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

1. Abstract

The Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6) is a psychometric instrument engineered to evaluate disease-specific attitudes, risk appraisals, and clinical concerns regarding immunization against COVID-19 among clinical populations with underlying severe or chronic health conditions. Developed by an interdisciplinary team of clinician-researchers and patient representatives (Grech et al., 2023), the DIVAS-6 addresses a critical methodological omission in existing general-population vaccine hesitancy instruments, which systematically overlook the complex health-related calculus of individuals managing chronic pathologies such as cancer, diabetes mellitus, and multiple sclerosis (MS). The scale consists of six items structured across two distinct theoretical subscales: Disease Complacency (reflecting the perceived urgency of vaccination driven by personal health vulnerability and the reliance on physician endorsement) and Vaccine Vulnerability (capturing apprehensions regarding adverse vaccine-disease interactions, immunogenicity deficits, and therapeutic interference).

The scale employs a 5-point Likert scale anchored from “strongly disagree” to “strongly agree,” accompanied by an explicit “don’t know” administrative option. Psychometric evaluation conducted in a diverse Australian cohort of medically vulnerable adults established that Exploratory Structural Equation Modeling (ESEM) provides superior construct representation over traditional Confirmatory Factor Analysis (CFA), exhibiting exceptional fit indices (χ² = 21.67, df = 4, p < .001, CFI = 0.995, TLI = 0.980, RMSEA = 0.042, SRMR = 0.009). The internal consistency coefficients are robust (α = .73 for Disease Complacency; α = .85 for Vaccine Vulnerability). Furthermore, receiver operating characteristic analyses demonstrate strong discriminative capacity for identifying actual vaccination uptake across clinical subgroups. The DIVAS-6 provides a concise, theoretically grounded diagnostic tool suitable for clinical triage, health behavior tracking, and targeted communication interventions in immunocompromised and chronically ill cohorts.

2. Keywords

COVID-19 Vaccine Hesitancy, Disease Influence, Chronic Illness, Vaccine Vulnerability, Disease Complacency, Vaccine Acceptance, Health Belief Model, Multiple Sclerosis, Cancer, Diabetes, Psychometrics, Exploratory Structural Equation Modeling

3. Authors

The Disease Influenced Vaccine Acceptance Scale-Six was conceptualized, operationalized, and psychometrically validated by a multi-institutional consortium of clinical psychologists, oncologists, neurologists, and quantitative psychometricians:

• Lisa Grech, Ph.D.: Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia. ORCID: 0000-0003-0914-8573. Correspondence: [email protected].
• Bao Sheng Loe, Ph.D.: The Psychometrics Centre, University of Cambridge, Cambridge, United Kingdom.
• Daphne Day, MBBS, FRACP, Ph.D.: Department of Medicine, School of Clinical Sciences, Monash University, and Monash Health, Melbourne, Australia.
• Daniel Freeman, Ph.D., CPsychol, FBPsS: Department of Psychiatry, University of Oxford, Oxford, United Kingdom. ORCID: 0000-0002-2541-2197.
• Alastair Kwok, MBBS: Department of Oncology, Monash Health, Melbourne, Victoria, Australia. ORCID: 0000-0002-8064-867X.
• Mike Nguyen, MBBS: Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia. ORCID: 0000-0003-3044-1707.
• Nathan Bain, BMedSc: Department of Oncology, Monash Health, Melbourne, Victoria, Australia.
• Eva Segelov, MBBS, FRACP, Ph.D.: Department of Medicine, School of Clinical Sciences, Monash University, and Monash Health, Melbourne, Victoria, Australia. ORCID: 0000-0002-4410-6144.

4. Purpose

During global health crises, such as the pandemic triggered by SARS-CoV-2, individuals diagnosed with severe chronic illnesses face disproportionate risks of morbidity, hospitalization, intensive care admission, and mortality. While public health messaging broadly emphasizes universal vaccination as an imperative social and individual safeguard, the psychological reality of medically vulnerable individuals is far more nuanced. Standard vaccine hesitancy frameworks—such as the World Health Organization’s (WHO) 3Cs (Confidence, Complacency, Convenience) or 5C models—were primarily formulated to assess general population samples. Consequently, these metrics prioritize general vaccine safety mistrust, needle phobias, anti-establishment conspiracy beliefs, or logistical inertia. They routinely fail to capture the specific disease- and therapy-related cognitive deliberations faced by patients receiving immunosuppressive, cytotoxic, or immunomodulatory regimens.

The primary purpose of the DIVAS-6 is to isolate and measure the dual, counteracting forces governing vaccine decision-making in chronic disease cohorts: the heightenings of perceived risk from natural infection versus the fears of adverse iatrogenic effects caused by the vaccine interacting with pre-existing conditions or ongoing pharmacotherapies. Clinically, chronic disease patients often express idiosyncratic anxieties that do not align with classic anti-vaccine sentiment. A patient receiving systemic chemotherapy or disease-modifying therapy (DMT) for multiple sclerosis may fully accept epidemiological science yet exhibit intense hesitancy due to apprehension that an activated immune response might trigger a relapse of autoimmune neuroinflammation, blunt the efficacy of antineoplastic agents, or yield negligible serological protection due to lymphopenia.

The DIVAS-6 serves three critical clinical and investigative functions:

• Rapid Clinical Risk Stratification: With only six items, the inventory can be seamlessly embedded into routine digital oncology, endocrinology, or neurology patient portals, rapidly identifying individuals whose hesitancy is rooted in modifiable medical misconceptions rather than generalized societal mistrust.
• Targeted Medical Communication: By distinguishing between lack of concern regarding the disease (disease complacency) and heightened fear of therapeutic interference (vaccine vulnerability), clinicians can tailor their counseling. For example, high vulnerability scores signal the need for an oncologist or neurologist to explicitly clarify pharmacodynamic interactions and timing strategies, rather than relying on generic public safety brochures.
• Epidemiological and Translational Research: The scale provides behavioral epidemiologists with a standardized, invariant measurement tool capable of tracking how disease-specific concerns evolve across clinical phases, therapeutic regimens, and booster rollout campaigns.

5. Psychological Construct

The DIVAS-6 operationalizes vaccine decision-making in chronic illness as a multidimensional construct determined by the interplay of two inversely oriented cognitive-affective dimensions: Disease Complacency and Vaccine Vulnerability. These two dimensions reflect distinct psychological pathways through which an individual’s medical history influences their health-protective behaviors.

Disease Complacency (Items 1–3)

Within the psychometric architecture of the DIVAS-6, the term “Disease Complacency” is adapted from the classical WHO taxonomy to denote the inverse of health vigilance; it represents the patient’s recognition of their heightened systemic risk from natural pathogen exposure and their willingness to rely on clinical guidance to mitigate that risk. Higher scores on this subscale reflect low complacency (i.e., high illness concern, high recognition of vaccine importance, and strong trust in physician authority). The construct comprises three distinct cognitive elements:

• Perceived Infection Vulnerability: The cognitive appraisal that one’s pre-existing chronic disease status elevates personal susceptibility to severe illness or catastrophic outcomes upon contracting the targeted pathogen (operationalized in Item 1: “My history of [disease] makes me more worried about being infected with COVID-19”).
• Attributed Preventive Value: The rationalization that the chronic diagnosis renders immunization significantly more vital for oneself than for the average, healthy member of the general population (Item 2: “My history of [disease] means having the vaccine is more important to me”).
• Clinical Heuristic Dependence: The extent to which the patient relies on the expert recommendation of their managing physician as a central decision-making criterion (Item 3: “My doctor’s recommendation regarding the vaccine is important to me”).

Together, these items assess an adaptive cognitive pathway wherein the reality of living with an illness functions as an internal catalyst, elevating the subjective utility of preventive vaccination.

Vaccine Vulnerability (Items 4–6)

Conversely, the Vaccine Vulnerability dimension encapsulates the maladaptive appraisal that the biological mechanism of the vaccine represents an active threat to personal somatic stability. This construct does not reflect generalized anti-science beliefs or conspiracy mentation; rather, it reflects disease-specific anxiety regarding the safety and pharmacological compatibility of the vaccine within an already compromised physiological system. It encompasses three specific threat appraisals:

• Perceived Immunogenicity Deficit (Vaccine Inefficacy): The fear that one’s underlying condition or pharmacological immunosuppression will prevent the vaccine from eliciting a protective immune response, rendering administration futile (Item 4: “My history of [disease] makes me worried about how well the vaccine will work for me”).
• Somatic Destabilization Fear: The anxiety that the systemic reactogenicity of the vaccine will exacerbate underlying symptoms, induce disease flares, or trigger irreversible clinical decline (Item 5: “My history of [disease] makes me worried about how the vaccine will affect me”).
• Treatment-Interference Apprehension: The targeted operational fear that receiving the vaccine will alter the pharmacokinetics, pharmacodynamics, safety profile, or scheduling of life-sustaining disease therapies (Item 6: “I am worried about how the vaccine will affect my [disease] treatment”).

In clinical cohorts, these two factors operate in psychological tension. While Disease Complacency acts as an approach motivator toward immunization, Vaccine Vulnerability acts as an avoidance motivator. The empirical independence of these factors (evidenced by a low factor correlation, r = -0.10) confirms that high awareness of disease threat can coexist simultaneously with acute fears of vaccine-induced therapeutic disruption.

6. Theoretical Framework

The conceptual foundation of the DIVAS-6 is rooted in a convergence of classical health psychology and cognitive appraisal paradigms, notably the Health Belief Model (HBM) (Rosenstock, 1974; Janz & Becker, 1984) and Protection Motivation Theory (PMT) (Rogers, 1975, 1983). These behavioral frameworks posit that engagement in health-protective action is determined by an individual’s evaluation of threat appraisal alongside their coping appraisal.

Integration with the Health Belief Model

Under the classical HBM, preventive action is predicted by four core cognitive dimensions: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, modulated by specific “cues to action.” The DIVAS-6 maps directly onto this architecture while embedding the idiosyncratic context of chronic disease:

• Perceived Susceptibility and Severity: Captured via the Disease Complacency subscale, where the baseline disease condition amplifies the perceived severity of the viral infection, transforming an abstract infectious risk into an imminent threat to survival.
• Perceived Barriers: Operationalized through the Vaccine Vulnerability subscale. In healthy cohorts, barriers typically consist of time expenditure, needle discomfort, or common adverse side effects (such as pyrexia or fatigue). In chronic disease populations, however, perceived barriers escalate into profound existential and physiological threats—specifically, the destabilization of disease remission or the contraindication of ongoing biological, chemotherapy, or immunosuppressive therapy.
• Cues to Action: Institutional public health advisories frequently fail as effective cues for chronically ill populations due to perceived medical non-specificity. Item 3 specifically measures the authoritative clinical recommendation of the personal physician as the primary external cue necessary to override latent vulnerability anxieties.

Protection Motivation Theory and Dual-Appraisal Calculus

According to Protection Motivation Theory, individuals exposed to a health hazard simultaneously execute a Threat Appraisal (evaluating intrinsic/extrinsic rewards versus severity and vulnerability) and a Coping Appraisal (evaluating response efficacy, self-efficacy, and response costs). In the context of chronic disease, vaccination induces an atypical appraisal paradox:

The individual must evaluate two parallel and competing threats: (1) the external threat of SARS-CoV-2 infection, and (2) the internal threat of the medical intervention itself (the vaccine) interacting negatively with their underlying pathology. If the coping appraisal indicates that the response cost (risk of disease flare or treatment interruption) outweighs the response efficacy (protection against infection), the individual adopts non-adherent or avoidant coping strategies, culminating in vaccine delay or refusal. By isolating these dual appraisals into quantifiable metrics, the DIVAS-6 translates complex cognitive coping mechanics into measurable, actionable scores.

7. Validity

The psychometric validation of the DIVAS-6 was conducted by Grech and colleagues (2023) across a well-characterized multi-disease cohort comprising Australian adults diagnosed with cancer, multiple sclerosis, and diabetes. The validation protocol established rigorous evidence supporting construct, convergent, concurrent, and discriminative validity.

Convergent and Concurrent Validity

To demonstrate convergent validity, the DIVAS-6 subscales were correlated against two established, widely validated COVID-19 vaccine attitudes inventories: the Oxford COVID-19 Vaccine Hesitancy Scale and the Oxford COVID-19 Vaccine Confidence and Complacency Scale. As theoretically predicted:

• The Disease Complacency subscale demonstrated statistically significant negative correlations with general vaccine hesitancy scores and positive correlations with broader vaccine acceptance measures, substantiating that heightened awareness of personal health risks fosters positive immunization intent.
• The Vaccine Vulnerability subscale exhibited significant positive correlations with general vaccine hesitancy and negative correlations with vaccine confidence, confirming that concerns regarding adverse disease-treatment interactions are direct drivers of vaccine resistance.

Discriminant and Factorial Validity

The orthogonality and empirical distinctiveness of the two subscales were supported by an inter-factor correlation of r = -0.10. This near-zero association confirms that Disease Complacency and Vaccine Vulnerability measure distinct psychological phenomena rather than representing polar ends of a single continuum. A patient can score highly on disease concern while concurrently expressing profound alarm regarding vaccine safety in the context of their medical treatment.

Discriminative Ability and Clinical Utility

The total score of the DIVAS-6 demonstrated good-to-excellent discriminative capacity for identifying actual vaccination status across distinct clinical diagnostic groups using Receiver Operating Characteristic (ROC) curve analyses:

• Low Cutoff (≥ 13): Setting a total score threshold of ≥ 13 achieved an overall sensitivity of 0.90 for identifying vaccinated individuals, accompanied by a specificity of 0.43. This threshold serves effectively in broad epidemiological screening to capture the vast majority of vaccine-willing patients.
• High Cutoff (≥ 18): Increasing the score threshold to ≥ 18 shifted the diagnostic balance, yielding an elevated specificity of 0.90 alongside a sensitivity of 0.45. This high-specificity threshold is optimized for clinical triage, enabling healthcare teams to precisely identify unvaccinated individuals whose decision-making is heavily encumbered by disease-related concerns.

8. Reliability

The reliability of the DIVAS-6 has been evaluated across parameters of internal consistency and structural measurement stability.

Internal Consistency

Reliability estimates indicate acceptable to strong internal consistency across both operational dimensions despite the brevity of the scale (three items per factor):

• Disease Complacency Subscale: Demonstrated a Cronbach’s alpha coefficient of α = .73. In short scales (fewer than ten items), an alpha exceeding .70 is widely accepted as indicative of solid construct homogeneity without redundancy.
• Vaccine Vulnerability Subscale: Yielded a robust Cronbach’s alpha coefficient of α = .85, reflecting a high degree of inter-item correlation and conceptual coherence among the items assessing perceived treatment disruption, adverse physiological reactions, and immunogenicity doubts.

Measurement Invariance

A critical consideration for instruments applied across heterogenous clinical cohorts is measurement invariance. Grech et al. (2023) evaluated multi-group invariance across distinct chronic disease profiles—specifically comparing patients with cancer, diabetes mellitus, and multiple sclerosis. The scale demonstrated robust psychometric equivalence across groups, establishing that variations in observed scores reflect true differences in underlying psychological constructs rather than artifactual shifts in item interpretation across different medical diagnoses.

9. Factor Analysis

The dimensional architecture of the DIVAS-6 was examined through an analytical sequence combining Parallel Analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Exploratory Structural Equation Modeling (ESEM).

Exploratory Factor Analysis (EFA)

Initial extraction via parallel analysis decisively indicated a two-factor latent structure. Following oblique rotation (promax), the two identified factors accounted for over 58.0% of the total cumulative variance in the item pool. Items 1, 2, and 3 loaded heavily onto Factor 1 (Disease Complacency), whereas Items 4, 5, and 6 loaded prominently onto Factor 2 (Vaccine Vulnerability). Cross-loadings were negligible, demonstrating clean structural divergence.

ESEM vs. CFA Model Comparison

To assess structural adequacy, the authors compared traditional CFA against an Exploratory Structural Equation Modeling (ESEM) framework. While conventional CFA forces non-target item cross-loadings to zero, ESEM permits cross-loadings while retaining latent variable modeling rigor. This distinction proved critical:

• Confirmatory Factor Analysis (CFA): The strict CFA model failed to achieve acceptable goodness-of-fit benchmarks, indicating that zero-loading constraints were overly restrictive given the clinical overlap between systemic health concerns and therapeutic anxieties.
• Exploratory Structural Equation Modeling (ESEM): The ESEM framework demonstrated an exceptional fit to the empirical data across all established psychometric metrics:

Fit Statistic	Observed Value	Standard Benchmark for Good Fit
Chi-Square (χ2)	21.67 (df = 4, p < .0002)	Lower values indicate better fit
Comparative Fit Index (CFI)	0.995	≥ 0.95 (Excellent)
Tucker-Lewis Index (TLI)	0.980	≥ 0.95 (Excellent)
Root Mean Square Error of Approximation (RMSEA)	0.042	≤ 0.05 (Close fit)
Standardized Root Mean Square Residual (SRMR)	0.009	≤ 0.08 (Excellent)
Akaike Information Criterion (AIC)	42278.37	Lower than CFA model
Bayesian Information Criterion (BIC)	42411.84	Lower than CFA model

A formal chi-square difference test confirmed the significant statistical superiority of the ESEM model over the CFA structure (Δχ² = 438.13, df = 4, p < .0001). Information criteria (both AIC and BIC) were substantially lower for the ESEM model, establishing it as the most parsimonious, theoretically coherent representation of the underlying data.

10. Instrument / Measurement Tool

• Instrument Name: Disease Influenced Vaccine Acceptance Scale-Six (DIVAS-6)
• Instrument Type: Self-report psychometric inventory / clinical questionnaire
• Administration Modality: Standardized for electronic web-based administration (computer, tablet, or smartphone); adaptable for clinical paper-and-pencil delivery
• Target Population: Adult clinical cohorts (≥ 18 years of age) with pre-existing, chronic, or severe medical conditions (including oncology, rheumatology, endocrinology, and neurology patients)
• Item Count: 6 core items divided evenly across two distinct subscales:
• Disease Complacency: 3 items (Items 1, 2, and 3)
• Vaccine Vulnerability: 3 items (Items 4, 5, and 6)
• Response Format: A 5-point Likert scale utilizing the options: “strongly agree,” “somewhat agree,” “neither disagree nor agree,” “somewhat disagree,” and “strongly disagree.” An explicit “don’t know” option is also provided to distinguish medical uncertainty from neutral conviction.
• Disease Personalization Architecture: The questionnaire embeds an adaptive token, [disease], within item stems, allowing dynamic substitution with the patient’s primary medical diagnosis (e.g., “cancer”, “multiple sclerosis”, “diabetes”, “rheumatoid arthritis”).
• Scoring Protocol: Items are typically coded from 1 to 5. Subscale scores are derived by calculating the mean or sum of the constituent three items. Total composite scores range from 6 to 30:
• Disease Complacency Subscale: High scores reflect high perceived disease vulnerability and high recognized necessity of vaccination (low complacency).
• Vaccine Vulnerability Subscale: High scores indicate heightened worry regarding adverse somatic events, treatment interactions, or diminished vaccine effectiveness.
• Screening Cutoffs: Total composite scores ≥ 13 demonstrate a sensitivity of 0.90 for general vaccine uptake; scores ≥ 18 demonstrate a specificity of 0.90 for distinguishing unvaccinated patients requiring specialized clinical consultation.
• Completion Time: Approximately 2 to 3 minutes, rendering it suitable for rapid pre-consultation triaging.

11. Permissions & Fee and Test Year

• Publication Year: 2023
• Copyright and Licensing: © 2022 The Authors. Published by Taylor & Francis Group in Behavioral Medicine.
• Access and Usage Rights: The article is published under an open-access model. Non-commercial academic research and clinical use are generally permitted under creative commons frameworks or by citing the primary source article. For commercial deployment, proprietary software integration, or formal reproduction rights, users should consult the publisher (Taylor & Francis) or contact the corresponding author directly.
• Financial Cost / Royalty Fee: None for non-commercial research, academic inquiry, or direct clinical practice ($0).
• Primary Author Contact: Dr. Lisa Grech, Monash University, Department of Medicine, School of Clinical Sciences, 246 Clayton Road, Melbourne, Victoria, Australia, 3168. E-mail: [email protected].

12. 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
• Janz, N. K., & Becker, M. H. (1984). The Health Belief Model: A decade later. Health Education Quarterly, 11(1), 1–47. https://doi.org/10.1177/109019818401100101
• Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700
• Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91(1), 93–114. https://doi.org/10.1080/00223980.1975.9915803
• Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Social Psychophysiology: A Sourcebook (pp. 153–176). Guilford Press.
• Rosenstock, I. M. (1974). Historical origins of the Health Belief Model. Health Education Monographs, 2(4), 328–335. https://doi.org/10.1177/109019817400200403

13. Items of the Scale