Browsing by Author "Saroj RK"

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Development and validation of a novel scale for antiretroviral therapy readiness among pregnant women in urban Zambia with newly diagnosed HIV infection.
(2023-Apr-06) Mubiana-Mbewe M; Bosomprah S; Saroj RK; Kadota J; Koyuncu A; Thankian K; Vinikoor MJ; Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana.; Centre for Infectious Diseases Research in Zambia, Plot 34620 Off Alick Nkhata Road, P.O. Box 34681, Lusaka, Zambia. Mwangelwa.Mbewe@cidrz.org.; Department of Medicine, University of Alabama at Birmingham, Birmingham, USA.; Centre for Infectious Diseases Research in Zambia, Plot 34620 Off Alick Nkhata Road, P.O. Box 34681, Lusaka, Zambia.; UCSF Center for Tuberculosis and Division of Pulmonary and Critical Care Medicine San Francisco General Hospital, University of California, San Francisco, CA, USA.; Department of Gender Studies, University of Zambia, Lusaka, Zambia.; Department of Epidemiology, Johns Hopkins University, Maryland, USA.; School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
BACKGROUND: Women who are newly diagnosed with HIV infection during pregnancy may not be ready to immediately initiate lifelong antiretroviral therapy (ART; called Option B +) as is recommended. Lack of "readiness" drives early disengagement from care and undermines prevention of HIV transmission to infants. Several studies have shown high early attrition of women initiating ART in pregnancy. Although poor ART uptake and adherence have been attributed to various factors including stigma, disclosure issues and structural issues, there is no standard way of determining which pregnant woman will face challenges and therefore need additional support. We developed and validated a novel ART readiness tool in Lusaka, Zambia. METHODS: The aim of this study was to develop and validate a tool that could be used to assess how ready a newly diagnosed pregnant woman living with HIV would be to initiate ART on the day of diagnosis. Using a mixed method design, we conducted this study in three public-setting health facilities in Lusaka, Zambia. Informed by qualitative research and literature review, we identified 27 candidate items. We assessed content validity using expert and target population judgment approaches. We administered the 27-item questionnaire to 454 newly diagnosed pregnant women living with HIV, who were enrolled into a randomized trial (trials number NCT02459678). We performed item reduction analysis and used Cronbach's alpha coefficient of 0.70 as threshold for reliability. RESULTS: A total of 454 pregnant women living with HIV enrolled in the study between March 2017 and December 2017; 452 had complete data for analysis. The correlation coefficient between the 27 items on the completed ART readiness scale ranged from 0.31 to 0.70 while item discrimination index ranged from -0.01 to 2.38. Sixteen items were selected for the final scale, representing three domains, which we classified as "internalized and anticipated HIV stigma", "partner support" and "anticipated structural barriers". CONCLUSION: We developed and validated a tool that could be used to assess readiness of newly diagnosed women living with HIV to initiate ART. This ART readiness tool could allow clinics to tailor limited resources to pregnant women living with HIV needing additional support to initiate and remain on ART.
Machine Learning Algorithms for understanding the determinants of under-five Mortality.
(2022-Sep-24) Saroj RK; Yadav PK; Singh R; Chilyabanyama ON; Department of Biostatistics and Epidemiology, International Institute for Population Sciences, Mumbai, 400088, India.; Department of Mathematics and Statistics, Banasthali Vidyapith, Vanasthali Rd, Aliyabad, Tonk, Rajasthan, 304022, India.; Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences-Sikkim Manipal University, Gangtok, Sikkim, 737102, India. rakesh.saroj@bhu.ac.in.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; African Centre of Excellency in Data Science (ACEDS), University of Rwanda, KK 737 Street, Gikondo, Kigali, Rwanda.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
BACKGROUND: Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality. METHOD: The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model's accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen's Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software. RESULT: By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen's Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother's education level, and birth order were identified as important factors influencing under-five mortality. CONCLUSION: The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.
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.