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Lim AY, Jafari Y, Caldwell JM, Clapham HE, Gaythorpe KAM, Hussain-Alkhateeb L, Johansson MA, Kraemer MUG, Maude RJ, McCormack CP, Messina JP, Mordecai EA, Rabe IB, Reiner RC, Ryan SJ, Salje H, Semenza JC, Rojas DP, Brady OJ. A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk. BMC Infect Dis 2023; 23:708. [PMID: 37864153 PMCID: PMC10588093 DOI: 10.1186/s12879-023-08717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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Affiliation(s)
- Ah-Young Lim
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
| | - Yalda Jafari
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jamie M Caldwell
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Laith Hussain-Alkhateeb
- School of Public Health and Community Medicine, Sahlgrenska Academy, Institute of Medicine, Global Health, University of Gothenburg, Gothenburg, Sweden
- Population Health Research Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Michael A Johansson
- Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA
| | | | - Richard J Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Clare P McCormack
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, Oxford, UK
- Oxford School of Global and Area Studies, University of Oxford, Oxford, UK
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Ingrid B Rabe
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Sadie J Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Jan C Semenza
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, Umeå, Sweden
| | - Diana P Rojas
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
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Lee KH, Lee S, Ryu J, Chun S, Heo J. Geographically varying associations between mentally unhealthy days and social vulnerability in the USA. Public Health 2023; 222:13-20. [PMID: 37499437 DOI: 10.1016/j.puhe.2023.06.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 07/29/2023]
Abstract
OBJECTIVES A growing body of research has incorporated the Social Vulnerability Index (SVI) into an expanded understanding of the social determinants of health. Although each component of SVI and its association with individual-level mental health conditions have been well discussed, variation in mentally unhealthy days (MUDs) at a county level is still unexplored. To systematically examine the geographically varying relationships between SVI and MUDs across the US counties, our study adopted two different methods: 1) aspatial regression modeling (ordinary least square [OLS]); and 2) locally calibrated spatial regression (geographically weighted regression [GWR]). STUDY DESIGN This study used a cross-sectional statistical design and geospatial data manipulation/analysis techniques. Analytical unit is each of the 3109 counties in the continental USA. METHODS We tested the model performance of two different methods and suggest using both methods to reduce potential issues (e.g., Simpson's paradox) when researchers apply aspatial analysis to spatially coded data sets. We applied GWR after checking the spatial dependence of residuals and non-stationary issues in OLS. GWR split a single OLS equation into 3109 equations for each county. RESULTS Among 15 SVI variables, a combination of eight variables showed the best model performance. Notably, unemployment, person with a disability, and single-parent households with children aged under 18 years especially impacted the variation of MUDs in OLS. GWR showed better model performance than OLS and specified each county's varying relationships between subcomponents of SVI and MUDs. For example, GWR specified that 69.3% (2157 of 3109) of counties showed positive relationships between single-parent households and MUDs across the USA. Higher positive relationships were concentrated in Michigan, Kansas, Texas, and Louisiana. CONCLUSIONS Our findings could contribute to the literature regarding social determinants of community mental health by specifying spatially varying relationships between SVI and MUDs across US counties. Regarding policy implementation, in counties containing more social and physical minorities (e.g., single-parent households and disabled population), policymakers should attend to these groups of people and increase intervention programs to reduce potential or current mental health illness. The results of GWR could help policymakers determine the specific counties that need more support to reduce regional mental health disparities.
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Affiliation(s)
- Kyung Hee Lee
- Department of Recreation, Parks and Leisure Services Administration, Central Michigan University, USA.
| | - Sunwoo Lee
- The Faculty of Physical Culture, Palacký University Olomouc, Třída Míru 117, 77111 Olomouc, Czech Republic
| | - Jungsu Ryu
- Department of Sport Management, Marshall University, USA
| | - Sanghee Chun
- Department of Recreation & Leisure Studies, Brock University, Canada
| | - Jinmoo Heo
- Department of Sports Industry Studies, Yonsei University, South Korea
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Hoseinzadeh N, Gu Y, Zhang H, Han LD, Kim H, Freeze PB. Mobility Dynamics amid COVID-19 with a Case Study in Tennessee. TRANSPORTATION RESEARCH RECORD 2023; 2677:946-959. [PMID: 37153202 PMCID: PMC10149353 DOI: 10.1177/03611981211063199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.
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Affiliation(s)
- Nima Hoseinzadeh
- Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN
| | - Yangsong Gu
- Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN
| | | | - Lee D. Han
- Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN
| | - Hyun Kim
- Department of Geography, The University of Tennessee, Knoxville, TN
| | - Phillip Brad Freeze
- Traffic Operations Division, Tennessee Department of Transportation, Nashville, TN
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Zhang L, Han Y, Fang Y. Non-human and human service efficiency of long-term care facilities in China. Front Public Health 2023; 11:1066190. [PMID: 36935680 PMCID: PMC10018177 DOI: 10.3389/fpubh.2023.1066190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/03/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction Care services provided by long-term care facilities (LTCFs) are currently plagued by care resource shortages and insufficient utilization. The analysis on the temporal and spatial distribution of human resources and non-human resources in LTCFs, could provide a basis to optimize resource allocation and efficient use of limited resources. Methods This study used data envelopment analysis to comprehensively evaluate the efficiency of human and non-human resources in different time spans and regions. The spatial Markov chain and spatial correlation were also applied to explore the heterogeneity of and correlation between the service efficiency of LTCFs in different regions and then analyzes the influencing factors of efficiency using Tobit regression model. Results The quantitative changes in the service efficiency of LTCFs in various provinces showed a "W" shape in two periods, ranging from 0.8 to 1.6. The overall efficiency of LTCFs in different regions had a lower probability to achieve short-term cross-stage development. Non-human resource efficiency presented a "cluster" distribution mode, demonstrating a great probability to achieve cross-stage development, which might be due to the regional disparities of economic development and land resource. Tobit regression analysis results also showed that the comprehensive efficiency of LTCFs decreases by 0.210 for every square increase in construction space variation. However, human resource efficiency had a significant spatial polarization, making it difficult to develop area linkages. The reason for this might be the nursing staff have relatively stable regional characteristics, weakening the inter-provincial spatial connection. We also found that female workers, aged between 35 and 45 can positively affect the efficiency of LTCFs. Those staff stay focused and improve their skills, which might improve the efficiency of LTCFs. So improving technology and service quality changes by increasing female workers, aged between 35 and 45, and avoiding excessive construction space changes can enhance the growth of service quality and personnel stability of LTCFs. Conclusion There is an urgent trade-off among staff quality improvement, resource reduction, construction excessive and substantial regional variation in efficiency. Therefore, strengthening policy support to encourage inter-regional initiatives, particularly highlighting the development of human resources interaction and common development is urgent.
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Affiliation(s)
- Liangwen Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- School of Economics, Xiamen University, Xiamen, China
| | - Ying Han
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
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Tangena JAA, Mategula D, Sedda L, Atkinson PM. Unravelling the impact of insecticide-treated bed nets on childhood malaria in Malawi. Malar J 2023; 22:16. [PMID: 36635658 PMCID: PMC9837906 DOI: 10.1186/s12936-023-04448-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 01/06/2023] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND To achieve malaria elimination it is essential to understand the impact of insecticide-treated net (ITNs) programmes. Here, the impact of ITN access and use on malaria prevalence in children in Malawi was investigated using Malaria Indicator Survey (MIS) data. METHODS MIS data from 2012, 2014 and 2017 were used to investigate the relationship between malaria prevalence in children (6-59 months) and ITN use. Generalized linear modelling (GLM), geostatistical mixed regression modelling and non-stationary GLM were undertaken to evaluate trends, spatial patterns and local dynamics, respectively. RESULTS Malaria prevalence in Malawi was 27.1% (95% CI 23.1-31.2%) in 2012 and similar in both 2014 (32.1%, 95% CI 25.5-38.7) and 2017 (23.9%, 95% CI 20.3-27.4%). ITN coverage and use increased during the same time period, with household ITN access growing from 19.0% (95% CI 15.6-22.3%) of households with at least 1 ITN for every 2 people sleeping in the house the night before to 41.7% (95% CI 39.1-44.4%) and ITN use from 41.1% (95% CI 37.3-44.9%) of the population sleeping under an ITN the previous night to 57.4% (95% CI 55.0-59.9%). Both the geostatistical and non-stationary GLM regression models showed child malaria prevalence had a negative association with ITN population access and a positive association with ITN use although affected by large uncertainties. The non-stationary GLM highlighted the spatital heterogeneity in the relationship between childhood malaria and ITN dynamics across the country. CONCLUSION Malaria prevalence in children under five had a negative association with ITN population access and a positive association with ITN use, with spatial heterogeneity in these relationships across Malawi. This study presents an important modelling approach that allows malaria control programmes to spatially disentangle the impact of interventions on malaria cases.
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Affiliation(s)
- Julie-Anne A. Tangena
- grid.48004.380000 0004 1936 9764Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Donnie Mategula
- grid.48004.380000 0004 1936 9764Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, UK ,grid.419393.50000 0004 8340 2442Malawi-Liverpool Wellcome Trust Clinical Research Programme, Blantyre, Malawi
| | - Luigi Sedda
- grid.9835.70000 0000 8190 6402Lancaster Ecology and Epidemiology Group, Lancaster University, Lancaster, UK
| | - Peter M. Atkinson
- grid.9835.70000 0000 8190 6402Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster, LA1 4YR UK ,grid.5491.90000 0004 1936 9297Geography and Environmental Science, University of Southampton, Highfield, Southampton, SO17 1BJ UK ,grid.9227.e0000000119573309Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Beijing, 100101 China
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Sharif M, Khan MAS, Hasan MJ, Naher T, Rudra S, Fardous J, Gozal D, Rahman MK, Amin MR. Spatial association of Aedes aegypti with dengue fever hotspots in an endemic region. Heliyon 2022; 8:e11640. [PMID: 36439726 PMCID: PMC9694391 DOI: 10.1016/j.heliyon.2022.e11640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/25/2022] [Accepted: 11/09/2022] [Indexed: 11/22/2022] Open
Abstract
Background and objective Dengue is a vector-borne viral disease usually transmitted by Aedes mosquitoes. Around the world, the relationship between local vector density and frequency of dengue cases is being explored and needs further evidence. This study aimed to analyze the potential spatial relationships between the dengue vector (Aedes aegypti) and dengue cases in the megacity of Bangladesh during the 2019 dengue outbreak. Methods Vector density measures were used to estimate spatial associations with dengue case distribution. Location was determined for 364 dengue cases who were admitted to Dhaka Medical College Hospital over a period of 4 months. Data were collected using a semi-structured questionnaire, and prior consent was ensured before participation. The Moran global index, Getis-Ord Gi∗, ordinary least squares regression, geographically weighted regression and count data regression methods were used for spatial analysis. Results We found that dengue case distribution was not associated with immature Aedes aegypti mosquito (larvae) density across the city. The relationship between larval density measured by the Breteau Index (BI) and House Index (HI) with dengue cases was nonstationary and not statistically significant. Conclusion The location of dengue cases appears to be unrelated to vector distribution and vector density. These findings should prompt the search for other transmission risk factors.
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Affiliation(s)
- Mohiuddin Sharif
- Department of Medicine, Dhaka Medical College Hospital, Dhaka, Bangladesh
| | | | | | - Tanzin Naher
- Department of Clinical Pathology, Dhaka Medical College Hospital, Dhaka, Bangladesh
| | - Sujan Rudra
- Department of Statistics, University of Chittagong, Chottogram, Bangladesh
| | | | - David Gozal
- Department of Child Health and the Child Health Research Institute, University of Missouri School of Medicine, Columbia, MO 65201, USA
| | - Md Khalilur Rahman
- National Malaria Elimination and Aedes Transmitted Diseases Control Programme, Disease Control Division, Directorate General of Health Services, Bangladesh
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Nazia N, Law J, Butt ZA. Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada. Spat Spatiotemporal Epidemiol 2022; 43:100534. [PMID: 36460444 PMCID: PMC9411108 DOI: 10.1016/j.sste.2022.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/19/2022] [Accepted: 08/24/2022] [Indexed: 12/15/2022]
Abstract
The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada,Corresponding author at: School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada,School of Planning, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
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Lu X, Bambrick H, Frentiu FD, Huang X, Davis C, Li Z, Yang W, Devine GJ, Hu W. Species-specific climate Suitable Conditions Index and dengue transmission in Guangdong, China. Parasit Vectors 2022; 15:342. [PMID: 36167577 PMCID: PMC9516795 DOI: 10.1186/s13071-022-05453-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/26/2022] [Indexed: 12/02/2022] Open
Abstract
Background Optimal climatic conditions for dengue vector mosquito species may play a significant role in dengue transmission. We previously developed a species-specific Suitable Conditions Index (SCI) for Aedes aegypti and Aedes albopictus, respectively. These SCIs rank geographic locations based on their climatic suitability for each of these two dengue vector species and theoretically define parameters for transmission probability. The aim of the study presented here was to use these SCIs together with socio-environmental factors to predict dengue outbreaks in the real world. Methods A negative binomial regression model was used to assess the relationship between vector species-specific SCI and autochthonous dengue cases after accounting for potential confounders in Guangdong, China. The potential interactive effect between the SCI for Ae. albopictus and the SCI for Ae. aegypti on dengue transmission was assessed. Results The SCI for Ae. aegypti was found to be positively associated with autochthonous dengue transmission (incidence rate ratio: 1.06, 95% confidence interval: 1.03, 1.09). A significant interaction effect between the SCI of Ae. albopictus and the SCI of Ae. aegypti was found, with the SCI of Ae. albopictus significantly reducing the effect of the SCI of Ae. aegypti on autochthonous dengue cases. The difference in SCIs had a positive effect on autochthonous dengue cases. Conclusions Our results suggest that dengue fever is more transmittable in regions with warmer weather conditions (high SCI for Ae. aegypti). The SCI of Ae. aegypti would be a useful index to predict dengue transmission in Guangdong, China, even in dengue epidemic regions with Ae. albopictus present. The results also support the benefit of the SCI for evaluating dengue outbreak risk in terms of vector sympatry and interactions in the absence of entomology data in future research. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13071-022-05453-x.
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Affiliation(s)
- Xinting Lu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Hilary Bambrick
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.,National Centre for Epidemiology and Population Health, The Australian National University, Canberra ACT, Australia
| | - Francesca D Frentiu
- Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Xiaodong Huang
- Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Callan Davis
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Zhongjie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Weizhong Yang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, China.,School of Population Medicine & Public Health, Chinese Academy of Medical Science, Peking Union Medical College, Beijing, China
| | - Gregor J Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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Lin CH, Wen TH. How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission. Trop Med Infect Dis 2022; 7:tropicalmed7080164. [PMID: 36006256 PMCID: PMC9413673 DOI: 10.3390/tropicalmed7080164] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 02/06/2023] Open
Abstract
Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.
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Affiliation(s)
- Chia-Hsien Lin
- Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei City 10610, Taiwan
- Department of Geography, National Taiwan University, Taipei City 10617, Taiwan;
- Correspondence:
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei City 10617, Taiwan;
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Kawano Y, Matsumoto R, Motomura E, Shiroyama T, Okada M. Bidirectional Causality between Spreading COVID-19 and Individual Mobilisation with Consumption Motives across Prefectural Borders in Japan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159070. [PMID: 35897432 PMCID: PMC9332297 DOI: 10.3390/ijerph19159070] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/22/2022] [Accepted: 07/22/2022] [Indexed: 01/27/2023]
Abstract
A combination of pharmaceutical and non-pharmaceutical interventions as well as social restrictions has been recommended to prevent the spread of coronavirus disease 2019 (COVID-19). Therefore, social contact surveys play an essential role as the basis for more effective measures. This study attempts to explore the fundamental basis of the expansion of COVID-19. Temporal bidirectional causalities between the numbers of newly confirmed COVID-19 cases (NCCC) and individual mobilisations with consumption motives across prefecture borders in three metropolitan regions in Japan were analysed using vector autoregression models. Mobilisation with consumption in pubs from Kanto to Tokai contributed to the spread of COVID-19 in both regions. Meanwhile, causal mobilisation with consumption motives in Kansai also contributed to the expansion of COVID-19; however, the pattern was dependent on the industrial characteristics of each prefecture in Kansai. Furthermore, the number of pub visitors in Kanto immediately decreased when NCCC increased in Kanto. In contrast, the causal mobilisations for the expansion of COVID-19 in the Tokai and Kansai regions were unaffected by the increasing NCCC. These findings partially proved the validity of the conventional governmental measures to suppress pub visitors across prefectural borders. Nevertheless, the individual causal mobilisations with consumption motives that contributed to the increasing COVID-19 cases are not identical nationwide, and thus, regional characteristics should be considered when devising preventive strategies.
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Murphy AK, Salazar FV, Bonsato R, Uy G, Ebol AP, Boholst RP, Davis C, Frentiu FD, Bambrick H, Devine GJ, Hu W. Climate variability and Aedes vector indices in the southern Philippines: An empirical analysis. PLoS Negl Trop Dis 2022; 16:e0010478. [PMID: 35700164 PMCID: PMC9197058 DOI: 10.1371/journal.pntd.0010478] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/09/2022] [Indexed: 11/18/2022] Open
Abstract
Background
Vector surveillance is an essential public health tool to aid in the prediction and prevention of mosquito borne diseases. This study compared spatial and temporal trends of vector surveillance indices for Aedes vectors in the southern Philippines, and assessed potential links between vector indices and climate factors.
Methods
We analysed routinely collected larval and pupal surveillance data from residential areas of 14 cities and 51 municipalities during 2013–2018 (House, Container, Breteau and Pupal Indices), and used linear regression to explore potential relationships between vector indices and climate variables (minimum temperature, maximum temperature and precipitation).
Results
We found substantial spatial and temporal variation in monthly Aedes vector indices between cities during the study period, and no seasonal trend apparent. The House (HI), Container (CI) and Breteau (BI) Indices remained at comparable levels across most surveys (mean HI = 15, mean CI = 16, mean BI = 24), while the Pupal Productivity Index (PPI) was relatively lower in most months (usually below 5) except for two main peak periods (mean = 49 overall). A small proportion of locations recorded high values across all entomological indices in multiple surveys. Each of the vector indices were significantly correlated with one or more climate variables when matched to data from the same month or the previous 1 or 2 months, although the effect sizes were small. Significant associations were identified between minimum temperature and HI, CI and BI in the same month (R2 = 0.038, p = 0.007; R2 = 0.029, p = 0.018; and R2 = 0.034, p = 0.011, respectively), maximum temperature and PPI with a 2-month lag (R2 = 0.031, p = 0.032), and precipitation and HI in the same month (R2 = 0.023, p = 0.04).
Conclusions
Our findings indicated that larval and pupal surveillance indices were highly variable, were regularly above the threshold for triggering vector control responses, and that vector indices based on household surveys were weakly yet significantly correlated with city-level climate variables. We suggest that more detailed spatial and temporal analyses of entomological, climate, socio-environmental and Aedes-borne disease incidence data are necessary to ascertain the most effective use of entomological indices in guiding vector control responses, and reduction of human disease risk.
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Affiliation(s)
- Amanda K. Murphy
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane Australia
- Mosquito Control Laboratory, Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Ferdinand V. Salazar
- Department of Medical Entomology, Research Institute for Tropical Medicine (RITM), Manila, The Philippines
| | - Ryan Bonsato
- Department of Medical Entomology, Research Institute for Tropical Medicine (RITM), Manila, The Philippines
| | - Gemma Uy
- Department of Health, Center for Health Development 10, Northern Mindanao, Cagaya de Oro, The Philippines
| | - Antonietta P. Ebol
- Department of Health, Center for Health Development 11, Davao City, Davao del Sur, The Philippines
| | - Royfrextopher P. Boholst
- Department of Health, Center for Health Development Soccskargen Region, Cotabato City, The Philippines
| | - Callan Davis
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane Australia
| | - Francesca D. Frentiu
- Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Hilary Bambrick
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane Australia
| | - Gregor J. Devine
- Mosquito Control Laboratory, Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane Australia
- * E-mail:
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12
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Lei C, Wang Q, Wang Y, Han L, Yuan J, Yang L, Xu Y. Spatially non-stationary relationships between urbanization and the characteristics and storage-regulation capacities of river systems in the Tai Lake Plain, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 824:153684. [PMID: 35134417 DOI: 10.1016/j.scitotenv.2022.153684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/25/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
Given environmental or hydrological functions influenced by changing river networks in the development of rapid urbanization, a clear understanding of the relationships between comprehensive urbanization (CUB) and river network characteristics (RNC), storage capacity (RSC), and regulation capacity (RRC) is urgently needed. In the rapidly urbanized Tai Lake Plain (TLP), China, various methods and multisource data were integrated to estimate the dynamics of RNC, RSC, and RRC as well as their interactions with urbanization. The bivariate Moran's I methods were applied to detect and visualize the spatial dependency of RNC, RSC, or RRC on urbanization. Geographically weighted regression (GWR) model was set up to characterize spatial heterogeneity of urbanization influences on RNC, RSC and RRC. Our results indicated that RNC, RSC and RRC variables each showed an overall decreasing trend across space from 1960s to 2010s, particularly in those of tributary rivers. RNC, RSC, or RRC had globally negative correlations with CUB, respectively, but looking at local scale the spatial correlations between each pair were categorized as four types: high-high, high-low, low-low, and low-high. GWR was identified to accurately predict the response of most RNC, RSC, or RRC variables to CUB (R2: 0.6-0.8). The predictive ability of GWR was spatially non-stationary. The obtained relationships presented different directions and strength in space. All variables except for the water surface ratio (Wp) were more positively affected by CUB in the middle eastern parts of TLP. Drainage density, RSC and RRC variables were more negatively influenced by CUB in the northeast compared to other parts. The quantitative results of spatial relationships between urbanization and RNC, RSC or RRC can provide location-specific guidance for river environment protection and regional flood risk management.
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Affiliation(s)
- Chaogui Lei
- Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, Olshausenstr. 75, 24118 Kiel, Germany; School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China.
| | - Qiang Wang
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
| | - Yuefeng Wang
- School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
| | - Longfei Han
- College of Geographic Science, Hunan Normal University, Changsha 410081, China
| | - Jia Yuan
- School of History Culture and Tourism, Fuyang Normal University, Fuyang 236037, China
| | - Liu Yang
- College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China
| | - Youpeng Xu
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
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13
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Ha H, Xu Y. An ecological study on the spatially varying association between adult obesity rates and altitude in the United States: using geographically weighted regression. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2022; 32:1030-1042. [PMID: 32940052 DOI: 10.1080/09603123.2020.1821875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 09/07/2020] [Indexed: 06/11/2023]
Abstract
In this research, we evaluated the relationship between obesity rates and altitude using a cross-county study design. We applied a geographically weighted regression (GWR) to examine the spatially varying association between adult obesity rates and altitude after adjusting for four predictor variables including physical activity. A significant negative relationship between altitude and adult obesity rates were found in the GWR model. Our GWR model fitted the data better than OLS regression (R2 = 0.583), as indicated by an improved R2 (average R2 = 0.670; range: 0.26-0.77) and a lower Akaike Information Criteria (AIC) value (14,736.88 vs. 15,386.59 in the OLS model). These approaches, evidencing spatial varying associations, proved very useful to refine interpretations of the statistical output on adult obesity. This study underscored the geographic variation in relationships between adult obesity rates and mean county altitude in the United States. Our study confirmed a varying overall negative relationship between county-level adult obesity rates and mean county altitude after taking other confounding factors into account.
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Affiliation(s)
- Hoehun Ha
- Department of Biology and Environmental Science, Auburn University at Montgomery, Montgomery, AL, USA
| | - Yanqing Xu
- Department of Geography and Planning, University of Toledo, Toledo, OH, USA
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14
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Seabra IL, Pedroso AO, Rodrigues TB, Ferreira GRON, da Silva Ferreira AL, Arcêncio RA, Gomes D, da Silva RAR, Botelho EP. Temporal trend and spatial analysis of the HIV epidemic in young men who have sex with men in the second largest Brazilian Amazonian province. BMC Infect Dis 2022; 22:190. [PMID: 35209850 PMCID: PMC8867691 DOI: 10.1186/s12879-022-07177-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 02/15/2022] [Indexed: 11/17/2022] Open
Abstract
Background After 40 years of its starting, the HIV epidemic in Brazilian Amazon region remains on an increasing trend. The young men who have sex with men (MSM) have been the most impacted by the HIV in the last decade. However, much more than attributing the risk behavior to HIV uniquely to the individual, behaviors are shaped by social determinants of health (SDH). Despite the problem, there is a scarcity of studies evaluating the impact of SDH on HIV among young MSM and none of them were done in the Northern of Brazil. Therefore, the main goal of this study was to analyse the HIV epidemic among Brazilian Amazonian young MSM using temporal trends and spatial analysis. Methods We conducted an ecological study using reported cases of HIV/AIDS in young MSM living in Pará, the second larger Brazilian Amazonian province, between 2007 and 2018. Data were obtained from the Information System for Notifiable Diseases. For the temporal analysis, we employed a Seasonal and Trend decomposition using Loess Forecasting model (STLF), which is a hybrid time-series forecast model, that combines the Autoregressive-Integrated Moving Average (ARIMA) forecasting model with the Seasonal-Trend by Loess (STL) decomposition method. For the spatial analysis, Moran’s spatial autocorrelation, spatial scan, and spatial regression techniques were used. Results A total of 2192 notifications were included in the study. Greater variabilities in HIV/AIDS population-level diagnosis rates were found in the festive months. The HIV/AIDS population-level diagnosis rates exhibited an upward trend from 2013 and this trend is forecasted to continue until 2022. Belém, the capital of Pará, presented the highest spatial risk for HIV/AIDS and was the only city to present spatiotemporal risk from 2014 to 2018. The geographic variation of the HIV epidemic was associated with the number of men with formal jobs, the average salary of men, and the percentage of people over 18 years old with elementary education. Conclusion The upward trend of HIV/AIDS population-level diagnosis rate forecasted until 2022 and the variability of the epidemic promoted by the SDH brings an alert and subsidies to health authorities to implement more efficient and focalized public policies against HIV among young MSM in Pará.
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Affiliation(s)
- Iaron Leal Seabra
- Universidade Federal do Pará, Programa de Pós-Graduação em Enfermagem, Rua Augusto Correia, 01 - Setor saúde, GuamáBelém, 66075-110, Pará, Brasil
| | - Andrey Oeiras Pedroso
- Universidade Federal do Pará, Programa de Pós-Graduação em Enfermagem, Rua Augusto Correia, 01 - Setor saúde, GuamáBelém, 66075-110, Pará, Brasil
| | - Taymara Barbosa Rodrigues
- Universidade Federal do Pará, Programa de Pós-Graduação em Enfermagem, Rua Augusto Correia, 01 - Setor saúde, GuamáBelém, 66075-110, Pará, Brasil
| | | | - Ana Lucia da Silva Ferreira
- Departamento de Vigilância Epidemiológica, Secretaria de Saúde Pública do Pará, Av. Doutor Freitas, 235 Sacramenta, 66123-050, Belém, Pará, Brasil
| | - Ricardo Alexandre Arcêncio
- Maternal-Infant and Public Health Nursing Departament, University of São Paulo, College of Nursing at Ribeirão Preto, Avenida Dos Bandeirantes, 3900 - Campus Universitário, Monte Alegre, 14040-902, Ribeirão Preto, São Paulo, Brasil
| | - Dulce Gomes
- Departamento de Matemática, Colégio Luís António Verney, Universidade de Évora, Rua Romão Ramalho,, 597000-671, Évora, Portugal
| | - Richardson Augusto Rosendo da Silva
- Departamento de Enfermagem, Centro de Ciências da Saúde,, Universidade Federal do Rio Grande do Norte, Rua General Cordeiro de Faria, S/N. Petrópolis, 59012-570, Natal, Rio Grande do Norte, Brasil
| | - Eliã Pinheiro Botelho
- Universidade Federal do Pará, Programa de Pós-Graduação em Enfermagem, Rua Augusto Correia, 01 - Setor saúde, GuamáBelém, 66075-110, Pará, Brasil.
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15
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Li P, Zhong H, Zhang JZ. Spatial Patterns and Development Characteristics of China's Postgraduate Education. INT J SEMANT WEB INF 2022. [DOI: 10.4018/ijswis.313190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Using four types of publicly available datasets and ArcGIS software, the authors identify the spatial characteristics of postgraduate education in China at three scales: comprehensive economic zone, provincial, and city. They also employ geographically weighted regression and ordinary least squares to study the factors influencing the spatial pattern of postgraduate education in Gin at the city scale. The findings show that the number of postgraduate education institutions increases as the longitude of a city increases, but the number decreases from coast to inland. Second, postgraduate education institutions tend to group together in provincial capitals and megacities. Finally, GDP, per capita GDP, population size, local income, and total retail sales of consumer goods significantly impact postgraduate education development. The study contributes to the literature and provides insights for practitioners in promoting urban planning and infrastructure development.
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Affiliation(s)
- Ping Li
- Zhejiang Wanli University, China
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16
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Li H, Zhu L, Dai Z, Gong H, Guo T, Guo G, Wang J, Teatini P. Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 799:149244. [PMID: 34365261 DOI: 10.1016/j.scitotenv.2021.149244] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/17/2021] [Accepted: 07/20/2021] [Indexed: 06/13/2023]
Abstract
The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.
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Affiliation(s)
- Huijun Li
- Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China
| | - Lin Zhu
- Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China.
| | - Zhenxue Dai
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Huili Gong
- Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing 100048, China
| | - Tao Guo
- Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
| | - Gaoxuan Guo
- Beijing Institute of Hydrogeology and Engineering Geology, Beijing 100048, China
| | - Jingbo Wang
- National Computational Infrastructure, Australian National University, Canberra, Australia
| | - Pietro Teatini
- Dept. of Civil, Environmental and Architectural Engineering, University of Padova, Padova 35121, Italy; UNESCO-LaSII (Land Subsidence International Initiative), Querétaro, Mexico
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Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182212018. [PMID: 34831785 PMCID: PMC8618682 DOI: 10.3390/ijerph182212018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/31/2021] [Accepted: 11/10/2021] [Indexed: 11/17/2022]
Abstract
The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.
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18
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The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit-A systematic review. PLoS Negl Trop Dis 2021; 15:e0009631. [PMID: 34499653 PMCID: PMC8428665 DOI: 10.1371/journal.pntd.0009631] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background This systematic review aims to assess how different urbanisation patterns related to rapid urban growth, unplanned expansion, and human population density affect the establishment and distribution of Aedes aegypti and Aedes albopictus and create favourable conditions for the spread of dengue, chikungunya, and Zika viruses. Methods and findings Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted using the PubMed, Virtual Health Library, Cochrane, WHO Library Database (WHOLIS), Google Scholar, and and the Institutional Repository for Information Sharing (IRIS) databases. From a total of 523 identified studies, 86 were selected for further analysis, and 29 were finally analysed after applying all inclusion and exclusion criteria. The main explanatory variables used to associate urbanisation with epidemiological/entomological outcomes were the following: human population density, urban growth, artificial geographical space, urban construction, and urban density. Associated with the lack of a global definition of urbanisation, several studies provided their own definitions, which represents one of the study’s limitations. Results were based on 8 ecological studies/models, 8 entomological surveillance studies, 7 epidemiological surveillance studies, and 6 studies consisting of spatial and predictive models. According to their focus, studies were categorised into 2 main subgroups, namely “Aedes ecology” and “transmission dynamics.” There was a consistent association between urbanisation and the distribution and density of Aedes mosquitoes in 14 of the studies and a strong relationship between vector abundance and disease transmission in 18 studies. Human population density of more than 1,000 inhabitants per square kilometer was associated with increased levels of arboviral diseases in 15 of the studies. Conclusions The use of different methods in the included studies highlights the interplay of multiple factors linking urbanisation with ecological, entomological, and epidemiological parameters and the need to consider a variety of these factors for designing effective public health approaches. The expansion of urbanisation is often associated with the emergence and spread of vector-borne diseases by creating favourable conditions for the survival of Aedes species and the spread of dengue, chikungunya, and Zika viruses. This systematic review examined the relationship of urbanisation to the emergence and spread of Aedes mosquito–borne diseases and epidemics. From a total of 523 identified studies, 29 were included in the analysis. Studies were categorised into 2 main subgroups, namely “Aedes ecology” and “transmission dynamics” according to the main influence factors posed by urbanisation. Selected articles showed a clear relationship of urbanisation with distribution and density of Aedes mosquitoes and a robust association between vector production, human population density, and disease transmission. Differing definitions of ’urbanisation’ and the interplay of numerous factors linking urbanisation with ecological, entomological, and epidemiological parameters highlight the need for a multidimensional perspective when assessing the impacts of rapid and unplanned urban expansion and when designing effective control programmes.
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Lu J, Lin A, Jiang C, Zhang A, Yang Z. Influence of transportation network on transmission heterogeneity of COVID-19 in China. TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES 2021; 129:103231. [PMID: 34092940 PMCID: PMC8169317 DOI: 10.1016/j.trc.2021.103231] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 04/26/2021] [Accepted: 05/17/2021] [Indexed: 05/04/2023]
Abstract
In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.
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Affiliation(s)
- Jing Lu
- College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Anrong Lin
- College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Changmin Jiang
- Asper School of Business, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Anming Zhang
- Sauder School of Business, University of British Columbia, Vancouver, BC V6T1Z2, Canada
| | - Zhongzhen Yang
- Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
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20
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Gao F, languille C, karzazi K, Guhl M, Boukebous B, Deguen S. Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization. Int J Health Geogr 2021; 20:22. [PMID: 34011390 PMCID: PMC8136234 DOI: 10.1186/s12942-021-00276-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/08/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Healthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship. METHODS This study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored. RESULTS GWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists. CONCLUSIONS Our findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.
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Affiliation(s)
- Fei Gao
- HESP, 35000 Rennes, France
- Recherche en Pharmaco-Épidémiologie Et Recours Aux Soins, L’équipe REPERES, UPRES EA-7449, Rennes, France
- Department of Quantitative Methods for Public Health, EHESP School of Public Health, Avenue du Professeur Léon Bernard, 35043 Rennes, France
| | - Clara languille
- HESP, 35000 Rennes, France
- Univ Rennes, Ensai, 35000 Rennes, France
| | - Khalil karzazi
- HESP, 35000 Rennes, France
- Univ Rennes, Ensai, 35000 Rennes, France
| | - Mélanie Guhl
- HESP, 35000 Rennes, France
- Univ Rennes, Ensai, 35000 Rennes, France
| | - Baptiste Boukebous
- ECAMO, UMR1153, CRESS, INSERM, Paris, France
- Hoptial Bichât /Beaujon, APHP, Paris, France
| | - Séverine Deguen
- HESP, 35000 Rennes, France
- Department of Social Epidemiology, INSERM, Sorbonne Université, Institut Pierre Louis D’Épidémiologie Et de Santé Publique, IPLESP, 75012 Paris, France
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21
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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22
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Dangisso MH, Datiko DG, Lindtjørn B. Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions. Glob Health Action 2021; 13:1785737. [PMID: 32746745 PMCID: PMC7480636 DOI: 10.1080/16549716.2020.1785737] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Background Previous studies from Ethiopia detected disease clustering using broader geographic settings, but limited information exists on the spatial distribution of the disease using residential locations. An assessment of predictors of spatial variations of TB at community level could fill the knowledge gaps, and helps in devising tailored interventions to improve TB control. Objective To assess the pattern of spatial distribution of pulmonary tuberculosis (PTB) based on geographic locations of individual cases in the Dale district and Yirga Alem town in southern Ethiopia. Methods The socio-demographic characteristics of PTB cases were collected using a structured questionnaire, and spatial information was collected using geographic position systems. We carried out Getis and Ord (Gi*) statistics and scan statistics to explore the pattern of spatial clusters of PTB cases, and geographically weighted regression (GWR) was used to assess the spatial heterogeneities in relationship between predictor variables and PTB case notification rates (CNRs). Results The distribution of PTB varied by enumeration areas within the kebeles, and we identified areas with significant hotspots in various areas ineach year. In GWR analysis, the disease distribution showed a geographic heterogeneity (non-stationarity) in relation to physical access (distance to TB control facilities) and population density (AICc = 5591, R2 = 0.3359, adjusted R2 = 0.2671). The model explained 27% of the variability in PTB CNRs (local R2 ranged from 0.0002–0.4248 between enumeration areas). The GWR analysis showed that areas with high PTB CNRs had better physical accessibility to TB control facilities and high population density. The effect of physical access on PTB CNRs changed after the coverage of TB control facilities was improved. Conclusion We report a varying distribution of PTB in small and different areas over 10 years. Spatial and temporal analysis of disease distribution can be used to identify areas with a high burden of disease and predictors of clustering, which helps in making policy decisions and devising targeted interventions.
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Affiliation(s)
- Mesay Hailu Dangisso
- Department of Public Health, College of Medicine and Health Sciences, Hawassa University , Hawassa, Ethiopia
| | - Daniel Gemechu Datiko
- Department of Clinical Sciences, Liverpool School of Tropical Medicine , Liverpool, UK
| | - Bernt Lindtjørn
- Department of Public Health, College of Medicine and Health Sciences, Hawassa University , Hawassa, Ethiopia.,Centre for International Health, Faculty of Medicine, University of Bergen , Bergen, Norway
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23
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Hass FS, Jokar Arsanjani J. The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18062803. [PMID: 33802001 PMCID: PMC7998460 DOI: 10.3390/ijerph18062803] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 12/19/2022]
Abstract
The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus' rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability events with massive impacts. Certainly, similar to many phenomena, the Covid-19 pandemic has shown us its own geography presenting its emergence and evolving patterns as well as taking advantage of our geographical settings for escalating its spread. Hence, this study aims at presenting a data-driven approach for exploring the spatio-temporal patterns of the pandemic over a regional scale, i.e., Europe and a country scale, i.e., Denmark, and also what geographical variables potentially contribute to expediting its spread. We used official regional infection rates, points of interest, temperature and air pollution data for monitoring the pandemic's spread across Europe and also applied geospatial methods such as spatial autocorrelation and space-time autocorrelation to extract relevant indicators that could explain the dynamics of the pandemic. Furthermore, we applied statistical methods, e.g., ordinary least squares, geographically weighted regression, as well as machine learning methods, e.g., random forest for exploring the potential correlation between the chosen underlying factors and the pandemic spread. Our findings indicate that population density, amenities such as cafes and bars, and pollution levels are the most influential explanatory variables while pollution levels can be explicitly used to monitor lockdown measures and infection rates at country level. The choice of data and methods used in this study along with the achieved results and presented discussions can empower health authorities and decision makers with an interactive decision support tool, which can be useful for imposing geographically varying lockdowns and protectives measures using historical data.
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24
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Poverty and Physical Geographic Factors: An Empirical Analysis of Sichuan Province Using the GWR Model. SUSTAINABILITY 2020. [DOI: 10.3390/su13010100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Given the complexity of the poverty problem, efforts and policies aiming at reducing poverty should be tailored to local conditions, including cultural, economic, social, and geographic aspects. Taking the Sichuan Province of China as the study area, this paper explores the impact of physical geographic factors on poverty using the Ordinary Least Squares (OLS) Regression and the Geographically Weighted Regression (GWR) models at the county level. In total, 28 factors classified in seven groups were considered as variables, including terrain (relief degree of the land surface, altitude, and slope); vegetation (forest coverage rate); water (drainage network density); climate (temperature, annual average rainfall); and natural disaster (landslide, debris flow, and torrential flood). The 28 variables were then tested using correlations and regressions. A total of six physical variables remained significant for the OLS and GWR models. The results showed that the local GWR model was superior to the OLS regression model and, hence, more suitable for explaining the associations between the poverty rate and physical geographic features in Sichuan.
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25
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Zeng D, Wu X. Exposure to suicide in residential neighborhood and mental distress symptoms in Hong Kong: A spatiotemporal analysis. Health Place 2020; 67:102472. [PMID: 33316602 DOI: 10.1016/j.healthplace.2020.102472] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/24/2020] [Accepted: 10/29/2020] [Indexed: 10/22/2022]
Affiliation(s)
- Donglin Zeng
- Center for Applied Social and Economic Research, The Hong Kong University of Science and Technology, Hong Kong.
| | - Xiaogang Wu
- Center for Applied Social and Economic Research, NYU Shanghai, China; Department of Sociology, New York University, USA.
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26
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Song I, Kim OJ, Choe SA, Kim SY. Spatial heterogeneity in the association between particulate matter air pollution and low birth weight in South Korea. ENVIRONMENTAL RESEARCH 2020; 191:110096. [PMID: 32871145 DOI: 10.1016/j.envres.2020.110096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 08/07/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
As many studies showed the spatial heterogeneity in the association between particulate matter (PM) air pollution and low birth weight (LBW), few studies focused on the variation of local associations at the national scale and related areal characteristics. This study aimed to explore different approaches to estimating local effects of PM with an aerodynamic diameter ≤10 μm (PM10) on LBW across 235 districts in South Korea, to investigate the spatial pattern of local associations, and to examine the relationship with local socio-demographic and environmental characteristics. LBW was identified in 5,692,650 mothers from birth certificate data for 2001-2013. We estimated individual annual-average concentrations of PM10 at centroids of mothers' residential districts by using a previously-validated prediction model. Then, we estimated district-specific odds ratios of LBW for PM10 using modified geographically weighted logistic regression. Here, we applied four approaches with different neighborhood definitions: the distance-based approach within 20- and 40-km bandwidth and the hybrid approach replacing with adjacent districts for urban districts <100 km2. In addition, we compared district-specific socioeconomic indicators and emission estimates across three groups of districts that showed significantly positive, no, and significantly negative associations. Medians of district-specific estimates of four approaches were similar to the global estimate and between each other. However, their variability differed with some unreasonably high estimates when a small distance was applied as the neighborhood definition, although spatial pattern was generally similar among the four. The hybrid approach based on the different neighborhood definition by urban and rural areas provided stable risk estimates. Higher risk districts in rural areas were found in more socioeconomically-deprived areas, whereas urban areas showed higher risk districts when their air pollution emissions were higher. Our approach and findings will help identify high risk areas and enhance understanding of geographic determinants.
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Affiliation(s)
- Insang Song
- Department of Geography, University of Oregon, Eugene, OR, 97403, United States
| | - Ok-Jin Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Gyeonggi, 10408, Republic of Korea
| | - Seung-Ah Choe
- Department of Preventive Medicine, Korea University Medical College, Seoul, 02841, Republic of Korea; Department of Epidemiology & Health Informatics, Graduate School of Public Health, Korea University, Seoul, 02841, Republic of Korea
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Gyeonggi, 10408, Republic of Korea.
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27
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Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression. MATHEMATICS 2020. [DOI: 10.3390/math8091523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We introduce a new multivariate regression model based on the generalized Poisson distribution, which we called geographically-weighted multivariate generalized Poisson regression (GWMGPR) model, and we present a maximum likelihood step-by-step procedure to obtain parameters for it. We use the maximum likelihood ratio test to examine the significance of the regression parameters and to define their critical region.
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28
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Garjito TA, Hidajat MC, Kinansi RR, Setyaningsih R, Anggraeni YM, Mujiyanto, Trapsilowati W, Jastal, Ristiyanto, Satoto TBT, Gavotte L, Manguin S, Frutos R. Stegomyia Indices and Risk of Dengue Transmission: A Lack of Correlation. Front Public Health 2020; 8:328. [PMID: 32793541 PMCID: PMC7393615 DOI: 10.3389/fpubh.2020.00328] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 06/15/2020] [Indexed: 01/24/2023] Open
Abstract
Dengue is present in 128 countries worldwide and is still expanding. There is currently no treatment or universally approved vaccine available. Therefore, prevention and control of mosquito vectors remain the most efficient ways of managing the risk of dengue outbreaks. The Stegomyia indices have been developed as quantitative indicators of the risk of dengue outbreaks. However, conflictual data are circulating about their reliability. We report in this article the first extensive study on Stegomyia indices, covering 78 locations of differing environmental and socio-economic conditions, climate, and population density across Indonesia, from West Sumatra to Papua. A total of 65,876 mosquito larvae and pupae were collected for the study. A correlation was found between incidence and human population density. No correlation was found between the incidence of dengue and the Stegomyia indices.
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Affiliation(s)
- Triwibowo Ambar Garjito
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia.,Université de Montpellier, Montpellier, France.,HydroSciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD), CNRS, Université de Montpellier, Montpellier, France
| | - Muhammad Choirul Hidajat
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Revi Rosavika Kinansi
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Riyani Setyaningsih
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Yusnita Mirna Anggraeni
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Mujiyanto
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Wiwik Trapsilowati
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Jastal
- Health Research and Development Unit Banjarnegara, National Institute of Health Research Development (NIHRD), MoH, Banjarnegara, Indonesia
| | - Ristiyanto
- Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research Development (NIHRD), MoH, Salatiga, Indonesia
| | - Tri Baskoro Tunggul Satoto
- Department of Parasitology, Faculty of Medicine, Public Health and Nursing, Gadjah Mada University, Yogyakarta, Indonesia
| | | | - Sylvie Manguin
- Université de Montpellier, Montpellier, France.,HydroSciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD), CNRS, Université de Montpellier, Montpellier, France
| | - Roger Frutos
- Université de Montpellier, Montpellier, France.,CIRAD, Intertryp, Montpellier, France.,IES, Université de Montpellier-CNRS, Montpellier, France
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29
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Yang Q, Yuan Q, Yue L, Li T. Investigation of the spatially varying relationships of PM 2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 262:114257. [PMID: 32146364 DOI: 10.1016/j.envpol.2020.114257] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 02/21/2020] [Accepted: 02/21/2020] [Indexed: 06/10/2023]
Abstract
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.
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Affiliation(s)
- Qianqian Yang
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
| | - Qiangqiang Yuan
- School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China; Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, 430079, Hubei, China.
| | - Linwei Yue
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei, 430074, China
| | - Tongwen Li
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, 430079, China
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30
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Wu S, Ren H, Chen W, Li T. Neglected Urban Villages in Current Vector Surveillance System: Evidences in Guangzhou, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010002. [PMID: 31861276 PMCID: PMC6981632 DOI: 10.3390/ijerph17010002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/14/2019] [Accepted: 12/15/2019] [Indexed: 12/28/2022]
Abstract
Numerous urban villages (UVs) with substandard living conditions that cause people to live there with vulnerability to health impacts, including vector-borne diseases such as dengue fever (DF), are major environmental and public health concerns in highly urbanized regions, especially in developing countries. It is necessary to explore the relationship between UVs and vector for effectively dealing with these problems. In this study, land-use types, including UVs, normal construction land (NCL), unused land (UL), vegetation, and water, were retrieved from the high-resolution remotely sensed imagery in the central area of Guangzhou in 2017. The vector density from May to October in 2017, including Aedes. albopictus (Ae. albopictus)’s Breteau index (BI), standard space index (SSI), and adult density index (ADI) were obtained from the vector surveillance system implemented by the Guangzhou Center for Disease Control and Prevention (CDC). Furthermore, the spatial and temporal patterns of vector monitoring sites and vector density were analyzed on a fine scale, and then the Geodetector tool was further employed to explore the relationships between vector density and land-use types. The monitoring sites were mainly located in NCL (55.70%–56.44%) and UV (13.14%–13.92%). Among the total monitoring sites of BI (79), SSI (312), and ADI (326), the random sites accounted for about 88.61%, 97.12%, and 98.47%, respectively. The density of Ae. albopictus was temporally related to rainfall and temperature and was obviously differentiated among different land-use types. Meanwhile, the grids with higher density, which were mostly concentrated in the Pearl River fork zone that collects a large number of UVs, showed that the density of Ae. albopictus was spatially associated with the UVs. Next, the results of the Geodetector illustrated that UVs posed great impact on the density of Ae. albopictus across the central region of Guangzhou. We suggest that the number of monitoring sites in the UVs should be appropriately increased to strengthen the current vector surveillance system in Guangzhou. This study will provide targeted guidance for local authorities, making more effective control and prevention measures on the DF epidemics.
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Affiliation(s)
- Sijia Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;
- College of Geographical Science, Fujian Normal University, No.8 Shangsan Road, Fuzhou 350007, China;
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;
- Correspondence: (H.R.); (T.L.)
| | - Wenhui Chen
- College of Geographical Science, Fujian Normal University, No.8 Shangsan Road, Fuzhou 350007, China;
| | - Tiegang Li
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
- Correspondence: (H.R.); (T.L.)
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31
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Zhou S, Zhou S, Liu L, Zhang M, Kang M, Xiao J, Song T. Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16245013. [PMID: 31835451 PMCID: PMC6950619 DOI: 10.3390/ijerph16245013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/05/2019] [Accepted: 12/06/2019] [Indexed: 12/25/2022]
Abstract
Environment and human mobility have been considered as two important factors that drive the outbreak and transmission of dengue fever (DF). Most studies focus on the local environment while neglecting environment of the places, especially epidemic areas that people came from or traveled to. Commuting is a major form of interactions between places. Therefore, this research generates commuting flows from mobile phone tracked data. Geographically weighted Poisson regression (GWPR) and analysis of variance (ANOVA) are used to examine the effect of commuting flows, especially those from/to epidemic areas, on DF in 2014 at the Jiedao level in Guangzhou. The results suggest that (1) commuting flows from/to epidemic areas affect the transmission of DF; (2) such effects vary in space; and (3) the spatial variation of the effects can be explained by the environment of the epidemic areas that commuters commuted from/to. These findings have important policy implications for making effective intervention strategies, especially when resources are limited.
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Affiliation(s)
- Shuli Zhou
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
| | - Suhong Zhou
- School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
- Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
- Correspondence: (S.Z.); (T.S.)
| | - Lin Liu
- Center of Geo-Informatics for Public Security, School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;
- Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; (M.Z.); (M.K.)
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; (M.Z.); (M.K.)
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China;
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China; (M.Z.); (M.K.)
- Correspondence: (S.Z.); (T.S.)
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32
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Yamana TK, Shaman J. A framework for evaluating the effects of observational type and quality on vector-borne disease forecast. Epidemics 2019; 30:100359. [PMID: 31439454 PMCID: PMC7315892 DOI: 10.1016/j.epidem.2019.100359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/31/2019] [Accepted: 08/02/2019] [Indexed: 11/03/2022] Open
Abstract
Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy.
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Affiliation(s)
- Teresa K Yamana
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, United States.
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, United States
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33
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Geostatistical modeling of dengue disease in Lahore, Pakistan. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-0428-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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34
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Ha H. Using geographically weighted regression for social inequality analysis: association between mentally unhealthy days (MUDs) and socioeconomic status (SES) in U.S. counties. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2019; 29:140-153. [PMID: 30230366 DOI: 10.1080/09603123.2018.1521915] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 09/05/2018] [Indexed: 06/08/2023]
Abstract
This research explores geographic variability of factors on social inequality related to mental health in the United States using county-level data in 2014. First, we account for complex design factors in Behavioural Risk Factor Surveillance System (BRFSS) data such as clustering, stratification, and sample weight using Complex Samples General Linear Model (CSGLM). Then, three variables are used in the model as indicators of social inequality, low socioeconomic status (SES): unemployment, education status, and social association status. A geographically weighted regression analysis is applied to examine the spatial variations in the associations of mentally unhealthy days (MUDs) with the indicators of SES in the United States. The results demonstrate that unemployment and education level show global positive and negative influences respectively on MUDs. Social association status ranged from positive to negative across the United States, implying some geographic clustering. These findings suggest that social and health policies should be adjusted to address the different effects of indicators of social inequality on mental health across different social characteristics of communities to more effectively manage mental health problems.
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Affiliation(s)
- Hoehun Ha
- Department of Biology and Environmental Science, Auburn University at Montgomery, Montgomery, AL, USA
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Zheng L, Ren HY, Shi RH, Lu L. Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China. Infect Dis Poverty 2019; 8:24. [PMID: 30922405 PMCID: PMC6440137 DOI: 10.1186/s40249-019-0533-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 03/12/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people's health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. METHODS A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. RESULTS DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. CONCLUSIONS The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.
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Affiliation(s)
- Lan Zheng
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China.,State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,School of Geographic Sciences, East China Normal University, Shanghai, China.,Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University and Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Shanghai, China
| | - Hong-Yan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
| | - Run-He Shi
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China. .,School of Geographic Sciences, East China Normal University, Shanghai, China. .,Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University and Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Shanghai, China.
| | - Liang Lu
- Department of Vector Biology and Control, Chinese Center for Disease Control and Prevention, Natural Institute for Communicable Disease Control and Prevention, Beijing, China
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The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8010051] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services.
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Mahmood S, Irshad A, Nasir JM, Sharif F, Farooqi SH. Spatiotemporal analysis of dengue outbreaks in Samanabad town, Lahore metropolitan area, using geospatial techniques. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 191:55. [PMID: 30617862 DOI: 10.1007/s10661-018-7162-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 12/13/2018] [Indexed: 06/09/2023]
Abstract
Dengue is endemic to Pakistan with its usual peak incidence in the post-monsoon period. In the last decade, dengue outbreaks have occurred in major urban areas particularly Karachi and Lahore, affecting large numbers of people. This study is an attempt to analyze the spatiotemporal variation of dengue fever (DF) in Samanabad town, Lahore metropolitan area. The study is based on secondary data, acquired from concerned government departments. Point level geo-coding is used to transform the relative location to the absolute location using Google Earth, and Global Position System (GPS) is used to validate the geo-coded location. Geographic information system (GIS) has been used to perform spatial analysis. It has been found that temporally DF prevalence varies from month to month and year to year. Major outbreak was observed in the year 2013 with more than 900 confirmed DF cases. Rainfall, temperature, and humidity have played a central role in outbreaks. The land cover pattern and population density further intensified the outbreak. Spatially, the number of DF incidence was high in those localities where the entire land is built-up and with little/no green space areas. Analysis reveals that DF is still a major threat to the area as socioeconomic and geographic conditions favor vector breeding and transfer of disease from one person/place to another. This study presents useful information regarding spatiotemporal patterns of dengue outbreak and may bring the attention of public health departments to formulate dengue-combating strategies. The methodology is general for spatiotemporal analysis and can be applied to other infectious diseases as well.
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Affiliation(s)
- Shakeel Mahmood
- Department of Geography, Government College University Lahore, Lower Mall, District Lahore, Lahore, 54000, Pakistan.
| | - Ahtisham Irshad
- Department of Geography, Government College University Lahore, Lower Mall, District Lahore, Lahore, 54000, Pakistan
| | | | - Faiza Sharif
- Sustainable Development Study Center, Government College University Lahore, Lahore, Pakistan
| | - Shahid Hussain Farooqi
- Department of Clinical Medicine and Surgery, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan
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Acharya BK, Cao C, Lakes T, Chen W, Naeem S, Pandit S. Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2018; 62:1973-1986. [PMID: 30182200 DOI: 10.1007/s00484-018-1601-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 07/31/2018] [Accepted: 08/13/2018] [Indexed: 05/26/2023]
Abstract
Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran's I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.
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Affiliation(s)
- Bipin Kumar Acharya
- Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China
| | - ChunXiang Cao
- Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China.
| | - Tobia Lakes
- Department of Geography, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - Wei Chen
- Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China
| | - Shahid Naeem
- Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing, 100094, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China
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Zhen Z, Cao Q, Shao L, Zhang L. Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children's Lead Poisoning in Syracuse, NY, USA. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E2300. [PMID: 30347704 PMCID: PMC6210516 DOI: 10.3390/ijerph15102300] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/15/2018] [Accepted: 10/17/2018] [Indexed: 12/16/2022]
Abstract
Objective: The purpose of this study was to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. Methods: Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to model the relationships between children's lead poisoning and three environmental factors at different quantiles (25th, 50th, 75th, and 90th). The response variable was the incident rate of children's blood lead level ≥ 5 µg/dL in each census block, and the three predictor variables included building year, town taxable values, and soil lead concentration. Results: At each quantile, the regression coefficients of both global QR and GWQR models were (1) negative for both building year and town taxable values, indicating that the incident rate of children lead poisoning reduced with newer buildings and/or higher taxable values of the houses; and (2) positive for the soil lead concentration, implying that higher soil lead concentration around the house may cause higher risks of children's lead poisoning. Further, these negative or positive relationships between children's lead poisoning and three environmental factors became stronger for larger quantiles (i.e., higher risks). Conclusions: The GWQR models enabled us to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse, which would provide useful information to assist the government agencies to make better decisions on where and what the lead hazard treatment should focus on.
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Affiliation(s)
- Zhen Zhen
- Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China.
| | - Qianqian Cao
- Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA.
| | - Liyang Shao
- Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA.
| | - Lianjun Zhang
- Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA.
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Atique S, Chan TC, Chen CC, Hsu CY, Iqtidar S, Louis VR, Shabbir SA, Chuang TW. Investigating spatio-temporal distribution and diffusion patterns of the dengue outbreak in Swat, Pakistan. J Infect Public Health 2018; 11:550-557. [PMID: 29287804 DOI: 10.1016/j.jiph.2017.12.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 11/29/2017] [Accepted: 12/06/2017] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Dengue has been endemic to Pakistan in the last two decades. There was a massive outbreak in the Swat valley in 2013. Here we demonstrate the spatio-temporal clustering and diffusion patterns of the dengue outbreak. METHODS Dengue case data were acquired from the hospital records in the Swat district of Pakistan. Ring maps visualize the distribution and diffusion of the number of cases and incidence of dengue at the level of the union council. We applied space-time scan statistics to identify spatio-temporal clusters. Ordinary least squares and geographically weighted regression models were used to evaluate the impact of elevation, population density, and distance to the river. RESULTS The results show that dengue distribution is not random, but clustered in space and time in the Swat district. Males constituted 68% of the cases while females accounted for about 32%. A majority of the cases (>55%) were younger than 40 years of age. The southern part was a major hotspot affected by the dengue outbreak in 2013. There are two space-time clusters in the spatio-temporal analysis. GWR and OLS show that population density is a significant explanatory variable for the dengue outbreak, while GWR exhibits better performance in terms of 'R2=0.49 and AICc=700'. CONCLUSION Dengue fever is clustered in the southern part of the Swat district. This region is relatively urban in character, with most of the population of the district residing here. There is a need to strengthen the surveillance system for reporting dengue cases in order to respond to future outbreaks in a robust way.
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Affiliation(s)
- Suleman Atique
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan
| | - Ta-Chien Chan
- Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taiwan
| | - Chien-Chou Chen
- Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taiwan
| | - Chien-Yeh Hsu
- Master's Program in Global Health and Development, Taipei Medical University, Taiwan; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Somia Iqtidar
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Valérie R Louis
- Institute of Public Health, Heidelberg University, Heidelberg, Germany
| | - Syed A Shabbir
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan
| | - Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
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Mayfield HJ, Lowry JH, Watson CH, Kama M, Nilles EJ, Lau CL. Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study. Lancet Planet Health 2018; 2:e223-e232. [PMID: 29709286 PMCID: PMC5924768 DOI: 10.1016/s2542-5196(18)30066-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 04/03/2018] [Accepted: 04/16/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR) to provide insights into the ecoepidemiology of human leptospirosis in Fiji. METHODS We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1-90 years) was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month) on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR) for each covariate. FINDINGS The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each covariate. Maximum rainfall had the least variation across space (median OR 1·30, IQR 1·27-1·35), and distance to river varied the most (1·45, 1·35-2·05). The predictive risk map indicated that the highest risk was in the interior of Viti Levu, and the agricultural region and southern end of Vanua Levu. INTERPRETATION GWLR provided a valuable method for modelling spatial heterogeneity of covariates for leptospirosis infection and their relative importance over space. Results of GWLR could be used to inform more place-specific interventions, particularly for diseases with strong environmental or sociodemographic drivers of transmission. FUNDING WHO, Australian National Health & Medical Research Council, University of Queensland, UK Medical Research Council, Chadwick Trust.
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Affiliation(s)
- Helen J Mayfield
- Department of Global Health, Research School of Population Health, The Australian National University, Canberra, ACT, Australia
| | - John H Lowry
- School of People, Environment and Planning, Massey University, Palmerston North, New Zealand; School of Geography, Earth Science and Environment, The University of the South Pacific, Suva, Fiji
| | - Conall H Watson
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Mike Kama
- Fiji Centre for Communicable Disease Control, Ministry of Health and Medical Services, Suva, Fiji
| | - Eric J Nilles
- Division of Pacific Technical Support, World Health Organization, Suva, Fiji
| | - Colleen L Lau
- Department of Global Health, Research School of Population Health, The Australian National University, Canberra, ACT, Australia; Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
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Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity. ATMOSPHERE 2018. [DOI: 10.3390/atmos9040156] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Simionato de Assis I, Arcoverde MAM, Ramos ACV, Alves LS, Berra TZ, Arroyo LH, de Queiroz AAR, dos Santos DT, Belchior ADS, Alves JD, Pieri FM, Silva-Sobrinho RA, Pinto IC, Tavares CM, Yamamura M, Frade MAC, Palha PF, Chiaravalloti-Neto F, Arcêncio RA. Social determinants, their relationship with leprosy risk and temporal trends in a tri-border region in Latin America. PLoS Negl Trop Dis 2018; 12:e0006407. [PMID: 29624595 PMCID: PMC5906021 DOI: 10.1371/journal.pntd.0006407] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 04/18/2018] [Accepted: 03/24/2018] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Brazil is the only country in Latin America that has adopted a national health system. This causes differences in access to health among Latin American countries and induces noticeable migration to Brazilian regions to seek healthcare. This phenomenon has led to difficulties in the control and elimination of diseases related to poverty, such as leprosy. The aim of this study was to evaluate social determinants and their relationship with the risk of leprosy, as well as to examine the temporal trend of its occurrence in a Brazilian municipality located on the tri-border area between Brazil, Paraguay and Argentina. METHODS This ecological study investigated newly-diagnosed cases of leprosy between 2003 and 2015. Exploratory analysis of the data was performed through descriptive statistics. For spatial analysis, geocoding of the data was performed using spatial scan statistic techniques to obtain the Relative Risk (RR) for each census tract, with their respective 95% confidence intervals calculated. The Bivariate Moran I test, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to analyze the spatial relationships of social determinants and leprosy risk. The temporal trend of the annual coefficient of new cases was obtained through the Prais-Winsten regression. A standard error of 5% was considered statistically significant (p < 0.05). RESULTS Of the 840 new cases identified in the study, there was a predominance of females (n = 427, 50.8%), of white race/color (n = 685, 81.6%), age range 15 to 59 years (n = 624, 74.3%), and incomplete elementary education (n = 504, 60.0%). The results obtained from multivariate analysis revealed that the proportion of households with monthly nominal household income per capita greater than 1 minimum wage (β = 0.025, p = 0.036) and people of brown race (β = -0.101, p = 0.024) were statistically-significantly associated with risk of illness due to leprosy. These results also confirmed that social determinants and risk of leprosy were significantly spatially non-stationary. Regarding the temporal trend, a decrease of 4% (95% CI [-0.053, -0.033], p = 0.000) per year was observed in the rate of detection of new cases of leprosy. CONCLUSION The social determinants income and race/color were associated with the risk of leprosy. The study's highlighting of these social determinants can contribute to the development of public policies directed toward the elimination of leprosy in the border region.
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Affiliation(s)
- Ivaneliza Simionato de Assis
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- * E-mail:
| | - Marcos Augusto Moraes Arcoverde
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Antônio Carlos Viera Ramos
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Luana Seles Alves
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Thais Zamboni Berra
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Luiz Henrique Arroyo
- Graduate Program Interunit Doctoral Program in Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil
| | | | - Danielle Talita dos Santos
- Graduate Program Interunit Doctoral Program in Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil
| | - Aylana de Souza Belchior
- Graduate Program Interunit Doctoral Program in Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil
| | - Josilene Dália Alves
- Graduate Program Interunit Doctoral Program in Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil
| | | | | | - Ione Carvalho Pinto
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Clodis Maria Tavares
- Department of School of Nursing and Pharmacy, Federal University of Alagoas, Maceió, Alagoas, Brazil
| | - Mellina Yamamura
- Graduate Program Interunit Doctoral Program in Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil
| | - Marco Andrey Cipriani Frade
- Division of Dermatology of the Department of Internal Medicine of the Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Pedro Fredemir Palha
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | - Ricardo Alexandre Arcêncio
- Graduate Program in Public Health Nursing, Nursing College of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Graduate Program Interunit Doctoral Program in Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil
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Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7040143] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15040671. [PMID: 29617301 PMCID: PMC5923713 DOI: 10.3390/ijerph15040671] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 03/28/2018] [Accepted: 04/01/2018] [Indexed: 12/23/2022]
Abstract
Suicide is a serious but preventable public health issue. Several previous studies have revealed a positive association between altitude and suicide rates at the county level in the contiguous United States. We assessed the association between suicide rates and altitude using a cross-county ecological study design. Data on suicide rates were obtained from a Web-based Injury Statistics Query and Reporting System (WISQARS), maintained by the U.S. National Center for Injury Prevention and Control (NCIPC). Altitude data were collected from the United States Geological Survey (USGS). We employed an ordinary least square (OLS) regression to model the association between altitude and suicide rates in 3064 counties in the contiguous U.S. We conducted a geographically weighted regression (GWR) to examine the spatially varying relationship between suicide rates and altitude after controlling for several well-established covariates. A significant positive association between altitude and suicide rates (average county rates between 2008 and 2014) was found in the dataset in the OLS model (R2 = 0.483, p < 0.001). Our GWR model fitted the data better, as indicated by an improved R2 (average: 0.62; range: 0.21–0.64) and a lower Akaike Information Criteria (AIC) value (13,593.68 vs. 14,432.14 in the OLS model). The GWR model also significantly reduced the spatial autocorrelation, as indicated by Moran’s I test statistic (Moran’s I = 0.171; z = 33.656; p < 0.001 vs. Moran’s I = 0.323; z = 63.526; p < 0.001 in the OLS model). In addition, a stronger positive relationship was detected in areas of the northern regions, northern plain regions, and southeastern regions in the U.S. Our study confirmed a varying overall positive relationship between altitude and suicide. Future research may consider controlling more predictor variables in regression models, such as firearm ownership, religion, and access to mental health services.
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Low socioeconomic condition and the risk of dengue fever: A direct relationship. Acta Trop 2018; 180:47-57. [PMID: 29352990 DOI: 10.1016/j.actatropica.2018.01.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 01/11/2018] [Accepted: 01/15/2018] [Indexed: 02/05/2023]
Abstract
This study aimed to characterize the first dengue fever epidemic in Várzea Paulista, São Paulo, Brazil, and its spatial and spatio-temporal distribution in order to assess the association of socioeconomic factors with dengue occurrence. We used autochthonous dengue cases confirmed in a 2007 epidemic, the first reported in the city, available in the Information System on Diseases of Compulsory Declaration database. These cases where geocoded by address. We identified spatial and spatio-temporal clusters of high- and low-risk dengue areas using scan statistics. To access the risk of dengue occurrence and to evaluate its relationship with socioeconomic level we used a population-based case-control design. Firstly, we fitted a generalized additive model (GAM) to dengue cases and controls without considering the non-spatial covariates to estimate the odds ratios of the occurrence of the disease. The controls were drawn considering the spatial distribution of the household of the study area and represented the source population of the dengue cases. After that, we assessed the relationship between socioeconomic variables and dengue using the GAM and obtained the effect of these covariates in the occurrence of dengue adjusted by the spatial localization of the cases and controls. Cluster analysis and GAM indicated that northeastern area of Várzea Paulista was the most affected area during the epidemic. The study showed a positive relationship between low socioeconomic condition and increased risk of dengue. We studied the first dengue epidemic in a highly susceptible population at the beginning of the outbreak and therefore it may have allowed to identify an association between low socioeconomic conditions and increased risk of dengue. These results may be useful to predict the occurrence and to identify priority areas to develop control measures for dengue, and also for Zika and Chikungunya; diseases that recently reached Latin America, especially Brazil.
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A Spatial Analysis of the Relationship between Vegetation and Poverty. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7030083] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Chuang TW, Ng KC, Nguyen TL, Chaves LF. Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15030396. [PMID: 29495351 PMCID: PMC5876941 DOI: 10.3390/ijerph15030396] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 02/23/2018] [Accepted: 02/23/2018] [Indexed: 12/29/2022]
Abstract
The metropolitan region of Tainan City in southern Taiwan experienced a dengue outbreak in 2015. This manuscript describes basic epidemiological features of this outbreak and uses spatial and temporal analysis tools to understand the spread of dengue during the outbreak. The analysis found that, independently of gender, dengue incidence rate increased with age, and proportionally affected more males below the age of 40 years but females above the age of 40 years. A spatial scan statistic was applied to detect clusters of disease transmission. The scan statistic found that dengue spread in a north-south diffusion direction, which is across the North, West-Central and South districts of Tainan City. Spatial regression models were used to quantify factors associated with transmission. This analysis indicated that neighborhoods with high proportions of residential area (or low wetland cover) were associated with dengue transmission. However, these association patterns were non-linear. The findings presented here can help Taiwanese public health agencies to understand the fundamental epidemiological characteristics and diffusion patterns of the 2015 dengue outbreak in Tainan City. This type of information is fundamental for policy making to prevent future uncontrolled dengue outbreaks, given that results from this study suggest that control interventions should be emphasized in the North and West-Central districts of Tainan city, in areas with a moderate percentage of residential land cover.
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Affiliation(s)
- Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing Street, Xinyi District, Taipei 11031, Taiwan.
| | - Ka-Chon Ng
- College of Public Health, National Taiwan University, Taipei 10607, Taiwan.
| | - Thi Luong Nguyen
- College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
| | - Luis Fernando Chaves
- Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud (INCIENSA), Apartado Postal 4-2250, Tres Ríos, Cartago, Costa Rica.
- Programa de Investigación en Enfermedades Tropicales (PIET), Escuela de Medicina Veterinaria, Universidad Nacional, Apartado Postal 304-3000, Heredia, Costa Rica.
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Liu Y, Lam KF, Wu JT, Lam TTY. Geographically weighted temporally correlated logistic regression model. Sci Rep 2018; 8:1417. [PMID: 29362396 PMCID: PMC5780421 DOI: 10.1038/s41598-018-19772-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 01/08/2018] [Indexed: 11/18/2022] Open
Abstract
Detecting the temporally and spatially varying correlations is important to understand the biological and disease systems. Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data, by incorporating spatial and temporal information for joint inference. The local likelihood method is adopted to estimate the spatial relationship, while the smoothing method is employed to estimate the temporal variation. We present the construction and implementation of GWTCLR and the study of the asymptotic properties of the proposed estimator. Simulation studies were conducted to evaluate the robustness of the proposed model. GWTCLR was applied on real epidemiologic data to study the climatic determinants of human seasonal influenza epidemics. Our method obtained results largely consistent with previous studies but also revealed certain spatial and temporal varying patterns that were unobservable by previous models and methods.
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Affiliation(s)
- Yang Liu
- Center of Influenza Research, State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong, China.,School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Kwok-Fai Lam
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Joseph T Wu
- School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Tommy Tsan-Yuk Lam
- Center of Influenza Research, State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Pokfulam, Hong Kong, China. .,School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong, China.
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Pinto AA, Zilberman D. Prospective Study About the Influence of Human Mobility in Dengue Transmission in the State of Rio de Janeiro. SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS 2018. [PMCID: PMC7123889 DOI: 10.1007/978-3-319-74086-7_21] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Dengue is a human arboviral disease transmitted by Aedes mosquitoes and it is currently a major public health problem in which around 2–5 billion people are at risk of infection each year. Climate changes and human mobility contribute to increase the number of cases and to spread the disease all around the world. In this work, the influence of human mobility is evaluated by analyzing a sequence of correlations of dengue incidence between cities in southeastern Brazil. The methodology initially identifies the cities were the epidemy begins, considered as focus for that epidemic year. The strength of the linear association between all pairs of cities were calculated identifying the cities which have high correlations with the focus-cities. The correlations are also calculated between all pairs considering a time lag of 1, 2 or 3 weeks ahead for all cities except the focus ones. Centred differences of the notification number are used to detect the outbreaks. The tests were made with DATASUS-SINAN data of the state of Rio de Janeiro, from January 2008 to December 2013. Preliminary results indicate that the spread of dengue from one city to another can be characterized by the development of the sequence of shifted correlations. The proposal may be useful to consider control strategies against disease transmission.
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Affiliation(s)
- Alberto A. Pinto
- LIAAD—INESC TEC, Department of Mathematics, Faculty of Science, University of Porto, Porto, Portugal
| | - David Zilberman
- Department of Agricultural and Resource Economics, University of California, Berkeley, California USA
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