The Role of Health Belief Model in HIV Screening Decision among International Students in the United States: A Pilot Study

Background of the Study

Most new Human Immunodeficiency Virus (HIV) infections are acquired through sexual intercourse.¹ HIV transmissions from persons unaware of their HIV status occur more commonly than those who are aware of their HIV status.² People Living with HIV (PLHIV) who are aware of their HIV status are more likely to prevent transmission through behavioral changes than those unaware of their HIV status. In the United States, statistics show that out of the 1.2 million people living with HIV, 156,300 people do not know they are infected.¹ It is estimated that about 45,000 people are diagnosed with HIV annually, and 30% of new infections are transmitted by people who are infected but are yet to be diagnosed.¹

One of the behavioral theories with significant relevance to disease prevention is the health belief model (HBM). The HBM was first developed in the 1950s by social psychologists in the United States Public Health Service for preventive health behavior. The primary concern at that time was the failure of people to accept disease prevention or screening, which was to enhance early diagnosis of asymptomatic diseases. Although these preventive measures were provided at little or no cost to the people, the acceptance was still very low. This observation led to the development of a theory that could explain preventive health behavior. ^3,4 The HBM was subsequently extended to include responses of individuals to disease symptoms and their behaviors towards diagnosis and treatment compliance.^5,6

The HBM constructs also show that disease preventive action is based on individual perception.⁷ The perceptions include susceptibility to a disease condition (perceived susceptibility), the deleterious consequences of disease (perceived severity), positive outcomes that can be derived from a behavioral modification (perceived benefits), and the perceived negative factors that can affect their health actions (perceived barriers).⁷ Over the years, HBM scholars introduced two additional constructs. The first is called self-efficacy, which is the belief that the behavioral change is achievable despite the perceived barriers.⁸ Second, is the specific cues to action, which helps in behavioral modification.⁹ These cues to action can be internal or external. Symptoms of illnesses, exposure to screening, physician counsels, health education and social marketing efforts supporting behavioral modification are specific cues to action that have been recently considered.¹⁰

While HIV testing is paramount for those who are living with undiagnosed HIV to reduce the spread of the virus,¹ their perception of susceptibility is equally vital for screening to be accepted. Undiagnosed HIV infections limit the effectiveness of HIV programs because testing is the entry point for HIV care and treatment.^11,12 A high proportion of HIV infected young people remain undiagnosed despite the individual and public health benefits of HIV testing.¹³

Although the early diagnosis of HIV is important because it provides prompt access to treatment that prevents progression to AIDS, there is a disproportionate rise in the incidence of HIV among college students.¹⁴ This increasing incidence of HIV may be explained by the low perception of susceptibility among college students. The perception of HIV risk has been shown to increase with age among college students.¹⁵ There is limited evidence about the perception of HIV risk among international students, especially in the United States, where students from diverse nationalities pursue tertiary education. There is also no HIV screening policy for these international students. We anticipate that the provision of routine screening for international students may contribute to the overall HIV prevention in the United States.

Objectives of the Study

The objectives of this study were to: (1) determine how Health Belief Model constructs influence HIV screening decisions among international students, and (2) identify which of the HBM constructs is most relevant for HIV screening decisions.

Specific Aims and Hypothesis

Although there is a considerable variation in the burden of the HIV epidemic among countries and regions, the interactions among international students may increase the perception of susceptibility. Drawing upon the HBM, we hypothesized that the perception of international students about HIV may play a role in their acceptance of the screening. We aimed to conduct a pilot study of international students at Western Illinois University (WIU) regarding the role of HBM in HIV screening decisions. We anticipate that the findings from this study may inform a more robust study of international students that can lead to a policy-driven conclusion.

Study Variables

The questionnaire consisted of three-sections with 25- item responses selected from a drop-down list menu. The first section consisted of the independent variables, which are the sociodemographic information of the respondents. The second section assessed the perceived knowledge of the students about HIV; the third section assessed the acceptance of HIV screening among the students; the fourth section identified the perceptions of the respondents about HIV screening as it affects their decisions. Essentially, these sections asked about the respondents' reactions to the HBM constructs: perceived susceptibility to HIV, the benefit of the screening, the barrier to the screening, perceived severity of the disease, and cues to action that can influence their decision to accept HIV screening. Figure 1 depicts the relationships between the HBM constructs.

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Figure 1.

The Health Belief Model Framework as a predictor of preventive health behavior (Adapted from Becker et al., 1974).

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Statistical Analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 24.²¹ The baseline characteristics of the respondents were analyzed in a spreadsheet using a simple proportion of respondents. A chi-square test was done to explore associations between demographic characteristics, perceived knowledge, and acceptance of HIV testing. Logistic regression analysis was used to determine associations of the dependent variable (acceptance of HIV screening) with the independent variables. All levels of significance were set at p < 0.05.

Sociodemographic Characteristics

A total of 185 respondents participated in the online survey, yielding a 38% response rate. Table 1 summarizes the sociodemographic characteristics of study participants. There were 107 (57.8%) males and 78 (42.8%) females (Table 1). A hundred and seventy-nine respondents (97%) were aged between 15 and 34 years, while 100 (54.1%) respondents were aged between 25 and 34 years. 149 (80.5%) and 28 (15.1%) respondents were full-time graduate and undergraduate students, respectively. Most of the respondents were single and never-married (73.0%), and 94 (50.8%) of them were not sexually active. Most of the respondents were from Asia (64.9%) and Africa (24.9%). One-third (33.5%) of the respondents were from India, while 17.3% were from Nigeria; in fact, most of the international students were from these two countries.

HIV Screening Decision

One hundred and thirty-six respondents accepted to do HIV screening if offered. Only 41 respondents refused HIV screening if offered, while 8 participants did not respond. Thus, the prevalence of acceptance of HIV screening among the study population is 73.5% (Table 2).

Figure 2 shows a bar graph presenting the association between the independent variable (country of origin) and the dependent variable (acceptance of HIV screening). Acceptance of HIV screening was highest among international students from the continents of Asia and Africa. However, there is no sufficient statistical power to determine any significant association between the continent of origin and HIV screening decisions because of the skewness of the study population (Figure 2).

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Figure 2.

Acceptance of HIV screening and continent of origin of the respondents

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Health Belief Model Constructs and HIV Screening Decisions

Table 2 and Figure 3 summarize the association between the HBM constructs and the HIV screening decisions. The two main significant HBM constructs associated with acceptance of the screening if offered, were the perceived benefit of the screening and the cues to action. Meanwhile, the refusal of HIV screening was associated with perceived susceptibility, perceived outcome severity, perceived threat and perceived benefit of the test (Figure 3).

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Figure 3.

HBM Constructs and HIV screening decisions

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Application of Health Belief Model to the Acceptance of HIV Screening

Our study found a relevant application of the constructs of HBM to address behavioral attitude towards disease prevention.^3,4 The HBM suggests that the responses of individuals to health promotion or disease prevention strategies are based on the following: the individual's belief that they are at risk of developing a specific condition; the belief that the risk is severe and has undesirable consequences; the belief that a specific behavior change will offer a benefit that leads to reduction of the risk; and the belief that one can overcome the barriers to the behavior change.²²

The perceived threat is the first condition in the HBM. There are two types of perceived threats: perceived susceptibility and perceived severity. Susceptibility refers to how much risk a person perceives he or she has; severity refers to how serious the consequences might be. Perceived benefit is the belief that a behavioral change will be beneficial to the health of an individual. The self- efficacy is the belief that an individual is confident in his or her ability to make a behavioral change. The cues to action are considered necessary in assisting all stages of change in this model. A cue for action is what motivates behavior. In our study, for instance, the offering of HIV screening to the international students was the cue to action, while the acceptance of the screening was based on their self-efficacy. Becker et al. ²³ described this model, and we adapted his description to create an algorithm for HIV screening (Figure 1).

Some studies have shown the applicability of the HBM in behavioral changes.^24,25 Perceived benefit and perceived susceptibility were found to be the most important predictors of behavioral change. Our finding agrees with that of Kabiru et al. ²⁴ In a study conducted on the correlates and motivations for HIV testing among youth in an urban settlement in Kenya, they reported that perceived risk for HIV infection may drive testing among youth. For example, about half of the youth who had ever had sex but had never been tested reported that they had not been tested because they were not at risk. Similarly, in a study conducted by Buldeo and colleagues exploring the HBM and first-year students' responses to HIV/ AIDS and voluntary counseling and testing (VCT) at a South African University, some students did not access VCT because of their low individual risk perception for HIV due to sexual abstinence.²⁶ Based on our findings, we propose that with more cues to action, more people would be willing to screen for HIV, and those found to be positive would want to proceed to treatment. We, therefore, adapted the HBM to create an algorithm that can be used to describe the relevance of this theoretical framework in HIV screening (Figure 1).

Limitations and Feasibility

While we cannot generalize the findings from our study because of the small sample size, our study forms a pilot to inform the design of a multicenter study of international students in the United States. With the internal validity of our study, our findings are reproducible, and our survey instruments are replicable. There was a significant skewness of our population to the continents with the highest number of international students in the institution of this study. Perhaps, if the international students were evenly distributed from all the six continents assessed, we would be able to have a statistical significance relating to their countries of origin. Since this study is about international students, it is, however, challenging to have all continents evenly distributed, but a larger sample size may help in solving this problem. The number of international students in this institution is relatively small, and that also reflected on the difficulties faced in using a random sampling method, hence convenience sampling. However, given that this is a pilot study, we anticipate that our findings will inform the decision of researchers to conduct a more robust study where there will be a possibility of random sampling that will generate enough statistical power. Due to the sampling limitations, we were unable to generate a statistical power for our logistic regression analysis. Nonetheless, we deemed it fit to report our findings in order to stimulate further interest in this area of research.

Further studies should explore the benefits of HIV screening and consider making it the focus of health education during the process of introducing the screening to international students. Similarly, further research should determine if those who accept HIV screening would uptake the screening and whether it might be necessary for better understanding the interface between accepting the screening and getting the screening done. Finally, a large multi-center study may be necessary to help inform a potential policy of offering HIV screening for college students, especially during admission screening programs.

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