Women's post-abortion contraceptive use: Are predictors the same for immediate and future uptake of contraception? Evidence from Ghana.
Esinam Afi KayiAdriana Andrea Ewurabena BineyNaa Dodua DodooCharlotte Abra Esime OforiFrancis Nii-Amoo DodooPublished in: PloS one (2021)
This study seeks to identify the socio-demographic, reproductive, partner-related, and facility-level characteristics associated with women's immediate and subsequent use of post-abortion contraception in Ghana. Secondary data from the 2017 Ghana Maternal Health Survey were utilized in this study. The weighted data comprised 1,880 women who had ever had an abortion within the five years preceding the survey. Binary logistic regression analyses were performed to examine the associations between the predictor and outcome variables. Health provider and women's socio-demographic characteristics were significantly associated with women's use of post-abortion contraception. Health provider's counselling on family planning prior to or after abortion and place of residence were associated with both immediate and subsequent post-abortion uptake of contraception. Among subsequent post-abortion contraceptive users, older women (35-49), women in a union, and women who had used contraception prior to becoming pregnant were strong predictors. Partner-related and reproductive variables did not predict immediate and subsequent use of contraception following abortion. Individual and structural/institutional level characteristics are important in increasing women's acceptance and use of contraception post abortion. Improving and intensifying family planning counselling services at the health facility is critical in increasing contraceptive prevalence among abortion seekers.
Keyphrases
- polycystic ovary syndrome
- pregnancy outcomes
- healthcare
- public health
- mental health
- primary care
- breast cancer risk
- pregnant women
- magnetic resonance imaging
- metabolic syndrome
- skeletal muscle
- health information
- risk assessment
- machine learning
- big data
- artificial intelligence
- smoking cessation
- deep learning
- weight loss
- health insurance
- health promotion
- weight gain
- long term care
- contrast enhanced