Login / Signup

Does Voluntary Family Planning Contribute to Food Security? Evidence from Ethiopia.

Geteneh Moges AssefaMuluken Dessalegn MulunehSentayehu TsegayeSintayehu AbebeMisrak MakonnenWoldu KidaneKasahun NegashAbebaye GetanehVirginia Stulz
Published in: Nutrients (2023)
This study aims to explore the effects of voluntary family planning (FP) utilization on food security in selected districts of Ethiopia. Quantitative research methods were used to conduct a community-based study among a sample of 737 women of reproductive age. The data were analyzed using a hierarchical logistic regression constructed in three models. The findings showed 579 (78.2%) were using FP at the time of the survey. According to the household-level food insecurity access scale, 55.2% of households experienced food insecurity. The likelihood of food security was lower by 64% for women who used FP for less than 21 months (AOR = 0.64: 95%CI: 0.42-0.99) in comparison to mothers who used FP for more than 21 months. Households having positive adaptive behaviors were three times more likely (AOR = 3.60: 95%CI 2.07-6.26) to have food security in comparison to those not having positive adaptive behaviors. This study also revealed that almost half of the mothers (AOR: 0.51: 95%CI: 0.33-0.80) who reported being influenced by other family members to use FP had food security, in comparison to their counterparts. Age, duration of FP use, positive adaptive behaviors, and influence by significant others were found to be independent predictors of food security in the study areas. Culturally sensitive strategies need to be considered to expand awareness and dispel misconceptions that lead to hesitancy around FP utilization. Design strategies should take into account households' resilience in adaptive skills during shocks, natural disasters, or pandemics which will be invaluable for food security.
Keyphrases
  • global health
  • human health
  • public health
  • type diabetes
  • healthcare
  • climate change
  • high resolution
  • adipose tissue
  • big data
  • electronic health record
  • insulin resistance
  • data analysis