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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

3. 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

5. 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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6. 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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7. 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

11. 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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13. 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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14. 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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17. 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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18. 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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19. 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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20. 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

21. 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

23. 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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25. 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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29. Missing Outcome Data in Epidemiologic Studies.

Author

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

Subjects
STATISTICS, DATABASE evaluation, DATA analysis, RESEARCH bias, ELECTRONIC publications, EPIDEMIOLOGICAL research
Abstract

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Metabolic gestational age assessment in low resource settings: a validation protocol

Author

Bota, A. Brianne, primary, Ward, Victoria, additional, Hawken, Stephen, additional, Wilson, Lindsay A., additional, Lamoureux, Monica, additional, Ducharme, Robin, additional, Murphy, Malia S. Q., additional, Denize, Kathryn M., additional, Henderson, Matthew, additional, Saha, Samir K., additional, Akther, Salma, additional, Otieno, Nancy A., additional, Munga, Stephen, additional, Atito, Raphael O., additional, Stringer, Jeffrey S. A., additional, Mwape, Humphrey, additional, Price, Joan T., additional, Mujuru, Hilda Angela, additional, Chimhini, Gwendoline, additional, Magwali, Thulani, additional, Mudawarima, Louisa, additional, Chakraborty, Pranesh, additional, Darmstadt, Gary L., additional, and Wilson, Kumanan, additional

Published
2021
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33. Metabolic gestational age assessment in low resource settings: a validation protocol

Author

Bota, A. Brianne, primary, Ward, Victoria, additional, Hawken, Stephen, additional, Wilson, Lindsay A., additional, Lamoureux, Monica, additional, Ducharme, Robin, additional, Murphy, Malia S. Q., additional, Denize, Kathryn M., additional, Henderson, Matthew, additional, Saha, Samir K., additional, Akther, Salma, additional, Otieno, Nancy A., additional, Munga, Stephen, additional, Atito, Raphael O., additional, Stringer, Jeffrey S. A., additional, Mwape, Humphrey, additional, Price, Joan T., additional, Mujuru, Hilda Angela, additional, Chimhini, Gwendoline, additional, Magwali, Thulani, additional, Mudawarima, Louisa, additional, Chakraborty, Pranesh, additional, Darmstadt, Gary L., additional, and Wilson, Kumanan, additional

Published
2020
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34. Acceptability of a trial of vaginal progesterone for the prevention of preterm birth among HIV-infected women in Lusaka, Zambia: A mixed methods study

Author

Price, Joan T., primary, Mabula-Bwalya, Chileshe M., additional, Freeman, Bethany L., additional, Carda-Auten, Jessica, additional, Phiri, Winifreda M., additional, Chibwe, Kasapo, additional, Kantumoya, Patricia, additional, Vwalika, Bellington, additional, Stringer, Jeffrey S. A., additional, and Golin, Carol E., additional

Published
2020
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41. Unlocking the global health potential of dried blood spot cards.

Author

Bota, Brianne, Ward, Victoria, Lamoureux, Monica, Santander, Emeril, Ducharme, Robin, Hawken, Steven, Potter, Beth K., Atito, Raphael, Nyamanda, Bryan, Munga, Stephen, Otieno, Nancy, Chakraborty, Sowmitra, Saha, Samir, Stringer, Jeffrey S. A., Mwape, Humphrey, Price, Joan T., Mujuru, Hilda Angela, Chimhini, Gwendoline, Magwali, Thulani, and Chakraborty, Pranesh

Published
2022
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46. The Zambian Preterm Birth Prevention Study (ZAPPS): Cohort characteristics at enrollment

Author

Castillo, Marcela C., primary, Fuseini, Nurain M., additional, Rittenhouse, Katelyn, additional, Price, Joan T., additional, Freeman, Bethany L., additional, Mwape, Humphrey, additional, Winston, Jennifer, additional, Sindano, Ntazana, additional, Baruch-Gravett, Courtney, additional, Chi, Benjamin H., additional, Kasaro, Margaret P., additional, Litch, James A., additional, Stringer, Jeffrey S. A., additional, and Vwalika, Bellington, additional

Published
2019
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50. Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol

Author

Murphy, Malia S Q, Hawken, Steven, Atkinson, Katherine M, Milburn, Jennifer, Pervin, Jesmin, Gravett, Courtney, Stringer, Jeffrey S A, Rahman, Anisur, Lackritz, Eve, Chakraborty, Pranesh, and Wilson, Kumanan

Subjects
obstetrics, validation study, newborn screening, screening, Protocol, epidemiology, gestational age, metabolomics
Abstract

Background Knowledge of gestational age (GA) is critical for guiding neonatal care and quantifying regional burdens of preterm birth. In settings where access to ultrasound dating is limited, postnatal estimates are frequently used despite the issues of accuracy associated with postnatal approaches. Newborn metabolic profiles are known to vary by severity of preterm birth. Recent work by our group and others has highlighted the accuracy of postnatal GA estimation algorithms derived from routinely collected newborn screening profiles. This protocol outlines the validation of a GA model originally developed in a North American cohort among international newborn cohorts. Methods Our primary objective is to use blood spot samples collected from infants born in Zambia and Bangladesh to evaluate our algorithm’s capacity to correctly classify GA within 1, 2, 3 and 4 weeks. Secondary objectives are to 1) determine the algorithm's accuracy in small-for-gestational-age and large-for-gestational-age infants, 2) determine its ability to correctly discriminate GA of newborns across dichotomous thresholds of preterm birth (≤34 weeks

Published
2017

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