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Blencowe H, Chou VB, Lawn JE, et al. Modelling stillbirth mortality reduction with the Lives Saved Tool. BMC Public Health. 2017;17:784doi: 10.1186/s12889-017-4742-5.
Blencowe, H., Chou, V. B., Lawn, J. E., & Bhutta, Z. A. (2017). Modelling stillbirth mortality reduction with the Lives Saved Tool. BMC public health, 17784. https://doi.org/10.1186/s12889-017-4742-5
Blencowe, Hannah, et al. "Modelling stillbirth mortality reduction with the Lives Saved Tool." BMC public health vol. 17 (2017): 784. doi: https://doi.org/10.1186/s12889-017-4742-5
Blencowe H, Chou VB, Lawn JE, Bhutta ZA. Modelling stillbirth mortality reduction with the Lives Saved Tool. BMC Public Health. 2017 Nov 07;17:784. doi: 10.1186/s12889-017-4742-5. PMID: 29143647; PMCID: PMC5688483.
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BMC Public Health. 2017 Nov 07;17:784. doi: 10.1186/s12889-017-4742-5.

Modelling stillbirth mortality reduction with the Lives Saved Tool.

BMC public health

Hannah Blencowe, Victoria B Chou, Joy E Lawn, Zulfiqar A Bhutta

Affiliations

  1. Maternal Adolescent Reproductive and Child Health (MARCH) Centre, London School of Hygiene & Tropical Medicine, G11, Keppel Street, London, WC1E 7HT, UK. [email protected].
  2. Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Room E5518, Baltimore, MD, 21205, USA.
  3. Maternal Adolescent Reproductive and Child Health (MARCH) Centre, London School of Hygiene & Tropical Medicine, G11, Keppel Street, London, WC1E 7HT, UK.
  4. Centre for Global Child Health, Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.

PMID: 29143647 PMCID: PMC5688483 DOI: 10.1186/s12889-017-4742-5

Abstract

BACKGROUND: The worldwide burden of stillbirths is large, with an estimated 2.6 million babies stillborn in 2015 including 1.3 million dying during labour. The Every Newborn Action Plan set a stillbirth target of ≤12 per 1000 in all countries by 2030. Planning tools will be essential as countries set policy and plan investment to scale up interventions to meet this target. This paper summarises the approach taken for modelling the impact of scaling-up health interventions on stillbirths in the Lives Saved tool (LiST), and potential future refinements.

METHODS: The specific application to stillbirths of the general method for modelling the impact of interventions in LiST is described. The evidence for the effectiveness of potential interventions to reduce stillbirths are reviewed and the assumptions of the affected fraction of stillbirths who could potentially benefit from these interventions are presented. The current assumptions and their effects on stillbirth reduction are described and potential future improvements discussed.

RESULTS: High quality evidence are not available for all parameters in the LiST stillbirth model. Cause-specific mortality data is not available for stillbirths, therefore stillbirths are modelled in LiST using an attributable fraction approach by timing of stillbirths (antepartum/ intrapartum). Of 35 potential interventions to reduce stillbirths identified, eight interventions are currently modelled in LiST. These include childbirth care, induction for prolonged pregnancy, multiple micronutrient and balanced energy supplementation, malaria prevention and detection and management of hypertensive disorders of pregnancy, diabetes and syphilis. For three of the interventions, childbirth care, detection and management of hypertensive disorders of pregnancy, and diabetes the estimate of effectiveness is based on expert opinion through a Delphi process. Only for malaria is coverage information available, with coverage estimated using expert opinion for all other interventions. Going forward, potential improvements identified include improving of effectiveness and coverage estimates for included interventions and addition of further interventions.

CONCLUSIONS: Known effective interventions have the potential to reduce stillbirths and can be modelled using the LiST tool. Data for stillbirths are improving. Going forward the LiST tool should seek, where possible, to incorporate these improving data, and to continually be refined to provide an increasingly reliable tool for policy and programming purposes.

Keywords: Lives saved tool; Mortality modelling; Stillbirths

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