Estimating unintended pregnancy and induced abortion in Liberia: A nationally representative cross-sectional survey

Estimating unintended pregnancy and induced abortion in Liberia: A nationally representative cross-sectional survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimating unintended pregnancy and induced abortion in Liberia: A nationally representative cross-sectional survey Boniface Ayanbekongshie Ushie, Margaret Giorgio, Kenneth Juma, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4595818/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: More than 60% of unintended pregnancies end in induced abortions globally. In Liberia, limited access to contraceptives caused by health system challenges and sociocultural barriers leads to many unintended pregnancies. Nevertheless, very little is known about the incidence of induced abortion and unintended pregnancy in the country. This study aimed to estimate the incidence of induced abortion and unintended pregnancy in Liberia in 2021. Methods: The study utilized the Abortion Incidence Complications Method. First, we estimated the total number of induced abortions that resulted in women seeking facility-based post-abortion care in 2021 using data from a nationally representative sample of health facilities (n=128). Next, we used data from a survey of experts on abortion in Liberia (n=89) to estimate the proportion of induced abortions that resulted in complications treated in health facilities in 2021. The inverse of this proportion is a multiplier, which was applied to the estimate of the total number of induced abortion-related complications treated in health facilities to obtain the total number of induced abortions in 2021. We complemented this estimate of induced abortion with the Demographic Health Survey data to estimate the incidence of unintended pregnancies in Liberia in 2021. Results: We estimated that 14,555 patients received post-abortion (PAC) treatment in Liberia in 2021. Approximately 58% of these post-abortion care cases (n=8,461) were due to induced abortions. Accounting for induced abortions that had complications that did not require facility-based treatment and those without complications, we estimated that 38,779 induced abortions occurred in Liberia in 2021, corresponding to a national induced abortion incidence rate of 30.7 per 1,000 women of reproductive age (15–49). The unintended pregnancy rate among women of reproductive age was 86.54 per 1,000, and approximately 35% of all pregnancies ended in an abortion. Conclusions: Despite the legal restrictions on abortions, study findings show that unintended pregnancy and induced abortion are common in Liberia. There is a need for strengthened efforts to increase access to contraceptives, safe abortion care under legal indications, and quality PAC to improve socioeconomic and health outcomes for women and girls in Liberia. Abortion Incidence Complications Method unintended pregnancy Liberia post-abortion care Introduction While unsafe abortion and its associated complications are a leading public health concern in many parts of Africa, abortion is regulated by the criminal code rather than a public health framework in most countries ( 1 ). In the context of Liberia, under the Liberian Penal Code (1978), Section 16.3, abortion is justified on the following grounds: to save the life of the woman; to preserve the physical health of the woman; to preserve the mental health of the woman; rape or incest or other felonious intercourse; and fetal impairment ( 2 ). The Penal Code felony offences for contravening these provisions of the penal code. Though preventable, unsafe abortions contribute an estimated 10% to Liberia’s maternal mortality - one of the highest globally at 1,072 deaths per 100,000 live births ( 3 ). It is also possible that this is an underestimate because of the clandestine nature of the practice; other notable contributors to maternal mortality in Liberia are hemorrhage (25%), pre-eclampsia (16%), and sepsis (10%) ( 4 ). There is, also, evidence to suggest that providers in restrictive contexts use these diagnoses to conceal or deliberately misclassify cases of induced abortion ( 5 ). For this reason, the number of abortion-related morbidities and mortalities is grossly underestimated in national health databases ( 6 ). Globally, the main driver of induced abortion is unintended pregnancy ( 7 ). While modern and safe contraceptive methods exist for preventing unintended pregnancies ( 8 ), there remain notable gaps in the provision and uptake of family planning services in Liberia, as only 23.9% of all reproductive-age women reported using modern contraceptives ( 9 ). Consequently, unintended pregnancy in Liberia is among the highest in Africa, especially among adolescents and young women ( 10 ). In addition to being highly restrictive, induced abortion is highly stigmatized in Liberia, even when legally indicated. Both factors create diverse challenges in generating reliable data and estimates of the incidence of abortion, the conditions under which abortions take place and the burden of unsafe abortions, including the types and severity of complications ( 5 ). First, administrative health records on induced abortion are often inaccurate and incomplete. In addition, research has consistently shown that women responding to social surveys are likely to underreport experiences with abortion ( 11 , 12 ), and women presenting with post-abortion complications in facilities often report induced abortions as miscarriages ( 13 ). As a result of these difficulties, there is very little available evidence on abortion in Liberia. According to the 2007 Liberia Demographic and Health Survey (DHS), only 6% of women reported ever having had an induced abortion ( 14 ), yet estimates from recent global statistical models suggest that this is a dramatic underestimation ( 10 ). A population-based study in 2015 also attempted to measure lifetime induced abortion prevalence using an indirect method, finding that 32% of women reported ever having an abortion ( 15 ). However, limitations to the methods used make it difficult to determine the validity of this estimate or the frequency of abortion annually. The most recent legal reforms by the parliament of Liberia to the Public Health Law (Title 33) gained momentum in 2022, with proposed amendments including sections addressing the legality of induced abortion. This process requires sound, robust, and nationally representative data to inform and guide decision-makers in legal reforms that could reduce maternal morbidity and mortality through the availability of safe abortion services. This study provides the first nationally representative estimate of the incidence of induced abortion and unintended pregnancy in Liberia. Methods Study Design We used the Abortion Incidence Complications Method (AICM) to estimate the 2021 incidence of induced abortion in Liberia ( 16 ). The AICM is an indirect method of abortion measurement that has enjoyed considerable usage and acceptability across several geographies globally, including 11 countries in sub-Saharan Africa in the last decade ( 17 ). Data for the study are drawn largely from two 2021 national cross-sectional surveys, namely, 1) the Health Facilities Survey (HFS), which involves health facility interviews with post-abortion care (PAC) service providers, and 2) a Knowledgeable Informant Survey (KIS), which was a survey of purposively selected key informants with extensive knowledge on abortion in their regions. The other data required for abortion incidence estimate using the AICM, such as the national population of women of reproductive age (with subnational distributions), age-specific fertility rates, and data on poverty or wealth distribution by subnational groupings and rural and urban locations, come from secondary sources, chiefly the national census or the Liberia Demographic and Health Survey. Health Facility Survey We collected data from a nationally representative sample of health facilities in Liberia that are capable of providing post-abortion care services. We used stratified random sampling to select facilities based on administrative region (South Central, North Western, North-Central, South Eastern A, and South Eastern B) and level of care (clinic, health center, and county hospital). In addition to public health facilities, there are private facilities, including private-for-profit, faith-based, and concession facilities across Liberia. We used the Liberia Ministry of Health’s (MOH) Master Facility List (obtained on May 31, 2021) to identify eligible public and private facilities. The Master List had a universe of 894 facilities, comprising clinics (802), health centers (59), and hospitals (34). We selected 132 health facilities for the HFS, which included 31 hospitals (91.2%), 41 health centers (69.4%), and 60 clinics (7.5%). One hundred and twenty-eight (128) (97%) of the sampled facilities completed the HFS (see Table 1 ). We excluded four hospitals because one was burnt down during the study, two were specialized hospitals, and one refused participation. Table 1 Liberia Health Facilities Survey sample by region and facility type Region Clinic Health center County hospital Total facilities National total 57 43 28 128 North Central 17 11 10 38 North Western 5 3 3 11 South Central 22 18 9 49 South Eastern A 7 3 3 13 South Eastern B 6 8 3 17 At each selected facility, we identified and interviewed a healthcare provider most knowledgeable about PAC. In large facilities, such as hospitals, we interviewed the head of the maternal health or labor or delivery unit or an obstetrician/gynecologist who oversees PAC services. In lower-level facilities, we interviewed nurses, midwives, officers in charge, or health workers who could provide information about abortion-related care in that facility. Each respondent was interviewed using a face-to-face structured questionnaire. The HFS collected retrospective estimates of the number of PAC patients (inpatient and outpatient) treated at each facility in the past month and in a typical month. Caseloads for past and typical months were collected to account for abortion seasonality. If the respondent could not give the monthly estimates, retrospective estimates of the past year and typical year were requested. Other information captured in the survey included the health facilities’ provision of PAC services, including PAC management approaches, provider knowledge of comprehensive abortion care (CAC, which is inclusive of safe abortion and PAC), and the provision of post-abortion family planning. Knowledgeable Informant Survey The knowledgeable informant survey (KIS) involved in-person structured interviews with a sample of purposively selected respondents who were knowledgeable about the provision of abortion and PAC in the country (nationally or in specific regions). Informants were not restricted to clinicians providing direct CAC services to patients. Potential key informants were identified through purposeful and snowball sampling. The individuals recruited for the KIS were in the following roles: 1) Key national MOH and County Health Team (subnational) staff involved in SRH programs; 2) Heads/informed members of non-governmental organizations implementing SRH programs; 3) Heads/informed members of SRH advocacy groups, including civil society groups; 4) Researchers on SRHR from different regions of Liberia. Selected individuals were asked to list up to two referrals they believed were knowledgeable on the provision of abortion and PAC in Liberia. A total of 89 key informants were interviewed on topics that included their perceptions regarding the type of providers women seek abortions from, the likelihood of women experiencing complications that require treatment in a facility according to the type of abortion provider and method used, and the likelihood that women who need treatment would receive it at a health facility. These questions were asked for four major sub-groups of women within the population: rural poor, rural non-poor, urban poor, and urban non-poor. This information was used to estimate the proportion of all induced abortions in Liberia that ended in a complication that was treated in a health facility. The inverse of this proportion is called the “multiplier”. This indicator represents, for every one woman who receives postabortion care in health facilities in Liberia, the additional number of women/girls who have induced abortions who either did not have a complication or had a complication that were not treated in the formal health system. Other Data Sources Other data sources used for calculating abortion incidence were the 2019/2020 Liberia Demographic and Health Survey (LDHS) ( 18 ), which provided information on fertility, contraceptive prevalence, unmet need for contraception, pregnancy wantedness, and measures of access to health care. We also drew from the 2021 population projection data for the number of women of reproductive age (15–49) (WRA) in the different regions of Liberia ( 19 ). We used the Poverty Equity Brief for Liberia 2021 ( 20 ) and the Global Multidimensional Poverty Index Liberia Country Briefing 2021 ( 21 ) to estimate the proportion of poor/non-poor women in urban/rural settings in Liberia. Data analysis Estimating PAC caseloads We followed the steps for estimating abortion incidence through the AICM approach described in Singh et al. (2019) ( 17 ). First, we calculated the health facility PAC caseloads using the HFS data. We generated the annual PAC caseload in each facility by averaging the number of women treated in the past month and an average month for in-patients and outpatients and adding the two estimates. To create annual estimates, we then multiplied this number by 12. For facilities with low caseloads where respondents could not provide PAC estimates by past month or typical month, we averaged past year and typical year estimates for inpatients and outpatients. The resulting facility-level estimates were weighted to generate nationally representative data, accounting for sampling proportions and non-response. After this, we removed cases referred to other (usually higher-level) facilities to avoid double-counting PAC cases. Accounting for PAC caseload due to spontaneous abortion The PAC caseload estimates at this point include those resulting from spontaneous and induced abortion because the AICM does not require HFS respondents to separate complications due to the two types of pregnancy loss. This practice is based on the understanding that complications from induced and spontaneous abortion have similar clinical presentations ( 16 ). As such, the next step in the analysis involves removing cases due to spontaneous abortion. The AICM accounts for PAC cases that were due to spontaneous abortion by estimating the number of second-trimester miscarriages, as they are most likely to require facility-based treatment. Previous work has suggested that the number of second-trimester miscarriages in a population is approximately 3.41% of live births ( 22 ). Therefore, we estimated the number of spontaneous abortions by taking 3.41% of all live births within each region. After subtracting the number of PAC cases due to miscarriages from the regional PAC caseload estimates, we weighted these new regional estimates to generate the total number of women treated in health facilities for complications resulting from induced abortions in Liberia in 2021. Calculating the multiplier and creating abortion incidence estimates The next step is the creation of a multiplier. A detailed description of the steps for calculating the multiplier is provided elsewhere by Sully et al. (2018) ( 23 ). In brief, we used data from the KIS to multiply the proportion of women obtaining abortions from each provider type within each abortion method by the respective probabilities of experiencing complications from each provider type-abortion method combination. Next, we used information on the likelihood of receiving treatment for complications to create an estimate for the proportion of women who have an induced abortion and receive treatment for a complication. This is done separately for four different sub-populations within Liberia (urban poor, urban nonpoor, rural poor and rural nonpoor) to account for differences in abortion and treatment-seeking behaviors by women as a result of these factors. We then weighted these estimates by the population distribution of the four groups in each region using population census estimates for 2021 ( 19 ), and the poverty profiles for Liberia by region ( 18 , 24 ), which resulted in estimates of the proportion of all abortions that resulted in PAC treatment in a facility in each region. The multiplier for each region is the inverse of this proportion. Applying the regional multiplier to the regional estimates of PAC cases due to induced abortion provides the total estimated number of women who obtained an induced abortion in Liberia in 2021. Using data on the population of women of reproductive age in Liberia ( 19 ), we calculated the abortion incidence (number of induced abortions per year), abortion rate (number of induced abortions per 1,000 women of reproductive age [15–49]), and abortion ratio (number of induced abortions per 1,000 live births). Unintended pregnancy estimation Finally, we used the total number of abortions to estimate the number and rate of pregnancies and the unintended pregnancy rates. The prevalence of intended pregnancy was calculated as the total number of unintended pregnancies divided by the total number of all pregnancies in Liberia in 2021. To calculate the total number of unintended pregnancies, we assumed that all induced abortions were from unintended pregnancies. We defined unintended pregnancies to be composed of all unplanned births, which we obtained from the Liberia Demographic and Health Survey of 2019/2020 ( 18 ), plus all induced abortions. Intended pregnancies include planned births and miscarriages. In line with previous AICM studies, we estimated miscarriages to represent 20% of live births and 10% of induced abortions ( 16 , 25 ). Results There were an estimated 14,223 PAC cases treated in Liberian health facilities in 2021 (Table 2 ). Accounting for the underlying population of women of reproductive age, the national PAC treatment rate is 11.3 per 1,000 women aged 15–49. After removing second-trimester miscarriages, we estimate a total of 8,461 PAC cases were a result of an induced abortion. Table 2 Post-abortion care caseloads by region Region Total PAC caseloads (referrals removed) PAC treatment rate (caseloads per 1,000) PAC caseloads due to induced abortion Induced PAC treatment rate (per 1,000) Liberia total 14,223 11.3 8,461 6.7 North Central 3,444 8.0 1,157 2.7 North Western 1,835 17.7 1,254 12.1 South Central 7,347 12.7 5,243 9.1 South Eastern A 898 11.1 456 5.7 South Eastern B 699 9.6 351 4.8 The KIS multiplier for each region ranges from 2.4 in the North Central region to 5.5 in South Central (Table 3 ). After adjusting each regional PAC caseload by applying the corresponding multipliers, we estimate that a total of 38,779 induced abortions occurred in Liberia in 2021, corresponding to a national induced abortion incidence rate of 30.7 per 1,000 women aged 15–49. As such, the national PAC caseloads represented approximately 22% of all induced abortions in Liberia in 2021. There were significant regional variations, with the abortion incidence rate ranging from 6.6 per 1,000 in the North Central region to 49.7 per 1,000 in the South-Central region. Table 3 Induced abortion in Liberia, 2021 Region PAC cases due to induced abortion Multiplier Number of abortions Abortion rate per 1,000 women 15–49 Liberia total 8,461 -- 38,779 30.7 North Central 1,157 2.4 2,819 6.6 North Western 1,254 3.5 4,402 42.5 South Central 5,243 5.5 28,725 49.7 South Eastern A 456 3.5 1,601 19.9 South Eastern B 351 3.5 1,232 16.8 Accounting for all live births, miscarriages, and induced abortions, we estimate a total of 109,262 unintended pregnancies occurred in Liberia in 2021 (Table 4 ). This corresponds to a national unintended pregnancy rate of 86.5 per 1,000 (Table D). Close to half (44%) of all pregnancies in Liberia were unintended, with significant regional differences. The North Western region had the highest unintended pregnancy rate (118.0 per 1,000), while the largest proportion of unintended pregnancies was observed in South Central (55%). Nationally, we estimate that approximately 35% of all unintended pregnancies ended in an induced abortion in 2021. Table 4 Unintended pregnancy in Liberia, 2021 Region Number of unintended pregnancies Unintended pregnancy rate per 1,000 women 15–49 Proportion of pregnancies unintended Proportion of unintended pregnancies ending in abortion Liberia total 109,262 86.5 44% 35% North Central 28,308 66.0 34% 10% North Western 12,208 118.0 48% 36% South Central 57,658 99.8 55% 50% South Eastern A 6,312 78.3 36% 25% South Eastern B 4,776 65.3 35% 26% Discussion Our study is the first nationally representative study designed to estimate the incidence of unintended pregnancy and induced abortion in Liberia. Our findings reveal that induced abortion and unintended pregnancy are common experiences in Liberia. The study estimates of abortion incidence and unintended pregnancy rates are slightly lower than recently released model-based estimates for 2015–2019 (42 per 1,000 and 106 per 1,000, respectively) ( 26 ), but similar to earlier estimates for West at 31 per 1,000 WRA between 2010–2014 ( 7 ). However, that model relies heavily on estimates from neighboring countries when country-specific estimates are unavailable, which may have resulted in a slightly inflated estimate for Liberia. Further, we found significant variations in the abortion rate across regions in Liberia, with North Central having the lowest abortion rate (6.6/1000) and South-Central having the highest (49.7/1000). These variations in abortion rates could be reflective of the regional differences in access to services such as contraceptive and abortion care, including post-abortion care, but also of the preponderance towards abortions to navigate shame and stigma around unintended pregnancies ( 10 ). However, we note that the regional incidence rates must be cautiously interpreted because it does not necessarily mean that the people who had these induced abortions are domiciled in South Central; for example, it is possible that South Central (where the capital city is located) may see more induced abortions due to accessible, safe abortion services, whether performed formally or informally. Our findings also highlight the important limitations of existing indicators. This study’s estimate of facility-based PAC caseloads (14,223 PAC cases) is markedly higher than the official number reported to the government through the DHIS2 for the same year (2636, including PAC cases from induced abortions and miscarriages). This finding is unsurprising because there is a consensus that official records significantly underreport abortions ( 11 ). However, what we did not expect was the similarity of our findings to the most recent DHS, which reported that 33% of births were mistimed and 8% were unwanted. This is surprising, given known biases in self-reported abortion data ( 27 ). Some reasons why DHS underestimate unintended pregnancy include its use of the timing-based measure, which produces ambivalent responses and fails to consider the partner’s intention ( 28 ) and the nature of questions asked about pregnancy intentions and unwillingness to consider pregnancy as unwanted rather than mistimed ( 29 ). The unintended pregnancy rate of 86.5 per 1,000 is considerably higher than the subregional average (75 per 1,000) ( 26 ). The high unintended pregnancy rates in Liberia suggest that women and girls have a high unmet need for family planning. For instance, in 2020, one-third of married women had an unmet need for family planning (21% for spacing and 13% for limiting the number of children), and this gap increases among adolescents and young people ( 18 ). Other studies have summarized factors influencing existing rates of unintended pregnancy and abortion, including contraceptive failure because of incorrect and inconsistent use, unprotected sexual activity, and strong motivation to have small families among women and couples ( 30 ). The role of broad ecological factors such as urbanization, educational attainment, and the changing status and roles of women in society have also been documented ( 31 ). Further, connecting the high unintended pregnancy rate to the high abortion rate reinforces the well-established fact that legal restrictions do not necessarily reduce the occurrence of abortions, but instead drive women toward unsafe abortion methods likely to result in complications and death ( 32 ). Our study also indicates that most women seeking PAC services do so in public (67%) and primary-level health facilities (74%) compared to health centers and hospitals. Yet, despite these public and primary-level facilities being the dominant access points to women seeking PAC services in Liberia, they are most often the least equipped with trained staff, essential equipment, commodities, and supplies for PAC, thus impeding access to quality and comprehensive abortion-related services ( 33 ). Greater investment by the Liberian government in PAC provision at lower-level public facilities would markedly improve access to safe services. Our study has some limitations. The weaknesses of the AICM have been documented in previous research ( 16 ). In sum, we depended on facility-based estimates of PAC caseloads from a survey of providers assumed to have the best knowledge of PAC services in the selected health facilities. By selecting providers to take the interview on behalf of the facility, we assumed that the respondents could estimate the number of PAC cases accurately. However, the PAC caseload estimated at each facility is as good as the respondent’s knowledge of patient volume and memory. Because it is challenging to differentiate PAC due to induced and spontaneous abortion, we assumed that only late-stage miscarriages require facility-based care to enable us to separate the two since we have evidence of a biological relationship between late-term miscarriage and live births. Furthermore, this biological relationship was derived from a decades-old study conducted in the USA. This relationship between late-term miscarriage and live births may not be reflective of the Liberian context and may result in an over- or under-estimation of unintended pregnancy and abortion rates. Further, to account for abortions that do not result in complications or complications not treated in the facility (multiplier), we selected individuals from the community considered knowledgeable about abortion in Liberia for the KIS component. This approach creates a weakness because the information for calculating the multiplier is based on the informants’ subjective opinions. Future abortion incidence studies could consider triangulating data sources with respondent-driven sampling surveys of women who have had abortions to capture real-life experiences of abortions. In this study, we are unable to provide reasons for unintended pregnancies or abortions or why women seek abortions outside the healthcare facilities and only present after complications because the approach focuses on obtaining the estimates of incidence. Notwithstanding these limitations, there are no better alternatives to these assumptions and no better methodological alternative to the AICM. This study has several strengths. It provides the first nationwide estimates of unintended pregnancy and abortion and utilizes a well-established method, which allows a cross-national comparison of the findings. The finding provides estimates from which future studies in Liberia can make trend analysis; at the time of our study, there were no comprehensive estimates to facilitate decision-making, especially on methodological issues. We also believe that the estimates of abortion incidence and rates, generated directly from health facilities, represent a significant improvement on any other data sources existing before this study, and we could not compare our findings in a trend format because of that. Finally, our study comes at the most opportune time to feed into the legal and policy processes currently underway in Liberia. Conclusions Based on these findings, several recommendations targeting diverse stakeholder groups are warranted. The government of Liberia and its partners must continue to invest in the provision of contraceptives and ensure access to quality FP services and effective modern methods for all women and men, particularly to implement high-impact practices in family planning. It is also necessary to undertake community education and awareness on the burdens and dangers of unsafe abortion, current legal provisions on abortion, current access points for both safe abortion and PAC, stigma reduction, and awareness of the wide range of family planning methods available in Liberia. Of paramount importance is the improvement of the health system and health facilities to ensure that they have the necessary equipment and supplies, including medications and contraceptives, and trained personnel to provide quality sexual and reproductive health services, including abortion and post-abortion care. Expanding access to quality post-abortion care, including post-abortion contraceptive counseling and method provision at all health system levels, could improve uptake and reduce unintended pregnancies. This also includes strengthening the capacity of lower-level health facilities and mid-level providers to utilize appropriate uterine evacuation technologies, including MVA, misoprostol, and mifepristone/misoprostol combi pack, availing essential equipment and supplies, and ensuring pre-service and in-service training PAC providers. Abbreviations Abortion Incidence Complications Method AICM Comprehensive Abortion Care CAC Demographic and Health Survey DHS Family Planning FP Health Facility Survey HFS Knowledgeable Informant Interviews KIS Liberia Demographic and Health Survey LDHS Manual Vacuum Aspiration MVA Ministry of Health MOH Post abortion care–PAC Sexual and Reproductive Health SRH Sexual and Reproductive Health and Rights SRHR Declarations Ethics Approval and Consent to Participate The University of Liberia-Pacific Institute for Research and Evaluation Institutional Review Board (UL-PIRE) (now the Atlantic Center for Research and Evaluation (ACRE) Institutional Review Board, Protocol #21-07-275 approved the study. The Ministries of Health and Sanitation and the APHRC Institutional Review Board also reviewed and approved the study. All investigators on the team completed the human subjects’ protection training before engaging in the study. All respondents provided signed informed consent before participation. Consent for Publication Not applicable Competing Interests The authors declare that they have no competing interests. Funding The research was supported by a grant from the African Regional Office of the Swedish International Development Cooperation Agency, Sida (Contribution No. 12103), to the African Population and Health Research Center (APHRC) under the Challenging the Politics of Social Exclusion project. Author Contribution BU, KJ, and RO conceptualized the original study. BU, KJ, VD, LL, MM, and ND were primarily involved in the data collection. MG, JP, and AB performed data cleaning and analysis. BU and MG wrote the initial draft, and all authors reviewed, edited, and approved the final manuscript. Acknowledgement We appreciate the field teams' hard work and diligence in collecting the study data. We also thank all the respondents for their participation. We thank Esther Mutuku, who supported the data management processes. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Also, according to the APHRC policies (the organization hosting the datasets), all deidentified datasets will be publicly available on the APHRC microdata portal after three years (https://aphrc.org/microdata-portal/). References Center for Reproductive Rights. The World’s Abortion Law. New York; 2016. Penal Law. Penal Law - Title 26 - Liberian Code of Laws Revised [Internet], Volume. IV Liberia; 1978. http://www.liberlii.org/lr/legis/codes/plt26lcolr367 . World Health Organization. WHO-Country Cooperation Strategy 2018–2021 [Internet]. Geneva. 2019. https://www.afro.who.int/sites/default/files/2019-09/CCS_Liberia_ISBN_Final_18Sep2019_0.pdf . Ministry of Health L. Investment Case for Reproductive, Maternal, New-Born, Child, and Adolescent Health 2016–2020. Monrovia; 2016. 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Sully EA, Madziyire MG, Riley T, Moore AM, Crowell M, Nyandoro MT et al. Abortion in Zimbabwe: A national study of the incidence of induced abortion, unintended pregnancy and post-abortion care in 2016. PLoS One [Internet]. 2018;13(10):e0205239. https://doi.org/10.1371/journal.pone.0205239 . World Bank. World Development Report 2012 [Internet]. World Development Report. The World Bank. 2012. 426 p. https://doi.org/10.1596/978-0-8213-8810-5 . Frisch RE. Human Fertility: The Basic Components. Henri Leridon, Judith F, Helzner. Q Rev Biol [Internet]. 1978;53(4):500. https://doi.org/10.1086/410955 . Bearak JM, Popinchalk A, Beavin C, Ganatra B, Moller AB, Tunçalp Ö et al. Country-specific estimates of unintended pregnancy and abortion incidence: a global comparative analysis of levels in 2015–2019. BMJ Glob Heal [Internet]. 2022 Mar 1 [cited 2023 Mar 30];7(3):e007151. https://gh.bmj.com/content/7/3/e007151 . Moore, Gebrehiwot Y, Fetters T, Dibaba Y, Bankole, Singh S et al. The Estimated Incidence of Induced Abortion in Ethiopia, 2014: Changes in the Provision of Services Since 2008. Int Perspect Sex Reprod Health. 2016;42. Khan MM, Taylor S, Morry C, Sriram S, Demir I, Siddiqi M. How reliable is the asset score in measuring socioeconomic status? Comparing asset ownership reported by male and female heads of households. PLoS One [Internet]. 2023;18(2):e0279599. https://doi.org/10.1371/journal.pone.0279599 . Yargawa J, Machiyama K, Ponce Hardy V, Enuameh Y, Galiwango E, Gelaye K et al. Pregnancy intention data completeness, quality and utility in population-based surveys: EN-INDEPTH study. Popul Health Metr [Internet]. 2021;19(1):6. https://doi.org/10.1186/s12963-020-00227-y . Ayamolowo LB, Ayamolowo SJ, Adelakun DO, Adesoji BA. Factors influencing unintended pregnancy and abortion among unmarried young people in Nigeria: a scoping review. BMC Public Health [Internet]. 2024;24(1):1494. https://doi.org/10.1186/s12889-024-19005-8 . Beguy D, Mumah J, Gottschalk L. Unintended Pregnancies among Young Women Living in Urban Slums: Evidence from a Prospective Study in Nairobi City, Kenya. PLoS ONE. 2014;9:e101034. Bearak J, Popinchalk A, Ganatra B, Moller AB, Tunçalp Ö, Beavin C et al. Unintended pregnancy and abortion by income, region, and the legal status of abortion: estimates from a comprehensive model for 1990–2019. Lancet Glob Heal [Internet]. 2020 Sep 1 [cited 2023 Mar 30];8(9):e1152–61. http://www.thelancet.com/article/S2214109X20303156/fulltext . Owolabi O, Biddlecom A, Whitehead HS. Health systems’ capacity to provide post-abortion care: a multicountry analysis using signal functions. Lancet Glob Heal [Internet]. 2019;7(1):e110–8. https://doi.org/10.1016/S2214-109X(18)30404-2 . Additional Declarations No competing interests reported. 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Ushie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACAyA+UAFhszEkMNgAacbGAwS1nEFoSQNpaSCohQGuhYHhMJiFV4s5e+/BAwdq7OQYJNKfPXhQcd5ubfthoC01NtG4tFj2nEs4cOBYsjGDRI65QcKZ28nbziQCtRxLy23A5bAbOQaHP7AdSGyQyGGTSGy7nWwGZB9gbDiMV8uBA/8O1DcAHSaR+O9cstn5h0RoOdh2IIFBIsFMAmi+ndkNArZY9pwBaulLNmzjeWMmkXAsOcHsBtCWBDx+MWfvMf5w4JudPD97+jPJHzV29mbn0x8++FBjg1MLHLBB6USwygRCypGBPSmKR8EoGAWjYGQAAMRCaGt6qY2vAAAAAElFTkSuQmCC","orcid":"","institution":"Beshi King Development Services","correspondingAuthor":true,"prefix":"","firstName":"Boniface","middleName":"Ayanbekongshie","lastName":"Ushie","suffix":""},{"id":321669998,"identity":"b12278c4-2b30-4aad-ac14-fbee43c11438","order_by":1,"name":"Margaret Giorgio","email":"","orcid":"","institution":"Guttmacher Institute","correspondingAuthor":false,"prefix":"","firstName":"Margaret","middleName":"","lastName":"Giorgio","suffix":""},{"id":321669999,"identity":"7fec1644-4576-4ddf-91cd-3d2394bb864f","order_by":2,"name":"Kenneth Juma","email":"","orcid":"","institution":"African Population and Health Research Center","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Juma","suffix":""},{"id":321670000,"identity":"094eac47-924a-401c-9866-86b27ef717fc","order_by":3,"name":"Vekeh Donzo","email":"","orcid":"","institution":"Clinton Health Access Initiative","correspondingAuthor":false,"prefix":"","firstName":"Vekeh","middleName":"","lastName":"Donzo","suffix":""},{"id":321670003,"identity":"bd660bcc-7d52-439c-99fd-267e5a972d26","order_by":4,"name":"Jesse Philbin","email":"","orcid":"","institution":"Guttmacher Institute","correspondingAuthor":false,"prefix":"","firstName":"Jesse","middleName":"","lastName":"Philbin","suffix":""},{"id":321670006,"identity":"089c8ddc-2442-4bb0-a0be-6a9e47df35db","order_by":5,"name":"Lily Lu","email":"","orcid":"","institution":"Clinton Health Access Initiative","correspondingAuthor":false,"prefix":"","firstName":"Lily","middleName":"","lastName":"Lu","suffix":""},{"id":321670008,"identity":"8e5a3885-88af-4bd3-a75d-c3193d0fd828","order_by":6,"name":"Akinrinola Bankole","email":"","orcid":"","institution":"Guttmacher Institute","correspondingAuthor":false,"prefix":"","firstName":"Akinrinola","middleName":"","lastName":"Bankole","suffix":""},{"id":321670011,"identity":"8eb2120f-51fb-45d0-9b34-5b25eadd34c1","order_by":7,"name":"Moses Massaquoi","email":"","orcid":"","institution":"Clinton Health Access Initiative","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"","lastName":"Massaquoi","suffix":""},{"id":321670012,"identity":"9489d801-5ba8-429f-bf65-766421530787","order_by":8,"name":"Ramatou Ouedraogo","email":"","orcid":"","institution":"African Population and Health Research Center","correspondingAuthor":false,"prefix":"","firstName":"Ramatou","middleName":"","lastName":"Ouedraogo","suffix":""},{"id":321670022,"identity":"a011c913-f8c0-4eed-886f-c7d52bcfd8a2","order_by":9,"name":"Nelson Dunbar","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Nelson","middleName":"","lastName":"Dunbar","suffix":""}],"badges":[],"createdAt":"2024-06-17 19:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4595818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4595818/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83073169,"identity":"efc3188e-30d8-446d-b3bf-c14753d03ce8","added_by":"auto","created_at":"2025-05-19 17:16:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4595818/v1/85799104-2bc6-48b9-b27e-61848dfc0a67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating unintended pregnancy and induced abortion in Liberia: A nationally representative cross-sectional survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhile unsafe abortion and its associated complications are a leading public health concern in many parts of Africa, abortion is regulated by the criminal code rather than a public health framework in most countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In the context of Liberia, under the Liberian Penal Code (1978), Section 16.3, abortion is justified on the following grounds: to save the life of the woman; to preserve the physical health of the woman; to preserve the mental health of the woman; rape or incest or other felonious intercourse; and fetal impairment (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The Penal Code felony offences for contravening these provisions of the penal code.\u003c/p\u003e \u003cp\u003eThough preventable, unsafe abortions contribute an estimated 10% to Liberia’s maternal mortality - one of the highest globally at 1,072 deaths per 100,000 live births (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is also possible that this is an underestimate because of the clandestine nature of the practice; other notable contributors to maternal mortality in Liberia are hemorrhage (25%), pre-eclampsia (16%), and sepsis (10%) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). There is, also, evidence to suggest that providers in restrictive contexts use these diagnoses to conceal or deliberately misclassify cases of induced abortion (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For this reason, the number of abortion-related morbidities and mortalities is grossly underestimated in national health databases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, the main driver of induced abortion is unintended pregnancy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While modern and safe contraceptive methods exist for preventing unintended pregnancies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), there remain notable gaps in the provision and uptake of family planning services in Liberia, as only 23.9% of all reproductive-age women reported using modern contraceptives (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Consequently, unintended pregnancy in Liberia is among the highest in Africa, especially among adolescents and young women (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to being highly restrictive, induced abortion is highly stigmatized in Liberia, even when legally indicated. Both factors create diverse challenges in generating reliable data and estimates of the incidence of abortion, the conditions under which abortions take place and the burden of unsafe abortions, including the types and severity of complications (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). First, administrative health records on induced abortion are often inaccurate and incomplete. In addition, research has consistently shown that women responding to social surveys are likely to underreport experiences with abortion (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and women presenting with post-abortion complications in facilities often report induced abortions as miscarriages (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As a result of these difficulties, there is very little available evidence on abortion in Liberia. According to the 2007 Liberia Demographic and Health Survey (DHS), only 6% of women reported ever having had an induced abortion (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), yet estimates from recent global statistical models suggest that this is a dramatic underestimation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A population-based study in 2015 also attempted to measure lifetime induced abortion prevalence using an indirect method, finding that 32% of women reported ever having an abortion (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, limitations to the methods used make it difficult to determine the validity of this estimate or the frequency of abortion annually.\u003c/p\u003e \u003cp\u003eThe most recent legal reforms by the parliament of Liberia to the Public Health Law (Title 33) gained momentum in 2022, with proposed amendments including sections addressing the legality of induced abortion. This process requires sound, robust, and nationally representative data to inform and guide decision-makers in legal reforms that could reduce maternal morbidity and mortality through the availability of safe abortion services. This study provides the first nationally representative estimate of the incidence of induced abortion and unintended pregnancy in Liberia.\u003c/p\u003e \n\n \n\n \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch3\u003eStudy Design\u003c/h3\u003e\u003cp\u003eWe used the Abortion Incidence Complications Method (AICM) to estimate the 2021 incidence of induced abortion in Liberia (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The AICM is an indirect method of abortion measurement that has enjoyed considerable usage and acceptability across several geographies globally, including 11 countries in sub-Saharan Africa in the last decade (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Data for the study are drawn largely from two 2021 national cross-sectional surveys, namely, \u003cem\u003e1)\u003c/em\u003e the Health Facilities Survey (HFS), which involves health facility interviews with post-abortion care (PAC) service providers, and \u003cem\u003e2)\u003c/em\u003e a Knowledgeable Informant Survey (KIS), which was a survey of purposively selected key informants with extensive knowledge on abortion in their regions. The other data required for abortion incidence estimate using the AICM, such as the national population of women of reproductive age (with subnational distributions), age-specific fertility rates, and data on poverty or wealth distribution by subnational groupings and rural and urban locations, come from secondary sources, chiefly the national census or the Liberia Demographic and Health Survey.\u003c/p\u003e\u003ch2\u003eHealth Facility Survey\u003c/h2\u003e\u003cp\u003eWe collected data from a nationally representative sample of health facilities in Liberia that are capable of providing post-abortion care services. We used stratified random sampling to select facilities based on administrative region (South Central, North Western, North-Central, South Eastern A, and South Eastern B) and level of care (clinic, health center, and county hospital). In addition to public health facilities, there are private facilities, including private-for-profit, faith-based, and concession facilities across Liberia. We used the Liberia Ministry of Health’s (MOH) Master Facility List (obtained on May 31, 2021) to identify eligible public and private facilities. The Master List had a universe of 894 facilities, comprising clinics (802), health centers (59), and hospitals (34). We selected 132 health facilities for the HFS, which included 31 hospitals (91.2%), 41 health centers (69.4%), and 60 clinics (7.5%). One hundred and twenty-eight (128) (97%) of the sampled facilities completed the HFS (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We excluded four hospitals because one was burnt down during the study, two were specialized hospitals, and one refused participation.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLiberia Health Facilities Survey sample by region and facility type\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth center\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCounty hospital\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal facilities\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNational total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e128\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Western\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern A\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern B\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt each selected facility, we identified and interviewed a healthcare provider most knowledgeable about PAC. In large facilities, such as hospitals, we interviewed the head of the maternal health or labor or delivery unit or an obstetrician/gynecologist who oversees PAC services. In lower-level facilities, we interviewed nurses, midwives, officers in charge, or health workers who could provide information about abortion-related care in that facility. Each respondent was interviewed using a face-to-face structured questionnaire.\u003c/p\u003e\u003cp\u003eThe HFS collected retrospective estimates of the number of PAC patients (inpatient and outpatient) treated at each facility in the past month and in a typical month. Caseloads for past and typical months were collected to account for abortion seasonality. If the respondent could not give the monthly estimates, retrospective estimates of the past year and typical year were requested. Other information captured in the survey included the health facilities’ provision of PAC services, including PAC management approaches, provider knowledge of comprehensive abortion care (CAC, which is inclusive of safe abortion and PAC), and the provision of post-abortion family planning.\u003c/p\u003e\u003ch2\u003eKnowledgeable Informant Survey\u003c/h2\u003e\u003cp\u003eThe knowledgeable informant survey (KIS) involved in-person structured interviews with a sample of purposively selected respondents who were knowledgeable about the provision of abortion and PAC in the country (nationally or in specific regions). Informants were not restricted to clinicians providing direct CAC services to patients. Potential key informants were identified through purposeful and snowball sampling. The individuals recruited for the KIS were in the following roles: 1) Key national MOH and County Health Team (subnational) staff involved in SRH programs; 2) Heads/informed members of non-governmental organizations implementing SRH programs; 3) Heads/informed members of SRH advocacy groups, including civil society groups; 4) Researchers on SRHR from different regions of Liberia. Selected individuals were asked to list up to two referrals they believed were knowledgeable on the provision of abortion and PAC in Liberia.\u003c/p\u003e\u003cp\u003eA total of 89 key informants were interviewed on topics that included their perceptions regarding the type of providers women seek abortions from, the likelihood of women experiencing complications that require treatment in a facility according to the type of abortion provider and method used, and the likelihood that women who need treatment would receive it at a health facility. These questions were asked for four major sub-groups of women within the population: rural poor, rural non-poor, urban poor, and urban non-poor. This information was used to estimate the proportion of all induced abortions in Liberia that ended in a complication that was treated in a health facility. The inverse of this proportion is called the “multiplier”. This indicator represents, for every one woman who receives postabortion care in health facilities in Liberia, the additional number of women/girls who have induced abortions who either did not have a complication or had a complication that were not treated in the formal health system.\u003c/p\u003e\u003ch3\u003eOther Data Sources\u003c/h3\u003e\u003cp\u003eOther data sources used for calculating abortion incidence were the 2019/2020 Liberia Demographic and Health Survey (LDHS) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which provided information on fertility, contraceptive prevalence, unmet need for contraception, pregnancy wantedness, and measures of access to health care. We also drew from the 2021 population projection data for the number of women of reproductive age (15–49) (WRA) in the different regions of Liberia (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). We used the Poverty Equity Brief for Liberia 2021 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and the Global Multidimensional Poverty Index Liberia Country Briefing 2021 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) to estimate the proportion of poor/non-poor women in urban/rural settings in Liberia.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003ch2\u003eEstimating PAC caseloads\u003c/h2\u003e\u003cp\u003eWe followed the steps for estimating abortion incidence through the AICM approach described in Singh et al. (2019) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). First, we calculated the health facility PAC caseloads using the HFS data. We generated the annual PAC caseload in each facility by averaging the number of women treated in the past month and an average month for in-patients and outpatients and adding the two estimates. To create annual estimates, we then multiplied this number by 12. For facilities with low caseloads where respondents could not provide PAC estimates by past month or typical month, we averaged past year and typical year estimates for inpatients and outpatients. The resulting facility-level estimates were weighted to generate nationally representative data, accounting for sampling proportions and non-response. After this, we removed cases referred to other (usually higher-level) facilities to avoid double-counting PAC cases.\u003c/p\u003e\u003ch2\u003eAccounting for PAC caseload due to spontaneous abortion\u003c/h2\u003e\u003cp\u003eThe PAC caseload estimates at this point include those resulting from spontaneous and induced abortion because the AICM does not require HFS respondents to separate complications due to the two types of pregnancy loss. This practice is based on the understanding that complications from induced and spontaneous abortion have similar clinical presentations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). As such, the next step in the analysis involves removing cases due to spontaneous abortion. The AICM accounts for PAC cases that were due to spontaneous abortion by estimating the number of second-trimester miscarriages, as they are most likely to require facility-based treatment. Previous work has suggested that the number of second-trimester miscarriages in a population is approximately 3.41% of live births (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Therefore, we estimated the number of spontaneous abortions by taking 3.41% of all live births within each region. After subtracting the number of PAC cases due to miscarriages from the regional PAC caseload estimates, we weighted these new regional estimates to generate the total number of women treated in health facilities for complications resulting from induced abortions in Liberia in 2021.\u003c/p\u003e\u003ch2\u003eCalculating the multiplier and creating abortion incidence estimates\u003c/h2\u003e\u003cp\u003eThe next step is the creation of a multiplier. A detailed description of the steps for calculating the multiplier is provided elsewhere by Sully et al. (2018) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In brief, we used data from the KIS to multiply the proportion of women obtaining abortions from each provider type within each abortion method by the respective probabilities of experiencing complications from each provider type-abortion method combination. Next, we used information on the likelihood of receiving treatment for complications to create an estimate for the proportion of women who have an induced abortion and receive treatment for a complication. This is done separately for four different sub-populations within Liberia (urban poor, urban nonpoor, rural poor and rural nonpoor) to account for differences in abortion and treatment-seeking behaviors by women as a result of these factors. We then weighted these estimates by the population distribution of the four groups in each region using population census estimates for 2021 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and the poverty profiles for Liberia by region (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which resulted in estimates of the proportion of all abortions that resulted in PAC treatment in a facility in each region. The multiplier for each region is the inverse of this proportion.\u003c/p\u003e\u003cp\u003eApplying the regional multiplier to the regional estimates of PAC cases due to induced abortion provides the total estimated number of women who obtained an induced abortion in Liberia in 2021. Using data on the population of women of reproductive age in Liberia (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), we calculated the abortion incidence (number of induced abortions per year), abortion rate (number of induced abortions per 1,000 women of reproductive age [15–49]), and abortion ratio (number of induced abortions per 1,000 live births).\u003c/p\u003e\u003ch2\u003eUnintended pregnancy estimation\u003c/h2\u003e\u003cp\u003eFinally, we used the total number of abortions to estimate the number and rate of pregnancies and the unintended pregnancy rates. The prevalence of intended pregnancy was calculated as the total number of unintended pregnancies divided by the total number of all pregnancies in Liberia in 2021. To calculate the total number of unintended pregnancies, we assumed that all induced abortions were from unintended pregnancies. We defined unintended pregnancies to be composed of all unplanned births, which we obtained from the Liberia Demographic and Health Survey of 2019/2020 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), plus all induced abortions. Intended pregnancies include planned births and miscarriages. In line with previous AICM studies, we estimated miscarriages to represent 20% of live births and 10% of induced abortions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere were an estimated 14,223 PAC cases treated in Liberian health facilities in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Accounting for the underlying population of women of reproductive age, the national PAC treatment rate is 11.3 per 1,000 women aged 15\u0026ndash;49. After removing second-trimester miscarriages, we estimate a total of 8,461 PAC cases were a result of an induced abortion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePost-abortion care caseloads by region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal PAC caseloads (referrals removed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePAC treatment rate (caseloads per 1,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePAC caseloads due to induced abortion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInduced PAC treatment rate (per 1,000)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiberia total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14,223\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8,461\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Western\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe KIS multiplier for each region ranges from 2.4 in the North Central region to 5.5 in South Central (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting each regional PAC caseload by applying the corresponding multipliers, we estimate that a total of 38,779 induced abortions occurred in Liberia in 2021, corresponding to a national induced abortion incidence rate of 30.7 per 1,000 women aged 15\u0026ndash;49. As such, the national PAC caseloads represented approximately 22% of all induced abortions in Liberia in 2021. There were significant regional variations, with the abortion incidence rate ranging from 6.6 per 1,000 in the North Central region to 49.7 per 1,000 in the South-Central region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInduced abortion in Liberia, 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAC cases due to induced abortion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiplier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of abortions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbortion rate per 1,000 women 15\u0026ndash;49\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiberia total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8,461\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e--\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e38,779\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e30.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Western\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28,725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccounting for all live births, miscarriages, and induced abortions, we estimate a total of 109,262 unintended pregnancies occurred in Liberia in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This corresponds to a national unintended pregnancy rate of 86.5 per 1,000 (Table D). Close to half (44%) of all pregnancies in Liberia were unintended, with significant regional differences. The North Western region had the highest unintended pregnancy rate (118.0 per 1,000), while the largest proportion of unintended pregnancies was observed in South Central (55%). Nationally, we estimate that approximately 35% of all unintended pregnancies ended in an induced abortion in 2021.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnintended pregnancy in Liberia, 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of unintended pregnancies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnintended pregnancy rate per 1,000 women 15\u0026ndash;49\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion of pregnancies unintended\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProportion of unintended pregnancies ending in abortion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiberia total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e109,262\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e86.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e44%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e35%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28,308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Western\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Eastern B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is the first nationally representative study designed to estimate the incidence of unintended pregnancy and induced abortion in Liberia. Our findings reveal that induced abortion and unintended pregnancy are common experiences in Liberia. The study estimates of abortion incidence and unintended pregnancy rates are slightly lower than recently released model-based estimates for 2015\u0026ndash;2019 (42 per 1,000 and 106 per 1,000, respectively) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), but similar to earlier estimates for West at 31 per 1,000 WRA between 2010\u0026ndash;2014 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, that model relies heavily on estimates from neighboring countries when country-specific estimates are unavailable, which may have resulted in a slightly inflated estimate for Liberia. Further, we found significant variations in the abortion rate across regions in Liberia, with North Central having the lowest abortion rate (6.6/1000) and South-Central having the highest (49.7/1000). These variations in abortion rates could be reflective of the regional differences in access to services such as contraceptive and abortion care, including post-abortion care, but also of the preponderance towards abortions to navigate shame and stigma around unintended pregnancies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, we note that the regional incidence rates must be cautiously interpreted because it does not necessarily mean that the people who had these induced abortions are domiciled in South Central; for example, it is possible that South Central (where the capital city is located) may see more induced abortions due to accessible, safe abortion services, whether performed formally or informally.\u003c/p\u003e \u003cp\u003eOur findings also highlight the important limitations of existing indicators. This study\u0026rsquo;s estimate of facility-based PAC caseloads (14,223 PAC cases) is markedly higher than the official number reported to the government through the DHIS2 for the same year (2636, including PAC cases from induced abortions and miscarriages). This finding is unsurprising because there is a consensus that official records significantly underreport abortions (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, what we did not expect was the similarity of our findings to the most recent DHS, which reported that 33% of births were mistimed and 8% were unwanted. This is surprising, given known biases in self-reported abortion data (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Some reasons why DHS underestimate unintended pregnancy include its use of the timing-based measure, which produces ambivalent responses and fails to consider the partner\u0026rsquo;s intention (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and the nature of questions asked about pregnancy intentions and unwillingness to consider pregnancy as unwanted rather than mistimed (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe unintended pregnancy rate of 86.5 per 1,000 is considerably higher than the subregional average (75 per 1,000) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The high unintended pregnancy rates in Liberia suggest that women and girls have a high unmet need for family planning. For instance, in 2020, one-third of married women had an unmet need for family planning (21% for spacing and 13% for limiting the number of children), and this gap increases among adolescents and young people (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Other studies have summarized factors influencing existing rates of unintended pregnancy and abortion, including contraceptive failure because of incorrect and inconsistent use, unprotected sexual activity, and strong motivation to have small families among women and couples (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The role of broad ecological factors such as urbanization, educational attainment, and the changing status and roles of women in society have also been documented (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Further, connecting the high unintended pregnancy rate to the high abortion rate reinforces the well-established fact that legal restrictions do not necessarily reduce the occurrence of abortions, but instead drive women toward unsafe abortion methods likely to result in complications and death (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study also indicates that most women seeking PAC services do so in public (67%) and primary-level health facilities (74%) compared to health centers and hospitals. Yet, despite these public and primary-level facilities being the dominant access points to women seeking PAC services in Liberia, they are most often the least equipped with trained staff, essential equipment, commodities, and supplies for PAC, thus impeding access to quality and comprehensive abortion-related services (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Greater investment by the Liberian government in PAC provision at lower-level public facilities would markedly improve access to safe services.\u003c/p\u003e \u003cp\u003eOur study has some limitations. The weaknesses of the AICM have been documented in previous research (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In sum, we depended on facility-based estimates of PAC caseloads from a survey of providers assumed to have the best knowledge of PAC services in the selected health facilities. By selecting providers to take the interview on behalf of the facility, we assumed that the respondents could estimate the number of PAC cases accurately. However, the PAC caseload estimated at each facility is as good as the respondent\u0026rsquo;s knowledge of patient volume and memory. Because it is challenging to differentiate PAC due to induced and spontaneous abortion, we assumed that only late-stage miscarriages require facility-based care to enable us to separate the two since we have evidence of a biological relationship between late-term miscarriage and live births. Furthermore, this biological relationship was derived from a decades-old study conducted in the USA. This relationship between late-term miscarriage and live births may not be reflective of the Liberian context and may result in an over- or under-estimation of unintended pregnancy and abortion rates. Further, to account for abortions that do not result in complications or complications not treated in the facility (multiplier), we selected individuals from the community considered knowledgeable about abortion in Liberia for the KIS component. This approach creates a weakness because the information for calculating the multiplier is based on the informants\u0026rsquo; subjective opinions. Future abortion incidence studies could consider triangulating data sources with respondent-driven sampling surveys of women who have had abortions to capture real-life experiences of abortions. In this study, we are unable to provide reasons for unintended pregnancies or abortions or why women seek abortions outside the healthcare facilities and only present after complications because the approach focuses on obtaining the estimates of incidence. Notwithstanding these limitations, there are no better alternatives to these assumptions and no better methodological alternative to the AICM.\u003c/p\u003e \u003cp\u003eThis study has several strengths. It provides the first nationwide estimates of unintended pregnancy and abortion and utilizes a well-established method, which allows a cross-national comparison of the findings. The finding provides estimates from which future studies in Liberia can make trend analysis; at the time of our study, there were no comprehensive estimates to facilitate decision-making, especially on methodological issues. We also believe that the estimates of abortion incidence and rates, generated directly from health facilities, represent a significant improvement on any other data sources existing before this study, and we could not compare our findings in a trend format because of that. Finally, our study comes at the most opportune time to feed into the legal and policy processes currently underway in Liberia.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on these findings, several recommendations targeting diverse stakeholder groups are warranted. The government of Liberia and its partners must continue to invest in the provision of contraceptives and ensure access to quality FP services and effective modern methods for all women and men, particularly to implement high-impact practices in family planning. It is also necessary to undertake community education and awareness on the burdens and dangers of unsafe abortion, current legal provisions on abortion, current access points for both safe abortion and PAC, stigma reduction, and awareness of the wide range of family planning methods available in Liberia. Of paramount importance is the improvement of the health system and health facilities to ensure that they have the necessary equipment and supplies, including medications and contraceptives, and trained personnel to provide quality sexual and reproductive health services, including abortion and post-abortion care. Expanding access to quality post-abortion care, including post-abortion contraceptive counseling and method provision at all health system levels, could improve uptake and reduce unintended pregnancies. This also includes strengthening the capacity of lower-level health facilities and mid-level providers to utilize appropriate uterine evacuation technologies, including MVA, misoprostol, and mifepristone/misoprostol combi pack, availing essential equipment and supplies, and ensuring pre-service and in-service training PAC providers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAbortion Incidence Complications Method\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAICM\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eComprehensive Abortion Care\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCAC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDemographic and Health Survey\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDHS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFamily Planning\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHealth Facility Survey\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHFS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKnowledgeable Informant Interviews\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKIS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLiberia Demographic and Health Survey\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLDHS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eManual Vacuum Aspiration\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMVA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMinistry of Health\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMOH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePost\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eabortion care\u0026ndash;PAC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSexual and Reproductive Health\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSRH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSexual and Reproductive Health and Rights\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSRHR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003e The University of Liberia-Pacific Institute for Research and Evaluation Institutional Review Board (UL-PIRE) (now the Atlantic Center for Research and Evaluation (ACRE) Institutional Review Board, Protocol #21-07-275 approved the study. The Ministries of Health and Sanitation and the APHRC Institutional Review Board also reviewed and approved the study. All investigators on the team completed the human subjects\u0026rsquo; protection training before engaging in the study. All respondents provided signed informed consent before participation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe research was supported by a grant from the African Regional Office of the Swedish International Development Cooperation Agency, Sida (Contribution No. 12103), to the African Population and Health Research Center (APHRC) under the Challenging the Politics of Social Exclusion project.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBU, KJ, and RO conceptualized the original study. BU, KJ, VD, LL, MM, and ND were primarily involved in the data collection. MG, JP, and AB performed data cleaning and analysis. BU and MG wrote the initial draft, and all authors reviewed, edited, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe appreciate the field teams' hard work and diligence in collecting the study data. We also thank all the respondents for their participation. We thank Esther Mutuku, who supported the data management processes.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Also, according to the APHRC policies (the organization hosting the datasets), all deidentified datasets will be publicly available on the APHRC microdata portal after three years (https://aphrc.org/microdata-portal/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCenter for Reproductive Rights. The World\u0026rsquo;s Abortion Law. New York; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePenal Law. Penal Law - Title 26 - Liberian Code of Laws Revised [Internet], Volume. IV Liberia; 1978. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.liberlii.org/lr/legis/codes/plt26lcolr367\u003c/span\u003e\u003cspan address=\"http://www.liberlii.org/lr/legis/codes/plt26lcolr367\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO-Country Cooperation Strategy 2018\u0026ndash;2021 [Internet]. Geneva. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afro.who.int/sites/default/files/2019-09/CCS_Liberia_ISBN_Final_18Sep2019_0.pdf\u003c/span\u003e\u003cspan address=\"https://www.afro.who.int/sites/default/files/2019-09/CCS_Liberia_ISBN_Final_18Sep2019_0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health L. Investment Case for Reproductive, Maternal, New-Born, Child, and Adolescent Health 2016\u0026ndash;2020. Monrovia; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoseson H, Massaquoi M, Dehlendorf C, Bawo L, Dahn B, Zolia Y et al. Reducing under-reporting of stigmatized health events using the List Experiment: results from a randomized, population-based study of abortion in Liberia. 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Lancet Glob Heal [Internet]. 2020 Sep 1 [cited 2023 Mar 30];8(9):e1152\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.thelancet.com/article/S2214109X20303156/fulltext\u003c/span\u003e\u003cspan address=\"http://www.thelancet.com/article/S2214109X20303156/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwolabi O, Biddlecom A, Whitehead HS. Health systems\u0026rsquo; capacity to provide post-abortion care: a multicountry analysis using signal functions. Lancet Glob Heal [Internet]. 2019;7(1):e110\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(18)30404-2\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(18)30404-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Abortion Incidence Complications Method, unintended pregnancy, Liberia, post-abortion care","lastPublishedDoi":"10.21203/rs.3.rs-4595818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4595818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e More than 60% of unintended pregnancies end in induced abortions globally. In Liberia, limited access to contraceptives caused by health system challenges and sociocultural barriers leads to many unintended pregnancies. Nevertheless, very little is known about the incidence of induced abortion and unintended pregnancy in the country. This study aimed to estimate the incidence of induced abortion and unintended pregnancy in Liberia in 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study utilized the Abortion Incidence Complications Method. First, we estimated the total number of induced abortions that resulted in women seeking facility-based post-abortion care in 2021 using data from a nationally representative sample of health facilities (n=128). Next, we used data from a survey of experts on abortion in Liberia (n=89) to estimate the proportion of induced abortions that resulted in complications treated in health facilities in 2021. The inverse of this proportion is a multiplier, which was applied to the estimate of the total number of induced abortion-related complications treated in health facilities to obtain the total number of induced abortions in 2021. We complemented this estimate of induced abortion with the Demographic Health Survey data to estimate the incidence of unintended pregnancies in Liberia in 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We estimated that 14,555 patients received post-abortion (PAC) treatment in Liberia in 2021. Approximately 58% of these post-abortion care cases (n=8,461) were due to induced abortions. Accounting for induced abortions that had complications that did not require facility-based treatment and those without complications, we estimated that 38,779 induced abortions occurred in Liberia in 2021, corresponding to a national induced abortion incidence rate of 30.7 per 1,000 women of reproductive age (15–49). The unintended pregnancy rate among women of reproductive age was 86.54 per 1,000, and approximately 35% of all pregnancies ended in an abortion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Despite the legal restrictions on abortions, study findings show that unintended pregnancy and induced abortion are common in Liberia. There is a need for strengthened efforts to increase access to contraceptives, safe abortion care under legal indications, and quality PAC to improve socioeconomic and health outcomes for women and girls in Liberia.\u003c/p\u003e","manuscriptTitle":"Estimating unintended pregnancy and induced abortion in Liberia: A nationally representative cross-sectional survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-11 16:51:55","doi":"10.21203/rs.3.rs-4595818/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1901f01c-fc6a-48a7-a546-1f69ec5b524c","owner":[],"postedDate":"July 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T17:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-11 16:51:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4595818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4595818","identity":"rs-4595818","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}