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Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan
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Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan Shintoku Takahashi a,∗ , Kazuhiro Sato a , Yukinori Kusaka a , Akihito Hagihara b a Division of Environmental Health, Department of International and Social Medicine, Faculty of Medical Sciences, University of Fukui, Fukui 910-1193, Japan b Department of Health Communication, Graduate School of Medicine, Kyushu University, Fukuoka 812-8582, Japan
a r t i c l e
i n f o
Article history: Received 20 August 2016 Received in revised form 12 April 2017 Accepted 28 April 2017 Keywords: Infection Influenza Epidemic Preventive awareness Preventive behavior
a b s t r a c t Background: As an influenza epidemic poses a serious public health threat, it is important for the public to adopt behaviors that effectively prevent influenza infection. Methods: In the winter of 2009, by using a structured questionnaire, we conducted an Internet survey with respect to residents (n = 2788) in Fukui prefecture, Japan. The main aim is to obtain information about effective prevention, factors related to preventive awareness and behaviors during the influenza epidemic. A factor analysis and linear regression models were used in the analysis. Results: Three types of preventive awareness were identified by factor analysis: “avoidance of influenza infection,” “awareness of the benefits of mask use,” and “awareness of the need for a rapid diagnosis.” Gender, age, residence, being medical person and being vaccinated were related to these preventive awareness and behaviors. Avoidance of influenza Infection was related to all preventive behavior, awareness of the benefits of mask use was related to hand disinfectant use, and awareness of the need for a rapid diagnosis was related to avoidance of face touch, gargling and attention to health care, respectively. Conclusion: Three types of preventive awareness during the influenza epidemic were emerged, and were related to preventive behaviors against influenza infection. © 2017 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction In the winter of 2009, the pandemic (H1N1) influenza virus caused a massive outbreak of disease in Japan. In Fukui Prefecture, the prefectural government issued an “influenza warning” on October 28, 2009, as the number of influenza patients per sentinel was 14.00 from October 19 to 25. On November 18, it issued an “influenza alert” as the number of patients per sentinel between November 9 and 15 was 32.16. From November 23 to 29, the number of patients per sentinel climbed to 95.44, which was the highest morbidity in all prefectures that winter. From February 15 to February 21, 2010, the number of patients per sentinel decreased to 5.19, and, on February 24, the influenza alert was lifted [1,2].
∗ Corresponding author at: 23-3 Matsuoka-shimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui Prefecture 910-1193, Japan, Division of Environmental Health, Department of International and Social Medicine, Faculty of Medical Sciences, University of Fukui. Fax: +81 776 61 8107. E-mail address: [email protected] (S. Takahashi).
Compared with other countries where pandemic (H1N1) influenza caused outbreaks of similar size, Japan was unique because of its lower mortality rate [3]. As of November 6, 2009, the reported mortality rate (deaths per million individuals) was 0.2 in Japan, which was much lower than those in Canada (2.8), the UK (2.2), Mexico (2.9), the USA (3.3), South Africa (1.8), Argentina (14.6), Australia (8.6), Brazil (7.0), Chile (8.1), and New Zealand (4.4) [4]. By the time the pandemic (H1N1) was over, 198 deaths had occurred in Japan, including two deaths in Fukui Prefecture, by March 23, 2010 [1,2]. Public awareness and behavioral practices conducive to the prevention of influenza infection have been studied by several groups. However, the awareness of medical students in Karachi, Pakistan regarding disease transmission, preventive measures, vaccinations, and available treatment was inadequate with respect to H1N1 [5]. Health messages delivered through various media, especially television, were effective in informing the public of disease-related prevention measures during a developing influenza pandemic along the Mississippi Gulf Coast, USA [6]. Residents of Shuangqiao District, Chengde, China lacked comprehensive knowl-
http://dx.doi.org/10.1016/j.jiph.2017.04.002 1876-0341/© 2017 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
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edge about influenza A (H1N1), and specific health education was, therefore, needed [7]. Most of the many reports on influenza epidemics are based on government statistics, and little is known about the level of preventive awareness and/or about the most effective preventive practices in relation to an influenza infection in the general population. Dr. Koji Omi pointed out that there are three explanations for why pandemic (H1N1) influenza resulted in a small number of casualties in Japan [8]: (i) school was suspended across a wide geographical area, particularly at the early stage of the outbreak; (ii) antiviral drugs were administered to a large proportion of infected individuals; and (iii) public awareness and personal hygiene, especially regular hand-washing, were promoted. This study focuses on the third reason. Specifically, we evaluated the relationship between a citizen’s personal attributes and his/her preventive awareness and behavior during an influenza outbreak.
Subject recruitment Fig. 1 depicts the subject recruitment process. Written informed consent forms regarding participation in the survey were distributed to 2788 people, including Fukui prefectural government members and officials and students of Fukui University (Fukui Prefecture). During the same period, 2315 signed consent forms were returned. These individuals were asked to provide their e-mail address for an Internet survey. On November 17, 2009, the 920 individuals (39.7%) who had provided demographic information were sent a questionnaire regarding their preventive awareness level and behaviors during an influenza outbreak. The time required to answer all of the questionnaire items was approximately 10 min. Among the 577 (24.9%) returned questionnaires, 492 (21.3%) were complete. Questionnaire items
Material and methods Study design and ethics This was a cross-sectional online survey. This study was approved by the Ethics Committee of the University of Fukui. Study period The study was conducted from October 9, 2009 to January 6, 2010. Representativeness It has been reported that almost all Japanese individuals know their own blood type [9]. Thus, making use of this characteristics, we determined the blood types of participants based on their responses to the questionnaire. To assess the representativeness of the sample, we compared the prevalence of blood types in study subjects with national prevalence.
2788 written informed consent forms were distributed 473 did not want to participate 2315 signed the consent forms 1395 lacked a correct e-mail address 920 were sent an e-mail with the questionnaire 343 did not reply to the e-mail 577 replied to the e-mail and answered the questionnaire 85 had missing values 492 were complete Fig. 1. Study participant selection.
Questions addressing three domains were included in the Internet survey: (i) demographic characteristics and influenza vaccination and infection history; (ii) preventive awareness of influenza infection; and (iii) preventive behaviors adopted to avoid influenza infection. Demographic information and data on influenza vaccination and infection included sex (male, female), address (Fukui City, others), whether respondents were living with children under the age of 18 years (yes, no), age, occupation (medical, non-medical), prior experience with influenza infection (yes, no), blood type (ABO expression), e-mail address, vaccination in the past 3 months (yes, no), diagnosis of influenza infection in the past 3 months (yes, no), and family history of influenza diagnosis in the past 3 months (yes, no). Items included in the section addressing preventive awareness of influenza infection evaluated how active a respondent was in taking measures to avoid either the influenza infection or spreading the influenza virus. Responses were provided on a scale from 0 (“not at all”) to 10 (“very much”) and were used in a factor analysis. Items addressing preventive behaviors to avoid influenza infection evaluated the extent to which respondents practiced six types of preventive behavior in their daily lives: hand-washing, using hand disinfectant, avoiding contact with their face, gargling, attending to healthcare, and collecting flu-related information. Responses were provided on a scale from 0 (“not at all”) to 10 (“very much”). Analysis The aim of this study was to determine how the personal attributes of Japanese citizens were related to their level of preventive awareness and to the preventive behaviors they adopted during the pandemic (H1N1) influenza outbreak. To these ends, we performed three analyses. First, factor analyses with varimax rotation and promax rotation were performed using the responses to the nine items included in the section addressing preventive awareness of influenza infection. Factor analysis is a statistical method used to describe the variability among observed, correlated variables in terms of fewer, unobserved variables, called factors [10]. Varimax and promax are commonly available orthogonal and oblique methods, respectively. Orthogonal rotations produce factors that are uncorrelated, while oblique methods allow the factors to correlate [11]. In the study, we tried both methods, and a Heywood case was detected in varimax rotation. Thus, promax rotation was adopted in the analysis. The following three factors were identified: “avoidance of influenza infection,” “awareness of the benefits of mask use,” and “awareness of the need for a rapid diagnosis.” By summing the item scores for each of the three factors, a factor score with a range of 0–30 was calculated.
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
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Table 1 Results of a factor analysis of preventive awareness of influenza infection. Communality
Being careful if he/she has to go to where an influenza epidemic is occurring Being careful not to catch influenza virus Being careful with those who have flu-like symptoms Using a mask if he/she is diagnosed of influenza Being careful not to infect others if he/she is diagnosed of Influenza Awareness to use a mask when an influenza epidemic is occurring in the area Seeing a physician immediately if he/she has the flu-like symptoms Awareness to call “the counseling and consultation center for fever” having fever Awareness to check body temperature with thermometer feeling feverish
0.68 0.47 0.42 0.56 0.43 0.37 0.81 0.19 0.30
Contribution ratio %
Second, to identify the factors related to public preventive awareness, a multiple regression model in which one of the three preventive awareness scores was treated as a dependent variable and demographic, influenza vaccination, and infection data were treated as independent variables were fitted to the data. Third, to identify factors related to preventive behaviors, a multiple logistic regression analysis was carried out in which one of the six preventive behaviors was the dependent variable and demographic, influenza vaccination, and infection data plus the three preventive awareness scores were the independent variables. If factors related to the preventive awareness or behaviors are identified, the finding might be useful for an interventional effort to increase the level of preventive awareness or to promote preventive behaviors. Thus, the second and third analyses were performed. Preventive behavior scores were initially entered into a multiple regression model as a continuous variable. However, as the model did not fit the data well, a logistic regression model was used. Each of the six types of preventive behavior was treated a dependent variable and coded using a score of 6 [i.e., 1 = score ≥6 (“practitioner” of the behavior), 0 = score <6 (“non-practitioner” of the behavior)]. All analyses were performed using the statistical software PASW Statistics (ver. 18; SPSS, IBM, Chicago, IL, USA). A p-value (two-sided) <0.05 was considered to indicate statistical significance. Results Table 1 shows the results of the factor analysis related to preventive awareness. Three types of public preventive awareness were identified by the analysis. Principal factor method and the Kaiser–Guttman rule were used. The Kaiser–Meyer–Olkin measure of specimen validity was 0.84. The significance of the Bartlett sphericity test was 0.001. As shown in the table, we defined three factors by simply adding the values of three items (resulting in a range from 0 to 30). The first factor (F1) was related to the degree of care taken to avoid infection (“avoidance of influenza infection”). The second factor (F2) was related to the level of awareness about the importance of using a mask (“awareness of the benefits of mask use”), and the third factor (F3) was related to the degree of awareness about the importance of a rapid diagnosis (“awareness of the need for a rapid diagnosis”). The Cronbach ˛ statistics for F1, F2, and F3 were 0.76, 0.61, and 0.61, respectively; all values were above the acceptance level 0.6 [12]. Table 2 shows the demographic characteristics of the participants. Their mean age was 40.73 years (±11.38), with a slightly higher proportion of females (52.24%) than males (47.76%). Slightly fewer individuals lived in Fukui City than in other areas (46.14% vs. 53.86%). The degree to which participants engaged in preventive behaviors in their daily lives to avoid influenza infection was initially evaluated using an 11-point Likert scale, ranging from “0”
Factors F1. Avoidance of influenza Infection
F2. Awareness of the benefits of mask use
F3. Awareness of the need for a rapid diagnosis
0.86 0.68 0.63 −0.13 0.16 0.24 −0.07 0.09 0.14
−0.06 0.01 0.06 0.83 0.58 0.37 −0.02 −0.08 0.11
−0.01 0.01 −0.06 0.001 −0.08 0.09 0.94 0.43 0.40
33.40%
10.80%
8.70%
Table 2 Demographic characteristics of the study participants recruited between October 9, 2009 and January 6, 2012 in Fukui, Japan (n = 492). Variables
Number/mean of categories
(Demographics information and influenza vaccination and infection) Sex 235 (47.76) Male n(%) Female n(%) 257 (52.24) Address Urban area (Fukui city) n(%) Other areas n(%)
227 (46.14) 265 (53.86)
Living with children under the age of 18 (yes) n(%)
208 (42.28)
Age (yr.) (Mean ± SD) (Range)
40.73 ± 11.38 (19–65)
Occupation Medical n(%) Non-medical n(%)
170 (34.55) 322 (65.45)
Prior experience with influenza infection (yes) n(%)
249 (50.61)
Blood type A n(%) O n(%) B n(%) AB n(%)
198 (40.24) 142 (28.86) 105 (21.34) 47 (9.55)
Flu-related events in this three months Had been vaccinated against influenza (yes) n(%) Had been diagnosed with Influenza (yes) n(%) Having a family member who had been diagnosed with influenza n (yes) (%) (Preventive awareness of influenza infection) F1. Avoidance of influenza infection (mean ± SD) (range) F2. Awareness of the benefits of mask use (mean ± SD) (range) F3. Awareness of the need for a rapid diagnosis (mean ± SD) (range) (Preventive behaviors to avoid influenza infection) Hand-washing (score ≥6) n (%) (range) Hand disinfectant use (score ≥6) n (%) (range) Avoidance of face touch (score ≥6) n (%) (range) Gargling (score ≥6) n (%) (range) Attention to health care (score ≥6) n (%) (range) Flu information collection (score ≥6) n (%) (range)
268 (54.47) 6 (1.22) 99 (20.12)
21.96 ± 5.85 (3–30) 26.02 ± 4.51 (6–30) 18.69 ± 6.86 (0–30)
392 (79.67) (0–10), 242 (49.19) (0–10), 134 (27.24) (0–10), 239 (48.58) (0–10), 330 (67.07) (0–10), 265 (53.86) (0–10),
Abbreviation: SD, standard deviation.
(“not at all”) to “10” (“definitely, always”), and the proportion of respondents with a score ≥ 6 (defined as a “practitioner” of the behavior) was reported. The proportion of practitioners was highest for hand-washing (79.67%) and lowest for avoidance of touching one’s own face (27.24%). The results of the multiple regression analyses are shown in Table 3, which presents the factors related to preventive aware-
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
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Table 3 Factors related to preventive awareness of influenza infection as determined in multiple regression analyses. Items
F3. Awareness of the need for a rapid diagnosis
F1. Avoidance of influenza infection
F2. Awareness of the benefits of mask use
B
SE
p-Value
B
SE
p-Value
B
SE
p-Value
3.03 −0.70 0.42 0.11 2.33 0.12
0.53 0.49 0.51 0.02 0.56 0.49
0.001 0.16 0.41 0.001 0.001 0.80
2.48 −0.70 0.73 0.05 0.87 −0.28
0.42 0.40 0.41 0.02 0.45 0.39
0.001 0.08 0.07 0.01 0.05 0.47
3.34 0.54 1.30 0.19 −2.95 0.25
0.63 0.58 0.60 0.03 0.66 0.58
0.001 0.36 0.03 0.001 0.001 0.66
Flu-related events 1. Had been vaccinated against influenza (ref. no) 2. Had been diagnosed with influenza (ref. no) 3. Having a family member who had been diagnosed with influenza (ref. no)
0.45 -0.18 1.44
0.54 2.23 0.62
0.41 0.94 0.02
-0.08 0.15 0.53
0.44 1.79 0.49
0.86 0.94 0.28
0.49 3.51 -0.33
0.64 2.64 0.73
0.44 0.18 0.65
R2 (adjusted R2 )
0.17 (0.16)
Demographic characteristics 1. Female (ref. male) 2. Living in an urban area (ref. others) 3. Living with children under the age of 18 (ref. no) 4. Age (yr.) 5. Medical jobs (ref. non-medical jobs) 6. Prior experience with influenza infection (ref. no)
0.10 (0.09)
0.16 (0.14)
Abbreviations: B, standardized partial regression coefficient; SE, standard error.
ness of influenza infection. All explanatory variables were entered simultaneously. With respect to all models, the p-values of the ANOVA were 0.00, implying no problem with multi-collinearity. Female sex and age were both related to the three types of preventive awareness (p = 0.001 or 0.01). Although holding a medical job was positively related to taking care to avoid infection (p = 0.001), it was inversely related to awareness of the need for a rapid diagnosis (p = 0.001). Having a family member who had been diagnosed with influenza was related to taking care to avoid infection (p = 0.02). Table 4 presents the factors related to the preventive behaviors designed to avoid influenza infection identified by the multiple logistic regression analyses. As noted previously, preventive behaviors were dummy coded by assigning “1” when the score was ≥6 and “0” when the score was <6. All explanatory variables were entered simultaneously. The p-value for the Hosmer–Lemeshow test was >0.05 in all models, except for the model treating attention to healthcare as the dependent variable, reflecting that the five other models fit the data well. Younger age (OR = 0.98) and awareness of the benefits of mask use (OR = 1.07) were related to the use of hand disinfectant, as were urban residence compared with living elsewhere (OR = 1.80). Compared with unvaccinated individuals, people who were vaccinated against influenza were more likely to engage in hand-washing (OR = 1.77). Compared with people holding non-medical jobs, those employed in medical jobs were more likely to avoid touching their face (OR = 2.76) and to seek flu-related information (OR = 2.30). Compared with people who were not careful to avoid influenza infection, those who did take care to avoid infection were more likely engage in hand-washing (OR = 1.16), use hand disinfectant (OR = 1.11), avoid touching their face (OR = 1.17), gargle (OR = 1.14), and seek flu-related information (OR = 1.14). Discussion We examined factors related to preventive awareness and to behaviors designed to avoid influenza infection during the period of the pandemic (H1N1) influenza outbreak. Several findings were obtained. First, it has been established that a mother will act as the caregiver and role model in mother–child interactions [13]. The same relationship between these care-givers and children has been reported among nurses in Japan [14]. Thus, the present finding that people living with children were aware of the need for a rapid diagnosis of flu-like symptoms by a clinician might be understood as an example of a mother’s role modeling. Second, hand disinfectants significantly reduce viral counts on the hands [15]. In Japan, it is common for hand disinfectant to be placed at the entrances of med-
ical facilities, including hospitals, nursing homes, day-care centers, and long-term care facilities [16], so that visitors can use it before entering the facility to prevent the spread of infection inside the facility. Generally, there are more medical facilities in urban areas than in other areas in Japan, and this is the case in Fukui Prefecture [17]; this greater availability might account for the more frequent use of hand disinfectant by residents of urban areas. Third, there are administrative health services that provide citizens with advice via telephone before suggesting a visit to a medical institution [18]. It was found that people holding medical jobs were unlikely to seek attention from counseling and consultation centers for fever. This same group did not see a doctor immediately upon flu suspicion. The finding could be due to the medical knowledge of people in the healthcare professions. This study revealed multiple behaviors and factors related to preventive awareness, and several of the findings are consistent with those of previous studies concerning preventive behaviors related to an influenza outbreak [5–8]. Specifically, females and older individuals were more likely than males or younger people, respectively, to have a higher level of preventive awareness, such as taking care to avoid infection, being aware of the benefits of mask use, and realizing the need for a rapid diagnosis. Previous studies have reported the same association of female sex and age with preventive awareness [19,20]. In addition, people holding medical jobs were aware of the need to avoid infection, avoid touching their face, and obtain flu-related information. The latter two are important measures in preventing influenza infection [21]. We also found that people who were careful to avoid influenza infection were more likely to adopt preventive behaviors, such as hand-washing, hand disinfectant use, avoidance of touching their face, gargling, and obtaining flu information. In Japan, masks are used not only to avoid spreading the flu virus to others but also to prevent being infected by someone else. The effectiveness of mask use for preventing infection, especially pandemic transmission in households, has been established [22]. Hand-washing is a well-established means of preventing infection [23,24]. Gargling is also a very common practice in Japan. The efficacy of gargling for preventing respiratory tract infection was recently demonstrated in studies from Japan. Specifically, gargling with water can protect against upper respiratory tract infections [25], and gargling could be effective in the prevention of febrile diseases in children [26]. It is interesting to note that the preventive behaviors adopted by those who were careful to avoid influenza infection are evidence-based. Our results have important practical implications. Additional information about public awareness and preventive behaviors
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
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Items
2. Living in an urban area (ref. others) 3. Living with children under the age of 18 (ref. no) 4. Age (yr.) 5. Medical jobs (ref. Non-medical jobs) 6. Prior experience with influenza infection (ref. no) Flu-related events 1. Had been vaccinated against influenza (ref. no) 2. Had been diagnosed with influenza (ref. no) 3. Family members diagnosed with influenza (ref. no) Preventive awareness F1. Avoidance of influenza Infection F2. Awareness of the benefits of mask use F3. Awareness of the need for a rapid diagnosis Hosmer–Lemeshow test
Hand disinfectant use OR (95%CI) p-Value
Avoidance of face touch OR (95%CI) p-Value
Gargling OR (95%CI) p-Value
Attention to health care OR (95%CI) p-Value
Obtaining flu information OR (95%CI) p-Value
1.61 (0.91–2.84) 0.10 1.17 (0.71–1.93) 0.54 1.01 (0.60–1.72) 0.97 0.98 (0.96–1.00) 0.11 1.66 (0.86–3.19) 0.13 0.72 (0.43–1.19) 0.20
0.81 (0.52–1.26) 0.35 1.80 (1.21–2.68) 0.001 1.08 (0.72–1.61) 0.72 0.98 (0.96–1.00) 0.03 0.86 (0.54–1.36) 0.51 1.02 (0.69–1.51) 0.92
1.86 (1.11–3.10) 0.02 1.29 (0.82–2.04) 0.28 1.10 (0.69–1.75) 0.69 1.01 (0.99–1.03) 0.32 2.76 (1.66–4.61) 0.001 0.91 (0.58–1.43) 0.68
1.06 (0.69–1.64) 0.78 1.06 (0.72–1.56) 0.78 1.26 (0.85–1.88) 0.25 0.99 (0.97–1.01) 0.34 0.69 (0.44–1.09) 0.11 1.29 (0.88–1.90) 0.19
1.51 (0.93–2.45) 0.10 1.20 (0.77–1.87) 0.41 0.89 (0.57–1.41) 0.62 1.00 (0.98–1.02) 0.79 1.97 (1.15–3.39) 0.01 0.93 (0.60–1.44) 0.74
1.41 (0.90–2.23) 0.14 0.97 (0.65–1.47) 0.89 1.16 (0.76–1.77) 0.49 1.01 (0.99–1.03) 0.17 2.30 (1.42–3.73) 0.001 0.83 (0.55–1.25) 0.38
1.77 (1.03–3.03) 0.04 0.001 (0.001–0.001) 1.00 1.24 (0.63–2.44) 0.53
1.41 (0.92–2.16) 0.11 1.06 (0.18–6.08) 0.95 0.91 (0.56–1.47) 0.69
0.89 (0.53–1.47) 0.64 1.04 (0.13–8.32) 0.97 1.24 (0.71–2.15) 0.45
1.13 (0.74–1.72) 0.58 0.41 (0.06–2.58) 0.34 1.48 (0.91–2.41) 0.12
1.03 (0.64–1.65) 0.90 2.77 (0.22–35.39) 0.43 1.55 (0.86–2.78) 0.14
1.12 (0.72–1.74) 0.61 7.40 (0.61–90.32) 0.12 0.99 (0.60–1.65) 0.98
1.16 (1.10–1.22) 0.001 0.98 (0.93–1.04) 0.53 1.03 (0.99–1.07) 0.20
1.11 (1.06–1.16) 0.001 1.07 (1.01–1.13) 0.03 1.03 (0.99–1.06) 0.13
1.17 (1.10–1.24) 0.001 1.00 (0.93–1.08) 0.93 1.03 (0.99–1.07) 0.10
1.14 (1.09–1.19) 0.001 0.97 (0.92–1.03) 0.31 1.03 (1.00–1.06) 0.10
1.15 (1.10–1.21) 0.001 0.99 (0.93–1.04) 0.66 1.06 (1.02–1.10) 0.001
1.14 (1.09–1.20) 0.001 1.00 (0.95–1.06) 0.88 1.02 (0.99–1.06) 0.24
2 = 6.63 (p = 0.58)
2 = 12.15 (p = 0.15)
2 = 10.74 (p = 0.22)
2 = 6.09 (p = 0.64)
2 = 17.09 (p = 0.03)
2 = 13.17 (p = 0.11)
Abbreviations: OR, odds ratio; CI, confidence interval.
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Demographic characteristics 1. Female (ref. male)
Hand-washing OR (95%CI) p-Value
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Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
Table 4 Factors related to preventive behaviors to avoid influenza infection as determined in multiple logistic regression analyses.
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should lead to more effective measures and behaviors to prevent the spread of influenza infection. Specifically, only females and the elderly showed high levels of preventive awareness regarding influenza infection. Consequently, health promotion efforts to increase the level of preventive awareness during influenza outbreaks should specifically target males and younger people. Age was also inversely correlated with the use of hand disinfectant. As the effectiveness of virus removal by hand disinfectant is widely established [15], we suggest that hand disinfectant use be emphasized in messages to the elderly. This study has several limitations, and the results should be interpreted with caution. First, the survey was performed using an e-mailed questionnaire; therefore, it was not anonymous (openlabel method). Thus, people who knew the researchers, such as healthcare workers, might have felt unintentionally pressured to provide consent for study participation, which would have introduced selection bias. Second, of the initial 2788 potential participants, the responses of only 492 were used in the analysis (21.3%). In 1976, the proportions of blood types A, O, B, and AB among the Japanese were 38%, 31%, 22%, and 9%, respectively [27]. The corresponding percentages in the study were 41%, 28%, 22%, and 9%, respectively. The difference between the national and the study sample data was not significant (x2 = 0.53, p > 0.05); the study sample was therefore representative of the Japanese population, as was the age range of the study participants. Accordingly, we believe that we accurately captured the levels of public preventive awareness and preventive practices during the largest influenza epidemic in Japan. Third, because the study population consisted of members of the Fukui prefectural government and officials and students of Fukui University in Fukui Prefecture, the external validity of our findings is limited to individuals employed in local government or working or attending academic institutions. Finally, the influenza outbreak in 2009 resulted in a small number of casualties in Japan because of the high level of public awareness and personal hygiene, especially with regard to regular hand-washing. Identification of the factors that promote hand-washing might lead to the development of effective programs to prevent influenza epidemics. Additional studies are needed to examine these factors. Conclusion In conclusion, in a study of the citizens in Fukui Prefecture, Japan during the pandemic (H1N1) 2009 influenza pandemic, we identified factors related to preventive awareness levels and behaviors regarding influenza infection. To better protect the general public against future influenza epidemics, health-promotion efforts should be aimed at increasing the level of preventive awareness of influenza infection among males and young people and at encouraging the use of hand disinfectants by elderly individuals. Conflict of interest None declared. Funding No funding sources. Competing interests None declared. Ethical approval Not required.
Acknowledgments We express our sincere thanks to the study participants; Fukui Prefectural Government members, officials and students of University of Fukui, University of Fukui Hospital members, and all of our colleagues of in the Division of Environmental Health, University of Fukui.
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Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan Shintoku Takahashi a,∗ , Kazuhiro Sato a , Yukinori Kusaka a , Akihito Hagihara b a Division of Environmental Health, Department of International and Social Medicine, Faculty of Medical Sciences, University of Fukui, Fukui 910-1193, Japan b Department of Health Communication, Graduate School of Medicine, Kyushu University, Fukuoka 812-8582, Japan
a r t i c l e
i n f o
Article history: Received 20 August 2016 Received in revised form 12 April 2017 Accepted 28 April 2017 Keywords: Infection Influenza Epidemic Preventive awareness Preventive behavior
a b s t r a c t Background: As an influenza epidemic poses a serious public health threat, it is important for the public to adopt behaviors that effectively prevent influenza infection. Methods: In the winter of 2009, by using a structured questionnaire, we conducted an Internet survey with respect to residents (n = 2788) in Fukui prefecture, Japan. The main aim is to obtain information about effective prevention, factors related to preventive awareness and behaviors during the influenza epidemic. A factor analysis and linear regression models were used in the analysis. Results: Three types of preventive awareness were identified by factor analysis: “avoidance of influenza infection,” “awareness of the benefits of mask use,” and “awareness of the need for a rapid diagnosis.” Gender, age, residence, being medical person and being vaccinated were related to these preventive awareness and behaviors. Avoidance of influenza Infection was related to all preventive behavior, awareness of the benefits of mask use was related to hand disinfectant use, and awareness of the need for a rapid diagnosis was related to avoidance of face touch, gargling and attention to health care, respectively. Conclusion: Three types of preventive awareness during the influenza epidemic were emerged, and were related to preventive behaviors against influenza infection. © 2017 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction In the winter of 2009, the pandemic (H1N1) influenza virus caused a massive outbreak of disease in Japan. In Fukui Prefecture, the prefectural government issued an “influenza warning” on October 28, 2009, as the number of influenza patients per sentinel was 14.00 from October 19 to 25. On November 18, it issued an “influenza alert” as the number of patients per sentinel between November 9 and 15 was 32.16. From November 23 to 29, the number of patients per sentinel climbed to 95.44, which was the highest morbidity in all prefectures that winter. From February 15 to February 21, 2010, the number of patients per sentinel decreased to 5.19, and, on February 24, the influenza alert was lifted [1,2].
∗ Corresponding author at: 23-3 Matsuoka-shimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui Prefecture 910-1193, Japan, Division of Environmental Health, Department of International and Social Medicine, Faculty of Medical Sciences, University of Fukui. Fax: +81 776 61 8107. E-mail address: [email protected] (S. Takahashi).
Compared with other countries where pandemic (H1N1) influenza caused outbreaks of similar size, Japan was unique because of its lower mortality rate [3]. As of November 6, 2009, the reported mortality rate (deaths per million individuals) was 0.2 in Japan, which was much lower than those in Canada (2.8), the UK (2.2), Mexico (2.9), the USA (3.3), South Africa (1.8), Argentina (14.6), Australia (8.6), Brazil (7.0), Chile (8.1), and New Zealand (4.4) [4]. By the time the pandemic (H1N1) was over, 198 deaths had occurred in Japan, including two deaths in Fukui Prefecture, by March 23, 2010 [1,2]. Public awareness and behavioral practices conducive to the prevention of influenza infection have been studied by several groups. However, the awareness of medical students in Karachi, Pakistan regarding disease transmission, preventive measures, vaccinations, and available treatment was inadequate with respect to H1N1 [5]. Health messages delivered through various media, especially television, were effective in informing the public of disease-related prevention measures during a developing influenza pandemic along the Mississippi Gulf Coast, USA [6]. Residents of Shuangqiao District, Chengde, China lacked comprehensive knowl-
http://dx.doi.org/10.1016/j.jiph.2017.04.002 1876-0341/© 2017 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
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edge about influenza A (H1N1), and specific health education was, therefore, needed [7]. Most of the many reports on influenza epidemics are based on government statistics, and little is known about the level of preventive awareness and/or about the most effective preventive practices in relation to an influenza infection in the general population. Dr. Koji Omi pointed out that there are three explanations for why pandemic (H1N1) influenza resulted in a small number of casualties in Japan [8]: (i) school was suspended across a wide geographical area, particularly at the early stage of the outbreak; (ii) antiviral drugs were administered to a large proportion of infected individuals; and (iii) public awareness and personal hygiene, especially regular hand-washing, were promoted. This study focuses on the third reason. Specifically, we evaluated the relationship between a citizen’s personal attributes and his/her preventive awareness and behavior during an influenza outbreak.
Subject recruitment Fig. 1 depicts the subject recruitment process. Written informed consent forms regarding participation in the survey were distributed to 2788 people, including Fukui prefectural government members and officials and students of Fukui University (Fukui Prefecture). During the same period, 2315 signed consent forms were returned. These individuals were asked to provide their e-mail address for an Internet survey. On November 17, 2009, the 920 individuals (39.7%) who had provided demographic information were sent a questionnaire regarding their preventive awareness level and behaviors during an influenza outbreak. The time required to answer all of the questionnaire items was approximately 10 min. Among the 577 (24.9%) returned questionnaires, 492 (21.3%) were complete. Questionnaire items
Material and methods Study design and ethics This was a cross-sectional online survey. This study was approved by the Ethics Committee of the University of Fukui. Study period The study was conducted from October 9, 2009 to January 6, 2010. Representativeness It has been reported that almost all Japanese individuals know their own blood type [9]. Thus, making use of this characteristics, we determined the blood types of participants based on their responses to the questionnaire. To assess the representativeness of the sample, we compared the prevalence of blood types in study subjects with national prevalence.
2788 written informed consent forms were distributed 473 did not want to participate 2315 signed the consent forms 1395 lacked a correct e-mail address 920 were sent an e-mail with the questionnaire 343 did not reply to the e-mail 577 replied to the e-mail and answered the questionnaire 85 had missing values 492 were complete Fig. 1. Study participant selection.
Questions addressing three domains were included in the Internet survey: (i) demographic characteristics and influenza vaccination and infection history; (ii) preventive awareness of influenza infection; and (iii) preventive behaviors adopted to avoid influenza infection. Demographic information and data on influenza vaccination and infection included sex (male, female), address (Fukui City, others), whether respondents were living with children under the age of 18 years (yes, no), age, occupation (medical, non-medical), prior experience with influenza infection (yes, no), blood type (ABO expression), e-mail address, vaccination in the past 3 months (yes, no), diagnosis of influenza infection in the past 3 months (yes, no), and family history of influenza diagnosis in the past 3 months (yes, no). Items included in the section addressing preventive awareness of influenza infection evaluated how active a respondent was in taking measures to avoid either the influenza infection or spreading the influenza virus. Responses were provided on a scale from 0 (“not at all”) to 10 (“very much”) and were used in a factor analysis. Items addressing preventive behaviors to avoid influenza infection evaluated the extent to which respondents practiced six types of preventive behavior in their daily lives: hand-washing, using hand disinfectant, avoiding contact with their face, gargling, attending to healthcare, and collecting flu-related information. Responses were provided on a scale from 0 (“not at all”) to 10 (“very much”). Analysis The aim of this study was to determine how the personal attributes of Japanese citizens were related to their level of preventive awareness and to the preventive behaviors they adopted during the pandemic (H1N1) influenza outbreak. To these ends, we performed three analyses. First, factor analyses with varimax rotation and promax rotation were performed using the responses to the nine items included in the section addressing preventive awareness of influenza infection. Factor analysis is a statistical method used to describe the variability among observed, correlated variables in terms of fewer, unobserved variables, called factors [10]. Varimax and promax are commonly available orthogonal and oblique methods, respectively. Orthogonal rotations produce factors that are uncorrelated, while oblique methods allow the factors to correlate [11]. In the study, we tried both methods, and a Heywood case was detected in varimax rotation. Thus, promax rotation was adopted in the analysis. The following three factors were identified: “avoidance of influenza infection,” “awareness of the benefits of mask use,” and “awareness of the need for a rapid diagnosis.” By summing the item scores for each of the three factors, a factor score with a range of 0–30 was calculated.
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Table 1 Results of a factor analysis of preventive awareness of influenza infection. Communality
Being careful if he/she has to go to where an influenza epidemic is occurring Being careful not to catch influenza virus Being careful with those who have flu-like symptoms Using a mask if he/she is diagnosed of influenza Being careful not to infect others if he/she is diagnosed of Influenza Awareness to use a mask when an influenza epidemic is occurring in the area Seeing a physician immediately if he/she has the flu-like symptoms Awareness to call “the counseling and consultation center for fever” having fever Awareness to check body temperature with thermometer feeling feverish
0.68 0.47 0.42 0.56 0.43 0.37 0.81 0.19 0.30
Contribution ratio %
Second, to identify the factors related to public preventive awareness, a multiple regression model in which one of the three preventive awareness scores was treated as a dependent variable and demographic, influenza vaccination, and infection data were treated as independent variables were fitted to the data. Third, to identify factors related to preventive behaviors, a multiple logistic regression analysis was carried out in which one of the six preventive behaviors was the dependent variable and demographic, influenza vaccination, and infection data plus the three preventive awareness scores were the independent variables. If factors related to the preventive awareness or behaviors are identified, the finding might be useful for an interventional effort to increase the level of preventive awareness or to promote preventive behaviors. Thus, the second and third analyses were performed. Preventive behavior scores were initially entered into a multiple regression model as a continuous variable. However, as the model did not fit the data well, a logistic regression model was used. Each of the six types of preventive behavior was treated a dependent variable and coded using a score of 6 [i.e., 1 = score ≥6 (“practitioner” of the behavior), 0 = score <6 (“non-practitioner” of the behavior)]. All analyses were performed using the statistical software PASW Statistics (ver. 18; SPSS, IBM, Chicago, IL, USA). A p-value (two-sided) <0.05 was considered to indicate statistical significance. Results Table 1 shows the results of the factor analysis related to preventive awareness. Three types of public preventive awareness were identified by the analysis. Principal factor method and the Kaiser–Guttman rule were used. The Kaiser–Meyer–Olkin measure of specimen validity was 0.84. The significance of the Bartlett sphericity test was 0.001. As shown in the table, we defined three factors by simply adding the values of three items (resulting in a range from 0 to 30). The first factor (F1) was related to the degree of care taken to avoid infection (“avoidance of influenza infection”). The second factor (F2) was related to the level of awareness about the importance of using a mask (“awareness of the benefits of mask use”), and the third factor (F3) was related to the degree of awareness about the importance of a rapid diagnosis (“awareness of the need for a rapid diagnosis”). The Cronbach ˛ statistics for F1, F2, and F3 were 0.76, 0.61, and 0.61, respectively; all values were above the acceptance level 0.6 [12]. Table 2 shows the demographic characteristics of the participants. Their mean age was 40.73 years (±11.38), with a slightly higher proportion of females (52.24%) than males (47.76%). Slightly fewer individuals lived in Fukui City than in other areas (46.14% vs. 53.86%). The degree to which participants engaged in preventive behaviors in their daily lives to avoid influenza infection was initially evaluated using an 11-point Likert scale, ranging from “0”
Factors F1. Avoidance of influenza Infection
F2. Awareness of the benefits of mask use
F3. Awareness of the need for a rapid diagnosis
0.86 0.68 0.63 −0.13 0.16 0.24 −0.07 0.09 0.14
−0.06 0.01 0.06 0.83 0.58 0.37 −0.02 −0.08 0.11
−0.01 0.01 −0.06 0.001 −0.08 0.09 0.94 0.43 0.40
33.40%
10.80%
8.70%
Table 2 Demographic characteristics of the study participants recruited between October 9, 2009 and January 6, 2012 in Fukui, Japan (n = 492). Variables
Number/mean of categories
(Demographics information and influenza vaccination and infection) Sex 235 (47.76) Male n(%) Female n(%) 257 (52.24) Address Urban area (Fukui city) n(%) Other areas n(%)
227 (46.14) 265 (53.86)
Living with children under the age of 18 (yes) n(%)
208 (42.28)
Age (yr.) (Mean ± SD) (Range)
40.73 ± 11.38 (19–65)
Occupation Medical n(%) Non-medical n(%)
170 (34.55) 322 (65.45)
Prior experience with influenza infection (yes) n(%)
249 (50.61)
Blood type A n(%) O n(%) B n(%) AB n(%)
198 (40.24) 142 (28.86) 105 (21.34) 47 (9.55)
Flu-related events in this three months Had been vaccinated against influenza (yes) n(%) Had been diagnosed with Influenza (yes) n(%) Having a family member who had been diagnosed with influenza n (yes) (%) (Preventive awareness of influenza infection) F1. Avoidance of influenza infection (mean ± SD) (range) F2. Awareness of the benefits of mask use (mean ± SD) (range) F3. Awareness of the need for a rapid diagnosis (mean ± SD) (range) (Preventive behaviors to avoid influenza infection) Hand-washing (score ≥6) n (%) (range) Hand disinfectant use (score ≥6) n (%) (range) Avoidance of face touch (score ≥6) n (%) (range) Gargling (score ≥6) n (%) (range) Attention to health care (score ≥6) n (%) (range) Flu information collection (score ≥6) n (%) (range)
268 (54.47) 6 (1.22) 99 (20.12)
21.96 ± 5.85 (3–30) 26.02 ± 4.51 (6–30) 18.69 ± 6.86 (0–30)
392 (79.67) (0–10), 242 (49.19) (0–10), 134 (27.24) (0–10), 239 (48.58) (0–10), 330 (67.07) (0–10), 265 (53.86) (0–10),
Abbreviation: SD, standard deviation.
(“not at all”) to “10” (“definitely, always”), and the proportion of respondents with a score ≥ 6 (defined as a “practitioner” of the behavior) was reported. The proportion of practitioners was highest for hand-washing (79.67%) and lowest for avoidance of touching one’s own face (27.24%). The results of the multiple regression analyses are shown in Table 3, which presents the factors related to preventive aware-
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Table 3 Factors related to preventive awareness of influenza infection as determined in multiple regression analyses. Items
F3. Awareness of the need for a rapid diagnosis
F1. Avoidance of influenza infection
F2. Awareness of the benefits of mask use
B
SE
p-Value
B
SE
p-Value
B
SE
p-Value
3.03 −0.70 0.42 0.11 2.33 0.12
0.53 0.49 0.51 0.02 0.56 0.49
0.001 0.16 0.41 0.001 0.001 0.80
2.48 −0.70 0.73 0.05 0.87 −0.28
0.42 0.40 0.41 0.02 0.45 0.39
0.001 0.08 0.07 0.01 0.05 0.47
3.34 0.54 1.30 0.19 −2.95 0.25
0.63 0.58 0.60 0.03 0.66 0.58
0.001 0.36 0.03 0.001 0.001 0.66
Flu-related events 1. Had been vaccinated against influenza (ref. no) 2. Had been diagnosed with influenza (ref. no) 3. Having a family member who had been diagnosed with influenza (ref. no)
0.45 -0.18 1.44
0.54 2.23 0.62
0.41 0.94 0.02
-0.08 0.15 0.53
0.44 1.79 0.49
0.86 0.94 0.28
0.49 3.51 -0.33
0.64 2.64 0.73
0.44 0.18 0.65
R2 (adjusted R2 )
0.17 (0.16)
Demographic characteristics 1. Female (ref. male) 2. Living in an urban area (ref. others) 3. Living with children under the age of 18 (ref. no) 4. Age (yr.) 5. Medical jobs (ref. non-medical jobs) 6. Prior experience with influenza infection (ref. no)
0.10 (0.09)
0.16 (0.14)
Abbreviations: B, standardized partial regression coefficient; SE, standard error.
ness of influenza infection. All explanatory variables were entered simultaneously. With respect to all models, the p-values of the ANOVA were 0.00, implying no problem with multi-collinearity. Female sex and age were both related to the three types of preventive awareness (p = 0.001 or 0.01). Although holding a medical job was positively related to taking care to avoid infection (p = 0.001), it was inversely related to awareness of the need for a rapid diagnosis (p = 0.001). Having a family member who had been diagnosed with influenza was related to taking care to avoid infection (p = 0.02). Table 4 presents the factors related to the preventive behaviors designed to avoid influenza infection identified by the multiple logistic regression analyses. As noted previously, preventive behaviors were dummy coded by assigning “1” when the score was ≥6 and “0” when the score was <6. All explanatory variables were entered simultaneously. The p-value for the Hosmer–Lemeshow test was >0.05 in all models, except for the model treating attention to healthcare as the dependent variable, reflecting that the five other models fit the data well. Younger age (OR = 0.98) and awareness of the benefits of mask use (OR = 1.07) were related to the use of hand disinfectant, as were urban residence compared with living elsewhere (OR = 1.80). Compared with unvaccinated individuals, people who were vaccinated against influenza were more likely to engage in hand-washing (OR = 1.77). Compared with people holding non-medical jobs, those employed in medical jobs were more likely to avoid touching their face (OR = 2.76) and to seek flu-related information (OR = 2.30). Compared with people who were not careful to avoid influenza infection, those who did take care to avoid infection were more likely engage in hand-washing (OR = 1.16), use hand disinfectant (OR = 1.11), avoid touching their face (OR = 1.17), gargle (OR = 1.14), and seek flu-related information (OR = 1.14). Discussion We examined factors related to preventive awareness and to behaviors designed to avoid influenza infection during the period of the pandemic (H1N1) influenza outbreak. Several findings were obtained. First, it has been established that a mother will act as the caregiver and role model in mother–child interactions [13]. The same relationship between these care-givers and children has been reported among nurses in Japan [14]. Thus, the present finding that people living with children were aware of the need for a rapid diagnosis of flu-like symptoms by a clinician might be understood as an example of a mother’s role modeling. Second, hand disinfectants significantly reduce viral counts on the hands [15]. In Japan, it is common for hand disinfectant to be placed at the entrances of med-
ical facilities, including hospitals, nursing homes, day-care centers, and long-term care facilities [16], so that visitors can use it before entering the facility to prevent the spread of infection inside the facility. Generally, there are more medical facilities in urban areas than in other areas in Japan, and this is the case in Fukui Prefecture [17]; this greater availability might account for the more frequent use of hand disinfectant by residents of urban areas. Third, there are administrative health services that provide citizens with advice via telephone before suggesting a visit to a medical institution [18]. It was found that people holding medical jobs were unlikely to seek attention from counseling and consultation centers for fever. This same group did not see a doctor immediately upon flu suspicion. The finding could be due to the medical knowledge of people in the healthcare professions. This study revealed multiple behaviors and factors related to preventive awareness, and several of the findings are consistent with those of previous studies concerning preventive behaviors related to an influenza outbreak [5–8]. Specifically, females and older individuals were more likely than males or younger people, respectively, to have a higher level of preventive awareness, such as taking care to avoid infection, being aware of the benefits of mask use, and realizing the need for a rapid diagnosis. Previous studies have reported the same association of female sex and age with preventive awareness [19,20]. In addition, people holding medical jobs were aware of the need to avoid infection, avoid touching their face, and obtain flu-related information. The latter two are important measures in preventing influenza infection [21]. We also found that people who were careful to avoid influenza infection were more likely to adopt preventive behaviors, such as hand-washing, hand disinfectant use, avoidance of touching their face, gargling, and obtaining flu information. In Japan, masks are used not only to avoid spreading the flu virus to others but also to prevent being infected by someone else. The effectiveness of mask use for preventing infection, especially pandemic transmission in households, has been established [22]. Hand-washing is a well-established means of preventing infection [23,24]. Gargling is also a very common practice in Japan. The efficacy of gargling for preventing respiratory tract infection was recently demonstrated in studies from Japan. Specifically, gargling with water can protect against upper respiratory tract infections [25], and gargling could be effective in the prevention of febrile diseases in children [26]. It is interesting to note that the preventive behaviors adopted by those who were careful to avoid influenza infection are evidence-based. Our results have important practical implications. Additional information about public awareness and preventive behaviors
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
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2. Living in an urban area (ref. others) 3. Living with children under the age of 18 (ref. no) 4. Age (yr.) 5. Medical jobs (ref. Non-medical jobs) 6. Prior experience with influenza infection (ref. no) Flu-related events 1. Had been vaccinated against influenza (ref. no) 2. Had been diagnosed with influenza (ref. no) 3. Family members diagnosed with influenza (ref. no) Preventive awareness F1. Avoidance of influenza Infection F2. Awareness of the benefits of mask use F3. Awareness of the need for a rapid diagnosis Hosmer–Lemeshow test
Hand disinfectant use OR (95%CI) p-Value
Avoidance of face touch OR (95%CI) p-Value
Gargling OR (95%CI) p-Value
Attention to health care OR (95%CI) p-Value
Obtaining flu information OR (95%CI) p-Value
1.61 (0.91–2.84) 0.10 1.17 (0.71–1.93) 0.54 1.01 (0.60–1.72) 0.97 0.98 (0.96–1.00) 0.11 1.66 (0.86–3.19) 0.13 0.72 (0.43–1.19) 0.20
0.81 (0.52–1.26) 0.35 1.80 (1.21–2.68) 0.001 1.08 (0.72–1.61) 0.72 0.98 (0.96–1.00) 0.03 0.86 (0.54–1.36) 0.51 1.02 (0.69–1.51) 0.92
1.86 (1.11–3.10) 0.02 1.29 (0.82–2.04) 0.28 1.10 (0.69–1.75) 0.69 1.01 (0.99–1.03) 0.32 2.76 (1.66–4.61) 0.001 0.91 (0.58–1.43) 0.68
1.06 (0.69–1.64) 0.78 1.06 (0.72–1.56) 0.78 1.26 (0.85–1.88) 0.25 0.99 (0.97–1.01) 0.34 0.69 (0.44–1.09) 0.11 1.29 (0.88–1.90) 0.19
1.51 (0.93–2.45) 0.10 1.20 (0.77–1.87) 0.41 0.89 (0.57–1.41) 0.62 1.00 (0.98–1.02) 0.79 1.97 (1.15–3.39) 0.01 0.93 (0.60–1.44) 0.74
1.41 (0.90–2.23) 0.14 0.97 (0.65–1.47) 0.89 1.16 (0.76–1.77) 0.49 1.01 (0.99–1.03) 0.17 2.30 (1.42–3.73) 0.001 0.83 (0.55–1.25) 0.38
1.77 (1.03–3.03) 0.04 0.001 (0.001–0.001) 1.00 1.24 (0.63–2.44) 0.53
1.41 (0.92–2.16) 0.11 1.06 (0.18–6.08) 0.95 0.91 (0.56–1.47) 0.69
0.89 (0.53–1.47) 0.64 1.04 (0.13–8.32) 0.97 1.24 (0.71–2.15) 0.45
1.13 (0.74–1.72) 0.58 0.41 (0.06–2.58) 0.34 1.48 (0.91–2.41) 0.12
1.03 (0.64–1.65) 0.90 2.77 (0.22–35.39) 0.43 1.55 (0.86–2.78) 0.14
1.12 (0.72–1.74) 0.61 7.40 (0.61–90.32) 0.12 0.99 (0.60–1.65) 0.98
1.16 (1.10–1.22) 0.001 0.98 (0.93–1.04) 0.53 1.03 (0.99–1.07) 0.20
1.11 (1.06–1.16) 0.001 1.07 (1.01–1.13) 0.03 1.03 (0.99–1.06) 0.13
1.17 (1.10–1.24) 0.001 1.00 (0.93–1.08) 0.93 1.03 (0.99–1.07) 0.10
1.14 (1.09–1.19) 0.001 0.97 (0.92–1.03) 0.31 1.03 (1.00–1.06) 0.10
1.15 (1.10–1.21) 0.001 0.99 (0.93–1.04) 0.66 1.06 (1.02–1.10) 0.001
1.14 (1.09–1.20) 0.001 1.00 (0.95–1.06) 0.88 1.02 (0.99–1.06) 0.24
2 = 6.63 (p = 0.58)
2 = 12.15 (p = 0.15)
2 = 10.74 (p = 0.22)
2 = 6.09 (p = 0.64)
2 = 17.09 (p = 0.03)
2 = 13.17 (p = 0.11)
Abbreviations: OR, odds ratio; CI, confidence interval.
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Demographic characteristics 1. Female (ref. male)
Hand-washing OR (95%CI) p-Value
S. Takahashi et al. / Journal of Infection and Public Health xxx (2017) xxx–xxx
Please cite this article in press as: Takahashi S, et al. Public preventive awareness and preventive behaviors during a major influenza epidemic in Fukui, Japan. J Infect Public Health (2017), http://dx.doi.org/10.1016/j.jiph.2017.04.002
Table 4 Factors related to preventive behaviors to avoid influenza infection as determined in multiple logistic regression analyses.
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should lead to more effective measures and behaviors to prevent the spread of influenza infection. Specifically, only females and the elderly showed high levels of preventive awareness regarding influenza infection. Consequently, health promotion efforts to increase the level of preventive awareness during influenza outbreaks should specifically target males and younger people. Age was also inversely correlated with the use of hand disinfectant. As the effectiveness of virus removal by hand disinfectant is widely established [15], we suggest that hand disinfectant use be emphasized in messages to the elderly. This study has several limitations, and the results should be interpreted with caution. First, the survey was performed using an e-mailed questionnaire; therefore, it was not anonymous (openlabel method). Thus, people who knew the researchers, such as healthcare workers, might have felt unintentionally pressured to provide consent for study participation, which would have introduced selection bias. Second, of the initial 2788 potential participants, the responses of only 492 were used in the analysis (21.3%). In 1976, the proportions of blood types A, O, B, and AB among the Japanese were 38%, 31%, 22%, and 9%, respectively [27]. The corresponding percentages in the study were 41%, 28%, 22%, and 9%, respectively. The difference between the national and the study sample data was not significant (x2 = 0.53, p > 0.05); the study sample was therefore representative of the Japanese population, as was the age range of the study participants. Accordingly, we believe that we accurately captured the levels of public preventive awareness and preventive practices during the largest influenza epidemic in Japan. Third, because the study population consisted of members of the Fukui prefectural government and officials and students of Fukui University in Fukui Prefecture, the external validity of our findings is limited to individuals employed in local government or working or attending academic institutions. Finally, the influenza outbreak in 2009 resulted in a small number of casualties in Japan because of the high level of public awareness and personal hygiene, especially with regard to regular hand-washing. Identification of the factors that promote hand-washing might lead to the development of effective programs to prevent influenza epidemics. Additional studies are needed to examine these factors. Conclusion In conclusion, in a study of the citizens in Fukui Prefecture, Japan during the pandemic (H1N1) 2009 influenza pandemic, we identified factors related to preventive awareness levels and behaviors regarding influenza infection. To better protect the general public against future influenza epidemics, health-promotion efforts should be aimed at increasing the level of preventive awareness of influenza infection among males and young people and at encouraging the use of hand disinfectants by elderly individuals. Conflict of interest None declared. Funding No funding sources. Competing interests None declared. Ethical approval Not required.
Acknowledgments We express our sincere thanks to the study participants; Fukui Prefectural Government members, officials and students of University of Fukui, University of Fukui Hospital members, and all of our colleagues of in the Division of Environmental Health, University of Fukui.
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