Résumé
Objective To analyze the correlation between detection of influenza pathogenic subtypes and epidemic situation in Hefei City during the surveillance years of 2015-2021, and to provide references for developing influenza prevention and control strategies. Methods The relevant data of influenza-like illness (ILI) surveillance, influenza etiology, and outbreak/cluster outbreaks reported from influenza surveillance sentinel hospitals in Hefei City from 2015 to 2021 in the China Influenza Surveillance Information System were analyzed. The statistical analysis was carried out. Results A total of 3 332 553 outpatient and emergency visits in 2 sentinel hospitals were reported in Hefei City from 2015 to 2021, of which 139,082 were ILI cases, accounting for 4.17%. The monthly proportion ranged from 1.60% to 7.15%. A total of 14 663 ILI specimens were submitted for detection, the submission rate was 10.54%, and the positive rate of influenza virus detection was 11.30%. There was no correlation between the proportion of monthly influenza like cases and the detection rate (rs=0.176, P=0.107). The composition ratio of each subtype of influenza virus varied greatly, showing diversified seasonality, and the epidemic strains changed constantly. The Victoria subtype (BV subtype) was dominant (40.21%). There was a moderate correlation between the positive rate of ILI in sentinel hospitals and the number of outbreaks per month (rs=0.696, P=0.000). There was a strong correlation between the monthly number of outbreaks and the detection rate (rss=0.696, P=0.000). There was a strong correlation between influenza A virus H3 subtype and BV subtype (rs -H3=0.686, P=0.030; rs -BV=0.632, P=0.000). There was a moderate correlation between the new A1 subtype and B Yamagata subtype (rs -new A H1=0.481, P=0.000 0; rs -BY=0.515, P=0.000). Conclusion There are two epidemic peaks in spring and winter in Hefei. Influenza subtypes are diverse. The results of ILI etiology can predict the outbreaks of different subtypes of influenza. Prediction and surveillance should be used for influenza control and outbreak management in a timely manner.
Résumé
Objective To investigate the effect of humidex combined with mean temperature and relative humidity on the incidence of bacillary dysentery in Hefei. Methods Daily counts of bacillary dysentery cases and weather data in Hefei were collected from January 1, 2006 to December 31, 2013. Then, the humidex was calculated from temperature and relative humidity. A Poisson generalized linear regression combined with distributed lag non-linear model was applied to analyze the relationship between humidex and the incidence of bacillary dysentery, after adjusting for long-term and seasonal trends, day of week and other weather confounders. Stratified analyses by gender, age and address were also conducted. Results The risk of bacillary dysentery increased with the rise of humidex. The adverse effect of high humidex (90 percentile of humidex) appeared in 2- days lag and it was the largest at 4-days lag (RR=1.063, 95%CI:1.037-1.090). Subgroup analyses indicated that all groups were affected by high humidex at lag 2-5 days. Conclusion High humidex could significantly increase the risk of bacillary dysentery, and the lagged effects were observed.
Résumé
Objective To investigate the effect of humidex combined with mean temperature and relative humidity on the incidence of bacillary dysentery in Hefei. Methods Daily counts of bacillary dysentery cases and weather data in Hefei were collected from January 1, 2006 to December 31, 2013. Then, the humidex was calculated from temperature and relative humidity. A Poisson generalized linear regression combined with distributed lag non-linear model was applied to analyze the relationship between humidex and the incidence of bacillary dysentery, after adjusting for long-term and seasonal trends, day of week and other weather confounders. Stratified analyses by gender, age and address were also conducted. Results The risk of bacillary dysentery increased with the rise of humidex. The adverse effect of high humidex (90 percentile of humidex) appeared in 2- days lag and it was the largest at 4-days lag (RR=1.063, 95%CI:1.037-1.090). Subgroup analyses indicated that all groups were affected by high humidex at lag 2-5 days. Conclusion High humidex could significantly increase the risk of bacillary dysentery, and the lagged effects were observed.