Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.
Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.
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DOI：
10.1038/s41598-021-95004-8
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发表时间：
2021-08-02
影响因子：
4.6
通讯作者：
van der Schaar M
中科院分区：
文献类型：
--
作者：
Baqui P;Marra V;Alaa AM;Bica I;Ercole A;van der Schaar M
COVID-19疫情继续对巴西造成破坏性影响。巴西的社会、卫生和经济危机因严重的社会不平等和持续的政治混乱而加剧。这种复杂的情况促使人们仔细研究导致巴西SARS-CoV-2死亡风险增加的临床、社会经济、人口和结构因素。我们考虑巴西SIVEP-Gripe目录，这是一个非常丰富的呼吸道感染数据集，使我们能够估计几个非实验室和社会地理因素对COVID-19死亡率的重要性。我们使用机器学习算法分析目录，以解释指标之间可能存在的复杂相互依赖关系。XGBoost算法实现了出色的性能，产生的AUC-ROC为0.813（95% CI 0.810-0.817），优于逻辑回归。使用我们的模型，我们发现，在巴西，社会经济，地理和结构因素比个人合并症更重要。特别重要的因素是：居住状态及其发展指数;到医院的距离（特别是农村和欠发达地区）;教育水平;医院筹资模式和压力。种族也被证实比合并症更重要，但低于上述因素。总括而言，社会经济及结构性因素与生物因素在决定COVID-19的结果方面同样重要。这对政策制定，特别是疫苗接种/非药物预防措施、医院管理和医疗保健网络组织具有重要影响。
The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil’s social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.
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影响因子：
34.3
作者：
Baqui, Pedro;Bica, Ioana;van der Schaar, Mihaela
通讯作者：
van der Schaar, Mihaela
影响因子：
13.8
作者：
Bravata DM;Perkins AJ;Myers LJ;Arling G;Zhang Y;Zillich AJ;Reese L;Dysangco A;Agarwal R;Myers J;Austin C;Sexson A;Leonard SJ;Dev S;Keyhani S
通讯作者：
Keyhani S
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
DOI：
10.1016/j.lanepe.2020.100021
发表时间：
2021-03
期刊：
The Lancet regional health. Europe
影响因子：
--
作者：
Campos-Matos I;Mandal S;Yates J;Ramsay M;Wilson J;Lim WS
通讯作者：
Lim WS
影响因子：
105.7
作者：
Docherty, Annemarie B.;Harrison, Ewen M.;Semple, Malcolm G.
通讯作者：
Semple, Malcolm G.