Prognostic factors of time to first abortion after sexual debut among fragile state Congolese women: a survival analysis

BMC Public Health, Volume 21, No. 1, Year 2021

Interpretation

Background: Despite the common restrictive abortion laws, abortion remains widespread in sub-Saharan Africa (SSA) countries. Women still utilize abortion services and put their lives and health at risk because abortion can only be procured illegally in private facilities such as mid-level or small patent medicine store that may be manned by unskilled providers or through a non-medicated approach. The objective of this study was to investigate the prevalence of abortion, the reasons women had abortions, median years to first abortion after sexual debut and examine the factors of time to first abortion among women of reproductive age in the Republic of Congo. Methods: We used data from the most recent Republic of Congo Demographic and Health Survey (DHS). A total sample of 3622 women aged 15–49 years was analyzed. We estimated the overall prevalence of abortion and median years to first abortion. Furthermore, we examined the factors of time to first abortion after sexual debut using multivariable Cox regression and reported the estimates using adjusted Hazard Ratio (aHR) and 95% confidence intervals (CI). Statistical significance was determined at p < 0.05. Results: The prevalence of abortion was 60.0% and median years of time to first abortion after sexual debut was 9.0. The prominent reasons for abortion were due to too short birth interval (23.8%), lack of money (21.0%) and that husband/partner did not need a child at that time (14.0%). Women’s age and region were notable factors in timing to first abortion. Furthermore, women from poorer, middle, richer and richest households had 34, 67, 86 and 94% higher risk of abortion respectively, when compared with women from poorest households (all p < 0.05). Women currently in union/living with a man and formerly in union had 41 and 29% reduction in the risk of abortion respectively, when compared with those never in union (all p < 0.05). In addition, women with primary and secondary+ education had 42 and 76% higher risk of abortion respectively, when compared with women with no formal education (all p < 0.05). Conclusion: There was high prevalence of abortion with short years at first abortion. Abortion was associated with women’s characteristics. There is need for unwanted pregnancy prevention intervention and the improvement in pregnancy care to reduce adverse pregnancy outcomes among women. A cross-sectional data extracted from the Republic of Congo DHS 2012 was analyzed. A nationally representative sample of 3622 women who have had sex and aged 15–49 years were included in this study. On the other hand, the exclusion criterion was women with history of sexual abstinence. This was to ensure that only women exposed to pregnancy occurrence were analyzed. DHS data was collected through a stratified multistage cluster sampling technique. The procedure for stratification approach divides the population into groups by geographical region and commonly crossed by place of residence – urban vs. rural. A multi-level stratification approach is used to divide the population into first-level strata and to subdivide the first-level strata into second-level strata, and so on. DHS data is available in the public domain and accessed at; http://dhsprogram.com/data/available-datasets.cfm. DHS has been conducted in over 85 countries and repeated every years since 1984. A major advantage is that the sampling design and data collection approach are similar across countries which makes the results of different settings comparable. Though from the onset, DHS was designed to expand on fertility, demographic and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, nonetheless, it has become the prominent source of population surveillance for the monitoring of population health indices particularly in resource-constrained settings. DHS elicits information from respondents in a wide range of health-related areas including vaccination, child and maternal mortality, fertility, intimate partner violence, female genital mutilation, nutrition, lifestyle, infectious and non-infectious diseases, family planning, water and sanitation amongst others. DHS has great merits in collecting high-quality data through proper interviewer training, national coverage, standardized data collection instrument and proper operational definition of concepts to enhance understanding among policy and decision makers. DHS data is useful in formulating epidemiological research to estimate prevalence, trends and inequalities. The details of DHS has been reported previously [28]. The main outcome variable in this study was “abortion” also known as induced pregnancy termination. It was derived from the question; “Number of abortions” and responses were coded as “no” if a woman reported “0” indicating no history of abortion, and coded as “yes” if a woman reported “1”, “2”, “3”, “4” and so forth indicating history of abortion. In addition, the time to first abortion after sexual debut was also utilized. It was derived from the question; “Age at first abortion”. The difference in years between age at first abortion and “Age at first sex” was used as the time to first abortion. The main reason for abortion was derived from the question; “Main reason for putting an end to this pregnancy?” in the DHS individual woman dataset. The factors examined in this study are based on previous studies related to abortion and presented in Table 1 below [9, 11, 30–32]. Categories and operational definition of independent variables aFor the calculation of household wealth status, household assets such as ownership of television, radio, bicycle possessed by the household and housing quality such as type of floor, wall and roof were taken into consideration. Each item is assigned a factor score generated through principal component analysis which are then summed and standardized for the households. These standardised scores places the households in a continuous scale based on relative wealth scores. The scores thus obtained from a continuous scale are subsequently categorised into quintiles to rank the household as poorest/poorer/middle/richer/richest to richest [29] We used publicly available data in this study. Since the data was not collected by the authors of this manuscript, we sought permission from MEASURE DHS/ICF International and access to the data was provided after our intent for the request was assessed and approved. MEASURE DHS Program is consistent with the standards for ensuring the protection of respondents’ privacy. ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of human subjects. No further approval was required for this study. More details about data and ethical standards are available at http://goo.gl/ny8T6X. The survey (‘svy’) module was used to adjust for stratification, clustering and sampling weights to compute the estimates of abortion. To check multicollinearity, variance-inflation factor was employed and a value below 10 was considered acceptable [33, 34]. Consequently, no variable was excluded from the model as they were not found to be interdependent. We use percentage, Kaplan-Meier and Cox regression models to account for censoring in the estimation of exposure time to abortion [35, 36]. Statistical significance was determined at p < 0.05. Stata Version 14 (StataCorp., College Station, TX, USA) was used for data analysis.

AI Digest

Study Justification:

– The study aims to investigate the prevalence of abortion, reasons for abortion, and the factors influencing the time to first abortion among Congolese women.
– Understanding these factors is crucial for developing interventions to prevent unwanted pregnancies and improve pregnancy care, thereby reducing adverse pregnancy outcomes.

Study Highlights:

– The prevalence of abortion among Congolese women was found to be 60.0%.
– The median time to first abortion after sexual debut was 9.0 years.
– The main reasons for abortion were a too short birth interval, lack of money, and the husband/partner not wanting a child at that time.
– Factors such as women’s age, region, household wealth status, marital status, and education level were associated with the timing of the first abortion.

Study Recommendations:

– Implement unwanted pregnancy prevention interventions to reduce the prevalence of abortion.
– Improve pregnancy care to minimize adverse pregnancy outcomes among women.
– Address the underlying factors contributing to the high prevalence of abortion, such as birth spacing, financial constraints, and partner involvement in family planning decisions.

Key Role Players:

– Policy makers and government officials responsible for reproductive health policies and programs.
– Healthcare providers and organizations involved in family planning and reproductive healthcare services.
– Non-governmental organizations (NGOs) working on women’s health and rights.
– Community leaders and influencers who can promote awareness and education about contraception and safe abortion practices.

Cost Items for Planning Recommendations:

– Development and implementation of educational campaigns on contraception and family planning methods.
– Training programs for healthcare providers on safe abortion procedures and post-abortion care.
– Provision of affordable and accessible contraceptive methods.
– Strengthening healthcare infrastructure to ensure quality pregnancy care services.
– Monitoring and evaluation systems to assess the effectiveness of interventions and track progress.
Please note that the cost items provided are general suggestions and may vary based on the specific context and resources available in the Republic of Congo.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study utilized data from a nationally representative sample and employed appropriate statistical analysis methods. The prevalence of abortion and factors associated with time to first abortion were reported. However, the abstract lacks information on the specific methodology used, such as the sampling technique and data collection instruments. Additionally, the abstract does not provide information on potential limitations of the study or suggestions for future research. To improve the evidence, the authors could provide more details on the study design and methodology, discuss potential limitations, and suggest areas for further investigation.

Abstract

A cross-sectional data extracted from the Republic of Congo DHS 2012 was analyzed. A nationally representative sample of 3622 women who have had sex and aged 15–49 years were included in this study. On the other hand, the exclusion criterion was women with history of sexual abstinence. This was to ensure that only women exposed to pregnancy occurrence were analyzed. DHS data was collected through a stratified multistage cluster sampling technique. The procedure for stratification approach divides the population into groups by geographical region and commonly crossed by place of residence – urban vs. rural. A multi-level stratification approach is used to divide the population into first-level strata and to subdivide the first-level strata into second-level strata, and so on. DHS data is available in the public domain and accessed at; http://dhsprogram.com/data/available-datasets.cfm. DHS has been conducted in over 85 countries and repeated every years since 1984. A major advantage is that the sampling design and data collection approach are similar across countries which makes the results of different settings comparable. Though from the onset, DHS was designed to expand on fertility, demographic and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, nonetheless, it has become the prominent source of population surveillance for the monitoring of population health indices particularly in resource-constrained settings. DHS elicits information from respondents in a wide range of health-related areas including vaccination, child and maternal mortality, fertility, intimate partner violence, female genital mutilation, nutrition, lifestyle, infectious and non-infectious diseases, family planning, water and sanitation amongst others. DHS has great merits in collecting high-quality data through proper interviewer training, national coverage, standardized data collection instrument and proper operational definition of concepts to enhance understanding among policy and decision makers. DHS data is useful in formulating epidemiological research to estimate prevalence, trends and inequalities. The details of DHS has been reported previously [28]. The main outcome variable in this study was “abortion” also known as induced pregnancy termination. It was derived from the question; “Number of abortions” and responses were coded as “no” if a woman reported “0” indicating no history of abortion, and coded as “yes” if a woman reported “1”, “2”, “3”, “4” and so forth indicating history of abortion. In addition, the time to first abortion after sexual debut was also utilized. It was derived from the question; “Age at first abortion”. The difference in years between age at first abortion and “Age at first sex” was used as the time to first abortion. The main reason for abortion was derived from the question; “Main reason for putting an end to this pregnancy?” in the DHS individual woman dataset. The factors examined in this study are based on previous studies related to abortion and presented in Table 1 below [9, 11, 30–32]. Categories and operational definition of independent variables aFor the calculation of household wealth status, household assets such as ownership of television, radio, bicycle possessed by the household and housing quality such as type of floor, wall and roof were taken into consideration. Each item is assigned a factor score generated through principal component analysis which are then summed and standardized for the households. These standardised scores places the households in a continuous scale based on relative wealth scores. The scores thus obtained from a continuous scale are subsequently categorised into quintiles to rank the household as poorest/poorer/middle/richer/richest to richest [29] We used publicly available data in this study. Since the data was not collected by the authors of this manuscript, we sought permission from MEASURE DHS/ICF International and access to the data was provided after our intent for the request was assessed and approved. MEASURE DHS Program is consistent with the standards for ensuring the protection of respondents’ privacy. ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of human subjects. No further approval was required for this study. More details about data and ethical standards are available at http://goo.gl/ny8T6X. The survey (‘svy’) module was used to adjust for stratification, clustering and sampling weights to compute the estimates of abortion. To check multicollinearity, variance-inflation factor was employed and a value below 10 was considered acceptable [33, 34]. Consequently, no variable was excluded from the model as they were not found to be interdependent. We use percentage, Kaplan-Meier and Cox regression models to account for censoring in the estimation of exposure time to abortion [35, 36]. Statistical significance was determined at p < 0.05. Stata Version 14 (StataCorp., College Station, TX, USA) was used for data analysis.

Materials

N/A

Innovations

Based on the information provided, it seems that the study is focused on understanding the prevalence and factors associated with abortion among women in the Republic of Congo. The study utilizes data from the Republic of Congo Demographic and Health Survey (DHS) to analyze the prevalence of abortion, reasons for abortion, and factors influencing the timing of first abortion after sexual debut.

To improve access to maternal health, some potential innovations and recommendations could include:

1. Strengthening reproductive health education: Implement comprehensive and accurate reproductive health education programs that provide information on contraception, family planning, and safe abortion methods. This can help reduce the need for abortions and ensure that women have access to the knowledge and resources they need to make informed decisions about their reproductive health.

2. Increasing access to contraception: Improve access to a wide range of contraceptive methods, including long-acting reversible contraceptives (LARCs) such as intrauterine devices (IUDs) and implants. This can help prevent unintended pregnancies and reduce the need for abortions.

3. Expanding access to safe abortion services: Advocate for the decriminalization of abortion and ensure that safe and legal abortion services are available and accessible to all women. This includes training healthcare providers in safe abortion procedures and ensuring that facilities are equipped to provide these services.

4. Addressing socio-economic factors: Address the underlying socio-economic factors that contribute to the high prevalence of abortion, such as poverty, lack of education, and limited access to healthcare. This can be done through targeted interventions that aim to improve economic opportunities, increase educational attainment, and provide social support to women and families.

5. Strengthening healthcare systems: Invest in strengthening healthcare systems, particularly in rural and underserved areas, to ensure that women have access to quality maternal healthcare services. This includes improving infrastructure, training healthcare providers, and ensuring the availability of essential medicines and supplies.

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 Republic of Congo.

AI Innovations Description

The study mentioned in the description focuses on the prevalence of abortion and factors influencing the timing of the first abortion among women in the Republic of Congo. The study used data from the Republic of Congo Demographic and Health Survey (DHS) conducted in 2012. The sample included 3622 women aged 15-49 years who have had sex.

The study found that the prevalence of abortion among Congolese women was 60.0%, and the median time to the first abortion after sexual debut was 9.0 years. The main reasons reported for having an abortion were a too short birth interval, lack of money, and the husband/partner not wanting a child at that time.

Several factors were found to be associated with the timing of the first abortion. Women’s age and region were notable factors, with younger women and those from certain regions having a higher risk of having an abortion earlier. Household wealth status also played a role, with women from poorer households having a lower risk of abortion compared to women from richer households. Women currently in a union or living with a man had a lower risk of abortion compared to those who were never in a union. Education level was also a factor, with women with primary or secondary+ education having a higher risk of abortion compared to those with no formal education.

The study highlights the need for interventions to prevent unwanted pregnancies and improve pregnancy care to reduce adverse outcomes among women. Based on these findings, a recommendation to improve access to maternal health could be to implement comprehensive sexual and reproductive health education programs that address the reasons women have abortions, such as birth spacing and financial constraints. These programs could also focus on empowering women to make informed decisions about their reproductive health and provide access to affordable and safe contraceptive methods. Additionally, efforts should be made to improve the availability and quality of maternal health services, particularly in regions with higher rates of abortion and among women from poorer households.

AI Innovations Methodology

Based on the provided information, it seems that the study focuses on investigating the prevalence of abortion, reasons for abortion, and factors influencing the timing of the first abortion among women in the Republic of Congo. The study utilized data from the Republic of Congo Demographic and Health Survey (DHS) conducted in 2012, which included a nationally representative sample of 3622 women aged 15-49 years.

To improve access to maternal health in the context of this study, potential recommendations could include:

1. Strengthening reproductive health education: Providing comprehensive and accurate information about contraception, family planning, and safe abortion methods can help women make informed decisions and prevent unwanted pregnancies.

2. Increasing availability of safe abortion services: Expanding access to safe and legal abortion services can help reduce the risks associated with unsafe abortions. This could involve training healthcare providers, ensuring the availability of necessary equipment and medications, and establishing appropriate referral systems.

3. Addressing socio-economic barriers: Implementing interventions to address socio-economic factors such as poverty, lack of financial resources, and limited access to healthcare facilities can help reduce the need for abortion due to financial constraints.

4. Promoting gender equality and empowerment: Addressing gender inequalities and empowering women to make decisions about their reproductive health can contribute to reducing the incidence of unwanted pregnancies and the need for abortion.

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

1. Baseline assessment: Collect data on the current prevalence of abortion, reasons for abortion, and factors influencing the timing of the first abortion among women in the Republic of Congo.

2. Define indicators: Identify specific indicators that can measure the impact of the recommendations, such as the reduction in the prevalence of abortion, improvement in access to safe abortion services, or increase in contraceptive use.

3. Develop a simulation model: Create a simulation model that incorporates the potential recommendations and their expected effects on the identified indicators. This model should consider factors such as population demographics, healthcare infrastructure, and socio-economic conditions.

4. Data input: Input the baseline data into the simulation model to establish the starting point for the analysis.

5. Implement recommendations: Simulate the implementation of the recommendations by adjusting relevant variables in the model. For example, increase the availability of safe abortion services, provide reproductive health education programs, or address socio-economic barriers.

6. Run simulations: Run multiple simulations using different scenarios and assumptions to assess the potential impact of the recommendations on the identified indicators. This can help estimate the magnitude of change and identify the most effective strategies.

7. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. Assess the feasibility, cost-effectiveness, and sustainability of the proposed interventions.

8. Refine and validate: Refine the simulation model based on feedback and validation from experts in the field. Incorporate additional data or adjust assumptions as necessary.

9. Communicate findings: Present the findings of the simulation analysis in a clear and concise manner, highlighting the potential benefits of the recommendations in improving access to maternal health. Use the results to inform policy decisions and guide the implementation of interventions.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. The steps outlined above provide a general framework for conducting such an analysis.

Authors & Co-Authors

Statistics:

Authors: 3

Identifiers:

DOI: 10.1186/s12889-021-10599-x

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Violence and Injury

Study Countries:

Congo

Study Design:

Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female