Effects of temperature and humidity on hospitalizations for metabolic syndrome with cerebral infarction among older adults in Panzhihua: a distributed lag non-linear model analysis.

High temperature was associated with a reduced risk of hospital admission for metabolic syndrome combined with cerebral infarction among older adults.

• The relative risk reached its minimum at lag day 17 (RR = 0.940, 95% CI: 0.887–0.996).
• Study population consisted of adults aged over 60 admitted to Panzhihua Central Hospital.
• Data were collected from 2016 to 2020 in Panzhihua, a dry-hot valley and high-altitude region.
• A distributed lag nonlinear model (DLNM) was used to analyze the relationship between daily mean temperature and admission risk.

Both relatively high humidity and high humidity were associated with reduced hospital admission risk for metabolic syndrome with cerebral infarction.

• The lowest RR for relatively high humidity was observed at lag day 19.
• The lowest RR for high humidity was observed at lag day 18.
• Daily relative humidity data were collected alongside temperature data from 2016 to 2020.
• Air pollution data were also incorporated as part of the analytic model.

Sex-based subgroup analysis revealed differential effects of temperature and humidity on admission risk.

• Men experienced reduced admission risk when exposed to high temperature and high humidity.
• Women showed reduced admission risk under low temperature conditions.
• These findings suggest that the protective or harmful meteorological effects differ by sex.

Age-based subgroup analysis showed that protective meteorological effects were present in the 60–75 age group but not in those over 75.

• In the 60–75 age group, protective effects were observed with exposure to relatively high temperature, low humidity, relatively high humidity, and high humidity.
• No statistically significant effects of temperature or humidity exposure were found among individuals over 75 years of age.
• This suggests that older-old adults (>75) may not benefit from or respond to the same meteorological protective factors as younger-old adults.

The study used a distributed lag nonlinear model (DLNM) to examine delayed and nonlinear associations between meteorological factors and hospitalizations.

• Daily meteorological data, air pollution data, and hospital admission records from Panzhihua Central Hospital were collected from 2016 to 2020.
• The DLNM framework allowed assessment of lagged effects extending to at least 19 days after exposure.
• The study setting was a dry-hot valley and high-altitude climate region, described as a 'special climatic region.'
• The outcome was hospital admission for metabolic syndrome complicated with cerebral infarction among adults aged over 60.