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Population attributable fractions for risk factors for spontaneous preterm births in 81 low- and middle-income

Emily Bryce1, Sabi Gurung1, Hannah Tong1

  • 1Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.

Journal of Global Health
|March 31, 2022
PubMed

Insights

Identifying modifiable risk factors for preterm birth (PTB) is crucial for reducing child mortality. This study found that maternal undernutrition, infections, and environmental exposures significantly contribute to spontaneous PTB (sPTB) in low- and middle-income countries.

Area of Science:

  • Global child survival strategies
  • Public health interventions
  • Epidemiology of preterm birth

Background:

  • Preterm birth (PTB) complications are a leading cause of mortality in children under five globally.
  • Addressing PTB is essential for achieving Sustainable Development Goals.
  • Identifying modifiable risk factors and effective prevention strategies is critical.

Purpose of the Study:

  • To identify and quantify the contribution of spontaneous PTB (sPTB) risk factors in low- and middle-income countries (LMICs).
  • To estimate population attributable fractions (PAFs) for sPTB risk factors.
  • To inform the development of targeted prevention strategies for sPTB.

Main Methods:

  • Conducted a literature review to identify established sPTB risk factors.
  • Estimated prevalence and risk relationships for identified risk factors.
  • Calculated PAFs for statistically significant sPTB risk factors in 81 LMICs.

Main Results:

  • Twenty-four significant sPTB risk factors were identified.
  • These factors accounted for 73% of sPTB in the studied LMICs.
  • Maternal undernutrition (17.5%), infections (16.6%), and environmental exposures (16%) were the leading contributors.

Conclusions:

  • Multiple risk factors contribute to sPTB, with no single factor dominating.
  • A significant proportion (27%) of sPTB cases lack identified risk factors.
  • Addressing sPTB requires understanding population-specific mechanisms and implementing effective interventions, despite data gaps in LMICs.
Abstract

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