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Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action

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TRANSLATIONAL RESEARCH 2021年 234卷 88-113页

作者： Marks, Charles Carrasco-Escobar, Gabriel Carrasco-Hernandez, Rocio Johnson, Derek Ciccarone, Dan Strathdee, Steffanie A. Smith, Davey Borquez, Annick San Diego State Univ Interdisciplinary Res Subst Use Joint Doctoral Pr San Diego CA 92182 USA Univ Calif San Diego San Diego CA 92103 USA Univ Calif San Diego Div Infect Dis & Global Publ Hlth San Diego CA 92103 USA San Diego State Univ Sch Social Work San Diego CA 92182 USA Univ Peruana Cayetano Heredia Inst Trop Med Alexander von Humboldt Hlth Innovat Lab Lima Peru Univ Calif San Francisco Dept Family & Community Med San Francisco CA 94143 USA

The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.

关键词： US United States CDC Centers for disease control ORE opioid use-related epidemics IDU injection drug use PWID people who inject drugs HIV human immunodeficiency virus HCV hepatitis C virus TB tuberculosis SUD substance-use disorders OUD opioid use disorders ARIMA autoregressive integrated moving average ESDA exploratory spatial data analysis LISA Local Indicators of spatial Association SaTScan spatial scan statistic

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The Geography of Long Term Exposure to Particulate Matter 2.5 and COVID-19 Mortality;An Assessment of the Fragility and spatial Sensitivity of a Significant Finding

The Geography of Long Term Exposure to Particulate Matter 2....

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作者： Badger, Jennifer Beth University of California Los Angeles

学位级别：M.A., Master of Arts

Air pollution is directly linked to death. In December 2020, a UK coroner ruled that air pollution was the cause of a fatal asthma attack that led to the 2013 death of nine-year-old Ella Adoo-Kissi Debrah who lived adjacent to a busy motorway (BBC News, 2022). The assignment of air pollution as the official cause of death on a death certificate was the first of its kind in the world (Reynolds, 2020). Though this was the first official assignment of air pollution as a cause of death, there are numerous studies linking air pollution exposure with mortality all over the world. Before the COVID-19 pandemic, the air pollutant PM 2.5 was identified as the “largest environmental risk factor in the United States” (Goodkind et al. 2019, p. 8780) and the cause of more annual premature deaths than traffic accidents and homicides combined (Goodkind et al. 2019). With the onset of the COVID-19 pandemic, researchers began assessing the impact of air pollution exposure on COVID-19 incidence and death. In a widely received, nationwide study linking air pollution exposure to COVID-19 mortality, Harvard T.H. Chan School of Public Health researchers, Wu et al., produced significant findings linking the impact of long term exposure to PM 2.5 to COVID-19 mortality across the contiguous United States. This 2020 study, published in ScienceAdvances, has been cited over 600 times, covered by 131 news outlets and downloaded over 15,000 times. Georeferenced data is routinely used in public health research such as this, however, the substantive influence of geography in the relationship between the treatment and outcome variable is often not considered in the model specifications, research design, nor the sampling strategy (Goldhagen et al., 2005; Matisziw, Grubesic, and Wei 2008). Additionally, the mechanism of data aggregation to an administrative unit may spatially misrepresent the data (Delmelle et al., 2022). As air pollution is a local, regional, and transboundary phenomenon (Nordenstam

关键词： Air pollution COVID-19 exploratory spatial data analysis Health geography PM2.5 United States

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Measurement and characteristics of the temporal-spatial evolution of China's healthcare services efficiency

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ARCHIVES OF PUBLIC HEALTH 2023年第1期81卷 1页

作者： Ye, Yizhong Tao, Qunshan Anhui Univ Chinese Med Sch Hosp Econ & Management Hefei 230000 Peoples R China Key Lab Data Sci & Innovat Dev Chinese Med Anhui P Hefei 230000 Peoples R China

Background Healthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country's coordinated economic and social *** In this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010-2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the *** The study found that China's HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an "East > Central > West" distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the "club convergence" phenomenon of "high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression" was formed in the spatial *** The results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper.

关键词： Healthcare services Efficiency analysis Temporal-spatial evolution Markov chain model exploratory spatial data analysis

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A study of primary health care service efficiency and its spatial correlation in China

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BMC HEALTH SERVICES RESEARCH 2023年第1期23卷 247-247页

作者： Mei, Kangni Kou, Ruxin Bi, Yuqing Liu, Yuzhuo Huang, Jingwen Li, Wei Weifang Med Univ Sch Publ Hlth Weifang 261053 Shandong Peoples R China Weifang Med Univ Sch Management Weifang 261053 Shandong Peoples R China

BackgroundChina's primary health care system has undergone major changes since the new round of medical reform in 2009, but the current status of primary health care institution service efficiency is still unsatisfactory. The purpose of this study is to compare and evaluate the China's primary health care institution service efficiency and provide a reference for improving the efficiency and promoting the development of primary health care *** on panel data of 31 provinces (municipalities directly under the central government and autonomous regions) in mainland China from 2011 to 2020, using the super efficiency slack-based measure-data envelopment analysis model, to analyze the data from a static perspective, and the changes in the efficiency of primary health care services were analyzed from a dynamic perspective by using the Malmquist index method. spatial autocorrelation analysis method was used to verify the spatial correlation of primary health care service efficiency among various *** number of Primary health care institutions increased from 918,000 in 2011 to 970,000 in 2020. The average primary health care institution service efficiency in the northeastern region including Jilin (0.324), Heilongjiang (0.460), Liaoning (0.453) and northern regions such as Shaanxi (0.344) and Neimenggu (0.403) was at a low level, while the eastern coastal regions such as Guangdong (1.116), Zhejiang (1.211), Shanghai (1.402) have higher average service efficiency levels. The global Moran's I showed the existence of spatial autocorrelation, and the local Moran's I index suggested that the problem of uneven regional development was prominent, showing a contiguous regional distribution pattern. Among them, H-H (high-efficiency regions) were mainly concentrated in Jiangsu, Anhui and Shanghai, and L-L regions (low-efficiency regions) were mostly in northern and northeastern *** service efficiency of primary health care institution

关键词： Primary health care institution Service efficiency data envelopment analysis exploratory spatial data analysis spatial autocorrelation Moran's I

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spatial effects and impact factors of food nitrogen footprint in China based on spatial durbin panel model

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ENVIRONMENTAL RESEARCH 2022年第PartB期204卷 112046-112046页

作者： Liu, Chun Nie, Guihua Wuhan Univ Technol Sch Econ Wuhan 430070 Hubei Peoples R China Wuhan Technol & Business Univ Sch Management Wuhan 430065 Hubei Peoples R China Hubei Prov Res Ctr E Business Big Data Engn Techn Wuhan 430070 Hubei Peoples R China

Reactive nitrogen (Nr) has far-reaching advantages and disadvantages on human beings. Nitrogen footprint (NF) is a tool to quantify the use of Nr in the environment. Food nitrogen footprint (FNF) accounts for the largest proportion of the total NF, and the differences between provinces in China exist objectively. In order to explore the spatial correlation and socio-economic driving factors of China's FNF, this paper uses N-calculator tool to calculate the FNF of 30 provinces in China from 2000 to 2018, and uses exploratory spatial data to analyze the spatial correlation and changes of provincial FNF, The driving factors and spatial effects of FNF change in the province were analyzed by using spatial Durbin panel model and spatial regression partial differential method. The results showed that: (1) There is a significant and stable positive spatial dependence and heterogeneity in the FNF among provinces;(2) The direct effect factors of promoting the growth of FNF in the province are urban household Engel coefficient, per capita disposable income of rural residents and rural household Engel coefficient. The main factors of restraining the growth of FNF in the province are wastewater discharge per unit GDP and per capita GDP;(3) the spillover effect is mainly manifested as the negative effect of the increase of urban household Engel coefficient on neighboring provinces, and the spillover effect of per capita disposable income of urban residents and nitrogen fertilizer application rate per unit grain yield on the growth of FNF of neighboring provinces is significant. From the policy level, it is necessary to guide healthy and scientific eating habits, reduce the proportion of meat and fish in the diet structure, reduce the nitrogen fertilizer application per unit grain yield, and improve the efficiency of chemical fertilizer utilization. When formulating relevant policies, government departments should give consideration to the cooperation between provincial and region

关键词： Food nitrogen footprint exploratory spatial data analysis spatial durbin panel Spillover effect

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Agricultural Water Utilization Efficiency in China: Evaluation, spatial Differences, and Related Factors

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WATER 2022年第5期14卷 684-684页

作者： Liu, Kai Xue, Yuting Lan, Yu Fu, Yuxuan Shandong Normal Univ Coll Geog & Environm Jinan 250358 Peoples R China Shandong Normal Univ Univ Shandong Collaborat Innovat Ctr Human Nat & Green Dev Jinan 250358 Peoples R China

Agricultural water utilization efficiency (AWUE) reflects the rational utilization of water resources in agricultural production. Improving AWUE is important for both improving the levels of agricultural production and reducing consumption of water resources, and it is significant to explore the spatial differences between different cities and regions and the various factors related to AWUE, both theoretically and practically. The AWUE of totally 281 cities at the prefecture level or above in China between 2003 and 2018 was evaluated using the super-efficiency slacks-based measure (SBM). The spatial differences in AWUE were simulated by exploratory spatial data analysis (ESDA), and the various factors affecting AWUE were simulated using the graphical statistical tool, Geodetector. The results of this study are as follows: (1) The mean value of AWUE across the country was merely 0.23 when it registered a record high in 2018, indicating that the AWUE in China was low;(2) AWUE showed significant spatial differences judging from the results of ESDA, and the low-low type was the principal spatial type, which was distributed mainly in the North China Plain and the Loess Plateau;and (3) agricultural technology was the main factor affecting AWUE.

关键词： agricultural water utilization efficiency spatial differences related factors super-efficiency slacks-based measure exploratory spatial data analysis Geodetector

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Spatio-Temporal Heterogeneity of Ecological Quality in Hangzhou Greater Bay Area (HGBA) of China and Response to Land Use and Cover Change

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REMOTE SENSING 2022年第21期14卷 5613-5613页

作者： Yang, Zhenjie Sun, Chao Ye, Junwei Gan, Congying Li, Yue Wang, Lingyu Chen, Yujun Ningbo Univ Dept Geog & Spatial Informat Tech Ningbo 315211 Peoples R China Ningbo Univ Donghai Acad Ningbo 315211 Peoples R China Ningbo Univ Ningbo Univ Collaborat Innovat Ctr Land & Marine Ningbo 315211 Peoples R China

Human activities have been stressing the ecological environment since we stepped into the Anthropocene Age. It is urgent to formulate a sustainable plan for balancing socioeconomic development and ecological conservation based on a thorough understanding of ecological environment changes. The ecological environment can be evaluated when multiple remote sensing indices are integrated, such as the use of the recently prevalent Remote Sensing-based Ecological Index (RSEI). Currently, most of the RSEI-related studies have focused on the ecological quality evolution in small areas. Less attention was paid to the spatio-temporal heterogeneity of ecological quality in large-scale urban agglomerations and the potential links with Land Use and Cover Change (LUCC). In this study, we monitored the dynamics of the ecological quality in the Hangzhou Greater Bay Area (HGBA) during 1995-2020, using the RSEI as an indicator. During the construction of the RSEI, a percentile de-noising normalization method was proposed to overcome the problem of widespread noises from large-scale regions and make the RSEI-based ecological quality assessment for multiple periods comparable. Combined with the land use data, the quantitative relationship between the ecological quality and the LUCC was revealed. The results demonstrated that: (1) The ecological quality of the HGBA degraded after first improving but was still good (averaged RSEI of 0.638). It was divergent for the prefecture-level cities of the HGBA, presenting degraded, improved, and fluctuant trends for the cities from north to south. (2) For ecological quality, the improved regions have larger area (57.5% vs. 42.5%) but less increment (0.141 vs. -0.195) than the degraded regions. Mountains, downtowns, and coastal wetlands were the hot spots for the improvement and urbanization, and reclamation processes were responsible for the degradation. (3) The ecological quality was improved for forests and urban areas (oRSEI > 0.07) but degraded

关键词： ecological quality remote sensing-based ecological index (RSEI) Hangzhou Greater Bay Area land use and cover change (LUCC) exploratory spatial data analysis

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Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis

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GEOCARTO INTERNATIONAL 2022年第1期38卷

作者： Roy, Subham Bose, Arghadeep Majumder, Suranjan Roy Chowdhury, Indrajit Abdo, Hazem Ghassan Almohamad, Hussein Abdullah Al Dughairi, Ahmed Univ North Bengal Dept Geog & Appl Geog Siliguri West Bengal India Univ Tartous Fac Arts & Humanities Geog Dept Tartous Syria Univ Damascus Fac Arts & Humanities Geog Dept Damascus Syria Univ Tishreen Fac Arts & Humanities Geog Dept Latakia Syria Qassim Univ Coll Arab Language & Social Studies Dept Geog Buraydah Saudi Arabia

Urban environmental quality consisting of ecological, physical, and socio-economic components, often deteriorates due to rapid urbanization. Therefore, using Remote sensing and GIS environment, a composite measure is applied to quantify the spatial heterogeneity of urban environmental quality for the Class-1 Indian city (Siliguri). In this study, the Urban Environmental Quality Index was constructed using 15 indicators and three interconnected dimensions (eco-environment, landscape and built-up, and socio-economy). The three domains and Urban Environmental Quality Index were computed utilizing Principal Component analysis with average aggregation techniques. exploratory spatial data analysis includes Moran's I and Local indicator of spatial auto-correlation, were used to leverage the information of spatial clusters, spatial heterogeneity, and outliers based on the Urban Environmental Quality Index. The results show that Siliguri's northern, north-western, and southern parts experience good environmental quality. The effectiveness of the employed model was checked using R-2 (0.832), providing a good fit for the model. Moreover, the spatial pattern of urban environmental quality and the constructed domains (except socio-economy) revealed that the Low-Low values were predominantly clustered in the city centre, while High-High patterns are concentrated towards the periphery. Also, the value of Moran's I indicated the existence of spatial autocorrelation and non-randomness pattern in Siliguri City. The results obtained from the analysis indicate spatial heterogeneity and spatial differentiation across the study area. The study's outcome is relevant for urban planning, frequent monitoring of urban environmental quality, urban governance, and the well-being of urban inhabitants for a sustainable urban space.

关键词： Urban environment quality remote sensing GIS exploratory spatial data analysis LISA

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A spatio-temporal perspective of China's industrial circular economy development

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 706卷 135754-000页

作者： Tang, Jiahui Tong, Menghua Sun, Yuhuan Du, Juntao Liu, Ningning Dongbei Univ Finance & Econ Sch Econ Dalian 116025 Liaoning Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Liaoning Peoples R China

The circular economy pattern is the key to solve the contradiction between industrial development and resource protection. Based on the principle of the "3Rs" of the circular economy (reduction, reuse, and resource utilization), and combined with the characteristics of industrial and provincial circular economy development from the perspective of material flow analysis, this study constructs an evaluation index system of industrial circular economy in China's provinces. A combination of the global entropy and the coefficient of variation methods is used to calculate the comprehensive index of industrial circular economy development. Furthermore, this study analyzes the development of China's provincial industrial circular economy from the two dimensions of time and space. The results show that China's industrial circular economy developed rapidly during the Eleventh and Twelfth live-Year Plan periods. Resource output, resource consumption, and resource reuse and waste disposal have improved over time, but the outlook for emissions of major pollutants is not optimistic. China's industrial circular economy has significant spatial correlation and obvious regional differences, since the development of the circular economy requires a large amount of capital input, and the capacity of capital input fundamentally depends on the regional economic situation. Therefore, the spatial agglomeration pattern of China's industrial circular economy is roughly the same as that of China's economic development. Eastern China has the fastest development, followed by the central and northeast regions, while the western region, especially the northwest, lags the other regions. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Material flow analysis exploratory spatial data analysis Spatio-temporal characteristic

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spatial heterogeneity of factors influencing transportation CO2emissions in Chinese cities: based on geographically weighted regression model

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AIR QUALITY ATMOSPHERE AND HEALTH 2020年第8期13卷 977-989页

作者： Wang, Huiping Zhang, Xueying Xian Univ Finance & Econ Western Collaborat Innovat Res Ctr Energy Econ & Xian 710100 Peoples R China

Based on the 2015 transportation CO(2)emissions and economic and social data for the 286 cities in China, exploratory spatial data analysis (ESDA) method and a geographically weighted regression (GWR) model were used to analyze the spatial distribution characteristics of transportation CO(2)emissions and their influencing factors. The results showed that the CO(2)emissions from urban transportation in China featured significant spatial agglomeration. The high emission areas were mainly concentrated in Beijing, Shanghai, Chongqing, Chengdu, Nanjing, and other regional core cities, while the low emission areas were mainly concentrated in the cities of Gansu, Guizhou, Yunnan, and other underdeveloped provinces. Considering the overall evolution of the factors affecting transportation CO(2)emissions, private car ownership, technological innovation, and industrial structure correlated positively with transportation CO(2)emissions. Population density, urbanization rate, per capita urban road area, and transportation structure could significantly inhibit transportation CO(2)emissions. The impact of per capita GDP, public transportation, and environmental regulation on transportation CO(2)emissions was insignificant. From the perspective of spatial heterogeneity, there were significant regional differences in the impact of various factors on CO(2)emissions. Private car ownership, technological innovation capacity, and per capita GDP all had a positive impact on transportation CO(2)emissions. Urbanization rate, urban road area per capita, and transportation structure all had a negative impact on transportation CO(2)emissions. Population density, industrial structure, public transportation, and environmental regulation all had a two-way impact on transportation CO(2)emissions in different cities.

关键词： Transportation CO(2)emissions exploratory spatial data analysis Geographically weighted regression spatial heterogeneity

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