A set of exposure-response coefficients between fine particulate matter (PM2.5) pollution and different health endpoints were determined through the meta-analysis method based on 2254 studies collected from the Web of Science database. With data including remotely-sensed PM2.5 concentration, demographic data, health data, and survey data, a Poisson regression model was used to assess the health losses and their economic value caused by PM2.5 pollution in cities of atmospheric pollution transmission channel in the Beijing-Tianjin-Hebei region, China. The results showed the following: (1) Significant exposure-response relationships existed between PM2.5 pollution and a set of health endpoints, including all-cause death, death from circulatory disease, death from respiratory disease, death from lung cancer, hospitalization for circulatory disease, hospitalization for respiratory disease, and outpatient emergency treatment. Each increase of 10 g/m(3) in PM2.5 concentration led to an increase of 5.69% (95% CI (confidence interval): 4.12%, 7.85%), 6.88% (95% CI: 4.94%, 9.58%), 4.71% (95% CI: 2.93%, 7.57%), 9.53% (95% CI: 6.84%, 13.28%), 5.33% (95% CI: 3.90%, 7.27%), 5.50% (95% CI: 4.09%, 7.38%), and 6.35% (95% CI: 4.71%, 8.56%) for above-mentioned health endpoints, respectively. (2) PM2.5 pollution posed a serious threat to residents' health. In 2016, the number of deaths, hospitalizations, and outpatient emergency visits induced by PM2.5 pollution in cities of atmospheric pollution transmission channel in the Beijing-Tianjin-Hebei region reached 309,643, 1,867,240, and 47,655,405, respectively, accounting for 28.36%, 27.02% and 30.13% of the total number of deaths, hospitalizations, and outpatient emergency visits, respectively. (3) The economic value of health losses due to PM2.5 pollution in the study area was approximately $28.1 billion, accounting for 1.52% of the gross domestic product. The economic value of health losses was higher in Beijing, Tianjin, Shijiazhuang, Zhengzhou, Handan, Baoding, and Cangzhou, but lower in Taiyuan, Yangquan, Changzhi, Jincheng, and Hebi.