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Background: Globally 10% of women have an unmet need for contraception, with higher rates in sub-Saharan Africa. Programs to improve family planning (FP) outcomes require data on how service characteristics (e.g., geographic access, quality) and women’s characteristics are associated with contraceptive use. Materials and Methods: We combined data from health facility assessments (2018 and 2019) and a population-based regional household survey (2018) of married and in-union women ages 15-49 in the Kigoma Region of Tanzania. We assessed the associations between contraceptive use and service (i.e., distance, methods available, personnel) and women’s (e.g., demographic characteristics, fertility experiences and intentions, attitudes toward FP) characteristics. Results: In this largely rural sample (n = 4,372), 21.7% of women used modern reversible contraceptive methods. Most variables were associated with contraceptive use in bivariate analyses. In multivariate analyses, access to services located <2 km of one's home that offered five methods (adjusted odds ratio [aOR] = 1.57, confidence interval [CI] = 1.18-2.10) and had basic amenities (aOR = 1.66, CI = 1.24-2.2) increased the odds of contraceptive use. Among individual variables, believing that FP benefits the family (aOR = 3.65, CI = 2.18-6.11) and believing that contraception is safe (aOR = 2.48, CI = 1.92-3.20) and effective (aOR = 3.59, CI = 2.63-4.90) had strong associations with contraceptive use. Conclusions: Both service and individual characteristics were associated with contraceptive use, suggesting the importance of coordination between efforts to improve access to services and social and behavior change interventions that address motivations, knowledge, and attitudes toward FP.
The Kigoma region is located in the northwest corner of the country where 83% of households are classified as rural.20 The region has relatively low rates of contraceptive use; nationally 38% of 15–49 year old married women used contraception, but only 18% of 15–49 year old married women in Kigoma region used contraception in 2015–2016.21 We drew on data used to evaluate a project to improve maternal and neonatal outcomes in Kigoma.22 In its first phase, the “Reducing Maternal Mortality in Tanzania” project (2006–2012) supported hospitals and health centers (e.g., provide supplies and training) and created demand for obstetric services. In Phase 2 (2013–2019), the project expanded support to dispensaries and included FP components. Implemented by a consortium of partners (i.e., Engender Health, Thamini Uhai, Ministry of Health, Community Development, Gender, Elderly, and Children, President's Office—Regional Administrative and Local Government, Global Health Advocacy Incubator, and local officials), the project was supported by Bloomberg Philanthropies and the Foundation H&B Agerup. The U.S. Centers for Disease Control and Prevention (CDC) led the evaluation. We used data from health facility assessments (HFAs) and a reproductive health survey (RHS); protocols were approved by the Tanzania Ministry of Health and Social Welfare and the Tanzania National Health Research Ethics Review Committee and classified as nonresearch by CDC. Participants did not receive incentives. HFAs, conducted in 2013, 2016, 2018, and 2019, documented changes in capacity (e.g., staffing, commodities).23 We used data from HFAs conducted in January and February of 2018 and 2019 for all facilities (N = 197 hospitals, health centers, and dispensaries) with at least 90 deliveries per year. The HFA documented geographic coordinates of facilities, amenities, number and types of providers, training, drug stocks, and practices (e.g., neonatal care). Cross-sectional household-based RHSs, in 2014, 2016, and 2018, drew representative samples of women ages 15–49.24 The 2018 survey, which we used, sampled ∼10,000 women using a multistage design. First, we drew a random sample of 120 primary sampling units (PSUs), proportional to population size, using 2012 census EAs20; PSUs were composed of one or two EAs, which were visited before field work to update household listings and capture geographic coordinates. In the second stage, between 36 and 109 households were sampled in each PSU. All women aged 15–49 in selected households were eligible. From September to November 2018, interviewers visited 10,021 households and obtained informed consent before conducting interviews; the household response rate was 98.8%. Of the 10,542 eligible women identified, 10,181 (96.6%) completed interviews. Questionnaires asked about demographics, fertility, contraceptive behaviors, and knowledge of and attitudes toward contraception. We limited our analysis to women who: were married to or living with a man; were not currently pregnant; had sex in the year before the interview; did not report that she or her partner was sterilized; and had not started using a long-acting method before January of 2017. Of the 10,181 survey respondents, 4,804 met these criteria. Because 17% of women reported that whether they become pregnant is “up to God,” we included fertility intentions as a covariate rather than limiting the sample to women who wanted to delay pregnancy. We explored access to services by linking each woman in the sample to the nearest health facility in the HFA. Using ArcGIS software, we measured the straight-line distance between the center of the EA to the nearest facility (i.e., all women in an EA were matched to the same facility). Because the HFAs did not include all facilities in Kigoma, some women were closer to a non-HFA facility than to the HFA facility to which they were matched. In line with similar analyses, we further restricted our analyses to women matched to a facility within 10 km of the center of their EA.7,9 This resulted in the exclusion of 432 women, for an analytic sample of 4,372. Using Ministry of Health data, we determined that 76.7% of women in our analytic sample were matched to their closest facility. From the HFAs, we measured the availability of contraceptive methods and basic amenities overtime, assuming that women would experience or hear about the services and be more likely to use geographically closer and higher quality services. We assessed whether a characteristic was present in 2018 and 2019 HFAs and combined that with our measure of distance; the resulting variables had three levels: characteristic was NOT present at the facility in both or in either year; characteristic present in both years at a facility that was within 1.9 km from the center of the EA; and characteristic present in both years at a facility that was 2–10 km from the center of the EA. The HFA observed if five modern reversible methods—condoms, pills, injectables, implants, and intrauterine devices (IUDs)—were available on the day of the assessment and had no stock-outs in the previous year. We counted facilities as having methods available if all five were not stocked out (i.e., assessed availability continuously from approximately January 2017 to January 2019). To measure basic amenities, we summed “yes” responses to whether each of eight amenities were present (i.e., electricity, backup generator, running water, toilet, private space, telephone/radio, internet access, motor vehicle with fuel) and dichotomized the resulting score (i.e., 0–3 vs. 4–8). In addition, in 2019, the HFA measured the number of providers with any FP training; our measure had three levels that combined data on the presence of at least two FP trained providers and distance. The RHS measured several individual characteristics. We defined current use of modern reversible contraception as use in the 30 days before the interview of the pill, injectable, condoms, implant, lactational amenorrhea method, or IUDs; women in the sample did not report using other modern methods (e.g., diaphragm). The comparison group included women who were not currently using contraception or who used a traditional method. We measured four sets of individual characteristics: demographics; fertility history and fertility intentions; empowerment; and FP awareness and attitudes. We measured demographic variables with dichotomous or ordinal-level variables (Table 1). Measures of fertility history and intentions included parity, intendedness at the time of conception of the last birth since 2016, and fertility intentions. We included three measures of women's empowerment and agency. We summed “yes” responses to five items to measure economic empowerment (e.g., had a job and received pay, had own cash) and created a categorical variable. We created a household decision-making score using the answers to the question about which members of the household (i.e., woman alone or woman and her partner vs. her partner alone or others) had input into seven household decisions (e.g., her health care, money she brings into the household, how many children to have); we treated the resulting score (ranging from 0 to 7) as a categorical variable. Initially, we considered the item assessing input into decisions about how many children to have as a single item; because it was not associated with contraceptive use, we included it in the scale of input into household decision-making. Because early marriage is often associated with lower relationship power (e.g., increased controlling behaviors), we included age at first marriage.25 Bivariate Associations Between Modern Contraceptive Use and Service Characteristics, Demographic Characteristics, Fertility History and Intentions, Women's Empowerment, and Family Planning Awareness and Attitudes for Nonpregnant Married/In-Union Women, Kigoma Tanzania (n = 4,372) FP, family planning. Finally, we measured awareness of, perception of, and attitudes toward FP. We summed “yes” responses and created categorical variables to measure: the number of modern methods ever heard about (range 0–9); the number of channels (e.g., radio, billboard, doctor/nurse) from which she heard FP information in the past 6 months (range 0–10); and whether it would be acceptable to hear about FP from different channels (e.g., radio, school, religious leader) (range 0–6). We summed “very effective” and “effective” responses to questions asking about effectiveness of four modern methods (i.e., condom, pill, injectable, IUD/implant) and did the same for responses to questions about the safety (i.e., lack of side effects) of the same methods; we created dichotomous variables measuring whether a woman believed that none versus at least one method was effective or safe. We asked women whether they agreed to five statements about FP; for each we contrasted “agree” and “strongly agree” to “neither agree nor disagree,” “disagree,” and “strongly disagree” responses (Table 1). We began with bivariate analyses to assess the relationship between each service or individual characteristic and contraceptive use (Table 1). Next, we ran multivariate logistic regression models for each set of variables: demographics; fertility history and intentions; women's empowerment and agency; and awareness, perceptions of, and attitudes toward FP. For each set, we included variables that were significant at p < 0.10 in bivariate analysis (Table 2). Finally, we ran a full model that included all variables with at least one dummy variable that was significant at p < 0.10 in the first set of models. Models were constrained to retain variables that were significant at p < 0.20, although we focused on variables that were statistically significant at <0.05. All analyses were weighted for the inverse probability of selection at each sampling stage, and analyses were run using the “complex sampling” package in IBM SPSS Statistics (Build 1.0.0.1114). Adjusted Odds Ratios and 95% Confidence Intervals for the Associations Between Modern Contraceptive Use and Service Characteristics, Demographic Characteristics, Fertility History and Intentions, Women's Empowerment, and Family Planning Awareness and Attitudes for Nonpregnant Married/In-Union Women, Kigoma Tanzania (n = 4,372) CI, confidence interval; OR, odds ratio.
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