Scholarlink

<p>The magnitude and timing of short-term associations of weather and air quality with ophthalmic attendance remain poorly quantified. We analysed completed attendances from a tertiary ophthalmology department in Jiangsu, China (2015–2025; 3,788&#xa0;days) using a time-stratified case-crossover design with conditional Poisson regression. Exposures included catchment-weighted meteorology, nitrogen dioxide (NO₂), and fine particulate matter (PM₂.₅); extreme heat, cold, and heavy rainfall were accumulated over 0–3 and 0–7&#xa0;days. Over 0–3&#xa0;days, each additional extreme-heat or heavy-rainfall day was associated with lower completed attendance (rate ratios 0.961 [95% CI 0.954–0.967] and 0.937 [95% CI 0.923–0.952], respectively; both <i>p</i> &lt; 0.0001), whereas extreme cold showed no clear association. From a baseline of 293 visits/day, two heat days and two heavy-rainfall days corresponded to approximately 23 and 36 fewer visits/day, respectively. Lag analyses suggested delayed positive rainfall associations compatible with partial compensation, but no comparable heat pattern within 21&#xa0;days. The retrospective trigger simulation had 39% precision and 59% recall; approximately 61% of triggered days were false alarms. Locally derived heat thresholds and rainfall amount-related estimates may support preparedness planning. Because completed attendance—not underlying ophthalmic need—was measured, real-time use requires prospective validation of safety, equity, and unmet-need outcomes.</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

Short term associations of weather and air quality with ophthalmic outpatient attendance in eastern China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月25日

查看全文
  • Tian Yang,
  • Chen Qin,
  • Li Ding,
  • Jie Zhu

摘要

The magnitude and timing of short-term associations of weather and air quality with ophthalmic attendance remain poorly quantified. We analysed completed attendances from a tertiary ophthalmology department in Jiangsu, China (2015–2025; 3,788 days) using a time-stratified case-crossover design with conditional Poisson regression. Exposures included catchment-weighted meteorology, nitrogen dioxide (NO₂), and fine particulate matter (PM₂.₅); extreme heat, cold, and heavy rainfall were accumulated over 0–3 and 0–7 days. Over 0–3 days, each additional extreme-heat or heavy-rainfall day was associated with lower completed attendance (rate ratios 0.961 [95% CI 0.954–0.967] and 0.937 [95% CI 0.923–0.952], respectively; both p < 0.0001), whereas extreme cold showed no clear association. From a baseline of 293 visits/day, two heat days and two heavy-rainfall days corresponded to approximately 23 and 36 fewer visits/day, respectively. Lag analyses suggested delayed positive rainfall associations compatible with partial compensation, but no comparable heat pattern within 21 days. The retrospective trigger simulation had 39% precision and 59% recall; approximately 61% of triggered days were false alarms. Locally derived heat thresholds and rainfall amount-related estimates may support preparedness planning. Because completed attendance—not underlying ophthalmic need—was measured, real-time use requires prospective validation of safety, equity, and unmet-need outcomes.