Estimating the changing burden of disease attributable to high body mass index in South Africa for 2000, 2006 and 2012

被引:2
作者
Bradshaw, D. [1 ,2 ]
Joubert, J. D. [1 ]
Abdelatif, N. [3 ]
Cois, A. [1 ,4 ]
Turawa, E. B. [1 ]
Awotiwon, O. F. [1 ]
Roomaney, R. A. [1 ]
Neethling, I. [1 ,5 ]
Pacella, R. [5 ]
Pillay-van Wyk, V. [1 ]
机构
[1] South African Med Res Council, Burden Dis Res Unit, Cape Town, South Africa
[2] Univ Cape Town, Fac Hlth Sci, Dept Family Med & Publ Hlth, Rondebosch, South Africa
[3] South African Med Res Council, Biostat Res Unit, Cape Town, South Africa
[4] Stellenbosch Univ, Div Hlth Syst & Publ Hlth, Dept Global Hlth, Fac Med & Hlth Sci, Cape Town, South Africa
[5] Univ Greenwich, Fac Educ Hlth & Human Sci, Inst Lifecourse Dev, London, England
来源
SAMJ SOUTH AFRICAN MEDICAL JOURNAL | 2022年 / 112卷 / 8B期
基金
英国医学研究理事会;
关键词
D O I
10.7196/SAMJ.2022.v112i8b.16488
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background. A high body mass index (BMI) is associated with several cardiovascular diseases, diabetes and chronic kidney disease, cancers, and other selected health conditions. Objectives. To quantify the deaths and disability-adjusted life years (DALYs) attributed to high BMI in persons aged >= 20 years in South Africa (SA) for 2000, 2006 and 2012. Methods. The comparative risk assessment (CRA) methodology was followed. Meta-regressions of the BMI mean and standard deviation from nine national surveys spanning 1998 - 2017 were conducted to provide estimates by age and sex for adults aged >= 20 years. Population attributable fractions were calculated for selected health outcomes using relative risks identified by the Global Burden of Disease Study (2017), and applied to deaths and DALY estimates from the second South African National Burden of Disease Study to estimate the burden attributed to high BMI in a customised Microsoft Excel workbook. Monte Carlo simulation-modelling techniques were used for the uncertainty analysis. BMI was assumed to follow a log-normal distribution, and the theoretical minimum value of BMI below which no risk was estimated was assumed to follow a uniform distribution from 20 kg/m(2) to 25 kg/m(2). Results. Between 2000 and 2012, mean BMI increased by 6% from 27.7 kg/m(2) (95% confidence interval (CI) 27.6 - 27.9) to 29.4 kg/m(2) (95% CI 29.3 - 29.5) for females, and by 3% from 23.9 kg/m(2) (95% CI 23.7 - 24.1) to 24.6 kg/m2 (95% CI 24.5 - 24.8) for males. In 2012, high BMI caused 58 757 deaths (95% uncertainty interval (UI) 46 740 - 67 590) or 11.1% (95% UI 8.8 - 12.8) of all deaths, and 1.42 million DALYs (95% UI 1.15 - 1.61) or 6.9% (95% UI 5.6 - 7.8) of all DALYs. Over the study period, the burden in females was similar to 1.5 - 1.8 times higher than that in males. Type 2 diabetes mellitus became the leading cause of death attributable to high BMI in 2012 (n=12 382 deaths), followed by hypertensive heart disease (n=12 146), haemorrhagic stroke (n=9 141), ischaemic heart disease (n=7 499) and ischaemic stroke (n=4 044). The age-standardised attributable DALY rate per 100 000 population for males increased by 6.6% from 3 777 (95% UI 2 639 - 4 869) in 2000 to 4 026 (95% UI 2 831 - 5 115) in 2012, while it increased by 7.8% for females from 6 042 (95% UI 5 064 - 6 702) to 6 513 (95% UI 5 597 - 7 033). Conclusion. Average BMI increased between 2000 and 2012 and accounted for a growing proportion of total deaths and DALYs. There is a need to develop, implement and evaluate comprehensive interventions to achieve lasting change in the determinants and impact of overweight and obesity, particularly among women.
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页码:583 / 593
页数:11
相关论文
共 49 条
  • [1] Ahmad OB, 2001, Age standardisation of rates: A new WHO standard
  • [2] Mechanism linking diabetes mellitus and obesity
    Al-Goblan, Abdullah S.
    Al-Alfi, Mohammed A.
    Khan, Muhammad Z.
    [J]. DIABETES METABOLIC SYNDROME AND OBESITY-TARGETS AND THERAPY, 2014, 7 : 587 - 591
  • [3] [Anonymous], 2019, South Africa Demographic and Health Survey 2016
  • [4] [Anonymous], 2013, The national strategic plan for nurse education, training and practice 2012/13- 2016/17
  • [5] [Anonymous], 2018, National Income Dynamics Study
  • [6] Adiposity and fat distribution outcome measures: Assessment and clinical implications
    Aronne, LJ
    Segal, KR
    [J]. OBESITY RESEARCH, 2002, 10 : 14S - 21S
  • [7] The Effect Size in Uncertainty Analysis
    Barendregt, Jan J.
    [J]. VALUE IN HEALTH, 2010, 13 (04) : 388 - 391
  • [8] Barendregt JJ., 2017, Ersatz version 1.35
  • [9] BarendregtJJ, EpigearXL
  • [10] Tobacco and obesity epidemics: not so different after all?
    Chopra, M
    Darnton-Hill, I
    [J]. BMJ-BRITISH MEDICAL JOURNAL, 2004, 328 (7455): : 1558 - 1560