Evaluation of Anthropometric Parameters of Central Obesity among Professional Drivers, A Receiver Operating Characteristic (ROC) Analysis

Muhammad Asif, Muhammad Aslam, Saima Altaf, Abdul Majid, Saima Atif


Different anthropometric parameters have been proposed for assessing central obesity. The diagnostic performance of these anthropometric parameters and their ability to correctly measure central obesity for the professional community, like drivers, is questionable and needs to be assessed. The study aimed to examine the diagnostic performance of anthropometric parameters as indicators of central obesity in drivers as measured by waist circumference (WC) and to determine the best cut-off values for these parameters that would identify obese drivers. Anthropometric measurements from a cross-sectional sample of 197 professional drivers were taken under standard protocol. Receiver operating characteristics (ROC) analysis was used to examine the diagnostic performance and to determine the optimal cut-off point of each anthropometric parameter to identify centrally obese drivers. It was found that WC had a significant positive correlation with all other obesity indicators. The ROC curve analysis indicated that all the parameters analyzed had a good performance, but the waist-to-height ratio (WHtR) had a more predictive value of the area under the curve (AUC). Optimal cut-offs to identify central obesity in drivers were 0.55, 2.06, 0.95, and 25.44 for WHtR, conicity index, waist-to-hip ratio, and body mass index, respectively. These cut-off points for different indicators can be used to detect central obesity for drivers.



anthropometric parameters, central obesity, professional drivers, Receiver Operating Characteristic (ROC) curve, waist-to-height ratio


 1. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. The Lancet. 2011; 377(9765):557-567. doi: 10.1016/S0140-6736(10)62037-5

 2. James PT, Rigby N, Leach R. International Obesity Task Force. The obesity epidemic, metabolic syndrome and future prevention strategies. European Journal of Cardiovascular Prevention & Rehabilitation. 2004; 11(1):3-8. doi:10.1097/01.hjr.0000114707.27531.48

 3. Aslam M, Asif M, Altaf S. Obesity; prevalence among drivers and conductors in Multan, Pakistan. Professional Medical Journal. 2015; 22(7):859-864.

 4. Joshi BA, Joshi AV, Katti SM, Mallapur MD, Karikatti SS. A cross-sectional study of prevalence of overweight and obesity among bus drivers and conductors of North-West Karnataka Road Transport Corporation (NWKRTC) in Belgaum Division, Belgaum. Journal of Indian Medical Association. 2013; 111(3):157-159.

 5. Aguilar-Zinser JV, Irigoyen-Camacho ME, Ruiz-Garcia-Rubio V, et al. Prevalence of overweight and obesity among professional bus drivers in Maxico. Gaceta Medica de Mexico. 2007; 143(1):21-25.

 6. Huang B, Rodreiguez BL, Burchfiel CM, Chyou PH, Curb JD, Sharp DS. Association of adiposity with prevalent coronary heart disease among elderly men: The Honolulu heart program. International Journal of Obesity and Related Metabolic Disorder. 1997; 21(5): 340-348.

 7. Dalton M, Cameron AJ, Zimmet PZ, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. Journal Internal Medicine.2003; 254(6): 555-563.

 8. Hsieh SD, Yoshinaga H. Abdominal fat distribution and coronary heart disease risk factors in men-waist/height ratio as a simple and useful predictor. International Journal of Obesity and Related Metabolic Disorder. 1995; 19(8): 585-589.

 9. Valdez R, Seidell JC, Ahn YI, Weiss KM. A new index of abdominal adiposity as an indicator of risk for cardiovascular disease. A cross population study. International Journal of Obesity and Related Metabolic Disorder. 1993; 17(2):77-82.

 10. Udayar SE, Sampath S, Arun D, Sravan S. Epidemiological study of cardiovascular risk factors among public transport drivers in rural area of Chittoor district of Andhra Pradesh. International Journal of Community Medicine and Public Health. 2015; 2(4):415-420.

 11. Priya PL and P. Sathya. A study to find out cardiovascular risk in Bus drivers by using waist to height ratio and WHO/ISH risk prediction chart. International Journal of Innovation Research Science Engineering and Technology. 2015; 4(6):3933-3940.

 12. Tuchsen F, Hannerz F, Roepstorff C, Krause N. Stroke among male professional drivers in Denmark 1994-2003. Occupational and Environmental Medicine. 2006; 63(7): 456-460.

 13. Amira CO, Oke DA, Mabayoje MO, Bandele EO, Adewunmi JA. Prevalence of hypertension and metabolic risk factors among commercial transport workers in Lagos. Journal of Clinical Science. 2006; 6(2):25-30.

 14. International Diabetes Federation: the IDF consensus worldwide definition of the metabolic syndrome.http://www.idf.org/webdata/docs/Metabolic_syndrome_defination.pdf. Accessed 2 July 2007.

 15. Perkins NJ, Schisterman EF. The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. American Journal of Epidemiology. 2006; 163(7):670-675.

 16. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988; 240(4857):1285-1293.

 17. Martin BC, Church TS, Bonnell R, Ben-Joseph R, Borgstadt T. The impact of overweight and obesity on the direct medical costs of truck drivers. Journal of Occupational and Environmental Medicine. 2009 51(2): 180-4.

 18. French SA, Harnack LJ, Hnnan PJ, Mitchell NR, Gerlach AF, Toomey TL. Worksite environment intervention to prevent obesity among metropolitan transit workers. Preventive Medicine. 2010; 50(4): 180-5.

 19. Moreno CR, Louzada FM, Teixeira LR, Borges F, Lorenzi-Filho G. Short sleep is associated with obesity among truck drivers. Chronobiology International. 2006; 23 (6): 1295-303.

 20. Hannerz H, Tuchsen F. Hospital admissions among male drivers in Denmark. Occupational and Environmental Medicine. 2001; 58(4): 253-260.

 21. Aekplakorn W, Pakpeankitwatana V, Lee CM, et al. Abdominal obesity and coronary heart disease in Thai men. Obesity. 2007; 15(4): 1036-1042.

 22. Bullappa A, Harish BR, Mahendra BJ. Evaluation of anthropometric measurements of central obesity as screening tools in children: multi receiver operating characteristics analysis. International Journal of Community Medicine and Public Health. 2017; 4(1): 251-255.

 23. Shao J, Yu L, Shen X, Li D, Wang K. Waist-to-height ratio, an optimal predictor for obesity and metabolic syndrome in Chinese adults. Journal of Nutritional Health and Aging. 2010; 14(9): 782-785.

 24. Lin WY, Lee LT, Chen CY, et al. Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. International Journal of Obesity and Related Metabolic Disorder. 2002; 26(9): 1232-1238.

 25. World Health Organization. Waist circumference and waist-hip ratio: Report of a WHO expert consultation. Geneva, 8-11 December 2008.

 26. World Health Organization. The atlas of heart disease and stroke 2004. Available at: http:// www. Who.int/cardiovascular_diseases/resources/atlas/en/.Accessed 16 Jul 2015.

 27. Pitanga FJG, Lessa I. Sensitivity and specificity of the conicity index as discriminating coronary risk adults in Salvador, Brazil. Revista Brasileira Epidemiologia. 2004; 7(3):259-269.

 28. Flora MS, Mascie-Taylor CGN, Rahman M. Conicity index of adult Bangladeshi population and their socio-demographic characteristics. Ibrahim Medical College Journal. 2009; 3(1):1-8.

 29. Gu JJ, Rafalson L, Zhao GM, et al. Anthropometric measurements for prediction of metabolic risk among Chinese adults in Pudong new area of Shanghai. Experimental and Clinical Endocrinol & Diabetes. 2011; 119(7):387-394.

 30. Balkau B, Sapinho D, Petrella A, et al. Prescreening tools for diabetes and obesity-associated dyslipidemia: comparing BMI, waist and waist hip ratio. The DESIR study. European Journal of Clinical Nutrition. 2006; 60(3):295-304.

DOI: http://dx.doi.org/10.21109/kesmas.v15i3.3218


  • There are currently no refbacks.