Study Design and Data Source
The 2018 NDHS data used for this study adopted a cross-sectional research design. The women’s individual recode (IR) data extracted from the survey was used for the study. The data is accessible at https://dhsprogram.com/data/available-datasets.cfm.
Participants
The 2018 DHS was a national representative data of approximately 42,000 selected households. The survey targeted women aged 15–49 across the country. The sample was selected using a two-stage stratified random sampling technique. A multi-stage sampling technique was employed in the survey. Firstly, the country was stratified into urban and rural areas and then the primary sampling units were selected using a two-stage procedure. The selection of households was done through systematic random sampling. In all, 41,821 women were completely interviewed in the survey. Details information about the sampling procedure is available in the survey report and on the DHS website (https://dhsprogram.com).
Study Context
Nigeria has 36 States, the Federal Capital Territory (Abuja), and 774 constitutionally recognised local government areas across the six geo-political zones. It has 374 identifiable ethnic groups and three major ethnic groups (Hausa, Yoruba, and Igbo) [32, 33]. The country has a fast-growing population, with an estimated population of over 180 million [18]. About 46 million of this population are women of reproductive age with a total fertility rate (TFR) of 5.3 and low use of family planning services [18]. In addressing the fast-growing population and unintended pregnancies, as well as preventing maternal, infant, and child mortality, the Federal Government of Nigeria (FGoN), through the Federal Ministry of Health (FMoH), has set a target of increasing women’s use of FP services from the 2013 contraceptive prevalence rate (CPR) of 15–36% by 2018 [38]. In addition, a scale-up plan; the revised National Family Planning Blueprint (2020–2024), was also set up with the main goal of increasing the modern contraceptive prevalence rate (mCPR) to 27% by the year 2024 [38], representing a projected 3% annual growth from the 2018 national mCPR [32]. This blueprint is expected to avert around 400,000 infants, 700,000 child deaths and 1.6 million unintended pregnancies [18]. However, extant literature has shown that in the last two decades in Nigeria, among currently married women of reproductive age (15–49), the overall use of contraception has increased from 15–17% [28]. Within the same period, the use of any modern method of contraception has increased by 2% (from 10–12%) with a noticeable rise in the use of implants and injectables from 0–3% (NPC [28]. Despite the overall increase in modern contraceptive use in the 5 years preceding the 2018 Demographic and Health Survey [DHS] in Nigeria, two out of every five (40.6%) contraceptive users discontinued the method within 12 months [28]. In addition, the proportion of women who switched to another method after discontinuation of the previous method has reduced from 4.8% in 2013 to 4.5% in 2018 [28].
Data Collection
The DHS’s reproductive calendar data derived from the women’s questionnaire was used for this study. The data captured reliable and valid information on contraceptive use (Callahan & Becker, 2012). The data covers month-by-month strong retrospective information among women aged 15–49 on episodes of contraceptive use and non-use for five years preceding the DHS survey [31, 39, 40, 41]. Hence, it provides strong historical information on contraceptive discontinuation. The reproductive calendar data are recorded in a series of string variables (vcal variables) for each of the columns in the calendar [42, 43]. These string variables were converted into event data files to make each reproductive event (duration in months) become one observation in the dataset. These event variables were then used to calculate a contraceptive discontinuation rate and the number of episodes. The string functions command in Stata 14 that transform and restructure the calendar data into a single month was used to create event data files.
The discontinuation rate was calculated using a life table that generated the net discontinuation rates. The rate was calculated by dividing the number of episodes discontinued in a month by the total number of episodes that complete the duration. A 12-month duration for each of the discontinuation reasons was also calculated. In this study, a woman may contribute more than one episode to the calculation. Episodes in the last two months of the interview and the month of the interview were left out to avoid bias that may be introduced by unrecognized or unnoticed pregnancy in the analysis [42]. The duration of the episodes of contraceptive use was also calculated by examining each position in sequential order (starting at the end of the string and moving towards the beginning) for a contraceptive code. The first code following a position without that code indicates the start of a new episode of use. The last position (e.g., January 2013) is ignored in this examination, since a code in that position may represent an episode of use that began before the calendar start date. The number of continuous positions with the same contraceptive code indicates the number of months of use in the episode. An episode ends if the following month does not have the same contraceptive code (discontinuation) or corresponds to the month of the interview (a censored duration). The episodes are then tabulated by duration and reasons for ending for each contraceptive method and all methods combined. Standard life table calculations are then applied to the terminations to calculate months of exposure and the number of discontinuations by month of an episode. The cumulative proportion that is discontinued by 12 months is taken as the 12-month discontinuation rate. The unit of analysis in this study is the episodes of modern contraceptives used from 3–62 months preceding the date of the survey. Thus, the first episode of contraceptive use in the observation period was analysed.
Inclusion And Exclusion Criteria
Women who were not sexually active and not married/in-union, women who had never used contraceptives, women who had indicated that they were pregnant at the time of the survey, infecund women who were self-reported, and women with incomplete contraceptive information were excluded in this study. Also, out of the total sample of 41,821 women (15–49 years) in the IR data file, a weighted sample size of 3,433 currently sexually active married or in the union who were using a modern contraceptive and not sterilised or declared infecund were included and analysed.
Handling of missing values
In DHS, the use of contraception is generally not allowed to be missing in any month in the calendar. In the few surveys where it is missing, they are treated as months of non-use of contraception. Thus, missing and unknown reasons for discontinuation are treated as “Other” reasons in this study.
Definition And Measurement Of Variables
Dependent variable
The dependent variable is the modern contraceptive discontinuation (Pill, IUD, Injections, diaphragm, male condom, female sterilisation, male sterilisation, implants, female condom, foam/jelly, and lactational amenorrhea). Discontinuation refers to the disruption of the use of modern contraceptive methods for at least 12 months before the survey. It was operationalised as a dichotomous variable, coded as '1' for currently married women who are using contraception 12 months before the survey, but discontinue before the end of the 12 months and coded as '0' otherwise. However, we are not concerned with all contraceptive discontinuation because the fertility desires of currently married women change over time. Therefore, in this analysis, discontinuation was further disaggregated based on whether discontinuation occurred even though they are still at risk of unwanted pregnancy or not. Thus, discontinuation while still at risk was coded as “1” and as ‘0’ if otherwise.
Independent Variables
The independent variables considered in this study were: age, household wealth, region, and education. Others include the history of visiting a health facility in the last 12 months, media exposure and marital duration. Age was categorised into 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49. Household wealth; women’s household ownership of assets, was classified into the poorest, poorer, middle, richer and richest. The region of residence in which respondents were interviewed was grouped into North West, North East, North Central, South West, South East and South-South. Women’s highest level of education attained was grouped into no education, primary, secondary, and higher. The history of visiting health facilities in the last 12 months was grouped as yes and no. Media exposure was defined based on the use of at least one of the news outlets. It was grouped as exposed and not exposed. Finally, marital duration was calculated by subtracting the age at the time of marriage from the age at the time of the survey, in completed years. It was categorised as 0–4, 5–9, 10–14, 15–19, 20 years or more. The variables were included in the study and used to build models based on evidence from existing literature regarding factors associated with contraceptive discontinuation and/or based on a theoretical association
Data Analysis
Data were analysed in three stages (univariate, bivariate and multivariable) using the Stata statistical package version 14 [44]. First, frequencies and percentages were used to describe the categorical variables while the mean values were used for the continuous variables. In the second stage, Pearson's Chi-square goodness of fit test was performed to examine predictors that had a statistically and meaningfully crude association with contraceptive discontinuation using the confidence interval (CI) and the p-values. In addition to this, tests for multi-collinearity among variables were performed using the variance inflation factor threshold of < 5 [45]. A threshold means a score of 3.12 was obtained. Thus, all predictor variables that were significantly correlated with contraceptive discontinuation were kept in the logistic regression model in the third stage. Furthermore, sample weights and the Stata complex survey (svy) commands were also employed to cater for stratified sample design and the effect of oversampling or under-sampling of some regions or areas as recommended by DHS [18]. Finally, in the multivariable analysis, a logistic regression model with a 95% confidence interval was used to examine the determinants of contraceptive discontinuation up to 12 months. In this study, we focused on women who discontinued using modern contraceptive methods (pills, IUDs, injectables, implants, female condoms, and male condoms). To avoid bias as a result of the failure of some women to realise that they are pregnant in their first trimester, we censored discontinuation on the month of the interview or two months before the survey.