Regional Temperature-Sensitive Diseases and Attributable Fractions in China

Int J Environ Res Public Health. 2019 Dec 26;17(1):184. doi: 10.3390/ijerph17010184.

Abstract

Few studies have been carried out to systematically screen regional temperature-sensitive diseases. This study was aimed at systematically and comprehensively screening both high- and low-temperature-sensitive diseases by using mortality data from 17 study sites in China located in temperate and subtropical climate zones. The distributed lag nonlinear model (DLNM) was applied to quantify the association between extreme temperature and mortality to screen temperature-sensitive diseases from 18 kinds of diseases of eight disease systems. The attributable fractions (AFs) of sensitive diseases were calculated to assess the mortality burden attributable to high and low temperatures. A total of 1,380,713 records of all-cause deaths were involved. The results indicate that injuries, nervous, circulatory and respiratory diseases are sensitive to heat, with the attributable fraction accounting for 6.5%, 4.2%, 3.9% and 1.85%, respectively. Respiratory and circulatory diseases are sensitive to cold temperature, with the attributable fraction accounting for 13.3% and 11.8%, respectively. Most of the high- and low-temperature-sensitive diseases seem to have higher relative risk in study sites located in subtropical zones than in temperate zones. However, the attributable fractions for mortality of heat-related injuries were higher in temperate zones. The results of this research provide epidemiological evidence of the relative burden of mortality across two climate zones in China.

Keywords: attributable fraction; extreme temperature; multi-region study; regional differences; sensitive disease.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cause of Death*
  • China / epidemiology
  • Climate*
  • Cold Temperature / adverse effects*
  • Epidemiological Monitoring
  • Forecasting
  • Hot Temperature / adverse effects*
  • Humans
  • Mortality / trends*
  • Nonlinear Dynamics
  • Risk Factors