West Caroline, Ploth David, Fonner Virginia, Mbwambo Jessie, Fredrick Francis, Sweat Michael
College of Medicine, Medical University of South Carolina, Charleston, South Carolina.
Department of Nephrology, Medical University of South Carolina, Charleston, South Carolina.
Am J Med Sci. 2016 Apr;351(4):408-15. doi: 10.1016/j.amjms.2016.01.012. Epub 2016 Feb 10.
Noncommunicable diseases are on pace to outnumber infectious disease as the leading cause of death in sub-Saharan Africa, yet many questions remain unanswered with concern toward effective methods of screening for type II diabetes mellitus (DM) in this resource-limited setting. We aim to design a screening algorithm for type II DM that optimizes sensitivity and specificity of identifying individuals with undiagnosed DM, as well as affordability to health systems and individuals.
Baseline demographic and clinical data, including hemoglobin A1c (HbA1c), were collected from 713 participants using probability sampling of the general population. We used these data, along with model parameters obtained from the literature, to mathematically model 8 purposed DM screening algorithms, while optimizing the sensitivity and specificity using Monte Carlo and Latin Hypercube simulation.
An algorithm that combines risk assessment and measurement of fasting blood glucose was found to be superior for the most resource-limited settings (sensitivity 68%, sensitivity 99% and cost per patient having DM identified as $2.94). Incorporating HbA1c testing improves the sensitivity to 75.62%, but raises the cost per DM case identified to $6.04. The preferred algorithms are heavily biased to diagnose those with more severe cases of DM.
Using basic risk assessment tools and fasting blood sugar testing in lieu of HbA1c testing in resource-limited settings could allow for significantly more feasible DM screening programs with reasonable sensitivity and specificity.
在撒哈拉以南非洲地区，非传染性疾病有望超过传染病，成为主要死因，但在这种资源有限的环境中，关于II型糖尿病（DM）有效筛查方法仍有许多问题未得到解答。我们旨在设计一种II型糖尿病筛查算法，以优化识别未确诊糖尿病个体的敏感性和特异性，以及对卫生系统和个人的可承受性。
采用一般人群概率抽样方法，从713名参与者中收集基线人口统计学和临床数据，包括糖化血红蛋白（HbA1c）。我们利用这些数据以及从文献中获得的模型参数，对8种目标糖尿病筛查算法进行数学建模，同时使用蒙特卡罗和拉丁超立方模拟优化敏感性和特异性。
发现在资源最有限的环境中，一种结合风险评估和空腹血糖测量的算法更为优越（敏感性68%，特异性99%，识别出的糖尿病患者人均成本为2.94美元）。纳入HbA1c检测可将敏感性提高到75.62%，但识别出的每例糖尿病病例成本提高到6.04美元。首选算法严重倾向于诊断那些患有更严重糖尿病的患者。
在资源有限的环境中，使用基本风险评估工具和空腹血糖检测代替HbA1c检测，可以实现更可行的糖尿病筛查项目，同时具有合理的敏感性和特异性。
