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Erschienen in: Reproductive Health 1/2021

Open Access 01.12.2021 | Research

Women’s empowerment, intrahousehold influences, and health system design on modern contraceptive use in rural Mali: a multilevel analysis of cross-sectional survey data

verfasst von: Caroline Whidden, Youssouf Keita, Emily Treleaven, Jessica Beckerman, Ari Johnson, Aminata Cissé, Jenny Liu, Kassoum Kayentao

Erschienen in: Reproductive Health | Ausgabe 1/2021

Abstract

Background

Persistent challenges in meeting reproductive health and family planning goals underscore the value in determining what factors can be leveraged to facilitate modern contraceptive use, especially in poor access settings. In Mali, where only 15% of reproductive-aged women use modern contraception, understanding how women’s realities and health system design influence contraceptive use helps to inform strategies to achieve the nation’s target of 30% by 2023.

Methods

Using household survey data from the baseline round of a cluster-randomized trial, including precise geolocation data from all households and public sector primary health facilities, we used a multilevel model to assess influences at the individual, household, community, and health system levels on women’s modern contraceptive use. In a three-level, mixed-effects logistic regression, we included measures of women’s decision-making and mobility, as well as socio-economic sources of empowerment (education, paid labor), intrahousehold influences in the form of a co-residing user, and structural factors related to the health system, including distance to facility.

Results

Less than 5% of the 14,032 women of reproductive age in our study used a modern method of contraception at the time of the survey. Women who played any role in decision-making, who had any formal education and participated in any paid labor, were more likely to use modern contraception. Women had three times the odds of using modern contraception if they lived in a household with another woman, typically a co-wife, who also used a modern method. Compared to women closest to a primary health center, those who lived between 2 and 5 km were half as likely to use modern contraception, and those between 5 and 10 were a third as likely.

Conclusions

Despite chronically poor service availability across our entire study area, some women—even pairings of women in single households—transcended barriers to use modern contraception. When planning and implementing strategies to expand access to contraception, policymakers and practitioners should consider women’s empowerment, social networks, and health system design. Accessible and effective health systems should reconsider the conventional approach to community-based service delivery, including distance as a barrier only beyond 5 km.
Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1186/​s12978-020-01061-z.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
AOR
Adjusted odds ratio
CHW
Community health worker
CI
Confidence interval
DHS
Demographic and Health Survey
GIS
Geographic information system
ICC
Intracluster correlation coefficient
IQR
Interquartile Range
IUD
Intrauterine device
LAM
Lactational amenorrhea method
OCP
Oral contraceptive pill
PHC
Primary Health Center
WHO
World Health Organization

Plain English Summary

Despite needing methods to avoid unwanted pregnancies and safely space births, many women around the globe are unable to obtain modern contraception (for example, condoms, implants, etc.) particularly in middle and western Africa. In Mali, less than one in six women aged 15 to 49 years use modern contraception. In order to help design strategies to increase use, we need to understand what factors support women to use contraception in settings where access to healthcare is poor. In December 2016 and January 2017, we surveyed 14,032 women in Bankass, rural Mali, and asked them about themselves and their use of reproductive health services, among other topics. Less than five percent used modern contraception at the time of the survey. In a statistical regression analysis, we determined that women who were involved in decisions pertaining to her own health, visiting her relatives, and household spending were more likely to use contraception than those who were not, as were women who had any education and any paid work. Living with another woman in the household who used contraception meant that a woman was three times more likely to use herself. We also found that the further a woman lived from a health center, the less likely she was to use, even within 5 kilometers. When designing and rolling out targeted strategies to expand access to contraception, we ought to consider these elements related to women’s empowerment, intimate relationships, and the broader health system.

Background

Ensuring access to contraception and women’s family planning needs are met with modern methods is essential to meeting the Sustainable Development Goals related to universal access to reproductive healthcare, gender equality, and the empowerment of women and girls. Among all women of reproductive age globally, the use of modern contraception has increased only marginally between 2000 and 2019 from 42.0 to 44.3%, with the greatest unmet need persisting in middle and western Africa [1]. In Mali, only 15% of women aged 15–49 years used modern contraception at the time of the last Demographic and Health Survey (DHS) in 2018 [2].
Mali’s total fertility rate is among the highest in the world. Women have an average of 6.3 children, with women in rural areas having almost two more children than those in urban areas (6.8 versus 4.9 children per woman) [2]. Although fertility has declined in Mali since 1987 when the average was 7.1 children per woman, certain regions today have fertility rates as high as the 30 year old national average [2]. Despite national policy and law promoting sexual and reproductive health and rights, more than one in five reproductive-aged women in Mali report an unmet need for family planning, including one quarter of women in union and over half of sexually active women not in union [2].
A number of structural barriers may inhibit or delay access to contraception and other basic healthcare services within Mali’s decentralized, pluralistic, fee-for-service healthcare system. Family planning services are theoretically integrated into all levels of public sector care in Mali: national, regional, district, health catchment area, and community. In some communities greater than 5 km from a primary health center (PHC), community health workers (CHW) are stationed in fixed community health posts to provide counseling, services and referral, including for family planning, to patients who seek care and pay the fees for service. However, direct and indirect costs to care, including distance, are well-established barriers to timely, appropriate healthcare across a variety of settings [38]. Furthermore, service delivery at all levels of care in Mali suffers from a shortage and inequitable distribution of the health workforce, inadequate clinical mentoring and supervision, and poor infrastructure and frequent stock-outs, which undermine quality of care and patient confidence. Major system-wide reforms in Mali to improve access to care are were announced in February 2019 and expected to take full effect by 2022, including removing user fees for contraceptives and maternal and child health, strengthening the CHW cadre, and increasing national budget allocations to health.
Beyond barriers related to health system design and implementation, women in this context may be further hindered in fulfilling their contraceptive needs due to infringements on their empowerment, defined here as the expansion in people’s ability to make strategic life choices through resources, agency, and achievements [9]. Socio-economic disadvantages such as poor access to formal education and the paid labor force, constraints on physical mobility, limited decision-making power, and gender norms and attitudes have been shown to limit women’s ability to exercise contraceptive choices in settings across sub-Saharan Africa [1017]. The expansion in women’s ability to make strategic choices related to reproduction in such a prevailing context may be influenced by household composition, familial relationships, and decision-making dynamics. In South Asian settings where extended family ties are strong, intrafamilial influences, such as spousal communications and interactions with mothers-in-law, may play an important role in women’s contraceptive use [18, 19]. In rural Mali, where women in union typically live with their husband’s extended family and 40% are in a polygynous arrangement [2], the role, autonomy, and contraceptive use status of their female household members may expand women’s access to contraceptive choice.
Mali recently developed a renewed five-year national strategic plan for family planning, with the ambitious goal of increasing female modern contraceptive use to 30% by 2023 [20]. Building on the experiences and lessons learned in implementing the previous 5-year plan (2014–2018), the renewed plan for family planning is based on five strategic pillars: demand generation; availability and access to services; supply chain management; an enabling political environment and financing; and monitoring and supervision. In order to achieve this new goal, Mali must attain a rapid growth rate in contraceptive use comparable only to that achieved by Sierra Leone in the West African region [20]. Further elucidating how health system design and women’s realities influence modern contraceptive use helps to determine how such ambitious plans should be operationalized in order to improve access.
In this study, we aim to: (1) describe modern contraceptive use among women of reproductive age in the under-studied, high fertility, rural Malian context of the Bankass district, including methods and procurement among users; (2) explore descriptively and visually household and village composition of reproductive-aged women and their use of modern contraception; and (3) identify predictors of modern contraceptive use in this context where utilization is exceptionally low. We use a multilevel modeling approach using detailed household survey data, including geolocated measures of distance, to assess influences on women’s modern contraceptive use at the individual, household, community, and health system levels. We include ‘direct’ measures of women’s empowerment (decision-making and mobility in the public domain), as well as indirect socio-economic sources of empowerment (education and paid labor force participation). We explore the role of intimate female social networks by assessing how living in a household with another woman who uses modern contraception influences adoption. Determining what structural barriers to dismantle and social relationships to leverage in order to expand contraceptive access is key to meeting national and international goals for women’s wellness, health, and survival.

Methods

Study design

We conducted a cross-sectional household survey in the communities of seven health catchment areas of the rural Bankass district, Mali from December 2016 to January 2017. This survey served as the baseline for an ongoing cluster-randomized controlled trial (trial registration number NCT02694055; N = 137 village-clusters) to assess the effects of a proactive approach to community-based healthcare delivery on child mortality and access to care over three years, including access to modern and long-acting reversible contraception (secondary trial endpoint) [21]. Here, we analyze baseline survey data from women of reproductive age to assess contraceptive use before the launch of intervention activities.

Study setting

The Bankass health district is part of the Mopti region in central Mali, approximately 600 km northeast of the nation’s capital, Bamako. The district has a population of approximately 300,000 people and is served by a public, secondary referral hospital located in Bankass, the largest town in the district [22]. It was chosen for the trial in collaboration with the Malian Ministry of Health and Social Affairs based on high under-five mortality and low healthcare utilization in the region, similar to other rural Malian settings [23], as well as few concurrent health interventions in the district and interest from local authorities to collaborate. Within the Bankass health district, the study was conducted in seven (of 22) contiguous, rural health catchment areas: Dimbal, Doundé, Ende, Kani Bozon, Koulongon, Lessagou, and Soubala, an area with a population of approximately 100,000 people. Each health catchment area is served by a public sector PHC.

Study participants

In the context of rural Mali, extended families often live together in family compounds comprised of multiple households. Our survey definition of a household within a family compound was a monogamous or polygynous marital arrangement with or without children and additional relatives, or a single mother with or without additional relatives. All women aged 15 to 49 years permanently residing in the study area with no plans to leave during the trial period and who provided consent or assent were eligible to participate in the women’s questionnaire component of the household survey. From the present analysis we excluded all women who reported being pregnant at the time of the survey (N = 2022) or who reported having reached menopause or having had a hysterectomy (N = 299).

Data sources and measurement

We adapted our household survey instrument (see Additional File 1) from the Mali DHS and programmed it in Open Data Kit. The survey captured detailed information on household and individual socio-demographic characteristics, utilization of reproductive, maternal and child health services, and recorded household geographic coordinates, among other topics. All surveyors were women who were not members of the villages they surveyed, due to the sensitive nature of questions related to contraception and reproductive health. Respondents participated in French, Bamanankan, Peulh, or the Dogon dialects of Tomokan and Tingu.

Measures

Outcomes

We evaluated women’s self-reported use of a modern method of contraception at the time of the survey. We defined modern methods according to the World Health Organization (WHO) [24] and Malian guidelines, and included female and male sterilization, female and male condoms, intrauterine device (IUD), implant, injectable contraceptive, oral contraceptive pill (OCP), diaphragm, foam/spermicidal jelly, the lactational amenorrhea method (LAM), and the standard days method (e.g., cycle beads). Traditional methods included the rhythm/calendar method, withdrawal, herbal, and other methods. For women who reported using multiple methods concurrently (N = 5), the more efficacious method was chosen for analysis (i.e., sterilization > implant > IUD > injectable > other modern method > traditional method).
We descriptively examined length of use and place and cost of last procurement among all contraceptive users. Length of contraceptive use in months was calculated by subtracting the month and year that the woman reported using the current method without interruption from the month and year of the survey. Initiation month was assigned between one and 12 at random using the runiform function in Stata 15 if it was missing (N = 123/710). The place where the current method was last procured was categorized as within the health sector or outside the health sector. Within the health sector included national, regional, or district hospitals, PHCs, CHWs, and private clinics. Outside the health sector included at home, at boutiques, kiosks, bars, or nightclubs, black market vendors, or personal contacts.

Predictors

We elaborated a list of potential predictors a priori based on existing evidence and contextual knowledge. At the individual level, these included: women’s age (5-year categories); number of living children (none, one or two, three or four, five or six, seven or more); marital status (monogamous, polygynous, not currently married); tolerant attitudes for spousal violence (coded any tolerance versus none, based on whether she believed a husband was justified in hitting or beating a wife under any of the seven circumstances evaluated, including if she used contraception without his consent); education (any formal schooling versus none); participation in paid labor (any versus none); and empowerment measures we adapted from existing scales, including mobility and decision-making power. We coded women’s mobility categorically based on their having ever been to the market place, health center, women’s group, or outside the village (never been to any, been to some or all but never alone, been to at least one alone—with which we capture independent mobility) [25]. Women were coded as having any involvement in decision-making versus none, based on whether they reported making decisions, either independently or jointly with someone else in the family, for any of the three domains asked (i.e., her own healthcare, visiting her relatives, household purchasing) [26].
Household level predictors included: another woman of reproductive age in the household using modern contraception; household wealth quintiles constructed using principal components analysis of asset indicators; [27] household food insecurity (coded as any versus none, based on whether the respondent affirmed that in the last 30 days, there was no food to eat due to a lack of resources, or someone went to sleep hungry because there was not enough food, or someone went a whole day and night without eating because there was not enough food); [28] and household distance to nearest public sector health facility. Orthodromic (great-circle) distance estimates were based on Geographic Information System (GIS) data for the entrance to the family compound, each PHC, and the district referral hospital. When GIS data for the family compound was missing (N = 560), we approximated household distance using GIS collected at the central gathering place in the village. We included a community level factor for the availability of CHW services at the time of the survey (coded as having a CHW posted in the village or hamlet at a fixed community health site, having a CHW provide services in the village or hamlet but not posted there, or not having any CHW services available), based on documentation from the Ministry of Health and Social Affairs.

Statistical analysis

All statistical analyses were performed using Stata version 15 (Stata Corporation, Texas, USA).

Descriptive data

We first examined descriptively sample characteristics and contraceptive use outcomes. Categorical sample characteristics were calculated as proportions of all women in the sample (i.e., including those with missing characteristics data). The main outcome was calculated among those with non-missing modern contraceptive use data. We compared sample characteristics between those with and without missing data on the main outcome. For continuous variables, we calculated summary statistics appropriate to the variable distribution in the sample population (e.g., mean, median).
We georeferenced the concession location data using OpenStreetMap. Kernel Density Estimation was employed to generate a density raster (heatmap) in QGIS v3.4.7 and to visualize the spatial/geographic clustering of women using modern contraception as well as multi-user households and village-clusters with no users, across the study area. The radius was set at 0.01 map unit.
Within households that had multiple women of reproductive age where some but not all were using modern methods of contraception, we explored descriptively how, within the same household, users compared with non-users in terms of their socio-demographic characteristics, role in the household, and empowerment measures. We also explored descriptively how household level factors, including household decision-making dynamics, compared between households where there was at least one modern contraceptive user and households where there were none.

Regression analysis

We conducted a multilevel regression analysis to assess factors at multiple levels influencing modern contraceptive use among women of reproductive age. As the percent missing on outcome data and covariates was small, these observations were dropped from the regression analysis. Due to the clustering of female modern contraceptive users within households, family compounds, village-clusters, and health catchment areas, we employed a multilevel modeling approach. We used a three-level, mixed-effects logistic regression with random effects at the family compound and village-cluster levels, and fixed effects for health catchment area in order to adjust for any time-invariant unobserved heterogeneity across catchment areas, such as availability of contraceptive methods or characteristics of provision at the PHCs.
$${\text{Level 1}}: \eta_{ijk} = \alpha_{0jk} + \beta_{1} X_{ijk} + \varepsilon_{ijk}$$
$${\text{Level 2}}:\alpha_{0jk} = \delta_{00k} + \gamma_{1} Z_{0jk} + \mu_{0jk}$$
$${\text{Level 3}}: \,\delta_{00k} = \theta_{000} + \lambda_{1} C_{00k} + \upsilon_{00k}$$
The Level 1 equation represents variation at the individual woman level. \({\eta }_{ijk}=\) \(log\left(\frac{{\pi }_{ijk}}{1-{\pi }_{ijk}}\right)\), \({\pi }_{\mathrm{ijk}}\) denotes the probability that the ith woman in the jth family compound and the kth village-cluster uses modern contraception. \({X}_{ijk}\) denotes a vector of individual woman-level and household-level (e.g., wealth, food insecurity) variables of interest, and \({\beta }_{1}\) represents the coefficients for this set of covariates. \({\varepsilon }_{ijk}\) is the woman-level error term, with variance \({\sigma }_{(1)}^{2}\). The Level 2 equation represents variation at the level of the family compound, where \({\alpha }_{0jk}\) is a function of: \({Z}_{0jk}\), which denotes family compound-level covariates (i.e., distance to the nearest public sector health facility); \({\delta }_{00k}\), which is a systematic component modelled as the compound specific random intercept; and \({\mu }_{0jk}\), representing the family compound-level random effect with variance \({\sigma }_{(2)}^{2}\). The Level 3 equation represents variation at the level of the village-cluster. \({C}_{00k}\) denotes cluster-level covariates (i.e., availability of CHW services), \({\theta }_{000}\) represents the village-cluster specific intercept, and \({\upsilon }_{00k}\) is the village-cluster level random effect with variance \({\sigma }_{(3)}^{2}\).
We estimated adjusted odds ratios (AORs) and reported 95% confidence intervals (CIs). For all categorical variables, we conducted a likelihood ratio test to assess the evidence of an association between the variable and the outcome.

Sensitivity analyses

To assess the extent to which missing outcome data affected the results, we ran the model assuming women with missing contraceptive use data were all not modern contraceptive users, and again assuming they were all modern contraceptive users.

Results

Sample characteristics

Our sample included 10,872 households within 4987 family compounds, with a median household size of six members (IQR 4, 9) (Table 1). Median household distance to the nearest health center was 5.41 km. These households included 14,032 women of reproductive age (Table 2), with a median age of 30 years. Nine out of ten women (90.4%) were married; 38.6% were in a polygynous marital arrangement and 51.8% in a monogamous arrangement. Women had a median of three living children (IQR 1, 5). Only one in ten women (10.7%) had received any formal education and only slightly more (13.7%) participated in any paid labor. Approximately one quarter (26.9%) participated to any extent in decision-making; this was the same for women in monogamous (26.3%) and polygynous (26.7%) marital arrangements.
Table 1
Household level sample characteristics for women of reproductive age by modern contraceptive use status
Household level characteristic
Modern users
N = 626
Non modern users
N = 13,357
All women
N = 14,032
n
%
n
%
n
%
Household size
      
 Median/IQR
6
4, 9
6
4, 9
6
4, 9
 Missing
1
0.2
27
0.2
28
0.2
Distance to health center
      
 < 2 km
243
38.8
2455
18.4
2708
19.3
 2–4.99 km
149
23.8
3327
24.9
3489
24.9
 5–6.99 km
104
16.6
3159
23.7
3273
23.3
 7–9.99 km
52
8.3
2527
18.9
2584
18.4
 ≥ 10
61
9.7
1426
10.7
1494
10.7
 Missing
17
2.7
463
3.5
484
3.5
Household wealth quintilea
      
 Poorest
85
13.6
2294
17.2
2382
17.0
 Poor
108
17.3
2479
18.6
2591
18.5
 Middle
111
17.7
2589
19.4
2709
19.3
 Rich
139
22.2
2844
21.3
2996
21.4
 Richest
175
28.0
2983
22.3
3178
22.7
 Missing
8
1.3
168
1.3
176
1.3
Water and sanitationb
      
 Unimproved toilet facilities
235
37.5
6869
51.4
7127
50.8
 Improved toilet facilities
388
62.0
6437
48.2
6851
48.8
 Missing
3
0.5
51
0.4
54
0.4
 Unimproved water source
150
24.0
6118
45.8
6284
44.8
 Improved water source
476
76.0
7237
54.2
7746
55.2
 Missing
0
0
2
0.0
2
0.0
Food insecurity in past 30 days
      
 Little to no hunger in the household
554
88.5
11,685
87.5
12,280
87.5
 Moderate hunger in the household
45
7.2
932
7.0
980
7.0
 Severe hunger in the household
25
4.0
725
5.4
755
5.4
 Missing
2
0.3
15
0.1
17
0.1
CHW services available
      
 None
469
74.9
9148
68.5
9649
68.8
 Satellite villagec
33
5.3
1868
14.0
1908
13.6
 Posted village
124
19.8
2341
17.5
2475
17.6
Health catchment area
      
 Dimbal
89
14.2
3051
22.8
3145
22.4
 Lessagou
111
17.7
2083
15.6
2195
15.6
 Doundé
68
10.9
1684
12.6
1756
12.5
 Ende
89
14.2
639
4.8
728
5.2
 Soubala
35
5.6
2371
17.8
2407
17.2
 Kanibozon
128
20.5
1302
9.8
1448
10.3
 Koulongon
106
16.9
2227
16.7
2353
16.8
aHousehold wealth quintile here excludes water, sanitation, and hygiene measures, which are reported separately
bWe used the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation harmonized survey questions and definitions to define improved drinking water and improved sanitation [39]
cSatellite villages are within the CHW’s catchment area, 5 km or less of the village where the CHW has a fixed site (posted village)
Table 2
Individual level sample characteristics for women of reproductive age by modern contraceptive use status
Individual level characteristic
Modern users
N = 626
Non modern users
N = 13,357
All women
N = 14,032
n
%
n
%
n
%
Age
      
 15–19
49
7.8
1770
13.3
1839
13.1
 20–24
138
22.0
2232
16.7
2377
16.9
 25–29
120
19.2
2620
19.6
2746
19.6
 30–34
135
21.6
2308
17.3
2447
17.4
 35–39
99
15.8
1894
14.2
2998
14.2
 40–44
58
9.3
1440
10.8
1499
10.7
 45–49
27
4.3
1093
8.2
1126
8.0
Number of living children
      
 Median/IQR
3
1, 5
3
1, 5
3
1, 5
 None
76
12.1
2284
17.1
2376
16.9
 1–2
184
29.4
3656
27.4
3858
27.5
 3–4
157
25.1
3513
26.3
3674
26.2
 5–6
128
20.5
2555
19.1
2688
19.2
 7 + 
79
12.6
1331
10.0
1416
10.1
 Missing
2
0.3
18
0.1
20
0.1
Ethnicity
      
 Dogon
585
93.5
12,024
90.0
12,646
90.1
 Peulh
14
2.2
1012
7.6
1036
7.4
 Other
27
4.3
320
2.4
350
2.5
Religion
      
 Muslim
597
95.4
13,087
98.0
13,732
97.9
 Catholic
19
3.0
162
1.2
181
1.3
 Other
10
1.6
108
0.8
119
0.9
Marital status
      
 Never married
36
5.8
1131
8.5
1179
8.4
 Divorced/widowed
4
0.6
158
1.2
162
1.2
 Polygynous marriage
246
39.3
5141
38.5
5409
38.6
 Monogamous marriage
340
54.3
6917
51.8
7272
51.8
 Married, arrangement unspecified
0
0
10
0.1
10
0.1
Education
      
 None
457
73.0
12,016
90.0
12,515
89.2
 Primary
120
19.2
1191
8.9
1318
9.4
 Secondary or higher
47
7.5
134
1.0
181
1.3
 Missing
2
0.3
16
0.1
18
0.1
Participates in paid labor
      
 No
458
73.2
11,608
86.9
12,111
86.3
 Yes
168
26.8
1746
13.1
1917
13.7
 Missing
0
0
3
0.0
4
0.0
Mobility
      
 Been to no place
241
38.5
4517
33.8
4767
34.0
 Been to some/all places but none alone
90
14.4
2404
18.0
2498
17.8
 Been to some places alone
196
31.3
3870
29.0
4094
29.2
 Been to all places alone
97
15.5
2532
19.0
2634
18.8
 Missing
2
0.3
34
0.3
39
0.3
Tolerant attitudes for spousal violence
      
 Never tolerated
168
26.8
3677
27.5
3853
27.5
 Sometimes tolerated
280
44.7
5680
42.5
5991
42.7
 Always tolerated
163
26.0
3519
26.4
3687
26.3
 Missing
15
2.4
481
3.6
501
3.6
Decision-making
      
 Not involved in any domains
421
67.3
9747
73.0
10,210
72.8
 Involved in some domains
142
22.7
2318
17.4
2465
17.6
 Involved/independent in all domains
61
9.7
1242
9.3
1204
9.3
 Missing
2
0.3
50
0.4
53
0.4
Just 49 women (0.4%) were missing main outcome data on contraceptive use at the time of the survey and were therefore excluded from subsequent analyses. A relatively larger proportion of these women were Peulh, young (15 to 19 years), never married, nulliparous, having independent mobility, not involved in decision-making and in wealthier households compared to those with non-missing contraceptive use data (See Supplementary Tables 1 and 2, Additional File 2).

Descriptive results

The geographic distribution of female modern contraceptive users across the study area is depicted in Fig. 1. Approximately a third of village-clusters (N = 44/137) had no modern contraceptive users at all (blank circles, Fig. 1) and 51.8% (N = 71/137) had more than one user. More than a quarter of all women who lived with a modern contraceptive user in the same household also used modern contraception (N = 85/324). Put another way, 14% of all female modern contraceptive users lived with another modern contraceptive user in the same household (N = 85/626).
Table 3 represents household composition of reproductive-aged women and their use of modern contraception. Among households that had more than one woman of reproductive age (N = 2640/10,872; column 4, Table 3), less than 2% (N = 42/2640) had more than one woman who used modern contraception (stars, Fig. 1). One household had three users (a mother and her two daughters) and the rest had two users, most commonly first and second co-wives (N = 30/41), followed by other pairings of co-wives in households with three or four wives (7/41) and mother-daughter dyads (4/41). Within multi-woman households in which some but not all women were using modern contraception (N = 195 households containing 446 women; Table 3), those who were not using tended to be younger, unmarried and nulliparous (results not shown). Households with at least one female modern contraceptive user (N = 584 households containing 867 women; Table 3) tended to be richer, closer to the nearest health center, and more inclusive of women in decision-making than households with none (results not shown).
Table 3
Household composition of women of reproductive age and their use of modern contraception
 
Among WRA
N = 14,032
n (%)
Among HHs
N = 10,872
n (%)
Among multi-WRA HHs
N = 2640
n (%)
Household with no users
13,140 (93.6)
10,265 (94.4)
2412 (91.4)
 No user in a single-woman household
7853 (56.0)
7853 (72.2)
NA
 No users in a multi-woman household
5287 (37.7)
2412 (22.2)
2412 (91.4)
Household with one user
765 (5.5)
542 (5.0)
184 (7.0)
 User in a single-woman household
358 (2.6)
358 (3.3)
NA
 User in a multi-woman household
407 (2.9)
184 (1.7)
184 (7.0)
Household with more than one user
102 (0.7)
42 (0.4)
42 (1.6)
 Some users in a multi-woman householda
39 (0.3)
11 (0.1)
11 (0.4)
 All users in a multi-woman householdb
63 (0.5)
31 (0.3)
31 (1.2)
Missing
25 (0.2)
23 (0.2)
 
aPrecisely, 2 women using modern contraception within a household of 3, 4 or 5 reproductive-aged women;
bPrecisely, 2 women using modern contraception within a household of 2 reproductive-aged women (N = 30 households) or 3 women using modern contraception within a household of 3 reproductive-aged women (N = 1 household);
HH household; WRA women of reproductive age
Less than 5% of women (4.5%) reported using any method of contraception at the time of the survey, ranging from 1.5 to 12.2% across health catchment areas, the vast majority (98.6%) of whom used a modern method (Table 4). Among women who used modern methods, half used the injectable contraceptive (49.7%), one quarter used the implant (26.2%), 15.3% the OCP, and 5.1% the IUD. Five women used two options concurrently: four used the injectable with another method (two with implant, one with OCP, one with rhythm/calendar method) and one used the OCP with jelly. Over three quarters of all contraceptive users (78.4%) most recently acquired their method at the PHC. The method most commonly procured outside the health sector was the OCP (19.8% of all OCP users; 52.6% of whom procured from black market vendors). The median cost of the most recent procurement was 0.81 USD (IQR 0.32, 1.61). The least expensive method was the OCP (0.16 USD) and among the most expensive was the implant (1.21 USD).
Table 4
Methods and characteristics of contraceptive use among women of reproductive age
Method
Current utilization
Last procurement for current method
n
%
Median lengtha of use in months (IQR)
Median costa,d in USD (IQR)
Sitea %
PHC
Othere health sector
Outsidef health sector
Any method
635
4.5b
14 (8, 34)
0.81 (0.32, 1.61)
78.4
13.6
8.1
 Traditional method
9
1.4
21 (13, 22)
1.21 (0.40, 2.54)
87.5
0
12.5
 Modernc method
626
98.6
14 (8, 34)
0.81 (0.32, 1.61)
78.2
13.8
8.0
  Injectable
311
49.7
14 (7.5, 34)
0.81 (0.81, 1.61)
82.0
10.9
7.1
  Implant
164
26.2
13 (7, 33)
1.21 (0.00, 4.03)
78.7
17.7
3.7
  OCP
96
15.3
17 (8, 47)
0.16 (0.16, 0.81)
64.6
15.6
19.8
  IUD
32
5.1
16.5 (11.5, 37)
0.81 (0.16, 3.23)
80.7
16.1
3.2
  Other modern
23
3.7
17.5 (9, 25)
1.21 (0.00 1.61)
78.3
13.0
8.7
aSummary statistics are among those with complete/non-missing length or cost or site data. Missing year of initiation data: N = 13 total users; N = 7 OCP users; N = 3 injectable users; N = 2 implant users; N = 1 other modern method users. Missing cost of method: N = 17 total users; N = 6 implant users; N = 4 IUD users; N = 3 injectable users; N = 2 other modern method users; N = 1 OCP users; N = 1 traditional method users. Missing location of last procurement: N = 2 total users; N = 1 IUD users; N = 1 traditional method users
bPercentage among the 13,983 women (99.6% of the 14,032 in the sample population) who had complete/non-missing outcome data on contraceptive use. Percentage ranged by health catchment area: 1.5% in Soubala; 2.8% in Dimbal; 3.9% in Doundé; 4.5% in Koulongon; 5.2% in Lessagou; 9.4% in Kanibozon; 12.2% in Ende
cModern is defined according to WHO definition, which includes male and female sterilisation, intrauterine device (IUD), implant, injectable, oral contraceptive pill (OCP), male and female condom, diaphragm, jelly, cycle beads, and lactational amenorrhea method (LAM)
dCost converted at the approximate rate at the time of the survey: 620FCFA per 1USD
eThe majority procured from the district referral hospital in Bankass (41.9%; 36/86) or CHW (34.9%; 30/86) or private clinic (11.6%; 10/86)
fThe majority procured from home (43.1%; 22/51) or black market vendors (25.5%; 13/51) or boutiques (13.7%; 7/51)

Regression results

Ninety-five percent of observations (N = 13,376 complete cases) were retained in the regression analysis (Table 5). Women had more than three times the odds of using modern contraception if they had any formal education (AOR 3.28; 95% CI 2.52, 4.27), and were 71% more likely to use modern contraception if they participated in any paid labor (AOR 1.71; 95% CI 1.35, 2.17). Living in the same household as another woman who used modern contraception was the strongest factor influencing modern contraceptive use after education (AOR 3.04; 95% CI 1.95, 4.73). Women were 29% more likely to use modern contraception if they exerted any, even shared, power over decision-making (AOR 1.29; 95% CI 1.04, 1.60); this was after controlling for all covariates, including women’s education and paid labor force participation. Women who reported some mobility but none independently were 42% less likely to use modern contraception compared to women who reported no mobility at all (AOR 0.58; 95% CI 0.42, 0.79). There was no evidence of an association between women’s independent mobility or tolerant attitudes for spousal violence and modern contraceptive use.
Table 5
Three-level mixed-effects logistic regression modeling associations between individual, household, and community level factors and women’s modern contraceptive use
Variables
n
%
Adjusted OR (95% CI)
p valueb
Individual level
    
 Age in years
   
 < 0.0001
  15–19
1839
13.1
1.0
Ref
  20–24
2377
16.9
2.37 (1.52, 3.68)
 < 0.001
  25–29
2746
19.6
1.89 (1.18, 3.04)
0.008
  30–34
2447
17.4
2.29 (1.39, 3.77)
0.001
  35–39
2998
14.2
1.97 (1.17, 3.32)
0.010
  40–44
1499
10.7
1.31 (0.75, 2.27)
0.342
  45–49
1126
8.0
0.80 (0.43, 1.49)
0.485
 Number of living children
   
0.0053
  None
2376
17.0
1.0
Ref
  1–2
3858
27.5
1.52 (1.05, 2.20)
0.028
  3–4
3674
26.2
1.42 (0.95, 2.13)
0.084
  5–6
2688
19.2
1.60 (1.04, 2.46)
0.031
  7 + 
1416
10.1
2.39 (1.49, 3.83)
 < 0.001
 Marital status
   
0.0501
  Not currently married
1341
9.6
1.0
Ref
  Polygynous
5409
38.6
1.61 (0.97, 2.67)
0.067
  Monogamous
7272
52.9
1.79 (1.09, 2.92)
0.020
 Education
    
  None
12,515
89.3
1.0
Ref
  Any
1499
10.7
3.28 (2.52, 4.27)
 < 0.001
 Participates in paid labor
    
  No
12,111
86.3
1.0
Ref
  Yes
1917
13.7
1.71 (1.35, 2.17)
 < 0.001
 Mobility
   
0.0028
  Been to no place
4767
34.1
1.0
Ref
  Been to any place but none alone
2498
17.9
0.58 (0.42, 0.79
0.001
  Been to any place alone
6728
48.1
0.82 (0.64, 1.03)
0.093
 Spousal violence
    
  Not tolerated
3853
28.1
1.0
Ref
  Tolerated
9872
71.9
1.00 (0.80, 1.25)
0.978
 Decision-making
    
  None
10,210
73.0
1.0
Ref
  Any
3769
27.0
1.29 (1.04, 1.60)
0.019
Household level
    
 Someone else in the household using modern contraception
    
  No
13,682
97.7
1.0
Ref
  Yes
324
2.3
3.04 (1.95, 4.73)
 < 0.001
 Distance to health centerc
   
 < 0.0001
  < 2 km
2859
20.4
1.0
Ref
  2–4.99 km
3586
25.6
0.50 (0.33, 0.75)
0.001
  5–6.99 km
3329
23.7
0.33 (0.20, 0.53)
 < 0.001
  7–9.99 km
2677
19.1
0.33 (0.19, 0.55)
 < 0.001
  ≥ 10
1581
11.3
0.70 (0.38, 1.31)
0.266
 Food insecurity
    
  None
11,931
85.1
1.0
Ref
  Any
2084
14.9
1.05 (0.78, 1.41)
0.745
 Wealth quintiled
   
0.4582
  Richest
2385
17.2
1.0
Ref
  Rich
2592
18.7
1.04 (0.79, 1.37)
0.788
  Middle
2689
19.4
0.91 (0.68, 1.23)
0.554
  Poor
2997
21.6
0.86 (0.64, 1.18)
0.354
  Poorest
3193
23.0
0.78 (0.55, 1.09)
0.140
Community level
    
 CHW services available
   
0.2920
  None
9649
68.8
1.0
 
  Satellite village
1908
13.6
0.81 (0.48, 1.39)
0.452
  Posted village
2475
17.6
1.27 (0.82, 1.97)
0.291
 Health catchment areae
   
 < 0.0001
  Dimbal
3145
22.4
1.0
Ref
  Lessagou
2195
15.6
1.90 (1.14, 3.17)
0.014
  Doundé
1756
12.5
1.75 (1.00, 3.07)
0.051
  Ende
728
5.2
3.28 (1.67, 6.44)
0.001
  Soubala
2407
17.2
0.56 (0.30, 1.03)
0.063
  Kanibozon
1448
10.3
4.05 (2.36, 6.97)
 < 0.001
  Koulongon
2353
16.8
1.86 (1.10, 3.14)
0.020
Random effects
    
 Village-cluster level (level three) variance (SD)
0.22 (0.09)
 Compound-within-cluster level (level two) variance (SD)
0.98 (0.29)
 Level three ICC (95% CI)
 
0.05 (0.02, 0.10)
 Level two ICC (95% CI)
 
0.27 (0.18, 0.38)
Log likelihood
 
− 2081.39
aN = 13,376 complete cases, or 95% of all women in the analytic sample
bValue provided in line with the categorical variable name is the result of the likelihood ratio test
cVillage distance to health center substituted if household distance to health center was missing (N = 560)
dHousehold wealth quintile in the regression models includes water, sanitation, and hygiene measures, as these are not explored separately
eLargest health catchment area in terms of sample population is used as the reference category
There was very strong evidence (p < 0.0001) that greater distance to a public sector health facility reduced the odds of modern contraceptive use. Compared to women who lived less than 2 km from a health center, those who lived between 2 and 5 km were 50% less likely to use modern contraception (AOR 0.50; 95% CI 0.33, 0.75); those who lived between 5 and 10 km were 67% less likely (AOR 0.33; 95% CI 0.19, 0.55); and those who lived 10 km or more were 30% less likely (AOR 0.70; 95% CI 0.38, 1.31). The strength of the evidence declined in the group farthest away, where approximately 70% were serviced by a CHW compared to 45% for those between 5 and 10 km. Controlling for distance to health center and all other covariates, there was no evidence (p = 0.2920) that having CHW services available in the community as a base or satellite site was associated with modern contraceptive use. The intracluster correlation coefficient (ICC) at the village-cluster level was 0.05 (95% CI 0.02, 0.10) and the family compound-within-cluster ICC was 0.27 (95% CI 0.18, 0.38).
The odds of using a modern method of contraception were greater if the woman was in a polygynous (AOR 1.61; 95% CI 0.97, 2.67) or monogamous (AOR 1.79; 95% CI 1.09, 2.92) marital arrangement than if she was not currently married. Both age (p < 0.0001) and number of living children (p = 0.0053) were also significant predictors in the model. Regression results were consistent in sensitivity analyses (results not shown).

Discussion

Our study in seven health catchment areas of the Bankass district in the Mopti region of Mali found a modern contraceptive prevalence below 5%. This is similar to, but even lower than the 8.7% regional average in 2018 (an increase from 2.7% in 2012–2013 [23]), despite over a third of all women in Mopti desiring family planning [2]. Another study in the Youwarou district of Mopti found 8.8% of non-pregnant, reproductive-aged women visiting PHCs used modern contraception [29]. The injectable contraceptive was the most common method used in our study population, followed by the implant, which were also the two most common in the 2018 DHS (34% and 44%, respectively) [2]. Anecdotally, there is a preference for these methods in our context due to their long-acting and discrete nature. We may have had underreporting of traditional methods, although the DHS also reports that less than 1% of all contraceptive users used traditional methods [2].
Such low modern contraceptive prevalence may be partly explained by the services available. Within a global context of shortages and inequitable distribution of human resources for health, approximately 37% of doctors, nurses, and midwives in Mali work in rural areas where three quarters of the population resides [20]. Where healthcare workers are available in Mali’s rural areas, distance, quality, and cost create barriers to basic health services [8]; contraceptive options can be limited and stock-outs frequent. Yet, despite chronically poor service availability and accessibility across our entire study area, some women—and even some pairings of women within single households—used modern contraception. Distilling individual, household, community, and health system level factors associated with contraceptive use in this context helps to inform the design of strategies to reduce unmet need for contraception where access is at its absolute worst.
We found that women who played any role in decision-making, who had any formal education, and participated in any paid labor, were more likely to use modern contraception. In addition, a greater percentage of households that had at least one modern contraceptive user included any reproductive-aged woman in decision-making, compared to households that had no users (37% versus 29%). We found unexpected results related to women’s mobility, where women with some mobility were less likely to use modern contraception than those who had none. Our findings on the association between women’s education and contraceptive use are consistent with other studies from sub-Saharan Africa [1012, 17]. The evidence base for the role of women’s empowerment, as measured by decision-making and mobility, on contraceptive use is mixed and dominated by research conducted in South Asia [17, 18, 26, 30]. Our results suggest that having any involvement in decision-making related to healthcare, visiting relatives, or household purchasing more adequately captured women’s capabilities to make strategic choices related to contraceptive use in this context than having freedom of movement to the marketplace, health center, women’s group, or outside the village. It may be that having ever been or been alone to these places does not reflect a woman’s physical autonomy in this context, but rather their availability or distribution. Alternatively, it may be that having recently (rather than ever) been to these places—as mobility is so dependent on age or phase of life [30]—would be a more appropriate predictor of current contraceptive use.
Living in the same household as another woman who used modern contraception was strongly associated with an individual’s uptake in our study. Our findings contribute to the broader healthcare utilization literature on the importance of engaging social networks including in Mali [31, 32], by illustrating the power of intimate intrahousehold female relations—among cowives, and among mothers and their daughters—in influencing contraceptive use. One woman’s ‘functioning achievement’ [9] in accessing modern contraception that she desires may transform the intrahousehold context in which another woman makes a strategic life choice to use. These findings, taken together with education, paid labor, and decision-making, suggest that utilizing contraceptive services in this poor access, low use context may have required considerable assertiveness on the part of women. Strategies to expand women’s ability to make contraceptive choices might engage direct axes of empowerment, sources of empowerment, and the settings for empowerment [33]—decision-making, education and paid labor, and intrahousehold female relations, in this context.
Health systems must be designed to meet women most of the way. Distance to nearest public sector health facility, where 85% of contraceptive users procured their method, was a strong predictor of modern contraceptive use. Compared to women closest to a health center, those who lived between 2 and 5 km were half as likely to use modern contraception, and those between 5 and 10 km were a third as likely. A growing body of literature suggests that even relatively short distances from health facilities are associated with adverse health outcomes [34]; however, the 5 km cut-off continues to dominate research, policy, and practice [7]. Although CHWs offered family planning counseling and services in some villages 5 km or more from a PHC at the time of the survey, only condoms and the OCP were offered and women were referred to the more distant health centers for other methods. CHW capabilities to deliver a range of specific health interventions and contribute to health outcomes, including contraceptive use, is well established [35]—when CHW programmes are appropriately designed and implemented, and supported by health system enablers [36]. In our study setting, CHWs services were accessible only to patients who initiated their own care-seeking from the fixed community health post, and who paid a fee for service—a practice known to hinder utilization across settings and interventions. Our findings suggest that this conventional approach to CHW service delivery is insufficient to increase contraceptive use. Home visits by CHWs have shown particular promise as an alternative approach to community-based contraceptive service delivery [37, 38]
Finally, variation in modern contraceptive prevalence and methods between PHC catchment areas and the parameter estimates for PHC catchment areas in the regression model suggest that the availability and quality of contraceptive services differed in important ways between the seven neighboring PHCs in the Bankass district. The intracluster correlation coefficients (ICC) in the multilevel model indicated that the local village-cluster environment and the family compound environment within a given village-cluster played a role in contraceptive use in addition to the individual, household, and health catchment area fixed effects. We note that the two catchment areas with the highest prevalence of modern contraceptive use were the smallest in terms of population and tended to be wealthier, and anecdotally, are better connected to societal resources through tourism.
Our study was subject to some important limitations. First, we were unable to measure unmet need for contraception and thus analyzed use among all women of reproductive age. We were unable to exclude women intending to become pregnant at the time of the survey, as respondents did not report this data. Furthermore, while we used the current WHO definition of a modern contraceptive method, we note that women may not encounter the same barriers to using fertility awareness based methods, such as LAM and the standard days methods, as they do for methods procured at a health facility e.g., distance. Given the small number of users in the sample population, we were unable to perform subgroup analyses on users of specific methods or method types. We did not have geolocation data for contraceptive procurement sites other than the PHCs and district referral hospital, and were therefore unable to measure distance to these other locations. Although we assessed relative poverty on contraceptive use, wealth quintiles may be less meaningful in a context where absolute poverty is so widespread. Over three quarters (77.4%) of our sample fell in the poorest wealth quintile relative to a nationally representative sample, and only 5.5% were in Mali’s top two quintiles [2]. Furthermore, small holder wealth in a context like West Africa is difficult to measure as it is accumulated through shifting and diversifying sources (e.g., productive assets, land, labor, remittances, social networks, etc.). Due to seasonality and social desirability bias, we may have underestimated the prevalence of food insecurity. Finally, although we consider the inclusion of empowerment measures a strength of our study on contraceptive use in sub-Saharan Africa, we acknowledge that these measures are “simple windows into complex realities” and thus inherently limited [9]. However, it is likely that our measure of decision-making would underestimate the actual agency women exercise over resources and choice, which may also be exerted through informal or subtle negotiation.
Our multilevel modeling technique allowed us to appropriately model the nested structure of individuals within households within family compounds within communities, and to assess the influences of higher level factors on individual level outcomes. Although women with missing outcome data were different in some observable characteristics, the percentage of missing data was very low (0.4%) and our complete case regression analysis included 95% of women in the sample; therefore, this should not have impacted our results. Finally, by using precise geolocation data at the household and facility levels, our study was able to examine household distance to health center as a predictor of modern contraceptive use and to explore how users were grouped together at the community level. This sets our research apart from much of the multilevel research on the use of reproductive health services that relies on DHS data.

Conclusion

Women’s decision-making, education, and paid labor force participation, as well as living in a household nearer to a health center and with another women who used modern contraception, were associated with use in this poor access environment. In designing and implementing strategies to expand access to contraception, policymakers and practitioners should consider these axes, sources, and settings for underlying female empowerment. Relevant to the design of accessible and effective community health systems more broadly, our findings suggest that distance to facility is an important barrier to care even within a 5 km radius, and that care available from a fixed community health post on a fee-for-service basis is insufficient to increase utilization.

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1186/​s12978-020-01061-z.

Acknowledgements

We are very grateful to Boubacar Diaroumba, Issa Diarra and their team at Integral Consulting 4 Development for configurating the data collection platform in Open Data Kit; to Amadou Beydi Cissé, Mohamed Bana Traoré, Naimatou Koné, Seydou Sidibé and Yacouba Samaké of Muso for their dynamism and leadership in the field in preparation for and throughout data collection; to all 120 surveyors and 40 surveyor supervisors; and to Siriman Traoré, Ismaila Thera and their team at Malaria Research & Training Center for providing real-time data quality assurance and quality control. We thank Tracy Lin at University California, San Francisco for providing assistance with GIS data. We thank Brian Greenwood and Daniel Chandramohan of the London School of Hygiene & Tropical Medicine, Nancy Padian of the University of California, San Francisco, David Boettiger from the University of New South Wales, and Ousmane Sylla of the Malian General Directorate of Health and Public Hygiene for their review and feedback on drafts of the manuscript. We are grateful to Clémence Leyrat at London School of Hygiene & Tropical Medicine who provided valuable statistical guidance to the lead author. We thank Faith Cole at University of Michigan for her help with copy editing and formatting the final version of the manuscript. Finally, thank you to the district health authorities and community leaders and members for their partnership in the conduct of this research.
The larger cluster-randomized control trial (NCT026940550) received ethical approval from the Ethics Committee of the Faculty of Medicine, Pharmacy and Odonto-Stomatology at the University of Sciences Techniques and Technologies of Bamako (Ref: 2016/03/CE/FMPOS). Secondary analysis of trial data was approved by the Observational/Interventions Research Ethics Committee at the London School of Hygiene & Tropical Medicine (Ref: 13832), and was excepted by the University of California, San Francisco (Ref: 154824).
Not applicable.

Competing interests

The authors declare that they have no competing interests.
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Metadaten
Titel
Women’s empowerment, intrahousehold influences, and health system design on modern contraceptive use in rural Mali: a multilevel analysis of cross-sectional survey data
verfasst von
Caroline Whidden
Youssouf Keita
Emily Treleaven
Jessica Beckerman
Ari Johnson
Aminata Cissé
Jenny Liu
Kassoum Kayentao
Publikationsdatum
01.12.2021
Verlag
BioMed Central
Erschienen in
Reproductive Health / Ausgabe 1/2021
Elektronische ISSN: 1742-4755
DOI
https://doi.org/10.1186/s12978-020-01061-z

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