Scholarlink

<p>To investigate the correlation between environmental-meteorological factors and daily visits for hypertension in Beijing, China. Daily outpatient and emergency visits for hypertension during January 1st, 2015 and December 31st, 2019 were collected from Capital Medical University affiliated Beijing Shijitan Hospital. We used a time-series generalized additive model and cross-basis to analyze the correlation between atmospheric pollutants, temperature and hypertension emergency visits, with adjustments for time trends, additional meteorological variables, weekday effects and holiday effects. The R 3.6.3 software was engaged to estimate Spearman’s correlation coefficients, plot lag-response curves depicting the specific cumulative effects (SCE) and incremental cumulative effects (ICE) of relative risk (RR), alongside the response diagram and three-dimensional diagram of the predicted exposure lag effect. The fitted model was used to forecast the lag RR and 95% confidence interval (95% CI) of SCE and ICE for arbitrary air pollutants at varying concentration degrees. The overall lag-response RR curves for SCE and ICE of PM2.5, PM10, SO2, NO2 and CO were statistically significant. When the concentrations of PM2.5, PM10, SO2, NO2 and CO surpassed certain thresholds, and temperature dropped below 45&#xa0;°F (with 70&#xa0;°F as reference value), there was an observed increase in the number of hypertension, accompanied by a time lag effect. Elevated atmospheric concentrations of PM2.5, PM10, SO2, CO and NO2 are significantly correlated with an increase in hypertension-related emergency visits. The peak specific cumulative effects (SCE) and peak incremental cumulative effects (ICE) demonstrating a virtually consistent time lag effect, highlight the imperative for a holistic approach to air pollution management. Additionally, when temperatures fall below 45&#xa0;°F, there is a notable increase in hypertension visits, accompanied by a lag effect.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Air pollution and the number of daily visits of hypertension in northern china: a time-series analysis on generalized additive model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

查看全文
  • Chunmei Guo,
  • Aizhu Liu,
  • Hong Liu,
  • Jing Wu

摘要

To investigate the correlation between environmental-meteorological factors and daily visits for hypertension in Beijing, China. Daily outpatient and emergency visits for hypertension during January 1st, 2015 and December 31st, 2019 were collected from Capital Medical University affiliated Beijing Shijitan Hospital. We used a time-series generalized additive model and cross-basis to analyze the correlation between atmospheric pollutants, temperature and hypertension emergency visits, with adjustments for time trends, additional meteorological variables, weekday effects and holiday effects. The R 3.6.3 software was engaged to estimate Spearman’s correlation coefficients, plot lag-response curves depicting the specific cumulative effects (SCE) and incremental cumulative effects (ICE) of relative risk (RR), alongside the response diagram and three-dimensional diagram of the predicted exposure lag effect. The fitted model was used to forecast the lag RR and 95% confidence interval (95% CI) of SCE and ICE for arbitrary air pollutants at varying concentration degrees. The overall lag-response RR curves for SCE and ICE of PM2.5, PM10, SO2, NO2 and CO were statistically significant. When the concentrations of PM2.5, PM10, SO2, NO2 and CO surpassed certain thresholds, and temperature dropped below 45 °F (with 70 °F as reference value), there was an observed increase in the number of hypertension, accompanied by a time lag effect. Elevated atmospheric concentrations of PM2.5, PM10, SO2, CO and NO2 are significantly correlated with an increase in hypertension-related emergency visits. The peak specific cumulative effects (SCE) and peak incremental cumulative effects (ICE) demonstrating a virtually consistent time lag effect, highlight the imperative for a holistic approach to air pollution management. Additionally, when temperatures fall below 45 °F, there is a notable increase in hypertension visits, accompanied by a lag effect.