Ntumy Michael Yao, Tetteh John, Aguadze Stephen, Swaray Swithin M, Udofia Emilia Asuquo, Yawson Alfred Edwin
Department of Obstetrics and Gynaecology, University of Ghana Medical School, College of Health Sciences, Accra, Ghana.
Department of Community Health, University of Ghana Medical School, College of Health Sciences, Accra, Ghana.
Epidemiol Infect. 2024 Dec 6;152:e160. doi: 10.1017/S0950268824001444.
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 -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; -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.
为提高医疗系统基层和二级机构对生殖道感染的早期有效管理能力，我们开发了一种预测模型，并在内部进行了验证，以帮助预测加纳社区层面自我报告的生殖道感染（sGTIs）的个体风险。该研究纳入了2003年至2014年三轮加纳人口与健康调查中32973名年龄在15至49岁之间的男性和女性。结局指标为sGTIs。基于对文献的预先回顾，我们将最小绝对收缩和选择算子（LASSO）惩罚回归与10倍交叉验证模型应用于11个预测变量。还采用了自抽样技术作为敏感性分析以生成稳健的模型。我们进一步进行判别分析和校准分析来评估模型的性能。设定统计学显著性为P值<0.05。平均年龄±标准差为29.1±9.7岁，女性占多数（60.7%）。在此期间sGTIs的患病率为11.2%（95%CI = 4.5 - 17.8），范围从2003年的5.4%（95%CI = 4.8 - 5.86）到2014年的17.5%（95%CI = 16.4 - 18.7）。LASSO回归模型保留了所有11个预测变量。采用自抽样技术，模型区分有sGTIs者和无sGTIs者的能力约为73.50%（95%CI = 72.50 - 74.26）（曲线下面积）。自抽样校准带图未显示校准错误的证据（检验统计量 = 17.30；P值 = 0.060）。模型性能被判定为良好且可接受。在缺乏临床测量的情况下，该预测工具可用于识别加纳社区层面15至49岁有高sGTIs风险的个体。一线医护人员可使用此工具进行筛查和早期检测。因此，我们建议对该模型进行外部验证，以确认其在不同人群中的可推广性和可靠性。
