You are here

  • Home
  • Archive
  • Volume 5, Issue 10
  • Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health

Article Text

Article menu
XML
Original research
Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health
  1. Vincent S Huang1,
  2. Kasey Morris1,
  3. Mokshada Jain1,
  4. Banadakoppa Manjappa Ramesh2,
  5. Hannah Kemp1,
  6. James Blanchard2,
  7. Shajy Isac2,3,
  8. Bidyut Sarkar2,4,
  9. Vikas Gothalwal2,4,
  10. Vasanthakumar Namasivayam2,
  11. Pankaj Kumar5,
  12. Sema K Sgaier1,6,7
  1. 1Surgo Foundation, Washington, DC, USA
  2. 2Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
  3. 3India Health Action Trust, New Delhi, India
  4. 4India Health Action Trust, Lucknow, India
  5. 5National Health Mission, Government of Uttar Pradesh, Lucknow, India
  6. 6Department of Global Health, University of Washington, Seattle, WA, USA
  7. 7Department of Global Health and Population, Harvard University T.H. Chan School of Public Health, Boston, Massachusetts, USA
  1. Correspondence to Dr Sema K Sgaier; semasgaier{at}surgofoundation.org

Abstract

Introduction Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India.

Methods Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women’s behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups.

Results Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning.

Conclusion These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

  • mathematical modelling
  • child health
  • maternal health
  • health services research
  • cross-sectional survey
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjgh-2020-002340

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    View Full Text

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • VSH and KM are joint first authors.

    • Handling editor Sanni Yaya

    • VSH and KM contributed equally.

    • Contributors VSH, KM, MJ and SKS planned, conducted, analysed and reported the work described in the article. HK, BMR, JB, SI, BS, VG, VN and PK planned and conducted the data required for the work.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Patient consent for publication Not required.

    • Ethics approval The study was approved by Sigma Institutional Review Board (approval number 10032/IRB/D/16-17, New Delhi, India). Analysis of the data was approved by University of Manitoba Health Research Ethics Board (HS18479, HS19849 and HS20187). All subjects included in the analyses gave consent.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon request. Data are available upon request and may require the approval from the Government of Uttar Pradesh, India.