Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa.

Journal: Journal of global health
PMID: 35493781

Abstract

BACKGROUND: Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature.

Authors

  • Sunil Sazawal
    Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India. ssazawal@jhu.edu.
  • Sayan Das
    Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India.
  • Kelli K Ryckman
    College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA.
  • Rasheda Khanam
    School of Business, University of Southern Queensland, Toowoomba, Australia.
  • Imran Nisar
    Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan.
  • Saikat Deb
    Public Health Laboratory-IDC, Chake Chake, Pemba, Tanzania.
  • Elizabeth A Jasper
    University of Iowa, Iowa City, Iowa, USA.
  • Sayedur Rahman
    Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh.
  • Usma Mehmood
    Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan.
  • Arup Dutta
    Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India.
  • Nabidul Haque Chowdhury
    Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh.
  • Amina Barkat
    Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan.
  • Harshita Mittal
    Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India.
  • Salahuddin Ahmed
    Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh.
  • Farah Khalid
    Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan.
  • Said Mohammed Ali
    Public Health Laboratory-IDC, Chake Chake, Pemba, Tanzania.
  • Rubhana Raqib
    International Centre for Diarrhoeal Disease Research, Mohakhali, Dhaka, 1212, Bangladesh.
  • Muhammad Ilyas
    Department of Community Medical Science, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
  • Ambreen Nizar
    Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan.
  • Alexander Manu
    Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland.
  • Donna Russell
    University of California, Seattle, Washington, USA.
  • Sachiyo Yoshida
    Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland.
  • Abdullah H Baqui
    Department of International Health, Johns Hopkins Bloomberg School for Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
  • Fyezah Jehan
    Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan.
  • Usha Dhingra
    Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India.
  • Rajiv Bahl
    Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland. bahlr@who.int.

Keywords

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