The FINDRISC tool has largely been validated in Caucasian populations and widely recommended as a simple T2D screening tool across Europe and in other various population settings (24, 41–43) but has to a less extent been validated in the African setting (44). Using data from a large national cross-sectional survey, the obtained AUC of the modified and simplified FINDRISC were 0.748 and 0.749 respectively. This performance was lower than the AUC of 0.87 obtained in the first validation study among the Finnish population (20) but better than some other validation studies conducted in: Finland with AUC of 0.727 (24); Bulgaria with AUC of 0.70 (42) and Greece with AUC of 0.724 (41). More so, the performance was again better than the findings from a large primary care study conducted in Spain with AUC of 0.69 (45). This study findings were generally similar to other several validation studies conducted in diverse settings, with AUC ranging between 0.65 and 0.88 (41, 44, 46). As such, the FINDRISC can be adopted and utilized in community screening of T2D in Kenya and other similar contexts.
Although, there are insufficient data on prior FINDRISC use in the African settings, a study from South Africa that used a simplified FINDRISC with only 6 risk factor (age, BIM, waist circumference, personal histories of hypertension and diabetes, and parental/family history of diabetes) in a diverse racial population of Cape Town, had comparable AUC findings of identifying T2D. Similarly, findings from another cross-sectional study conducted in Bostwana had comparable but slightly lower AUC of 0.63 (47). Additionally, findings from a study conducted in Algeria to identify individuals with dysglycemia using FINDRISC was similarly lower, with AUC of 0.64 (48). Despite the low performance of the FINDRISC documented in a few studies conducted in some Africa countries, this study’s overall findings of AUC of 0.748 is better than in other contexts, and thus useful in detecting people who might have undiagnosed T2D and should receive a diagnostic test (49).
The observed differences could be in part, attributable to diverse population characteristics and the genetic variants inherent in the African population, and the possible use of different diabetes diagnostic tests such as: glycated hemoglobin, FBG or Oral glucose tolerance test (OGGT) that have varying sensitivities and specificities in different settings (50, 51).
At univariate and multivariate analyses, physical activity and fruit or vegetable consumption variables were excluded because they were not associated with undiagnosed T2D. Additionally, a weighted proportion of 1.6 % of those who reported engaging in daily physical activity for at least 30 minutes had undiagnosed T2D and 0.8 % of those who reported daily consumption of fruits and/or vegetables too had undiagnosed T2D. With these adjustments, there was no statistically significant difference (P = 0.9118) between the simplified FINDRISC and the modified FINDRISC in detecting individuals with undiagnosed T2D.
To maximize true positive rates and minimize false negative rates, the optimal cut-off score of the FINDRISC was selected based on the trade-off between sensitivity and specificity, diagnostic accuracy, predictive values and the diagnostic odds. With a cut-off score of ≥ 7 the simplified FINDRISC had a fairly acceptable sensitivity and specificity and, attained reasonable predictive values and diagnostic odds of detecting more individuals with undiagnosed T2D than with the modified FINDRISC. However, with a cut-off of ≥ 8, both the modified FINDRISC and simplified FINDRISC had similar diagnostic accuracy as with a cut-off of ≥ 7 but with lower sensitivity of 55.6% and 50.5% respectively. A cut-off score of ≥ 7 was therefore settled for, sacrificing a few PPV and diagnostic odds points but with a better sensitivity gain.
Using a simplified FINDRISC and the cut-off of 7 or more, 57.6% of those with a total score ≥ 7 will be true positive while 83.0% of those whose total scores ≤ 7 will be true negatives. On the other hand, using a modified FINDRISC and its optimal cut-off of 7 or more, 59.6% will be true positives while 79.7% will be true negatives. For this study, using a simplified FINDRISC than the modified FINDRISC, and taking the same risk cut-off of 7 or more, the number of people identified for a laboratory test narrowed from 858 (21%) to 723 (18.0%) and the prevalence of undiagnosed T2D increased from a pre-test probability of 1.8% to a post-test probability of 7.9% for simplified FINDRISC compared to 6.9% for modified FINDRISC.
Study strengths and limitations.
The main strength of this study is the use of a large national cross-sectional survey data. On the other hand, one of the limitations of this study is that it did not evaluate the detection of future incident diabetes because the STEPwise cross-sectional survey did not provide follow up data. However, FINDRISC was developed to detect undiagnosed diabetes and predict future incident diabetes (20). The other limitation is the use of self-reported physical activity and fruit and/or vegetable consumption data among other forms of self-reported data that may have attenuated this study findings. There is also potential uncertainty surrounding the realization of participant fasting glucose state although study procedures required measurement of blood glucose after participant overnight fasting.
Conclusion
The performance of the modified and simplified FINDRISC in detecting undiagnosed T2D in Kenya was lower than the findings of the original FINDRISC but with an acceptable diagnostic AUC of 0.7486. With the modifications made to the FINDRISC, the tool can be usefully applied for community screening of diabetes in the Kenyan population and at cut-off score of ≥ 7 the simplified FINDRISC can reasonably discriminate individuals with diabetes from those without, making diabetes diagnosis more cost effective.