Browsing by Author "Ngaruye I"

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Comparing growth velocity of HIV exposed and non-exposed infants: An observational study of infants enrolled in a randomized control trial in Zambia.
(2021) Chilyabanyama ON; Chilengi R; Laban NM; Chirwa M; Simunyandi M; Hatyoka LM; Ngaruye I; Iqbal NT; Bosomprah S; Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana.; College of Science and Technology, University of Rwanda, Kigali, Rwanda.; Research Division, Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; Aga Khan University Hospital, Karachi, Pakistan.; African Centre of Excellence in Data Science (ACEDS), University of Rwanda, Kigali, Rwanda.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
BACKGROUND: Impaired growth among infants remains one of the leading nutrition problems globally. In this study, we aimed to compare the growth trajectory rate and evaluate growth trajectory characteristics among children, who are HIV exposed uninfected (HEU) and HIV unexposed uninfected (HUU), under two years in Zambia. METHOD: Our study used data from the ROVAS II study (PACTR201804003096919), an open-label randomized control trial of two verses three doses of live, attenuated, oral RotarixTM administered 6 &10 weeks or at 6 &10 weeks plus an additional dose at 9 months of age, conducted at George clinic in Lusaka, Zambia. Anthropometric measurements (height and weight) were collected on all scheduled and unscheduled visits. We defined linear growth velocity as the rate of change in height and estimated linear growth velocity as the first derivative of the mixed effect model with fractional polynomial transformations and, thereafter, used the second derivative test to determine the peak height and age at peak heigh. RESULTS: We included 212 infants in this study with median age 6 (IQR: 6-6) weeks of age. Of these 97 (45.3%) were female, 35 (16.4%) were stunted, and 59 (27.6%) were exposed to HIV at baseline. Growth velocity was consistently below the 3rd percentile of the WHO linear growth standard for HEU and HUU children. The peak height and age at peak height among HEU children were 74.7 cm (95% CI = 73.9-75.5) and 15.5 months (95% CI = 14.7-16.3) respectively and those for HUU were 73 cm (95% CI = 72.1-74.0) and 15.6 months (95% CI = 14.5-16.6) respectively. CONCLUSION: We found no difference in growth trajectories between infants who are HEU and HUU. However, the data suggests that poor linear growth is universal and profound in this cohort and may have already occurred in utero.
Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia.
(2022-Jul-20) Chilyabanyama ON; Chilengi R; Simuyandi M; Chisenga CC; Chirwa M; Hamusonde K; Saroj RK; Iqbal NT; Ngaruye I; Bosomprah S; African Centre of Excellence in Data Science, College of Business Studies Kigali, University of Rwanda, Gikondo-Street, KK 737, Kigali P.O. Box 4285, Rwanda.; Department of Biostatistics, School of Public Health, University of Ghana, Accra P.O. Box LG13, Ghana.; College of Science of Technology, University of Rwanda, KN 7 Ave, Kigali P.O. Box 4285, Rwanda.; Enteric Disease and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia, Lusaka P.O. Box 34681, Zambia.; Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences (SIMMS) Sikkim Manipal University, Gangtok 03592, India.; Department of Paediatrics and Child Health, Biological and Biomedical Sciences, Aga Khan University Hospital, Karachi 74800, Pakistan.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.