Schroeder Lucas, de Souza Eniuce Menezes, Rosset Clévia, Marques Junior Ademir, Boquett Juliano André, Francisco Rofatto Vinicius, Brum Diego, Gonzaga Luiz, Zagonel de Oliveira Marcelo, Veronez Mauricio Roberto
Post Graduate Program in Applied Computing, Unisinos University. São Leopoldo, RS, Brazil.
Vizlab | X-Reality and Geoinformatics Lab, Unisinos University. São Leopoldo, RS, Brazil.
Lancet Reg Health Am. 2022 Feb;6:100102. doi: 10.1016/j.lana.2021.100102. Epub 2021 Nov 3.
Brazil has faced two simultaneous problems related to respiratory health: forest fires and the high mortality rate due to COVID-19 pandemics. The Amazon rain forest is one of the Brazilian biomes that suffers the most with fires caused by droughts and illegal deforestation. These fires can bring respiratory diseases associated with air pollution, and the State of Pará in Brazil is the most affected. COVID-19 pandemics associated with air pollution can potentially increase hospitalizations and deaths related to respiratory diseases. Here, we aimed to evaluate the association of fire occurrences with the COVID-19 mortality rates and general respiratory diseases hospitalizations in the State of Pará, Brazil.
We employed machine learning technique for clustering k-means accompanied with the elbow method used to identify the ideal quantity of clusters for the k-means algorithm, clustering 10 groups of cities in the State of Pará where we selected the clusters with the highest and lowest fires occurrence from the 2015 to 2019. Next, an Auto-regressive Integrated Moving Average Exogenous (ARIMAX) model was proposed to study the serial correlation of respiratory diseases hospitalizations and their associations with fire occurrences. Regarding the COVID-19 analysis, we computed the mortality risk and its confidence level considering the quarterly incidence rate ratio in clusters with high and low exposure to fires.
Using the k-means algorithm we identified two clusters with similar DHI (Development Human Index) and GDP (Gross Domestic Product) from a group of ten clusters that divided the State of Pará but with diverse behavior considering the hospitalizations and forest fires in the Amazon biome. From the auto-regressive and moving average model (ARIMAX), it was possible to show that besides the serial correlation, the fires occurrences contribute to the respiratory diseases increase, with an observed lag of six months after the fires for the case with high exposure to fires. A highlight that deserves attention concerns the relationship between fire occurrences and deaths. Historically, the risk of mortality by respiratory diseases is higher (about the double) in regions and periods with high exposure to fires than the ones with low exposure to fires. The same pattern remains in the period of the COVID-19 pandemic, where the risk of mortality for COVID-19 was 80% higher in the region and period with high exposure to fires. Regarding the SARS-COV-2 analysis, the risk of mortality related to COVID-19 is higher in the period with high exposure to fires than in the period with low exposure to fires. Another highlight concerns the relationship between fire occurrences and COVID-19 deaths. The results show that regions with high fire occurrences are associated with more cases of COVID deaths.
The decision-make process is a critical problem mainly when it involves environmental and health control policies. Environmental policies are often more cost-effective as health measures than the use of public health services. This highlight the importance of data analyses to support the decision making and to identify population in need of better infrastructure due to historical environmental factors and the knowledge of associated health risk. The results suggest that The fires occurrences contribute to the increase of the respiratory diseases hospitalization. The mortality rate related to COVID-19 was higher for the period with high exposure to fires than the period with low exposure to fires. The regions with high fire occurrences is associated with more COVID-19 deaths, mainly in the months with high number of fires.
No additional funding source was required for this study.
巴西面临与呼吸健康相关的两个同时存在的问题:森林火灾和因新冠疫情导致的高死亡率。亚马逊雨林是巴西受干旱和非法砍伐森林引发火灾影响最严重的生物群落之一。这些火灾会带来与空气污染相关的呼吸道疾病,巴西的帕拉州受影响最为严重。与空气污染相关的新冠疫情可能会增加与呼吸道疾病相关的住院人数和死亡人数。在此,我们旨在评估巴西帕拉州火灾发生情况与新冠死亡率以及一般呼吸道疾病住院人数之间的关联。
我们采用机器学习技术中的k均值聚类法,并结合肘部法则来确定k均值算法的理想聚类数量,对帕拉州的10组城市进行聚类,从中选取2015年至2019年火灾发生次数最多和最少的聚类。接下来,提出了自回归积分移动平均外生(ARIMAX)模型,以研究呼吸道疾病住院人数的序列相关性及其与火灾发生情况的关联。关于新冠分析,我们根据火灾暴露程度高和低的聚类中的季度发病率比计算了死亡风险及其置信水平。
使用k均值算法,我们从将帕拉州划分成的10个聚类中识别出两个具有相似人类发展指数(DHI)和国内生产总值(GDP)的聚类,但在亚马逊生物群落中的住院人数和森林火灾方面表现不同。从自回归移动平均模型(ARIMAX)可以看出,除了序列相关性外,火灾发生会导致呼吸道疾病增加,对于火灾暴露程度高的情况,火灾发生六个月后观察到这种影响。一个值得关注的亮点是火灾发生与死亡之间的关系。从历史上看,火灾暴露程度高的地区和时期因呼吸道疾病导致的死亡风险更高(约为两倍),高于火灾暴露程度低的地区和时期。在新冠疫情期间同样如此,火灾暴露程度高的地区和时期新冠死亡风险高出80%。关于严重急性呼吸综合征冠状病毒2(SARS-CoV-2)分析,火灾暴露程度高的时期与新冠相关的死亡风险高于火灾暴露程度低的时期。另一个亮点是火灾发生与新冠死亡之间的关系。结果表明,火灾发生次数多的地区与更多新冠死亡病例相关。
决策过程是一个关键问题,尤其是当涉及环境和健康控制政策时。环境政策作为健康措施往往比使用公共卫生服务更具成本效益。这凸显了数据分析对于支持决策以及识别因历史环境因素和相关健康风险知识而需要更好基础设施的人群的重要性。结果表明,火灾发生会导致呼吸道疾病住院人数增加。火灾暴露程度高的时期与新冠相关的死亡率高于火灾暴露程度低的时期。火灾发生次数多的地区与更多新冠死亡病例相关,主要是在火灾数量多的月份。
本研究无需额外资金来源。