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利用人口数据了解甲型H1N1流感和新冠疫情病例在墨西哥联邦实体和各市的分布情况。

Using demographic data to understand the distribution of H1N1 and COVID-19 pandemics cases among federal entities and municipalities of Mexico.

作者信息

Sarria-Guzmán Yohanna, Bernal Jaime, De Biase Michele, Muñoz-Arenas Ligia C, González-Jiménez Francisco Erik, Mosso Clemente, De León-Lorenzana Arit, Fusaro Carmine

机构信息

Centro Regional de Investigación en Salud Pública, Instituto Nacional de Salud Pública, Tapachula, Chiapas, Mexico.

Facultad de Ingeniería y Ciencias Básicas, Fundación Universitaria del Área Andina, Valledupar, Cesar, Colombia.

出版信息

PeerJ. 2021 Mar 24;9:e11144. doi: 10.7717/peerj.11144. eCollection 2021.

Abstract

BACKGROUND

The novel coronavirus disease (COVID-19) pandemic is the second global health emergency the world has faced in less than two decades, after the H1N1 Influenza pandemic in 2009-2010. Spread of pandemics is frequently associated with increased population size and population density. The geographical scales (national, regional or local scale) are key elements in determining the correlation between demographic factors and the spread of outbreaks. The aims of this study were: (a) to collect the Mexican data related to the two pandemics; (b) to create thematic maps using federal and municipal geographic scales; (c) to investigate the correlations between the pandemics indicators (numbers of contagious and deaths) and demographic patterns (population size and density).

METHODS

The demographic patterns of all Mexican Federal Entities and all municipalities were taken from the database of "Instituto Nacional de Estadística y Geografía" (INEGI). The data of "Centro Nacional de Programas Preventivos y Control de Enfermedades" (CENAPRECE) and the geoportal of Mexico Government were also used in our analysis. The results are presented by means of tables, graphs and thematic maps. A Spearman correlation was used to assess the associations between the pandemics indicators and the demographic patterns. Correlations with a value < 0.05 were considered significant.

RESULTS

The confirmed cases (ccH1N1) and deaths (dH1N1) registered during the H1N1 Influenza pandemic were 72.4 thousand and 1.2 thousand respectively. Mexico City (CDMX) was the most affected area by the pandemic with 8,502 ccH1N1 and 152 dH1N1. The ccH1N1 and dH1N1 were positively correlated to demographic patterns; -values higher than the level of marginal significance were found analyzing the % ccH1N1 and the % dH1N1 vs the population density. The COVID-19 pandemic data indicated 75.0 million confirmed cases (ccCOVID-19) and 1.6 million deaths (dCOVID-19) worldwide, as of date. The CDMX, where 264,330 infections were recorded, is the national epicenter of the pandemic. The federal scale did not allow to observe the correlation between demographic data and pandemic indicators; hence the next step was to choose a more detailed geographical scale (municipal basis). The ccCOVID-19 and dCOVID-19 (municipal basis) were highly correlated with demographic patterns; also the % ccCOVID-19 and % dCOVID-19 were moderately correlated with demographic patterns.

CONCLUSION

The magnitude of COVID-19 pandemic is much greater than the H1N1 Influenza pandemic. The CDMX was the national epicenter in both pandemics. The federal scale did not allow to evaluate the correlation between exanimated demographic variables and the spread of infections, but the municipal basis allowed the identification of local variations and "red zones" such as the delegation of Iztapalapa and Gustavo A. Madero in CDMX.

摘要

背景

新型冠状病毒病(COVID-19)大流行是世界在不到二十年里面临的第二次全球卫生紧急事件,仅次于2009 - 2010年的甲型H1N1流感大流行。大流行的传播通常与人口规模和人口密度的增加有关。地理尺度(国家、区域或地方尺度)是确定人口因素与疫情传播之间相关性的关键要素。本研究的目的是:(a)收集墨西哥与这两次大流行相关的数据;(b)使用联邦和市级地理尺度创建专题地图;(c)调查大流行指标(传染数和死亡数)与人口模式(人口规模和密度)之间的相关性。

方法

所有墨西哥联邦实体和所有市的人口模式数据取自“国家统计与地理研究所”(INEGI)的数据库。我们的分析还使用了“国家预防和控制疾病计划中心”(CENAPRECE)的数据以及墨西哥政府的地理门户网站。结果通过表格、图表和专题地图呈现。使用斯皮尔曼相关性来评估大流行指标与人口模式之间的关联。相关性值<0.05被认为具有显著性。

结果

甲型H1N1流感大流行期间登记的确诊病例(ccH1N1)和死亡病例(dH1N1)分别为7.24万例和1200例。墨西哥城(CDMX)是受该大流行影响最严重的地区,有8502例ccH1N1和152例dH1N1。ccH1N1和dH1N1与人口模式呈正相关;在分析ccH1N1百分比和dH1N1百分比与人口密度时,发现相关性值高于边际显著性水平。截至目前,COVID-19大流行的数据显示全球有7500万确诊病例(ccCOVID-19)和100万死亡病例(dCOVID-19)。记录到264330例感染病例且是全国大流行中心的墨西哥城。联邦尺度无法观察到人口数据与大流行指标之间的相关性;因此下一步是选择更详细的地理尺度(市级)。ccCOVID-19和dCOVID-19(市级)与人口模式高度相关;同样,ccCOVID-19百分比和dCOVID-19百分比与人口模式呈中度相关。

结论

COVID-19大流行的规模远大于甲型H1N1流感大流行。墨西哥城在两次大流行中都是全国中心。联邦尺度无法评估所研究的人口变量与感染传播之间的相关性,但市级尺度能够识别局部差异和“红色区域”,如墨西哥城的伊萨帕拉帕区和古斯塔沃·A·马德罗区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e92d/8000468/ebc00f5246f8/peerj-09-11144-g001.jpg

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