Abstract
Cardiovascular diseases (CVD) stand as the primary causes of both mortality and morbidity on a global scale. Social factors such as low social support can increase the risk of developing heart diseases and have shown poor prognosis in cardiac patients. Resources such as PubMed and Google Scholar were searched using a boolean algorithm for articles published between 2003 and 2023. Eligible articles showed an association between social support and cardiovascular risks. A systematic review was conducted using the guidance published in the Cochrane Prognosis Method Group and the PRISMA checklist, for reviews of selected articles. A total of five studies were included in our final analysis. Overall, we found that participants with low social support developed cardiovascular events, and providing a good support system can decrease the risk of readmission in patients with a history of CVD. We also found that integrating social determinants in the cardiovascular risk prediction model showed improvement in accessing the risk. Population with good social support showed low mortality and decreased rate of readmission. There are various prediction models, but the social determinants are not primarily included while calculating the algorithms. Although it has been proven in multiple studies that including the social determinants of health (SDOH) improves the accuracy of cardiovascular risk prediction models. Hence, the inclusion of SDOH should be highly encouraged.
Keywords: social determinants of health, sdoh, social isolation, loneliness, social life, cardiovascular risk prediction models, risk prediction model, cardiovascular risk factors, cardiovascular diseases, social support
Introduction and background
Among the world's top causes of death and many significant medical disorders are cardiovascular diseases (CVD) [1]. Risk factors causing CVD include age, gender, obesity, genetics, smoking history, hypertension, diabetes, socio-economic status, lifestyle, etc [2]. Timely identification of risks in individuals is necessary as CVD advances over time, and waiting until the symptoms occur increases morbidity and mortality [3].
People with poor social health were 30% more likely to develop coronary heart disease (CHD) and stroke episodes, according to a systematic review published in 2016 constituting 23 studies [4]. The notion of "social health" refers to an individual's potential for fulfilling and significant acquaintances, their ability to adapt to social adjustment, and their interactions with and perception of support from other people, organizations, and services.
The terms social isolation, social support, and loneliness are frequently used to describe social health, more briefly described in Figure 1. Social support is a subjective indicator of an individual’s perception of how much they receive from others [4]. Several studies have shown a relation between social support and change in lifestyle, which can decrease the risk of CVD. While patients with low social support tend to show poor prognosis in cardiac diseases [5]. Some studies also showed the risk of mortality and readmission in patients who are already discharged after hospitalization for heart failure [6,7].
Figure 1. Components of social health.
Many risk prediction models and algorithms are being developed to assess the risks of developing CVD, such as systematic coronary risk evaluation (SCORE) [8], cardiovascular risk score (QRISK2) [9-11], Framingham [12], and Reynolds risk score [13]. However, none of them focuses solely on the social determinants. We have several data and literature that show us the association between CVD and other risk factors, such as diabetes, hypertension, age, family history, depression, etc. However, at present, we have very limited data and literature that focus on social factors. As we age, our social health becomes poor, which is influenced by several factors. As we get older, we tend to have less social interaction, network, and activities. We rely more on friends and families for emotional support. The loss of a loved one can lead to stress, isolation, and loneliness. By considering these determinants in conjunction with clinical data, healthcare professionals can have a more holistic approach to determining the risks.
Understanding the impact of social support on cardiovascular disease risk prediction models is essential in order to develop more comprehensive and effective strategies for reducing inequalities in cardiovascular health outcomes.
Review
Methods
We followed the recently published guide from the Cochrane Prognosis Method and used the PRISMA [14] checklist for the study of prediction models.
Search Strategy
A systematic review of articles was conducted using the reports of the association between social support and cardiovascular risks from 2003 to 2023. Search engines such as PubMed, Google Scholar, and Medline were used. We included the following terms in our searches on August 01, 2023, using the boolean algorithm - (social impact) OR (social support) AND (cardiovascular risk prediction model) OR (heart disease risk prediction model) OR (risk prediction model) OR (cardiovascular abnormalities) OR (risk prediction model), as described in Table 1.
Table 1. Search strategy, search engines used, and number of results displayed.
| Database | Search Strategy | Search Results |
| 1. PubMed | ((((((social impact[Title/Abstract]) OR (social support[Title/Abstract])) AND (cardiovascular risk prediction model[Title/Abstract])) OR (heart disease risk prediction model[Title/Abstract])) OR (risk prediction model[Title/Abstract])) OR (cardiovascular abnormalities[MeSH Terms])) OR (risk prediction model[MeSH Terms]) | 389 |
| 2. Google Scholar | (social impact) OR (social support)) AND (cardiovascular risk prediction model)) OR (heart disease risk prediction model)) OR (risk prediction model) | 18,400 |
Eligibility Criteria
We included the articles published in English between 2003 and 2023 with only free full text and studies, including clinical trials, randomized controlled trials, and systematic reviews. The studies consisting of non-randomized controlled trials, books, and documents with a population of age less than 19 years old were excluded, as described in Table 2.
Table 2. Inclusion and exclusion criteria adopted during the process of literature search.
| Inclusion | Exclusion | |
| Species | Human | Animals |
| Gender | All | - |
| Age | Adult: 19+ years | < 19 years old |
| Types of studies | Clinical trials, randomized controlled trials, systematic reviews | Non-randomized controlled trials, books, and documents |
| Text availability | Free full-text | Abstract, Free Text |
| Publication year | 2003-2023 | Before 2003 |
| Language | English | Non-English |
Screening and Quality Appraisal
The articles were reviewed for duplicates using Endnote [15] and thoroughly screened by two authors AN and LG to be included in the final analysis. We employed a range of quality assessment instruments to guarantee the reliability of the five chosen papers. In the case of systematic reviews and meta-analyses, we adhered to the PRISMA checklist. For randomized clinical trials, we utilized the Cochrane bias assessment tool. The quality appraisal of qualitative studies was conducted using critical appraisal skills, as shown in Table 3.
Table 3. Quality appraisal tools used.
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses, RCT: Randomized Controlled Trials
| Quality Appraisal Tools Used | Type of Studies |
| PRISMA checklist | Systematic reviews and meta-analysis |
| Cochrane bias tool assessment | RCT |
Results
A total of 18,789 articles were identified, including 389 articles from PubMed and 18,400 articles from Google Scholar. However, only five studies met the inclusion criteria, as discussed in Figure 2. Out of these, three studies focused on the impact of support on CVD, and two studies focused on cardiovascular risk prediction models.
Figure 2. PRISMA 2020 flow diagram for new systematic review showing database searches.
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
In Table 4, we discussed that, in populations with low social support, the risk of developing CVD is high.
Table 4. Studies showing the development of cardiovascular events in a low social support population.
| Study | Participants | Social Support Status | Key Findings |
| Freak-Poli et al. (2021) [16] | 11,486 | People with high social support = 11,258, low social support = 228 | Cardiovascular disease events in high social support = 1,388 (12.32%), low social support =56 (24.56%) |
| Shen et al. (2004) [17] | 142 | Social support provided post-myocardial infarction | Social support impacts directly and indirectly on cardiac health by decreasing depressive symptoms among patients post-infarction |
| Thurston et al. (2009) [18] | 3,003 | The incidence of cardiovascular diseases was assessed in a cohort of a population with low social support and loneliness | 237 non-fatal vs. 120 fatal cases of cardiovascular diseases observed |
Freak-Poli et al. [16] showed that, in a cohort of 11,486 participants (11,258 with high social support and 228 with low social support), 12.32% with high social support and 24.56% with low social support developed the events of CVD. Shen et al. [17] suggested that providing a better social support system decreased depression in participants, which in turn decreased the risk of readmission. Thurston et al. [18] also showed that, in a cohort of 3,003 participants with low social support, 237 participants developed non-fatal vs. 120 participants who developed fatal CVD in the future.
All three studies showed that, in a population with low social support, the chances of developing CVD are higher through multiple pathways, including its impact on stress, behaviors, medication adherence, and mental health.
Table 5 shows the results of integrating social determinants of health (SDOH) with cardiovascular risk prediction models. Both studies showed improvement in the accuracy of the cardiovascular risk prediction model with the integration of SDOH.
Table 5. Studies showing the integration of social determinants of health with cardiovascular risk prediction models.
| Study | Study Design | Participants | Social Determinants Included | Key Findings | Conclusion |
| Hammond et al. (2020) [19] | Retrospective observational study | 4,703 | Socio-economic status, access to care, neighborhood environment, habits and behavior (Alcohol usage), social support, education level | Population with low social support(unmarried) are more likely to develop the risk of cardiovascular diseases | Including the social determinants of health (SDOH) with the cardiovascular risk prediction model improved the accuracy |
| Hammond et al. (2019) [20] | Cohort study | 155,013 | Socio-economic status, ethnicity | SCORE2 prediction model underpredicted the risk in low socioeconomic subgroups and populations with Surinamese ethnicity | It is necessary to implement social determinants in the cardiovascular risk prediction model |
Discussion
This systematic review sheds light on the impact of social support on cardiovascular risk prediction models. At first, we compared different risk prediction models and their components in Table 6 and found out that the social determinants need to be integrated while using these risk prediction models.
Table 6. Cardiovascular risk prediction models and their components.
AF: Atrial Fibrillation, BMI: Basic Metabolic Index, BP: Blood Pressure, CAD: Coronary Artery Diseases, CKD: Chronic Kidney Diseases, CVD: Cardiovascular Diseases, DM: Diabetes Mellitus, HDL: High-Density Lipid, HTN: Hypertension, MI: Myocardial Infarction, RA: Rheumatoid Arthritis
| Risk Prediction Models | Components | Features |
| European systemic coronary risk evaluation algorithm (SCORE) | Age, gender, total cholesterol, systolic BP, smoking status | Estimates the risk of fatal CVD over 10 years |
| QRISK2 | Age, gender, systolic BP, ethnicity, smoking status, ratio of total cholesterol: HDL cholesterol, BMI, history of CAD, treated HTN, RA, CKD, type II DM, AF | Estimates lifetime risk of adverse events of CVD |
| Framingham | Age, gender, systolic BP, smoking status, total cholesterol | Estimates the risk of CAD over 10 years |
| Reynolds risk score (RSS) | Age, gender, systolic BP, Total cholesterol, HDL cholesterol, HS-C reactive protein, history of MI | Estimates risks of having MI, stroke, and major CVD in the next 10 years |
Our first finding was to establish a link between social support and CVD. In one of the studies, it was noted that 12.32% of the population with high social support and 24.56% of the population with low social support developed the events of CVD in their lifetime. Meanwhile, the second study showed that, in a cohort of 3,003 participants with low social support, 357 participants developed CVD.
Our second and most significant finding was that people with low social support and loneliness are more prone to develop depression, and depression can act as an indirect cause of CVD. We can decrease the incidence of re-hospitalization in patients with post-infarction by providing them with more support. Social support systems can contribute to reducing stress in the population. Stress can directly lead to hypertension. Social support can also influence lifestyle, such as smoking, diet, etc. Hence, positive social support has a positive effect on risk management and directly impacts the development of CVD.
The impact of social support on cardiovascular risk prediction models stands as a topic of paramount significance. Extensive research underscores the substantial influence of social support on cardiovascular health and risk factors. Numerous studies have ascertained that individuals possessing robust social support networks tend to experience more favorable cardiovascular outcomes and exhibit a reduced likelihood of developing CVD. To elaborate, social support has been associated with shortened hospital stays, enhanced treatment adaptation, and a diminished risk of mortality among patients dealing with chronic conditions [6,7].
Moreover, it is worth noting that inadequate social and economic resources, insufficient social support, and social isolation have all emerged as recognized risk factors contributing to the development of coronary heart disease. Social support manifests in various forms, encompassing instrumental, emotional, and informational support. Research findings consistently underscore the pivotal role that social support plays in mitigating the risk of CVD and enhancing overall cardiovascular well-being.
Our third finding focused on integrating SDOH with cardiovascular risk prediction models, and we found out that incorporating SDOH with these risk prediction models has increased their accuracy. SDOH are the conditions and components in the settings where individuals are born, live, learn, work, play, and age that have an impact on a variety of hazards and repercussions for health, functioning, and quality of life. To put it differently, social and economic issues have an effect on people's health and well-being both individually and collectively.
SDOH includes various elements such as economic stability, social and economic factors, education, surroundings, and access to healthcare.
Economic stability: This entails aspects such as earnings, employment, and access to necessities like food and housing. Health disparities are more likely to affect people with lower incomes or undetermined employment.
Social and economic factors: Income inequality, social cohesion, and discrimination are a few examples of social and economic issues that might have an impact on health.
Education: The availability of and achievement in schooling can influence health outcomes. Better health and well-being are frequently associated with higher levels of education.
Surrounding: Health can be impacted by the physical surroundings in which individuals reside, including elements such as accessibility to green areas, walkability, and exposure to environmental contaminants.
Access to healthcare: While healthcare itself is an important factor, access to healthcare services, the quality of care received, and health insurance coverage all play a role in determining health outcomes.
Therefore, healthcare professionals should consider integrating measures of social support into cardiovascular risk assessment tools to better understand and predict an individual's risk of developing CVD. However, the question arises of how can we move towards this holistic approach of integrating SDOH with cardiovascular risk prediction models. Here are some strategies in Table 7.
Table 7. Strategies to integrate SDOH with cardiovascular risk prediction models.
SDOH: Social Determinants of Health
| SDOH Incorporation Strategies | Components |
| SDOH data | Collect SDOH data of patients either by survey or pre-existing data. These data should include information on income, education, employment status, neighborhood characteristics, access to healthcare, social support networks, and more. |
| Incorporate variables | Include socioeconomic status (income, education) as covariates in the model. Use neighborhood-level variables like crime rates, access to healthy food, and proximity to parks as proxies for environmental factors. Include measures of social isolation. |
| SDOH score | Create a composite SDOH score that combines various SDOH variables to represent an individual's overall social and economic context. This score can be integrated into the risk model as a single variable. |
| Validation | Ensure that the SDOH-integrated risk model is validated and calibrated to accurately predict cardiovascular risk in diverse populations. This may require access to large datasets that include both clinical and SDOH data. |
| Implementation | Once developed and validated, integrate the SDOH-enhanced risk prediction model into clinical practice. |
Integrating SDOH into cardiovascular risk prediction models acknowledges that health outcomes are influenced by a complex interplay of factors beyond traditional clinical risk factors. This approach can help identify individuals at higher risk and guide interventions that address both clinical and social determinants to improve cardiovascular health and reduce health disparities.
Limitations
Our systematic review has certain constraints. We exclusively considered articles published in the English language within the two past decades. Our selection was limited to papers accessible for free on PubMed and Google Scholar. Additionally, we focused solely on studies involving individuals aged 19 years and older, and our search was restricted to papers addressing cardiovascular risk prediction models and social health. Due to less research in this particular area, there were limitations in articles retrieved for final analysis. To obtain more precise findings, further research is required.
Conclusions
The implications of increased social support are profound. Increased social support has been linked to lower levels of loneliness, perceived stress, and anxiety, as well as better mental and physical health. Additionally, increased social support has been found to reduce the odds of depressive and psychotic-like symptoms, which is especially important for individuals with severe and persistent mental illness who may be at an increased risk of isolation. Overall, evidence suggests that social support can have a protective effect on cardiovascular health and risk, as well as on overall mental and physical well-being. In light of this research and the evidence presented, it is clear that social support plays a significant role in cardiovascular risk prediction models.
Acknowledgments
As authors of this systematic review, we would like to extend our sincere appreciation to the individuals and entities who played a pivotal role in bringing this work to fruition. MS played a significant role in shaping this article, including initiating the idea for the study and gathering the necessary data. They meticulously reviewed and refined tables and figures, ensuring their accuracy and coherence. Additionally, MS took charge of drafting the manuscript, skillfully crafting its content from the introduction to the conclusion. AN's involvement encompassed data collection, conducting thorough error checks, and contributing to the drafting process. Specifically, they meticulously gathered data, diligently reviewed for potential errors, and took an active role in formulating both the introduction and method sections of the manuscript. LG actively engaged in data selection, diligently identifying and rectifying any instances of duplicated data or potential errors. Moreover, they made substantive contributions to drafting the method sections and tables, showcasing their comprehensive involvement in these aspects of the research process. JT took an active role in verifying the process of data collection and meticulously reviewing references for accuracy. Furthermore, they played a significant part in composing both the results section and the discussion, demonstrating their comprehensive contribution to these critical components.RR played a vital role in drafting the abstract, refining the discussion section, meticulously collecting data, and conducting rigorous error checks.BP contributed to data collection and abstract editing, ensuring all guidelines were met, and drafting limitation sections. Furthermore, MS, AN, LG, JT, and RR reviewed the entire article, ensuring the rectification of any grammatical and writing errors. The completion of this systematic review paper was made possible through the collaborative efforts of a dedicated team of authors. Lastly, we are grateful to our families and loved ones for their unwavering encouragement and understanding as we dedicated countless hours to this endeavor.
The authors have declared that no competing interests exist.
References
- 1.Organizational update: the world health organization global status report on noncommunicable diseases 2014; one more landmark step in the combat against stroke and vascular disease. Mendis S, Davis S, Norrving B. Stroke. 2015;46:0–2. doi: 10.1161/STROKEAHA.115.008097. [DOI] [PubMed] [Google Scholar]
- 2.Risk for incident heart failure: a subject-level meta-analysis from the heart “omics” in ageing (Homage) study. Jacobs L, Efremov L, Ferreira JP, et al. J Am Heart Assoc. 2017;6:0. doi: 10.1161/JAHA.116.005231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Systematic coronary risk evaluation (score): JACC focus seminar 4/8. Graham IM, Di Angelantonio E, Visseren F, et al. J Am Coll Cardiol. 2021;77:3046–3057. doi: 10.1016/S0735-1097(21)04401-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Loneliness, social isolation and risk of cardiovascular disease in the English longitudinal study of ageing. Valtorta NK, Kanaan M, Gilbody S, Hanratty B. Eur J Prev Cardiol. 2018;25:1387–1396. doi: 10.1177/2047487318792696. [DOI] [PubMed] [Google Scholar]
- 5.Loneliness and social isolation as risk factors for CVD: implications for evidence-based patient care and scientific inquiry. Holt-Lunstad J, Smith TB. Heart. 2016;102:987–989. doi: 10.1136/heartjnl-2015-309242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. Calvillo-King L, Arnold D, Eubank KJ, Lo M, Yunyongying P, Stieglitz H, Halm EA. J Gen Intern Med. 2013;28:269–282. doi: 10.1007/s11606-012-2235-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patients with heart failure. Ouwerkerk W, Voors AA, Zwinderman AH. JACC Heart Fail. 2014;2:429–436. doi: 10.1016/j.jchf.2014.04.006. [DOI] [PubMed] [Google Scholar]
- 8.Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Conroy RM, Pyörälä K, Fitzgerald AP, et al. Eur Heart J. 2003;24:987–1003. doi: 10.1016/s0195-668x(03)00114-3. [DOI] [PubMed] [Google Scholar]
- 9.Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. Hippisley-Cox J, Coupland C, Robson J, Brindle P. BMJ. 2010;341:0. doi: 10.1136/bmj.c6624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. BMJ. 2007;335:136. doi: 10.1136/bmj.39261.471806.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, Brindle P. BMJ. 2008;336:1475. doi: 10.1136/bmj.39609.449676.25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.The Framingham risk score: an appraisal of its benefits and limitations. Hemann BA, Bimson WF, Taylor AJ. Am Heart Hosp J. 2007;5:91–96. doi: 10.1111/j.1541-9215.2007.06350.x. [DOI] [PubMed] [Google Scholar]
- 13.Cardiovascular risk assessed by Reynolds risk score in relation to waist circumference in apparently healthy middle-aged population in Montenegro. Klisić A, Kavarić N, Bjelaković B, et al. Acta Clin Croat. 2018;57:22–30. doi: 10.20471/acc.2018.57.01.03. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. PLoS Med. 2009;6:0. [PMC free article] [PubMed] [Google Scholar]
- 15.EndNote and Reference Manager Citation formats compared to "instructions to authors" in top medical journals. Brahmi FA, Gall C. Med Ref Serv Q. 2006;25:49–57. doi: 10.1300/J115v25n02_04. [DOI] [PubMed] [Google Scholar]
- 16.Social isolation, social support and loneliness as predictors of cardiovascular disease incidence and mortality. Freak-Poli R, Ryan J, Neumann JT, et al. BMC Geriatr. 2021;21:711. doi: 10.1186/s12877-021-02602-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Independent and mediated contributions of personality, coping, social support, and depressive symptoms to physical functioning outcome among patients in cardiac rehabilitation. Shen BJ, McCreary CP, Myers HF. J Behav Med. 2004;27:39–62. doi: 10.1023/b:jobm.0000013643.36767.22. [DOI] [PubMed] [Google Scholar]
- 18.Women, loneliness, and incident coronary heart disease. Thurston RC, Kubzansky LD. Psychosom Med. 2009;71:836–842. doi: 10.1097/PSY.0b013e3181b40efc. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Social determinants of health improve predictive accuracy of clinical risk models for cardiovascular hospitalization, annual cost, and death. Hammond G, Johnston K, Huang K, Joynt Maddox KE. Circ Cardiovasc Qual Outcomes. 2020;13:0. doi: 10.1161/CIRCOUTCOMES.120.006752. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.A theoretical framework for clinical implementation of social determinants of health. Hammond G, Joynt Maddox KE. JAMA Cardiol. 2019;4:1189–1190. doi: 10.1001/jamacardio.2019.3805. [DOI] [PMC free article] [PubMed] [Google Scholar]