Objective : To evaluate the excess mortality risk related to heat wave in Ningbo, Zhejiang from 2013 to 2018, so as to provide a basis for formulating coping strategies for heat wave. Methods : The data of daily mortality, meteorological and air quality from May to October in Ningbo from 2013 to 2018 were obtained from Ningbo Center for Disease Control and Prevention, Ningbo Meteorological Bureau and Environmental Monitoring Center of Ningbo, respectively. The generalized linear model ( GLM ) and distributed lag non-linear model ( DLNM ) were used to estimate the associations between heat wave and cause-specific mortality. Results : Among 1 104 days of the study period, 18 heat waves occured and lasted for 132 days, accounting for 11.96%. A total of 102 954 deaths were reported in the same period. The risks of mortality in circulatory system diseases ( RR=1.09, 95%CI: 1.03-1.16 ), respiratory system diseases ( RR=1.14, 95%CI: 1.04-1.25 ), digestive system diseases ( RR=1.38, 95%CI: 1.15-1.65 ), nervous system diseases ( RR=1.32, 95%CI: 1.08-1.61 ), mental disorders ( RR=1.51, 95%CI: 1.12-2.03 ) and accidental injury ( RR=1.18, 95%CI: 1.06-1.32 ) and all causes ( RR=1.10, 95%CI: 1.06-1.14 ) increased at lag 0-1 day of heat wave. The total excess death related to heat wave was 1 218 ( 95%CI: 731-1 705 ) . The excess deaths of circulatory system diseases, respiratory system diseases, accidental injury, digestive system diseases, nervous system diseases, mental disorders, urinary system diseases and endocrine system diseases were 313 ( 95%CI: 104-556 ), 206 ( 95%CI: 59-368 ), 164 ( 95%CI: 55-292 ), 122 ( 95%CI: 48-208 ), 69 ( 95%CI: 17-131 ), 56 ( 95%CI: 13-113 ), 18 ( 95%CI: -15-64 ) and 3 ( 95%CI: -51-72 ). The excess deaths of urinary system and endocrine system diseases was not statistically significant ( P>0.05 ). Conclusion Heat wave can increase the mortality risk on the day and after a day in Ningbo from 2013 to 2018. Circulatory system diseases, respiratory system diseases and accidental injury rank top three in excess deaths.

Objective: To identify the most appropriate meteorological variable for forecasting the health risk of high temperatures. Methods: The surveillance on causes of death, meteorological data and surveillance on air quality among registered residents in Ningbo City, Zhejiang Province during the period between May and October from 2013 to 2019 were collected. The association models of daily minimum temperature, average daily temperature, daily maximum temperature, daily minimum heat index, average daily heat index, daily maximum heat index, average daily apparent temperature and torridity index with deaths and years of life lost (YLL) were created using time series analysis and distributed lag non-linear models, and the model fitting effect was evaluated using the minimum Akaike information criterion (AIC) procedure. The most appropriate meteorological variable for forecasting gender-, age- and mortality-specific health risks of high temperatures was identified. Results: A total of 120 628 deaths were reported during the study period, with daily deaths of 94 cases, and daily YLL rate of 19.74 person-years/105. Except for daily minimum heat index and torridity index, the exposure-response relationships between other six meteorological variables and deaths and overall YLL rate all appeared a “J” shape. The lowest AIC values and the optimal model fitting effects were measured for the association models between average daily temperature and whole populations, females, subjects at ages of 65 years and older, and deaths and YLL rates due to circulatory diseases and respiratory diseases. Conclusion High model fitting effects are observed between average daily temperature and deaths and YLL rates, which are more suitable for forecasting the health risk of high temperature.