Abstract: Objective To identify the factors influencing the performance of notifiable communicable diseases reporting in medical institutions in Guangxi and compare the differences in correlation strength estimated by logistic regression model and log-binomial regression model. Methods The field survey was conducted in medical institutions at county levels and above selected through multistage stratified sampling in Guangxi. Multivariable logistic regression analysis and log-binomial regression analysis were conducted with software R v3.3.3. Results A total of 2 458 cases of notifiable communicable diseases were surveyed, and the overall reporting rate was 95.08%. Among these cases, 97.74% were reported timely. And among all the reporting cards, 77.60% were filled completely, 61.24% were filled accurately, 95.27% were consistent with network reporting, and 75.59% were with personnel identification numbers. Results of multivariate log-binomial regression analysis indicated that self-check (PR=1.03, 95% CI:1.01-1.05) and staff training (PR=1.08, 95%CI:1.02-1.15) could improve the disease reporting effectively. Besides, qualified outpatient recording (PR=1.21, 95%CI:1.07-1.37), self-check (PR=1.09, 95%CI:1.03-1.14), reward and punishment system establishment (PR=2.03, 95% CI:1.49-2.78) and staff training (PR=1.18, 95%CI:1.02-1.37) can improve the completeness of reporting cards. The multivariate logistic regression analysis had consistent results, while with the increase of 1.00% in the frequency of study event and the absolute difference between groups, compared with PR, the bias of OR estimated by logistic regression would increase by 0.65% (95%CI:0.34%-0.95%) and 1.31% (95%CI:0.20%-2.41%) respectively. Conclusion The performance of notifiable communicable diseases reporting needs to be improved in medical institutions at county level and above in Guangxi through self-check, staff training, reward and punishment, qualified outpatient recording. In addition, the application of log-binomial regression analysis can be promoted in the quantitative estimation of relationship between exposure and outcome in cross-section study or cohort study.