Abstract
Understanding cardiovascular (CV) risk above a certain age is vital following a health-focused, balanced lifestyle. This cross-sectional study aims to identify distortions in CV perception and their potential predictors in a primary care sample. For 376 participants in Hungary, the Framingham cardiovascular risk score was calculated, and a questionnaire measuring demographic, medical, psychological characteristics, and perceived cardiovascular risk was administered. Analyses were conducted in a 3-group design comparing “realistic” (39%), “optimistic” (18%), and “pessimistic” (43%); (corresponding to accurate estimation, underestimation, and overestimation of calculated risk, respectively) perception groups. Binary logistic regression analysis showed that the main predictors of “optimistic” perception were male sex (OR = 0.853; P < .001), blood pressure (OR = 1.038; P = .006), and age (OR = 1.084; P = .018). Given that more than half of the participants had inaccurate cardiovascular self-assessments, these findings suggest that interventions addressing risk misperceptions may be needed in cardiovascular prevention.
Keywords
Introduction
Cardiovascular diseases (CVD) are a significant health burden worldwide. 1 The European Society of Cardiology (ESC) 2019 Fact Sheet reports that 11 million new cases were recorded globally, resulting in 3.9 million fatalities. 2 In Central and Eastern Europe, including Hungary, the cardiovascular disease burden is particularly high, and cardiovascular mortality exceeds the Western European average. 3 In 2021, the mortality registry states that heart and vascular diseases were responsible for approximately 40% of all deaths in Hungary. 4
The 2021 Cardiovascular Disease Prevention Guide of the ESC 5 highlights the importance of individual risk assessment followed by appropriate communication, which considers patients’ educational level and numeracy skills, as well as their subjective risk perception. However, optimal communication about CVD has its difficulties, as understanding one’s own disease risk is embedded in self-beliefs and self-perception.
Classical health psychological models, including the Health Belief Model and Protection Motivation Theory, conceptualize disease risk estimation as a subjective cognitive appraisal process.6 -9 Cognitive biases intertwined with emotions significantly influence the final result of risk estimation. Due to unrealistic “optimistic” or “pessimistic” perception, individuals tend to conclude that they are less or more at risk than others, which might affect behavior and attitudes toward managing health problems. “Pessimistic” perception is influenced by anxiety and fear, which can hinder health actions and behavior change.10 -12 These psychological models describe subjective disease risk assessment as critical to personal health decision-making toward protective health behaviors. As an essential aspect of risk estimation and communication, valuable research has studied the effects of this process and found no long-term psychological consequences after CVD screenings.13 -16
Katz et al 17 utilized the Framingham Risk Score (FRS) and Lifetime Risk Assessment (LRS) models to estimate cardiovascular risk for the study participants. Their findings indicated that adults with higher objective FRS risk tended to misperceive their CVD risk, that is, over- or underestimating it. The overall ratio of “realistic” people was 56.6%, the “pessimist” 27.3%, and the “optimist” 15%. Alwan et al 18 found that underestimation (3.8% of the participants) was associated with male sex, younger age, lower educational level, normal BMI, and a higher frequency of physical activity. Overestimation (48%) was associated with cardiovascular medication use, older age, and being overweight. Lee et al’s 19 study assessed the cardiovascular risk using the FRS and compared it with perceived health, depression, and stress scores. Their results showed that 54.7% of the participants accurately estimated their risk, 12% overestimated it, and 33.3% underestimated it. The authors also found that depressive symptoms were associated with overestimation. Helou et al 20 found that, among others, smoking, alcohol consumption, dyslipidemia, and physical activity were associated with underestimation when compared with accurate estimators. Meanwhile, a higher BMI, more depressive symptoms, and higher stress levels reduced the likelihood of underestimation.
Earlier studies6 -8,17,21 investigated personal health decision-making based on subjective perceptions and assessments of disease risk. These valuable research studies contributed to the development of several behavioral and psychological interventions to improve health behaviors. Cardiovascular health behavior is closely linked to subjective risk appraisal: underestimation of personal risk is associated with poorer adherence, less lifestyle modification, and delayed help-seeking in acute cardiac events.22 -26
However, to our knowledge, only a few research papers in the cardiovascular health domain focus specifically on the risk perception gap. Further, the lack of objective risk estimation,27 -29 and variance in methodologies due to the lack of involvement of all risk groups 20 and the use of different methods has led to controversial results. Further, to address the lack of recent regional data on this specific mismatch and its clinical implications, our study aims to fill this research gap.
This cross-sectional study investigates the discrepancy between objective and subjective cardiovascular risk assessments and the possible demographic, medical, and psychological associations of different types of misperception. We aimed to cover all risk groups and pay special attention to sex differences.
Methods
Data and Procedures
The Budakalász Study is a longitudinal epidemiological study initiated in 2012 aimed at monitoring cardiovascular risk and health behaviors of a primary care. The current phase of the study commenced in 2023 (approved by the Hungarian Scientific and Research Ethics Committee of the Health Science Council, BMEU/2437-2/2022/EKU). The dataset comprises a subset of participants from a previously established cohort (Budakalász Study, 2012-2014). However, the present analyses are cross-sectional, based on data collected at a single time point within this cohort. No longitudinal analyses were conducted for the purposes of this study, and no data were used from the original study. Participants (N = 376) were recruited based on the inclusion criteria (age 45-65 years, laboratory results (serum cholesterol, triglycerides, low-density lipoprotein [LDL], high-density lipoprotein [HDL], blood glucose levels, and hemoglobin A1C [HbA1c] obtained within the last 4 months, no CVD events), by general practitioners. Participants were recruited through cooperating General Practitioners (GPs) based on a patient list provided to them with the former participants of Budakalász Study 2012-14 for follow-up purposes (N = 203). Inclusion criteria required that participants had no history of cardiovascular events and had laboratory results obtained within the previous 4 months. If recent laboratory data were unavailable, the tests were arranged by the GPs. We selected individuals aged 45 to 65 years, as this age range is associated with an emerging cardiovascular risk profile, while most individuals have not yet experienced a manifest cardiovascular disease event. The GP’s nurse invited the participants consecutively within the recruitment period (October 2022-May 2023). In addition, 2 new populations were recruited: residents of the ninth district (N = 139) and employees of Semmelweis University (N = 34). As one of Semmelweis University’s main campuses is located in the ninth district, close collaboration was established with local GPs, who invited all patients who met the inclusion criteria. University employees were recruited voluntarily through internal announcements. Written and oral information was provided to the participants during the first visit, and informed consent was obtained. All participants underwent blood sampling and physical examination. Physical examinations included measurements of blood pressure and weight, as well as an InBody analysis. Concurrently, a questionnaire was administered in 2 parts:
Part 1 was interviewer-administered during the physical examination (approximately 30 min) by Semmelweis University Students (sociodemographic, health behavior, and medical history). Examiners received standardized training before the examination and followed a structured script to ensure uniform data collection. Responses were recorded electronically into the Semmelweis University Biobank data management system.
Part 2 consisted of self-administered questionnaires (psychological and self-reported health measures) completed on paper before the physical examination (approximately 20 min) and administered on-site at the examination facility. Participants completed the questionnaires independently; however, trained staff were available to provide clarification if needed. Completed paper forms were subsequently reviewed for completeness and entered electronically by the examiners.
Cardiovascular risk (objective risk) was estimated using the FRS algorithm and categorized into 3 clinically established 10-year risk strata: low (0%-9%), medium (10%-19%), and high (≥20%), in accordance with the Framingham Heart Study.30 -32 Participants also assessed their perceived 10-year cardiovascular risk using the same 3 predefined categories (low, medium, high) by answering the question: “What do you think is the likelihood that you will develop cardiovascular disease within the next 10 years?” used in several studies before.17,18,20 Following this, participants were categorized as “Realistic” if the 2 answers were concordant, “Optimistic” if their perceived risk was found to be lower than the objective risk, and “Pessimistic” if their subjective risk was higher than their objective risk.
Measures
Framingham Risk Score (FRS)
The Framingham Heart Study 30 is a longitudinal cardiovascular study that led to the development of a prediction tool to estimate a person’s 10-year cardiovascular risk based on established risk factors, including age, sex, systolic blood pressure, antihypertensive treatment, smoking status, diabetes mellitus, and lipid parameters. In our study, participants were categorized into 3 risk groups: low-risk (<10%), medium-risk (10%-20%), and high-risk (>20%) as defined by the original study. The Framingham score 33 is a preferred tool for estimating cardiovascular risk because it is easy to communicate and understand.34 -36
CVD Subjective Risk Perception (SRP)
We used a single-item tool to assess the subjective CVD risk. Participants were asked, “What do you think is the likelihood that you will experience cardiovascular diseases in the next 10 years?” with response options in 3 categories: low, medium, or high. As perceived risk is a global probability judgment rather than a multidimensional construct, single-item assessment is methodologically appropriate and has been widely used in cardiovascular risk perception research.17,18,20 The item was included in the self-administered questionnaire completed on-site after the physical examination and blood sampling, but before participants received any feedback regarding their calculated FRS. No additional explanatory preamble was provided. The term “cardiovascular disease” was not further defined, allowing respondents to rely on their own understanding of the concept, thereby reflecting their interpretation of perceived real-world risk.
Beck’s Depression Inventory-Shortened Version (BDI-S)
The shortened and validated version of Beck’s Depression Inventory, consisting of 9 items, was used.37,38
Scores ≤4 indicate minimal depressive symptoms that typically do not necessitate clinical intervention. This scale identifies depressive symptoms and their severity and is widely used in research (Cronbach’s alpha = .839).
Perceived Stress (PSS10)
PSS-1039 is a 10-item questionnaire that captures how individuals perceived stress in the previous 2 weeks. Respondents rated the statements on a 5-point Likert scale. The higher the total score, the higher the perceived stress. We used a shortened 4-item version of this scale (Cronbach’s alpha = .705). 39
Life Satisfaction – Satisfaction With Life Scale (SWLS)
A single-item questionnaire was used. Respondents provided ratings on a 10-point Likert scale, with higher scores indicating greater satisfaction. The psychometric evaluations of single-item measures have been shown to converge with multi-item scales, demonstrating adequate performance. 40
World Health Organization (WHO) Well-Being Scale
The 5-item shortened WHO Well-Being Scale 41 is one of the most frequently used self-report instruments for measuring subjective mental well-being. Participants responded to questions on a 4-point Likert scale (0 = not at all applicable, to 3 = fully applicable). The higher the total score, the better the mental well-being (Cronbach’s alpha = .837).
Statistical Analysis
Statistical analyses were performed using the IBM Corp. SPSS Statistics for Windows, Version 25.0. software package. For our purposes, we created groups labeled as “realistic,” “optimistic,” and “pessimistic” based on the concordance and discrepancies between FRS (low, medium, and high) and SRP (low, medium, and high). Chi-square and Kruskal-Wallis nonparametric tests were employed because the variables did not follow a normal distribution. After significant Kruskal-Wallis omnibus testing, Dunn-Bonferroni post hoc comparisons were used as the appropriate nonparametric pairwise comparisons, controlling type I error.
Binary logistic regression analysis was performed with “optimistic” and “non-optimistic” perception as the dependent variable. Age, sex, systolic blood pressure, smoking status, quality of life, and subjective health status were entered simultaneously into a multivariable model using the enter method. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. Independent variables were selected a priori based on theoretical relevance and previous cardiovascular risk perception literature. Three domains were represented: (1) demographic characteristics (age and sex), (2) clinically observable cardiovascular risk factors known to influence perceived risk (systolic blood pressure and smoking status), and (3) subjective health representation variables reflecting patients’ interpretation of their health state (well-being and subjective health status).
As this study is part of an epidemiological screening cohort study, no a priori sample size calculation was performed. Missing values were handled using SPSS default listwise exclusion within each analysis, which explains the varying N across tests.
Visualizations (Sankey diagram) were created with the assistance of an AI-based tool (ChatGPT, OpenAI), using author-provided data.
Results
Basic Characteristics of the Sample
Out of the 393 participants, 17 were excluded due to missing laboratory parameters (13 participants) or missing responses to the subjective risk question (4 participants). Among the remaining 376 participants, 132 were male, and 244 were female, with an average age of 54.36 ± 6.34 years. Most were in a relationship (82.7%) and had a higher education degree (64.6%). A significant proportion (83%) were non-smokers. Participants’ average body mass index (BMI) was 28.016 ± 5.58, average cholesterol level was (5.5 ± 1.09), LDL was 3.56 ± 1.00, HDL was 1.54 ± 0.44, and blood glucose levels were 4.99 ± 1.14. The average FRS was 10.76 ± 9.01 (56.1% low risk, 29% medium risk, and 14.9% high risk; see Table 1).
Basic Characteristics of Our Sample.
Abbreviations: BMI, Body Mass Index; FRS, Framingham score; HDL, high density lipoprotein; LDL, low density lipoprotein.
Number of patients (N) percentage (%) Mean ± standard deviation.
Objective: Cardiovascular Risk in the Sample
Descriptive analysis of the FRS, broken down by risk groups, showed that a significant proportion of the high-risk group was male (N = 44; 78.6%). The age distribution was: 60.05 ± 4.70 years for high-risk, 56.94 ± 5.54 years for medium-risk, and 51.53 ± 5.47 years for low-risk individuals. In the high-risk group, 53.6% (N = 30) were non-smokers and 46.4% (N = 26) were smokers. The high-risk group had the highest BMI values (30.19 ± 5.77 vs 28.95 ± 4.54 in the medium-risk and 26.95 ± 7.79 in the low-risk group). The psychological measures revealed worse mood (4.58 ± 5.24) and higher reported stress levels (5.07 ± 2.99) in the high-risk group (see Table 2).
Basic Characteristics of the Sample According to Framingham Risk.
Abbreviations: BMI, Body Mass Index; FRS, Framingham score; HDL, high density lipoprotein; LDL, low density lipoprotein.
Number of patients (N) percentage (%) Mean ± standard deviation.
Discrepancy of Objective Versus Self-Assessed Risk
We identified several discrepancies between objective and subjective perceptions. A total of 231 persons (61%) misjudged their cardiovascular risk (Table 3).
Discrepancy and Concordancy Between Objective and Self-assessed Cardiovascular Risk N (%) N = 376.
Number of patients (N) percentage (%).
Figure 1 illustrates a Sankey diagram that depicts the flow between objective cardiovascular risk categories and participants’ self-assessed risk. The figure shows an apparent misalignment between actual and perceived risk, particularly among those in the medium (N = 29) and high-risk (N = 40) groups, who frequently underestimate their risk. Overestimation also occurred, mainly within the medium-risk (N = 25) and low-risk (N = 31) categories. The diagram highlights the direction and extent of misperception and represents both “optimistic” perception (overestimation of low risk) and “pessimistic” perception (underestimation of high risk) in risk perception. The ratios of the different perception groups are shown in Table 3.
Flow of misperception: discrepancies between objective and self-assessed cardiovascular risk.
Clinical and Psychological Characteristics and Differences Among Risk Perception Groups
We examined the differences among the “realistic,” “optimistic,” and “pessimistic” groups regarding demographic, medical, and psychological characteristics. The results of the Kruskal-Wallis test are presented in Table 4. Means and standard deviations are shown in Supplemental File 1
Results of Kruskal-Wallis Test Between Realistic, Optimistic, and Pessimistic Groups.
χ Stat: effect size, H-statistic: Kruskal-Wallis test.
The clinical and psychological characteristics of participants across the different perception groups, reported as median (interquartile range [IQR]) for continuous variables and as number (column percentage) for categorical variables, are presented in Supplemental File 2
Demographics
Age
Significant differences were observed among the 3 groups (H = 47.982, P < .001). Post hoc tests indicated that the age of the “optimistic” group (58.84 ± 5.56) was significantly higher compared to the “realistic” (54.39 ± 6.07; P < .001) and “pessimistic” (52.44 ± 5.94; P < .001) group. A further significant difference in age was found between the “pessimistic” (52.44 ± 5.94) and “realistic” (54.39 ± 6.07) groups (P = .018).
Education
Significant differences were observed across the 3 groups (H = 11.976, P = .003). Post hoc comparisons revealed that the “optimistic” group had a significantly lower educational level than the “realistic” (2.36 ± 0.66 and 2.61 ± .63, respectively; P = .006) and “pessimistic” groups (2.64 ± 0.56; P = .003).
Medical Status
Blood Pressure
Average systolic and diastolic blood pressures were significantly different across the groups (H = 56.281, P < .001 and H = 26.238, P < .001, respectively). Post hoc comparisons indicated that the “optimistic” group had significantly higher systolic and diastolic values than the “realistic” (systolic: 136.54 ± 14.41 and 122.08 ± 15.57, respectively; P < .001; diastolic: 85.72 ± 8.38 and 80.26 ± 9.29, respectively; P < .001) and “pessimistic” groups (systolic: 119.71 ± 12.86; diastolic: 79.31 ± 8.76), P < .001).
BMI
Significant differences between the groups were revealed regarding BMI (H = 9.966, P = .007). The results of the post hoc analysis indicated that the “optimistic” and “pessimistic” groups had significantly higher BMI than the “realistic” group (28.54 ± 4.97; 28.78 ± 6.11; and 26.92 ± 5.09, respectively; P = .044 and P = .014).
Alcohol Consumption – Frequency
Significant differences between the groups were observed (H = 10.041, P = .007). According to the post hoc analysis, the “optimistic” group showed significantly higher alcohol consumption frequency in the last 12 months than the “realistic” and “pessimistic” groups (3.26 ± 1.52, 2.70 ± 1.19, and 2.59 ± 1.19, respectively; P = .046 and .005).
Alcohol Consumption – Quantity/Event
Significant differences were found across the groups (H = 11.165, P = .004). According to the post hoc test, the “optimistic” group consumed significantly more units of alcoholic beverages during 1 occasion compared to the “realistic” and “pessimistic” groups (0.62 ± 0.96, 0.29 ± 1.19, and 0.25 ± 0.56, respectively, P = .013 and .004).
Psychological Status
Depression
Significant differences between the groups were identified regarding depression (H = 14.913, P = .001). Post hoc analysis revealed that the “pessimistic” group had significantly higher depression scores than the “realistic” (4.80 ± 4.09 and 3.37 ± 3.21, respectively, P = .006) and “optimistic” (3.26 ± 4.06, P = .003) groups.
Perceived Stress
Significant differences were found across the groups regarding perceived stress (H = 10.699, P = .005). The post hoc analysis indicated that the “pessimistic” group showed a higher level of perceived stress than the “realistic” group (5.13 ± 2.41 vs 4.43 ± 2.72, respectively; P = .007), although no significant difference was observed with the “optimistic” group.
WHO Well-Being
The statistical analysis showed a significant difference between the groups (H = 16.553, P < .001). The results of the post hoc analysis indicated that the “optimistic” group rated their well-being significantly higher than the “pessimistic” and “realistic” groups (9.65 ± 2.33, 8.65 ± 2.54; and 9.5 ± 2.76, respectively, P = .002 and .002).
Life Satisfaction
Significant differences were found across the groups (H = 9.676, P = .008). Post hoc analysis showed that the “optimistic” group had higher life satisfaction than the “pessimistic” group (8.12 ± 1.36 vs 7.61 ± 1.39, respectively; P = .012).
Subjective Health Status
Significant differences were identified between the groups in subjective health status (H = 8.61, P = .013). The post hoc test showed that the “realistic” group had a higher self-perceived health status (3.83 ± 0.71) than the “pessimistic” group (3.59 ± 0.73).
Associations of “Optimistic” Perception – Predictors of Underestimation of Cardiovascular Risk
To explore potential predictors of “optimistic” and “pessimistic” perceptions, we conducted binary logistic regressions comparing “optimistic” versus “non-optimistic” groups (ie, “realistic” and “pessimistic”), stratified by participants with at least medium objective risk. Independent predictors were age, sex, systolic blood pressure, smoking status, well-being, and subjective health status (Table 5). The logistic regression model’s goodness-of-fit was assessed using a likelihood ratio test, yielding a significant chi-square statistic of χ2 (6) = 28.198; P < .001. The model correctly identifies 71.3% of the cases. Male sex was significantly associated with optimistic risk perception (OR = 2.34, 95% CI: 1.14-4.82, P = .020). Older age was also independently associated with the outcome (OR = 1.084 per year, 95% CI: 1.01-1.16, P = .018), as was higher systolic blood pressure (OR = 1.038 per mmHg, 95% CI: 1.01-1.07, P = .006).
Significant Variables to Predict Optimistic Bias in a Binary Logistic Model.
Smoking status (OR = 2.20, 95% CI: 0.98-4.94, P = .056), WHO score (OR = 1.13, 95% CI: 0.99-1.30, P = .067), and self-rated health status (OR = 1.66, 95% CI: 0.96-2.89, P = .071) showed trends toward association but did not reach statistical significance.
Discussion
The general pathways of the disease risk perception process are described in health psychology models. 7 Distortion is a natural part of any decision-making process, and the role of lingering background cognitive processes is also well known in theories. 42 However, in cardiovascular diseases, risk perception remains understudied, despite evidence supporting its role in health behavior and risk reduction.43,44 The available studies provide conflicting results, as some do not cover all risk groups by excluding pessimistic estimators, 20 or by not including objective risk assessment.27 -29 Our observation incorporated subjective and objective risk assessments across all relevant risk groups and analyzed the dataset across all types of perceivers.
In our sample, 61.5% of the participants misjudged their cardiovascular risk. 43.1% overestimating (“pessimistic”), 18.4% underestimating (“optimistic”), and 38.5% were “realistic” estimators. These findings indicate a substantial discrepancy between subjective and objective risk assessments at the population level.
An early study by Avis et al 45 on this topic used a comparative question to measure self-perceived cardiovascular risk and found 56% “optimistic,” 29% “realistic,” and 13% “pessimistic” responses. Despite the misperception ratios being very close to ours (69% and 61.5%, respectively), the distribution of these groups was the opposite of ours. In a similar study, Helou et al 20 identified 83.9% of participants as underestimators (“optimistic”) and only 16.1% as accurate estimators. However, their analysis included only moderate- and high-risk individuals, while ours covered all risk groups. Regarding sex differences, we found that optimism was more prevalent among men, whereas pessimism was more common among women. No significant sex differences were found among realistic perceivers. Both our findings and earlier literature19,20 suggest that “pessimistic” estimators report higher anxiety and depressive symptoms and lower well-being and life satisfaction. Previous literature has described sex differences in sensitivity to somatic signals, which may be reflected in differences in symptom perception and perceived health status.46 -48 At the same time, the literature also shows that men exhibit reduced symptom perception.45,49,50
In our regression analysis, “optimistic” perception was examined among patients with at least moderate objective cardiovascular risk. “Optimistic” perception occurred primarily in older, male patients with higher blood pressure. These findings should be interpreted not as causal explanations but as clinically relevant associations that may help identify individuals in whom risk misperception is more likely to occur.
Helou et al 20 found in their regression model that “optimistic” perception was associated with higher age, higher blood pressure and dyslipidemia, smoking, lower BMI, and lower levels of perceived stress or depressive symptoms. Our findings partly support these results, mainly regarding age-related patterns and somatic characteristics of risk misperception. However, Helou et al’s 20 study excluded “pessimists,” and the study population included a relatively small proportion of women (19.9%). In our research, descriptive analyses were performed across all perception groups (ie, “realistic,” “optimistic,” and “pessimistic”) in the full sample. Still, the regression analysis was intentionally restricted to patients with at least moderate objective cardiovascular risk. Within these clinically relevant strata, we examined factors explicitly associated with the “optimistic” subgroup. Thus, rather than modeling all perception categories simultaneously, we aimed to identify patient characteristics that may help clinicians recognize individuals in whom relevant risk misperception is most likely to occur.
Based on these observed associations, further behavioral implications can be hypothesized: different forms of risk misperception may be associated with distinct behavioral tendencies, as suggested by prior research.43,44 For example, the higher likelihood of optimistic risk perception among men is consistent with research on gender differences in health behavior, with men being less likely to utilize primary care, more likely to minimize symptoms, and less inclined to acknowledge illness.51 -54
Another possible explanation for unjustified optimism – requires further investigation – is the suppression of health anxiety and the denial, 55 which may also be related to higher reported life satisfaction. From a preventive perspective, such individuals may be less likely to perceive the need for behavioral change or to engage with health-related interventions.
However, such interpretations remain speculative in the absence of direct behavioral data and should be tested in longitudinal or interventional studies.
Study Limitations
The present study has several limitations. First, the relatively small sample size may have limited statistical power. In addition, the mixed recruitment strategy resulted in a heterogeneous sample, which may have introduced selection bias, as participants recruited from different populations may systematically differ in socioeconomic status, health awareness, and health-related behaviors. Further, the study participants were predominantly middle-aged and highly educated females, recruited by GPs (see further details in Method section), which may have introduced selection bias, as individuals attending primary care may differ from the general population in health awareness, healthcare utilization, and generalizability to the broader population. In addition, because of the cross-sectional design, the observed associations cannot be interpreted as causal. Second, several variables were based on self-report, and therefore, reporting and recall bias cannot be excluded. Third, in addition to the FRS, several population-specific estimation scales are used to measure cardiovascular risk (eg, Score 2). Nevertheless, we used FRS because of its good communicability.34,35 Further, it is worth noting that subjective cardiovascular risk perception was assessed using a single-item measure. However, such measures are widely used in risk-perception research,17,18,20 internal consistency cannot be evaluated, potentially limiting the ability to capture all nuances of the construct. Finally, the study was conducted in Hungary. Cultural and healthcare system characteristics may limit the generalizability of the findings.
Future studies should address these limitations to understand the pathways and consequences of subjective CVD risk perception on health behaviors.
Implications for Prevention and Clinical Practice
Our findings suggest that discrepancies between subjective and objective cardiovascular risk may be clinically relevant in primary care, as patients with higher objective risk may not accurately perceive their risk.
In practice, clinicians may benefit from briefly assessing patients’ subjective risk perception alongside objective risk estimation using validated tools (e.g. ASCVD calculator, ESC CVD risk calculator, MedCalc). Despite their ease of accessibility and application, their use in primary care settings remains suboptimal.56,57 A single question (eg, “How likely do you think it is that you will develop cardiovascular disease in the next 10 years?”) can help identify potential misperception, followed by a discussion comparing perceived and calculated risk.
This process may be supported by brief, structured communication approaches based on low-intensity psychological interventions (LIPI), 58 incorporating open questions, clarification of misunderstandings, and personalized explanations. Tailoring communication to patients with optimistic or pessimistic perceptions may facilitate more realistic risk appraisal and improve engagement with preventive recommendations.
Conclusion
Our results suggest that men are more “optimistic” in their cardiovascular risk assessment, that is, they tend to underestimate risk. While “pessimistic” perception is related to mood and anxiety disorders, which also may go together with poorer compliance.
Therefore, cardiovascular risk estimation and quantification, along with consideration of self-perceptions of CVD, followed by optimized communication, could be an optimal starting point for highly effective prevention programs to improve compliance and health-behavior change. The consequences of inaccurate risk estimation, from the perspectives of adherence and patient management, must also be considered.
Based on these findings, appropriate profiling and measurement procedures applicable in clinical settings should be developed to support preventive behavior and medical decision-making, helping enhance patients’ optimal risk estimation and decision-making abilities.
Supplemental Material
sj-docx-1-jpc-10.1177_21501319261442384 – Supplemental material for Biases in Cardiovascular Risk Perception: Psychological and Demographic Correlates: A Cross-Sectional Study in Hungary
Supplemental material, sj-docx-1-jpc-10.1177_21501319261442384 for Biases in Cardiovascular Risk Perception: Psychological and Demographic Correlates: A Cross-Sectional Study in Hungary by Zsofia Ocsovszky, Blanka Ehrenberger, Orsolya Papp-Zipernovszky, József Otohal, Alexandra Assabiny, Gergely Koplanyi, Magor Papp, Éva Borenszki-Gutási, Hajnalka Vago, Bela Merkely, Zsolt Bagyura and Marta Csabai in Journal of Primary Care & Community Health
Footnotes
Acknowledgements
Thanks to the students and volunteers of the Semmelweis Health Promotion Center their contribution to data acquisition.
Ethical Considerations
The research has been approved by the Hungarian Scientific and Research Ethics Committee of the Health Science Council in 2022, no: BNMÜ/2437-2/2022/EKU.
Consent to Participate
Written informed consent was signed by the participants.
Consent for Publication
Not applicable to our publication.
Author Contributions
OZS: conception, study design, interpretation, and writing. EB: interpretation and writing. POZ: revision of manuscript. OJ: revision of manuscript. AA: revision of manuscript. KG: statistical analysis. MP: conception and revision of manuscript. EG: data acquisition. .HV: revision of manuscript and project leader. BM: grant owner. ZSB: supervisor, conception, study design, interpretation, and revision of manuscript. MCS: supervisor, conception, study design, interpretation, and revision of manuscript.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Project no. TKP2021-NKTA-46 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development, and Innovation Fund, financed under the TKP2021-NKTA funding scheme.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due protection of medical information but are available from the corresponding author* on reasonable request.
Supplemental Material
Supplemental material for this article is available online.
References
Supplementary Material
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