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684 results on '"Stringer, Jeffrey S. A."'

1. Double Robust Variance Estimation with Parametric Working Models

Author

Shook-Sa, Bonnie E., Zivich, Paul N., Lee, Chanhwa, Xue, Keyi, Ross, Rachael K., Edwards, Jessie K., Stringer, Jeffrey S. A., and Cole, Stephen R.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or exposure model is correctly specified. However, for nonrandomized exposures the influence function based variance estimator frequently used with doubly robust estimators of the average causal effect is only consistent when both working models (i.e., outcome and exposure models) are correctly specified. Here, the empirical sandwich variance estimator and the nonparametric bootstrap are demonstrated to be doubly robust variance estimators. That is, they are expected to provide valid estimates of the variance leading to nominal confidence interval coverage when only one working model is correctly specified. Simulation studies illustrate the properties of the influence function based, empirical sandwich, and nonparametric bootstrap variance estimators in the setting where parametric working models are assumed. Estimators are applied to data from the Improving Pregnancy Outcomes with Progesterone (IPOP) study to estimate the effect of maternal anemia on birth weight among women with HIV., Comment: 32 pages, 14 figures, 13 tables

Published
2024

4. AI system for fetal ultrasound in low-resource settings

Author

Gomes, Ryan G., Vwalika, Bellington, Lee, Chace, Willis, Angelica, Sieniek, Marcin, Price, Joan T., Chen, Christina, Kasaro, Margaret P., Taylor, James A., Stringer, Elizabeth M., McKinney, Scott Mayer, Sindano, Ntazana, Dahl, George E., Goodnight III, William, Gilmer, Justin, Chi, Benjamin H., Lau, Charles, Spitz, Terry, Saensuksopa, T, Liu, Kris, Wong, Jonny, Pilgrim, Rory, Uddin, Akib, Corrado, Greg, Peng, Lily, Chou, Katherine, Tse, Daniel, Stringer, Jeffrey S. A., and Shetty, Shravya

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption. We developed and validated an artificial intelligence (AI) system that uses novice-acquired "blind sweep" ultrasound videos to estimate gestational age (GA) and fetal malpresentation. We further addressed obstacles that may be encountered in low-resourced settings. Using a simplified sweep protocol with real-time AI feedback on sweep quality, we have demonstrated the generalization of model performance to minimally trained novice ultrasound operators using low cost ultrasound devices with on-device AI integration. The GA model was non-inferior to standard fetal biometry estimates with as few as two sweeps, and the fetal malpresentation model had high AUC-ROCs across operators and devices. Our AI models have the potential to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings.

Published
2022

6. A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

Author

Gomes, Ryan G., Vwalika, Bellington, Lee, Chace, Willis, Angelica, Sieniek, Marcin, Price, Joan T., Chen, Christina, Kasaro, Margaret P., Taylor, James A., Stringer, Elizabeth M., McKinney, Scott Mayer, Sindano, Ntazana, Dahl, George E., Goodnight, III, William, Gilmer, Justin, Chi, Benjamin H., Lau, Charles, Spitz, Terry, Saensuksopa, T., Liu, Kris, Tiyasirichokchai, Tiya, Wong, Jonny, Pilgrim, Rory, Uddin, Akib, Corrado, Greg, Peng, Lily, Chou, Katherine, Tse, Daniel, Stringer, Jeffrey S. A., and Shetty, Shravya

Published
2022
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7. Author Correction: Prediction of gestational age using urinary metabolites in term and preterm pregnancies

Author

Contrepois, Kévin, Chen, Songjie, Ghaemi, Mohammad S., Wong, Ronald J., Jehan, Fyezah, Sazawal, Sunil, Baqui, Abdullah H., Stringer, Jeffrey S. A., Rahman, Anisur, Nisar, Muhammad I., Dhingra, Usha, Khanam, Rasheda, Ilyas, Muhammad, Dutta, Arup, Mehmood, Usma, Deb, Saikat, Hotwani, Aneeta, Ali, Said M., Rahman, Sayedur, Nizar, Ambreen, Ame, Shaali M., Muhammad, Sajid, Chauhan, Aishwarya, Khan, Waqasuddin, Raqib, Rubhana, Das, Sayan, Ahmed, Salahuddin, Hasan, Tarik, Khalid, Javairia, Juma, Mohammed H., Chowdhury, Nabidul H., Kabir, Furqan, Aftab, Fahad, Quaiyum, Abdul, Manu, Alexander, Yoshida, Sachiyo, Bahl, Rajiv, Pervin, Jesmin, Price, Joan T., Rahman, Monjur, Kasaro, Margaret P., Litch, James A., Musonda, Patrick, Vwalika, Bellington, Shaw, Gary, Stevenson, David K., Aghaeepour, Nima, and Snyder, Michael P.

Published
2022
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8. Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings

Author

Ryu, Dennis, Kim, Dong Hyun, Price, Joan T., Lee, Jong Yoon, Chung, Ha Uk, Allen, Emily, Walter, Jessica R., Jeong, Hyoyoung, Cao, Jingyue, Kulikova, Elena, Abu-Zayed, Hajar, Lee, Rachel, Martell, Knute L., Zhang, Michael, Kampmeier, Brianna R., Hill, Marc, Lee, JooHee, Kim, Edward, Park, Yerim, Jang, Hokyung, Arafa, Hany, Liu, Claire, Chisembele, Maureen, Vwalika, Bellington, Sindano, Ntazana, Spelke, M. Bridget, Paller, Amy S., Premkumar, Ashish, Grobman, William A., Stringer, Jeffrey S. A., Rogers, John A., and Xu, Shuai

Published
2021

10. Weekly 17 alpha-hydroxyprogesterone caproate to prevent preterm birth among women living with HIV: a randomised, double-blind, placebo-controlled trial

Author

Price, Joan T, Vwalika, Bellington, Freeman, Bethany L, Cole, Stephen R, Saha, Pooja T, Mbewe, Felistas M, Phiri, Winifreda M, Peterson, Marc, Muyangwa, Dorothy, Sindano, Ntazana, Mwape, Humphrey, Smithmyer, Megan E, Kasaro, Margaret P, Rouse, Dwight J, Goldenberg, Robert L, Chomba, Elwyn, and Stringer, Jeffrey S A

Published
2021
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12. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps.

Author

Stringer, Jeffrey S. A., Pokaprakarn, Teeranan, Prieto, Juan C., Vwalika, Bellington, Chari, Srihari V., Sindano, Ntazana, Freeman, Bethany L., Sikapande, Bridget, Davis, Nicole M., Sebastião, Yuri V., Mandona, Nelly M., Stringer, Elizabeth M., Benabdelkader, Chiraz, Mungole, Mutinta, Kapilya, Filson M., Almnini, Nariman, Diaz, Arieska N., Fecteau, Brittany A., Kosorok, Michael R., and Cole, Stephen R.

Subjects
*PREGNANT women, *IMAGE analysis, *RESOURCE-limited settings, *GESTATIONAL age, *BODY mass index
Abstract

Key Points: Question: Can novice clinicians accurately estimate gestational age using a low-cost, battery-powered ultrasonography probe with integrated artificial intelligence (AI) image interpretation? Findings: This prospective study enrolled 400 pregnant individuals with due dates confirmed by first-trimester ultrasonography. At follow-up visits randomly assigned throughout gestation, novice clinicians using an AI-enabled device estimated gestational age as accurately as credentialed sonographers using traditional ultrasonography devices (difference, 0.2 days). Meaning: Obstetrical care in low-resource settings may benefit from reliable gestational age assessment using AI integration with point-of-care ultrasonography. Importance: Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. Objective: To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. Design, Setting, and Participants: This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). Main Outcomes and Measures: The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. Results: In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, −0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Conclusions and Relevance: Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. Trial Registration: ClinicalTrials.gov Identifier: NCT05433519 This prospective diagnostic study examines the accuracy of an AI-enabled ultrasonography tool in estimating gestational age when used by those with no prior training in sonography. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

Author

Ross, Rachael K., Cole, Stephen R., Edwards, Jessie K., Zivich, Paul N., Westreich, Daniel, Daniels, Julie L., Price, Joan T., and Stringer, Jeffrey S. A.

Abstract

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Combination HIV prevention during pregnancy and the post‐partum period in Malawi and Zambia: a mathematical modelling analysis

Author

Powers, Kimberly A., primary, Mutale, Wilbroad, additional, Rosenberg, Nora E., additional, Graybill, Lauren A., additional, Mollan, Katie R., additional, Freeborn, Kellie, additional, Saidi, Friday, additional, Maman, Suzanne, additional, Mulenga, Priscilla L., additional, Jahn, Andreas, additional, Nyirenda, Rose K., additional, Stringer, Jeffrey S. A., additional, Vermund, Sten H., additional, and Chi, Benjamin H., additional

Published
2023
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25. Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries

Author

Espinosa, Camilo A., primary, Khan, Waqasuddin, additional, Khanam, Rasheda, additional, Das, Sayan, additional, Khalid, Javairia, additional, Pervin, Jesmin, additional, Kasaro, Margaret P., additional, Contrepois, Kévin, additional, Chang, Alan L., additional, Phongpreecha, Thanaphong, additional, Michael, Basil, additional, Ellenberger, Mathew, additional, Mehmood, Usma, additional, Hotwani, Aneeta, additional, Nizar, Ambreen, additional, Kabir, Furqan, additional, Wong, Ronald J., additional, Becker, Martin, additional, Berson, Eloise, additional, Culos, Anthony, additional, De Francesco, Davide, additional, Mataraso, Samson, additional, Ravindra, Neal, additional, Thuraiappah, Melan, additional, Xenochristou, Maria, additional, Stelzer, Ina A., additional, Marić, Ivana, additional, Dutta, Arup, additional, Raqib, Rubhana, additional, Ahmed, Salahuddin, additional, Rahman, Sayedur, additional, Hasan, A. S. M. Tarik, additional, Ali, Said M., additional, Juma, Mohamed H., additional, Rahman, Monjur, additional, Aktar, Shaki, additional, Deb, Saikat, additional, Price, Joan T., additional, Wise, Paul H., additional, Winn, Virginia D., additional, Druzin, Maurice L., additional, Gibbs, Ronald S., additional, Darmstadt, Gary L., additional, Murray, Jeffrey C., additional, Stringer, Jeffrey S. A., additional, Gaudilliere, Brice, additional, Snyder, Michael P., additional, Angst, Martin S., additional, Rahman, Anisur, additional, Baqui, Abdullah H., additional, Jehan, Fyezah, additional, Nisar, Muhammad Imran, additional, Vwalika, Bellington, additional, Sazawal, Sunil, additional, Shaw, Gary M., additional, Stevenson, David K., additional, and Aghaeepour, Nima, additional

Published
2023
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26. Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

Author

Hawken, Steven, primary, Ducharme, Robin, additional, Murphy, Malia S. Q., additional, Olibris, Brieanne, additional, Bota, A. Brianne, additional, Wilson, Lindsay A., additional, Cheng, Wei, additional, Little, Julian, additional, Potter, Beth K., additional, Denize, Kathryn M., additional, Lamoureux, Monica, additional, Henderson, Matthew, additional, Rittenhouse, Katelyn J., additional, Price, Joan T., additional, Mwape, Humphrey, additional, Vwalika, Bellington, additional, Musonda, Patrick, additional, Pervin, Jesmin, additional, Chowdhury, A. K. Azad, additional, Rahman, Anisur, additional, Chakraborty, Pranesh, additional, Stringer, Jeffrey S. A., additional, and Wilson, Kumanan, additional

Published
2023
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32. Maternal mid‐upper arm circumference to predict small for gestational age: Findings in a Zambian cohort

Author

Appiagyei, Ashley, primary, Vwalika, Bellington, additional, Spelke, M. Bridget, additional, Conner, Madelyn G., additional, Mabula‐Bwalya, Chileshe M., additional, Kasaro, Margaret P., additional, Honart, Anne West, additional, Kumwenda, Andrew, additional, Stringer, Elizabeth M., additional, Stringer, Jeffrey S. A., additional, and Price, Joan T., additional

Published
2022
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33. Missing Outcome Data in Epidemiologic Studies

Author

Cole, Stephen R, primary, Zivich, Paul N, additional, Edwards, Jessie K, additional, Ross, Rachael K, additional, Shook-Sa, Bonnie E, additional, Price, Joan T., additional, and Stringer, Jeffrey S A, additional

Published
2022
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36. Inertial Measurement Unit-Assisted Ultrasonic Tracking System for Ultrasound Probe Localization

Author

Cai, Qianqian, Hu, Jiale, Chen, Mengyue, Prieto, Juan, Rosenbaum, Alan J., Stringer, Jeffrey S. A., and Jiang, Xiaoning

Abstract

Ultrasonic tracking is a promising technique in indoor object localization. However, limited success has been reported in dynamic orientational and positional ultrasonic tracking for ultrasound (US) probes due to its instability and relatively low accuracy. This article aims at developing an inertial measurement unit (IMU)-assisted ultrasonic tracking system that enables a high accuracy positional and orientational localization. The system was designed with the acoustic pressure field simulation of the transmitter, receiver configuration, position-variant error simulation, and sensor fusion. The prototype was tested in a tracking volume required in typical obstetric sonography within the typical operation speed ranges (slow mode and fast mode) of US probe movement. The performance in two different speed ranges was evaluated against a commercial optical tracking device. The results show that the proposed IMU-assisted US tracking system achieved centimeter-level positional tracking accuracy with the mean absolute error (MAE) of 12 mm and the MAE of orientational tracking was less than 1°. The results indicate the possibility of implementing the IMU-assisted ultrasonic tracking system in US probe localization.

Published
2023
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38. Interpersonal therapy versus antidepressant medication for treatment of postpartum depression and anxiety among women with HIV in Zambia: a randomized feasibility trial

Author

Spelke, M. Bridget, primary, Paul, Ravi, additional, Blette, Bryan S., additional, Meltzer‐Brody, Samantha, additional, Schiller, Crystal E., additional, Ncheka, J. M., additional, Kasaro, Margaret P., additional, Price, Joan T., additional, Stringer, Jeffrey S. A., additional, and Stringer, Elizabeth M., additional

Published
2022
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39. Prediction of gestational age using urinary metabolites in term and preterm pregnancies

Author

Contrepois, Kévin, Chen, Songjie, Ghaemi, Mohammad S., Wong, Ronald J., Jehan, Fyezah, Sazawal, Sunil, Baqui, Abdullah H., Nisar, Muhammad I., Dhingra, Usha, Khanam, Rasheda, Ilyas, Muhammad, Dutta, Arup, Mehmood, Usma, Deb, Saikat, Hotwani, Aneeta, Ali, Said M., Rahman, Sayedur, Nizar, Ambreen, Ame, Shaali M., Muhammad, Sajid, Chauhan, Aishwarya, Khan, Waqasuddin, Raqib, Rubhana, Das, Sayan, Ahmed, Salahuddin, Hasan, Tarik, Khalid, Javairia, Juma, Mohammed H., Chowdhury, Nabidul H., Kabir, Furqan, Aftab, Fahad, Quaiyum, Md Abdul, Manu, Alexander, Yoshida, Sachiyo, Bahl, Rajiv, Rahman, Anisur, Pervin, Jesmin, Price, Joan T., Rahman, Monjur, Kasaro, Margaret P., Litch, James A., Musonda, Patrick, Vwalika, Bellington, Stringer, Jeffrey S. A., Shaw, Gary, Stevenson, David K., Aghaeepour, Nima, and Snyder, Michael P.

Subjects
Cohort Studies, Multidisciplinary, Pregnancy, Infant, Newborn, Humans, Metabolomics, Female, Gestational Age, Ultrasonography, Prenatal, Chromatography, Liquid
Abstract

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.

Published
2022

40. Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Pregnancy in Sub-Saharan Africa: A 6-Country Retrospective Cohort Analysis.

Author

UCL - SSS/IREC/EDIN - Pôle d'endocrinologie, diabète et nutrition, UCL - (SLuc) Service d'endocrinologie et de nutrition, Nachega, Jean B, Sam-Agudu, Nadia A, Machekano, Rhoderick N, Rosenthal, Philip J, Schell, Sonja, de Waard, Liesl, Bekker, Adrie, Gachuno, Onesmus W, Kinuthia, John, Mwongeli, Nancy, Budhram, Samantha, Vannevel, Valerie, Somapillay, Priya, Prozesky, Hans W, Taljaard, Jantjie, Parker, Arifa, Agyare, Elizabeth, Opoku, Akwasi Baafuor, Makarfi, Aminatu Umar, Abdullahi, Asara M, Adirieje, Chibueze, Ishoso, Daniel Katuashi, Pipo, Michel Tshiasuma, Tshilanda, Marc B, Bongo-Pasi Nswe, Christian, Ditekemena, John, Sigwadhi, Lovemore Nyasha, Nyasulu, Peter S, Hermans, Michel, Sekikubo, Musa, Musoke, Philippa, Nsereko, Christopher, Agbeno, Evans K, Yeboah, Michael Yaw, Umar, Lawal W, Ntakwinja, Mukanire, Mukwege, Denis M, Birindwa, Etienne Kajibwami, Mushamuka, Serge Zigabe, Smith, Emily R, Mills, Edward J, Otshudiema, John Otokoye, Mbala-Kingebeni, Placide, Tamfum, Jean-Jacques Muyembe, Zumla, Alimuddin, Tsegaye, Aster, Mteta, Alfred, Sewankambo, Nelson K, Suleman, Fatima, Adejumo, Prisca, Anderson, Jean R, Noormahomed, Emilia V, Deckelbaum, Richard J, Stringer, Jeffrey S A, Mukalay, Abdon, Taha, Taha E, Fowler, Mary Glenn, Wasserheit, Judith N, Masekela, Refiloe, Mellors, John W, Siedner, Mark J, Myer, Landon, Kengne, Andre-Pascal, Yotebieng, Marcel, Mofenson, Lynne M, Langenegger, Eduard, AFREhealth Research Collaboration on COVID-19 and Pregnancy, UCL - SSS/IREC/EDIN - Pôle d'endocrinologie, diabète et nutrition, UCL - (SLuc) Service d'endocrinologie et de nutrition, Nachega, Jean B, Sam-Agudu, Nadia A, Machekano, Rhoderick N, Rosenthal, Philip J, Schell, Sonja, de Waard, Liesl, Bekker, Adrie, Gachuno, Onesmus W, Kinuthia, John, Mwongeli, Nancy, Budhram, Samantha, Vannevel, Valerie, Somapillay, Priya, Prozesky, Hans W, Taljaard, Jantjie, Parker, Arifa, Agyare, Elizabeth, Opoku, Akwasi Baafuor, Makarfi, Aminatu Umar, Abdullahi, Asara M, Adirieje, Chibueze, Ishoso, Daniel Katuashi, Pipo, Michel Tshiasuma, Tshilanda, Marc B, Bongo-Pasi Nswe, Christian, Ditekemena, John, Sigwadhi, Lovemore Nyasha, Nyasulu, Peter S, Hermans, Michel, Sekikubo, Musa, Musoke, Philippa, Nsereko, Christopher, Agbeno, Evans K, Yeboah, Michael Yaw, Umar, Lawal W, Ntakwinja, Mukanire, Mukwege, Denis M, Birindwa, Etienne Kajibwami, Mushamuka, Serge Zigabe, Smith, Emily R, Mills, Edward J, Otshudiema, John Otokoye, Mbala-Kingebeni, Placide, Tamfum, Jean-Jacques Muyembe, Zumla, Alimuddin, Tsegaye, Aster, Mteta, Alfred, Sewankambo, Nelson K, Suleman, Fatima, Adejumo, Prisca, Anderson, Jean R, Noormahomed, Emilia V, Deckelbaum, Richard J, Stringer, Jeffrey S A, Mukalay, Abdon, Taha, Taha E, Fowler, Mary Glenn, Wasserheit, Judith N, Masekela, Refiloe, Mellors, John W, Siedner, Mark J, Myer, Landon, Kengne, Andre-Pascal, Yotebieng, Marcel, Mofenson, Lynne M, Langenegger, Eduard, and AFREhealth Research Collaboration on COVID-19 and Pregnancy

Abstract

BACKGROUND: Few data are available on COVID-19 outcomes among pregnant women in sub-Saharan Africa (SSA), where high-risk comorbidities are prevalent. We investigated the impact of pregnancy on SARS-CoV-2 infection and of SARS-CoV-2 infection on pregnancy to generate evidence for health policy and clinical practice. METHODS: We conducted a 6-country retrospective cohort study among hospitalized women of childbearing age between 1 March 2020 and 31 March 2021. Exposures were (1) pregnancy and (2) a positive SARS-CoV-2 RT-PCR test. The primary outcome for both analyses was intensive care unit (ICU) admission. Secondary outcomes included supplemental oxygen requirement, mechanical ventilation, adverse birth outcomes, and in-hospital mortality. We used log-binomial regression to estimate the effect between pregnancy and SARS-CoV-2 infection. Factors associated with mortality were evaluated using competing-risk proportional subdistribution hazards models. RESULTS: Our analyses included 1315 hospitalized women: 510 pregnant women with SARS-CoV-2, 403 nonpregnant women with SARS-CoV-2, and 402 pregnant women without SARS-CoV-2 infection. Among women with SARS-CoV-2 infection, pregnancy was associated with increased risk for ICU admission (adjusted risk ratio [aRR]: 2.38; 95% CI: 1.42-4.01), oxygen supplementation (aRR: 1.86; 95% CI: 1.44-2.42), and hazard of in-hospital death (adjusted sub-hazard ratio [aSHR]: 2.00; 95% CI: 1.08-3.70). Among pregnant women, SARS-CoV-2 infection increased the risk of ICU admission (aRR: 2.0; 95% CI: 1.20-3.35), oxygen supplementation (aRR: 1.57; 95% CI: 1.17-2.11), and hazard of in-hospital death (aSHR: 5.03; 95% CI: 1.79-14.13). CONCLUSIONS: Among hospitalized women in SSA, both SARS-CoV-2 infection and pregnancy independently increased risks of ICU admission, oxygen supplementation, and death. These data support international recommendations to prioritize COVID-19 vaccination among pregnant women.

Published
2022

41. In Reply

Author

Stringer, Elizabeth M., Vladutiu, Catherine J., Batra, Priya, Stringer, Jeffrey S. A., and Kathryn Menard, M.

Published
2017
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44. Global research priorities related to the World Health Organization Labour Care Guide: results of a global consultation.

Author

World Health Organization Labour Care Guide Research Prioritization Group, Abalos, Edgardo, Adanu, Richard, Bernitz, Stine, Binfa, Lorena, Dao, Blami, Downe, Soo, Hofmeyr, Justus G., Homer, Caroline S. E., Hundley, Vanora, GaladanciGogoi, Hadiza Aparajita, Lavender, Tina, Lissauer, David, Lumbiganon, Pisake, Pattinson, Robert, Qureshi, Zahida, Stringer, Jeffrey S. A., Pujar, Yeshita V., Vogel, Joshua P., and Yunis, Khalid

Subjects
MATERNAL health services, RESEARCH evaluation, PRIORITY (Philosophy), RESEARCH methodology, WORLD health, MEDICAL protocols, PREGNANCY outcomes, QUALITY assurance, RESEARCH funding, JUDGMENT sampling, INTRAPARTUM care
Abstract

Background: The World Health Organization (WHO) published the WHO Labour Care Guide (LCG) in 2020 to support the implementation of its 2018 recommendations on intrapartum care. The WHO LCG promotes evidence-based labour monitoring and stimulates shared decision-making between maternity care providers and labouring women. There is a need to identify critical questions that will contribute to defining the research agenda relating to implementation of the WHO LCG. Methods: This mixed-methods prioritization exercise, adapted from the Child Health and Nutrition Research Initiative (CHNRI) and James Lind Alliance (JLA) methods, combined a metrics-based design with a qualitative, consensus-building consultation in three phases. The exercise followed the reporting guideline for priority setting of health research (REPRISE). First, 30 stakeholders were invited to submit online ideas or questions (generation of research ideas). Then, 220 stakeholders were invited to score "research avenues" (i.e., broad research ideas that could be answered through a set of research questions) against six independent and equally weighted criteria (scoring of research avenues). Finally, a technical working group (TWG) of 20 purposively selected stakeholders reviewed the scoring, and refined and ranked the research avenues (consensus-building meeting). Results: Initially, 24 stakeholders submitted 89 research ideas or questions. A list of 10 consolidated research avenues was scored by 75/220 stakeholders. During the virtual consensus-building meeting, research avenues were refined, and the top three priorities agreed upon were: (1) optimize implementation strategies of WHO LCG, (2) improve understanding of the effect of WHO LCG on maternal and perinatal outcomes, and the process and experience of labour and childbirth care, and (3) assess the effect of the WHO LCG in special situations or settings. Research avenues related to the organization of care and resource utilization ranked lowest during both the scoring and consensus-building process. Conclusion: This systematic and transparent process should encourage researchers, program implementers, and funders to support research aligned with the identified priorities related to WHO LCG. An international collaborative platform is recommended to implement prioritized research by using harmonized research tools, establishing a repository of research priorities studies, and scaling-up successful research results. [ABSTRACT FROM AUTHOR]

Published
2023
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46. AI Estimation of Gestational Age from Blind Ultrasound Sweeps in Low-Resource Settings

Author

Pokaprakarn, Teeranan, primary, Prieto, Juan C., additional, Price, Joan T., additional, Kasaro, Margaret P., additional, Sindano, Ntazana, additional, Shah, Hina R., additional, Peterson, Marc, additional, Akapelwa, Mutinta M., additional, Kapilya, Filson M., additional, Sebastião, Yuri V., additional, Goodnight, William, additional, Stringer, Elizabeth M., additional, Freeman, Bethany L., additional, Montoya, Lina M., additional, Chi, Benjamin H., additional, Rouse, Dwight J., additional, Cole, Stephen R., additional, Vwalika, Bellington, additional, Kosorok, Michael R., additional, and Stringer, Jeffrey S. A., additional

Published
2022
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48. Seroreduction of syphilis nontreponemal titers during pregnancy for women with and without HIV coinfection

Author

Webster, Carolyn M., Kasaro, Margaret P., Price, Joan T., Stringer, Elizabeth M., Wiesen, Christopher A., Vwalika, Bellington, and Stringer, Jeffrey S. A.

Abstract

This is the accepted manuscript version of the work published in its final form as Webster, C. M., Kasaro, M. P., Price, J. T., Stringer, E. M., Wiesen, C. A., Vwalika, B., & Stringer, J. S. A. (2022). Seroreduction of syphilis non'treponemal titers during pregnancy for women with and without HIV co'infection.International Journal of Gynecology & Obstetrics. https://doi.org/10.1002/ijgo.14131 Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org. &nbsp

Published
2022
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