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1.
Bull World Health Organ ; 96(11): 772-781, 2018 Nov 01.
Article in English | MEDLINE | ID: mdl-30455532

ABSTRACT

OBJECTIVE: To compare the World Health Organization (WHO) body mass index (BMI)-for-age definition of obesity against measured body fatness in African children. METHODS: In a prospective multicentre study over 2013 to 2017, we recruited 1516 participants aged 8 to 11 years old from urban areas of eight countries (Ghana, Kenya, Mauritius, Morocco, Namibia, Senegal, Tunisia and United Republic of Tanzania). We measured height and weight and calculated BMI-for-age using WHO standards. We measured body fatness using the deuterium dilution method and defined excessive body fat percentage as > 25% in boys and > 30% in girls. We calculated the sensitivity and specificity of BMI z-score > +2.00 standard deviations (SD) and used receiver operating characteristic analysis and the Youden index to determine the optimal BMI z-score cut-off for classifying excessive fatness. FINDINGS: The prevalence of excessive fatness was over three times higher than BMI-for-age-defined obesity: 29.1% (95% CI: 26.8 to 31.4; 441 children) versus 8.8% (95% CI: 7.5 to 10.4; 134 children). The sensitivity of BMI z-score > +2.00 SD was low (29.7%, 95% CI: 25.5 to 34.2) and specificity was high (99.7%, 95% CI: 99.2 to 99.9). The receiver operating characteristic analysis found that a BMI z-score +0.58 SD would optimize sensitivity, and at this cut-off the area under the curve was 0.86, sensitivity 71.9% (95% CI: 67.4 to 76.0) and specificity 91.1% (95% CI: 89.2 to 92.7). CONCLUSION: While BMI remains a practical tool for obesity surveillance, it underestimates excessive fatness and this should be considered when planning future African responses to the childhood obesity pandemic.


Subject(s)
Adiposity/physiology , Body Mass Index , Deuterium , Pediatric Obesity/diagnosis , Pediatric Obesity/pathology , Africa/epidemiology , Body Weights and Measures , Child , Female , Humans , Male , Pediatric Obesity/epidemiology , Prospective Studies , ROC Curve , Sensitivity and Specificity , World Health Organization
2.
Scand J Public Health ; 40(3): 229-38, 2012 May.
Article in English | MEDLINE | ID: mdl-22637361

ABSTRACT

AIMS: To determine and compare the extent of the nutrition transition between Kenyan and South African women. METHODS: A nationally representative sample of women aged ≥15 years (n=1008) was assessed in Kenya. Weight, height, and waist and hip circumferences were measured. A 24-hour dietary recall was conducted with each participant. This data was compared with data of the Demographic and Health Survey (DHS) of women in South Africa (n=4481). Dietary intake of South African women was based on secondary data analysis of dietary studies using the 24-hour recall method (n=1726). RESULTS: In South Africa, 27.4% women had a BMI ≥30 kg/m(2) compared with 14.2% of Kenyan women. In both countries there were large urban-rural differences in BMI, with the highest prevalence in women in urban areas. BMI increased with age, as did abdominal obesity which was equally prolific in both countries with more than 45% of women in the older groups having a waist/hip ratio ≥0.85. The nutrient mean adequacy ratio (MAR) of the South African rural diet was lower than those of the Kenyans diet (55.9; 57.3%, respectively). Dietary diversity score (DDS) and food variety score (FVS) were significantly lower in South African rural women (3.3; 4.9) compared with Kenyans (4.5; 6.8). CONCLUSIONS: Urban-rural differences in diet and weight status indicates that the nutrition transition was similar in both countries despite large sociodemographic differences; however, rural Kenyan women had a better MAR, DDS, and FVS than South African women, most probably due to 60% having access to land.


Subject(s)
Body Weight , Diet/standards , Nutritional Status , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data , Adolescent , Adult , Age Factors , Body Height , Body Mass Index , Female , Humans , Kenya/epidemiology , Middle Aged , Obesity/epidemiology , South Africa/epidemiology , Waist Circumference , Young Adult
3.
Scand J Public Health ; 39(1): 88-97, 2011 Feb.
Article in English | MEDLINE | ID: mdl-20851847

ABSTRACT

AIM: To assess the determinants of overweight and obesity in Kenyan women considered to be undergoing the nutrition transition. METHODS: A nationally representative sample of women (n = 1008) was randomly drawn. Weight, height, waist, and hip circumference were measured. A 24-hour dietary recall was conducted with each participant and a socio-demographic questionnaire completed. Data was analysed by age, education, location, and socioeconomic status. Risk for obesity was calculated while adjusting for age and location. RESULTS: Overweight and obesity (BMI ≥ 25 kg/m(2)) were highly prevalent in Kenya (43.3%). Urbanisation appears to be an important determinant of obesity since obesity was most prevalent in urban women in the high income group. Women in the high income group (7278 kJ) and in urban areas (7049 kJ) had the highest mean energy intakes. There were also significant urban/rural and income differences in the contribution of macronutrients to energy intake. Total fat intake was 34.5% of energy (E) in urban areas and 29.7% E in rural areas; while carbohydrates contributed 69.9% E in rural areas and 57.4% E in urban areas (p < 0.0001). Overweight was significantly more likely in the highest income group; among households where room density was low; electricity or gas was used for cooking; and households had own tap and/or own flush toilet. CONCLUSIONS: This study suggests that urbanisation and its associated economic advancement as well as changes in dietary habits are among the most important determinants of overweight and obesity in Kenyan women.


Subject(s)
Obesity , Overweight , Adult , Body Mass Index , Developing Countries , Energy Intake , Female , Humans , Kenya/epidemiology , Nutrition Policy , Nutritional Status , Obesity/diagnosis , Obesity/epidemiology , Overweight/diagnosis , Overweight/epidemiology , Prevalence , Risk Factors , Rural Population , Socioeconomic Factors , Surveys and Questionnaires , Urban Population , Women's Health
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