"I did not plan … that is what hurts": Pregnancy intentions and contraceptive use among pregnant young women in KwaZulu-Natal, South Africa.
Jessica N ColemanCecilia MilfordNzwakie MoseryKarmel W ChoiLetitia Rambally GreenerLynn T MatthewsAbigail D HarrisonDavid R BangsbergSteven A SafrenJennifer A SmitChristina PsarosPublished in: African journal of AIDS research : AJAR (2021)
Unintended pregnancy impacts many young women in South Africa, and rates of consistent contraceptive use among this population are suboptimal. Limited empirical work has investigated reasons for inconsistency between pregnancy intention and contraceptive use behaviour with data collected during pregnancy. We explored pregnancy intentions and discordance between intentions and contraceptive use prior to conception among young pregnant women in KwaZulu-Natal, South Africa. In-depth qualitative interviews were conducted with 35 women during pregnancy (mean age = 19.3; range = 18-21) in 2011 and 2012. Data were analysed using content analysis. All participants reported unintended pregnancies; almost half were not using contraception near conception. Reasons for not intending to become pregnant spanned personal, social, health, and economic domains. Participants living with HIV (n = 13) expressed specific concerns related to impacts of pregnancy on HIV disease management and fear of transmission of HIV to the infant. Discordance between pregnancy intentions and contraceptive use prior to conception was attributed to personal, social, health and structural domains. Findings indicate a need for interventions that address barriers to contraceptive use in order to minimise unintended pregnancy and support safe, desired pregnancies among young women.
Keyphrases
- south africa
- pregnancy outcomes
- hiv positive
- preterm birth
- pregnant women
- healthcare
- mental health
- public health
- hepatitis c virus
- physical activity
- hiv infected
- hiv testing
- electronic health record
- adipose tissue
- type diabetes
- men who have sex with men
- health information
- polycystic ovary syndrome
- skeletal muscle
- deep learning
- big data
- climate change
- insulin resistance
- breast cancer risk