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Tong Z, Zhang H, Yu J, Jia X, Hou X, Kong Z. Spatial-temporal evolution of overweight and obesity among Chinese adolescents from 2016 to 2020. iScience 2024; 27:108742. [PMID: 38230263 PMCID: PMC10790006 DOI: 10.1016/j.isci.2023.108742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 11/19/2023] [Accepted: 12/12/2023] [Indexed: 01/18/2024] Open
Abstract
This study examines the spatial-temporal evolution of overweight and obesity among Chinese adolescents aged 14-17. Data from five national surveys conducted between 2016 and 2020 were analyzed to determine distribution patterns and trends. Results showed that overweight and obesity exhibit spatial clustering, with greater severity in the north and less severity in the south. The issue has spread from the northeast to the southwest of Mainland China. Using a local autocorrelation model, the regions were divided into a northern disease cold spot area (Inner Mongolia) and a southern disease hot spot area (Guangxi). Over the past five years, overweight rates among Chinese adolescents have not been effectively curbed, but obesity has shown some success in control and reversal until 2019. Future efforts should focus on the spatial-temporal pattern of disease spread, targeting hotspot areas and abnormal values for regional synergy and precise prevention and control.
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Affiliation(s)
- Zihan Tong
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
| | - Hanyue Zhang
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
- Institute of Physical Education, Northeast Normal University, Changchun 130024, China
| | - Jingjing Yu
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
| | - Xiao Jia
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
| | - Xiao Hou
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
- School of Sport Science, Beijing Sport University, Beijing 100084, China
| | - Zhenxing Kong
- Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing 100084, China
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Iyaniwura SA, Ringa N, Adu PA, Mak S, Janjua NZ, Irvine MA, Otterstatter M. Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19. PLoS Comput Biol 2023; 19:e1011123. [PMID: 37172027 DOI: 10.1371/journal.pcbi.1011123] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 05/24/2023] [Accepted: 04/24/2023] [Indexed: 05/14/2023] Open
Abstract
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
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Affiliation(s)
- Sarafa A Iyaniwura
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Notice Ringa
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Prince A Adu
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sunny Mak
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Naveed Z Janjua
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael A Irvine
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Otterstatter
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148267. [PMID: 35886114 PMCID: PMC9324591 DOI: 10.3390/ijerph19148267] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 02/04/2023]
Abstract
The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.
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Zhang S, Wang M, Yang Z, Zhang B. Do spatiotemporal units matter for exploring the microgeographies of epidemics? APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 142:102692. [PMID: 35399592 PMCID: PMC8982866 DOI: 10.1016/j.apgeog.2022.102692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 02/04/2022] [Accepted: 03/25/2022] [Indexed: 05/17/2023]
Abstract
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the "symptom onset" timestamp instead of only the "confirmed" data and that "spatial transmission" should not be confused with "spatial sprawling" of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.
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Affiliation(s)
- Sui Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Minghao Wang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Zhao Yang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Baolei Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
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Zhang S, Wang M, Yang Z, Zhang B. A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:13294. [PMID: 34948902 PMCID: PMC8704640 DOI: 10.3390/ijerph182413294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 11/24/2022]
Abstract
Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various "densities" were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the "densities" were actually an abstract reflection of the "contact" frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect "contact" frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional "densities". Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.
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Affiliation(s)
| | | | | | - Baolei Zhang
- College of Geography and Environment, Shandong Normal University, Jinan 250014, China; (S.Z.); (M.W.); (Z.Y.)
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