Stability and change in fertility intentions in response to the COVID-19 pandemic in Kenya.
Linnea A ZimmermanCelia KarpMary ThiongoPeter Bundi GichangiGeorges GuiellaAlison GemmillCaroline MoreauSuzanne O BellPublished in: PLOS global public health (2022)
Fertility intentions are expected to decline due to the COVID-19 pandemic but limited empirical research on this topic has been conducted in sub-Saharan Africa. Longitudinal data from Kenya, with baseline (November 2019) and follow-up (June 2020) data, were used to 1) assess the extent to which individual-level fertility intentions changed, and 2) examine how security, specifically economic and health security, affected fertility intentions. The final sample included 3,095 women. The primary outcomes were change in quantum and timing. Exploratory analyses described overall changes within the sample and logistic regression models assessed sociodemographic and COVID-19 related correlates of change, specifically income loss at the household level, food insecurity, and ability to socially distance. Approximately 85% of women reported consistent fertility intentions related to both the number and timing of childbearing. No COVID-19-related factors were related to changing quantum intentions. Women who reported chronic food insecurity had 4.78 times the odds of accelerating their desired timing to next birth compared to those who reported no food insecurity (95% CI: 1.53-14.93), with a significant interaction by wealth. The COVID-19 pandemic did not lead to widespread changes in fertility intentions in Kenya, though the most vulnerable women may have accelerated their childbearing intentions.
Keyphrases
- polycystic ovary syndrome
- coronavirus disease
- pregnancy outcomes
- sars cov
- childhood cancer
- public health
- healthcare
- mental health
- electronic health record
- cervical cancer screening
- breast cancer risk
- type diabetes
- molecular dynamics
- climate change
- physical activity
- pregnant women
- global health
- big data
- skeletal muscle
- young adults
- cross sectional
- drug induced
- deep learning