Prevalence and risk factors of obesity among undergraduate student population in Ghana: an evaluation study of body composition indices

• Main Authors: Christian Obirikorang (Author), Evans Asamoah Adu (Author), Enoch Odame Anto (Author), Anthony Afum-Adjei Awuah (Author), Angela Nana Bosowah Fynn (Author), George Osei-Somuah (Author), Patience Nyarkoa Ansong (Author), Alexander Owusu Boakye (Author), Ivy Ofori-Boadu (Author), Yaa Obirikorang (Author), Austin Gideon Adobasom-Anane (Author), Eric NY Nyarko (Author), Lois Balmer (Author)
• Format: Book
• Published: BMC, 2024-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12889-023-17175-5; 1471-2458
• Keywords: Adiposity; Obesity; Body composition; Undergraduate students; Risk factors; Public aspects of medicine; RA1-1270
• Source: BMC Public Health, Vol 24, Iss 1, Pp 1-12 (2024)

Abstract Background Obesity is a classified risk factor for several of the world's leading causes of death. In this study, we combined information contained in body mass index (BMI), total percentage body fat (TPBF) and relative fat mass (RFM) to estimate obesity prevalence and examine the risk factors associated with obesity. Methods The study recruited 1027 undergraduate students aged between 16 and 25 years using a cross-sectional study design and two-stage stratified random sampling between January and April 2019 from the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. Demographic, lifestyle, and family history of chronic disease data, were collected using a structured questionnaire. Bioelectrical impedance, along with height, weight, age, and gender, were used to estimate BMI and TPBF. The RFM was calculated using a published equation. The TPBF and RFM ranges were evaluated based on standard BMI thresholds and an informative combined obesity prevalence estimated in a Bayesian framework. Multiple logistic regression analysis was used to evaluate potential risk factors of overweight/obesity. Results Concordance between BMI, TPBF and RFM for obesity classification was 84% among female and 82.9% among male students. The Bayesian analysis revealed a combined prevalence means of obesity of 9.4% (95%CI: 6.9-12.2%) among female students and 6.7% (95%CI:4.3-9.5%) among male students. The odds of obesity were increased between 1.8 and 2.5 for females depending on the classification index. A significant increasing trend of obesity was observed with university-level. A family history of obesity was associated with a high estimate of general, central, and high TPBF. Conclusion Using multiple adiposity indicators conjointly in a Bayesian framework offers a greater power to examine obesity prevalence. We have applied this and reported high obesity prevalence, especially among female students. University level and family history of obesity were key determinants for obesity among the student population.