Caroline West1, David Ploth2, Virginia Fonner3, Jessie Mbwambo4, Francis Fredrick5, Michael Sweat6. 1. College of Medicine, Medical University of South Carolina, Charleston, South Carolina. Electronic address: westcm@musc.edu. 2. Department of Nephrology, Medical University of South Carolina, Charleston, South Carolina. 3. Department of Public Health, Johns Hopkins University, Baltimore, Maryland. 4. Department of Psychiatry, Muhimbili University of Health and Allied Sciences, Muhimbili National Hospital, Dar es Salaam, Tanzania. 5. School of Medicine, Muhimbili University of Health and Allied Sciences, Muhimbili National Hospital, Dar es Salaam, Tanzania. 6. Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
RCT Entities:
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Elizabeth Selvin; Michael W Steffes; Christie M Ballantyne; Ron C Hoogeveen; Josef Coresh; Frederick L Brancati Journal: Ann Intern Med Date: 2011-03-01 Impact factor: 25.391
Authors: Matthias B Schulze; Kurt Hoffmann; Heiner Boeing; Jakob Linseisen; Sabine Rohrmann; Matthias Möhlig; Andreas F H Pfeiffer; Joachim Spranger; Claus Thamer; Hans-Ulrich Häring; Andreas Fritsche; Hans-Georg Joost Journal: Diabetes Care Date: 2007-03 Impact factor: 19.112
Authors: G T Ko; J C Chan; V T Yeung; C C Chow; L W Tsang; J K Li; W Y So; H P Wai; C S Cockram Journal: Diabetes Care Date: 1998-08 Impact factor: 19.112