Data setting
We use data from two rounds of a prospective study on ‘Improving Measurement of Unintended Pregnancy and Unmet Need for Family Planning’. The information was collected from a cohort of married and cohabiting women between the ages of 15-39 years living in Nairobi Urban Health and Demographic Surveillance System (NUHDSS) and Homa Bay County in Kenya. The Nairobi study was nested in the NUHDSS implemented by the African Population and Health Research center (APHRC) where households are visited twice a year to collect data on key socio-demographic and health indicators. The NUDHSS covers two slum settlements of Korogocho and Viwandani in Nairobi. Data collection in Homa Bay County in western Kenya was carried out by the Population Council. The Homa Bay study used a two-stage cluster sampling design to identify households and individual respondents. Detailed information on the study sample size and sampling procedures were described elsewhere in Machiyama et al. 2017 [24], Mumah et al (2017) [25] and Odwe et al, 2017[26]. Ethical approval was obtained from the AMREF Ethics and Scientific Review Committee, and Institutional Review Boards at the Population Council’s and the London School of Hygiene and Tropical Medicine.
The first round of data collection occurred from August to December 2016 in both Nairobi and Homa Bay and covered 2812 and 2424 women from Nairobi and Homa Bay, respectively. In round 1, the surveys collected detailed information on respondent’s demographic and socio-economic characteristics, fertility preferences, contraceptive behavior, length of use and intended length of future use, as well as method related beliefs. These same women were re-interviewed a year later from September to December 2017. Those who reported themselves as infecund or sterilized at round one or not in union at round 2 were excluded from the second round. During round two survey, 2208 women from Nairobi and 2083 women from Homa Bay site were successfully re-interviewed with a response rate of 78% and 86%, respectively. The round two survey also covered respondent’s reproductive behavior, fertility preferences and contraceptive use as well method related beliefs. This round also inquired about contraceptive discontinuation, namely whether respondent was using same method reported at baseline, and if not reasons for stopping the method.
Calendar data was collected during the second round survey, with women asked about all episodes of contraceptive use from the first round interview to the second round interview. This was a monthly calendar with fields for births/pregnancies and for contraceptive use. In any month in which women reported discontinuing contraceptive use, they were asked for the primary reason for stopping the method. The instrument contained pre-coded reasons including became pregnant while using, desire to become pregnant, fear of side effects/health concerns, inconvenience to use, husband’s disapproval, access/availability problems, wanted more effective method, infrequent sex, menopause/infecundity, marital dissolution, and other method related reasons.
Measures/variables
The analysis is method-specific and carried out for the two most common contraceptive methods in these samples of Kenyan women, namely injectable and implant (If women reported use of more than one method, we give priority to the most effective method). The observation is episode of contraceptive use at the time of the round 1 interview. Across both sites, there were 2535 episodes of use of injectable and implant (1554 injectable, 981 implant). We examine predictors of the discontinuation of these episodes by the round 2 interview, which occurred about twelve months later. The discontinuations include transitioning to a different method (switching) and transitioning to no use. Our interest is discontinuation that women attribute to method related reasons; these included side effects, health concerns, access/availability problems, desire for a more effective method, inconvenience to use, cost and other unspecified reasons while still at risk of an unintended pregnancy [11]. All other reasons for discontinuation were combined together as ‘other reasons of discontinuation’ in the competing risk survival analysis.
The potential predictors of discontinuation of principal interest are method-specific beliefs about each of the two methods (injectable, implant). Women were asked about eleven method related attributes: their familiarity with the method, knowledge of source and access to the method, perceived use of, and satisfaction with, the method by others in the women’s social network, perception of the effectiveness of the method at preventing pregnancy, safety of the method for long-term use, and the likelihoods of the method causing unspecified health problems, unpleasant side effects, menstrual disruption and infertility. Respondents were also asked whether they believe their husband approves of each method. These method-specific beliefs were collected in both round 1 and round 2; we rely exclusively on round 1 measurement for prediction of discontinuation between rounds 1 and 2. More details on the ambitious measurement in this Kenyan data collection are published elsewhere [27, 28]. The precise wording and sequence of questions can be found at (http://stepup.popcouncil.org/library/STEPUP_questionnaire_31072016.pdf).
In the analysis, all method-specific beliefs are entered as binary variables. Most items were already simply yes or no response options. A few were reduced to binary form. Specifically, the item ‘How many of your friends, relatives and neighbors have tried the method?” has response options ‘most’, ‘about half’, ‘few’, ‘none’ and ‘don’t know’. A binary variable was created by combining women who responded ‘most’, ‘about half’, and ‘few’ into one category (coded “1” ) and the remaining ‘none’ and ‘don’t know’ to another category (coded as “0”).
Concerned about redundancy of method beliefs, we assessed strength of association between all pairs of beliefs using Cramers V, separately by method and site. For most the value of V was less than 0.2, indicating weak associations. The principal exception was the association between belief in unpleasant side effects and menstrual disturbance, for which the values of V were in the range of 0.41 and 0.54, indicating moderate associations. A particular concern was the possibility of strong associations of beliefs in serious health problems and beliefs about bleeding and other side effects. V values for the bleeding-health problems associations were low, between 0.19 and 0.29, but slightly higher for other side effects, in the range of 0.32 and 0.42. In short, this assessment revealed no associations so high that excluding one or more method beliefs seemed in order, and hence we include all beliefs in the analysis.
The second explanatory variable distinctive to this research was ‘strength of fertility preferences’ referring to women’s motivation to delay or avoid getting pregnant. The survey collected detailed information on the fertility desires of women and their spouses. The future fertility preferences question asks women whether they would like to have (a/another) child, or would they prefer not to have any (more) children. For those who want a child in the future, additional questions were asked to find out how long they want to wait from the date of the interview before the birth of another child. The fertility preferences variable was initially recoded into five categories; want a child soon or within 2 years, want a child in 2-4 years, want a child after 5 years, want no more child, and undecided/not sure or missing categories.
Beyond this standard DHS questioning, the survey assessed the strength of women’s motivation to avoid pregnancy by asking the following question; “How important is it to you to avoid becoming pregnant now: Would you say very important, somewhat important, or not at all important?”. Additional questions were asked to assess women’s feelings, asking if she becomes pregnant in the next few weeks whether she would be worried about telling her husband/partner; whether she would be worried about how she could afford to raise her children properly with an extra child; and whether she would be concerned or not about the effect on her own health. In the analysis, these variables on the importance of avoiding pregnancy and women’s feelings were recoded to binary form to create a variable of ‘strength of motivation’. Response options were coded as 1 for ‘worried’ and 0 for ‘not worried or unsure’. Then the responses were counted to categorize respondents into those that have ‘weak motivation’ and those that have ‘strong motivation’ based on their responses to the three survey questions. We then combined the two variables (fertility preferences and strength of motivation) and created a composite variable of strength of fertility preferences with five categories: want no more/want to wait 5 years or longer with strong motivation; want no more/want to wait 5 years or longer with weak motivation; want to wait 2-4 years with strong motivation; want to wait 2-4 years with weak motivation; and want a child soon (within two years) or undecided.
Background variables serving as controls are age of the respondent, education, and study site (Nairobi vs. Homa Bay). Age was recoded into two categories as 15-24 and 25-39 years. Respondent’s highest level of education was recoded into three categories; no schooling or some primary education, completed primary, and secondary or higher than secondary education.
Statistical Approach
Analysis focused on discontinuation of injectables and implants. Episodes of use of other methods were too few to sustain analysis. The unit of analysis is episode of continuous use of a specific method [1], and accordingly a switch to another contraceptive method initiates a new episode of use. The distinction between method-related and non-method-related reasons for discontinuation is fundamental to this analysis. Our interest is correlates of method-related discontinuation. Episodes discontinued for other reasons (e.g. wanting to become pregnant) are included in the regression analysis, which employs a competing risks approach with two competing risks: discontinuation for method-related and for non-method-related reasons.
In a first stage of the analysis, we examine the stated reasons for the discontinuation of episodes between round 1 and round 2. This analysis is confined to women who discontinued, and it is a bivariate analysis with no attention to covariates.
In the second stage of the analysis, we model the predictors of discontinuation, separately for injectable and implant. For this portion of the analysis, we adopt a formal survival analysis methodology because the contraceptive episodes are characterized by both left-censoring and right-censoring. The former, also known as “delayed entry”, occurs because episodes were already in process at the time of the round 1 survey; the latter occurs because some episodes were still in process at the time of the round 2 survey. Survival analysis is designed to accommodate both types of censoring when calculating relative rates of discontinuation [29]. This is a competing risks survival analysis (method-related vs. non-method-related reasons for discontinuation). We employ the Fine and Gray approach as implemented in Stata procedure “stcrreg”. [29, 30]. Incidence functions and sub-hazard ratios (and corresponding confidence intervals) can be calculated for each competing risk. In this analysis, we calculate these only for method-related discontinuation.
The survival analysis is conducted with the data pooled across the two sites (Nairobi and Homa Bay). We have carried out site-specific analysis and confirmed that the main findings do not differ between sites. Moreover, the site-specific analysis suffers from weak statistical power.