Pattern and determinants of contraceptive usage among women of reproductive age from the Digo community residing in Kwale, Kenya: Results from a cross-sectional household survey

BMC Women's Health, Volume 18, No. 1, Year 2018

Interpretation

Background: Contraceptive usage has been associated with improved maternal and child health (MCH) outcomes. Despite significant resources being allocated to programs, there has been sub-optimal uptake of contraception, especially in the developing world. It is important therefore, to granulate factors that determine uptake and utilization of contraceptive services so as to inform effective programming. Methods: Between March and December 2015, we conducted a cross-sectional survey among women of reproductive age (WRA) from the Digo community residing in Kwale County, Kenya. The study aimed to describe the pattern and determinants of contraceptive usage in this population. Respondents were selected using stratified, systematic sampling and completed a household sexual and reproductive health (SRH) questionnaire. Results: We interviewed 745 respondents from 15 villages in 2 out of 4 sub-counties of Kwale. Their median (interquartile range, IQR) age was 29 (23-37) years. 568 (76%) reported being currently in a marital union. Among these, 308 (54%) were using a contraceptive method. The total unmet need, unmet need for spacing and for limiting was 16%, 8% and 8%, respectively. Determinants of contraceptive usage were education [adjusted Odds Ratio, aOR = 2.1, 95% confidence interval, CI: 1.4-3.4, P = 0.001]; having children [aOR = 5.0, 95% CI: 1.7-15.0, P = 0.004]; having attended antenatal care (ANC) at last delivery [aOR = 4.0, 95% CI: 1.1-14.8, P = 0.04] as well as intention to stop or delay future birth [aOR = 6.7, 95% CI: 3.3-13.8, P < 0.0001]. Conclusions: We found high levels of contraceptive usage among WRA from the Digo community residing in Kwale. To further improve uptake and utilization of contraception in this setting, programs should address demand-side factors including ensuring female educational attainment as well as promotion of ANC and skilled birth attendance. This was a cross-sectional household survey conducted between March and December 2015 within the framework of two MCH projects funded by the European Commission and implemented in 2 out of 4 sub-counties of Kwale County. The MOMI project was implemented in Matuga sub-county while the MNM II project was implemented initially in Msambweni and later, in Lungalunga sub-counties. Data collection involved administration of a structured SRH questionnaire to female respondents aged 18–45 years in their households (Additional file 1). We estimated a sample size of 700 respondents based on a previously reported CPR of 30% in this setting; a sample design effect of 2.5; Z-statistic of 1.96 for a 95% confidence level in the estimation; 10% non-response rate and a 5% margin of error. Respondents were selected using stratified, systematic random sampling. Each sub-county was stratified into constituent wards, sub-locations and further into villages within each sub-location. From each village, we obtained a list of all households from the headman and randomly selected households to visit using a random number generator. The number of households selected was based on the proportion of households in that village relative to the total number of households in each sub-location with a sampling interval of 12 households. In each household, we administered the questionnaire to all female respondents who reported being from the Digo community and who were resident in the study area for more than 6 months. We excluded those who did not provide consent, those who were not resident in the study area and women aged <18 (unless they were emancipated minors) or >45 years old. Prior to any data collection activities, we held a series of meetings with community gatekeepers including religious leaders, village headmen, chiefs, sub-county commissioners and the Kwale county commissioner. This was meant to sensitize them on the proposed data collection procedures and obtain their buy-in. We also used this as an opportunity to introduce our study team consisting of the principal investigator and resident data enumerators who were from the Digo community but residing in different villages within the study area. Ethical approval for the study was obtained from the Research Ethics Committee of the Aga Khan University, Nairobi (2014/REC-51) and the Ethics Review Committee of the University of Nairobi and Kenyatta National Hospital (P502/08/2014). We also obtained a research permit from the National Commission for Science, Technology and Innovation (#4703) to facilitate the conduct of research activities in the community. All participants provided written informed consent. Quantitative data were entered into a Microsoft Access (2010) database (Microsoft Inc. Seattle, WA, USA) and after appropriate data cleaning checks, migrated to Stata version 12 (StataCorp Inc., College Station, TX, USA) for statistical analyses. For the descriptive statistics, we summarized the respondents’ demographic characteristics as counts (N) and percentages (%) for categorical data and median (IQR) for continuous data. We compared these characteristics using Pearson’s chi-squared test for categorical data and Wilcoxon rank-sum test for continuous data. For the inferential statistics, the outcome of interest was current usage of any contraceptive method. We calculated the odds of current contraceptive usage among women with each determinant of interest versus the reference group using multivariable logistic regression models and report the adjusted ORs and 95% CIs for each. All statistical tests were evaluated using a 2-sided α-value of 0.05.

AI Digest

Study Justification:

– Contraceptive usage has been linked to improved maternal and child health outcomes.
– Despite significant resources being allocated to contraceptive programs, uptake remains sub-optimal, especially in developing countries.
– It is important to understand the factors that determine contraceptive usage in order to inform effective programming.

Study Highlights:

– Conducted a cross-sectional survey among women of reproductive age from the Digo community residing in Kwale County, Kenya.
– Interviewed 745 respondents from 15 villages in 2 out of 4 sub-counties of Kwale.
– Found that 54% of women in marital unions were using a contraceptive method.
– Identified education, having children, attending antenatal care, and intention to delay future births as determinants of contraceptive usage.
– High levels of contraceptive usage were observed among women from the Digo community in Kwale.

Study Recommendations:

– Programs should focus on demand-side factors to further improve contraceptive uptake, including ensuring female educational attainment, promoting antenatal care, and skilled birth attendance.

Key Role Players:

– Researchers and data enumerators
– Community gatekeepers (religious leaders, village headmen, chiefs, sub-county commissioners, and the Kwale county commissioner)
– Research Ethics Committee of the Aga Khan University, Nairobi
– Ethics Review Committee of the University of Nairobi and Kenyatta National Hospital
– National Commission for Science, Technology and Innovation

Cost Items for Planning Recommendations:

– Research permit from the National Commission for Science, Technology and Innovation
– Data collection activities (questionnaire administration, data entry)
– Statistical analysis software (Stata)
– Research team salaries and allowances
– Ethical approval process
– Sensitization meetings with community gatekeepers
– Travel and transportation expenses for the research team

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides specific data and findings from a cross-sectional household survey conducted in Kwale, Kenya. The survey included a sample size of 745 respondents and collected data on contraceptive usage among women of reproductive age. The abstract also presents determinants of contraceptive usage and suggests actionable steps to improve uptake and utilization of contraception in the population, such as addressing demand-side factors like female educational attainment and promotion of antenatal care and skilled birth attendance. However, the abstract does not provide information on the methodology used for data collection and analysis, which could be improved by including more details on the sampling strategy, data cleaning procedures, and statistical analyses performed.

Abstract

This was a cross-sectional household survey conducted between March and December 2015 within the framework of two MCH projects funded by the European Commission and implemented in 2 out of 4 sub-counties of Kwale County. The MOMI project was implemented in Matuga sub-county while the MNM II project was implemented initially in Msambweni and later, in Lungalunga sub-counties. Data collection involved administration of a structured SRH questionnaire to female respondents aged 18–45 years in their households (Additional file 1). We estimated a sample size of 700 respondents based on a previously reported CPR of 30% in this setting; a sample design effect of 2.5; Z-statistic of 1.96 for a 95% confidence level in the estimation; 10% non-response rate and a 5% margin of error. Respondents were selected using stratified, systematic random sampling. Each sub-county was stratified into constituent wards, sub-locations and further into villages within each sub-location. From each village, we obtained a list of all households from the headman and randomly selected households to visit using a random number generator. The number of households selected was based on the proportion of households in that village relative to the total number of households in each sub-location with a sampling interval of 12 households. In each household, we administered the questionnaire to all female respondents who reported being from the Digo community and who were resident in the study area for more than 6 months. We excluded those who did not provide consent, those who were not resident in the study area and women aged 45 years old. Prior to any data collection activities, we held a series of meetings with community gatekeepers including religious leaders, village headmen, chiefs, sub-county commissioners and the Kwale county commissioner. This was meant to sensitize them on the proposed data collection procedures and obtain their buy-in. We also used this as an opportunity to introduce our study team consisting of the principal investigator and resident data enumerators who were from the Digo community but residing in different villages within the study area. Ethical approval for the study was obtained from the Research Ethics Committee of the Aga Khan University, Nairobi (2014/REC-51) and the Ethics Review Committee of the University of Nairobi and Kenyatta National Hospital (P502/08/2014). We also obtained a research permit from the National Commission for Science, Technology and Innovation (#4703) to facilitate the conduct of research activities in the community. All participants provided written informed consent. Quantitative data were entered into a Microsoft Access (2010) database (Microsoft Inc. Seattle, WA, USA) and after appropriate data cleaning checks, migrated to Stata version 12 (StataCorp Inc., College Station, TX, USA) for statistical analyses. For the descriptive statistics, we summarized the respondents’ demographic characteristics as counts (N) and percentages (%) for categorical data and median (IQR) for continuous data. We compared these characteristics using Pearson’s chi-squared test for categorical data and Wilcoxon rank-sum test for continuous data. For the inferential statistics, the outcome of interest was current usage of any contraceptive method. We calculated the odds of current contraceptive usage among women with each determinant of interest versus the reference group using multivariable logistic regression models and report the adjusted ORs and 95% CIs for each. All statistical tests were evaluated using a 2-sided α-value of 0.05.

Materials

https://efashare.b-cdn.net/materials/697f6417e492071077b38f88f4ef1550b06af659d0a3676a9ae0c1f2e5f3806c/12905_2017_497_MOESM1_ESM.pdf

Innovations

Based on the information provided, here are some potential recommendations for innovations to improve access to maternal health:

1. Mobile Health (mHealth) Solutions: Develop and implement mobile applications or text messaging services that provide information and reminders about contraceptive methods, antenatal care, and skilled birth attendance. These tools can help educate and empower women, especially those in remote areas, to make informed decisions about their reproductive health.

2. Community Health Workers (CHWs): Train and deploy CHWs to provide education, counseling, and support on contraceptive methods, antenatal care, and skilled birth attendance. CHWs can play a crucial role in reaching women in underserved communities and addressing their specific needs and concerns.

3. Task Shifting: Expand the roles and responsibilities of healthcare providers, such as nurses and midwives, to include the provision of contraceptive services, antenatal care, and skilled birth attendance. This can help alleviate the burden on doctors and increase access to these essential services.

4. Integration of Services: Strengthen the integration of contraceptive services with antenatal care and skilled birth attendance. This can be done by ensuring that healthcare facilities offer a comprehensive package of services, making it easier for women to access multiple services in one location.

5. Quality Improvement Initiatives: Implement quality improvement initiatives to enhance the overall quality of maternal health services. This can involve training healthcare providers on best practices, improving infrastructure and equipment, and ensuring the availability of essential supplies and medications.

6. Community Engagement: Engage the community, including religious leaders, village headmen, and community-based organizations, in promoting and supporting maternal health initiatives. This can help reduce cultural and social barriers to accessing contraceptive services, antenatal care, and skilled birth attendance.

7. Financial Support: Explore innovative financing mechanisms, such as health insurance schemes or conditional cash transfers, to reduce the financial barriers to accessing maternal health services. This can help ensure that cost is not a deterrent for women seeking these services.

It is important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the Digo community residing in Kwale, Kenya.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study is to implement targeted programs that address the determinants of contraceptive usage among women of reproductive age in the Digo community residing in Kwale, Kenya. These programs should focus on the following areas:

1. Education: Promote female educational attainment as a means to increase contraceptive usage. This can be done through awareness campaigns, scholarships, and incentives for girls to stay in school.

2. Antenatal care (ANC): Encourage women to attend ANC visits during pregnancy by providing information on the benefits of ANC and addressing any barriers to accessing these services. ANC visits can serve as an opportunity to provide contraceptive counseling and services.

3. Intention to stop or delay future birth: Support women in their decision to stop or delay future pregnancies by providing access to a range of contraceptive methods and ensuring that they have the information and resources needed to make informed choices.

By addressing these factors, programs can help increase the uptake and utilization of contraception among women in the Digo community, ultimately improving access to maternal health services and contributing to better maternal and child health outcomes.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase educational opportunities for women: The study found that education was a significant determinant of contraceptive usage. Therefore, implementing programs that focus on improving female educational attainment can help increase access to maternal health services.

2. Promote antenatal care (ANC) and skilled birth attendance: The study found that women who had attended ANC at their last delivery were more likely to use contraception. Therefore, it is important to promote ANC services and encourage women to seek skilled birth attendance, as this can lead to increased utilization of contraception.

3. Address demand-side factors: To further improve uptake and utilization of contraception, programs should address demand-side factors. This can include raising awareness about the benefits of contraception, addressing cultural and social norms that may hinder its use, and providing counseling and support services to women and couples.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Baseline data collection: Collect data on the current levels of contraceptive usage, educational attainment, ANC attendance, and other relevant factors in the target population.

2. Define indicators: Identify specific indicators that can measure the impact of the recommendations, such as the percentage increase in contraceptive usage, the percentage increase in ANC attendance, or the percentage increase in educational attainment.

3. Develop a simulation model: Use statistical software, such as Stata, to develop a simulation model that incorporates the baseline data and the recommended interventions. The model should consider the relationships between the different factors and simulate their impact on improving access to maternal health.

4. Run simulations: Run multiple simulations using different scenarios, such as varying levels of educational attainment or ANC attendance rates. This will help assess the potential impact of each recommendation on improving access to maternal health.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations. This can include quantifying the expected increase in contraceptive usage, ANC attendance, or educational attainment, as well as identifying any potential trade-offs or unintended consequences.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data or expert input. This will help ensure the accuracy and reliability of the simulation results.

7. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community leaders. This can help inform decision-making and guide the implementation of interventions to improve access to maternal health.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and data availability.

Author

Dima Health

Statistics:

Citations: 11

Identifiers:

DOI: 10.1186/s12905-017-0497-5

Research Areas:

Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Kenya

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female