通过使用基于在线国家数据源的回归分析，研究宏观尺度估计值对全球 COVID-19 发病和死亡的影响。

Investigating the effect of macro-scale estimators on worldwide COVID-19 occurrence and mortality through regression analysis using online country-based data sources.

机构信息

Department of Business Administration, Dokuz Eylül University, Izmir, Turkey.

Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey

出版信息

BMJ Open. 2022 Feb 14;12(2):e055562. doi: 10.1136/bmjopen-2021-055562.

DOI:10.1136/bmjopen-2021-055562

PMID:35165110

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8844970/

Abstract

OBJECTIVE

To investigate macro-scale estimators of the variations in COVID-19 cases and deaths among countries.

DESIGN

Epidemiological study.

SETTING

Country-based data from publicly available online databases of international organisations.

PARTICIPANTS

The study involved 170 countries/territories, each of which had complete COVID-19 and tuberculosis data, as well as specific health-related estimators (obesity, hypertension, diabetes and hypercholesterolaemia).

PRIMARY AND SECONDARY OUTCOME MEASURES

The worldwide heterogeneity of the total number of COVID-19 cases and deaths per million on 31 December 2020 was analysed by 17 macro-scale estimators around the health-related, socioeconomic, climatic and political factors. In 139 of 170 nations, the best subsets regression was used to investigate all potential models of COVID-19 variations among countries. A multiple linear regression analysis was conducted to explore the predictive capacity of these variables. The same analysis was applied to the number of deaths per hundred thousand due to tuberculosis, a quite different infectious disease, to validate and control the differences with the proposed models for COVID-19.

RESULTS

In the model for the COVID-19 cases (R=0.45), obesity (β=0.460), hypertension (β=0.214), sunshine (β=-0.157) and transparency (β=0.147); whereas in the model for COVID-19 deaths (R=0.41), obesity (β=0.279), hypertension (β=0.285), alcohol consumption (β=0.173) and urbanisation (β=0.204) were significant factors (p<0.05). Unlike COVID-19, the tuberculosis model contained significant indicators like obesity, undernourishment, air pollution, age, schooling, democracy and Gini Inequality Index.

CONCLUSIONS

This study recommends the new predictors explaining the global variability of COVID-19. Thus, it might assist policymakers in developing health policies and social strategies to deal with COVID-19.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04486508).

摘要

目的

研究各国 COVID-19 病例和死亡的宏观估计值变化。

设计

流行病学研究。

设置

来自国际组织公开在线数据库的国家数据。

参与者

本研究涉及 170 个国家/地区，每个国家/地区都有完整的 COVID-19 和结核病数据，以及特定的与健康相关的估计值（肥胖、高血压、糖尿病和高胆固醇血症）。

主要和次要结果

使用 17 个与健康相关、社会经济、气候和政治因素有关的宏观估计值，分析 2020 年 12 月 31 日每百万 COVID-19 病例和死亡的全球异质性。在 170 个国家中的 139 个国家中，使用最佳子集回归调查了国家之间 COVID-19 变异的所有潜在模型。进行了多元线性回归分析，以探索这些变量的预测能力。同样的分析应用于因结核病（一种截然不同的传染病）而导致的每十万死亡人数，以验证和控制与 COVID-19 提出的模型的差异。

结果

在 COVID-19 病例模型中（R=0.45），肥胖（β=0.460）、高血压（β=0.214）、阳光（β=-0.157）和透明度（β=0.147）；而在 COVID-19 死亡模型中（R=0.41），肥胖（β=0.279）、高血压（β=0.285）、饮酒（β=0.173）和城市化（β=0.204）是显著因素（p<0.05）。与 COVID-19 不同，结核病模型包含了肥胖、营养不良、空气污染、年龄、教育、民主和基尼不平等指数等重要指标。