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Background Women who start using contraception (“adopters”) are a key population for family planning goals, but little is known about characteristics that predict the adoption of contraception as opposed to current use. We used prospective data from women and facilities for five countries, (Democratic Republic of Congo, India, Kenya, Nigeria, and Burkina Faso) and identified baseline characteristics that predicted adoption of modern contraception in the short term. Methods We used data from the Performance Monitoring for Action (PMA) Agile Project. PMA Agile administered service delivery point (SDP) client exit interview (CEI) surveys in urban sites of these five countries. Female clients responding to the CEI were asked for phone numbers that were used for a phone follow-up survey approximately four months later. For our analysis, we used data from the SDP and CEI baseline surveys, and the phone follow up to compare women who start using contraception during this period with those who remain nonusers. We used characteristics of the facility and the woman at baseline to predict her contraception adoption in the future. Results Discussing FP with a partner at baseline was associated with greater odds of adoption in DRC (OR 2.34; 95% CI 0.97-5.66), India (OR 2.27; 95% CI 1.05-4.93), and Kenya (OR 1.65; 95% CI 1.16-2.35). Women who discussed family planning with any staff member at the health facility had 1.72 greater odds (95% CI 1.13-2.67) of becoming an adopter in Nigeria. The odds of adoption were lower in Nigerian facilities that had a stockout (OR 0.66 95% CI 0.44-1.00) at baseline. Other characteristics associated with contraception adoption across settings were education, age, wealth, parity, and marital status. Conclusions Characteristics of both the woman and the health facility were associated with adoption of modern contraception in the future. Some characteristics, like discussing family planning with a spouse, education, and parity, were associated with contraceptive adoption across settings. Other characteristics that predict contraceptive use, such as health facility measures, varied across countries.
Data for this study come from the Performance Monitoring for Action (PMA) Agile Project. PMA Agile was a continuous data monitoring and evaluation system that collected data every four to six months on the overall health service delivery environment. PMA Agile operated in urban areas of six countries, Burkina Faso, the Democratic Republic of Congo, India, Kenya, Niger, and Nigeria. PMA Agile had multiple sites in four countries: Lagos, Kano, and Ogun in Nigeria; Uasin Gishu, Migori, and Kiricho in Kenya; Indore, Firozabad, and Puri in India; Ouagadougou, and Koudougou in Burkina Faso. There was one PMA Agile site in each of the two remaining countries, Kinshasa, DRC; and Niamey, Niger. Data were collected in urban health facilities; the surveys were conducted at low cost with rapid turnaround. More information about PMA Agile can be found at the project’s website: www.pmadata.org/technical-areas/pma-agile, and in the PMA Agile Cohort Profile [22]. PMA Agile used a similar sampling approach in each country and site. PMA Agile started with a full list of public and private family planning service delivery points for each urban area, and then randomly selected 220 facilities in each site, with equal numbers public and private. The sample size accounted for 10% expected non-participation among selected facilities. PMA Agile then conducted both a service delivery point (SDP) and client exit interview (CEI) at each selected facility. The former was administered to a representative who was knowledgeable about the family planning services at the facility, and measured topics such as the availability of contraceptive methods and the cost of each method. The CEI was administered to both male (aged 18–59) and female (18–49) facility clients who visited one of the facilities in the PMA Agile sample. The CEI was administered to approximately 10 clients per SDP, which yielded a sample size of 1,500–2,000 per PMA Agile site. CEI participants were selected systematically using a sampling interval that was calculated from the daily client flow reported from the SDP survey. The CEI survey instrument included questions on sociodemographic characteristics, contraceptive use, service quality, and family planning product recognition. A mobile airtime card with a value of about one USD was provided to each respondent completing the interview. At the end of the CEI, female clients were asked if they would be willing to be followed up by telephone after four months and if so, to provide up to two telephone numbers. The same interviewer at baseline typically conducted the follow-up interview. Mobile phone airtime of one USD, transmitted electronically, was again provided to the followed-up female client. In the CEI follow-up survey, women were asked about continued contraceptive use, method switching, and satisfaction with services received. We used CEI baseline and follow up data from five of the six countries, omitting Niger due to a small sample size of adopters. The CEI follow up was administered to women, so men are not included in our analysis. In all five countries, the baseline CEI and SDP surveys were conducted in 2018. In Burkina Faso, baseline data collection occurred from August through October; in DRC from May through June; In India from July through October; and in Kenya and Nigeria from March through August. The phone follow-up CEIs were conducted between four to six months later starting in September 2018 through April 2019 in all countries. The PMA Agile study and data collection protocols were reviewed and approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board and the in-country counterpart review board: Kenyatta National Hospital-University of Nairobi Ethics Research Committee (KNH-UoN ERC P470/08/2017); National Health Research Ethics Committee of Nigeria (NHREC/01/01/2007-19/09/2019); MOH-Burkina Comité d’Ethique pour la Recherche en Santé (MOH 2018-02-027); University of Kinshasa School of Public Health Institutional Review Board (ESP/CE/070/2017); Indian Institute for Health Management Research Ethical Review Board (19/12/2017-15/01-2018); MOH- Niger Comité National d’Ethique pour la Recherche en Santé (027/2020/CNERS). In accordance with country specific approved consent procedures, participants provided informed verbal consent in DRC, Kenya, India, and Nigeria (consent was recorded in a checkbox by the interviewer into the smartphone); and participants provided informed written consent in Burkina Faso. The data used in this analysis were completely anonymised, deidentified, and aggregated before access and analysis. Our measures are consistent with the standard approaches. We defined women who adopted modern contraception as “someone who starts using family planning who was not currently using modern contraception at the time of her visit but may have used modern contraception in the past” [2]. This definition included both women who used modern contraception in the past but discontinued use before adopting again, as well as first-time users of modern contraception. Following WHO standards, we defined the following contraceptive methods as modern: oral pills, intrauterine devices, injectables, male and female sterilization, implants, condom, lactational amenorrhea method, vaginal barrier methods, emergency contraception, and cycle beads. To measure adoption, we used PMA Agile data from women interviewed in both the in-person baseline CEI and the telephone CEI follow-up, and limited our analysis to women who were not using any contraceptive method at baseline. Modern contraceptive method use at baseline was determined through two measures from the CEI survey. Clients that were attending an SDP seeking services besides family planning were asked about their current contraceptive use status. Among clients who were attending an SDP seeking family planning services, use status was assigned from the contraceptive method either prescribed or given. To capture contraceptive use at the follow-up survey, women were directly asked about their current contracepting status. Adopters were defined as female clients who were not using modern contraception at baseline and reported using modern contraception at the time of the follow-up survey. We compared these adopters with continued non-users of contraception, who were female clients not using a contraceptive method at baseline or follow up. Women using traditional contraceptive methods were included among non-users of modern contraception. Women using modern methods at baseline were excluded from this analysis. To identify the predictors of contraceptive adoption, we used CEI and SDP baseline characteristics. Our selection of specific characteristics was guided by the literature on this topic. We started with sociodemographic characteristics, such as age (separated into three categories, 18–24, 25–34, 35–49), parity (none, one, two, three or more), education (none/primary, secondary, higher), and marital status (currently married, not currently married). We used the Cantril ladder to measure household wealth, in which female clients ranked their household wellbeing on a 10-step staircase where the first step represents the poorest and the 10th step represents the richest (we separate into 1–3, 4, 5, 6–10) [23]. Research has shown that spousal communication is often associated with family planning use [24], so we included a measure of whether the woman discussed family planning use with their partner in the past six months. We included exposure to family planning programs, measured by seeing advertisement for FP on radio or television in the past three months, and being visited by a community health worker and discussing FP with a health care provider in the past 12 months. Media exposure to family planning was not collected in India. We distinguished between several reasons for the visit to the facility (family planning/maternal health, child health, general health/other). Reasonable access to a health facility is also associated with contraceptive use [17], so we included a measure of distance to the facility (less than 1 kilometer, between 1 and the median distance, and above the median distance). Our measures of facility characteristics focus on general service quality from the visit that took place on the day of the baseline interview. Women were asked about common problems clients face at health facilities and whether they experienced any of these problems at their visit. They were asked whether each of these items were a problem on that day, with response options ranging from 0 to 2, 0 being not a problem and 2 a major problem. The problems listed were time waited to see a provider, cleanliness of the facility, and cost of services or treatment. We also included an indicator of whether staff at the health facility discussed family planning during the visit. We also included measures of facility characteristics from the SDP survey. Specifically we measured the facility type (hospital, health center, pharmacy, other), as service quality and cost of contraceptives often vary by type [25]. Our categorization of facility types represented the four broadest categories that had comparable types across settings. Alternative categorizations did not change the substantive interpretation of our results. Distributions of SDP types prior to our recoding appear in S1 Table. The cost of contraception has been found to influence contraceptive behavior [26], so we included a dichotomous variable indicating if the facility where the client was interviewed at baseline charged a fee for providing family planning methods. Finally, we included contraceptive availability, measured as a binary variable that equal to one if the facility had any method out-of-stock or if they are not offered, and zero if they have all methods in stock, either short-acting or long-acting. These characteristics have all been identified as important potential influences on contraceptive use [12–15, 27]. We conducted our analysis in four steps. First, we presented sample numbers and response rates for each PMA Agile country. Next, we showed the percentage of women who were adopters and non-users in each country. Third, we tabulated percentages of all characteristics that influenced adoption, and performed bivariate chi-squared tests of differences in these characteristics between adopters and continuing non-users (separately for each country). Finally, we constructed a logistic regression model with site fixed effects to identify baseline characteristics that predicted modern contraception adoption in the future. Our outcome of interest is the proportion of female clients defined as adopters, which is characterized as a function of service delivery point and individual characteristics resulting in the following function: In this equation “Adopter” represents the proportion of female clients who adopted a modern contraceptive method in period t+1 given they were not using contraception in period t (t = baseline) for each site indexed by “i” (i = 1 in DRC, i = 2 in Burkina Faso and i = 3 in Kenya, Nigeria, and India). SDPi,t is a vector that represents service delivery point characteristics. SESi,t is a vector that represents the client’s sociodemographic characteristics. FPCommi,t represents spousal communication about family planning in the last six months. FPExposurei,t represents exposure to family planning information. RFi,t represents the relationship between the client and the facility by capturing the visit reason and distance to the facility. All service delivery point and individual characteristics were measured at time t. Finally, δs represents site fixed effects; and εit is the error term. The analysis was performed separately by country. For these regression models, we show odds ratios (OR) and 95% confidence intervals (95% CIs). As a robustness check we specified two additional models following a similar approach but restricting the covariates of the model. The first model only included SDP characteristics, while the second model only included socioeconomic factors, exposure to family planning, spousal communication, and the relationship between the client and the facility. We also tested for multicollinearity through variance inflation factors (VIFs) and did not include any measures that exceeded a value of 7.0 for VIF.
