Predictive model for genital tract infections among men and women in Ghana: An application of LASSO penalized cross-validation regression model.

Abstract

To enhance the capacity for early and effective management of genital tract infections at primary and secondary levels of the healthcare system, we developed a prediction model, validated internally to help predict individual risk of self-reported genital tract infections (sGTIs) at the community level in Ghana. The study involved 32973 men and women aged 15-49 years from three rounds of the Ghana Demographic Health Survey, from 2003 to 2014. The outcomes were sGTIs. We applied the least absolute shrinkage and selection operator (LASSO) penalized regression with a 10-fold cross-validation model to 11 predictors based on prior review of the literature. The bootstrapping technique was also employed as a sensitivity analysis to produce a robust model. We further employed discriminant and calibration analyses to evaluate the performance of the model. Statistical significance was set at P-value <0.05. The mean±standard deviation age was 29.1±9.7 years with female preponderance (60.7%). The prevalence of sGTIs within the period was 11.2% (95% CI = 4.5-17.8) and it ranged from 5.4% (95% CI = 4.8-5.86) in 2003 to 17.5% (95% CI = 16.4-18.7) in 2014. The LASSO regression model retained all 11 predictors. The model's ability to discriminate between those with sGTIs and those without sGTIs was approximately 73.50% (95% CI = 72.50-74.26) from the area under the curve with bootstrapping technique. There was no evidence of miscalibration from the calibration belt plot with bootstrapping (test statistic = 17.30; P-value = 0.060). The model performance was judged to be good and acceptable. In the absence of clinical measurement, this prediction tool can be used to identify individuals aged 15-49 years with a high risk of sGTIs at the community level in Ghana. Frontline healthcare staff can use this tool for screening and early detection. We, therefore, propose external validation of the model to confirm its generalizability and reliability in different population.