Wagner Ryan G, Crowther Nigel J, Micklesfield Lisa K, Boua Palwende Romauld, Nonterah Engelbert A, Mashinya Felistas, Mohamed Shukri F, Asiki Gershim, Tollman Stephen, Ramsay Michèle, Davies Justine I
MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa
Department of Chemical Pathology, National Health Laboratory Service, Johannesburg, Gauteng, South Africa.
BMJ Glob Health. 2021 Jan;6(1). doi: 10.1136/bmjgh-2020-003499.
Cardiovascular disease (CVD) risk factors are increasing in sub-Saharan Africa. The impact of these risk factors on future CVD outcomes and burden is poorly understood. We examined the magnitude of modifiable risk factors, estimated future CVD risk and compared results between three commonly used 10-year CVD risk factor algorithms and their variants in four African countries.
In the Africa-Wits-INDEPTH partnership for Genomic studies (the AWI-Gen Study), 10 349 randomly sampled individuals aged 40-60 years from six sites participated in a survey, with blood pressure, blood glucose and lipid levels measured. Using these data, 10-year CVD risk estimates using Framingham, Globorisk and WHO-CVD and their office-based variants were generated. Differences in future CVD risk and results by algorithm are described using kappa and coefficients to examine agreement and correlations, respectively.
The 10-year CVD risk across all participants in all sites varied from 2.6% (95% CI: 1.6% to 4.1%) using the WHO-CVD lab algorithm to 6.5% (95% CI: 3.7% to 11.4%) using the Framingham office algorithm, with substantial differences in risk between sites. The highest risk was in South African settings (in urban Soweto: 8.9% (IQR: 5.3-15.3)). Agreement between algorithms was low to moderate (kappa from 0.03 to 0.55) and correlations ranged between 0.28 and 0.70. Depending on the algorithm used, those at high risk (defined as risk of 10-year CVD event >20%) who were under treatment for a modifiable risk factor ranged from 19.2% to 33.9%, with substantial variation by both sex and site.
The African sites in this study are at different stages of an ongoing epidemiological transition as evidenced by both risk factor levels and estimated 10-year CVD risk. There is low correlation and disparate levels of population risk, predicted by different risk algorithms, within sites. Validating existing risk algorithms or designing context-specific 10-year CVD risk algorithms is essential for accurately defining population risk and targeting national policies and individual CVD treatment on the African continent.
撒哈拉以南非洲地区的心血管疾病(CVD)风险因素正在增加。人们对这些风险因素对未来CVD结局和负担的影响了解甚少。我们研究了可改变风险因素的程度,估计了未来CVD风险,并比较了四个非洲国家中三种常用的10年CVD风险因素算法及其变体之间的结果。
在非洲-威特沃特斯兰德大学-深入基因组研究伙伴关系(AWI-Gen研究)中,从六个地点随机抽取的10349名40至60岁的个体参与了一项调查,测量了血压、血糖和血脂水平。利用这些数据,生成了使用弗雷明汉姆、全球风险和世卫组织-CVD算法及其基于办公室的变体的10年CVD风险估计值。分别使用kappa系数和相关系数描述算法之间未来CVD风险和结果的差异,以检验一致性和相关性。
所有地点的所有参与者的10年CVD风险从使用世卫组织-CVD实验室算法的2.6%(95%CI:1.6%至4.1%)到使用弗雷明汉姆办公室算法的6.5%(95%CI:3.7%至11.4%)不等,各地点之间的风险存在显著差异。风险最高的是南非地区(索韦托市区:8.9%(IQR:5.3-15.3))。算法之间的一致性为低到中度(kappa系数从0.03到0.55),相关性在0.28到0.70之间。根据所使用的算法,正在接受可改变风险因素治疗的高危人群(定义为10年CVD事件风险>20%)比例从19.2%到33.9%不等,在性别和地点上都有很大差异。
本研究中的非洲地区处于正在进行的流行病学转变的不同阶段,这一点从风险因素水平和估计的10年CVD风险中都可以看出。在各地点内,不同风险算法预测的人群风险相关性较低且水平不同。验证现有的风险算法或设计针对特定情况的10年CVD风险算法对于准确界定人群风险以及在非洲大陆制定国家政策和个人CVD治疗目标至关重要。