ABSTRACT
The epidemic of coronavirus disease 2019 (COVID-19) broke out during the peak season of influenza in China. We aimed to assess the knowledge, attitudes, and practices (KAP) of influenza among Chinese adults during this special season. A cross-sectional online questionnaire survey was performed by recruiting 4822 participants. There were 76.09% of the participants reporting that they learned more knowledge of influenza during the COVID-19 epidemic. The mean knowledge score of participants was 5.51 ± 1.55 (78.7% correct rate), and participants who received influenza vaccination in the past year scored the highest (6.06 ± 1.30, p< .001). Nearly half of the participants (49.63%) agreed the threat to the functioning of society by influenza was far less than the COVID-19. 73.04% of the participants knew influenza vaccination was the most effective way to prevent influenza infection, while 54.18% did not know the vaccination location. The proportion of participants who were willing to get vaccinated would increase from 62.53% to 85.82% if clinicians recommended the vaccination. For influenza-like illness, merely 36.11% of participants would seek medical care from the hospital, and 60.53% agreed or showed a neutral attitude toward antibiotic use for influenza treatment. Regression analyses showed that the medical profession and history of influenza vaccination were both associated with higher knowledge or attitude score and participants’ use of face masks in previous seasons and their willingness to receive influenza vaccination. In conclusion, the awareness of influenza vaccination among adults in China should be reinforced and educational campaigns were warranted to increase the coverage of influenza vaccination.
KEYWORDS: Influenza, vaccination, knowledge, attitude, practice, COVID-19, China
1. Introduction
Influenza is a viral respiratory disease with each individual susceptible to its infection. Around 1 billion cases (3–5 million severe cases) of influenza infection occurred across the globe annually.1 In 2019, the World Health Organization (WHO) named the influenza pandemic as one of the ten threats to global health and launched the Global Influenza Strategy for 2019–2030,2,3 which aimed to prevent and control the spread of influenza in all countries.
In China, annual influenza-associated excess deaths (88100 cases) accounted for 8.2% of respiratory mortality,4 yet the substantial disease burden of seasonal influenza was underappreciated. The influenza vaccination rate among Chinese population is unexpectedly low (1.5–2.2%) even though influenza vaccination is recommended as the most effective approach to prevent influenza.5 Unlike some developed countries, China has not implemented the National Immunization Scheme for influenza vaccination, and residents need to pay out-of-pocket for immunization.5 Besides the reasons of immunization policy, previous studies reported that individuals’ inadequate awareness toward influenza and influenza vaccination was another major obstacle to increase the vaccination coverage.6-9
The epidemic of coronavirus disease 2019 (COVID-19) broke out during the peak season of influenza in China. The COVID-19 drew the indirect attention of influenza to the public because both were viral respiratory diseases with similar clinical symptoms, such as fever, cough, myalgia, and fatigue.10,11 It was difficult for the general population to differentiate influenza from COVID-19, which to some extent increased the public panic of COVID-19. The outbreak of COVID-19 warned all nations the necessity of preparedness of infectious diseases pandemic. The threat of pandemic influenza was ever-present, and we need to be vigilant against the potential risks of a major influenza outbreak.3 Previous studies supported that the knowledge, attitudes and practices (KAP) surveys had the potential to explore factors affecting individuals’ precautionary behaviors of influenza and influenza vaccination.12-15 The findings could provide policy recommendations for decision-makers on how to take appropriate preparedness measures for an influenza pandemic. Therefore, we aimed to assess the KAP of influenza among Chinese adults during the special period of COVID-19 outbreak in China.
2. Materials and methods
2.1. Participants
We conducted a cross-sectional online questionnaire survey from February 17 to March 7, 2020. During this period, most of the people in China were isolated at home due to the epidemic of COVID-19. The WeChat app, which is the most widely used social networking app in China (over 1.1 billion users), was used for data collection. All authors of the present study initially posted the survey link or a quick response code of the questionnaire on their moments or social groups in WeChat. WeChat users who were aged over 18 years and could complete the survey independently were eligible to participate in the survey. After participants completed the survey, they were encouraged to share the questionnaire with their WeChat social groups, and more individuals would enroll in the survey. A snowball sampling strategy was therefore utilized to finalize the study. No incentives were provided to the enrolled participants.
2.2. Study design
Two authors drafted the structured questionnaire, which was further reviewed and revised by gaining the feedback of two experts from the Shaanxi Provincial Center for Disease Control and Prevention for content validation. A pilot test (n = 60) was also performed to ensure the validity and interpretability of the questionnaire. The Cronbach alpha coefficient of our questionnaire in the pilot test was 0.71, indicating an acceptable level of internal consistency. The final version of the questionnaire (Supplementary file 1) was composed of two sections, including the demographics of the participants and their KAP toward influenza. Demographic variables included age, gender, place of residence, marital status, education level, profession, monthly income, self-assessed health condition, participants’ or their family members’ history of lab-confirmed influenza infection and influenza vaccination in the past year.
There were 21 questions included in the KAP sections, which were generated following the Technical Guidelines for Seasonal Influenza Vaccination in China.11 The knowledge section consisted of seven questions. Four items were related to influenza infection on its pathogeny, complications, and difference from the common cold. The other three items were associated with influenza vaccination. The question “Influenza is a fatal disease like current COVID-19 epidemic” aimed to explore whether participants were aware of the severity of influenza. In each question, there were three preexisting options, and participants could select “yes”, “no”, or “unknown”. To investigate participants’ knowledge level, we assigned 1 point for each correct item and 0 for the incorrect/unknown option. Thus, the total knowledge score ranged from 0 to 7.
The attitude section was composed of six questions. Three items were designed to measure participants’ attitudes toward influenza infection, including its disease burden and treatment. The question “Influenza is a common disease, and its social hazard is far less than the COVID-19” was used to compare participants’ attitudes of influenza with the current COVID-19 epidemic. Another three items were related to influenza vaccination, which aimed to assess participants’ attitudes on the necessity of vaccination, location of vaccination, and willingness to receive vaccination if it was free. A five-point Likert scale was used to record the response of the participants, including “strongly agree”, “agree”, “neutral”, “disagree”, and “strongly disagree” in each question. To determine the attitude score, we assigned 1–5 points from “strongly agree” to “strongly disagree”, respectively, to each item, and the total attitude score ranged from 6 to 30.
Eight questions were included in the practice section, which were designed to assess participants’ behaviors on the prevention and treatment of influenza infection. There were two questions used to evaluate participants’ behavioral change during the special time of COVID-19 epidemic, including “Do you learn more knowledge of influenza during the epidemic of COVID-19” and “If there was no COVID-19 outbreak, do you often wear face masks to go outdoor in flu season”. In this section, participants could select “yes” or “no” for each question. Another two multiple-choice questions aimed to investigate participants’ information sources of influenza and measures to prevent its infection. Additionally, two questions were also designed to explore participants’ willingness to receive influenza vaccination if recommended by clinicians. We did not assign score values for each item included in this section in order to objectively report participants’ practices toward influenza.
2.3. Statistical analysis
Data analyses were performed by SPSS 25.0. We calculated mean with standard deviation and median with interquartile range (IQR) to demonstrate the distribution of knowledge and attitude scores. Data in the practice section were described by frequency and proportions. To compare the differences in varied groups, we used the Kruskal–Wallis test for continuous variables and the Chi-square test for categorical variables. Multiple linear regression analysis was conducted to determine the association between demographic variables and knowledge or attitude scores, in which demographic variables were the independent variables and scores were the outcome variables. Coefficients (β) were estimated by the regression. Logistic regression analysis was performed to determine the association between demographic variables and participants’ behaviors in the practice section. Logistic regression analysis was carried out to estimate the odds ratio (OR) and its 95% confidence interval (CI). P-values below 0.05 were considered as statistically significant in the present study.
3. Results
A total of 4822 individuals completed the online survey. Among the participants, 2950 (61.18%) were female, 3186 (66.07%) were aged 18–40 years, and 3985 (82.64%) lived in urban districts. Other demographic characteristics are shown in Table 1.
Table 1.
Demographic characteristics of participants and their knowledge scores
| Characteristics | Proportion of overall Chinese adults# (%) | N (%) | Mean score (SD) | Median score (IQR) | Statistics | P-value |
|---|---|---|---|---|---|---|
| Total | - | 4822 (100.00) | 5.51 (1.55) | 6 (5,7) | ||
| Gender | −2.800 | 0.005 | ||||
| Male | 51.13 | 1872 (38.82) | 5.41 (1.63) | 6 (5,7) | ||
| Female | 48.87 | 2950 (61.18) | 5.57 (1.49) | 6 (5,7) | ||
| Age (years) | 89.000 | <0.001 | ||||
| 18–40 | 41.03 | 3186 (66.07) | 5.55 (1.49) | 6 (5,7) | ||
| 41–60 | 36.65 | 1458 (30.24) | 5.50 (1.61) | 6 (5,7) | ||
| >60 | 22.32 | 178 (3.69) | 4.81 (1.90) | 5 (4,6) | ||
| Place of residence | −7.457 | <0.001 | ||||
| URBAN | 59.58 | 3985 (82.64) | 5.58 (1.50) | 6 (5,7) | ||
| Rural | 40.42 | 837 (17.36) | 5.14 (1.69) | 5 (4,6) | ||
| Marital status | 30.582 | <0.001 | ||||
| Unmarried | 18.16 | 1330 (27.58) | 5.44 (1.40) | 6 (5,7) | ||
| Married | 74.12 | 3336 (69.18) | 5.55 (1.59) | 6 (5,7) | ||
| Divorced | 2.11 | 127 (2.63) | 5.14 (1.76) | 6 (4,7) | ||
| Widowed | 5.61 | 29 (0.60) | 4.97 (1.80) | 5 (4,6) | ||
| Education level | 196.423 | <0.001 | ||||
| High school or below | 85.99 | 536 (11.12) | 4.61 (1.94) | 5 (3,6) | ||
| College or undergraduate | 13.41 | 3103 (64.35) | 5.52 (1.50) | 6 (5,7) | ||
| Master or above | 0.60 | 1183 (24.53) | 5.87 (1.30) | 6 (5,7) | ||
| Profession | 378.137 | <0.001 | ||||
| Medical | 1.53 | 1834 (38.03) | 6.03 (1.16) | 6 (5,7) | ||
| Non-medical | 94.77 | 2322 (48.15) | 5.28 (1.59) | 6 (4,7) | ||
| No profession | 3.70 | 666 (13.81) | 4.83 (1.84) | 5 (4,6) | ||
| Monthly income (CNY) | 95.085 | <0.001 | ||||
| ≤5000 | 31.80 | 2084 (43.22) | 5.27 (1.63) | 6 (4,7) | ||
| 5000–10000 | 37.80 | 1817 (37.68) | 5.66 (1.46) | 6 (5,7) | ||
| ≥10000 | 30.40 | 921 (19.10) | 5.74 (1.45) | 6 (5,7) | ||
| Health condition | 24.719 | <0.001 | ||||
| Good | - | 3432 (71.17) | 5.58 (1.50) | 6 (5,7) | ||
| General | - | 1332 (27.62) | 5.36 (1.60) | 6 (4,7) | ||
| Poor | - | 58 (1.20) | 4.67 (2.22) | 5 (3,7) | ||
| Infected by influenza last year* | 31.416 | <0.001 | ||||
| Yes | - | 656 (13.60) | 5.52 (1.55) | 6 (5,7) | ||
| No | - | 3927 (81.44) | 5.54 (1.51) | 6 (5,7) | ||
| Unknown | - | 239 (4.96) | 4.85 (1.91) | 5 (4,6) | ||
| Received influenza vaccination last year* | 183.324 | <0.001 | ||||
| Yes | - | 768 (15.93) | 6.06 (1.30) | 7 (6,7) | ||
| No | - | 3866 (80.17) | 5.44 (1.55) | 6 (5,7) | ||
| Unknown | - | 188 (3.90) | 4.68 (1.82) | 5 (4,6) |
CNY: Chinese Yuan; SD: standard deviation; IQR: inter quartile range; #The data were derived from the 2019 Report of National Bureau of Statistics; *participants or their family members.
3.1. Knowledge
The mean knowledge score of participants was 5.51 ± 1.55, and the knowledge score varied significantly in all demographic groups (Table 1). Participants who received influenza vaccination in the past year had the highest mean knowledge score (6.06 ± 1.30, p< .001), while high school or less educated participants scored the lowest (4.61 ± 1.94, p< .001). Among the total participants, 80.67% were aware that “influenza is different from common cold” and 89.28% knowing “influenza infection may arise complications, such as pneumonia or cardiovascular diseases”. There were 79.80% of the participants knowing “influenza is a deadly disease like the COVID-19 epidemic”. A relatively lower proportion of participants (73.04%) were aware that “influenza vaccination is the most effective way to prevent influenza infection”. However, participants who knew “the annual optimum time of influenza vaccination is before the end of October” merely accounted for 53.71%.
The results of multiple linear regression analysis on the knowledge score are shown in Table 2. It was revealed that participants aged 41–60 years scored higher compared to the elderly aged over 60 years (β = 0.398, p< .001). Individuals living in urban districts had a better knowledge level than those who lived in rural districts (β = 0.302, p= .003). The knowledge score was observed to be higher among participants with higher education (βcollege or undergraduate = 0.535, p< .001; βmaster or above = 0.813, p< .001). In addition, medical profession (vs. non-medical, β = 0.684, p< .001), monthly income of CNY5000-10000 (vs. ≤CNY5000, β = 0.103, p= .045), general or good health condition (vs. poor health, βgood = 0.423, p= .029; βgeneral = 0.383, p= .048), and received influenza vaccination in the past year (vs. no vaccination, β = 0.564, p< .001) were significantly associated with higher knowledge score. Conversely, participants who were infected or unknown whether infected by influenza in the past year scored lower than those who were not infected (βinfected = −0.149, p= .0149; βunknown infected = −0.572, p< .001).
Table 2.
Multiple linear regression analysis on factors associated with knowledge score
| Variable | Coefficient (β) | Standard error | t | P-value |
|---|---|---|---|---|
| Gender | ||||
| Female | 0 | |||
| Male | −0.061 | 0.044 | −1.38 | 0.169 |
| Age (years) | ||||
| >60 | 0 | |||
| 18–40 | 0.185 | 0.118 | 1.56 | 0.118 |
| 41–60 | 0.398 | 0.117 | 3.41 | <0.001 |
| Place of residence | ||||
| Rural | 0 | |||
| Urban | 0.302 | 0.138 | 2.19 | 0.003 |
| Marital status | ||||
| Unmarried | 0 | |||
| Married | 0.032 | 0.056 | 0.57 | 0.570 |
| Divorced | −0.212 | 0.139 | −1.52 | 0.127 |
| Widowed | −0.153 | 0.276 | −0.56 | 0.579 |
| Education level | ||||
| High school or below | 0 | |||
| College or undergraduate | 0.535 | 0.079 | 6.78 | <0.001 |
| Master or above | 0.813 | 0.091 | 8.92 | <0.001 |
| Profession | ||||
| Non-medical | 0 | |||
| Medical | 0.684 | 0.046 | 14.705 | <0.001 |
| No profession | −0.108 | 0.071 | −1.532 | 0.1270 |
| Monthly income (CNY) | ||||
| ≤5000 | 0 | |||
| 5000–10000 | 0.103 | 0.051 | 2.01 | 0.045 |
| ≥10000 | 0.063 | 0.065 | 0.97 | 0.333 |
| Health condition | ||||
| Poor | 0 | |||
| Good | 0.423 | 0.193 | 2.19 | 0.029 |
| General | 0.383 | 0.194 | 1.97 | 0.048 |
| Infected by influenza last year* | ||||
| No | 0 | |||
| Yes | −0.149 | 0.061 | −2.432 | 0.0149 |
| Unknown | −0.572 | 0.097 | −5.138 | <0.001 |
| Received influenza vaccination last year* | ||||
| No | 0 | |||
| Yes | 0.564 | 0.058 | 9.734 | <0.001 |
| Unknown | −0.520 | 0.109 | −4.745 | <0.001 |
CNY: Chinese Yuan; *participants or their family members.
3.2. Attitudes
In the attitude section, the median score ranged from 2 to 4 (Table 3). Nearly half of the participants (49.63%) agreed that influenza was a common disease and its threat to the functioning of society was far less than the COVID-19. Less than one-third of the participants (26.82%) agreed that influenza would not cause severe health and economic burden. Participants who agreed or disagreed with antibiotic use for influenza treatment accounted for 29.18% and 39.45%, respectively. Merely 9.44% of the participants knew the location of influenza vaccination, and over half of the participants (54.63%) stated that they were not aware of the vaccination location. There were 18.89% of the participants who agreed with the statement that it was not necessary to have influenza vaccination, and 12.03% of participants said that they would not receive the vaccination even if it was offered free.
Table 3.
Attitudes of statement on influenza and influenza vaccination
| N (%) |
Attitude score |
||||||
|---|---|---|---|---|---|---|---|
| Strongly agree | Agree | Neutral | Disagree | Strongly disagree | Mean (SD) | Median (IQR) | |
| 1) Influenza is a common disease, and its social hazard is far less than the COVID-19. | 581 (12.05) | 1812(37.58) | 1121 (23.25) | 1155 (23.95) | 153 (3.17) | 2.68 (1.06) | 3 (2,4) |
| 2) Influenza infection will not cause severe health and economic burden. | 202 (4.19) | 1091 (22.63) | 1270 (26.34) | 1899 (39.38) | 360 (7.47) | 3.23 (1.01) | 3 (2,4) |
| 3) Antibiotics can be used to treat an influenza infection, such as amoxicillin and cephalosporins. | 130 (2.70) | 1277 (26.48) | 1513 (31.38) | 1272 (26.38) | 630 (13.07) | 3.20 (1.06) | 3 (2,4) |
| 4) I know the location of influenza vaccination. | 94 (1.95) | 361 (7.49) | 1733 (35.94) | 2001 (41.50) | 633 (13.13) | 2.43 (0.88) | 2 (2,3) |
| 5) I think it is not necessary to receive influenza vaccination. | 124 (2.57) | 787 (16.32) | 949 (19.68) | 2301 (47.72) | 661 (13.71) | 3.54 (1.00) | 4 (3,4) |
| 6) I would not receive influenza vaccination even if it was offered free. | 112 (2.32) | 468 (9.71) | 1035 (21.46) | 1918 (39.78) | 1289 (26.73) | 3.79 (1.02) | 4 (3,5) |
| Total | 4822 (100.00%) | 20.01 (3.50) | 20 (18,22) | ||||
SD: standard deviation; IQR: inter quartile range; COVID-19: coronavirus disease 2019.
The results of multiple linear regression analysis on the attitude score are shown in Table 4. Similar to the findings in the knowledge section, the younger age (vs. aged over 60 years, β18-40 = 1.697, p< .001; β41-60 = 0.803, p= .003), urban districts (β = 0.302, p= .028), master or above education (vs. high school or below, β = 0.830, p< .001), medical profession (vs. non-medical, β = 1.218, p< .001), received influenza vaccination in the past year (vs. no vaccination, β = 1.678, p< .001) were significantly associated with higher attitude score. Participants who were infected with influenza virus or participants who were unsure whether they were infected in the past year had a lower attitude score than those who were not infected (βinfected = −0.574, p< .001; βunknown infected = −0.572, p= .010).
Table 4.
Multiple linear regression analysis on factors associated with attitude score
| Variable | Coefficient (β) | Standard error | t | P-value |
|---|---|---|---|---|
| Gender | ||||
| Female | 0 | |||
| Male | −0.181 | 0.101 | −1.78 | 0.074 |
| Age (years) | ||||
| >60 | 0 | |||
| 18–40 | 1.697 | 0.271 | 6.26 | <0.001 |
| 41–60 | 0.803 | 0.267 | 3.00 | 0.003 |
| Place of residence | ||||
| Rural | 0 | |||
| Urban | 0.302 | 0.137 | 2.19 | 0.028 |
| Marital Status | ||||
| Unmarried | 0 | |||
| Married | −0.046 | 0.129 | −0.36 | 0.718 |
| Divorced | −0.307 | 0.320 | −0.96 | 0.336 |
| Widowed | −0.327 | 0.633 | −0.52 | 0.606 |
| Education level | ||||
| High school or below | 0 | |||
| College or undergraduate | 0.322 | 0.181 | 1.78 | 0.075 |
| Master or above | 0.830 | 0.209 | 3.97 | <0.001 |
| Profession | ||||
| Non-medical | 0 | |||
| Medical | 1.218 | 0.109 | 11.41 | <0.001 |
| No profession | 0.026 | 0.164 | 0.16 | 0.873 |
| Monthly income (CNY) | ||||
| ≤5000 | 0 | |||
| 5000–10000 | −0.037 | 0.118 | −0.32 | 0.752 |
| ≥10000 | 0.161 | 0.149 | 1.08 | 0.280 |
| Health condition | ||||
| Poor | 0 | |||
| Good | 0.532 | 0.443 | 1.20 | 0.229 |
| General | 0.508 | 0.445 | 1.14 | 0.253 |
| Infected by influenza last year* | ||||
| No | 0 | |||
| Yes | −0.574 | 0.141 | −4.08 | <0.001 |
| Unknown | −0.572 | 0.223 | −2.57 | 0.010 |
| Received influenza vaccination last year* | ||||
| No | 0 | |||
| Yes | 1.678 | 0.133 | 12.60 | <0.001 |
| Unknown | −0.113 | 0.251 | −0.45 | 0.652 |
CNY: Chinese Yuan; *participants or their family members.
3.3. Practices
The results of the participants’ practices are listed in Table 5. During the epidemic of COVID-19, the majority of the participants (76.09%) learned more knowledge of influenza, while 60.95% of participants said that they would not wear face masks to go outdoor if there was no COVID-19 outbreak. Participants who would take the initiative to focus on influenza-related information accounted for 58.90%. The most popular source (93.82%) for participants to derive influenza-related information was social media, such as WeChat and Weibo. Maintaining indoor ventilation (77.37%), washing hands frequently (75.38%), and strengthening physical exercise (69.45%) were participants’ top three measures to prevent influenza infection. If participants had influenza-like-illness, 5.37% would select no treatment, 43.41% would take leftover medicines at home, and 36.11% would seek medical care from the hospital. For influenza vaccination, 62.53% of the participants would consider receiving vaccination in the future, and 85.82% were willing to get vaccinated if they received recommendations from clinicians.
Table 5.
Practices toward influenza and influenza vaccination
| Questions | N (%) |
|---|---|
| 1) Do you learn more knowledge of influenza during the epidemic of COVID-19? | |
| Yes | 3669 (76.09) |
| No | 1153 (23.91) |
| 2) If there was no COVID-19 outbreak, do you often wear face mask to go outdoor in the flu season? | |
| Yes | 1883 (39.05) |
| No | 2939 (60.95) |
| 3) Will you take the initiative to focus on influenza-related information? | |
| Yes | 2840 (58.90) |
| No | 1982 (41.10) |
| 4) Which of the following sources will you choose to derive influenza-related information? (multiple choices) | |
| Community publicity | 2312 (47.95) |
| Social media, such as WeChat and Weibo | 4524 (93.82) |
| TV | 2924 (60.64) |
| Newspaper | 956 (19.83) |
| Magazines | 691 (14.33) |
| Radio | 730 (15.14) |
| Other sources | 1326 (27.50) |
| 5) Which of the following measures did you previously take to prevent influenza infection? (multiple choices) | |
| No measures | 571 (11.84) |
| Influenza vaccination | 1402 (29.08) |
| Maintain indoor ventilation | 3731 (77.37) |
| Wear a mask outside | 2460 (51.02) |
| Wash hands frequently | 3635 (75.38) |
| Take more physical exercises | 3349 (69.45) |
| Less go to crowded places | 3273 (67.88) |
| Other measures | 502 (10.41) |
| 6) Which of the following option is your first choice if you have influenza-like illness (such as fever, cough, and muscle pain)? | |
| No treatment | 259 (5.37) |
| Take leftover medicines at home | 2093 (43.41) |
| Take medicine from a retail pharmacy | 729 (15.12) |
| Seek medical care from hospital | 1741 (36.11) |
| 7) Will you consider receiving influenza vaccination in the future? | |
| Yes | 3015 (62.53) |
| No | 510 (10.58) |
| Uncertain | 1297 (26.9) |
| 8) If clinicians recommend influenza vaccination to you, are you willing to receive it? | |
| Yes | 4138 (85.82) |
| No | 684 (14.18) |
COVID-19: coronavirus disease 2019.
We further analyzed the associated factors on participants’ behaviors of influenza prevention, including the use of face masks and receiving influenza vaccination. The corresponding results of logistic regression analysis are shown in Table 6. The number of participants who used face mask in flu season or considered to receive influenza vaccination in the future would be significantly larger with the increase of knowledge or attitude score. For the behavior of wearing face masks, males were found to use less than females (OR = 0.675; 95%CI: 0.593–0.768; p< .01). Participants who had a master or above education used fewer masks than those who had high school or below education (OR = 0.599; 95%CI: 0.460–0.780; p< .01). Similar results were found in participants with higher monthly income compared to those who earned below CNY5000 monthly. By contrast, medical professionals (OR = 1.410; 95%CI: 1.232–1.613; p< .01) and individuals who received influenza vaccination in the past year (OR = 1.305; 95%CI: 1.106–1.541; p< .01) had significantly higher use of face masks. The medical profession and the history of influenza vaccination in the past year were also significantly associated with participants’ intention to receive influenza vaccination in the future. Compared to the non-medical professionals, medical professionals (OR = 1.268; 95%CI: 1.110–1.447; p< .05) were more willing to get vaccinated. Participants who received vaccination in the past year showed three-fold more willingness to get vaccinated compared to others (OR = 3.275; 95%CI: 2.679–4.003; p< .01).
Table 6.
Logistic regression analysis on factors associated with participants’ behaviors of influenza prevention
| Variable | If there was no COVID-19 outbreak, do you often wear face mask to go outdoor in the flu season? | Will you consider receiving influenza vaccination in the future? |
|---|---|---|
| Score | ||
| Knowledge | 1.112 (1.064–1.163) * | 1.238 (1.187–1.291) * |
| Attitude | 1.035 (1.016–1.055) * | 1.127 (1.105–1.149) * |
| Gender | ||
| Female | 1 | 1 |
| Male | 0.675 (0.593–0.768) ** | 0.959 (0.847–1.087) |
| Age (years) | ||
| >60 | 1 | 1 |
| 18–40 | 1.120 (0.794–1.581) | 1.179 (0.848–1.640) |
| 41–60 | 1.141 (0.813–1.600) | 0.870 (0.630–1.201) |
| Place of residence | ||
| Rural | 1 | 1 |
| Urban | 0.88 (0.739–1.048) | 1.028 (0.861–1.227) |
| Marital status | ||
| Unmarried | 1 | 1 |
| Married | 1.074 (0.911–1.266) | 1.036 (0.882–1.218) |
| Divorced | 1.450 (0.977–2.153) | 0.860 (0.587–1.259) |
| Widowed | 1.046 (0.477–2.292) | 0.908 (0.431–1.912) |
| Education level | ||
| High school or below | 1 | 1 |
| College or undergraduate | 0.902 (0.721–1.130) | 0.957 (0.765–1.198) |
| Master or above | 0.599 (0.460–0.780) ** | 0.786 (0.607–1.018) |
| Profession | ||
| Non-medical | 1 | 1 |
| Medical | 1.410 (1.232–1.613) ** | 1.268 (1.110–1.447) * |
| No profession | 0.950 (0.773–1.166) | 1.203 (0.978–1.415) |
| Monthly income (CNY) | ||
| ≤5000 | 1 | 1 |
| 5000–10000 | 0.767 (0.660–0.891) ** | 0.987 (0.850–1.147) |
| ≥10000 | 0.501 (0.408–0.613) ** | 0.855 (0.707–1.034) |
| Health condition | ||
| Poor | 1 | 1 |
| Good | 1.722 (0.962–3.086) | 1.643 (0.977–2.762) |
| General | 1.507 (0.839–2.707) | 1.332 (0.791–2.245) |
| Infected by influenza last year# | ||
| No | 1 | 1 |
| Yes | 0.961 (0.803–1.149) | 0.977 (0.820–1.165) |
| Unknown | 0.711 (0.531–0.953) * | 0.727 (0.559–0.946) * |
| Received influenza vaccination last year# | ||
| No | 1 | 1 |
| Yes | 1.305 (1.106–1.541) ** | 3.275 (2.679–4.003) ** |
| Unknown | 1.234 (0.902–1.688) | 1.061 (0.782–1.438) |
COVID-19: coronavirus disease 2019; CNY: Chinese Yuan; #participants or their family members; *P< 0.05; **P< 0.01.
4. Discussion
The present study sheds light on the KAP of influenza among Chinese adults during the special time of the COVID-19 epidemic in China. The results showed an acceptable knowledge score (78.7% correct rate) and a relatively lower attitude score (66.7% of the total score) of the participants. Multivariant linear regression analyses showed a significant association of knowledge and attitude scores with age, place of residence, education level, profession, and the history of influenza vaccination in the past year. Social media (mainly through WeChat and Weibo) was the most popular source to derive influenza-related information and maintaining indoor ventilation was the most widely used approach to prevent influenza infection.
Probably due to the mass media coverage on the comparison of clinical symptoms between influenza and COVID-19, the majority of Chinese adults (76.09%) reported that they learned more knowledge of influenza during the COVID-19 outbreak. A previous KAP study on influenza vaccination among general Chinese population (N = 10045) from six provinces in the 2017/18 season reported that 75% of participants were aware that influenza was different from a common cold and 82% of participants knew that influenza could cause severe complications.13 The study was performed by a telephone survey and 58% of the participants were aged over 15 years. The corresponding data in our study were 80.67% and 89.28%, respectively. Although both studies showed Chinese population had good knowledge of influenza, the participants in our study performed better during the COVID-19 epidemic. Our study also found that the elderly population and those with poor health conditions had significantly lower knowledge scores compared to others. Similar results were reported in a cross-sectional study from Italy, which assessed the awareness of influenza vaccination in 700 adults with chronic diseases.16 Participants who lived in urban districts or had higher education levels had better knowledge, which was similar to the findings in a telephone survey conducted to assess the KAP toward influenza pandemic 2009 among the general Chinese population.9 By contrast, participants who were diagnosed with lab-confirmed influenza in the past year showed lower knowledge scores, probably because this population group was comprised of a higher number of older people and individuals with poor health conditions than other groups.
Nearly half of the participants agreed that influenza was a common disease, and its threat to the functioning of society was far less than the COVID-19. This could possibly explain that 37.57% of the participants agreed or remained neutral attitude toward the statement that it was not necessary to receive influenza vaccination, and 12.03% of participants agreed or strongly agreed that they would not get vaccination even if the vaccine was offered free. The seasonal and preventable characteristics of influenza could overlook the severity of influenza owing to the mass panic across the globe caused by COVID-19. However, the 1918 Spanish flu and influenza A(H1N1) 2009 caused 20–50 million and 100–400 thousand excess mortality worldwide, respectively, and WHO predicted next pandemic influenza would inevitably emerge.17 Although the ongoing COVID-19 waned in China, there were great concerns that a second wave of COVID-19 might occur in the 2020–21 influenza season. The potential compounded respiratory disease burden of COVID-19 and influenza would overwhelm the healthcare system. To reduce the overall burden of respiratory viral infection before next influenza season, the general public should reinforce the awareness of influenza prevention. Effective strategies should be developed to inform the population on the severity of influenza and improve their ability of influenza preparedness, especially those with inadequate knowledge and attitude scores.
Wearing face masks in public venues and receiving influenza vaccination are both practical approaches to prevent influenza infection.18 In this special influenza season of the COVID-19 outbreak, almost all Chinese population used face masks. However, less than half of the participants (39.05%) in our study reported that they had also used masks in the previous flu seasons. We found that participants who had higher education levels or higher monthly income wore fewer masks, probably because the majority of them were students or non-medical indoor workers. In the present study, 15.93% of participants or their family members received influenza vaccination in the past year, which indicated even lower vaccination coverage if there were participants from the same family. Among all measures that participants previously took to prevent influenza infection, merely 29.08% of participants selected influenza vaccination, which suggested a substantial amount of work to be done on public education of influenza preparedness and influenza vaccination promotion in China. As for the willingness to receive influenza vaccination in the future, the intention to get vaccination would increase from 62.53% to 85.82% if clinicians recommended the vaccination. Among all participants, medical professionals and patients who received influenza vaccination in the past year were both associated with more willingness to get the vaccination, which may be due to the reason that they had higher knowledge and a better attitude toward influenza. It was surprising to notice that participants who knew the annual optimum time of influenza vaccination merely accounted for 53.71% and the proportion of participants who knew the location of vaccination was reasonably low (9.44%). The findings of the present study informed policy makers on immunization programs in China to reinforce public education of influenza and vaccination promotion to prevent the widespread of influenza infection.
Although the etiology of influenza infection was often viral, antibiotics were found to be commonly used or prescribed without indications by diagnostic testing.19 There were 76% of residents in the US–Mexico border communities who believed that individuals should take antibiotics to treat influenza.20 Similarly, over half of the participants (60.56%) in our study showed positive or neutral attitudes that antibiotics could be used for influenza treatment. Our results were consistent with findings in previous studies that the Chinese population had poor knowledge and attitude toward antibiotic use.21–24 A previous US study reported that 32% of the influenza-like-illness episodes (n = 19196) received no health-care visits.25 However, the proportion was found to be higher in our study that nearly half of the participants would prefer no treatment or taking leftover medicines at home if they had influenza-like-illness. Taking leftover medicines at home after influenza would possibly contribute to the increase of irrational use of medicine in China, such as antibiotic abuse. These findings indicated the need of educational campaigns, which should be provided to promote judicious antibiotic use for influenza management.
There are several limitations in the present study. The cross-sectional design of this study was inherently limited not to infer causality from the results. Although the majority of the participants reported that they learned more influenza knowledge during the epidemic of COVID-19, we could not further compare their KAP change before and after the COVID-19 outbreak. During the special time of COVID-19 epidemic, online questionnaire survey was one of the most feasible approaches to complete our study. We could not calculate the proportion of population who exposed or responded to the survey. Due to not applying random sampling, potential selection bias might exist and diminish the internal validity of the present study. The number of participants in the present study was also not large enough to depict the full views of adults in China. Besides, the participation of respondents aged over 60 years was low (3.69%), possibly due to the design of an online survey. Therefore, the results based on the comparison among different age groups should be interpreted cautiously.
The COVID-19 pandemic offers a unique opportunity to provide the suggested education and intervention regarding influenza vaccination to the general Chinese population. Due to increased concerns about viral respiratory disease in general and the potential of a second COVID-19 wave coincident with influenza season, people may be more receptive to learning about influenza and obtaining influenza vaccine than they otherwise would be. In conclusion, the present study indicates that the awareness of influenza vaccination among adults in China should be reinforced to prepare influenza epidemics and pandemics in future. Interventional strategies, such as public education through social medias or vaccination recommendation from clinicians, could be implemented by local or national health authorities to promote the coverage of influenza vaccination.
Supplementary Material
Funding Statement
This work was supported by the China Postdoctoral Science Foundation under Grant 2018M631179 and Shaanxi Natural Science Foundation under Grant 2020JQ-079.
Disclosure of potential conflicts of interest
No potential conflicts of interest were disclosed.
Ethical statement
The study protocol was approved by the Medical Ethics Review Committee of Xi’an Jiaotong University (Ref: 2020-1180) and was conducted in compliance with the Helsinki Declaration. The survey was anonymous, and all participants were informed of the study objectives. No sensitive individual information or clinical specimens were collected in the present study.
Supplementary material
Supplemental data for this article can be accessed on the publisher’s website.
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