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Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study

BACKGROUND: Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission o...

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Autores principales: Liu, Yonghong, Wang, Zengmiao, Rader, Benjamin, Li, Bingying, Wu, Chieh-Hsi, Whittington, Jason D, Zheng, Pai, Stenseth, Nils Chr, Bjornstad, Ottar N, Brownstein, John S, Tian, Huaiyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143730/
https://www.ncbi.nlm.nih.gov/pubmed/34045001
http://dx.doi.org/10.1016/S2589-7500(21)00059-5
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author Liu, Yonghong
Wang, Zengmiao
Rader, Benjamin
Li, Bingying
Wu, Chieh-Hsi
Whittington, Jason D
Zheng, Pai
Stenseth, Nils Chr
Bjornstad, Ottar N
Brownstein, John S
Tian, Huaiyu
author_facet Liu, Yonghong
Wang, Zengmiao
Rader, Benjamin
Li, Bingying
Wu, Chieh-Hsi
Whittington, Jason D
Zheng, Pai
Stenseth, Nils Chr
Bjornstad, Ottar N
Brownstein, John S
Tian, Huaiyu
author_sort Liu, Yonghong
collection PubMed
description BACKGROUND: Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide. METHODS: In this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high–middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression. FINDINGS: The reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=–0·47, p<0·0001). However, reductions in inter-city movement flow (both inflow and outflow intensity) were not associated with SEI and were only associated with government control measures. In the country-level analysis, countries with higher sociodemographic and Universal Health Coverage indexes had greater reductions in population mobility (ie, in transit stations, workplaces, and retail and recreation) following national emergency declarations than those with lower sociodemographic and Universal Health Coverage indexes. A higher sociodemographic index showed a greater reduction in mobility in transit stations (r=–0·27, p=0·0028), workplaces (r=–0·34, p=0·0002), and areas retail and recreation (rxs=–0·30, p=0·0012) than those with a lower sociodemographic index. INTERPRETATION: Although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level—eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality. FUNDING: Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology.
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spelling pubmed-81437302021-05-25 Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study Liu, Yonghong Wang, Zengmiao Rader, Benjamin Li, Bingying Wu, Chieh-Hsi Whittington, Jason D Zheng, Pai Stenseth, Nils Chr Bjornstad, Ottar N Brownstein, John S Tian, Huaiyu Lancet Digit Health Articles BACKGROUND: Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide. METHODS: In this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high–middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression. FINDINGS: The reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=–0·47, p<0·0001). However, reductions in inter-city movement flow (both inflow and outflow intensity) were not associated with SEI and were only associated with government control measures. In the country-level analysis, countries with higher sociodemographic and Universal Health Coverage indexes had greater reductions in population mobility (ie, in transit stations, workplaces, and retail and recreation) following national emergency declarations than those with lower sociodemographic and Universal Health Coverage indexes. A higher sociodemographic index showed a greater reduction in mobility in transit stations (r=–0·27, p=0·0028), workplaces (r=–0·34, p=0·0002), and areas retail and recreation (rxs=–0·30, p=0·0012) than those with a lower sociodemographic index. INTERPRETATION: Although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level—eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality. FUNDING: Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology. The Author(s). Published by Elsevier Ltd. 2021-06 2021-05-24 /pmc/articles/PMC8143730/ /pubmed/34045001 http://dx.doi.org/10.1016/S2589-7500(21)00059-5 Text en © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Articles
Liu, Yonghong
Wang, Zengmiao
Rader, Benjamin
Li, Bingying
Wu, Chieh-Hsi
Whittington, Jason D
Zheng, Pai
Stenseth, Nils Chr
Bjornstad, Ottar N
Brownstein, John S
Tian, Huaiyu
Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
title Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
title_full Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
title_fullStr Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
title_full_unstemmed Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
title_short Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study
title_sort associations between changes in population mobility in response to the covid-19 pandemic and socioeconomic factors at the city level in china and country level worldwide: a retrospective, observational study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143730/
https://www.ncbi.nlm.nih.gov/pubmed/34045001
http://dx.doi.org/10.1016/S2589-7500(21)00059-5
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