Objective To investigate the cognitive understanding of medical college students on statistical indicators and statistical charts, and to examine the effect of online testing tools on improving students′ ability to understand statistical charts.Methods The subjects were undergraduates majoring in statistics, clinical medicine, anesthesiology, imaging and laboratory science in a medical university. The survey was conducted by online questionnaire (questionnaire url, test one: https://ks.wjx.top/jq/92107364.aspx, test two: https://ks.wjx.top/jq/92106453.aspx). Frequency, rate and mean were used to describe qualitative and quantitative data. Quantitative data were compared by paired- t- test or rank sum test.Results Five hundred and forty-six students completed test one and 476 completed test two. We found that quite a few students were wrong about basic statistical charts. And 70% of medical students misunderstood the composition ratio of the two grouping factors, while 50% of medical students misunderstood the constituent ratio of a grouping factor. Between 30% and 50% of medical students misunderstood the rate, the difference between case mortality and mortality, and the correlation shown in the scatter plot.Conclusion If statistical charts can only be understood and mastered by classroom explanation， due to the limitation of class hours, the goal of accurate understanding can not be achieved. Through online testing and cases, combined with answer analysis, this part of knowledge can be mastered in a limited period of time. If students only understand and master statistical charts through classroom explanation, they cannot achieve the goal of accurate understanding due to the limitation of class hours. In the ways of online tests and the design of cases and questions, combined with the way of answer analysis, can students master this part of knowledge in a limited period of time.

Objective systematically review the short-term exposure effects of atmospheric PM_2.5 and respiratory disease outpatient visits in China.Methods Databases including CNKI, WanFang Data, VIP, PubMed, EMbase, The Cochrane Library and Web of science databases were electronically searched to collect studies on the association between atmospheric PM_2.5 and outpatient visits of respiratory diseases from January 2000 to August 2022 in China. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Metaanalysis was performed by using Stata 14.0 software.Results Forty-eight articles were included. The random effect model was used for meta-analysis. The results showed that the increase of PM_2.5  concentration by 10 μg·m^-3 was associated with an increase of 0.66%(95%CI: 0.56%-0.76%) in the number of respiratory disease outpatient visits. Subgroup analysis was conducted according to population (age, gender), season, study area, urban economic level, PM_2.5 concentration and effect type. The results showed that for every 10 μg·m^-3  increase in atmospheric PM_2.5 concentration, the increase of outpatient visits for respiratory diseases was the highest in the 0-14 years group 0.61%(95%CI: 0.50%-0.73%), followed by ≥65 years old group 0.50% (95%CI:0.35%-0.66%) and 15-64 years old group 0.44%(95%CI: 0.32%-0.56%). In spring, the highest increase was 0.61% (95%CI: 0.12%-1.11%). Compared with the second and third-tier cities, the increase of outpatient clinics for respiratory diseases in firsttier cities was the highest by 0.63% (95%CI: 0.50%-0.75%). The increase of outpatient clinics for respiratory diseases in southern China was 0.75%(95%CI: 0.63%-0.88%), higher than that in northern China 0.37%(95%CI: 0.28%-0.47%). PM_2.5 <75 μg·m^-3  groups of respiratory disease outpatient increment of 0.78% (95%CI: 0.63%-0.88%) was higher than PM_2.5 ≥75 μg·m^-3  group-0.26% (95%CI: 0.17%-0.36%). There was no significant difference in the increase of outpatient visits for respiratory diseases by gender and effect type. Sensitivity analysis showed that the results of this study were relatively robust and reliable. The funnel plot and Egger′s test showed that there was a certain publication bias.Conclusion There is a positive correlation between PM_2.5 and the number of respiratory diseases in China. With the increase ofPM_2.5 concentration in the atmosphere, the risk of respiratory diseases will increase, especially in children, the elderly, in spring, first-tier cities and the southern region, and the number of respiratory diseases outpatients increases.