Predictors of unmet need for contraception among adolescent girls and young women in selected high fertility countries in sub-Saharan Africa: A multilevel mixed effects analysis

Abstract

Introduction Despite the desire of adolescent girls and young women (AGYW) in sub-Saharan Africa (SSA) to use contraceptives, the majority of them have challenges with access to contraceptive services. This is more evident in high fertility countries in SSA. The purpose of this study was to examine the predictors of unmet need for contraception among AGYW in selected high fertility countries in SSA. Materials and methods Data from current Demographic and Health Surveys (DHS) carried out between 2010 and 2018 in 10 countries in SSA were analysed. A sample size of 24,898 AGYW who were either married or cohabiting was used. Unmet need for contraception was the outcome variable in this study. The explanatory variables were age, marital status, occupation, educational level, frequency of reading newspaper/magazine, frequency of listening to radio, frequency of watching television and parity (individual level variables) and wealth quintile, sex of household head, place of residence and decision-maker in healthcare (household/community level variables). Descriptive and multilevel logistic regression analyses were carried out. The results of the multilevel logistic regression analyses were reported using adjusted odds ratios at 95% confidence interval. Results The prevalence of unmet need for contraception in all the countries considered in this study was 24.9%, with Angola, recording the highest prevalence of 42.6% while Niger had the lowest prevalence of 17.8%. In terms of the individual level predictors, the likelihood of unmet need for contraception was low among AGYW aged 20-24

Intimate partner violence against adolescent girls and young women and its association with miscarriages, stillbirths and induced abortions in sub-Saharan Africa: Evidence from demographic and health surveys

“…First, the surveys used in this study were based on cross-sectional data, and hence, causal interpretations of the findings cannot be established. Second, both IPV and pregnancy termination were self -reported, and as a result, there is the possibility of under-and over-reporting of data ( Ahinkorah, 2020a , 2020b , 2020c ).…”

Modern contraceptive utilization and its associated factors among married women in Senegal: a multilevel analysis

“…Married women who were sterilized and declared as infecund were excluded from the analysis. Several individual and community-level explanatory variables were chosen based on prior evidence [ 6 , 14 – 16 , 18 , 19 , 36 , 47 – 50 ].…”

“…Results were presented using adjusted odds ratios (aOR) at 95% confidence intervals (CI). The multilevel logistic regression models consisted of fixed effects (measures of association) and random effects (measures of variability) [ 19 , 53 , 54 ]. Muli-collinearity test was done among the independent variables using the variance inflation factor (VIF) and the results indicated no evidence of high collinearity among the explanatory variables (Mean VIF = 1.43, Minimum VIF = 1.01, Maximum VIF = 2.26).…”

Breaking Barriers to Healthcare Access: A Multilevel Analysis of Individual- and Community-Level Factors Affecting Women’s Access to Healthcare Services in Benin

“…Fourth, we conducted a multilevel logistic regression analysis by constructing four models: (a) an empty model (called model 0), as the first model, which emphasizes the variance in the response variable (healthcare access problem), accredited to the clustering at the primary sampling units (PSUs); (b) then we constructed model 1, to measure the individual-level factors that had associations with healthcare access problem; (c) then we constructed model 2, which included the community-level factors to ascertain their association with healthcare access problem; (d) finally, we constructed the full model (called model 3) that included the individual- and community-level factors. The multilevel logistic regression model consisted of random and fixed effects [ 27 , 28 , 29 ].…”