This was a cross-sectional study with descriptive and analytical aims, conducted at the University Hospital Center of Owendo (CHUO), in Gabon. Data collection took place in March 2023, on the occasion of the open days of the cardiology department. The hospital services were grouped into two categories: care services, including medical, surgical, and pharmaceutical activities, and auxiliary services, grouping support services such as administration, logistics, and maintenance.

The study population consisted of the staff of the CHUO. The participants were divided into five professional categories: medical staff, paramedical staff, administrative and management staff, logistics and support staff, as well as technical and security staff.

All agents aged 18 years or older, who had been working in the facility for at least six months, present at the time of screening, and who had given their informed consent to participate in the study were included. Pregnant women were excluded.

The collected data included sociodemographic variables (age, sex), professional variables (type of service and professional category), as well as clinical and biological parameters. Anthropometric measurements included weight, height, and abdominal circumference. Hemodynamic parameters included systolic and diastolic blood pressure as well as heart rate. Capillary blood glucose was also measured.

Weight was measured using a calibrate scale, with participants lightly dressed and barefoot. Height was measured using a stadiometer. Waist circumference is measured midway between the last rib and the top of the iliac crest, with a tape measure placed horizontally, at the end of a normal exhalation. Blood pressure was measured after a least 5 minutes of rest, in seated position, using an electronic sphygmomanometer. Heart rate was read on the blood pressure monitor display. Three measurements of blood pressure and heart rate were taken and the average was used for analysis. Capillary blood glucose was measured in fasting patients using a glucometer. The measurements were taken by trained nurses and medical externs trained.

The body mass index (BMI), calculated as the ratio of weight in kilograms to height in meters squared (kg/m²), was used to classify participants as having normal weight status (BMI < 25 kg/m²), overweight (BMI between 25 and 29.9 kg/m²), or obesity (BMI ≥ 30 kg/m²). Abdominal obesity was defined by an abdominal circumference greater than 102 cm in men and 88 cm in women.

Elevated blood pressure was defined by a systolic blood pressure ≥ 140 mmHg and/or diastolic ≥ 90 mmHg, or by the use of antihypertensive treatment. Heart rate was considered elevated starting from 80 beats per minute. Capillary blood glucose was deemed elevated for a value ≥ 110 mg/dL, with suspicion of diabetes starting from 126 mg/dL.

The data were collected using a standardized survey form. They were then entered into Microsoft Excel 365 and analyzed using Epi Info version 7 for descriptive analysis, and the online tool pvalue.io for statistical analysis.

Quantitative variables were expressed as means with their standard deviations, and qualitative variables as frequencies and percentages. The analysis of factors associated with obesity was conducted in two steps. A bivariate analysis was first performed to identify potentially associated variables. Variables with a p < 0.2 in bivariate analysis, as well as those deemed clinically relevant, were included in the multivariate model. The significant variables were then introduced into a binary logistic regression model to determine independently associated factors. The results were expressed as odds ratios (OR) with their 95% confidence intervals. The statistical significance threshold was set at p < 0.05.

Prior authorization from the competent administrative authorities was obtained before conducting the study. The data were collected anonymously and processed confidentially. Informed consent from each participant was obtained before their inclusion in the study.

General Characteristics of the Population

Table 1.General characteristics of the study population.

Out of 800 staff working to CHUO, 257 (32.1%) agents participated in the survey. The mean age was 40.9 ± 9.7 years with a sex ratio of 0.4. The staff from care services represented 65.0% of the workforce, with 43.2% paramedics. The administrative and management staff grouped 23.4% of the workforce (Table 1).

Participants reported being hypertensive (13.6%) or diabetic (2.3%). The prevalence of smoking was 4.3% (Table 2).

The mean body mass index was 28.3 ± 6.0 kg/m². The prevalence of obesity was 38.5% (obese individuals). It consisted of obesity at stages I (n = 61; 23.7%), II (n = 27; 10.5%), or III (n = 11; 4.3%). Abdominal obesity was observed in 45.9% of the subjects.

Table 2.Clinical data of the participants.

BMI: body mass index; BP: blood pressure; DBP: diastolic blood pressure; SBP: systolic blood pressure.

The mean values of systolic and diastolic blood pressure were respectively 127.9 ± 21.6 mmHg and 79.2 ± 13.9 mmHg. Blood pressure was elevated in 34.6% of the participants. An elevated heart rate was found in 44.0% of the subjects.

The mean capillary blood glucose was 95.0 ± 35.4 mg/dL. An elevated blood glucose was observed in 14.8% of the participants (Table 2).

In bivariate analysis, the factors associated with obesity were female sex (p = 0.05), history of hypertension (p = 0.001), diabetes, and elevated blood pressure (p = 0.002). There was no association between obesity and the type of service or staff (Table 2).

After adjustment for age, sex, blood glucose, and blood pressure, the factors independently associated with obesity were female sex and elevated systolic blood pressure (Table 3).

Table 3. Factors associated with obesity in multivariate analysis.

The independent association between obesity, female sex, and elevated blood pressure is in agreement with the literature data. Obesity is a major risk factor for cardiovascular diseases, particularly arterial hypertension, as has been widely demonstrated [11].

The absence of association with professional category suggests that the determinants of obesity are cross-cutting and exceed the differences in functions within the hospital environment. This observation aligns with data showing that individual and environmental factors play a predominant role in the occurrence of obesity [11][12].

This study presents certain limitations. Its cross-sectional nature does not allow establishing a causal relationship between the studied factors and obesity. Recruitment during a screening campaign could introduce a selection bias by including participants who are potentially more sensitized to health issues.

Furthermore, the absence of variables such as physical activity, dietary habits, sleep quality, shift work or psychosocial factors limits the analysis of obesity determinants. Their absence may have resulted in residual confounding. Finally, the single-center nature of the study restricts the generalization of results to other facilities or the entire country.