Literature DB >> 23347500

Risk Factors for influenza A(H1N1)pdm09 among students, Beijing, China.

Yang Zheng1, Wei Duan, Peng Yang, Yi Zhang, Xiaoli Wang, Li Zhang, Surabhi S Liyanage, Quanyi Wang.   

Abstract

To identify risk factors associated with influenza A(H1N1)pdm09 among students in Beijing, China, we conducted a case-control study. Participants (304 case-patients and 608 controls, age range 6-19 years) were interviewed by using a standardized questionnaire. We found that in addition to vaccination, nonpharmaceutical interventions appeared to be protective.

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Year:  2013        PMID: 23347500      PMCID: PMC3559042          DOI: 10.3201/eid1902.120628

Source DB:  PubMed          Journal:  Emerg Infect Dis        ISSN: 1080-6040            Impact factor:   6.883


Influenza A(H1N1)pdm09 virus first emerged in Mexico and southern California, USA, in early April 2009 and rapidly spread worldwide (). The mode of transmission of this novel virus was similar to that of other influenza viruses. Notably, the virus disproportionately affected children and young adults (). Therefore, further research was required to understand etiologic factors associated with spread of influenza A(H1N1)pdm09 among school-age children to limit transmission within schools and in the community. We conducted a case–control study to identify risk factors associated with influenza A(H1N1)pdm09 among students in Beijing, China.

The Study

Beijing is one of the largest cities in China and has 18 districts and a population of >20 million persons. Although there is considerable variation in district size and a greater population density in urban areas, health care is accessible for residents in all districts. During the pandemic period, the Notifiable Disease Surveillance System (NDSS) was established in Beijing. Fifty-five collaborating laboratories covering all hospitals were authorized to conduct confirmation testing for influenza A(H1N1)pdm09 virus (). All confirmed cases were reported through the NDSS. Case-patients were students for whom diagnosis was confirmed during October 1, 2009–January 31, 2010. Stratified sampling was used to recruit case-patients through the NDSS. We randomly selected 3 urban and 3 rural districts from the 18 districts and listed all case-patients <18 years of age. We aimed to randomly select 50 patients from each district. Controls were matched with case-patients at a ratio of 2:1 by sex and age (± 1 year) and were recruited from the same school and grade but from different parallel classes than case-patients. Students who reported having influenza-like symptoms since September 2009 were excluded. The survey was conducted as a face-to-face interview by trained investigators from the Centers for Disease Control and Prevention in Beijing by using a standardized questionnaire. This interview had a 100% response rate and no data were missing. All variables were self-reported. Data entry and statistical analysis were conducted by using EpiData software version 3.1 (www.epidata.dk/download.php) and SPSS version 16.0 (IBM, Armank, NY, USA). Bivariate and multivariate conditional logistic regression analyses were used to determine risk factors associated with infection. All variables with p<0.05 in bivariate analysis were included in multivariate analysis. Collinearity was evaluated for all variables in the final model. Backward conditional logistic regression was conducted by removing variables with p>0.10, and statistical significance was defined as p<0.05 (Technical Appendix). A total of 304 case-patients and 608 controls were recruited from either primary or middle schools. Age range was 6–18 years for case-patients and 6–19 years for controls (median age 13 years for both groups). Bivariate analysis identified factors associated with having influenza A(H1N1)pdm09. These factors were vaccination history, eye rubbing, handwashing immediately after sneezing, handwashing after lessons in communal classrooms, sleep time per day, participation in outdoor activities after class, population density of classrooms, classroom ventilation, mode of transportation to and from school, and participation in clustered social activities after school (Table 1).
Table 1

Bivariate analysis of potential factors associated with influenza A(H1N1)pdm09 infection among students ≤18 years, Beijing, China*

VariableNo. (%) case-patients, n = 304 No. (%) controls, n = 608p valueOR (95%CI)
Vaccination against influenza A(H1N1)pdm09
No276 (90.8)264 (43.4)Referent
Yes28 (9.2)344 (56.6)<0.0010.08 (0.05–0.12)
Vaccination with pneumococcal vaccine
No279 (91.8)542 (89.1)Referent
Yes25 (8.2)66 (10.9)0.1930.72 (0.43–1.18)
Use of traditional Chinese medicine
No103 (33.9)175 (28.8)Referent
Yes201 (66.1)433 (71.2)0.0680.73 (0.51–1.02)
Eye rubbing
No163 (53.6)395 (65.0)Referent
Yes141 (46.4)213 (35.0)0.0011.68 (1.25–2.26)
Handwashing immediately after sneezing
No151 (49.7)205 (33.7)Referent
Yes153 (50.3)403 (66.3)<0.0010.48 (0.36–0.65)
Use of soap during handwashing
No37 (12.2)63 (10.4)Referent
Yes267 (87.8)545 (89.6)0.4020.83 (0.53–1.29)
Handwashing after lessons in communal classrooms
No176 (57.9)285 (46.9)Referent
Yes128 (42.1)323 (53.1)0.0020.63 (0.48–0.84)
Handwashing after participation in outdoor sports activities
No46 (15.1)69 (11.3)Referent
Yes258 (84.9)539 (88.7)0.0880.69 (0.45–1.06)
Duration of handwashing, s
<20176 (57.9)347 (57.1)Referent
≥20128 (42.1)261 (42.9)0.8000.96 (0.71–1.30)
Sleep time, h/day
<799 (32.6)162 (26.6)Referent
≥7205 (67.4)446 (73.4)0.0300.67 (0.47–0.96)
Sharing of tableware with classmates
No263 (86.5)534 (87.8)Referent
Yes41 (13.5)74 (12.2)0.5561.14 (0.74–1.75)
Classroom space/student, m2
<1.6223 (73.4)412 (67.8)Referent
≥1.681 (26.6)196 (32.2)<0.0010.17 (0.07–0.41)
Participation in outdoor activities after class
No232 (76.3)411 (67.6)Referent
Yes72 (23.7)197 (32.4)0.0030.58 (0.40–0.83)
Frequency of classroom ventilation
>1×/h109 (35.9)160 (26.3)Referent
1×/h195 (64.1)448 (73.9)0.0020.61 (0.44–0.83)
Having meals in small restaurants near school
No232 (76.3)460 (75.7)Referent
Yes72 (23.7)148 (24.3)0.8080.96 (0.67–1.37)
Modes of transportation to and from school
Closed (taxi, public transportation, school bus, car)188 (61.8)325 (53.5)Referent
Open (walking, bicycle, motorcycle)116 (38.2)283 (46.5)0.0090.66 (0.48–0.90)
Participation in clustered social activities after school closure
No266 (87.5)559 (91.9)Referent
Yes38 (12.5)49 (8.1)0.0231.76 (1.08–2.86)

*Bivariate conditional logistic regression was used to generate p values. OR, odds ratio.

*Bivariate conditional logistic regression was used to generate p values. OR, odds ratio. Multivariate analysis showed that in addition to vaccination, a series of environmental and behavioral factors were associated with reducing the risk for influenza A(H1N1)pdm09. These factors included provision of classroom space >1.6 m2/student, participation in outdoor activities after school, decreased interval of classroom ventilation, immediate handwashing after sneezing, having more sleep time (>7 h/day), and use of open modes of travel (walking, bicycle, and motorcycle) (Table 2).
Table 2

Multivariate analysis of independent factors associated with influenza A(H1N1)pdm09 infection among students <18 years of age, Beijing, China*

Variablep valueMatched OR (95% CI)
Vaccination against influenza A(H1N1)pdm09
NoReferent
Yes
<0.001
0.07 (0.04–0.11)
Handwashing immediately after sneezing
NoReferent
Yes
<0.001
0.49 (0.33–0.72)
Sleep time, h/day
<7Referent
>7
0.042
0.62 (0.38–0.98)
Classroom space/student, m2
<1.6Referent
≥1.6
<0.001
0.11 (0.04–0.31)
Participation in outdoor activities after class
NoReferent
Yes
0.029
0.60 (0.38–0.95)
Frequency of classroom ventilation
>1×/hReferent
1×/h
0.023
0.60 (0.39–0.93)
Mode of transportation to and from school
Closed (taxi, public transportation, school bus, car)Referent
Open (walking, bicycle, motorcycle)
0.010
0.58 (0.39–0.88)
Participation in clustered social activities after school
NoReferent
Yes0.0252.08 (1.10–3.95)

*Ten variables were included in multivariate conditional logistic regression analysis. Backward conditional logistic regression was conducted by removing variables with p>0.10, and 8 variables remained in the final regression model. All statistical tests were 2-sided, and significance was defined as p<0.05. The statistic for each variable was obtained after adjustment for other 7 variables in the final regression model.

*Ten variables were included in multivariate conditional logistic regression analysis. Backward conditional logistic regression was conducted by removing variables with p>0.10, and 8 variables remained in the final regression model. All statistical tests were 2-sided, and significance was defined as p<0.05. The statistic for each variable was obtained after adjustment for other 7 variables in the final regression model.

Conclusions

We found several variables that determined whether students would have influenza A(H1N1)pdm09. These factors were vaccination, classroom space, outdoor activities, classroom ventilation, handwashing, sleep time, and modes of travel. Vaccination against influenza A(H1N1)pdm09 was more common among controls than case-patients, suggesting its potential value of protection. However, the vaccination rate is low in Beijing, China. Limited knowledge and misconceptions regarding vaccination safety were contributing risk factors (–). Because transmission modes for this virus appeared to be similar to those for seasonal influenza viruses, involving close, unprotected contact with respiratory droplets (), we found that environmental issues appeared to be protective. When the interval of classroom ventilation exceeded 1 h, air renewal was determined to be inadequate, increasing potential risk for infection. These findings are consistent with those of other studies, which reported that influenza can spread in a confined space with insufficient air flow and that clustering of students within classrooms or during after-school activities can facilitate transmission of infectious diseases (,). Social distancing might be another protective nonpharmaceutical measure. When available classroom space per student was <1.6 m2, there was a greater chance that students having influenza A(H1N1)pdm09 would have close contact with healthy classmates, who would be at higher risk of acquiring this disease. We found that use of closed modes of transportation was also a risk factor. Although other studies reported that transmission rates of influenza A(H1N1)pdm09 were not increased by close and frequent contact with other persons on public transportation, we advocate use of open modes of transportation for travel to and from school, and self-protection measures when using closed modes of transportation (). For instance, because wearing of face masks is easily applicable and has been shown to be protective, it tended to be a preventative measure for students who use closed transport systems (). School closure has been identified as a protective measure for controlling influenza pandemics (). Some students after school closure continued to participate in clustered social activities, thereby having potentially increased their risk for contact with patients with influenza A(H1N1)pdm09 outside the school environment. Thus, after school closure, avoidance of large gatherings and clustered social activities may further reduce infection among students. Some variables that we analyzed were not risk factors (vaccination with pneumococcal vaccine, drug prophylaxis [using traditional Chinese medicine], some handwashing habits (e.g., duration of handwashing), and sharing of tableware with classmates. Further studies might be needed to determine their effects. There were limitations to this study. Case-patients were recruited into the study 3–8 months after receiving a confirmed diagnosis. Therefore, data collection was retrospective and had potential recall bias. Not all risk factors for influenza could be comprehensively assessed by the questionnaire. Controls were not subjected to laboratory testing, and some asymptomatic infected students may have been misclassified as controls, resulting in underestimation of odd ratios of certain risk factors and overestimation of odd ratios of certain protective factors. We did not include face mask use in the analysis because it was difficult to accurately categorize wearing face masks, given the large variety of face masks in different sizes and varying tightness in use during the pandemic in Beijing, and because we had no data for time, place, or duration of face mask use. In conclusion, administration of vaccine and nonpharmaceutical interventions were beneficial for control of influenza A(H1N1)pdm09. Thus, it is essential to increase awareness regarding severity of influenza A(H1N1)pdm09 to improve knowledge of the protective effect of influenza vaccine and to promote use of nonpharmaceutical interventions among school-age children.

Technical Appendix

Methods used to determine risk factors for influenza A(H1N1)pdm09 among students, Beijing, China.
  12 in total

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Authors:  Vincent Pestre; Bruno Morel; Nathalie Encrenaz; Amandine Brunon; Frédéric Lucht; Bruno Pozzetto; Philippe Berthelot
Journal:  Scand J Infect Dis       Date:  2011-12-08

2.  Severe, critical and fatal cases of 2009 H1N1 influenza in China.

Authors:  Peng Yang; Ying Deng; Xinghuo Pang; Weixian Shi; Xinyu Li; Lili Tian; Yi Zhang; Xiaoli Wang; Fang Huang; Macintyre C Raina; Quanyi Wang
Journal:  J Infect       Date:  2010-07-27       Impact factor: 6.072

3.  The importance of school and social activities in the transmission of influenza A(H1N1)v: England, April - June 2009.

Authors:  I Kar-Purkayastha; C Ingram; H Maguire; A Roche
Journal:  Euro Surveill       Date:  2009-08-20

4.  Pandemic (H1N1) 2009 virus outbreak in a school in London, April-May 2009: an observational study.

Authors:  L Calatayud; S Kurkela; P E Neave; A Brock; S Perkins; M Zuckerman; M Sudhanva; A Bermingham; J Ellis; R Pebody; M Catchpole; R Heathcock; H Maguire
Journal:  Epidemiol Infect       Date:  2009-11-20       Impact factor: 2.451

5.  [Factors associated with immunization of novel influenza A (H1N1) vaccine in Beijing, 2009].

Authors:  Xing-huo Pang; Dong-lei Liu; Li Lu; Xiao-li Wang; Zhen Yang; Jia-zi Zhang Zhu; Ying Deng
Journal:  Zhonghua Liu Xing Bing Xue Za Zhi       Date:  2010-05

6.  Seroprevalence of pandemic (H1N1) 2009 influenza and effectiveness of 2010/2011 influenza vaccine during 2010/2011 season in Beijing, China.

Authors:  Peng Yang; Li Zhang; Weixian Shi; Guilan Lu; Shujuan Cui; Xiaomin Peng; Daitao Zhang; Yimeng Liu; Huijie Liang; Xinghuo Pang; Quanyi Wang
Journal:  Influenza Other Respir Viruses       Date:  2011-12-30       Impact factor: 4.380

Review 7.  The 2009 A (H1N1) influenza virus pandemic: A review.

Authors:  Marc P Girard; John S Tam; Olga M Assossou; Marie Paule Kieny
Journal:  Vaccine       Date:  2010-05-27       Impact factor: 3.641

8.  Emergence of a novel swine-origin influenza A (H1N1) virus in humans.

Authors:  Fatimah S Dawood; Seema Jain; Lyn Finelli; Michael W Shaw; Stephen Lindstrom; Rebecca J Garten; Larisa V Gubareva; Xiyan Xu; Carolyn B Bridges; Timothy M Uyeki
Journal:  N Engl J Med       Date:  2009-05-07       Impact factor: 91.245

Review 9.  The first pandemic of the 21st century: a review of the 2009 pandemic variant influenza A (H1N1) virus.

Authors:  Nikole M Scalera; Sherif B Mossad
Journal:  Postgrad Med       Date:  2009-09       Impact factor: 3.840

10.  Effects of school closures, 2008 winter influenza season, Hong Kong.

Authors:  Benjamin J Cowling; Eric H Y Lau; Conrad L H Lam; Calvin K Y Cheng; Jana Kovar; Kwok Hung Chan; J S Malik Peiris; Gabriel M Leung
Journal:  Emerg Infect Dis       Date:  2008-10       Impact factor: 6.883

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