Effects of exposure to fine particulate matter in elderly hospitalizations due to respiratory diseases in the South of the Brazilian Amazon

Exposure to air pollution is an important cause of hospital admissions due to respiratory diseases. Nevertheless, few studies use pollutant concentration data estimated by mathematical models. A time-series ecological study was developed, using data from hospitalizations due to respiratory diseases in people over 60 years of age, residents of Cuiabá, Brazil, during 2012, obtained from the Brazilian Ministry of Health. The independent variables were the concentrations of fine particulate matter (PM2.5) and carbon monoxide (CO) estimated by mathematical modeling, minimum temperature, and relative humidity (obtained from the Brazilian Meteorological Agency), and the number of forest fires. The generalized linear regression model of Poisson was used, with lags of 0 to 7 days. The coefficients obtained were transformed into relative risk of hospitalization, with respective 95% confidence intervals; alpha=5% was adopted. In that year, 591 hospitalizations were evaluated, with a daily average of 1.61 (SD=1.49), the PM2.5 average concentration was 15.7 µg/m3, and the CO average concentration was 144.2 ppb. Significant associations between exposure to these contaminants and hospitalizations in lags 3 and 4 in 2012 were observed. There was a hospitalization risk increase of 31.8%, with an increase of 3.5 µg/m3 of PM2.5 concentrations and an increase of 188 in the total number of hospitalizations, with an expense of more than ≈US$ 96,000 for the Brazilian Public Health System. This study provided information on the cost of air pollution to the health system and the feasibility of using a mathematical model to estimate environmental concentration of air pollutants.

空气污染暴露是引发呼吸系统疾病住院的重要诱因。然而，目前鲜有研究采用数学模型估算得到的污染物浓度数据开展相关研究。本研究开展了一项时间序列生态学研究，研究数据取自2012年巴西库亚巴市常住老年群体（60岁以上）因呼吸系统疾病住院的病例数据，相关数据由巴西卫生部提供。本研究的自变量包括经数学模型估算得到的细颗粒物（fine particulate matter, PM2.5）与一氧化碳（carbon monoxide, CO）浓度、最低气温与相对湿度（数据取自巴西气象局），以及森林火灾发生次数。本研究采用泊松广义线性回归模型，设置0至7天的滞后效应。将得到的回归系数转换为住院相对风险，并给出对应的95%置信区间，检验水准α设为5%。2012年共计纳入591例住院病例，日均住院量为1.61例（标准差standard deviation, SD=1.49）；细颗粒物平均浓度为15.7 µg/m³，一氧化碳平均浓度为144.2 ppb。研究发现，2012年该地区污染物暴露与滞后3天、4天的呼吸系统疾病住院量存在显著关联。当细颗粒物浓度每升高3.5 µg/m³时，住院风险上升31.8%；当年总住院量增加188例，给巴西公共卫生系统带来的额外支出约合96000美元。本研究明确了空气污染给卫生系统带来的经济成本，并验证了采用数学模型估算环境空气污染物浓度的可行性。