Cardiovascular risk in young healthcare professionals: A cross-sectional study

Abstract

BACKGROUND:

In the last years cardiovascular risk has decreased in grown adults and elderly while it seems to be rising among young adults.

OBJECTIVE:

to assess the 10-year cardiovascular risk (CVR) in young healthcare professionals at the teaching hospital in Rome, using two scores, and identify possible determinants in order to design and implement preventive strategies.

METHODS:

A cross-sectional study was carried out between January 2019 and July 2020. Participants underwent medical history collection, physical examination, and blood tests. CVR was calculated using CUORE and Framingham Risk Scores. A multiple linear regression analysis was conducted having the scores as dependent variables. Diagnostic tests were used for checking model assumptions.

RESULTS:

The study was carried out including 525 participants, 58.5% physicians and 32.1% nurses. Multivariate analysis was carried out only for men, since the pp plot for the whole population and for females for the dependent variables showed some evidence of non-normality, and the residual plot shows variance of the residuals was not constant across the range of fitted values. CVR, using the Framingham equation, directly correlated with age (β = 0.260; p < 0.001). Using the CUORE score, qualification as a physician (p < 0.001) is associated with a lower risk of having a CVR, while age (p < 0.001) is directly proportional to this risk.

CONCLUSIONS:

Increasing age consistently emerges as a prominent factor, positively influencing both the Framingham risk score and CUORE score, but this association was found only for men. Being a doctor is a protective factor for the CUORE score.

Keywords

Framingham CUORE score healthcare workers cardiovascular diseases cardiac event

1 Introduction

Cardiovascular disease (CVD) is a general term that describes a disease of the heart and blood vessels [1]. The World Health Organization (WHO) approximates an annual global CVD-related mortality rate of 17.9 million individuals, constituting about 32% of total worldwide deaths [1]. Within Italy, CVDs persist as the primary cause of mortality, attributing to 34.8% of all recorded deaths [2]. Risk factors (RFs) for CVDs include high blood pressure, tobacco use, raised blood lipids, and diabetes, physical inactivity, and excessive use of alcohol [1]. Research has shown that the adoption of appropriate lifestyle changes could mitigate over 75% of fatal CVD occurrences [3].

In order to be able to adopt specific therapeutic interventions as part of primary prevention, it is useful to have a simple and straightforward tool to assess the cardiovascular risk (CVR) in an individual. Several CVR calculation scores that aim to assess the likelihood of developing a cardiovascular event (CVE) in the future can be found in the literature, including the Framingham risk score and the CUORE score.

The progress made in the prevention and treatment of CVDs over the last 40 years have led to a 40% reduction in mortality from these diseases. However, this reduction seems to have subsided despite optimal treatment of traditional RFs. Up to 50% of patients with known coronary artery disease have recurrent cardiac events, such as myocardial infarction and sudden cardiac death, even with aggressive treatment against traditional RFs. This evidence suggests that further interventions, particularly those implemented against non-traditional RFs, could be useful in continuing the reduction of cerebrovascular events.

In the past two decades there has been an increase in the prevalence of overweight or obesity among young adults resulting in the development of an ascending unhealthy cardiovascular profile [4 –9]. Furthermore, e-cigarette use, substance abuse, and rates of diabetes have increased in this population [10 –12]. Trends in the incidence of CVDs in the las two decades show a decrease in the elderly while are mostly stable or slightly increasing in young adults [13] which may result in an increased burden of CVDs in the future if appropriate strategies are not put in place.

Shift work is associated with increased risk of cardiovascular diseases or cardiovascular risk factors, such as diabetes (increase of about 10%), overweight (increase of 25–38%), obesity (increase of 5–18%) [14] and healthcare workers (HCWs) are continuously exposed to shift work, high demand from the superiors and the patients, and high levels of physical and mental stress which exposes them to a high risk of a cardiovascular event. Despite these findings, scant attention has been directed towards the exploration of CVR among young HCWs. Therefore, the present study aims (i) to calculate CVR at ten years in HCWs at the teaching hospital Policlinico Umberto I of Rome, using two different scores. The choice of the HCW category was due to the possibility to start a follow-study due to the availability of surveillance health checks. Moreover, we aimed at (ii) identifying the associated factors, in order to design and implement interventions in the workplace to reduce this risk.

2 Methods

2.1 Study design and population

A cross-sectional study was performed following the STROBE Statement, between January 2019 and July 2020 at the teaching hospital Policlinico Umberto I of Rome.

The study population consisted of different healthcare professionals, including all the medical specialists and those in specialty training, nurses, social workers, radiology technicians and medical engineering staff. The inclusion criteria were a working seniority of at least 1 year and age between 20 and 40 years old, since at the international level the age of 40 years is considered a cut-off value for classify people as young or not for cardiovascular diseases [15]. For HCWs that in the considered period had undergone more than one visit, only the first one was included.

2.2 Evaluation instruments

All information was obtained through the annual medical record compilation by medical history linkage and blood analysis performed in the morning (7–9 am) on an empty stomach. For shift workers it was performed before or during the morning shift. The collected data were stored in a database for subsequent analysis.

2.3 Variables evaluated

The parameters evaluated were [16, 17]:

Resting blood pressure (systolic, SBP, and diastolic DBP) taken in two consecutive measurements at 15 minutes apart. The value considered is the average of the two measurements. Five classes of Blood pressure were identified:

Optimal: SBP < 120 mmHg and DBP < 80 mmHg

Normal: SBP 120–129 mmHg and DBP 80–84 mmHg

Upper limits of standard: SBP 130–139 mmHg and DBP 85–89 mmHg

Stage I hypertension: SBP c 140–159 mmHg and DBP 90–99 mmHg

Stage II–IV hypertension: SBP≥160 or DBP≥100 mmHg.

When SBP and DBP fell into different categories, the higher category was chosen for classification purposes.

Total cholesterol, HDL cholesterol, Triglycerides, LDL cholesterol: peripheral venous blood sampling, performed after fasting at least 12 hours. They were classified as follows:

Total cholesterol, 5 classes: <160, 160 - 199, 200 - 239, 240 - 279,≥280 mg/dl

HDL cholesterol, 5 classes: <35, 35 - 44, 45 - 49, 50 - 59,≥60 mg/dl

Triglycerides, 2 classes: <150 mg/dl optimal levels;≥150 mg/dl elevated levels

LDL cholesterol, 5 classes: <100, 100 - 129, 130 - 159,≥160 mg/dl

Glycemia performed on peripheral venous blood sampling, performed after fasting at least 12 hours: ≤110 mg/dl indicating a good health status; 111–125 mg/dl indicating impaired fasting glucose;≥126 mg/dl high levels. Being treated with oral hypoglycemic drugs or insulin or if basal fasting blood glucose was≥126 mg/dl was considered an indication od diabetes.

Tobacco smoking: years of tobacco smoke and average number of cigarettes smoked per day. A smoker is defined as someone who smokes regularly every day (even one cigarette) or has quit for less than 12 months. A nonsmoker is defined as someone who has never smoked or has quit for more than 12 months.

Medication history: taking any drug (for hypertension, diabetes, and other).

Age was classified into 4 classes: 20–25, 26–30, 30–35, 36–40.

2.4 Statistical analysis

The collected data were entered into a database and analyzed using SPSS statistical software for Windows 10, version 25.0 (IBM, Armonk, NY, USA).

Descriptive analysis involved the use of means, medians, standard deviations (SDs). Univariate, bivariate and multivariate analysis was also performed. A statistically significant difference was accepted at a P-value of < 0.05.

Based on the available data, we calculated a score to estimate 10-year CVR in HCWs at the teaching hospital Policlinico Umberto I, adopting the mathematical equation of the Framingham study reported by Wilson et al. [18].

The biochemical and clinical data were converted into points and then summed: these determined the total score, which was then converted into a percentage of the predicted CVR for the next ten years.

We also calculated the risk of having a CVE at ten years with the CUORE Individual Score according to the CUORE algorithm [19]. An inclusion criterion to use the CUORE algorithm is not having had a previous CVE through standardized measurement of RFs. We entered the variables gender, age, tobacco smoking, SBP, total and HDL cholesterol, diabetes, and regular use of antihypertensives into adatabase.

Finally, a multiple linear regression analysis was conducted having Framingham score and CUORE score as dependent variables (not transformed and with a natural logarithm transformation), and as independent variables: age, gender (males as reference group), profession type (other health professionals as reference group), night work (No as reference group). Two analyses were conducted with all variables (full model) and with a stepwise procedure (stepwise with backward elimination). The results are presented in the form of coefficient β and p-value. The goodness of fit of the model was evaluated with the coefficient of determination R². We carried out multivariate analysis for the whole population, and separately for females and for males, since for the whole population the distribution of the dependent variables was not normal. We performed diagnostic tests for model assumptions when applying a linear regression model using Normal P-P plot of unstandardized Residual and scatterplot of the standardized predicted dependent variable against the standardized residuals, scatter plots for checking for equality of variances and correlation coefficients for the independent variable for looking at high values (over 0.7).

2.5 Ethical considerations

This study, as a descriptive study, is exempt from Institutional Review Board approval. Informed consent was obtained from all subjects involved in the study.

3 Results

Table 1 summarizes the characteristics of the study population.

Table 1
Socio-demographic and anamnestic data of the study population

Variable Number (%) or Mean (SD)

Gender

F 360 (68,6%)

M 165 (31,4%)

Job title

Physician 308 (58,5%)

Nurse 169 (32,1%)

Other health professions 48 (9,4%)

Age

20–25 years 38 (7,2%)

26–30 years 250 (47,5%)

31–35 years 180 (34,4%)

36–40 years 57 (10,9%)

Smoking status

No 380 (72.4%)

Yes 145 (27.6%)

Diabetes mellitus

No 523 (99.6%)

Yes 2 (0.4%)

Hypertension

No 524 (99.8%)

Yes 1 (0.2%)

Total cholesterolemia

Normal (<200 mg/dL) 402 (76.5%)

Borderline (200-239 mg/dL) 98 (18.7%)

High (>240 mg/dL) 25 (4.8%)

Framingham score 1.77 (1.47)

CUORE score 0.73 (0.38)

Variable	Number (%) or Mean (SD)
Gender
F	360 (68,6%)
M	165 (31,4%)
Job title
Physician	308 (58,5%)
Nurse	169 (32,1%)
Other health professions	48 (9,4%)
Age
20–25 years	38 (7,2%)
26–30 years	250 (47,5%)
31–35 years	180 (34,4%)
36–40 years	57 (10,9%)
Smoking status
No	380 (72.4%)
Yes	145 (27.6%)
Diabetes mellitus
No	523 (99.6%)
Yes	2 (0.4%)
Hypertension
No	524 (99.8%)
Yes	1 (0.2%)
Total cholesterolemia
Normal (<200 mg/dL)	402 (76.5%)
Borderline (200-239 mg/dL)	98 (18.7%)
High (>240 mg/dL)	25 (4.8%)
Framingham score	1.77 (1.47)
CUORE score	0.73 (0.38)

A total population of 638 HCWs who had undergone a medical visit by the occupational medicine service in the period considered had all the clinical records needed for this study. Of them, 525 met the inclusion criteria of age and were included in the study. As for occupation, the most represented category is physicians (58.5%) followed by nurses (32.1%).

3.1 Framingham risk score

Table 2 shows the calculated risk of a 10-year CVE in women and men.

Table 2
Risk of cardiovascular event calculated at 10 years using the Framingham risk score

Risk of cardiovascular Gender

event calculated at Female Male Total

10 years (%) n (%) n (%) n (%)

1,00 353 (98.3) 15 (9.1) 368 (70.2)

2,00 4 (1.1) 48(29.1) 52 (9.9)

3,00 2 (0.6) 20 (12.1) 22 (4.2)

4,00 0 (0.0) 57 (34.5) 57 (10.9)

6,00 0 (0.0) 13 (7.9) 13 (2.5)

7,00 0 (0.0) 11 (6.7) 11 (2.1)

9,00 0 (0.0) 1 (0.6) 1 (0.2)

Total 359 (100.0) 165 (100.0) 524 (100.0)

Risk of cardiovascular	Gender
1,00	353 (98.3)	15 (9.1)	368 (70.2)
2,00	4 (1.1)	48(29.1)	52 (9.9)
3,00	2 (0.6)	20 (12.1)	22 (4.2)
4,00	0 (0.0)	57 (34.5)	57 (10.9)
6,00	0 (0.0)	13 (7.9)	13 (2.5)
7,00	0 (0.0)	11 (6.7)	11 (2.1)
9,00	0 (0.0)	1 (0.6)	1 (0.2)
Total	359 (100.0)	165 (100.0)	524 (100.0)

3.1.1 Multivariate analysis

A multivariate analysis was carried out only for men, since the pp plot for the whole population and for females for the Framingham score showed some evidence of non-normality, and the residual plot shows that the variance of the residuals was not constant across the range of fitted values. Using the linear regression model for males, we related CVR to four variables: age, job title, and being a shift worker. CVR, as measured by the Framingham equation, directly correlated with age (β = 0.260; p < 0.001) (Table 3).

Table 3
Multivariate analysis of cardiovascular risk using Framingham risk score for males

Full model Stepwise model

β (p) standardized β (p) standardized

Shift worker 0.094(0.264) –

Physician –0.104 (0.361) –

Nurse –0.033 (0.78) –

Age 0.245 (0.003) 0.260 (<0.001)

R² 0.287 0.260

	Full model	Stepwise model
Shift worker	0.094(0.264)	–
Physician	–0.104 (0.361)	–
Nurse	–0.033 (0.78)	–
Age	0.245 (0.003)	0.260 (<0.001)
R²	0.287	0.260

3.2 Cuore score

The histogram depicted in Fig. 1 shows the distribution of CVR in the study population, regardless of gender. As can be seen from this histogram, the majority of participants (519 participants, i.e. 98.9% of the sample) has a risk of less than 1% of having a CVE in the next ten years.

Fig. 1

Risk of cardiovascular event calculated at 10 years using the CUORE score.

3.2.1 Multivariate analysis

For the same reason, as noted before, a multivariate analysis was carried out only for men. Using the linear regression model, as can be seen from Table 4, qualification as a physician (p < 0.001) is associated with a lower risk of having a CVE in the next ten years, while age (p < 0.001) is directly proportional to this risk (Table 4).

Table 4
Multivariate analysis of cardiovascular risk using CUORE score for males

Full model Stepwise model

β (p) standardized β (p) standardized

Shift worker 0.009 (0.879) –

Physician –0.098 (0.243) –0.116 (0.037)

Nurse 0.022 (0.801) –

Age 0.715 (<0.001) 0.720 (<0.001)

R² 0.716 0.716

	Full model	Stepwise model
Shift worker	0.009 (0.879)	–
Physician	–0.098 (0.243)	–0.116 (0.037)
Nurse	0.022 (0.801)	–
Age	0.715 (<0.001)	0.720 (<0.001)
R²	0.716	0.716

The goodness of fit using the R² were high for both models (0.26 and 0.716). The assumptions for linear regression indicated a non-violation only for the models carried out for males (Annex 1).

4 Discussion

In this study, we calculated the risk of having a CVE at ten years among healthcare workers aged 20–40 years at the teaching hospital Policlinico Umberto I through two scores, the Framingham score and the CUORE score.

Using the Framingham risk score, we found that there is a significant difference between women (median 1%) and men (median 3%), the latter having a higher CVR. In fact, the female population has a CVR within the limits of the average CVR calculated by age while in the male population it appears that about 15% have a higher CVR than the average CVR calculated by age [20]. This issue is an important issue to consider in interpreting our results. The distribution of the risk scores was not normal for the whole population, especially for the contribution of females. This result is in line with data from other studies in the literature. Actually, it is well known that the incidence of CVD is low in premenopausal women and increases after menopause. Indeed, the transition to menopause is associated with a worsening of CVR [21]. The endogenous estrogen plays a protective role against atherosclerotic manifestations during childbearing age [22]. In premenopausal age the rate of CVE in women is low and is mainly ascribed to smoking [23]. From the methodological point of view, some authors have explored the possibility that adding female-specific factors to models containing established cardiovascular risk factors could have been a tool for improve the prediction of cardiovascular events. However, they demonstrated that doing this improved a little bit the prediction and in a non-significant way [24].

We need to recognize that traditional risk assessment tools, such as the Framingham Risk Score, can underestimate in a significant way the risk in women, just classifying women as having a low risk for CVD, and other factors are needed to improve the assessment, including autoimmune conditions (i.e., systemic lupus erythematosus, rheumatoid arthritis). depression and other psycho-social factors [25].

Furthermore, data obtained from the first analysis of the lifetime risk of developing coronary heart disease in the general population produced by the Framingham Heart Study showed that the risk of developing Acute Myocardial Infarction at the age of 40 has been calculated. This risk is 1 in 2 for males and 1 in 3 for females. The difference is confirmed, although attenuated, at age 70 where the risk is 1 in 3 for males and 1 in 4 for females [26].

Another factor found to lead to an increase in RCV is night shifts. In the literature, there are numerous studies that have evaluated this correlation, also due to the progressive increase in shift work. In fact, it has been reported that up to one-third of the workers in Europe and in the United States are employed in shift working [27], and this trend is increasing [28]. Shift work increases the risk of CVDs through several pathways, including disruption of circadian rhythm, lifestyle changes, workplace tension, and social stress, [27 , 29–31].

A recent meta-analysis[26] performed a quantitative dose-response assessment between the duration of shift work and morbidity and mortality from cardiovascular causes. A 5-year increase in shift work was shown to be associated with a 5% increase in the risk of developing CVD, and a 6% and 4% increase in the risk of morbidity and mortality from cardiovascular causes, respectively. The authors concluded that shift work could increase CVR in a dose-responsive manner, which has never been reported, including CVD morbidity and mortality.

Another point to underscore is the influence of increasing age in increase the risk, calculated using both the Framingham and the CUORE score, and this is in line with the scientific literature. This issue needs to be considered with care, since large proportion of adults are not adequately classified for primary prevention of CVD, due to their low of very low short-time predicted CVD risk, and there is evidence of the utility of using lifetime risk [32].

In our analysis, a direct relationship between CVR and age was seen, i.e., as age increases, CVR also increases (β = 0.104 and p < 0.001). In fact, age is one of the non-modifiable, well-known RFs with regard to CVDs due to its effect on the arterial system and the heart [33].

With regard to the calculation of CVR with the CUORE score, it was found that the majority of the participants had a risk of having a CVE in the next ten years between 0.25% and 0.75%, with 98.9% of the sample having a risk of less than 1% and an average of this risk for the entire study population of 0.73%. The multivariate analysis carried out for males showed that, using the CUORE score as the dependent variable, physicians (β = -0.116; p = 0.037) were associated with a lower risk of having a CVE in the next ten years, while age was directly proportional to this risk (β = 0, 720; p < 0,001). On the other hand, using the Framingham score as the dependent variable, the only variable associated to the increasing of the risk was age (β = 0, 260;p < 0,001).

To our knowledge, there are two studies that have assessed CVR in HCWs using the FRS. One of them [34] did not assess risk by job category while the other study [35] reported an increased risk for doctors compared to nurses, however, these results were not statistically significant. Other studies that have investigated cardiovascular RFs among HCWs have reported greater occurrence among doctors than nurses [36, 37], in contrast to what we found using the CUORE score. Furthermore, a recent systematic review [38] reported that although physicians were more aware of their cardiovascular RFs, they were more prone to obesity and cardiovascular disorders and more at risk than nurses.

A similar study was conducted by Gonzalez-Velàzquez and Mendez in 2007 in Veracruz, Mexico [39]. In this study, CVR was calculated using the SCORE system in a population of 96 HCWs under 40 years of age and found that 14% of these participants had a 10-year CVR greater than 2% (which is twice what is expected in a young population of HCWs) and 51% of the population had a risk less than 1%.

Considering the fact that CUORE score, unlike the Framingham risk score, (I) did not identify shift work as a risk factor for CVDs, as has been well documented in the literature [28 , 40–42], and (II) found that physicians have a lower risk of CVE than nurses while several studies in the literature suggesting the opposite although there is not a clear consensus [35–38 , 44], we think that FRS presents a better fitting for measuring CVR in this particular group of HCW population.

This study has strengths and weaknesses.

The weaknesses are related to the methodology of the study. In fact, we were able to measure CVR in HCWs but cause-and-effect relationships cannot be determined. Also, being carried out in a specific Roman hospital, the results are not generalizable. Finally, we can mention selection bias as only young HCWs that had scheduled a visit in the period considered were included in the study.

We need to recognize that the provided P-P plots and scatterplot of the standardized predicted dependent variable against the standardized residuals, either for Framingham risk score and CUORE risk score (not transformed) show that the residuals from the two multiple linear regression models are not normally distributed for the whole population under study (males and females together). However, the model with only males for both scores (Framingham and CUORE) as dependent variables indicates that the residuals are not heavy tailed, which means that the outliers are more or less what we would expect in a normally distributed dataset.

This is the first study in Italy to evaluate CVR in healthcare workers and, in particular, in young healthcare workers. This study can be a starting point for subsequent evaluation of CVR over the years in order to identify RFs and plan targeted interventions with the aim of reducing this risk.

Other strengths are the large number of healthcare professionals who participated in the study and the reduction in response bias related to the methodology used.

5 Conclusions

In conclusion, we found a higher cardiovascular risk among male healthcare workers compared to the average cardiovascular risk calculated by age and in shift workers, while females have a cardiovascular risk within the limits. It is interesting to underline that we found a lower cardiovascular risk among Doctors (using the CUORE score as a dependent variable). It is plausible that future studies that compare the difference between these scores can be useful for helping researchers to choose the most appropriate one. Over the past two years, healthcare personnel have faced a constant barrage of intense physical and psychological strain, primarily stemming from the ongoing pandemic. This unrelenting pressure places them at a significant vulnerability to experiencing cardiovascular incidents. Healthcare organizations should put attention into protecting health professionals from the risk of cardiovascular events. Scheduled frequent visits should be implemented especially in those at higher risk. This study can be a starting point for subsequent cardiovascular risk assessment over the years in order to identify risk factors and plan targeted interventions to reduce this risk.

Funding

This research received no external funding.

Footnotes

Acknowledgments

The authors have no acknowledgments.

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

Declaration of interest

The authors declare no conflict of interest.

Ethical approval

Not required due to the nature of the study design.

The Annex part is available in the electronic version of this article: .

References

WHO CARDIOVASCULAR DISEASES. Available from: https://www.who.int/health-topics/cardiovascular-diseases/#tab=tab_1.

Ministero della salute. Malattie Cardiovascolari [Internet]. Available from: http://www.salute.gov.it/portale/donna/dettaglioContenutiDonna.jsp?lingua=italiano&id=4490&area=Salutedonna&menu=patologie#:∼:text=Lemalattiecardiovascolari (malattie ischemiche,invalidità e mortalità in Italia.

la Torre

, Palmeri

, Pagano

, Nati

, Sernia

, Mannocci

Evaluation of cardiovascular risk profile: A comparative analysis between CUORE algorithm and the Framingham risk scores. Eur Rev Med Pharmacol Sci. 2018;22(8):2398–404.

Andersson

, Vasan

Epidemiology of cardiovascular disease in young individuals. Vol. 15, Nature Reviews Cardiology. Nature Publishing Group; 2018. p. 230–40.

O’Flaherty

, Buchan

, Capewell

Contributions of treatment and lifestyle to declining CVD mortality: Why have CVD mortality rates declined so much since the 1960s? Vol. 99, Heart. 2013. p. 159–62.

Gooding

, Gidding

, Moran

, Redmond

, Allen

, Bacha

, et al. Challenges and opportunities for the prevention and treatment of cardiovascular disease among young adults: Report from a national heart, lung, and blood institute working group. Vol. 9, Journal of the American Heart Association. American Heart Association Inc.; 2020.

Arora

, Stouffer

, Kucharska-Newton

, Qamar

, Vaduganathan

, Pandey

, et al. Twenty Year Trends and Sex Differences in Young Adults Hospitalized with Acute Myocardial Infarction: The ARIC Community Surveillance Study. Circulation. 2019;139(8):1047–56.

Ford

, Capewell

Coronary Heart Disease Mortality Among Young Adults in the U.S. 1980 From Through 2002. Concealed Leveling of Mortality Rates. J Am Coll Cardiol. 2007;50(22):2128–32.

Ford

Trends in predicted 10-year risk of coronary heart disease and cardiovascular disease among U.S. adults from 1999 to 2010. J Am Coll Cardiol. 2013;61(22):2249–52.

10.

George

, Tong

, Bowman

Prevalence of cardiovascular risk factors and strokes in younger adults. JAMA Neurol. 2017;74(6):695–703.

11.

Pearson-Stuttard

, Guzman-Castillo

, Penalvo

, Rehm

, Afshin

, Danaei

, et al. Modeling future cardiovascular disease mortality in the United States. Circulation. 2016;133(10):967–78.

12.

Leppert

, Poisson

, Sillau

, Campbell

, Ho

, Burke

Is Prevalence of Atherosclerotic Risk Factors Increasing Among Young Adults? It Depends on How You Ask. J Am Heart Assoc. 2019;8(6).

13.

Groenewegen

, Rutten

, Mosterd

, Hoes

Epidemiology of heart failure. Vol. 22 European Journal of Heart Failure. John Wiley and Sons Ltd;. 2020. p.1342–56.

14.

Boini

, Bourgkard

, Ferrières

, Esquirol

What do we know about the effect of night-shift work on cardiovascular risk factors? An umbrella review. Front Public Health. 2022;10:1034195.

15.

Arnett

, Blumenthal

, Albert

, Buroker

, Goldberger

, Hahn

, Himmelfarb

, Khera

, Lloyd-Jones

, McEvoy

, Michos

, Miedema

, Muñoz

, Smith

Jr , Virani

, Williams

Sr , Yeboah

, Ziaeian

2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596–e646.

16.

Carta del rischio cardiovascolare [Internet]. [cited 2023 Jan 27]. Available from: https://www.cuore.iss.it/valutazione/carte.

17.

Sohn

, Kim

, Bae

The framingham risk score, diet, and inflammatory markers in Korean men with metabolic syndrome. Nutr Res Pract [Internet]. 2012;6(3):246. Available from: https://e-nrp.org/DOIx.php?id=10.4162/nrp.2012.6.3.246.

18.

Wilson

PWF

, D’Agostino

, Levy

, Belanger

, Silbershatz

, Kannel

Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–47.

19.

Palmieri

, Rielli

, Demattè

, Donfrancesco

, Ciccarelli

, de Sanctis Caiola

, et al. CUORE project: implementation of the 10-year risk score. Eur J Cardiovasc Prev Rehabil [Internet]. 2011 Aug [cited 2022 Dec 13];18(4):642–9. Available from: https://pubmed.ncbi.nlm.nih.gov/21450625/.

20.

Coronary Heart Disease (10-year risk) | Framingham Heart Study [Internet]. [cited 2022 Oct 18]. Available from: https://www.framinghamheartstudy.org/fhs-riskfunctions/coronary-heart-disease-10-year-risk/.

21.

el Khoudary

, Aggarwal

, Beckie

, Hodis

, Johnson

, Langer

, et al. Menopause Transition and Cardiovascular Disease Risk: Implications for Timing of Early Prevention: A Scientific Statement From the American Heart Association. Circulation. 142(25).

22.

Rami Helvaci

, Kaya

, Gundogdu

Gender differences in coronary heart disease in Turkey. Pak J Med Sci. 2012;28(1):40–44.

23.

Prescott

, Hippe

, Schnohr

, Hein

, Vestbo

Smoking and risk of myocardial infarction in women and men: longitudinal population study. BMJ. 1998;316(7137):1043–7.

24.

Tschiderer

, Seekircher

, Willeit

, Peters

SAE

Assessment of Cardiovascular Risk in Women: Progress so Far and Progress to Come. Int J Womens Health. 2023;15, 191–212.

25.

Park

, Pepine

Assessing cardiovascular risk in women: looking beyond traditional risk factors. Trends Cardiovasc Med. 2015;25(2):152–3.

26.

Lloyd-Jones

, Larson

, Beiser

, Levy

Lifetime risk of developing coronary heart disease. Lancet. 1999;353(9147):89–92.

27.

Boivin

, Boudreau

Impacts of shift work on sleep and circadian rhythms. Pathologie Biologie [Internet]. 2014;62(5):292–301. Available from: https://dx-doi-org.web.bisu.edu.cn/10.1016/j.patbio.2014.08.001.

28.

Wang

, Ruan

, Chen

, Peng

, Li

Shift work and risk of cardiovascular disease morbidity and mortality: A dose-response meta-analysis of cohort studies. Eur J Prev Cardiol. 2018;25(12):1293–302.

29.

Frost

, Kolstad

, Bonde

Shift work and the risk of ischemic heart disease – a systematic review of the epidemiologic evidence. Scand J Work Environ Health. 2009;35(3):163–79.

30.

Wang

, Armstrong

MEG

, Cairns

, Key

, Travis

Shift work and chronic disease: the epidemiological evidence. Occup Med (Chic Ill). 2011;61(2):78–89.

31.

Havakuk

, Zukerman

, Flint

, Sadeh

, Margolis

, Konigstein

, et al. Shift Work and the Risk of Coronary Artery Disease: A Cardiac Computed Tomography Angiography Study. Cardiology. 2018;139(1):11–6.

32.

Di Castelnuovo

, Costanzo

, Persichillo

, Olivieri

, de Curtis

, Zito

, Donati

, de Gaetano

, Iacoviello

; MOLI-SANI Project Investigators. Distribution of short and lifetime risks for cardiovascular disease in Italians. Eur J Prev Cardiol. 2012;19(4):723–30.

33.

Lakatta

, Levy

Arterial and cardiac aging: Major shareholders in cardiovascular disease enterprises: Part I: Aging arteries: A “set up” for vascular disease. Circulation. 2003;107(1):139–46.

34.

Amg

Risco cardiovascular em profissionais de saúde de serviços de atendimento pré-hospitalar [Internet]. Vol. 46,Rev Esc Enferm USP. 2012. Available from: www.ee.usbr/reeusp/.

35.

Olubiyi

, Rotimi

, Afolayan

, Alatishe-Muhammad

, Olubiyi

, Balami

The ten-year risk of developing cardiovascular disease among public health workers in North-Central Nigeria using Framingham and atherogenic index of plasma risk scores. BMC Public Health. 2022;22(1).

36.

Lin

, Li

Introduction Prevalence of Cardiovascular Risk Factors in Taiwanese Healthcare Workers. Vol. 47,Industrial Health 2009.

37.

Naik

, Jacob

, Reddy

Prediction of cardiovascular risk among healthcare professionals using atherosclerotic cardiovascular disease risk score in a tertiary care hospital in Aurangabad, India. Annals of Clinical Cardiology. 2021;3(2):69.

38.

Hassan

, Patel

, Peddemul

, Sikandar

, singh kahlon

simranjit

, Nair

, et al. Knowledge, Attitude and Health Practice towards Cardiovascular disease in Health care providers: A Systematic Review. Current Problems in Cardiology. Elsevier Inc.; 2022.

39.

González-Velázquez

, Mendez

Cardiovascular risk stratification by means of the SCORE system in health care workers in Veracruz, Mexico. Int J Cardiol. 2007;121(1):81–3.

40.

Knutsson

, Jonsson Bjorn

, Akerstedt

, Orth-Gomer

INCREASED RISK OF ISCHAEMIC HEART DISEASE IN SHIFT WORKERS. The Lancet. 1986;328(8498):89–92.

41.

, Huang

J tao

, Tan

, Yang

B ping

, Tang

Shift work and risk of stroke: A meta-analysis. Int J Cardiol. 2016;214:370–3.

42.

Vyas

M v

, Garg

, Iansavichus

A v

, Costella

, Donner

, Laugsand

, et al. Shift work and vascular events: systematic review and meta-analysis. BMJ [Internet]. 2012 Jul 26 [cited 2022 Dec 13]; 345(7871). Available from: https://www.bmj.com/content/345/bmj.e4800.

43.

Jjp

, Pei Xuan

, Weng Ken

, Pin Fen

, Yeo Jui Ping

, Samarahan

, et al. Lifestyle factors associated with cardiovascular risk among healthcare workers from the tertiary hospitals in Sarawak. Vol. 15, Malaysian Family Physician. 2020.

44.

Lin

, Yang

, Sung

, Li

Risks and Causes of Hospitalizations among Physicians in Taiwan. Health Serv Res. 2008;43(2):675–92.

Risk of cardiovascular	Gender
event calculated at	Female	Male	Total
10 years (%)	n (%)	n (%)	n (%)
1,00	353 (98.3)	15 (9.1)	368 (70.2)
2,00	4 (1.1)	48(29.1)	52 (9.9)
3,00	2 (0.6)	20 (12.1)	22 (4.2)
4,00	0 (0.0)	57 (34.5)	57 (10.9)
6,00	0 (0.0)	13 (7.9)	13 (2.5)
7,00	0 (0.0)	11 (6.7)	11 (2.1)
9,00	0 (0.0)	1 (0.6)	1 (0.2)
Total	359 (100.0)	165 (100.0)	524 (100.0)

	Full model	Stepwise model
	β (p) standardized	β (p) standardized
Shift worker	0.094(0.264)	–
Physician	–0.104 (0.361)	–
Nurse	–0.033 (0.78)	–
Age	0.245 (0.003)	0.260 (<0.001)
R²	0.287	0.260

Cardiovascular risk in young healthcare professionals: A cross-sectional study

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Methods

2.1 Study design and population

2.2 Evaluation instruments

2.3 Variables evaluated

2.4 Statistical analysis

2.5 Ethical considerations

3 Results

Table 3 Multivariate analysis of cardiovascular risk using Framingham risk score for males Full model Stepwise model β (p) standardized β (p) standardized Shift worker 0.094(0.264) – Physician –0.104 (0.361) – Nurse –0.033 (0.78) – Age 0.245 (0.003) 0.260 (<0.001) R2 0.287 0.260

Table 4 Multivariate analysis of cardiovascular risk using CUORE score for males Full model Stepwise model β (p) standardized β (p) standardized Shift worker 0.009 (0.879) – Physician –0.098 (0.243) –0.116 (0.037) Nurse 0.022 (0.801) – Age 0.715 (<0.001) 0.720 (<0.001) R2 0.716 0.716

5 Conclusions

Funding

Footnotes

Acknowledgments

Informed consent statement

Declaration of interest

Ethical approval

References

Table 3
Multivariate analysis of cardiovascular risk using Framingham risk score for males

Full model Stepwise model

β (p) standardized β (p) standardized

Shift worker 0.094(0.264) –

Physician –0.104 (0.361) –

Nurse –0.033 (0.78) –

Age 0.245 (0.003) 0.260 (<0.001)

R² 0.287 0.260