Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6)

Abstract

The Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6; Grech et al., 2023) is a six-item inventory designed to assess disease-related attitudes and concerns regarding COVID-19 vaccination, which may contribute to vaccine hesitancy. This measure was developed to address a gap in existing scales by incorporating disease or treatment-related hesitancy factors. A panel of specialist clinician-researchers and patient representatives developed the six items based on a comprehensive literature review. The DIVAS-6 was administered to adults in Australia diagnosed with severe and/or chronic illnesses, including cancer, diabetes, and multiple sclerosis (MS). The scale’s structure was examined through exploratory factor analysis and exploratory structural equation modeling, revealing a two-factor structure. The psychometric properties of the DIVAS-6, including measurement invariance, reliability, and validity, were subsequently reported.

Keywords

COVID-19 Vaccine Hesitancy; Disease Influence; Chronic Illness; COVID-19 Vaccination; COVID-19 Vaccine Attitudes; Disease Complacency; Treatment-Related Hesitancy; Vaccine Acceptance; Vaccine Effect on Disease Progression; Vaccine Interaction with Treatment; Vaccine Vulnerability

Authors

Grech, Lisa; Loe, Bao Sheng; Day, Daphne; Freeman, Daniel; Kwok, Alastair; Nguyen, Mike; Bain, Nathan; Segelov, Eva

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Purpose

The primary purpose of the DIVAS-6 is to evaluate attitudes and concerns related to COVID-19 vaccination, specifically focusing on potential complications of SARS-CoV-2 infection due to underlying disease (termed “disease complacency”) and the perceived impact of vaccination on disease progression and treatment (termed “vaccine vulnerability”).

Validity

Convergent/Concurrent Validity: Both factors of the DIVAS-6 demonstrated significant correlations with two established COVID-19 Vaccine scales assessing vaccine hesitancy and associated attitudes, indicating strong convergent validity.

Discriminative Ability: The total summary score of the DIVAS-6 showed an acceptable ability to differentiate vaccination status across various diseases. For the overall sample, the scale exhibited good-to-excellent discriminative ability. A cutoff score of ≥ 13 on the DIVAS-6 yielded a sensitivity of 0.90 for accurately classifying vaccinated participants, though with a specificity of 0.43. Increasing the cutoff score to ≥ 18 resulted in a reduced sensitivity of 0.45 but an increased specificity of 0.90 for correctly classifying unvaccinated individuals.

Reliability

Internal Consistency: The two factors comprising the DIVAS-6 demonstrated strong internal consistency, with Cronbach’s alpha values of 0.73 and 0.85, respectively.

Factor Analysis

Exploratory Factor Analysis (EFA): Based on parallel analysis, two factors were extracted and subjected to oblique rotation during factor analysis. These two factors collectively explained over 58.0% of the variance in the data, thereby supporting the presence of a two-factor structure within the questionnaire. The correlation between the two factors was found to be r = -0.10.

Structural Equation Modeling (SEM) & Confirmatory Factor Analysis (CFA): The Exploratory Structural Equation Modeling (ESEM) model exhibited an excellent model fit (χ2 = 21.67, df = 4, p < 0.0002, CFI = 0.995, TLI = 0.98, RMSEA = 0.042, SRMR = 0.009, AIC = 42278.37, BIC = 42411.84). In contrast, the Confirmatory Factor Analysis (CFA) model did not achieve a satisfactory model fit. A chi-square difference test indicated a significant difference between the two models (△χ2 = 438.13, df = 4, p = <0.0001). Furthermore, the AIC and BIC values were both lower for the ESEM model, signifying its superiority over the CFA model. Measurement invariance analysis revealed that the two factors exhibited psychometric equivalence across different patient groups.

Instrument

Test Type: Original Inventory/Questionnaire

Format: The DIVAS-6 utilizes a 5-point Likert scale with the following response options: “strongly agree,” “somewhat agree,” “neither disagree nor agree,” “somewhat disagree,” and “strongly disagree.” A “don’t know” option is also provided. The administration method is electronic.

Language Available: English

Population Group: Human; Male; Female, including individuals with Non-Binary/Other gender identities.

Age Group: Adulthood (18 years and older), encompassing Young Adulthood (18-29 years), Thirties (30-39 years), Middle Age (40-64 years), and Aged (65 years and older).

Population Details: The study participants were patients with chronic illnesses, specifically cancer, diabetes, and multiple sclerosis, located in Australia.

Test Methodology: The methodology involved Test Validity (including Concurrent Validity, Convergent Validity, and Discriminant Validity), Test Reliability (including Internal Consistency), Factor Analysis (including Confirmatory Factor Analysis, Exploratory Factor Analysis, and Structural Equation Modeling), Test Sensitivity, and Test Specificity.

Keywords

Chronic Illness; COVID-19 Vaccination; COVID-19 Vaccine Attitudes; Disease Complacency; Treatment-Related Hesitancy; Vaccine Acceptance; Vaccine Effect on Disease Progression; Vaccine Interaction with Treatment; Vaccine Vulnerability

Authors

Author ORCID Identifier:

• Grech, Lisa: http://orcid.org/0000-0003-0914-8573

• Freeman, Daniel: http://orcid.org/0000-0002-2541-2197

• Kwok, Alastair: http://orcid.org/0000-0002-8064-867X

• Nguyen, Mike: http://orcid.org/0000-0003-3044-1707

• Segelov, Eva: http://orcid.org/0000-0002-4410-6144

Affiliation and Email Addresses:

• Grech, Lisa: Department of Medicine, School of Clinical Sciences Monash University. Email: [email protected]

• Loe, Bao Sheng: Psychometrics Centre, University of Cambridge. No data is Available

• Day, Daphne: Department of Medicine, School of Clinical Sciences Monash University. No data is Available

• Freeman, Daniel: Department of Psychiatry, University of Oxford. No data is Available

• Kwok, Alastair: Department of Oncology, Monash Health. No data is Available

• Nguyen, Mike: Department of Medicine, School of Clinical Sciences Monash University. No data is Available

• Bain, Nathan: Department of Oncology, Monash Health. No data is Available

• Segelov, Eva: Department of Medicine, School of Clinical Sciences Monash University. No data is Available

Correspondence Address:

Grech, Lisa: Monash University, Department of Medicine, 246 Clayton Road, Melbourne, Victoria, Australia, 3168, [email protected]

Permissions & Fee and Test Year

Permissions: Contact Publisher

Fee: No

Commercial: No

Test Year: 2023

References

Grech, L., Loe, B. S., Day, D., Freeman, D., Kwok, A., Nguyen, M., Bain, N., & Segelov, E. (2023). The Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6): Validation of a measure to assess disease-related COVID-19 vaccine attitudes and concerns. Behavioral Medicine, 49(4), 402–411. https://doi.org/10.1080/08964289.2022.2082358

Items of the DIVAS-6

The DIVAS-6 consists of 6 items. The specific content of the items is available in the source reference: 2022-74553-001, Table 2, Page 406.

Factors and Subscales:

• Disease complacency

• Vaccine vulnerability

This instrument is comprised of two subscales: Disease Complacency and Vaccine Vulnerability.

Note. Responses are made on a 5-point Likert scale with an additional “don’t know” option (“strongly agree,” “somewhat agree,” “neither disagree nor agree,” “some what disagree,” “strongly disagree”).