Effects of Different Absenteeism Statistical Methods on Influenza Surveillance

Zhen Yang; Cheng-hua Jiang

doi:doi:10.11648/j.sjph.20221003.13

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Effects of Different Absenteeism Statistical Methods on Influenza Surveillance

Zhen Yang, Cheng-hua Jiang

Published in Science Journal of Public Health (Volume 10, Issue 3)

Received: 24 May 2022 Accepted: Published: 26 May 2022

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Abstract

Background The existing absenteeism surveillance systems in China rely heavily on school doctors to collect data manually, but the low prevalence rate of school doctors makes it difficult to popularize this mode. The method of absenteeism statistics requires new breakthroughs. Objective The purpose of this study was to evaluate the feasibility of an established absenteeism surveillance system based on face recognition, and to explore the appropriate surveillance index for this system. Methods A primary school of about 1900 students was selected. Absenteeisms reported by school doctors and this system from March 1, 2021 (week 9) to January 14, 2022 (week 2) were collected, as well as weekly positive rate of influenza virus (WPRIV) released by China National Influenza Center. Eight weekly absenteeism rate indicators were calculated: all-cause absenteeism rate reported by system (WAR1), all-cause absenteeism rate reported by school doctors (WAR2), sickness absenteeism rate reported by school doctors (WAR3), and the rate of students absent one time (WAR4), two times (WAR5), three to four times (WAR6), one to two times (WAR7) and two to four times (WAR8) a week reported by the system. Pearson correlation coefficients of eight indicators and WPRIV were analyzed, and the change trend of their time series diagram was investigated. Results During week 9-42, WAR1 (r=0.614, p=0.001), WAR4 (r=0.631, p<0.001), WAR5 (r=0.651, p<0.001), WAR6 (r=0.541, p<0.001), WAR7 (r=0.654, p<0.001) and WAR8 (r=0.644, p<0.001) were significantly correlated with WPRIV, while WAR2 (r=0.262, p>0.05) and WAR3 (r=0.239, p>0.05) were not. Throughout the surveillance period, WAR1 (r=0.671, p<0.001), WAR2 (r=0.638, p<0.001), WAR3 (r=0.752, p<0.001), WAR5 (r=0.682, p<0.001), WAR6 (r=0.535, p<0.001) and WAR8 (r=0.683, p<0.001) were significantly correlated with WPRIV, while WAR4 (r=0.086, p>0.05) and WAR7 (r=0.242, p>0.05) were not. Conclusions Absenteeism reported by the system was more effective for influenza surveillance than absenteeism reported by school doctors, especially when the influenza activity level was low. When WAR1, WAR5 and WAR8 were combined together, the epidemic situation of influenza could be more comprehensively aware.

Published in	Science Journal of Public Health (Volume 10, Issue 3)
DOI	10.11648/j.sjph.20221003.13
Page(s)	115-123
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Face Recognition, Absenteeism, Influenza, Syndromic Surveillance

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APA Style

Zhen Yang, Cheng-hua Jiang. (2022). Effects of Different Absenteeism Statistical Methods on Influenza Surveillance. Science Journal of Public Health, 10(3), 115-123. https://doi.org/10.11648/j.sjph.20221003.13

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ACS Style

Zhen Yang; Cheng-hua Jiang. Effects of Different Absenteeism Statistical Methods on Influenza Surveillance. Sci. J. Public Health 2022, 10(3), 115-123. doi: 10.11648/j.sjph.20221003.13

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AMA Style

Zhen Yang, Cheng-hua Jiang. Effects of Different Absenteeism Statistical Methods on Influenza Surveillance. Sci J Public Health. 2022;10(3):115-123. doi: 10.11648/j.sjph.20221003.13

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@article{10.11648/j.sjph.20221003.13,
  author = {Zhen Yang and Cheng-hua Jiang},
  title = {Effects of Different Absenteeism Statistical Methods on Influenza Surveillance},
  journal = {Science Journal of Public Health},
  volume = {10},
  number = {3},
  pages = {115-123},
  doi = {10.11648/j.sjph.20221003.13},
  url = {https://doi.org/10.11648/j.sjph.20221003.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjph.20221003.13},
  abstract = {Background The existing absenteeism surveillance systems in China rely heavily on school doctors to collect data manually, but the low prevalence rate of school doctors makes it difficult to popularize this mode. The method of absenteeism statistics requires new breakthroughs. Objective The purpose of this study was to evaluate the feasibility of an established absenteeism surveillance system based on face recognition, and to explore the appropriate surveillance index for this system. Methods A primary school of about 1900 students was selected. Absenteeisms reported by school doctors and this system from March 1, 2021 (week 9) to January 14, 2022 (week 2) were collected, as well as weekly positive rate of influenza virus (WPRIV) released by China National Influenza Center. Eight weekly absenteeism rate indicators were calculated: all-cause absenteeism rate reported by system (WAR1), all-cause absenteeism rate reported by school doctors (WAR2), sickness absenteeism rate reported by school doctors (WAR3), and the rate of students absent one time (WAR4), two times (WAR5), three to four times (WAR6), one to two times (WAR7) and two to four times (WAR8) a week reported by the system. Pearson correlation coefficients of eight indicators and WPRIV were analyzed, and the change trend of their time series diagram was investigated. Results During week 9-42, WAR1 (r=0.614, p=0.001), WAR4 (r=0.631, p0.05) and WAR3 (r=0.239, p>0.05) were not. Throughout the surveillance period, WAR1 (r=0.671, p0.05) and WAR7 (r=0.242, p>0.05) were not. Conclusions Absenteeism reported by the system was more effective for influenza surveillance than absenteeism reported by school doctors, especially when the influenza activity level was low. When WAR1, WAR5 and WAR8 were combined together, the epidemic situation of influenza could be more comprehensively aware.},
 year = {2022}
}

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TY  - JOUR
T1  - Effects of Different Absenteeism Statistical Methods on Influenza Surveillance
AU  - Zhen Yang
AU  - Cheng-hua Jiang
Y1  - 2022/05/26
PY  - 2022
N1  - https://doi.org/10.11648/j.sjph.20221003.13
DO  - 10.11648/j.sjph.20221003.13
T2  - Science Journal of Public Health
JF  - Science Journal of Public Health
JO  - Science Journal of Public Health
SP  - 115
EP  - 123
PB  - Science Publishing Group
SN  - 2328-7950
UR  - https://doi.org/10.11648/j.sjph.20221003.13
AB  - Background The existing absenteeism surveillance systems in China rely heavily on school doctors to collect data manually, but the low prevalence rate of school doctors makes it difficult to popularize this mode. The method of absenteeism statistics requires new breakthroughs. Objective The purpose of this study was to evaluate the feasibility of an established absenteeism surveillance system based on face recognition, and to explore the appropriate surveillance index for this system. Methods A primary school of about 1900 students was selected. Absenteeisms reported by school doctors and this system from March 1, 2021 (week 9) to January 14, 2022 (week 2) were collected, as well as weekly positive rate of influenza virus (WPRIV) released by China National Influenza Center. Eight weekly absenteeism rate indicators were calculated: all-cause absenteeism rate reported by system (WAR1), all-cause absenteeism rate reported by school doctors (WAR2), sickness absenteeism rate reported by school doctors (WAR3), and the rate of students absent one time (WAR4), two times (WAR5), three to four times (WAR6), one to two times (WAR7) and two to four times (WAR8) a week reported by the system. Pearson correlation coefficients of eight indicators and WPRIV were analyzed, and the change trend of their time series diagram was investigated. Results During week 9-42, WAR1 (r=0.614, p=0.001), WAR4 (r=0.631, p0.05) and WAR3 (r=0.239, p>0.05) were not. Throughout the surveillance period, WAR1 (r=0.671, p0.05) and WAR7 (r=0.242, p>0.05) were not. Conclusions Absenteeism reported by the system was more effective for influenza surveillance than absenteeism reported by school doctors, especially when the influenza activity level was low. When WAR1, WAR5 and WAR8 were combined together, the epidemic situation of influenza could be more comprehensively aware.
VL  - 10
IS  - 3
ER  -

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Author Information

Zhen Yang

School of Medicine, Tongji University, Shanghai, China
Cheng-hua Jiang

School of Medicine, Tongji University, Shanghai, China

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Zhen Yang, Cheng-hua Jiang. (2022). Effects of Different Absenteeism Statistical Methods on Influenza Surveillance. Science Journal of Public Health, 10(3), 115-123. https://doi.org/10.11648/j.sjph.20221003.13

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ACS Style

Zhen Yang; Cheng-hua Jiang. Effects of Different Absenteeism Statistical Methods on Influenza Surveillance. Sci. J. Public Health 2022, 10(3), 115-123. doi: 10.11648/j.sjph.20221003.13

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AMA Style

Zhen Yang, Cheng-hua Jiang. Effects of Different Absenteeism Statistical Methods on Influenza Surveillance. Sci J Public Health. 2022;10(3):115-123. doi: 10.11648/j.sjph.20221003.13

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@article{10.11648/j.sjph.20221003.13,
  author = {Zhen Yang and Cheng-hua Jiang},
  title = {Effects of Different Absenteeism Statistical Methods on Influenza Surveillance},
  journal = {Science Journal of Public Health},
  volume = {10},
  number = {3},
  pages = {115-123},
  doi = {10.11648/j.sjph.20221003.13},
  url = {https://doi.org/10.11648/j.sjph.20221003.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjph.20221003.13},
  abstract = {Background The existing absenteeism surveillance systems in China rely heavily on school doctors to collect data manually, but the low prevalence rate of school doctors makes it difficult to popularize this mode. The method of absenteeism statistics requires new breakthroughs. Objective The purpose of this study was to evaluate the feasibility of an established absenteeism surveillance system based on face recognition, and to explore the appropriate surveillance index for this system. Methods A primary school of about 1900 students was selected. Absenteeisms reported by school doctors and this system from March 1, 2021 (week 9) to January 14, 2022 (week 2) were collected, as well as weekly positive rate of influenza virus (WPRIV) released by China National Influenza Center. Eight weekly absenteeism rate indicators were calculated: all-cause absenteeism rate reported by system (WAR1), all-cause absenteeism rate reported by school doctors (WAR2), sickness absenteeism rate reported by school doctors (WAR3), and the rate of students absent one time (WAR4), two times (WAR5), three to four times (WAR6), one to two times (WAR7) and two to four times (WAR8) a week reported by the system. Pearson correlation coefficients of eight indicators and WPRIV were analyzed, and the change trend of their time series diagram was investigated. Results During week 9-42, WAR1 (r=0.614, p=0.001), WAR4 (r=0.631, p0.05) and WAR3 (r=0.239, p>0.05) were not. Throughout the surveillance period, WAR1 (r=0.671, p0.05) and WAR7 (r=0.242, p>0.05) were not. Conclusions Absenteeism reported by the system was more effective for influenza surveillance than absenteeism reported by school doctors, especially when the influenza activity level was low. When WAR1, WAR5 and WAR8 were combined together, the epidemic situation of influenza could be more comprehensively aware.},
 year = {2022}
}

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TY  - JOUR
T1  - Effects of Different Absenteeism Statistical Methods on Influenza Surveillance
AU  - Zhen Yang
AU  - Cheng-hua Jiang
Y1  - 2022/05/26
PY  - 2022
N1  - https://doi.org/10.11648/j.sjph.20221003.13
DO  - 10.11648/j.sjph.20221003.13
T2  - Science Journal of Public Health
JF  - Science Journal of Public Health
JO  - Science Journal of Public Health
SP  - 115
EP  - 123
PB  - Science Publishing Group
SN  - 2328-7950
UR  - https://doi.org/10.11648/j.sjph.20221003.13
AB  - Background The existing absenteeism surveillance systems in China rely heavily on school doctors to collect data manually, but the low prevalence rate of school doctors makes it difficult to popularize this mode. The method of absenteeism statistics requires new breakthroughs. Objective The purpose of this study was to evaluate the feasibility of an established absenteeism surveillance system based on face recognition, and to explore the appropriate surveillance index for this system. Methods A primary school of about 1900 students was selected. Absenteeisms reported by school doctors and this system from March 1, 2021 (week 9) to January 14, 2022 (week 2) were collected, as well as weekly positive rate of influenza virus (WPRIV) released by China National Influenza Center. Eight weekly absenteeism rate indicators were calculated: all-cause absenteeism rate reported by system (WAR1), all-cause absenteeism rate reported by school doctors (WAR2), sickness absenteeism rate reported by school doctors (WAR3), and the rate of students absent one time (WAR4), two times (WAR5), three to four times (WAR6), one to two times (WAR7) and two to four times (WAR8) a week reported by the system. Pearson correlation coefficients of eight indicators and WPRIV were analyzed, and the change trend of their time series diagram was investigated. Results During week 9-42, WAR1 (r=0.614, p=0.001), WAR4 (r=0.631, p0.05) and WAR3 (r=0.239, p>0.05) were not. Throughout the surveillance period, WAR1 (r=0.671, p0.05) and WAR7 (r=0.242, p>0.05) were not. Conclusions Absenteeism reported by the system was more effective for influenza surveillance than absenteeism reported by school doctors, especially when the influenza activity level was low. When WAR1, WAR5 and WAR8 were combined together, the epidemic situation of influenza could be more comprehensively aware.
VL  - 10
IS  - 3
ER  -

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