Keywords
contraceptive methods, women of reproductive age, cross sectional study survey, 11 counties in Kenya
Modern Contraceptive Methods (MCM) use is among the interventions preventing unplanned pregnancies and unsafe abortions globally. Nevertheless, MCM uptake is still low. We aimed at determining factors influencing contraceptive uptake among women of reproductive age 15 to 49 years, in Kenya.
We used secondary data collected by Performance Monitoring for Action (PMA). PMA used cross sectional multi-stage cluster survey design and collected the data between November and December 2019. The study was approved by NACOSTI/202974 and KNERC KNH/ERC/R/192.
The study obtained a sample size of 9477 women of reproductive age (WRA) from 11 counties in Kenya. Both descriptive and inferential statistical analysis with a P value of 0.05 was done using Stata 16.1. The prevalence of modern contraceptives uptake was 43.2% uptake was 43.2% among all WRA. The prevalence was lower among rural dwellers 41.4% (95% CI 39.62, 43.17) as compared to urban dwellers 47.5 (95% CI 44.39,50.55). More than half (53.4%) of the married women were using a modern contraceptive, while only about two in every 10 of the unmarried were using a modern contraceptive. Women affiliated with the Islam religion were less likely to use modern contraceptive (aPOR 0.6, 95% CI 0.42, 0.89 p=0.010) as compared to the Catholics. Family planning (FP) services were found to be lower (aPOR 0.535(95% CI 0.29,0.98 p=0.043) in National Hospital Insurance Fund (NHIF)-covered facilities than in non-NHIF-covered ones. Adolescent FP service provision and prescription was 4 times higher (aPOR 4.0 95% CI; 1.05,15.41, p=0.42) as compared to either the prescribed or provided.
Low uptake for MCM is influenced by sociodemographic factors and Health system factors. Efforts to increase MCM uptake should focus on rural residents, unmarried women, Islamic religion women and accreditation of NHIF services in all facilities.
contraceptive methods, women of reproductive age, cross sectional study survey, 11 counties in Kenya
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A modern contraceptive method is defined as a product or medical procedure that interferes with reproduction from acts of sexual intercourse1. Modern contraceptive methods (MCM) are in line with addressing reproductive health needs as reflected in Sustainable Development Goal 3.72 which calls for universal access to family planning by 20302. Birth spacing and limitation by utilizing current family planning (FP) methods have the potential to prevent 1.5 million maternal deaths and almost two million children deaths each year, as well as contribute to overall economic growth and development. Unmet FP demand on the other side contributes to unintended pregnancies and unsafe abortions3,4. Of the 1.11 billion women of reproductive age who needed family planning services in 2019 only 842 million got them, leaving 270 million women around the world with unmet demands. The unmet need for family planning services is significantly worse in the low and middle income countries where more than 232 million women cannot receive MCM when necessary5. Every year, approximately 14 million unintended pregnancies occur in sub-Saharan Africa alone6 with teenager girls and women between the ages of 15 and 24 being the most vulnerable. For example, in Benin, adolescence is seen as the start of the continuity of medical care for reproductive newborn, maternal, and pediatric health. During this time poor access to and utilization of contraception are likely to have a negative impact on health outcomes overall7.
Modern contraceptives are so effective that they reduce maternal death by more than 20% and newborn mortality by 17%8. Contraceptive prevalence rates (CPR) are often lower in areas with high fertility rates and the sub-Saharan Africa (SSA) region is not an exception9,10. The majority of underdeveloped countries including Kenya, however, utilize few contraceptive methods hence they have a lot of unmet needs11. According to the UN's 2017 World Family Planning highlight levels of unmet family planning need greater than 20% are considered high, and those less than 10% are deemed low12. Studies have shown that, in some countries in Africa, 38% of contraceptive users stopped using them after the first year13,14. Kenya is not exempt from this - 31% of users of contraceptives discontinued use within a year of beginning use15. Even though Kenya has made a sizable effort in raising knowledge about the methods and encouraging the use of contraceptives, some women with unmet need for family planning were previous contraceptive users. Modern contraceptive prevalence rates among married women are 57.0% and among sexually active unmarried women aged 15–49 years, 59%. which is still a low contraceptive prevalence rate15 by Kenyan survey. The availability of high-quality healthcare facilities such as hospitals and health centers have an impact on one's selection of contraception and the unmet need for FP16. In addition, insufficient stocks and restricted usage of available methods create conditions that ultimately restrict women’s choices and use and are likely contributing factors to the prevalence of contraceptive discontinuations17. To ensure that individuals and couples can select their chosen contraceptive method and meet their fertility objectives, it is crucial to have an appropriate choice of methods at various tiers of the health care system18.
Healthcare financing of reproductive health is critical that promotes equitable access due to affordability of services. The Kenyan health sector is financed from public, private, and donor sources accounting for 37%, 39.6%, and 23.4% of total health expenditure. Out-of-pocket (OOP) costs for households make up a sizable amount (26.1%) of all medical expenses19. It is the State’s fundamental duty to respect, protect, promote and fulfil the right to the highest attainable standard of reproductive health care by ensuring the provision of a health service package at all levels of the health care system including FP services20.
In this context, this study will examine the factors influencing the uptake of modern contraception among Kenyan women of reproductive age and fill this gap using the most up-to-date Performance, monitoring for Action (PMA) survey data available in Kenya which may help decision-makers, stakeholders and planners develop efficient tactics to reduce the terrible consequences of unintended pregnancy while also raising the level of socioeconomic status across the country and geographical area8. Finally, we shall come up with solid recommendations on how to improve contraceptive uptake and usage with the ultimate goal of accelerating the country's growth and development. This study's specific objectives were to describe the socio-demographic factors associated with contraceptive uptake among women of reproductive age, to describe the prevalence of MCM uptake among women of reproductive age and to determine the effects of health system factors on contraceptive use among Kenyan women of reproductive age.
Ethical approval was given by the Kenyatta National Ethics and Research Committee (KNH-UON ERC) Ref No KNH/ERC/R/192 and administrative approval by the National Council for Science, Technology, and Innovation (NACOSTI) Ref No 202974 and by all participating counties, on 1st October 2019 for the original collection of the data. All the consent obtained from the study participants were verbally read to the participants, and they were also given the hard copies for them to sign after reading. Two copies were signed, and they retained a copy, while the other copy was archived by the project. All participants above 18 years gave written informed consent, while for the minors, the study obtained both assent and their parent/guardian’s written informed consent.
This study used secondary data from the PMA cross-sectional survey conducted in November and December of 2019.
The research was conducted in Kenya, one of the countries where Performance Monitoring is done. Kenya is one of the East African countries, a lower-middle-income country with current fertility rate 3.3 births per woman, with a population of approximately 52 million people. The study was carried out in 11 of the 47 counties Nairobi, Kiambu, Kericho, Kitui, Kilifi, Bungoma, Siaya, Nyamira Nandi, Kakamega, and West Pokot.
The inclusion criteria were 15 to 49-year-old women who lived in the 35 households randomly selected in each of the 308 EAs within the 11 counties, and all service delivery points serving the EAs for the public facilities and a maximum of three private facilities within the enumeration area (EA). The PMA study excluded all men and women who were younger than 15 years and those older than 49 years, those who never consented for the interviews, those who were mentally challenged beyond the point of responding to the interviews and those outside the study areas.
Modern contraceptive uptake among women of reproductive age was our outcome variable. It was measured based on the question; “Are you or your partner currently doing something or using any method to delay or avoid getting pregnant?” which was coded as 1 for modern contraceptive users and 0 for traditional users and non-users.
The independent variables included sociodemographic which were age of respondents, education levels, wealth quintiles, parity, marital status, awareness of modern contraceptive, religion and county of residence. The ages were categorized as 15–19, 20–24, 25–29, 30–34, 35–39, 40–44 and 45–49 years. Marital statuses were classified into Married, living with a partner, divorced/separated, widow and never married. Levels of education were categorized into never educated, primary, secondary, vocational, college and university. Wealth quintiles were classified into five wealth quintiles; poorest, poor, middle, high and highest were computed based on wealth index generated using principal component analysis from the household assets, walls, flooring and roofing materials. Parity was categorized into 0–1, 2–3 and 4+. The counties of residence were Nairobi, Bungoma, Kericho, Kiambu, Kilifi, Kitui, Nandi, Nyamira, Siaya, Kakamega and West Pokot, while residence was either rural or urban.
The second independent variable is health system factors, which are defined as facility type (public and private) commodities stock outs (yes or no). This includes facilities that support community Health Volunteers (CHVs) (yes or no), FP providing days (less than seven days, 8–14 days, 15–21 days and 22–30 days), NHIF-covered FP services (yes or no), FP services available to adolescent aged 15 to 19 years (Never offered; counselled-(on the use, side effects and correct use); counselled and prescribed; counselled and provided; counselled; provided and prescribed; prescribed; provided or prescribed and prescribed), and whether the FP Client pays any fees (yes or no).
The study used pooled data from Kenya’s performance monitoring for action 2019 survey. The survey used a multi-stage stratified cluster design with urban-rural and county as strata. A total of 308 enumeration areas were selected from the Kenya National Bureau of Statistics (KNBS) master sampling frame. In each EA, 35 households were randomly selected where the household interviews were conducted. Within the selected households all females aged 15–19 were eligible for the female questionnaires. The survey interviewed 10,581 households and 9477 females. A total of 945 service delivery points serving the enumeration areas were also interviewed.
Data collection was done by PMA survey project that was conducted in November and December 2019. It used an open-source software program called Open Data Kit (ODK). That was customized for ease of updating and addition of security features. All of the survey were programmed and loaded into the project's smartphones. The ODK surveys had automatic skip-patterns and built-in reply limits to minimize data entry errors. Three main types of questionnaires were used: household, female and service delivery point (SDP).
The PMA data is free available upon request which is shared with all other study components. Coding was done for the different independent variables for ease of analysis. The analysis aimed at showing how certain socio-demographic and Health system variables are associated with contraceptive use among women in Kenya. Analysis was done using Stata 16.1. Survey weights were applied to account for the complex survey design. Statistical significance was set at p-value <0.05. The sample's variables were described using frequencies and percentages and bivariate and multivariable logistic regressions were used to depict the independent variables associated with the use of modern contraceptives.
A total of 9477 women and 945 service delivery points completed the survey and were eligible for the analysis. Table 1 shows the socio-demographic characteristics of the sample. Majority of the respondents (39.2%) in the survey were aged young women 15–24 years while about (7.8%) were 45–49 years. More than half (53.4%) of the women were married, about a third (32.0%) were unmarried. About eight in every 10 women had basic education (primary and secondary levels), while (4.5%) had no basic education level. Majority of the women were Protestants (70.7%) and nearly all study participants (98.4%) were aware of modern contraceptive methods. Women who had at most one child were more (44.1%), whereas those who had two to three or four and more children were less common (28.8%) and (27.1%), respectively. Dispensaries (30.1%) were the most preferred source of contraceptives. Half (50.7%) of the facilities that offered contraceptive methods experienced or had an episode of contraceptive stock outs. More than half of the facilities (53.4%) supported community health volunteers in providing family planning services. The NHIF-covered facilities with FP services were less prevalent (7.14%) than those without NHIF (92.86%). Adolescents were more (51.95%) likely to use FP services in facilities that offered counseling, provision, and prescription of family planning methods.
Variables | Percentage | N [Weighted] |
---|---|---|
MCM | ||
Yes | 43.2 | 4095 |
No | 56.8 | 5382 |
Age in years | ||
15–19 | 21.6 | 2047 |
20–24 | 17.6 | 1665 |
25–29 | 16.3 | 1546 |
30–34 | 15.4 | 1461 |
35–39 | 11.8 | 1119 |
40–44 | 9.5 | 904 |
45–49 | 7.8 | 735 |
Marital status | ||
Married | 53.4 | 5056 |
Living with a partner | 5.8 | 550 |
Divorced / separated | 5.9 | 562 |
Widow / widower | 2.9 | 278 |
Never married | 32.0 | 3029 |
Residency | ||
Urban | 30.2 | 2859 |
Rural | 69.8 | 6618 |
Education | ||
Never | 4.5 | 427 |
Primary | 45.2 | 4284 |
Secondary | 35.6 | 3378 |
Vocational | 2.1 | 199 |
College | 9.7 | 923 |
University | 2.8 | 266 |
Religion | ||
Protestants | 70.7 | 6702 |
Catholic | 18.1 | 1717 |
Islam | 3.7 | 354 |
Other | 2.4 | 229 |
No religion | 5.0 | 473 |
Awareness of modern contraceptive | ||
Yes | 98.4 | 9326 |
No | 1.6 | 151 |
Parity | ||
0–1 | 44.1 | 4177 |
2–3 | 28.8 | 2729 |
4+ | 27.1 | 2569 |
Wealth index | ||
Poorest | 20.4 | 1934 |
Poor | 22.6 | 2139 |
Middle | 21.0 | 1986 |
High | 18.8 | 1784 |
Highest | 17.2 | 1634 |
Visited by HCW who talked about FP | ||
Yes | 9.9 | 941 |
No | 90.1 | 8532 |
Visited a facility and discussed FP | ||
Yes | 40.1 | 2404 |
No | 59.9 | 3593 |
County | ||
Bungoma | 9.1 | 865 |
Kakamega | 16.8 | 1592 |
Kericho | 11.5 | 1092 |
Kiambu | 7.2 | 681 |
Kilifi | 10.4 | 985 |
Kitui | 7.5 | 706 |
Nairobi | 10.1 | 959 |
Nandi | 7.7 | 725 |
Nyamira | 5.5 | 521 |
Siaya | 7.4 | 702 |
West Pokot | 6.9 | 650 |
Health system characteristics | ||
Facility types where FP was obtained | ||
Hospital | 28.6 | 1146 |
Health center | 24.4 | 976 |
Dispensary | 30.1 | 1204 |
Pharmacy and others | 16.9 | 676 |
Commodity stock outs | ||
No | 49.3 | 457 |
Yes | 50.7 | 469 |
Facility supports CHVs* | ||
No | 46.6 | 402 |
Yes | 53.4 | 460 |
Days FP is offered in a month | ||
Less than 7days | 1.19 | 11 |
8-14 days | 1.94 | 18 |
15-21days | 79.16 | 733 |
22-30days | 17.71 | 164 |
Facility with FP service covered by NHIF | ||
Yes | 7.14 | 66 |
No | 92.86 | 859 |
Facilities offering FP services to adolescents | ||
Do not offer adolescents | 3.57 | 33 |
Counseled | 4.33 | 40 |
counseled prescribed | 1.08 | 10 |
counseled provided | 32.58 | 301 |
counseled provided prescribed | 51.95 | 480 |
Prescribed | 0.97 | 9 |
Provided | 3.79 | 35 |
provided prescribed | 1.73 | 16 |
Clients are charged for any FP service | ||
Yes | 6.05 | 56 |
No | 93.95 | 870 |
In Table 2 below, the overall modern contraceptive use was 43.2% (95% CI; 41.7, 44.8). Modern contraceptive use was high among the older women (greater than 20 years), 34.0% (95% CI; 29.74,38.46, p=0.000) among those above 45 years, while least among the adolescents 12.1% (95% CI; 10.4, 14.05). About five in every 10 married women were using a modern contraceptive, while only about two in every 10 of the unmarried were using a modern contraceptive. Modern contraceptive prevalence was low among rural dwellers 41.4% (95% CI; 39.62, 43.17, p=0.001) as compared to urban dwellers. MCM increased with education attainment from 24.7% (95%CI; 19.45, 30.7) among those with non-formal education, to 47.8% (95% CI; 40.79, 54.9, p=0.000) among those with tertiary education. Modern contraceptive use was also noted to be higher among women who had a contact with the community health workers 51.5% (95% CI; 47.29, 55.67, p=0.000) at their homes in comparison to those who were not visited by HCW. About six in 10 of the women who visited a facility and discussed family planning with the health providers were using a modern contraceptive method 59.5% (95% CI; 1.76, 2.37, P=0.000).
Bivariate model | Multivariable model | ||||||
---|---|---|---|---|---|---|---|
Variables | Prevalence | cPOR | [95% CI] | P value | aPOR | [95% CI] | P value |
MCM | |||||||
Yes | 43.21[41.7,44.8] | ||||||
No | 56.79[55.2,58.4] | ||||||
Age | |||||||
15–19 | 12.1 [10.4,14.05] | Ref | Ref | ||||
20–24 | 47.5 [44.41,50.54] | 6.6 | [5.4, 7.97] | 0.0000 | 2.4 | [1.87, 3.15] | 0.0000 |
25–29 | 56.2 [53.04,59.31] | 9.3 | [7.64, 11.36] | 0.0000 | 1.8 | [1.37, 2.47] | 0.0000 |
30–34 | 58.5 [55.64,61.2] | 10.2 | [8.34, 12.51] | 0.0000 | 1.5 | [1.13, 1.99] | 0.0050 |
35–39 | 56.3 [52.95,59.58] | 9.4 | [7.68, 11.38] | 0.0000 | 1.1 | [0.84, 1.56] | 0.388 |
40–44 | 50.4 [46.71,53.99] | 7.4 | [5.9, 9.19] | 0.0000 | 1.1 | [0.77, 1.5] | 0.676 |
45–49 | 34.0 [29.74,38.46] | 3.7 | [2.97, 4.69] | 0.0000 | 0.6 | [0.39, 0.78] | 0.0010 |
Marital status | |||||||
Married | 57 [55.08,58.79] | Ref | Ref | ||||
Living with a partner | 54.1 [47.05,61.08] | 0.9 | [0.66, 1.21] | 0.459 | 1 | [0.7, 1.36] | 0.8810 |
Divorced / separated | 46.4 [41.2,51.68] | 0.7 | [0.53, 0.81] | 0.0000 | 0.7 | [0.53, 0.96] | 0.0250 |
Widow / widower | 32.3 [25.91,39.4] | 0.4 | [0.26, 0.49] | 0.0000 | 0.4 | [0.26, 0.63] | 0.0000 |
Never married | 18.7 [16.61,20.96] | 0.2 | [0.15, 0.2] | 0.0000 | 0.5 | [0.39, 0.56] | 0.0000 |
Residency | |||||||
Urban | 47.5 [44.39,50.55] | Ref | Ref | ||||
Rural | 41.4 [39.62,43.17] | 0.78 | [0.68, 0.9] | 0.0010 | 0.8 | [0.63, 0.98] | 0.0330 |
Education | |||||||
Never | 24.7 [19.45,30.7] | Ref | Ref | ||||
Primary | 47.8 [45.49,50.04] | 2.8 | [2.05, 3.81] | 0.0000 | 2.7 | [1.82, 4.01] | 0.0000 |
Secondary | 38 [35.94,40.12] | 1.87 | [1.37, 2.57] | 0.0000 | 2.2 | [1.34, 3.55] | 0.0020 |
Vocational | 42.1 [35.25,49.34] | 2.23 | [1.49, 3.33] | 0.0000 | 2.5 | [1.61, 3.87] | 0.0000 |
College | 48.7 [44.08,53.28] | 2.9 | [2.05, 4.09] | 0.0000 | 2.6 | [1.68, 4.09] | 0.0000 |
University | 47.8 [40.79,54.9] | 2.8 | [1.87, 4.18] | 0.0000 | 2.7 | [1.62, 4.6] | 0.0000 |
Awareness of modern contraceptive | |||||||
No | Ref | Ref | |||||
Yes | 43.9 [42.29,45.48] | 31.38 | [6,164.23] | 0.0000 | 4.52 | [0.8, 25.46] | 0.0870 |
Religion | |||||||
Catholic | 43.6 [40.61,46.68] | Ref | Ref | ||||
Islam | 36.7 [27.83,46.48] | 0.75 | [0.5, 1.13] | 0.164 | 0.6 | [0.42, 0.89] | 0.0100 |
No religion | 34 [27.5,41.24] | 0.67 | [0.48, 0.93] | 0.016 | 0.8 | [0.54, 1.23] | 0.3310 |
Other | 46.9 [39.89,53.94] | 1.14 | [0.82, 1.58] | 0.432 | 1.1 | [0.8, 1.62] | 0.4640 |
Protestant | 44 [42.26,45.67] | 1.01 | [0.89,1.15] | 0.834 | 0.9 | [0.79, 1.12] | 0.466 |
Parity | |||||||
0–1 | 25.3 [23.3,27.39] | Ref | Ref | ||||
2–3 | 59.9 [57.58,62.09] | 4.4 | [3.88, 5] | 0.0000 | 2.5 | [2.05, 3] | 0.0000 |
4+ | 54.7 [52.15,57.28] | 3.57 | [3.11, 4.1] | 0.0000 | 3.6 | [2.74, 4.62] | 0.0000 |
Wealth index | |||||||
Poorest | 37.7 [34.79,40.78] | Ref | Ref | ||||
Poor | 42.3 [39.6,45.05] | 1.21 | [1.04, 1.4] | 0.0110 | 1 | [0.85, 1.27] | 0.7190 |
Middle | 44.9 [42.23,47.55] | 1.34 | [1.15, 1.57] | 0.0000 | 1.3 | [1.04, 1.57] | 0.0170 |
High | 46.5 [43.61,49.43] | 1.43 | [1.21, 1.7] | 0.0000 | 1.4 | [1.07, 1.8] | 0.0140 |
Highest | 45.3 [41.62,48.97] | 1.36 | [1.12, 1.66] | 0.0020 | 1.3 | [0.96, 1.79] | 0.0920 |
Visited by HCW who talked about FP | |||||||
No | 48.5 [40.63,44.01] | Ref | Ref | ||||
Yes | 51.5 [47.29,55.67] | 1.45 | [1.21, 1.73] | 0.0000 | 1 | [0.77, 1.29] | 0.987 |
Visited a facility and discussed FP | |||||||
No | 41.8 [39.4,44.27] | Ref | Ref | ||||
Yes | 59.5 [56.57,62.27] | 2.04 | [1.76, 2.37] | 0.000 | 1.5 | [1.27, 1.77] | 0.0000 |
County | |||||||
Bungoma | 50.5 [46.13,54.93] | | | | | | | | | ||
Kericho | 42.8 [38.02,47.76] | 0.73 | [0.56, 0.96] | 0.0220 | 0.6 | [0.45, 0.89] | 0.0080 |
Kiambu | 46.9 [41.38,52.42] | 0.86 | [0.65, 1.15] | 0.309 | 0.8 | [0.55, 1.15] | 0.225 |
Kilifi | 35.2 [29.69,41.23] | 0.53 | [0.39, 0.73] | 0.0000 | 0.6 | [0.38, 0.78] | 0.0010 |
Kitui | 41.3 [35.83,47.04] | 0.69 | [0.51, 0.92] | 0.0130 | 0.7 | [0.51, 0.98] | 0.0390 |
Nairobi | 47 [41.67,52.44] | 0.87 | [0.66, 1.15] | 0.3230 | 0.7 | [0.49, 1.12] | 0.1510 |
Nandi | 49.3 [44.9,53.69] | 0.95 | [0.74, 1.22] | 0.6950 | 0.8 | [0.6, 1.15] | 0.2660 |
Nyamira | 49.5 [43.89,55.12] | 0.96 | [0.72, 1.28] | 0.7760 | 1 | [0.71, 1.48] | 0.9060 |
Siaya | 41.2 [37.18,45.32] | 0.69 | [0.54, 0.87] | 0.0030 | 0.6 | [0.4, 0.76] | 0.0000 |
Kakamega | 47.3 [43.58,50.94] | 0.88 | [0.7, 1.1] | 0.2620 | 0.9 | [0.62, 1.18] | 0.3520 |
West Pokot | 19.3 [13.54,26.85] | 0.23 | [0.15, 0.37] | 0.0000 | 0.3 | [0.17, 0.44] | 0.0000 |
Health system characteristics | |||||||
Facility type where FP was obtained | |||||||
Hospital | 46.5 [36.2,57.2] | Ref | Ref | ||||
Health center | 42.7 [35.9,49.9] | 0.86 | [0.51,1.46] | 0.568 | |||
Dispensary | 37.8 [32.9,43.1] | 0.7 | [0.43,1.15] | 0.157 | |||
Pharmacy and others | 42.3 [30.5,55.2] | 0.84 | [0.44,1.61] | 0.606 | |||
Stockouts among facilities offering FP | |||||||
No | 43.5 [38.4,48.7] | Ref | Ref | ||||
Yes | 37.5 [32.8,42.5] | 0.779 | [0.59,1.03] | 0.5874 | |||
Facility supports CHVs* | |||||||
No | 42.6 [36.8,48.6] | Ref | Ref | ||||
Yes | 38.2 [33.5,43.0] | 0.832 | [0.61,1.13] | 0.233 | |||
FP offering days per month | |||||||
Less than 7 days | 32.9 [15.7,56.4] | Ref | |||||
8–14 days | 43.1 [20.1,69.5] | 1.542 | [0.39,6.02] | 0.532 | |||
15–21 days | 39 [34.8,43.4] | 1.303 | [0.48,3.52] | 0.600 | |||
22–30 days | 49.9 [40.8,59.0] | 2.03 | [0.74,5.59] | 0.170 | |||
NHIF covered FP services | |||||||
No | 41.4 [37.7,45.3] | Ref | |||||
Yes | 27 [16.9,40.3] | 0.523 | [0.29,0.96] | 0.040 | 0.535 | [0.29,0.98] | 0.043 |
FP services offered to unmarried adolescents [age 10–19] | |||||||
Do not offer | 41.7 [23.7,62.2] | Ref | |||||
counseled | 32.9 [19.2,50.2] | 0.685 | [0.23,2.01] | 0.489 | 0.682 | [0.23,1.99] | 0.484 |
counseled prescribed | 32.6 [10.9,65.7] | 0.677 | [0.14,3.28] | 0.627 | 0.713 | [0.15,3.37] | 0.668 |
counseled provided | 42.2 [35.8,48.9] | 1.022 | [0.43,2.44] | 0.962 | 1.045 | [0.44,2.5] | 0.920 |
counseled provided prescribed | 37.7 [33.2,42.4] | 0.845 | [0.35,2.01] | 0.702 | 0.857 | [0.36,2.04] | 0.726 |
prescribed | 64.2 [29.9,88.2] | 2.503 | [0.48,12.94] | 0.273 | 2.456 | [0.48,12.7] | 0.282 |
provided | 59.7 [39.7,77.0] | 2.072 | [0.6,7.18] | 0.250 | 2.077 | [0.6,7.17] | 0.246 |
provided and prescribed | 74.6 [50.9,89.3] | 4.105 | [1.07,15.71] | 0.039 | 4.029 | [1.05,15.41] | 0.042 |
FP clients pay any fee irrespective of obtaining a method | |||||||
No | 40.7 [36.8,44.7] | Ref | |||||
Yes | 43.1 [29.2,58.3] | 0.535 | [0.58,2.1] | 0.75 | |||
* This excludes pharmacies in the sample |
The variables that were significant in the bivariate were included in the multivariable model. The odds of modern contraceptive use were higher among young women aged 20–24 years (prevalence 47.5) (aPOR 2.4, 95% CI; 1.87, 3.15, p=0.000), middle-aged women aged 25–29 (prevalence 56.2) (aPOR 1.8, 95% CI; 1.37, 2.47, p=0.000) and aged 30–34 (prevalence 58.5) (aPOR 1.5, 95% CI; 1.13, 1.99, P= 0.0050) as compared to adolescents.
The odds of modern contraceptive use were (aPOR 0.7, 95% CI; 0.53, 0.96, P= 0.0250) less among divorcee (prevalence 46.4), (aPOR 0.4, 95% CI; 0.26, 0.63, P=0.000) less among the widow (prevalence 32.3) and (aPOR 0.5, 95% CI; 0.39, 0.56, p=0.000) less among the non-married women (prevalence 18.7) compared to the married women (prevalence 57.0) respectively.
Rural women (prevalence 41.4) were 80% less likely to modern contraceptive use as compared to the urban women (prevalence 47.5) (aPOR 0.8, 95% CI; 0.63, 0.98, p= 0.0330).
The odds of modern contraceptive use were three-fold higher among women with education, (aPOR 2.7, 95% CI; 1.82, 4.01, p=0.000) (aPOR 2.2, 95% CI; 1.34, 3.55, p=0.020), (aPOR 2.5, 95% CI; 1.61, 3.87, p=0.000), (aPOR 2.6, 95% CI; 1.68, 4.09, p=0.000) (aPOR 2.7, 95% CI; 1.62, 4.6, p=0.000) among those with primary (prevalence 47.8), secondary (prevalence 38.0), vocational (prevalence 42.1), college (prevalence 48.7) and university (prevalence 47.8) respectively as compared to those with no formal education.
Women affiliated with the Islam religion (prevalence 36.7) were 60% less likely to use modern contraceptive (aPOR 0.6, 95% CI; 0.42, 0.89, p=0.010), as compared to the Catholics (prevalence 43.6).
Among those who had children, the odds of modern contraceptive use were three and four folds higher among those with two to three and more than four children respectively as compared to those with less than two children (aPOR 2.5, 95% CI; 2.05, 3.0, p=0.000) prevalence 59.9) and (aPOR 3.6, 95% CI; 2.74, 4.62, p=0.000) (54.7) respectively.
Women from middle (prevalence 44.9) and high wealth quintiles (46.5) had a higher odd of modern contraceptive use (aPOR 1.3, 95% CI; 1.04, 1.57, p=0.017) and (aPOR 1.4, 95% CI; 1.07, 1.8, p=0.0140) as compared to those from the poorest wealth quintile. Among women who visited and discussed FP with the provider, the odds of their modern contraceptive use were 1.5 times higher as compared to those who never discussed family planning after visiting the facility.
County wise, the odds of contraceptive use were less likely, in Kericho (prevalence 42.8) (aPOR 0.6, 95% CI; 0.45, 0.89, p=0.008), Kilifi (prevalence 35.2) (aPOR 0.6, 95% CI; 0.38, 0.78, p=0.001), Kitui (prevalence 41.3) (aPOR 0.7, 95% CI; 0.51, 0.98, p=0.039), Siaya (prevalence 41.2) (aPOR 0.6, 95% CI; 0.4, 0.76, p=0.000) and West Pokot (prevalence 19.3) (aPOR 0.3, 95% CI; 0.17, 0.44, p=0.000) as compared to Bungoma (prevalence 50.5).
Women who received NHIF-covered FP services were (aPOR 0.5,95%CI; 0.29,0.98, p=0. 043) less likely to utilize MCM than their counterparts, and the difference was statistically significant. The odds of using MCM was higher (aPOR 4.0,95% CI; 1.05,15.41, P=0.042) among adolescents aged 15 to 19 years who were offered provision and prescription of FP services than those who were not.
We analyzed the secondary data collected by PMA 2019 survey. The survey gathered quantitative data to examine the factors associated with the uptake of modern contraception among Kenyan women of reproductive age. From these study findings, we found out that the prevalence of modern contraceptives in the 11 Counties in Kenya during the performance monitoring survey 2019 was 43.2%. A larger number (21.6%) of respondents were between the ages of 15 and 19, while most (53.4%) were married, the majority (69.8%) lived in rural areas, and just 4.5% had no formal education. We also discovered that the majority of health facilities (92.86%) with FP services were not covered by NHIF. The Kenya Demographic Health Survey21 reports modern contraceptive prevalence rates among married women is (57.0%) and sexually active unmarried women aged 15–49 years is 59%.
Our study found that modern contraceptive use was high among the older women (greater than 20 years) while least among the adolescents 12.1% (95% CI; 10.4, 14.05) and those above 45 years 34.0% (95% CI; 29.74,38.46, p=0.000). This is consistent with another prior studies in Kira-Uganda5 which reported more limited MCM use among adolescent girls when compared to older age groups. Similarly a previous study done in Kwale county-Kenya noted that healthcare system constraints such as a lack of youth-friendly services, sexual and reproductive health commodities, with deeply rooted negative attitudes, significant anxiety and myths and misconceptions among adolescents serve as impediments to the beginning and ongoing utilization of contraceptive use and side effects22. It is therefore critical to promote family planning education and information to dispel myths and misconceptions22 and provide a variety of service options to fit their needs22. The study further found that about roughly five in every 10 married women were using a modern contraceptive while only about two in every 10 of the unmarried were using a modern contraceptive. This suggests that married women have a greater desire to space or limit pregnancies23 by study done in Uganda. The study further shows that the level of education was a determinant of contraceptive use as the level of education increased from non-formal education 24.7% (95% CI; 19.45, 30.7) so was the increase in contraceptive use among those with tertiary level 47.8% (95% CI; 40.79, 54.9). Two previous studies23,24 in Uganda and Ghana reported that the use of contraceptives is significantly influenced by education this suggests that persons with higher levels of education were definitely more conscious of the advantages and significance of using contraceptives, they are also more informed which enhances access to services and offers them greater negotiating power when making decisions about using contraceptives. This study's further conclusion demonstrates that in comparison to Catholics, women associated with the Islam faith were less likely to use modern methods of contraception (aPOR 0.6, 95% CI; 0.42, 0.89). This is consistent with a study undertaken in five countries (Democratic Republic of the Congo, India, Kenya, Nigeria, and Burkina Faso)25 that found Muslim married women have lower probabilities of using MCM than their Catholic counterparts. Likewise, a study in three African nations26 found that having a strong religious identity can affect women's uptake of MCM.
The consumption of contraceptives was also linked to a woman's parity, according to our study, women who had two to three children used MCM at a rate of about 59.9%, compared to only 25.3% among women who have less than one child. This could mean that women of low parity are under pressure to have more children as reported by study5,27 from two countries Uganda and India. Another research study which was done in India reported that women with no child once married are forced to prove their fertility due to pressures from spouses, in laws and communities27.
Women from middle and high wealth quintiles had a higher odd of modern contraceptive use [aPOR 1.3, 95% CI; 1.04, 1.57] and [aPOR 1.4, 95% CI; 1.07, 1.8] as compared to those from the poorest wealth quintile. Findings from previous studies5,28 in Uganda agrees with our results, married women living in communities with high poverty levels were less likely to use modern contraceptives compared to those who live in communities that were in low poverty levels. Economically needy communities may be less likely to invest in women’s education, resulting in a lower level of understanding of contraceptive use, and less autonomy23 as was seen in a recent study in Uganda. Distance to health services may also be major barrier leading to very low contraceptive use29,30 as seen in Guatemala - Latin America and USA. Additionally, study findings in Ghana discovered that communities with more working married women have higher likelihood of using modern contraception which is consistent with the results of our study. Women who work are able and willing to spend their money on the necessary medical expenses and they also have the freedom to decide on family planning with their partners, which may not be the case for married women who are reliant on their husbands and do not work24. Residents of West Pokot [aPOR 0.3, 95% CI; 0.17, 0.44] were less likely to use modern contraceptives as compared to Bungoma county, this is in line with a study by Kenya health survey21 that reports that the percentage of currently married women using a modern method which is lowest in almost all counties of Northern part of Kenya; Mandera (2%), followed by Wajir (3%), Marsabit (6%), and Garissa (11%).
It was discovered from this study that the lower the level of the facility types, the less the MCM consumption, with dispensaries at 37.8% (95% CI; 32.9,43.1), and health centers at 42.7% (95% CI; 35.9,49.9) as compared to hospitals (46.5%) (95% CI; 36.2,57.2). The health system in a woman's environment may affect her choice to use contraception depending on factors like the availability of methods, accessibility to facilities and the level of care31,32. Contraceptive prevalence was also low at 37.5% (95% CI; 32.8,42.5) among women who visited facilities with episodes of contraceptive stock outs, unlike those who visited facilities which did not experience stock outs. This is in line with 18 who reported that insufficient stocks and restricted usage of available methods create conditions that ultimately restrict their use. Additional research done in Kenya indicates that providing high-quality FP services can raise user satisfaction which will encourage clients to use the service repeatedly and consistently whenever they need a method13. In addition, this study found that the uptake of FP services was lower in facilities covered by NHIF (aPOR 0.5,95%CI; 0.29,0.98, p=0. 043) than in those not covered. This is corroborated by research conducted in Laos16 which found that equitable advances in the modern contraceptive prevalence rate (mCPR) across demographic groups cannot be guaranteed by the formal inclusion of family planning services in health insurance benefits packages. It has been reported that insurance plans payment structures for repaying providers for family planning services can either promote or discourage the provision of specific methods. This is consistent with a recent study in a Kenya33 that indicates that despite the widespread belief that health care financing and spending lead to improvements in health status and the inclusion of family planning services, its data is scant and conflicting, especially for low and middle-income countries (LMICs) like Kenya.
Contraceptive prevalence is also higher among adolescents aged 15 to 19 years who were offered and prescribed FP services compared to those who were not offered any FP services. A previous study5 in Uganda states that the lack of youth-appropriate facilities limits young people's access to counseling and knowledge about contraception. Another study7 in Benin also demonstrates that unmarried young women are hesitant to obtain contraception despite their desire to avoid pregnancy since their communities perceive them as unsuitable for sexual activity. These can be addressed by providing a wide selection of modern contraceptive methods, whether through health institutions or community-based distributors, as well as client-centered thorough counseling to enable people to make informed and voluntary decisions about FP usage. Similarly, a steady availability of cheap contraceptive methods correlates to increasing and consistent use of modern contraception among this age group34 by a study done in Kinshasa – Uganda.
The randomized and representative sample created by the sampling methods was a major strength in this study. The researchers made certain that the data collection methods were well-structured and that all relevant study criteria were considered. The PMA survey only gathered quantitative data, as a result, some of the crucial details found in the qualitative aspects of the data which could give meaning to the quantitative data are missing. Second, survey design flaws like social desirability bias and recollection bias may be present when data was being gathered. Generalizability of the results may be limited.
Our study shows that participants with higher levels of formal education were more willing to embrace MCM adoption than those with low education attainment. The result also indicates a relatively low overall contraceptive prevalence of 43.2%. Results also show that there was a period of commodity stock out, indicating that women are missing out on their preferred FP options.
County school health coordinators should ensure integration of sexual reproductive health sessions in the school curriculum. The county reproductive health coordinators and health promotion officers should advocate on modern contraceptive methods to increase services access to comprehensive education on FP service availability and MCM service alongside client centred contraceptive counselling to support a patient’s reproductive autonomy. County health products and technologies coordinators should ensure timely quantification and forecasting and availability of all family planning commodities in all health facilities.
Contraceptive awareness in Kenya is high, but the uptake is still low, therefore the unmet needs still exist. Family planning is very crucial in terms of decreasing maternal mortality, reducing poverty and environmental degradation. Contraceptive method choices availability and accessibility promotes uptake of contraceptives.
To access PMA data used in the analysis of the manuscript, the procedure is as follows.
1. Login into www.pmadata.org
2. Choose a country as Kenya
3. Request for dataset and create account
4. Choose the thematic area which is family planning.
5. Choose household/female dataset and 2019 as the year and purpose of the data
6. Submit the request.
7. The admin will send a package of the data, tools and data use guide.
Jane Nyakundi: Conceptualization, Methodology, Validation, Writing – Original Draft Preparation, Writing – Review & Editing
Shadrack Yonge: Supervision, Validation, Writing – Review & Editing
Samuel Kiiru: Data Curation, Formal Analysis, Methodology, Writing – Review & Editing
Peter Gichangi: Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing
The authors wish to acknowledge the research assistants and the 11 counties that participated in this study.
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Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Epidemiology, Nutrition, Maternal and child health,
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
No
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Not applicable
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
No
Competing Interests: No competing interests were disclosed.
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
No
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: maternal and newborn health, family planning,
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Health behavior change, qualitative research, reproductive health, environmental health
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: adolescent sexual and reproductive health and adolescent sexual and reproductive health services (quality and access)
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: adolescent sexual and reproductive health and adolescent sexual and reproductive health services (quality and access)
Alongside their report, reviewers assign a status to the article:
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