/análise , Regressão Espacial
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