Logo-hpp
Submitted: 05 Dec 2019
Accepted: 04 Jan 2020
First published online: 28 Jan 2020
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - FireFox Plugin)

Abstract View: 294
PDF Download: 149
Full Text View: 80
Predicting Chikungunya disease personal protective behaviors: Results of a cross-sectional survey of US-Caribbean travelers

Health Promotion Perspectives, 10(1), 43-49; DOI:10.15171/hpp.2020.08

Original Article

Predicting Chikungunya disease personal protective behaviors: Results of a cross-sectional survey of US-Caribbean travelers

Kristina R Anderson1, Kevin Naaman2, Edna Omodior1, Grace Karikari3, Lori Pennington-Gray4, Oghenekaro Omodior1*

1 Department of Recreation, Park, and Tourism Studies, School of Public Health, Indiana University, Bloomington, USA
2 Department of Counseling and Educational Psychology, School of Education, Indiana University, Bloomington, USA
3 Department of Applied Health Sciences, School of Public Health, Indiana University, Bloomington, USA
4 Department of Tourism, Recreation and Event Management, University of Florida, Gainesville, USA

*Corresponding Author: Oghenekaro Omodior, Email: oomodior@indiana.edu

© 2020 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Incidents of vector-borne disease have recently tripled in the United States. Chikungunya disease is a particularly common disease in the Caribbean, posing a threat to international tourists. However, the relationship between psychological variables derived from the protection motivation theory (PMT), and adoption of protective behaviors against the disease, is uncertain. This study sought to identify the psychological predictors of travelers’ protective health behaviors, specifically (1) appropriate clothing use, and (2) indoor spatial repellent use.

Methods: An online, retrospective survey of U.S. international travelers to Caribbean destinations measured the five constructs of the PMT in the context of Chikungunya disease: Perceived severity, perceived vulnerability, perceived response efficacy, perceived self-efficacy, and knowledge. Hierarchical logistic regression analyzed whether these five theoretical constructs predicted the two protective behaviors in respondents who met study criteria (n = 184).

Results: Results suggest that the interaction between chikungunya knowledge and perceived chikungunya severity predicts both appropriate clothing use (odds ratio [OR]: 1.95, CI: 1.18-3.25, P =0.010) and indoor spatial repellent use (OR: 1.55, CI: 1.05-2.29, P =0.029). In the cases of appropriate clothing use, the interaction between perceived chikungunya severity and perceived vulnerability was also a significant predictor (OR: 9.67, CI:1.23-75.80, P =0.031). Additionally, indoor spatial repellent use was also predicted by the interaction of chikungunya knowledge and perceived vulnerability (OR: 1.88, CI:1.18-3.02, P =0.009).

Conclusion : Two-pronged educational approaches may be most efficacious in increasing protective health behaviors. Such efforts could reduce incidents of chikungunya disease and other vectorborne diseases in travel destinations featuring high exposure risks.

Keywords: Caribbean region, Chikungunya virus, Health education, Protective factors, Travel-related illness

Introduction

In the United States, incidents of vector-borne diseases tripled from 2004-2016.1 Dengue, Zika, and Chikungunya as viral diseases transmitted by mosquitoes have spread to new regions, and concerns about further spread have become particularly salient in light of increasing international travel and changing climatic conditions.2,3From 2013 through 2017, fewer than 200 cases of Chikungunya disease were reported in the United States, with the exception of 2014, when more than 2800 cases were reported.4While this might suggest a 2014 peak and subsequent decline, recent ecological niche and climate modelling work suggests geographic expansion ofAedes aegypti— the mosquito transmitting the Chikungunya virus into areas worldwide that have not experienced outbreaks previously.5 While Chikungunya rarely results in death, its symptoms can be severe and disabling; common symptoms include fever and severe joint pain.6 In 2018, the U.S. Food and Drug Administration added Chikungunya disease to its priority list for drug development; currently, there is neither a vaccine to prevent Chikungunya disease nor antiviral treatments.6,7 As a result, adoption of personal protection behaviors such as 1) use of insect repellents, 2) wearing long-sleeve shirts and pants, and 3) staying indoors or in screened-in spaces are frequently suggested disease mitigation strategies when traveling to known Chikungunya regions.8

The use of insect repellent as a means to protect against Chikungunya disease has been one area of focus of protective behavior education and research.9,10 However, avoiding exposure during peak daytime hours, wearing appropriate clothing (e.g. long sleeved shirts and pants), and using indoor spatial repellents (e.g., mosquito or spatial nets), are also effective protective behaviors.11Wearing appropriate clothing while outdoors, such as long-sleeve shirts and pants, is a personal protective behavior frequently recommended to prevent transmission of vector-borne disease.2,12-14One study on malaria found that wearing long sleeves and pants has a significant reduction in malarial incident 12-weeks post-exposure, more so, even, than repellants and insecticides.15 However, Omodior et al16 found that while nearly 75% of travelers to the Caribbean used insecticide and repellents during their outdoor time abroad, fewer than half that rate (31%) wore pants, long-sleeved shirts, boots, or hats when outdoors. As a result, given the lower rate of adoption of the latter personal protective behavior, investigation of the psychological constructs influencing appropriate clothing use is warranted. The use of indoor spatial repellents, such as a mosquito-repellent bed nets, represents another frequently-cited personal protective behavior against mosquito-borne disease.12,17 Window and door screens are also recommended mechanisms for reducing exposure while indoors.13,14 Among travelers to high-risk malaria destinations, approximately half report intention of using a mosquito net.18 However, adoption of this strategy may vary with perceived threat: In one study of travelers to countries with risk of malaria, dengue and Chikungunya, participants visiting high-risk malarial regions used bed nets 40%-60% of the time in contrast to only 5% of those in Chikungunya/dengue regions.19 This discrepancy in protective behaviors implicates a need for research into the factors that predict use of spatial netting against Chikungunya disease-carrying mosquitos.

The protectionmotivation theory (PMT) provides the theoretical framework for this investigation of individual adoption of two protective behaviors against the Chikungunya disease: Wearing appropriate protective clothing outdoors and using indoor spatial repellents.20The PMT posits that four constructs of perceived vulnerability, perceived severity, perceived response efficacy, and perceived self-efficacy predict the likelihood of an individual adopting a protective behavior to avoid a health threat. Additionally, subjects’ knowledge of the threat is a requisite condition. In the context of this study, perceived vulnerability is the degree to which one believes there is a real possibility of contracting Chikungunya disease, whereas perceived severity is an assessment of its negative effects. Research indicates that higher perceived severity of a negative health outcome can induce behaviors that protect against disease or infection.10,21 Perceived response efficacy indicates the belief that one’s coping actions—wearing long clothes or a spatial net—is effective, whereas perceived self-efficacy reflects whether the individual believes they can perform the protective behavior. Floyd et al’s22 meta-analysis indicated that increases in these two coping variables facilitated adaptive behaviors. More recently, both perceived response efficacy and perceived self-efficacy were shown to be significant predictors of self-reported insect repellent use.10This study will add to the body of literature by improving understanding of the theoretical constructs influencing adoption of two additional protective behaviors to prevent Chikungunya disease.

Material and Methods

Study design and participants

A cross-sectional study design using a survey instrument was conducted. The online, retrospective survey instrument was housed in Qualtrics©(http://www.qualtrics.com/) and study participants were recruited through Amazon© Mechanical Turk (http://www.mturk.com/). Inclusion criteria consisted of age (over 18 years old), current residence in the United States, recent travel to one of 34 Caribbean destinations in the previous year for either leisure or vacation purposes, and engagement in at least one outdoor activity during that travel which might have resulted in exposure to mosquitos. A group of 653 adult U.S. international travelers met this initial criteria and responded to the survey Of these initial study participants, data for 184 individuals (28%) who completed all items on the questionnaire and who acknowledged on a dichotomous yes/no question that they had “heard about Chikungunya disease previously,” a requirement necessary to the PMT, were ultimately included. Power analysis for a logistic regression was conducted under the guidelines established in Allen and Le23 and using G*Power 3.1.724 to determine a sufficient sample size using an alpha of 0.05, a power of 0.80, a medium effect size (odds ratio [OR] = 1.75) and two-tailed test. Based on these assumptions, the suggested sample size was below that of our final sample, indicating adequate size.

Variables and measurements

Data collected from study participants included (a) sociodemographic information (age, gender, income, level of education, and race/ethnicity); (b) reported participation in outdoor activities while in the Caribbean (c) reported Chikungunya personal protective behaviors, i.e. (i) used bed nets, mosquito nets, insecticides or spatial repellents, and (ii) wore long-pants, long-sleeved shirts, boots, or hat; (d) responses that reflected the four theoretical constructs of the PMT, as well as respondents’, and (e) Chikungunya knowledge.

A detailed methodological explanation of the survey instrument and subsequent exploratory factor analysis has been published previously.10 In brief, the list of outdoor activities was developed from previously published scales.25,26 Single items reflecting a binary 0/1 scale were used to determine both self-reported use of appropriate clothing and indoor spatial repellents. Eleven items were used to assess the four theoretical constructs of the PMT, and internal reliability of each multi-item construct was evaluated using Cronbach α. Items were retained based on a Cronbach α threshold of ≥0.70.Four items measured perceived severity of Chikungunya and two items measured perceived vulnerability; both were calculated using an average of a 5-point Likert scale response (1 = strongly disagree, 5 = strongly agree). A single item was used to measure perceived response efficacy of appropriate clothing practices, while another was used to determine perceived response efficacy of indoor spatial repellent use. Two questions were used to determine perceived self-efficacy of indoor spatial repellent use, while perceived self-efficacy of appropriate clothing practices only necessitated one item. The same five-point Likert scale was utilized for these measures as well, and averages were calculated when constructs were measured by more than one item. Twelve item, adapted from the US Centers for Disease Control and Prevention and comprising a combination of true/false and multiple choice responses, were used to develop a Chikungunya knowledge score index.27

Statistical analysis

Assumptions for logistic regression were evaluated following protocols outlined by Field.28 The assumption of linearity of the logic was not violated for either response variable; all continuous dependent variables were linearly related to the log of the outcome variable. Additionally, tests for multicollinearity indicated that several criteria for multicollinearity, including tolerance values, variance inflation factors, and variance proportions were within their target ranges. Absence of multicollinearity was further supported by results of bivariate correlations. Then, simple, unadjusted logistic regressions were performed to assess the impact of each of the five predictor variables—and the interactions between them—on the two protective behavior outcomes, measured on a dichotomous scale (0 = no, 1 = yes). Finally, a hierarchical multiple logistic regression was conducted to assess the combined ability of the four theoretical constructs and Chikungunya knowledge to explain additional variability for each outcome variable, beyond what was already explained by the sociodemographic variables. Data was analyzed using IBM© SPSS version 25.0 (https://www.ibm.com/products/spss-statistics).

Results

An exhaustive description of the study population has been published elsewhere.10,16 Of the 184 people included, nine percent of the sample (n = 17) reported income levels below $25 000, 29% (n = 54) reported income in the $25 001-$50 000 range, 31% (n = 57) reported income ranging from $50 001-$75,000, another 14% (n = 25) reported income of $75 001-$100 000, and 17% (n = 31) reported income above $100 000. There was nearly equal representation of gender identities, i.e., female (n = 91, 49.5%), male (n = 93, 50.5%). Age of participants ranged from 21 to 56 years old (mean = 32.8 years). Seven percent of the sample (n = 13) had at least a high school degree, 16% (n = 29) reported at least two years of college education, 66% (n =122) indicated attainment of a bachelor’s degree, and another 11% (n = 20) had completed a graduate program. After analyzing descriptive data for the study population, the relationships between Chikungunya knowledge and the theoretical constructs were investigated using the Pearson product moment correlation coefficient. Overall, results of the correlation analyses indicate several significant, but not strong, relationships (i.e., not greater than r = 0.70) between any two variables (Table 1).

Table 1. Pearson product moment correlations between chikungunya knowledge, perceived severity, perceived vulnerability, response efficacy- & self-efficacy- for wearing appropriate clothing & use of indoor spatial repellents respectively
1 2 3 4 5 6 7
1. Chikungunya knowledge - 0.32 (P  < 0.001) 0.22 (P  = 0.002) 0.30 (P  < 0.000) -0.03 (P  = 0.704) 0.26 (P  < 0.000) 0.08 (P  = 0.286)
2. Perceived severity - 0.18 (P  = 0.013) 0.20 (P  = 0.004) 0.05 (P  = 0.448) 0.073 (P  = 0.310) -0.05 (P  = 0.496)
3. Perceived vulnerability     - 0.58 (P  < 0.000) 0.43 (P  < 0.000) 0.47 (P  < 0.000) 0.16 (P  = 0.029)
4. Perceived response efficacy of wearing appropriate clothing - 0.54 (P  < 0.000)
5. Perceived self-efficacy for wearing appropriate clothing         -
6. Perceived response efficacy of indoor protective measures - - 0.34 (P  < 0.000)
7. Perceived self-efficacy for indoor protective measures -

Note. Items 4 & 5 were specific to the first outcome variable (wearing appropriate clothing), while items 6 & 7 were specific to the second outcome variable (use of indoor protective measures. Pearson product moment correlation tests for these paired items were conducted only against items 1, 2, & 3. That is, items 4/5 were not tested for correlation against items 6/7 & vice-versa.

Wearing appropriate clothing

Simple, unadjusted logistic regression models predicting respondents’ reported practice of wearing appropriate clothing, are shown in Table 2. Results indicate several individual predictor variables that significantly predicted the participants’ self-report of wearing appropriate clothing. Then, an initial multiple logistic regression model was run to identify model outliers with standardized residuals ≥ 2.58. This resulted in the removal of n = 4 cases (2.2% of 184) for appropriate clothing use; these were cases in which predicted outcomes of the regression model significantly varied from the observed behavior. This listwise deletion increased classification accuracy from 74.5% to 80.0%.

Table 2. Predictors of wearing appropriate clothing
Predictor Values Response Variable: Self-reported Wearing Appropriate Clothes
Unadjusted Models Adjusted Model
UOR 95% CI P value AOR 95% CI P value
Perceived response efficacy 0.97 0.62-1.53 0.910 4.25 0.05-400.48 0.533
Perceived self-efficacy 0.71 0.50-1.01 0.056 7.35 0.11-516.38 0.358
Perceived severity 1.73 1.11-2.70 0.015* <0.001 0.00-0.020 0.005**
Perceived vulnerability 0.75 0.48-1.15 0.187 0.01 0.00-6.14 0.168
Chikungunya knowledge 1.24 1.08-1.42 0.002*** 1.78 0.19-16.64 0.613
Perceived response efficacy * perceived self-efficacy 0.97 0.92-1.03 0.345 0.56 0.19-1.67 0.297
Perceived Chikungunya knowledge * perceived vulnerability 1.03 0.96-1.10 0.444 9.67 1.23-75.80 0.031*
Chikungunya knowledge * perceived severity 1.06 1.03-1.08 0.000*** 1.95 1.18-3.25 0.010**
Chikungunya knowledge * perceived vulnerability 1.02 0.10-1.05 0.102 0.54 0.27-1.09 0.087
*P value significance at P <0.05; **P value significance at P <0.01; ***P value significance at P <0.001.
Abbreviation: UOR, unadjusted odds ratio; AOR, adjusted odds ratio.
Note: Data reported under “unadjusted models” columns represents a model for each row. While not reported here, the hierarchical adjusted (multiple) logistic regression model also included socio-demographic factors (Step 1).

Table 2 also reports the results of the hierarchical, multiple logistic regression predicting participants’ self-report of wearing appropriate clothing. Socio-demographic factors of age, gender, income, and education were entered in Step 1; together they explained between 14% (Cox & Snell R2 = 13.8) and 19% (Nagelkerke R2 = 19.1) of variability in respondents’ practices of wearing appropriate clothing, χ2 (9, 180) = 26.82, P = 0.001). In step 2 we added the theoretical constructs of perceived severity, perceived vulnerability, perceived response efficacy, perceived self-efficacy, and Chikungunya knowledge. This increased the total variance explained by the model as a whole to between 34% (Cox & Snell R2 = 33.9) and 47% (Nagelkerke R2 = 46.8), χ2 (14, 180) = 74.58, P = 0.001). In the final Step 3, four interaction terms were added (Chikungunya knowledge/perceived vulnerability, perceived response efficacy of appropriate clothing use/perceived self-efficacy of appropriate clothing use, Chikungunya knowledge/perceived Chikungunya severity, and perceived Chikungunya severity/perceived vulnerability); this resulted in a total variance explained by the model as a whole between 40% (Cox & Snell R2 = 39.7) and 55% (Nagelkerke R2 = 54.9), χ2 (18, 181) = 91.17, P < 0.001). After controlling for sociodemographic variables, the variables added in steps 2 and 3 explained between 26% (Cox & Snell R2 = 25.9%) and 36% (Nagelkerke R2= 35.8) of additional variability in respondents use of spatial repellents.

Among the theoretical constructs, perceived severity was significantly associated with the self-reported use of appropriate clothing. After adjusting for other variables, each unit increase in the average Likert-scaled score for perceived severity resulted in a slightly reduced odds of use of appropriate clothing (OR < 0.001, 95% CI = 0.00 – 0.020, P = 0.005). However, the inclusion of interaction terms indicated an opposite effect with regard to perceived severity. The interaction term between perceived Chikungunya severity and perceived vulnerability was associated with participants being more likely to wear appropriate clothing, after adjusting for other variables (AOR = 9.67, CI = 1.23-75.80, P = 0.031). Finally, the interaction term between Chikungunya knowledge and perceived severity was associated with participants being more likely to wear appropriate clothing (AOR = 1.95, CI = 1.18-3.25, P = 0.010) (Table 3).

Table 3. Predictors of indoor spatial repellent use
Predictor Values Response Variable: Self-reported Use of Indoor Spatial Repellents
Unadjusted Models Adjusted Model
UOR 95% CI P value AOR 95% CI P value
Perceived response efficacy 0.95 0.68-1.33 0.78 0.03 0.00-3.29 0.145
Perceived self-efficacy 1.95 1.40-2.71 0.00* 0.19 0.00-18.56 0.480
Perceived severity 1.31 0.87-1.96 0.197 11.29 0.03-4569.93 0.429
Perceived vulnerability 0.74 0.49-1.13 0.162 0.21 0.00-10.20 0.429
Chikungunya knowledge 1.01 0.91-1.13 0.796 0.02 0.00-0.31 0.005**
Perceived response efficacy * perceived self-efficacy 1.07 1.02-1.12 0.008** 2.20 0.73-6.66 0.163
Perceived Chikungunya severity * perceived vulnerability 1 0.94-1.07 0.974 0.23 0.04-1.24 0.087
Chikungunya knowledge * perceived severity 1.02 0.99-1.04 0.199 1.55 1.05-2.29 0.029*
Chikungunya knowledge * perceived vulnerability 0.1 0.98-1.02 0.894 1.88 1.18-3.02 0.009**
*P value significance at P <0.05; **P value significance at P <0.01.
Abbreviation: UOR, unadjusted odds ratio; AOR, adjusted odds ratio.
Note: Data reported under “unadjusted models” columns represents a model for each row. While not reported here, the hierarchical adjusted (multiple) logistic regression model also included socio-demographic factors (Step 1).

Indoor spatial repellent use

Simple, unadjusted logistic regressions were again conducted to predict the impact of each independent variable on respondents’ reported indoor spatial repellent use. Study results reveal that two terms significantly predicted indoor spatial repellent use (Table 3). Then, outliers were identified through a logistic regression that found 3 cases (1.6% of 184) in which predicted model outcomes differed significantly from the actual observed behavior (standardized residuals ≥ 2.58); listwise deletion of these outliers resulted in a change in classification accuracy from 78.3% to 79.6%.

Results of the hierarchical, multiple logistic regression predicting indoor spatial repellent use, inclusive of all independent variables, can be found in Table 3. Step 1 evaluated explanatory power of age, gender, income, and education; together they explained between 23% (Cox & Snell R2 = 23.4) and 31% (Nagelkerke R2 = 31.4) of variability in respondents’ indoor spatial repellent use, χ2 (9, 181) = 48.18, P < 0.001). After entry of the theoretical constructs of perceived severity, perceived vulnerability, perceived response efficacy, perceived self-efficacy, and Chikungunya knowledge, the total variance explained by the model was between 42% (Cox & Snell R2 = 41.6) and 56% (Nagelkerke R2 = 56.0) of variability in respondents’ indoor spatial repellent use, χ2 (14, 181) = 97.48, P < 0.001). Finally, in Step 3, four interaction terms were added (Chikungunya knowledge/perceived vulnerability, Perceived response efficacy of indoor spatial repellent use/perceived self-efficacy of indoor spatial repellent use, Chikungunya knowledge/perceived Chikungunya severity, and perceived Chikungunya severity/perceived vulnerability). This resulted in a total variance explained by the model as a whole between 46% (Cox & Snell R2 = 45.8) and 62% (Nagelkerke R2 = 61.6), χ2 (18, 181) = 110.94, P = 0.009). After controlling for sociodemographic variables, the variables added in steps 2 and 3 explained between 22% (Cox & Snell R2 = 22.4) and 30% (Nagelkerke R2= 30.2) of additional variability in respondents use of spatial repellents.

Among the theoretical constructs, Chikungunya knowledge was significantly associated with self-reported spatial repellent use. After adjusting for other variables, each unit increase in Chikungunya knowledge, measured on a 12-point scale, resulted a lower odd (0.02x) of indoor spatial repellent use (AOR = 0.02, CI = 0.00-0.31, P = 0.005). However, interaction between Chikungunya knowledge and other variables indicated an opposite effect. Each unit increase in the interaction term of Chikungunya knowledge and perceived vulnerability was associated with participants being more likely to engage in spatial repellent use, after adjusting for other variables (AOR = 1.88, CI = 1.18-3.02, P = 0.009). Finally, each unit increase associated with the interaction term of Chikungunya knowledge and perceived severity was associated with greater odds of using indoor spatial repellents (AOR = 1.55, CI = 1.05-2.29, P = 0.029).

Discussion

This study explored the relationship between psychological variables and travelers’ adoption of specific protective health behaviors against Chikungunya disease among U.S. travelers to the Caribbean. This research is particularly relevant now, as the 30.6 and 29.9 million visitors that travelled to the Caribbean in 2017 and 2018 represented the 1st and 2nd highest number of travelers to the region in its history.29Industry officials projected that the region’s tourism sector would grow by 6%-7% in 2019, with cruise ship arrivals increasing by 4-5%.29Consequently, as Chikungunya disease and other vector-borne diseases spread globally due to climate change and increased global travel, understanding the psychological drivers of relevant protective behaviors is pivotal, especially given the lack of vaccinations or antiviral treatments against the disease.

Of the key constructs of the PMT, our results illustrate significant, negative relationships between (1) reported use of indoor spatial repellents and Chikungunya knowledge, (2) appropriate clothing use and perceived severity. However, several interaction terms were identified to be significant, positive predictors of the desired outcome behaviors. Notably, the interaction between perceived Chikungunya severity and Chikungunya knowledge predicted travelers’ practice of both (1) wearing appropriate clothing and (2) use of indoor spatial repellents. This finding suggests that knowledge alone in the presence of other variables did not significantly predict increased odds of adopting protective health behaviors, whereas its interaction with perceived Chikungunya severity did.

Additionally, the interaction between perceived Chikungunya severity and perceived vulnerability was found to be an additional predictor of travelers’ practice of wearing appropriate clothing; furthermore, the interaction of Chikungunya knowledge and perceived vulnerability was an additional predictor of indoor spatial repellent use. Notably, with both desired behaviors, increasing one construct alone was associated with lower odds of adopting the desired behavior (i.e., this was observed in Chikungunya knowledge in the case of spatial repellent use, and perceived Chikungunya severity in the case of wearing appropriate clothing). However, increasing two constructs—the interaction terms—was associated with a significant increase.

These results can be situated in a relatively inconclusive body of literature. One systematic review showed that risk perceptions, attitudes, and knowledge of chikungunya varied across populations and countries (the review, however, also largely represented Asian regions where the disease is endemic).30 However, among U.S. populations at risk to vector-borne or infectious diseases, significant relationships have been identified between perceived severity and indicators of heightened risk perception.10 Additionally, a broad review of protective behavior literature supports the general concept that higher appraisals of perceived risk influence health behavior.31-33 The results of our analysis aide in identifying strategies for health communication messaging and tourist education. While most literature studying the efficacy of educational interventions on disease prevention behaviors focus on endemic disease regions in the developing world, these strategies may also be transferable to international tourists from the developing world.34,35Additionally, for both protective behaviors, given that the interaction between perceived Chikungunya severity and Chikungunya knowledge was significant, a two-pronged educational approach is recommended to (1) warn travelers of the disease’s severity as well as (2) increase their knowledge and understanding of it.

One limitation of this study is the use of self-reported data. It is possible that survey respondents, seeking to appear as effectively engaging in a desirable, protective behavior, over reported their adoption of indoor spatial repellent or appropriate clothing use.The data collection method (online) required that eligible study participants have internet access, which may have excluded portions of the targeted population. Whereas 74% of householders 15 years and over report internet access at home, fewer (a little over half, or 58.3%) of householders 65 years and older report household internet access.36This is particularly important as travel among the senior population (over 60 years of age) is anticipated to increase, and yet this population is also at an increased risk regarding the severity of Chikungunya disease.37,38

Despite these limitations, this research provides important information for tourism and public health officials to consider in order to reduce transmission of vector-borne diseases, like Chikungunya, among international travelers. For travelers whose home region is endemic to the vectors carrying disease (like Aedes aegypti and Aedes albopictus mosquitoes), this is particularly important to avoid new disease outbreaks. Increasing the degree to which travelers are knowledgeable about the disease and perceive it to be severe are important considerations for those designing education efforts. To this end, one direction for future research would to be increased understanding of why individuals choose not to engage in protective health behaviors while traveling, as the challenges in changing individual behaviors may vary between individuals who live in the region and others who are merely visiting. For instance, international travelers may be less willing to wear long-sleeved clothing while traveling to wear more fashionable or less confining, warm-weather clothing. Additionally, to identify nuances of specific locales and risks, future research might consider the role of the constructs of the PMT in the adoption of protection motivation behaviors within the context of other travel destinations and disease risks.

Ethical approval

All research activities were approved by the University of Florida Institutional Review Board (IRB#: 2015-U-1040) with subject consent.

Competing interests

The authors declare that they have no competing interests.

Funding

This study received no funding or external support.

Authors’ contributions

All authors were involved in the conceptualization of the study, revising the manuscript, or interpreting the results. Author KRA completed the analyses and drafted the first draft of the manuscript.

References

  1. Silk BJ. Infectious disease threats and opportunities for prevention. J Public Health Manag Pract 2018;24(6):503-5. doi: 10.1097/phh.0000000000000910. [Crossref]
  2. Hennessey M, Fischer M, Staples JE. Zika virus spreads to new areas--region of the Americas, May 2015–January 2016. MMWR Morb Mortal Wkly Rep 2016;65(3):55-8. doi: 10.15585/mmwr.mm6503e1. [Crossref]
  3. Kuehn BM. Chikungunya virus transmission found in the United States: US health authorities brace for wider spread. JAMA 2014;312(8):776-7. doi: 10.1001/jama.2014.9916. [Crossref]
  4. Centers for Disease Control and Prevention (CDC). Chikungunya virus: Geographic distribution. Available from: https://www.cdc.gov/chikungunya/geo/index.html. Accessed 7 January 2020.
  5. Campbell LP, Luther C, Moo-Llanes D, Ramsey JM, Danis-Lozano R, Peterson AT. Climate change influences on global distributions of dengue and chikungunya virus vectors. Philos Trans R Soc Lond B Biol Sci 2015;370(1665). doi: 10.1098/rstb.2014.0135. [Crossref]
  6. Centers for Disease Control and Prevention (CDC). Chikungunya virus: Symptoms, diagnosis, & treatment. Available from: https://www.cdc.gov/chikungunya/symptoms/index.html. Accessed 7 January 2020.
  7. U.S. Food & Drug Administration (USDA). FDA in brief: FDA adds four tropical diseases to priority review voucher program to encourage drug development in areas of unmet need. Available from: https://www.fda.gov/NewsEvents/Newsroom/FDAInBrief/ucm618097.htm. Accessed 20 September 2018.
  8. Litin SC. What Is Chikungunya Fever, and Should I Be Worried? Rochester: Mayo Clinic; 2017.
  9. Moro ML, Gagliotti C, Silvi G, Angelini R, Sambri V, Rezza G, et al. Knowledge, attitudes and practices survey after an outbreak of chikungunya infections. Int Health 2010;2(3):223-7. doi: 10.1016/j.inhe.2010.07.003. [Crossref]
  10. Omodior O, Pennington-Gray L, Thapa B. Modeling insect-repellent use for chikungunya disease prevention among US-Caribbean travelers. Int J Travel Med Glob Health 2017;5(4):125-34. doi: 10.15171/ijtmgh.2017.25. [Crossref]
  11. Mutebi J, Hawley W, Brogdon W. Protection against mosquitoes, ticks, & other arthropods. CDC Health Information for International Travel, Centers for Disease Control and Prevention; 2017. Available from: https://wwwnc.cdc.gov/travel/yellowbook/2018/the-pre-travel-consultation/protection-against-mosquitoes-ticks-other-arthropods. Accessed 20 September 2018.
  12. Nasci RS, Zielinski-Gutierrez E, Wirtz RA, Brogdon WG. Protection against mosquitoes, ticks, & other insects & arthropods. CDC Health Information for International Travel 2014: The Yellow Book, Centers for Disease Control and Prevention; 2013. Available from: http://wwwnc.cdc.gov/travel/yellowbook/2012/chapter‐2‐the‐pre‐travel‐consultation/protectionagainst‐mosquitoes‐ticks‐and‐other‐insects‐and‐arthropods.htm. Accessed 20 September 2018.
  13. Sampathkumar P. West Nile virus: epidemiology, clinical presentation, diagnosis, and prevention. Mayo Clin Proc 2003;78(9):1137-43. doi: 10.4065/78.9.1137. [Crossref]
  14. World Health Organization (WHO). Fact sheet: Dengue and severe dengue. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. Accessed 7 January 2020.
  15. Schoepke A, Steffen R, Gratz N. Effectiveness of personal protection measures against mosquito bites for malaria prophylaxis in travelers. J Travel Med 1998;5(4):188-92. doi: 10.1111/j.1708-8305.1998.tb00505.x. [Crossref]
  16. Omodior O, Pennington-Gray L, Holland S, Thapa B, Kiousis S. Chikungunya disease awareness among US travelers to caribbean destinations. Int J Travel Med Glob Health 2017;5(1):20-7. doi: 10.15171/ijtmgh.2017.04. [Crossref]
  17. Ntonifor NH, Veyufambom S. Assessing the effective use of mosquito nets in the prevention of malaria in some parts of Mezam division, Northwest Region Cameroon. Malar J 2016;15(1):390. doi: 10.1186/s12936-016-1419-y. [Crossref]
  18. Van Herck K, Van Damme P, Castelli F, Zuckerman J, Nothdurft H, Dahlgren AL, et al. Knowledge, attitudes and practices in travel-related infectious diseases: the European airport survey. J Travel Med 2004;11(1):3-8. doi: 10.2310/7060.2004.13609. [Crossref]
  19. Lalani T, Yun H, Tribble D, Ganesan A, Kunz A, Fairchok M, et al. A comparison of compliance rates with anti-vectorial protective measures during travel to regions with dengue or chikungunya activity, and regions endemic for Plasmodium falciparum malaria. J Travel Med 2016;23(5). doi: 10.1093/jtm/taw043. [Crossref]
  20. Prentice-Dunn S, Rogers RW. Protection motivation theory and preventive health: beyond the health belief model. Health Educ Res 1986;1(3):153-61. doi: 10.1093/her/1.3.153. [Crossref]
  21. van der Snoek EM, de Wit JB, Götz HM, Mulder PG, Neumann MH, van der Meijden WI. Incidence of sexually transmitted diseases and HIV infection in men who have sex with men related to knowledge, perceived susceptibility, and perceived severity of sexually transmitted diseases and HIV infection: Dutch MSM-Cohort Study. Sex Transm Dis 2006;33(3):193-8. doi: 10.1097/01.olq.0000194593.58251.8d. [Crossref]
  22. Floyd DL, Prentice‐Dunn S, Rogers RW. A meta‐analysis of research on protection motivation theory. J Appl Soc Psychol 2000;30(2):407-29. doi: 10.1111/j.1559-1816.2000.tb02323.x. [Crossref]
  23. Allen J, Le H. An additional measure of overall effect size for logistic regression models. J Educ Behav Stat 2008;33(4):416-41. doi: 10.3102/1076998607306081. [Crossref]
  24. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007;39(2):175-91. doi: 10.3758/bf03193146. [Crossref]
  25. Kil N, Holland S, Stein T. Improving the management of natural resource recreation areas through understanding place-attached visitor segments. J Park Recreat Admi 2010;28(3):16-41.
  26. Hendee JC, Gale RP, Catton WR. A typology of outdoor recreation activity preferences. J Environ Educ 1971;3(1):28-34. doi: 10.1080/00958964.1971.10801604. [Crossref]
  27. Centers for Disease Control and Prevention (CDC). Chikungunya virus. Available from: https://www.cdc.gov/chikungunya/index.html. Accessed 7 January 2020.
  28. Field A. Discovering Statistics Using IBM SPSS Statistics. London: SAGE Publications; 2013.
  29. Turner M. Caribbean tourism looks to build on momentum that closed out 2018. Travel Agent Central; 2019. Available from: https://www.travelagentcentral.com/destinations/caribbean-tourism-looks-to-build-momentum-closed-out-2018. Accessed 7 January 2020.
  30. Corrin T, Waddell L, Greig J, Young I, Hierlihy C, Mascarenhas M. Risk perceptions, attitudes, and knowledge of chikungunya among the public and health professionals: a systematic review. Trop Med Health 2017;45:21. doi: 10.1186/s41182-017-0061-x. [Crossref]
  31. Brewer NT, Chapman GB, Gibbons FX, Gerrard M, McCaul KD, Weinstein ND. Meta-analysis of the relationship between risk perception and health behavior: the example of vaccination. Health Psychol 2007;26(2):136-45. doi: 10.1037/0278-6133.26.2.136. [Crossref]
  32. Ferrer R, Klein WM. Risk perceptions and health behavior. Curr Opin Psychol 2015;5:85-9. doi: 10.1016/j.copsyc.2015.03.012. [Crossref]
  33. Sheeran P, Harris PR, Epton T. Does heightening risk appraisals change people’s intentions and behavior? a meta-analysis of experimental studies. Psychol Bull 2014;140(2):511-43. doi: 10.1037/a0033065. [Crossref]
  34. Cropley L. The effect of health education interventions on child malaria treatment-seeking practices among mothers in rural refugee villages in Belize, Central America. Health Promot Int 2004;19(4):445-52. doi: 10.1093/heapro/dah406. [Crossref]
  35. Ghahremani L, Faryabi R, Kaveh MH. Effect of health education based on the protection motivation theory on malaria preventive behaviors in rural households of Kerman, Iran. Int J Prev Med 2014;5(4):463-71.
  36. File T, Ryan C. Computer and internet use in the United States: 2013. US Census Bureau; 2014. Available from: https://www.census.gov/history/pdf/2013computeruse.pdf. Accessed 7 January 2020.
  37. Staples JE, Hills SL, Powers AM. Chikungunya. Atlanta, Georgia: Centers for Disease Control and Prevention; 2018. Available from: https://wwwnc.cdc.gov/travel/yellowbook/2020/travel-related-infectious-diseases/chikungunya. Accessed 7 January 2020.
  38. Organisation for Economic Co-operation and Development (OECD). OECD Tourism Trends and Policies 2018. Paris: OECD Publishing; 2018. doi: 10.1787/tour-2018-en.