Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China.

BACKGROUND: With the spread of vaccines, more and more countries have controlled the outbreak of the COVID-19. In this post-epidemic era, these countries began to revive their economy. However, pollution remains in the environment, and people's physical and psychological health has been under threat due to some over-prevention behaviors. Instruments for governmental agencies to manage these behaviors are not yet available. This study aims to develop a measurement model to identify and measure the degree of over-prevention behaviors during the COVID-19 epidemic in China.
METHODS: A survey online was conducted to collect cognition from 1528 Chinese people, including descriptions of various over-prevention behaviors defined by health authorities. Factor analyses were used to develop the measurement model and test its validity. Logistic regression analyses were conducted to explore demographic characteristics, indicating people who are inclined to exhibit over-prevention behaviors.
RESULTS: Four main factors were extracted to develop the model (eigenvalue = 7.337, 3.157, 1.447, and 1.059, respectively). The overall reliability (Cronbach's α = 0.900), the convergent (AVE > 0.5, CR > 0.8 for each factor) and discriminant validity is good. There is also a good internal consistency among these factors (Cronbach's α = 0.906, 0.852, 0.882, and 0.763, respectively). In Factor 1, gender has a negative effect (Beta = - 0.294, P <  0.05, OR = 0.745), whereas employment has a positive effect. Workers in institutions exhibit the greatest effect (Beta = 0.855, P <  0.001, OR = 2.352). In Factor 2, employment has a negative effect, with workers in institutions exhibit the greatest role (Beta = - 0.963, P <  0.001, OR = 0.382). By contrast, education level has a positive effect (Beta = 0.430, P <  0.001, OR = 1.537). In Factor 3, age plays a negative role (Beta = - 0.128, P < 0.05, OR = 0.880).
CONCLUSIONS: People show a discrepancy in the cognition toward various over-prevention behaviors. The findings may have implications for decision-makers to reduce the contradiction between the epidemic and economic revival via managing these behaviors.

Introduction

The vaccination was proved to be useful in controlling the COVID-19 [1]. With the spread of vaccines in many countries, the pandemic may be mitigated gradually. Although there are still some lingering impacts on the original business, we have entered into the post-COVID-19 era [2]. Nowadays, many countries have restarted their operation. For example, the Chinese government has been opening schools and workplaces to resume classes and various works since April, 2020 [3]. By doing so, the country tries to strike a balance between reviving human activities and lowering the risk of another wave [4]. China strives to prevent a huge resurgence that, which can cause further losses of health and economy, by not loosening the regular control drastically [5]. However, a contradiction exists between outbreak prevention and economic recovery. For example, Italian governments have closed all schools across the country in a short time. They also restricted the population movement and closed various non-essential business [6]. They have been emphasizing the importance to control the social distance and behaviors. Especially for people with a high exposure risk, such as teachers, governments required them to wear surgical masks at their work time [7]. These prevention measures have played an essential role in handling the first wave of pandemic in Italy successfully [8]. However, when the first wave came to an end, some regions relaxed their previous distance measures to revive the economy. As a result, the second wave hit Italy and resurgences occurred in many regions, causing more deaths [9]. From Italy’s prevention lessons, to preclude the possibility of resurgence and transmission, the government may continue to implement various precautions, such as curbing population flow and disinfecting places that are accessible to people. However, such implementations may also result in some deficiencies. Last year, 150 wild animals in Chongqing, China had died from excessive usage of disinfectant [10]. The United Nations Conference on Trade and Development also reported that plastic wastes from masks, gloves, bottles of disinfectants, and other plastic materials had polluted many beaches and oceans [11]. Aside from the current situation of the environment, the negative effects of social isolation on humans are not negligible. For instance, research finds that in this year, adults have consumed considerable alcoholic drinks and cannabis, underlying latent effects on health [12]. Scientists also revealed some negative psychological effects, such as stress symptoms, frustration, confusion, and anger [13].

Research questions development

The above cases show that people took excessive measures to control the pandemic quickly, which in turn hurt the environment and human health. In this study, we call these measures over-prevention behaviors. Some researchers defined these behaviors as the unnecessary measures, which can only bring psychological comforts to people [14]. In the description of prevalence elastic theory, Philipson claimed that individuals would adjust their prevention levels with the spread of a pandemic. If their perceived risk is lower than a standard level named threshold prevalence, they will make inadequate prevention behaviors [15]. On this basis, we define over-prevention behaviors as excessive precautions that individuals take when their perceived risk is higher than the threshold prevalence, causing obvious or latent damages to their health and the environment, like physical or psychological diseases and water pollution. Economically, we want to achieve optimal goals while invested exceedingly, which curbs prevention efficiency.

In this post-COVID-19 era, misinformation related to the crisis can induce these over-prevention behaviors [16]. And this can exist in many countries. More reports about long-term harm to humans would emerge in the future if people would still not take efficient measures to avoid these behaviors. Thus, knowing how to hold an appropriate level to reduce these unnecessary harms under the condition of ensuring efficient prevention work is indispensable. Previous studies have explored the correlation between demographic characteristics and people’s prevention behaviors. For example, in the H1N1 influenza pandemic, scientists identified that age, gender, and education can determine protective behaviors [17]. Meanwhile, for the influenza in the United States, Singh et al. found that different demographic groups of people have various degrees of self-protective behaviors of social distancing and vaccination uptake [18]. What’s more, in the COVID-19 pandemic, researchers found that people’s knowledge, perceived risk, health status, and other demographic characteristics may have a relationship with over-prevention behaviors [14, 19]. And Min et al. also explored the role of knowledge and negative moods in the correlation between public trust and protective measures [16]. However, little is known about an instrument to measure the degree of over-prevention behaviors in the COVID-19. We also have no idea about how to use the demographic characteristics to identify people who are inclined to perform these behaviors in this crisis in China. So in this study, we aim to explore the following two research questions:

RQ1: How to measure the degree of over-prevention behaviors in the post-pandemic era in China?

RQ2: How to use the demographic characteristics to identify people who tend to perform over-prevention behaviors?

Methods

To invent an instrument to solve RQ1, We used an exploratory design and analysis method. Firstly we developed an item pool based on a broad literature review and governmental guidelines. The guidelines were introduced by experts in the press conference of authoritative institutions, including WHO, CDC, and the National Health Commission of the People’s Republic of China, to show various items describing over-prevention behaviors. Second, based on a five-point Likert scale, we developed our scale using these items. The scale incorporated a total of 27 items, which have been revised and approved by all the co-authors. Meanwhile, we conducted a pilot test with 100 people aged 20 to 55, collecting their comments to revise the scale further and provide explicit descriptions. Most of behaviors described among these items were excessive in the high-risk and low-risk regions. We defined high-risk regions as districts with an above 50 accumulative confirmed cases and an outbreak in clusters in the past 14 days [20]. Accordingly, we classified the remaining regions as low risk. However, some descriptions could only apply to the low-risk regions. For instance, one behavior describes a situation wherein one wears a mask indoors, such as in an office, chamber, school, and other ventilated rooms, where people can still exercise social distancing from one another. This behavior is excessive in the low-risk regions but not in the high-risk ones [21]. People may show a contrary cognition at different risk levels. Thus, we investigated their degree of agreement on each description in one or two scenarios. For example, one sentence describes that staff settled a disinfection shed at the gate of a community, cleansing the people who entered it thoroughly, which is an over-prevention behavior [20, 22]. Then, we asked Sam how he agrees with the statement concerning a high-risk or a low-risk region. He chose between one to five points, varying from greatly disagree to greatly agree. In accordance with his answers, we averaged the points in each two-scenario item to represent its final grade. X1 to X27 shows all items in Table 1.

Table 1

Items collected and descriptions in the questionnaire

Item	Description
X1	Disinfecting the surrounding outdoors rarely touched by hands, such as the ground, plants, and walls, is an over-prevention behavior in the high- and low-risk regions [23].
X2	Disinfecting clothes and soles by using alcohol and other disinfectants after getting home even without close contact with confirmed cases of infection is an over-prevention behavior in the high- and low-risk regions [24, 25].
X3	Using alcohol and other disinfectants to clean the house every day, even without patients living, is an over-prevention behavior in the high- and low-risk regions [24, 25].
X4	Using ultraviolet rays at home, even without patients living, is an over-prevention behavior in the high- and low-risk regions [25].
X5	Using alcohol and other disinfectants to clean packages of carry-out, parcel, and shopping commodities is an over-prevention behavior in the high- and low-risk regions [25].
X6	Using disinfectants (75% alcohol excepted) to clean hands every day even without close contact with infected cases is an over-prevention behavior in the high- and low-risk regions [25].
X7	Using alcohol to clean used medical or N95 masks is an over-prevention behavior in the high- and low-risk regions [22].
X8	Using converted mist cannon trucks and drones to spray disinfectants to the air outdoors is an over-prevention behavior exhibited by staff in the high- and low-risk regions [21, 23].
X9	Disinfecting wheels and surfaces of ordinary cars that did not carry patients is an over-prevention behavior exhibited by staff in the high- and low-risk regions [23, 25].
X10	Building disinfection shed at the gate of a community to clean people thoroughly who entered it is an over-prevention behavior exhibited by staff in the high- and low-risk regions [21, 23].
X11	Using high-concentration or large amounts of disinfectants to clean corridors in a community in non-focus of infection is an over-prevention behavior exhibited by staff in the high- and low-risk regions [25].
X12	Using disinfectants to clean outdoors in a community in non-focus of infection is an over-prevention behavior exhibited by staff in the high- and low-risk regions [21, 25].
X13	Requiring people to disinfect their clothes and soles before entering public areas is an over-prevention behavior in the high- and low-risk regions [21, 25].
X14	Disinfecting the air outdoors on rainy and snowy days is an over-prevention behavior exhibited by staff in the high- and low-risk regions [21].
X15	Casting disinfectants to lakes, reservoirs, and pools is an over-prevention behavior exhibited by staff in the high- and low-risk regions [21].
X16	Wearing masks indoors, such as in an office, chamber, school, and other ventilated rooms, where people can remain one meter apart, is an over-prevention behavior in the low-risk regions [26].
X17	Wearing masks in private cars without patients is an over-prevention behavior in the high- and low-risk regions [26].
X18	Wearing masks outdoors where people can remain one meter apart is an over-prevention behavior in the high- and low-risk regions [26].
X19	Enforcing people to wear masks indoors, such as in an office, chamber, school, and other ventilated rooms, where they can remain one meter apart, is an over-prevention behavior in the low-risk regions [26].
X20	Inhibiting people to go outdoors without wearing masks is an over-prevention behavior in the high- and low-risk regions [26].
X21	Restricting human rights violently by implementing preventive measures, such as breaking into houses and hitting people, is an over-prevention behavior in the high- and low-risk regions [27].
X22	Setting pandemic checkpoints inappropriately, which harms human rights, such as the steel wire accidentally killing a passer-by reported in China, is an over-prevention behavior in the high- and low-risk regions [27].
X23	Collecting private information frequently or forcing privacy disclosure is an over-prevention behavior in the high- and low-risk regions [28].
X24	Damaging individual property rights as part of pandemic prevention, for instance, staff throwing away students’ items from the dormitory without permission to make room for patients that hospitals could not accommodate, is an over-prevention behavior in the high- and low-risk regions [27].
X25	Isolating received parcels in the corner of the house for several days is an over-prevention behavior in the high- and low-risk regions [25].
X26	Wearing gloves in public areas without the need to nurse patients or clean infected areas is an over-prevention behavior in the high- and low-risk regions [22].
X27	Delaying the operation of some enterprises in places qualified to allow the opening of workplaces is an over-prevention behavior in the low-risk regions [29].

Third, we conducted an online survey to validate these items based on the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The recommended sample size of the EFA and CFA is at least 300 and 200 [30, 31]. We divided China into seven areas based on a common view formed by Chinese geographical experts. Then, we used stratified random sampling to hand out our questionnaire [32]. The online survey was conducted via the Tencent Questionnaire Platform. It’s a platform wherein editing our questionnaire and making it publicly available is possible. It supports us to specify the characteristics of our participants, and helps to seek our target automatically in its sample database. Figure 1 shows the screenshot of the webpage. We determined some demographic characteristics, including gender, age, region, degree, and marriage. We paid for the service charge, then the platform began to filter to search for the target population. For matching people, it posted the questionnaire to them via SMS or WeChat, and waiting for their answers. When the collected answers reached our specified number, the platform stopped handing out. Then we can download the collected data. Finally, we received a total of 1528 answers from the platform. We tested the reliability of our scale and demonstrated its construct validity by using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity (BTS). The Cronbach’s α value was 0.936, demonstrating a good internal consistency of the scale. The KMO value was 0.953, with a BTS result that was statistically significant (P < 0.001). Thus, the collected data was suitable for the EFA. We randomly used 1000 of the 1320 participants to construct the EFA model. We extracted factors on the basis of the principal component analysis (PCA). We followed three criteria to filter invalid items [33]. First, we would delete the one with factor loading less than 0.5. Second, if more than one factor loaded the same item, we would remove it. Third, we would exclude who loaded on unintended factors. Finally, we grouped the remainder and calculated their weight individually. What’s more, for the remaining 320 participants, we used the CFA to evaluate goodness-of-fit indices, including Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker Lewis Index (TLI), and Incremental Fit Index (IFI). We also applied the Average Variance Extracted (AVE) and Composite Reliability (CR) and calculated the correlation coefficient between every two factors to test the convergent and discriminant validity. Besides, we adjusted the model by using the modification indices (MI) reasonably.

Fig. 1

Screenshot of the Tencent Questionnaire Platform

To answer RQ2, we firstly collected participants’ basic information, including their gender, age, education degree, employment status, and provincial address. The average answering time was 8 min. We filtered our data by following three rules. First, we filtered 88 instances wherein the recorded time of answering the questionnaire was below 2 min [34]. Then, we set a question instructing the participants to choose number four [34]. Considering that some people may give a wrong choice accidentally or deliberately for fun, we deleted those who did not choose four and those who recorded an answering time of below 4 min, which was half of the average time. At this time, we removed at least 89 questionnaires. Although we considered that the participants must have chosen the same answer because they possess a consistent cognition, those who presented invariant responses were still questionable [35]. For example, John was extremely careful, and he thought that all of the behaviors described in the scale were not excessive. Certainly, he could disagree will all 27 statements. However, we excluded those who gave invariant responses continuously in more than 14 items (half of the total 27), with an answering time below 4 min. Accordingly, we removed 31 more. There are a total of 1320 remaining valid answers. Table 2 shows the sample distribution. Table 3 shows the characteristics of the remaining sample. Among them, 53.1% (701/1320) were males, and 46.9% (619/1320) were females. The distribution in age groups approximated a normality tendency, with people aged 30–39 years accounting for the largest proportion, i.e., 43.7% (577/1320). More than half of the participants had a university degree (66.4%, 876/1320), followed by those with a senior high school degree (17.8%, 235/1320). As for the employment status reported, the majority comprised staff in enterprises (30.1%, 397/1320). Those in governmental agency composed the smallest group, accounting for only 3.8% (50/1320). After developing the model, for all the 1320 participants, we calculated scores of each extracted factor on these items by applying factor score matrix. We integrated the items into cognition variables, representing these factors, respectively. Then, we explored the discrepancy of individual cognition and the relationship between it and demographic characteristics on the basis of the χ2 test and Logistic regression. From the statistical results, we identified which group of people tend to behave excessively. We used IBM SPSS 25.0 and IBM AMOS 24.0 to perform all data analyses.

Table 2

Sample distribution in seven areas of China

Area (Provinces covered)	Sample size
Northeast China (Heilongjiang, Jilin, and Liaoning)	191
North China (Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia)	204
East China (Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Fujian, and Taiwan)	215
Central China (Henan, Hubei, and Hunan)	179
South China (Guangdong, Guangxi, Hainan, Hongkong, and Macau)	170
Southwest China (Chongqing, Sichuan, Guizhou, Yunnan, and Tibet)	176
Northwest China (Shaanxi, Gansu, Ningxia, Qinghai, and Sinkiang)	185
Total	1320

Table 3

Characteristics of the remaining sample (n = 1320)

Variable	Value
Gender
Male	701 (53.1%)
Female	619 (46.9%)
Age
0–17 years	19 (1.4%)
18–29 years	422 (32.0)
30–39 years	577 (43.7%)
40–49 years	235 (17.8%)
50–59 years	59 (4.5%)
60 years and above	8 (0.6%)
Education degree
Primary school and lower	5 (0.4%)
Junior high school	122 (9.2%)
Senior high school	235 (17.8%)
College degree	876 (66.4%)
Postgraduate degree and higher	82 (6.2%)
Employment status
Student	292 (22.1%)
Staff in an enterprise	397 (30.1%)
Staff in an institution (science, education, culture, health, and other institutions)	236 (17.9%)
Staff in governmental an agency	50 (3.8%)
Self-employed	197 (14.9%)
Others (including retirement)	148 (11.2%)

Results

Measurement model developing

After performing five rounds of EFA, we filtered eight items, and the result was stable. Among the remaining 19 items, we extracted four factors with eigenvalues above 1. The cumulative variance contribution was 68.42%, showing an acceptable rate. Only one factor contained two items. Yet, we retained this factor, considering its practical significance. In the last round, KMO was 0.916, and the BTS result was statistically significant (P < 0.001). We used the varimax rotation to rotate the factor loading matrix. Table 4 shows the result. All communalities extracted in each item were above 0.4. This finding indicated that these factors could explain each item ideally (Communality > 0.4). Accordingly, we labeled these four factors as follows: Factor 1 as excessive disinfection behaviors that harm personal health directly (including X1 to X5 and X8 to X10), Factor 2 as wearing masks inappropriately (including X16 to X20), Factor 3 as unreasonable restraints of human activities (including X21 to X24), and Factor 4 as excessive disinfection behaviors that damage the environment directly (including X14 and X15). X1 seems to fit for Factor 4. However, when individuals use disinfectants outdoors, they rarely equip themselves with protection instruments. Thus, the chemicals may be much more harmful to people using them than to the environment. Personal practice of disinfection also tends to consume so little that we could neglect its side-effect on the environment. On this basis, we classified this item into Factor 1, not Factor 4.

Table 4

Rotated component matrix and communalities

Item	Factor loading				Communality
	Factor 1	Factor 2	Factor 3	Factor 4
X1	0.744				0.572
X2	0.831				0.731
X3	0.781				0.682
X4	0.538				0.463
X5	0.798				0.714
X8	0.719				0.674
X9	0.682				0.652
X10	0.655				0.650
X14				0.758	0.753
X15				0.832	0.785
X16		0.790			0.672
X17		0.591			0.555
X18		0.767			0.772
X19		0.826			0.747
X20		0.637			0.615
X21			0.825		0.704
X22			0.882		0.787
X23			0.800		0.683
X24			0.872		0.789

The eigenvalue of Factor 1 to Factor 4 was 7.337, 3.157, 1.447, and 1.059, respectively, with the variance devoting rates of 38.618, 16.613, 7.616, and 5.573%. On the basis of PCA and variance contribution, we calculated the weight of each item. From the perspective of public cognition, one item gaining five points means that Sam agreed that this description was excessive. We could infer that he had a strong awareness of it. Accordingly, he might tend to avoid this behavior in his daily life. By contrast, 1 point means people disagreed. They were not conscious of the excessiveness. In this case, they might tend to show this behavior. Therefore, items with higher grades were less important than those with lower grades. Consequently, we converted the weights to calculated the reciprocals. Then, we normalized them to represent the final weights. Table 5 shows the result.

Table 5

Final weights of each item based on PCA

Item	Weight	Item	Weight
X1	0.068	X16	0.044
X2	0.066	X17	0.041
X3	0.055	X18	0.041
X4	0.052	X19	0.037
X5	0.068	X20	0.045
X8	0.065	X21	0.043
X9	0.062	X22	0.038
X10	0.058	X23	0.036
X14	0.062	X24	0.040
X15	0.081	Total	1

We tested the internal consistency in the four factors. The Cronbach’s ɑ value of Factor 1 to Factor 4 was 0.906, 0.852, 0.882, and 0.763, respectively, mostly indicating a good internal consistency. The whole scale had a value of 0.900, denoting ideal reliability.

Validity testing of the model

The modification index (MI) between the residuals of X3 and X8 was up to 27.79. These two items had an affiliation with Factor 1. The Spearman correlation coefficient between them was 0.542 (P < 0.001). Thus, conducting the MI modification and building a new path between them were reasonable. The indices we tested showed a great fit to the data (χ2[145] = 426.51, RMSEA = 0.078, IFI = 0.922, TLI = 0.907, CFI = 0.921) [36].

Table 6 shows the standardized loadings of items and the AVE and CR of each factor. The model analyzed showed a good convergent validity, with AVE values of all the four factors above 0.5 and CR above 0.8. The loading of each item was higher than 0.6, indicating that we could explain these items to a large extent.

Table 6

Loadings of each item and AVE and CR tested

Item	Path	Factor	Loading	AVE	CR
X1	<−--	Factor 1	0.673	0.587	0.919
X2	<−--	Factor 1	0.788
X3	<−--	Factor 1	0.812
X4	<−--	Factor 1	0.664
X5	<−--	Factor 1	0.765
X8	<−--	Factor 1	0.842
X9	<−--	Factor 1	0.790
X10	<−--	Factor 1	0.775
X16	<−--	Factor 2	0.672	0.535	0.851
X17	<−--	Factor 2	0.655
X18	<−--	Factor 2	0.835
X19	<−--	Factor 2	0.750
X20	<−--	Factor 2	0.730
X21	<−--	Factor 3	0.814	0.625	0.869
X22	<−--	Factor 3	0.814
X23	<−--	Factor 3	0.696
X24	<−--	Factor 3	0.830
X14	<−--	Factor 4	0.833	0.684	0.812
X15	<−--	Factor 4	0.821

Table 7 shows the estimated correlation coefficients between every two factors. We listed AVE and calculated the square root. All of the coefficients were statistically significant (P < 0.01 or P < 0.001). Most of the values indicated a weak correlation among these factors. Although only one (0.759) between Factor 2 and Factor 1 was higher than the Sqrt (AVE) of Factor 2 (0.731), others are lower than their corresponding Sqrt (AVE) values. Overall, the results denoted acceptable discrimination between every two factors while showing a correlation to some degree. Therefore, the model possessed reasonable discriminant validity. Figure 2 shows the modified structural equation modeling.

Table 7

Correlation coefficients and AVE

Factor	Factor 1	Factor 2	Factor 3	Factor 4
Factor 1	0.587a
Factor 2	0.759c	0.535a
Factor 3	0.213b	0.383c	0.625a
Factor 4	0.532c	0.612c	0.567c	0.684a
Sqrt (AVE)	0.766	0.731	0.791	0.827

aWe listed the AVE of each factor diagonally. The last row was the square root.

bP < 0.01.

cP < 0.001.

Fig. 2

Structural equation modeling in CFA

Demographic characteristics identification

Based on the factor score matrix gained by SPSS software, we converted these factors in PCA into four variables. The matrix is shown in Table 8. We named them as F1 to F4, representing Factor 1 to Factor 4. Then for each survey response, we calculated the integrated scores among F1 to F4. The conversion is shown in (1) as follows.

Table 8

Scores of each factor on the 19 items

	Factor scores
	Factor 1	Factor 2	Factor 3	Factor 4
X1	0.255	−0.136	0.055	−0.127
X2	0.268	−0.088	0.024	−0.181
X3	0.224	−0.090	0.034	−0.065
X4	0.124	−0.067	0.064	0.053
X5	0.230	−0.048	− 0.013	− 0.131
X8	0.154	−0.061	− 0.050	0.103
X9	0.134	−0.058	− 0.044	0.138
X10	0.106	0.009	−0.056	0.104
X14	−0.062	− 0.031	− 0.071	0.503
X15	−0.100	−0.075	− 0.063	0.591
X16	−0.147	0.363	−0.041	0.013
X17	−0.126	0.232	0.009	0.152
X18	−0.020	0.302	−0.011	−0.138
X19	−0.120	0.373	0.014	−0.081
X20	0.011	0.223	−0.007	−0.094
X21	−0.002	−0.034	0.280	−0.036
X22	0.032	−0.025	0.320	−0.133
X23	0.006	0.043	0.285	−0.130
X24	0.001	−0.038	0.295	−0.036

Integrated scores were continuous varying from negative to positive values. We re-coded them categorically. The original item with a high grade implied one agreed that the description was excessive. We also observed no zero in the data, so we coded the values above zero as 1. Then, we coded those below zero as 0. Now, these four variables were binary (0 = Disagree, 1 = Agree). We regarded them as dependent cognition variables. We re-coded the data concerning personal information to make independent variables. Gender (0 = Male and 1 = Female), Age (0 = 0–17 years, 1 = 18–29 years, 2 = 30–39 years, 3 = 40–49 years, 4 = 50–59 years, and 5 = 60 years and above), Area (0 = Northeast China, 1 = North China, 2 = East China, 3 = Central China, 4 = South China, 5 = Southwest China, and 6 = Northwest China), Employment (0 = Student, 1 = Staff in an enterprise, 2 = Staff in an institution, 3 = Staff in a governmental agency, 4 = Self-employed person, and 5 = Others), and Education Degree (0 = Primary school and lower, 1 = Junior high school, 2 = Senior high school, 3 = College degree, and 4 = Postgraduate degree and higher) were the five variables applied to test the relationship with four cognition variables.

Cognition discrepancy test

Table 9 shows the χ2 test result. People of opposing genders showed a significant discrepancy in the cognition toward excessive behaviors in F1 (P < 0.01). The cognition of different ages of people had statistical significance in F1, F2 (P < 0.001), and F3 (P < 0.05). People with diverse employments exhibited disagreement on F1 and F2 (P < 0.001), with a significant difference. A discrepancy also exists in F2 for people with different educational backgrounds (P < 0.001).

Table 9

χ2 test between attitude and personal information variables (n = 1320)

	F1		F2		F3		F4
	Agree	Disagree	Agree	Disagree	Agree	Disagree	Agree	Disagree
Gender
Male	384	317	373	328	361	340	334	367
Female	289	330	304	315	305	314	310	309
χ2	8.611b		2.210		0.651		0.780
Age
0–17 years	11	8	10	9	6	13	7	12
18–29 years	178	244	253	169	241	181	204	218
30–39 years	306	271	279	298	275	302	281	296
40–49 years	140	95	110	125	112	123	116	119
50–59 years	32	27	21	38	30	29	31	28
60 years and above	6	2	4	4	2	6	5	3
χ2	23.457c		22.394c		14.797a		2.087
Area of China
Northeast	104	87	86	105	89	102	85	106
North	103	101	100	104	91	113	102	102
East	113	102	122	93	117	98	115	100
Central	78	101	100	79	82	97	86	93
South	78	92	83	87	98	72	73	97
Southwest	92	84	88	88	93	83	94	82
Northwest	105	80	98	87	96	89	89	96
χ2	9.437		8.222		10.895		7.329
Employment
Student	111	181	189	103	155	137	138	154
In an enterprise	214	183	209	188	197	200	197	200
In an institution	27	23	29	21	27	23	28	22
In a governmental agency	140	96	96	140	112	124	112	124
Self-employed	104	93	89	108	108	89	87	110
Others	77	71	65	83	67	81	82	66
χ2	28.081c		39.095c		5.111		5.872
Education Degree
Primary and lower	2	3	1	4	3	2	3	2
Junior high school	63	59	42	80	62	60	60	62
Senior high school	129	106	106	129	117	118	124	111
College degree	432	444	467	409	447	429	414	462
Postgraduate degree and higher	47	35	61	21	37	45	43	39
χ2	3.992		38.389c		1.278		3.003

aP < 0.05, bP < 0.01, and cP < 0.001. These figures are the number of cases in groups with different characteristics, followed by the Pearson χ2 test in the last row

Influences of demographic characteristics

We conducted a binary logistic regression to explore the influence of these five variables on the four dependent variables. We made categorical variables, including Area and Employment, into dummy ones. The Age and Education Degree were ordinal, so we did not convert them. We used forward regression, and Table 10 shows the result. For Factor 1, which includes excessive disinfection behaviors that harm personal health directly, people of opposing genders and different employments showed a discrepancy in cognition. Gender had a negative effect on F1 (Beta = − 0.294, P < 0.05, OR = 0.745). Females were apt to think these behaviors were less excessive than their male counterparts. Compared with the student group (Dummy coding = 00000), people who worked in institutions (Dummy coding = 00100) were the most likely to believe these were excessive (Beta = 0.855, P < 0.001, OR = 2.352). Students showed the least probability. For wearing masks inappropriately in Factor 2, we observed that the dummy variable of Employment mostly had negative effects, whereas Education Degree had a positive effect on cognition. Compared with the student group, staffs working in institutions were the most likely to consider these behaviors to be non-excessive (Beta = − 0.963, P < 0.001, OR = 0.382). People with a higher education degree were likely to recognize these over-prevention behaviors better than those with a lower education degree (Beta = 0.430, P < 0.001, OR = 1.537). Age could only affect the cognition toward the behaviors in Factor 3. The elderly tended to consider these behaviors to be less excessive than the younger ones (Beta = − 0.128, P < 0.05, OR = 0.880). While for the behaviors included in Factor 4, we did not obtain any significant variables.

Table 10

Logistic regression results (n = 1320)

	F1		F2		F3		F4
	Beta	OR(95% CI)	Beta	OR(95% CI)	Beta	OR(95% CI)	Beta	OR(95% CI)
Gender	−0.294a	0.745 (0.596, 0.931)
Age					−0.128a	0.880 (0.778, 0.994)
Area of China (0)
Area of China (1)
Area of China (2)
Area of China (3)
Area of China (4)
Area of China (5)
Area of China (6)
Employment (0)	—c		—c
Employment (1)	0.594c	1.811 (1.328, 2.472)	−0.467b	0.627 (0.459, 0.857)
Employment (2)	0.585	1.796 (0.978, 3.296)	−0.360	0.698 (0.377, 1.291)
Employment (3)	0.855c	2.352 (1.654, 3.345)	−0.963c	0.382 (0.267, 0.546)
Employment (4)	0.580b	1.786 (1.237, 2.579)	−0.526b	0.591 (0.401, 0.871)
Employment (5)	0.591b	1.806 (1.209, 2.697)	−0.468a	0.626 (0.405, 0.967)
Education Degree			0.430c	1.537 (1.294, 1.826)

aP < 0.05, bP < 0.01, and cP < 0.001. The variables followed by a number in a bracket are the dummy variables. Considering the student group, we coded Employment (0) to Employment (5) into 00000 to 00001, varying from student to others, respectively. We did not observe any significant result in Employment (2) in F1 and F2. The dummy variable of Staff in institutions showed a lesser significant result than the dummy variable of Student

Discussion

Principal findings

As the results show, females tend to regard these over-prevention behaviors as correct measures. It may because they perceive a higher epidemic risk and greater vulnerability than males, as Boguszewski et al. suggested in their study [37]. Besides, we found that staffs working in institutions have the most proper cognition of excessive behaviors in Factor 1 that directly and greatly damage personal health. It may because they have a better command of expertise on using the chemicals appropriately than the public. But compared with them, we found students are easier to approve of these behaviors, indicating that they may tend to show them in the epidemic prevention. It is consistent with the previous study that students are less likely to take proper prevention measures than other employment groups [38]. Thus, when managing the over-prevention behaviors involving excessive use of disinfectants, decision-makers should pay attention to the female and student groups.

For behaviors in Factor 2, staff working in institutions tends to have a high prevention consciousness out of their professional instinct. Although they know how to prevent excessive usage of disinfectants, they don’t agree the behaviors about wearing masks in Factor 2 are excessive. They think it’s reasonable and proper for protection, as shown by the results, indicating that they may be easier to perform these behaviors. However, inappropriate usage of masks can still cause huge damages. For example, several students had died from wearing them when going outdoors on hot days, as reported in China several months ago. Moreover, Ozdemir et al. has studied that people who are highly educated have higher adoption of preventive behaviors in the COVID-19 [39]. But for the excessive prevention, we found they tend to recognize and avoid. It may because they have acquired knowledge about scientific precautions and can protect themselves easily and rationally. This is consistent with Zhao’s study which suggested that highly educated participants are more likely to perform proper prevention measures [38]. Therefore, crisis managers should concern people with less educational background, especially those who are ignorant in medical knowledge and those who work in institutions, when taking measures to intervene in their over-prevention behaviors in Factor 2.

Although restraints of human activities can effectively reduce the overall incidence of COVID-19 [40], the behaviors described in Factor 3 have been a trifle going against morals. Thus, regardless of their employment and educational backgrounds, people may have the ability to identify them. However, older people may be more cautious than youngsters, and thus they tend to consider these behaviors to be less excessive, holding an inappropriate cognition. This result is consistent with the findings of Perrota’s study [41]. She has proved that higher threat can always be perceived by older people. On this basis, authorities should supervise the old people to intervene in the excessive behaviors in Factor 3. In Factor 4, we did not obtain any influential variables. However, we could not ignore the health education to other people. In Table 9, approximately half of the subjects thought that these described behaviors were not excessive, with some even reasonable. Therefore, promoting the popularization of knowledge about prevention and helping the public to improve their health literacy are urgent. They should know how to take measures to protect themselves appropriately and avoid over-prevention that can severely harm them.

Implication and limitation

The results we obtained can help health authorities to manage prevention practices. They can know which group of people need their attention via demographic characteristics. This study can help them reduce the contradiction between pandemic and economic revival. It can also support the governments in adjusting their guidelines and policies on pandemic prevention to avoid the damage of excessive behaviors.

However, there are some limitations in this study. First, questionnaires can only be distributed to users who have registered on the platform. So the online survey may lead to selection bias, limiting the generalizability of our findings [42]. Second, there may be more factors that can affect the results, such as people’s social network, their participation in online health communities, and other social and environmental factors. These may influence their cognition toward various over-prevention behaviors. Thus, future studies should consider more external factors to extend this measurement model. Third, in this study, we use the Chinese sample to construct our model. Researchers should test the applicability of our results to other countries or use the sample of other races to adjust our model.

Conclusions

In this study, we developed a measurement model, proving ideal content, convergent, and discriminant validity. We tested our model to fit the investigated data well. We also helped to identify demographic characteristics that can indicate groups of people who should be the focus of decision-makers when promoting health literacy when managing a public crisis. Health literacy for the public is critical because holding appropriate prevention helps reduce the prevalence of infection and harms on human and nature [43].