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Prevalent risk factors and determinants of cardiovascular disease among staff in South-South Nigeria
, Iniobong Boniface Williams1,, Margaret Inemesit Akpan1, Peter Alexander1, Obiageli Chiezey Onwusaka1, Aniekanabasi Jonathan Okon1, Ekpereonne Babatunde Esu1, Antor Odu Ndep1
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Received: ,
Accepted: ,
How to cite this article: Ekpenyong BN, Williams IB, Akpan MI, Alexander P, Onwusaka OC, Okon JA, et al. Prevalent risk factors and determinants of Cardiovascular disease among staff in South-South Nigeria. Calabar J Health Sci. doi: 10.25259/CJHS_19_2025
Abstract
Objectives:
Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, yet routine risk assessment is not standard practice in many institutions, especially in low- and middle-income countries. This study aimed to assess the prevalence and determinants of CVD risk factors among staff of Federal Universities in Nigeria’s South-South geopolitical zone.
Materials and Methods:
A cross-sectional, comparative, and analytical study was conducted among 372 academic and non-academic staff members from two universities selected through stratified random sampling. Data collection involved a semi-structured questionnaire administered through the KoboCollect app, alongside physical and biochemical assessments. Anthropometric measurements were obtained using standard procedures. Height was measured to the nearest 0.1 cm with a stadiometer, and weight to the nearest 0.1 kg using a calibrated digital scale, with participants barefoot and in light clothing. Body mass index was calculated as weight (kg) divided by height squared (m2). Blood glucose and lipid profile (total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol [LDL-C], very-LDL-C, triglycerides) were analyzed using the Point of Care Accu-Chek Active and CardioChek Professional Analyzer. Data was cleaned in Microsoft Excel 2013 and analyzed using international business machines the Statistical Package for the Social Sciences version 23.0.
Results:
The mean age of participants was 51.20 ± 6.97 years. Obesity (15.1%) was the most prevalent CVD risk factor, followed by diabetes mellitus (7.5%), hypertension (4.6%), and elevated LDL-C (3.8%). Non-academic staff exhibited significantly higher rates of diabetes mellitus (χ2 = 6.165, P = 0.046) and hypertension (χ2 = 8.029, P = 0.018) compared to academic staff, while academic staff had a significantly higher prevalence of obesity (χ2 = 7.365, P = 0.022). Although LDL-C was also higher among academic staff (4.8%) than non- academic staff members (2.7%), the difference was not statistically significant (P = 0.276). Notably, 37.9% of respondents, particularly non-academic staff, demonstrated poor knowledge of CVD risk factors. Undesirable lifestyle behaviors such as smoking and excessive alcohol use (x̄ ≥ 4 bottles/month) were significantly associated with non-academic staff (p <0.001).
Conclusion:
The major CVD risk factors identified are obesity, hypertension, diabetes, and low-density lipoprotein cholesterol. Physical inactivity, lack of awareness, smoking, and alcohol use are the identified modifiable life behaviors. This study highlights the need for institutional-level interventions, including regular screening and targeted health education. Strengthening awareness and early detection programs within Nigerian universities is essential to reducing the long-term burden of CVD and promoting a healthier workforce.
Keywords
Cardiovascular diseases
Determinants
Prevalent risk factors
Public tertiary institutions
Staff
INTRODUCTION
Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, accounting for an estimated 17.9 million deaths annually, approximately 31% of all global deaths.[1] Over 75% of these deaths occur in low-and middle-income countries (LMICs), including Nigeria. While infectious diseases once dominated sub-Saharan Africa’s health landscape, rapid urbanization, industrialization, and lifestyle changes have triggered a growing epidemic of non-communicable diseases (NCDs), especially CVDs.[2,3] This epidemiological transition is fueled by modifiable risk factors such as unhealthy diets, physical inactivity, tobacco use, harmful alcohol consumption, and increasing rates of hypertension, obesity, and diabetes.[4]
In Nigeria, CVDs are increasingly contributing to the national disease burden, affecting individuals during their most productive years and placing significant economic strain on households and health systems.[5] According to the Nigerian Heart Foundation in 2022, heart-related ailments are now among the top causes of hospital admissions and mortality, particularly in urban and semi-urban areas.[6] Despite this alarming trend, awareness, screening, and prevention remain suboptimal, especially among working populations who are often assumed to be at lower risk.
The workforce in Federal tertiary institutions, comprising academic and non-academic staff, represents a vital but understudied population in Nigeria’s health and research landscape. Staff in these institutions often face sedentary routines, chronic occupational stress, long working hours, and limited opportunities for physical activity.[7,8] These factors can contribute to a high prevalence of cardiovascular risk, yet there is limited empirical data on the magnitude and determinants of such risk in this professional group. Unlike the general population, university staff may be uniquely vulnerable due to the combination of intellectual labor, administrative responsibilities, and lifestyle constraints.
Previous studies have highlighted that occupation-specific risk factors such as prolonged sitting, high job demands, irregular meals, and mental stress can significantly elevate the likelihood of developing CVD.[9,10] University staff, particularly in academic roles, are often under pressure to meet publication requirements, engage in research, teach large classes, and handle administrative tasks. These stressors can lead to unhealthy coping mechanisms such as smoking, alcohol consumption, and poor dietary habits.[11,12] Moreover, the increasing digitization of academic work has reduced physical activity further, while access to regular health checks and routine exercise programs is often minimal or underutilized.
Compounding this occupational exposure are socio-demographic, behavioral, and lifestyle factors that serve as critical determinants of cardiovascular health. Age, gender, marital status, income level, and educational attainment all influence CVD risk status differently in LMIC contexts compared to high-income countries.[13] In Nigeria, some studies have noted a higher prevalence of obesity and hypertension among individuals with lower socio-economic status[14], while others indicate that even highly educated individuals, such as those found within the perimeter of the university, may have poor knowledge or misperceptions about their cardiovascular health.[15]
The South-South geopolitical zone of Nigeria, comprising Rivers, Bayelsa, Akwa Ibom, Cross River, Delta, and Edo states, is particularly relevant for this study due to its economic and educational significance. The region hosts several public tertiary institutions yet faces ongoing public health challenges, including environmental pollution, high rates of alcohol use, and a sedentary lifestyle culture, especially among the urban educated elite. Despite this, little is known about the cardiovascular health profile of staff in these institutions. The lack of structured health promotion programs and preventive screening within university settings may contribute to underdiagnosis and delayed treatment of risk factors related to CVD.
Globally, the importance of early detection and management of CVD risk factors is well-established. Laboratory and non-laboratory-based cardiovascular risk prediction models have been widely recommended as cost-effective screening tools, especially in resource-limited settings.[16] These models typically assess factors such as age, blood pressure (BP), body mass index (BMI), cholesterol levels, smoking status, and diabetes history. However, in Nigeria, the routine application of such models in occupational settings, including universities, is rare. A comprehensive assessment involving both biochemical and physical parameters is even less common, despite its potential to inform early intervention and lifestyle modifications.
Evidence from other countries shows that academic and non-academic staff in higher institutions often have high rates of overweight, hypertension, and sedentary behavior. For example, a Brazilian study reported that over 58% of university staff self-identified as obese, while over one-third reported not engaging in regular physical activity.[12] Similar trends are emerging in Nigeria, yet national data are lacking. The few studies conducted in Nigeria have often been limited in scope, relying on self-reported data without objective biochemical assessments.[11,15]
As Nigeria works toward achieving the Sustainable Development Goals (SDGs), particularly SDG 3.4, which aims to reduce premature mortality from NCDs by one-third by 2030, identifying high-risk populations and addressing preventable determinants of CVD are critical public health priorities. Given the strategic role that universities play in shaping national development, ensuring the health and productivity of their staff is imperative.
This study, therefore, aimed to investigate the prevalent risk factors and determinants of CVDs among staff in public tertiary institutions in South-South Nigeria. Specifically, it sought to determine the prevalence of CVD risk factors among academic and non-academic staff and identify lifestyle choices and behavioral determinants associated with CVD risk.
By focusing on this understudied population, the study hopes to fill a critical gap in public health research in Nigeria. The findings could inform institutional policies on staff health, support the design of workplace wellness programs, and contribute to national strategies aimed at curbing the rise of CVDs.
MATERIALS AND METHODS
Study setting
This study was conducted in the South-South geopolitical zone of Nigeria. The zone comprises Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers states,[17] covering approximately 85,303 km2. As of the 2006 census, the population was 21,014,655, with an annual growth rate of 3.2%.[17] The region hosts seven federal universities, mostly in urban areas, with access to healthcare and basic amenities.[18]
Study design
An analytic cross-sectional design was employed to determine the prevalence and determinants of CVD among staff in public tertiary institutions in South-South Nigeria.
Study population
The study included asymptomatic staff aged 40 years and above from the University of Calabar and the University of Uyo.
Sample size determination
The sample size was determined using the WHO STEPS[5] approach, with an assumed CVD risk prevalence of 4.1%.[19] Applying the standard formula, n = Z2Pq/d2, an initial sample size of 60 was obtained. This value was then adjusted using the finite population correction formula, to account for the total staff population of 26,723. The sample size was calculated as 59 for both tertiary institutions. Multiplied by design effect (1.5) and age-sex groups,[4] the total was 352.26. With 5% added for non-response, the final sample size was 371. To avoid bias, 372 participants (equal males/females) were enrolled.
A 62-item WHO STEPS-based semi-structured questionnaire collected socio-demographics, CVD knowledge, lifestyle, and clinical data through KoboCollect. Clinical data were self-reported and researcher-verified.
Height, weight, and BP were measured by standard methods using standard tools (Seca®, Precision Hana®, Omron®). BP was taken thrice and averaged per the WHO guidelines. Ethical clearance was obtained from the Institutional Health Research Ethics Committee of the University of Uyo (Approval No. UU/CHS/IHREC/VOL.1/018) and from the Cross River State Health Research Ethics Committee, Cross River State Ministry of Health (Approval No. CRS/MH/ HREC/2021/VOL.VI/210).
Fasting samples of venous blood were collected for glucose and lipid profile tests using Accu-Chek and an ultraviolet-visible spectrophotometer, following standardized protocols.
Trained assistants administered tools, took measurements, and collected fasting blood samples. BMI was calculated; BP was measured per WHO cutoffs: High BP was defined as ≥130/85 mmHg based on the WHO criterion.
Inclusion criteria were asymptomatic staff aged ≥40 years based on epidemiological evidence indicating that the risk of developing NCDs increases significantly from mid-adulthood; those symptomatic or <40 were excluded. A multistage sampling method (involving selection of universities, faculties, and administrative units, as well as participants) was used to select 372 staff from the University of Calabar and the University of Uyo, matched by age and sex.
Ethical consideration
Ethical approval was obtained from relevant health authorities and university administrations. Participation was voluntary with written informed consent and confidentiality assured.
Method of data analysis
International Business Machines Statistical Package for the Social Sciences version 23 and Excel were used to analyze coded data. Chi-square was used to test the significance of the association between categorical variables at a 0.05 level of significance.
RESULTS
Socio-demographic characteristics of participants
Participants were staff of two Federal Universities in South-South Nigeria, with a mean age of 51.20 ± 6.97 years. The 50–54 age group made up 27.4%. The male-to-female and academic-to-non-academic staff ratios were 1:1. Pentecostals constituted 50.5%, Orthodox Christians 45.4%, while Muslims (1.1%) and Atheists (3.0%) were minimal. Nearly half (49.5%) had 3–4 adults per household. Most participants earned between ₦61,000 and ₦150,000 monthly, which was the study’s lowest income category. Two-thirds (67.5%) were married; 24.2% were single. Over 78.2% had tertiary education, whereas only 3.8% had primary or technical education [Table 1].
| Characteristics | Frequency (n = 372) | Percentage |
|---|---|---|
| Age (in years) | ||
| 40–44 | 78 | 21.0 |
| 45–49 | 68 | 18.3 |
| 50–54 | 102 | 27.4 |
| 55–59 | 53 | 14.3 |
| 60–64 | 71 | 19.0 |
| Mean age | 51.20±6.97 | |
| Sex | ||
| Male | 186 | 50.0 |
| Female | 186 | 50.0 |
| Staff category | ||
| Academic | 186 | 50.0 |
| Non-academic | 186 | 50.0 |
| Religious affiliation | ||
| Atheism | 4 | 1.1 |
| Islam | 11 | 3.0 |
| Orthodox | 169 | 45.4 |
| Pentecostal | 188 | 50.5 |
| Household size | ||
| 1–2 | 86 | 23.1 |
| 3–4 | 184 | 49.5 |
| 5–6 | 102 | 27.4 |
| Mean household size | 3.56±1.36 | |
| Estimated monthly income (n) | ||
| Low (61,000–150,000) | 156 | 42.0 |
| Fair (151,000–250,000) | 115 | 30.9 |
| Moderate (251,000–350,000) | 89 | 23.9 |
| High (351,000–450,000) | 12 | 3.2 |
| Marital status | ||
| Married | 251 | 67.5 |
| Separated | 31 | 8.3 |
| Single | 90 | 24.2 |
| Highest level of education | ||
| Primary/Technical | 14 | 3.8 |
| Secondary | 67 | 18.0 |
| Tertiary | 291 | 78.2 |
Prevalence of CVD risk factors
Four major CVD risk factors were identified: Obesity, diabetes, hypertension, and elevated LDL-C. Obesity was the most prevalent, affecting 15.1% of participants, followed by diabetes (7.5%), hypertension (4.6%), and elevated LDL-C (3.8%) [Figure 1].
Obesity
Obesity was more prevalent among academic staff (18.2%) than non-academic staff (11.8%). Similarly, 33.9% of academic staff were overweight compared to 26.4% of non-academic staff. The mean BMI was 29.2 ± 8.0 kg/m2 for academic and 27.7 ± 6.0 kg/m2 for non-academic staff (P = 0.022), with an overall mean BMI of 28.5 ± 7.0 kg/m2.
Diabetes
Pre-diabetes and diabetes were also more common among non-academic staff (18.9%, 9.1%) than academic staff (13.4%; 4.3%). Mean blood glucose was higher in non-academic staff (135 ± 27 mg/dL) than in academic staff (115 ± 12 mg/dL). The overall mean glucose level was 125 ± 20 mg/dL.
Hypertension
Non-academic staff showed a higher prevalence of prehypertension (26.9%) and hypertension (5.9%) than academic staff (16.7%; 3.2%). Mean systolic/diastolic BP in academic staff was 145.8 ± 15.0 mmHg/91.6 ± 23.4 mmHg, and in non-academic staff, 165.5 ± 16.0 mmHg/100.5 ± 19.7 mmHg. Overall means were 155.7 ± 15.5 mmHg and 96.1 ± 21.6 mmHg.
Low-density lipoprotein cholesterol (LDL-C)
Non-academic staff had a higher prevalence of elevated LDL-C (4.8%) compared to academic staff (2.7%), indicating greater CVD risk. Mean LDL-C levels were 59.2 ± 19.5 mg/dL for academic staff and 74.5 ± 23.4 mg/dL for non-academic staff, with an overall mean of 66.9 ± 21.5 mg/dL.
DISCUSSION
This study highlights a significant burden of CVD risk factors among staff of some public tertiary universities in Nigeria’s South-South geopolitical zone, with notable disparities between academic and non-academic personnel.
Obesity emerged as the most prevalent CVD risk factor, affecting 15.1% of participants [Figure 1]. The present study revealed that academic staff had higher rates of overweight (33.9%) and obesity (18.2%) compared to their non-academic counterparts (26.4% and 11.8%, respectively), with an overall mean BMI of 28.5 ± 7.0 kg/m2 [Table 2]. These findings are consistent with previous studies among Nigerian university staff. For instance, Abuo et al. (2018)[20] reported similar trends at the University of Calabar, where 35.5% of staff were overweight, and 19.6% were obese. Comparable patterns were also observed at the University of Nigeria, Nsukka, although with slight variations; overweight and obesity prevalences among academic staff were 35.1% and 6.6%, respectively, while non-academic staff had rates of 38.4% and 6.7% [Table 2].[21] Collectively, these results suggest that overweight and obesity are prevalent among university staff in Nigeria, with academic staff appearing more susceptible to higher BMI levels, potentially reflecting lifestyle or occupational differences. Similarly, a study among health service providers in Lagos found obesity and overweight prevalences of 27.3% and 44.7%, respectively. Prediabetes and diabetes were observed to be more prevalent among non-academic staff (18.9% and 9.1%, respectively) compared to academic staff (13.4% and 4.3%), with an overall mean fasting blood glucose level of 125 ± 20 mg/dL [Table 2]. These findings agree with previous research among administrative staff at the University College Hospital, Ibadan, where a prediabetes prevalence of 22.3% was reported. The higher rates among non-academic staff may reflect differences in lifestyle, occupational physical activity, and dietary patterns, suggesting the need for targeted interventions to reduce the risk of progression to diabetes in this group.[22]
| Staff category | BMI status | Total (%) | χ2 | p-value | ||
| Healthy weight | Overweight | Obesity | 7.365 | 0.022* | ||
| Academic | 89 (47.9) | 63 (33.9) | 34 (18.2) | 186 (100.0) | - | - |
| Non-academic | 115 (61.8) | 49 (26.4) | 22 (11.8) | 186 (100.0) | - | - |
| Total | 204 (54.8) | 112 (30.1) | 56 (15.1) | 372 (100.0) | - | - |
| Blood sugar status (mg/dL) | ||||||
| Normal (<100) | Pre-diabetes (100–125) | Diabetes (≥126) | 6.165 | 0.046* | ||
| Academic | 153 (82.3) | 25 (13.4) | 8 (4.3) | 186 (100.0) | - | - |
| Non-academic | 134 (72.0) | 35 (18.9) | 17 (9.1) | 186 (100.0) | - | - |
| Total | 287 (77.2) | 60 (16.1) | 25 (6.7) | 372 (100.0) | - | - |
| Blood pressure classification | ||||||
| Normal (SBP=90–120, DBP=60–80) | Prehypertension (SBP=121–139, DBP=81–89) | Stage 1 hypertension (SBP=140–159, DBP=90–99) | - | 8.029 | 0.018* | |
| Academic | 149 (80.1) | 31 (16.7) | 6 (3.2) | 186 (100.0) | ||
| Non-academic | 125 (67.2) | 50 (26.9) | 11 (5.9) | 186 (100.0) | ||
| Total | 274 (73.7) | 81 (21.8) | 17 (4.5) | 372 (100.0) | ||
| LDL-C status | ||||||
| Normal (%) | At risk (%) | 1.188 | 0.276 | |||
| Academic | 177 (95.2) | 9 (4.8) | 186 (100.0) | |||
| Non-academic | 181 (97.3) | 5 (2.7) | 186 (100.0) | |||
| Total | 358 (96.2) | 14 (3.8) | 372 (100.0) | |||
Hypertension was more prevalent among non-academic staff (5.9%) compared to academic staff (3.2%). The overall mean systolic and diastolic BPs were 155.7 ± 15.5 mmHg and 96.1 ± 21.6 mmHg, respectively [Table 2]. This aligns with findings from the University of Maiduguri, where undiagnosed hypertension prevalence was 36.1% among staff.[23] Elevated LDL-C levels were observed in 4.8% of non-academic staff and 2.7% of academic staff. The overall mean LDL-C level was 66.9 ± 21.5 mg/dL [Table 2].
Significant differences were noted in lifestyle choices between academic and non-academic staff. Current smoking was reported by 7.0% of academic staff and 32.3% of non-academic staff (p <0.001). Alcohol use was higher among non-academic staff (52.7%) compared to academic staff (19.9%) (p <0.001) [Table 3]. Physical inactivity was prevalent, with only 10.2% of academic staff and 17.2% of non-academic staff engaging in daily recreational activities (p = 0.05) [Table 4]. These findings are consistent with a study among healthcare professionals in Southwest Nigeria, which reported a smoking prevalence of 9.13%[24] and highlighted the need for targeted interventions.
| Lifestyle choices | Staff category n=372, (%) | Total (%) | χ2 | p-value | |
|---|---|---|---|---|---|
| Academic | Non-academic | ||||
| Tobacco or smoking status | - | - | - | 45.288 | <0.001* |
| Never smoked | 154 (82.8) | 97 (52.2) | 251 (67.5) | - | - |
| Past smoker | 19 (10.2) | 29 (15.6) | 48 (12.9) | - | - |
| Current smoker | 13 (7.0) | 60 (32.3) | 73 (19.6) | - | - |
| Daily tobacco or smoking use | - | - | - | 0.601 | 0.896 |
| Manufactured cigarettes (x̄=4 sticks) | 3 (1.6) | 18 (9.7) | 21 (5.7) | - | - |
| Hand-rolled cigarettes (x̄=2 wraps) | 4 (2.2) | 13 (7.0) | 17 (4.6) | - | - |
| Pipes full of cigarettes (x̄=1 pipe) | 1 (0.5) | 4 (2.2) | 5 (1.3) | - | - |
| None | 5 (2.7) | 25 (13.4) | 30 (8.1) | - | - |
| Daily passive smoking | 6.350 | 0.012* | |||
| Yes (x̄=2 days) | 15 (8.1) | 31 (16.7) | 46 (12.4) | - | - |
| No | 171 (91.9) | 155 (83.3) | 326 (87.6) | - | - |
| Alcohol use | - | - | - | 43.263 | <0.001* |
| Yes | 37 (19.9) | 98 (52.7) | 135 (36.3) | - | - |
| No | 149 (80.1) | 88 (47.3) | 237 (63.7) | - | - |
| The largest monthly alcohol use | - | - | - | 12.883 | <0.001* |
| Yes (x̄=4 bottles) | 12 (6.5) | 35 (18.8) | 47 (12.6) | - | - |
| No | 174 (93.5) | 151 (81.2) | 325 (87.4) | - | - |
| Weekly fruit consumption | - | - | - | 0.469 | 0.494 |
| Yes (x̄=3 days) | 21 (11.3) | 17 (9.1) | 38 (10.2) | - | - |
| No | 165 (88.7) | 169 (90.9) | 334 (89.8) | - | - |
| Lifestyle choice | Staff category n = 372, (%) | Total (%) | χ2 | p-value | |
|---|---|---|---|---|---|
| Academic | Non-academic | ||||
| Household fat or oil use | - | - | 4.948 | 0.176 | |
| Lard or suet | 13 (7.0) | 22 (11.9) | 35 (9.4) | - | - |
| Vegetable oil | 163 (87.7) | 160 (86.0) | 323 (86.8) | - | - |
| Butter or ghee | 2 (1.0) | 1 (0.5) | 3 (0.8) | - | - |
| Margarine | 8 (4.3) | 3 (1.6) | 11 (3.0) | - | - |
| Weekly consumption of home-made food | - | - | - | 24.294 | <0.001* |
| Yes | 38 (20.4) | 7 (3.8) | 45 (12.1) | - | - |
| No (x̄=11 meals) | 148 (79.6) | 179 (96.2) | 327 (87.9) | - | - |
| Daily engagement in sporting/recreational activities for at least 10 min | - | - | - | 3.840 | 0.05 |
| Yes (x̄=2 days) | 19 (10.2) | 32 (17.2) | 51 (13.7) | - | - |
| No | 167 (89.8) | 154 (41.4) | 321 (86.3) | - | - |
| Reasons for not engaging in physical activity* | - | - | - | 44.340 | <0.001* |
| Physical disability | 8 (4.3) | 5 (2.7) | 13 (3.5) | - | - |
| No functional facility | 38 (20.4) | 11 (5.9) | 49 (13.2) | - | - |
| Long working hours | 144 (77.4) | 62 (33.3) | 206 (55.4) | - | - |
| Lack of motivation | 76 (40.9) | 113 (60.8) | 189 (50.8) | - | - |
*Significant at 95% level of confidence, p < 0.05
Limitations
This study relied on self-reported data for behavioral risk factors, which may be subject to recall or reporting bias. In addition, social desirability bias may have led some respondents to underreport behaviors considered unhealthy (e.g., alcohol or tobacco use), thereby potentially underestimating the actual prevalence of these risk factors. Furthermore, the exclusion of state and private university staff limits generalizability in the region. Institutional policies, work environments, access to healthcare, and staff welfare practices may differ significantly between federal, state, and private institutions, potentially influencing cardiovascular risk profiles. Furthermore, only selected CVD risk factors were evaluated because they were feasibly assessed within the research timeframe and available resources. As a result, other important CVD determinants, mental health indicators (such as depression or anxiety), and inflammatory biomarkers were not evaluated.
The study employed a cross-sectional design, which captures a snapshot of risk factors at a single point in time. This approach limits causal inference, as it cannot determine whether identified risk factors preceded or resulted from health outcomes. While occupational and lifestyle factors were assessed, broader environmental determinants of cardiovascular health, exposure to pollution, built environment, workplace wellness policies, or community-level healthcare access, were not comprehensively examined.
CONCLUSION
The study found a high prevalence of modifiable CVD risk factors among university staff, particularly among non-academics. These findings highlight the need for targeted health promotion interventions, including regular screening, lifestyle modification programs, and workplace wellness initiatives, to mitigate the burden of CVDs in this population.
Data availability:
The data that support the findings of this study are available through the corresponding author upon reasonable request.
Ethical approval:
The study was approved by the Institutional Ethics Committee at University of Uyo, bearing number (UU/CHS/ IHREC/ VOL.1/018), dated 30th January 2022, and by the Cross River State Health Research Ethics Committee, at Cross River State Ministry of Health, bearing number CRS/MH/HREC/2021/VOL. V1/210, dated 18th December 2024.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient has given consent for clinical information to be reported in the journal. The patient understands that the patient’s names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: The study received funding from the Tertiary Education Trust Fund under the institution-based research grant scheme, bearing number UC/DR&D/GAL/039.
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