The severity and management of postabortion care complications in Liberia

The severity and management of postabortion care complications in Liberia | 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 The severity and management of postabortion care complications in Liberia Margaret M Giorgio, Boniface Ayanbekongshie Ushie, Kenneth Juma, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4757559/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 : Complications from unsafe abortion are a major contributor to maternal morbidity and mortality in resource poor settings. This study aims to assess the severity and management of abortion complications in Liberia. Methods : Data were collected among a nationally representative sample of health facilities in Liberia (n=100). Study staff administered a survey to all postabortion care (PAC) patients and their health providers arriving at a study facility over the course of 30 days. A total of 387 patient surveys and 429 provider surveys were included in the final analysis. Postabortion complication severity was classified into five categories, ranging from mild to near miss. Likely induced abortions were identified though patient self-reports reports and provider reports of clinical evidence of a foreign body or mechanical injury. We conducted bivariate tests to determine whether PAC management practices varied by complication severity. Poisson regression models were used to assess the relationship between patient characteristics and complication severity, as well as between complication severity and receipt of a family planning method. Results : Overall, 10.9% of PAC patients were classified as near miss and 34.7% had severe complications. Likely induced abortions were identified in 38% of women. Having a more severe complication was associated with marital status (uIRR 0.69, 95% CI 0.48,0.99) and the indicator for likely induced abortion (uIRR 1.74, 95% CI 1.11,2.74). Most women accessed PAC at primary level facilities (76.7%). The most common uterine evacuation methods were MVA (67.5%) and misoprostol (16.3%). Only 38.9% of patients received a family planning method prior to discharge. Controlling for age, residence, future intentions to use, and provider type, women classified as having severe or near miss complications were approximately twice as likely (aIRR 1.98, 95% CI 1.18,3.32) to receive a contraceptive method. Conclusions : Postabortion complications are a major public health concern in Liberia. Our results underscore the need for high quality postabortion care and greater access to safe abortion care. Liberia should invest in primary level facilities and strengthen their ability to manage postabortion complications. Future research is needed to understand how provider practices/attitudes shape the provision of postabortion family planning services. Post-abortion care induced abortion complication severity maternal morbidity and mortality family planning services Introduction Complications that arise from pregnancy termination, either due to spontaneous abortion or induced abortion, are a major driver of maternal morbidity and mortality in many resource-limited settings. The World Health Organization (WHO) estimated that in 2020 the maternal mortality ratio (MMR) for sub-Saharan Africa (SSA) was 536 maternal deaths per 100,000 live births.[ 1 ] While maternal death is the most severe outcome of pregnancy-related complications, women often experience less severe but significant health events that can have short or long-term consequences for their health and the well-being of their families.[ 2 , 3 ] In 2009, the WHO introduced the concept of ‘maternal near miss’, defined as a woman who nearly died but survived a complication that occurred during pregnancy, childbirth, or within 42 days of termination of pregnancy.[ 4 ] A recent review of the literature suggests that in settings where access to safe abortion care is limited, approximately 1 in 10 postabortion care hospital admissions were a near-miss event.[ 5 ] In recent years, several studies have investigated a broad array of maternal outcomes, ranging from mild complications to near miss to death,[ 6 – 9 ] and the evidence from this research has been a useful tool for understanding and improving the quality of postabortion care. While induced abortions rarely result in medical complications when conducted in accordance with internationally accepted standards, [ 10 ] it is estimated that only 24% of abortions that occur across Africa would be classified as safe (i.e., performed by a trained provider using a recommended method).[ 11 ] Abortion-related stigma, abortion under-reporting, and incomplete or unreliable medical record data make it difficult to generate reliable estimates of the relationship between unsafe abortion and maternal outcomes in many settings, particularly in those settings where abortion is highly legally restrictive. A recent report suggests that the SSA region has the highest rate of abortion-related deaths globally, at 185 deaths per 100,000 abortions, although this may be an underestimate.[ 12 ] Similar global or regional estimates for unsafe abortion’s impact on maternal morbidity are not available due to the measurement and documentation challenges described above. However, a small number of country-specific and sub-national studies have found a relationship between postabortion care patients who have likely had an induced abortion and more severe postabortion complications.[ 8 , 13 ] In the context of Liberia, maternal morbidity and mortality is a major public health concern. The maternal mortality ratio for 2020 is estimated to be 652 per 100,000 live births, representing one of the highest rates in the region.[ 1 ] However, to date there are no national estimates for the severity of postabortion complications or the frequency of near-miss cases in Liberia. Women have limited access to safe abortion care in Liberia; the 1978 Offenses Against the Family Penal Law only allows abortion when there is a risk to the life, physical health or mental health of the pregnant woman, or if the pregnancy is a result of rape or incest, or in the case of fetal impairment.[ 14 ] Despite these legal restrictions, induced abortion is a common occurrence in Liberia; a recent study estimated a one-year abortion incidence rate of 30.7 per 1,000 women aged 15–49 in 2021.[ 15 ] According to the 2013 demographic and health survey in Liberia (LDHS), approximately 10% of maternal deaths in the country are due to unsafe abortion, and it is unknown if this estimate has changed in the past decade.[ 16 ] Further, while two recent studies have provided some evidence of the existence of unsafe abortion among postabortion care patients in Liberia, this data was only collected retrospectively in a small number of hospitals and was limited by incomplete documentation of medical records.[ 17 , 18 ] In light of these gaps in knowledge, the goal of this paper is to investigate the severity of postabortion complications among patients accessing postabortion care within nationally representative sample of health facilities in Liberia. In addition, we describe how these postabortion complications are managed by the Liberian health system. Finally, we investigate the likelihood that women seeking postabortion care in our sample did so as a result of an induced abortion. Methods Sampling and data collection Data from this analysis come from a larger study investigating the incidence of induced abortion and unintended pregnancy in Liberia in 2021.[ 15 ] That study included a Health Facility Survey (HFS), which was administered among a nationally representative sample of health facilities capable of providing postabortion care (PAC) services. A detailed description of the sampling strategy is described elsewhere.[ 15 ] In brief, the study team sampled a total of 132 facilities for the HFS, which represents 100% of hospitals that provide PAC in Liberia in 2021, 84% of all health centers, and 7.5% of all clinics. The response rate for the survey was 97%, which corresponded to a final sample of 128 facilities. The Prospective Morbidity Survey (PMS), from which the data for this analysis are derived, aimed to include all 128 facilities that participated in the HFS. To participate in the PMS, each facility was required to send at least one health provider (typically a nurse, midwife or a physician assistant), to attend a week-long training in Monrovia, which covered topics such as the study aims and rationale, research ethics, how to conduct interviews, and the specific study instruments. Overall, 78% of the HFS facilities (n = 100) met this eligibility criteria and participated in the PMS. Data were collected prospectively over 30 days in each participating facility. All patients who arrived at the facility during the study period seeking treatment for postabortion complications and whose pregnancy gestation was 28 weeks or lower were eligible to participate in the study, with the exception of women who presented with ectopic pregnancies, molar pregnancies, or blighted ovum. Study enumerators worked with the providers at each facility to determine whether each patient was eligible to participate. The PMS consisted of two surveys. The first was a survey of postabortion care patients that captured data on their socio-demographic characteristics, reproductive histories, experiences accessing and receiving care, as well as the circumstances surrounding the abortion (spontaneous or induced) that led them to seek care. The second survey was administered to the patient’s health provider and captured the clinical details of the patient’s case. After the study enumerator determined eligibility, PAC patients were taken through the informed consent process and were asked to consent separately for the patient survey as well as allowing their provider to be interviewed in the provider survey. In the event of a maternal death, the study team received permission from the ethical review board to interview the woman’s provider in order to capture her clinical details. During the study period, a total of 455 women who met the study eligibility criteria presented at one of the selected study facilities for PAC. Of these, 390 provided consent for both the patient and provider surveys (response rate = 86%). An additional 11 patients agreed to participate in the patient survey but did not provide consent for the provider survey, resulting in a final patient sample of 401. Further, 40 patients declined the patient interviewed but allowed study staff to interview their provider, and there were 2 PAC patient deaths during the course of the study. This resulted in a total of 432 provider surveys. During the analysis phase, we identified 3 cases where the patient did not meet the study’s clinical eligibility criteria. As such, the final samples for this study are 387 for the patient survey and 429 for the provider survey. We created composite facility-level weights by multiplying the sampling weights by the facility- level non-response weights. This approach allowed us to appropriately adjust for the complexities associated with our sampling design and the potential biases introduced by non-response. Ethical approval for this study was received from 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; the Clinton Health Access Initiative’s internal Scientific and Ethical Review Committee (SERC); and the Institutional Review Board of the African Population and Health Research Center. All study investigators completed the human subjects’ protection training before engaging in the study. Measures Patient sociodemographic characteristics and reproductive health histories We collected data on the patient’s age, marital status, highest level of education completed, and area of residence (urban, peri-urban, or rural). Women also provided information on the total number of pregnancies and live births they have experienced in their lifetime. We also collected data on pregnancy intentions before conceiving the pregnancy for which the patient was seeking postabortion care. Women were asked whether at the time they became pregnant, whether they wanted to be pregnant, they wanted to be pregnant at that time but then later changed their minds, or they did not want to be pregnant at all. Clinical indicators and the severity of postabortion complications To measure the severity of abortion complications, we adapted a categorization approach that has been used in several prior studies on abortion-related complications.[ 6 – 8 ] Using data from the provider survey, each PAC patient’s clinical signs and symptoms, diagnosis, and interventions were examined to generate five hierarchically distinct categories of complications – mild, moderate, severe, near-miss, or death. Box 1 describes the specific definitions used for this analysis. Women were classified as “near miss” if they experienced any of the following: hemorrhagic or septic shock, generalized peritonitis, uterine perforation with an accompanying surgical repair or procedure, or system or organ failure. Previous studies have also included massive blood transfusion (defined as the provision of two or more units of blood) as part of the near miss definition.[ 9 ] However, the PMS instrument in this study only measured whether a blood transfusion occurred and not the amount of blood given. As such, we were unable to include this criterion in our severity definition. Women with severe complications were those who experienced a least one of the following symptoms or diagnoses: temperature ≥ 39°C or ≤ 35°C and a clinical sign of infection, sepsis/septicemia with no signs of septic shock, pelvic abscess or pelvic peritonitis with no sign of shock, uterine perforation without surgical repair or procedure, or anemia with the presence of hemorrhage and blood transfusion. Moderate severity is characterized by the experience of at least one of the following: elevated temperature (37.3°C-38.9°C), clinical signs of infection, or hemorrhage that did not require blood transfusion. Finally, women classified as having mild severity must meet all of the following criteria: temperature 35.1°C-38.9°C with no clinical signs of infection, systolic blood pressure ≥ 90mm Hg, no hemorrhage OR hemorrhage not requiring blood transfusion, and having been discharged from care in a stable state. Management of postabortion patients We investigated several indicators of PAC clinical management and treatment by the severity of postabortion complications. First, we determined methods of evacuation, which were either manual or electric vacuum aspiration (MVA/EVA), other non-surgical uterine evacuation (i.e. digital evacuation), the use of misoprostol, dilatation and curettage (D&C), or none. We also investigate the provider type (OBGYN or other general physician vs. lower-level providers such as nurses, midwives, and physician’s assistants), length of stay in the hospital, gestational age at the time the pregnancy was terminated, and whether the patient received contraceptive counseling and/or a contraceptive method before the completion of care. Evidence of an induced abortion It is widely documented that women often underreport experiences with induced abortion when asked directly.[ 19 , 20 ] As such, solely relying on women’s direct reports would likely underestimate the true prevalence of induced abortion in our sample. To address this issue, several previous studies have employed a WHO developed algorithm to help determine the likelihood that PAC patients have experienced an induced abortion.[ 8 , 21 – 23 ] Using this approach, abortion definitions include certain induced abortion, probably induced abortion, possible induced abortion, and likely spontaneous abortion. Classification into one of these definitions depends on different combinations of the following indicators: evidence of foreign bodies or trauma to the vagina, evidence of sepsis or peritonitis, provider perceptions that the abortion was induced, patient reports that the abortion was induced, and the intendedness of the pregnancy.[ 21 ] However, this approach could overestimate the existence of induced abortion for several reasons: provider perceptions may be inappropriately influenced by stereotypes about the demographic profile of women who induce abortions; many unintended pregnancies will result in miscarriage given the high prevalence of spontaneous abortion in pregnancy; and sepsis and peritonitis can commonly occur after a miscarriage if women experience delays in receiving care or receive poor quality care. Therefore, this study uses a more conservative approach and defines induced abortion in our sample using the WHO’s criteria for certain abortion: either the patient reported that she did something to intentionally end the pregnancy and/or the provider reported clinical evidence of a foreign body or mechanical injury (e.g., lacerated cervix). Analysis We first present descriptive statistics for our study sample, as well as for the distribution of the abortion complication severity indicator. Next, we investigate whether any sociodemographic characteristics are associated with complication severity by calculating unadjusted incidence rate ratios (uIRRs) and 95% confidence intervals from Poisson regression models estimating the relationship between selected patient characteristics and experiencing severe or near miss complications (vs. experiencing moderate or mild complications.) We also present descriptive statistics for indicators describing the management of postabortion care and conduct bivariate tests of significance to determine whether PAC management practices vary by the level of complication severity. Our final set of analyses focuses on a specific component of PAC management: the provision of postabortion care family planning. To understand whether the receipt of a family planning method varies by the level of a patient’s complication severity, we first identify which patient and facility characteristics are also associated with receiving a method at the end of care. We then construct Poisson regression models for the relationship between complication severity and family planning method receipt, controlling for patient and facility-level characteristics. Results Table 1 displays the unweighted descriptive statistics for women who responded to the patient survey. Approximately 1 in 5 (21.2%, n = 82) PAC clients were adolescents between the ages of 12 and 19, and half of patients were between the ages of 20 and 29 (49.6%, n = 192). The majority of patients were either currently married or cohabitating with a partner (74.2%, n = 285). Approximately 20% of patients (n = 79) had no formal education, 10.6% (n = 41) had completed a level of education greater than high school, and only 27.4% (n = 106) lived in a rural area. Approximately one quarter of PAC patients (24.3%, n = 94) reported that the pregnancy that brought them to the facility was their first, and 30.5% (n = 118) reported they had never given birth. Table 1 Sociodemographic characteristics of the PMS sample and characteristics of the pregnancy that led the respondent to seek PAC (n = 387)♠ Sociodemographic characteristics Age, %(n) 12–19 21.2% (82) 20–24 26.1% (101) 25–29 23.5% (91) 30–34 14.5% (56) 35+ 14.7% (57) Married/cohabitating, %(n) 74.2% (285) Highest level of education No schooling 20.4% (79) Primary school 35.1% (136) High school 33.9% (131) Greater than high school 10.6% (41) Residence, %(n) Urban 43.4% (168) Peri-urban 29.2% (113) Rural 27.4% (106) Number of pregnancies 1 24.3% (94) 2–3 34.1% (132) 4+ 41.6% (161) Number of children Never given birth 30.5% (118) 1–3 children 51.4% (199) 4 + children 18.1% (70) Characteristics of pregnancy that led respondent to seek PAC Intention status Intended 56.3% (218) Wanted to get pregnant but then later changed my mind 5.4% (21) Did not want to get pregnant 38.2% (148) Evidence of induced abortion 37.5% (161) Self-reported induced abortion 26.6% (103) Evidence of a foreign body ** 17.5% (75) ♠ Unweighted n’s and percentages shown **Denominator = 429, as includes evidence from the provider survey Just over half of women (56.3%, n = 148) reported that their current pregnancy was intended, 5.4% (n = 21) reported that they wanted to get pregnant at the time but later changed their mind, and 38.2% (n = 148) reported that they did not want to get pregnant at the time of conception. One in four women (26.6%, n = 103) reported having done something to intentionally end their pregnancy. Further, providers indicated that there was evidence of a foreign body or mechanical injury in 17.5% of cases for which there was clinical data (n = 75 out of 429 total cases). After combining these indicators, we identified 161 cases (37.5%) that had at least one of these criteria and were therefore classified as postabortion care as a result of an induced abortion. The distribution of the severity of postabortion complications is presented in Table 2 . After applying facility-level weights, we estimate that approximately 34.7% (95% CI 26.1, 44.6) of postabortion care patients in Liberia are classified as having severe complications and 10.9% (95% CI 6.3, 18.2) are near miss. Two women in our sample died, corresponding to an estimated less than 0.1% of PAC patients nationally. Approximately 1 in 4 women (24.2%, 95% CI 15.3, 35.9) were classified as having moderate morbidity, and 30.1% (95% CI 18.2, 45.4) had mild morbidity, meaning that these patients presented with normal temperatures, no clinical signs of infection, normal blood pressure, bleeding that was not classified as hemorrhage or did not require any blood transfusion, and were discharged in stable state. Table 2 Severity of Postabortion Complications (n = 429)♠ Weighted proportion 95% CI Unweighted n Death < 0.1 2 Near miss 10.9 6.3 18.2 31 Severe 34.7 26.1 44.6 138 Moderate 24.2 15.3 35.9 123 Mild 30.1 18.2 45.4 135 ♠ Weighted using facility-level weights, unweighted n’s shown Table 3 displays unadjusted incidence rate ratios for the relationship between key patient characteristics and the severity of their postabortion complications. The only sociodemographic factor that was significantly associated with complication severity was marital status; women who were married or currently cohabitating were less likely to experience the most severe complications, with an unadjusted incidence rate ratio of 0.69 (95% CI 0.48, 0.99). Further, women who were identified as likely having an induced abortion were at a higher risk of experiencing severe or near miss complications as compared to women who either did not self-report an induced abortion or whose providers did not indicate evidence of a foreign body or mechanical injury (uIRR 1.74, 95% CI 1.11, 2.74). Table 3 Unadjusted incidence rate ratios (uIRRs) and 95% confidence intervals from Poisson regression models estimating the relationship between selected patient characteristics and experiencing severe or near miss complications♠ Characteristic Severe/near miss complications (vs. moderate/mild) uIRR 95% CI Age 12–19 ref 20–24 0.92 0.41 2.07 25–29 1.07 0.52 2.19 30–34 1.15 0.53 2.51 35+ 1.48 0.75 2.92 Married/cohabitating 0.69** 0.48 0.99 Highest level of education No schooling ref Primary school 0.88 0.47 1.66 High school 1.07 0.55 2.08 Greater than high school 0.75 0.32 1.73 Residence Urban ref Peri-urban 0.67 0.40 1.11 Rural 1.00 0.62 1.63 Second trimester pregnancy 0.76 0.44 1.37 Self-reported unintended pregnancy 0.98 0.65 1.46 Likely induced abortion 1.74* 1.11 2.74 Self-reported induced abortion 1.52* 1.07 2.16 Evidence of a foreign body ** 1.62* 1.01 2.70 ♠ Weighted using facility-level weights *Significant at p < 0.05 ** Significant at p < 0.01 Indicators for the management of postabortion cases are displayed in Table 4 . The majority of PAC cases were not managed by an OB/GYN or other general physician but instead by a lower-level provider, such as nurses, midwives, or physician assistants (79.2%). Most women access PAC at primary level facilities in Liberia (76.7%), and more than half of women accessed services at public facilities (60.5%). A small proportion of women in Liberia sought PAC for a second trimester abortion (15%). Approximately half of patients were treated as outpatients (56.4%) and few spent more than 1 night admitted to a facility (17.7%). Overall, the most common uterine evacuation method was MVA [1] , with approximately 67.5% of PAC cases treated with this method in Liberia. The second most common uterine evacuation method was the use of misoprostol, with approximately 16.3% of cases where this medication was used. Although dilation and curettage (D&C) is not currently recommended for uterine evacuation by the WHO, we estimate that it is used in approximately 8.4% of PAC cases in Liberia. Finally, we estimate that 6.3% of cases did not require uterine evacuation. This may be because either all products of conception were expelled without the need for medical intervention, or the patient had received an abortion procedure or medication prior to arriving at the facility for PAC. Almost all patients (87.5%) received pain medication during their treatment. While providers reported that most patients (89.7%) received contraceptive counseling before leaving, only 38.9% of PAC patients received a contraceptive method prior to or upon discharge. We We found no statistically significant differences for any of the care management indicators by the level of severity of postabortion complications, with the exception of receipt of a contraceptive method; while half of women (49.9%) classified as having severe or near miss complications received a contraceptive method, this proportion drops to 39.8% for women with moderate levels of severity and 21.6% for women with mild complications. Table 4 Management of PAC, overall and by severity of postabortion complications♠ Overall Mild Moderate Severe/ near miss Method of Evacuation MVA 67.5% 62.3% 69.6% 69.7% Other uterine evacuation method 1.5% 1.2% 4.4% 0.2% Misoprostol 16.3% 22.2% 7.4% 17.0% D&C 8.4% 4.0% 14.2% 8.3% None 6.3% 10.1% 4.5% 4.8% Provider type OB/GYN or general physician 20.8% 33.1% 21.1% 12.5% Lower-level provider (nurse, midwife, physician’s assistant) 79.2% 66.9% 78.9% 87.6% Facility Level Primary 76.7% 75.8% 71.5% 80.1% Secondary – health center 10.0% 7.3% 12.0% 10.8% Secondary – county hospital 13.3% 16.9% 16.5% 9.1% Facility ownership Public 60.5% 53.9% 60.1% 65.2% Private 39.5% 46.1% 39.9% 34.9% Patient received pain medication 87.5% 83.5% 89.8% 89.0% Gestational age second trimester 15.0% 11.6% 24.8% 12.0% Length of stay Outpatient 56.4% 56.1% 66.7% 51.3% 1 night or < 24 hours 25.9% 30.3% 22.5% 24.7% 2–3 nights 15.6% 13.0% 7.3% 21.7% 4 or more nights 2.1% 0.1% 3.5% 2.4% Patient received contraceptive counseling§ 89.7% 93.3% 92.4% 84.5% Patient received a contraceptive method§* 38.9% 21.6% 39.8% 49.9% ♠ Weighted using facility-level weights §Proportion excludes individuals who died or were referred to higher level facilities to complete care *Significant at p < 0.05 The results from the unadjusted Poisson regression models revealed that several other factors were associated with whether a patient received a contraceptive method before discharge (Table 5 ). As compared to the youngest patients (aged 12–19), women of all ages were less likely to receive a contraceptive method, although this relationship was only statistically significant for women aged 20–24 (uIRR 0.46, 95% CI 0.23, 0.94) and women aged 35 or older (uIRR 0.27, 95% CI 0.14, 0.52). Women living in rural areas were approximately twice as likely to receive a contraceptive method as compared to women in urban areas (uIRR 2.24, 95% CI 1.02, 4.95). Finally, the factors most strongly associated with whether a patient received a contraceptive method in the unadjusted models were the patient’s future contraceptive intentions and the provider type; the unadjusted incidence rate ratio for the relationship between a woman receiving a method and reporting that she planned to use a contraceptive method in the next 12 months was 6.68 (95% CI 3.10, 13.99), and women who received care from mid-level providers (i.e. nurse, midwife, physician’s assistant) were 7.41 (95% CI 2.40, 22.89) times more likely to receive a contraceptive method than those whose provider was an OBGYN or general physician. Table 5 Unadjusted and adjusted incidence rate ratios (IRRs) and 95% confidence intervals from Poisson regression models estimating the relationship between selected patient characteristics and receiving a family planning method before discharge♠ Unadjusted IRR 95% CI Adjusted IRR 95% CI Severity of postabortion complications Mild ref ref Moderate 1.84 0.80 4.25 1.51 0.85 2.67 Severe or near miss 2.31* 1.10 4.82 1.98** 1.18 3.32 Patient characteristics Age 12–19 ref ref 20–24 0.46* 0.23 0.94 0.63 0.39 1.04 25–29 0.62 0.26 1.46 0.95 0.66 1.36 30–34 0.59 0.30 1.15 0.87 0.46 1.66 35+ 0.27** 0.14 0.52 0.56 0.30 1.03 Married/cohabitating 0.68 0.44 1.05 Highest level of education - - - No schooling ref Primary school 0.87 0.50 1.50 - - - High school 0.57 0.28 1.19 - - - Greater than high school 0.43 0.09 2.04 - - - Residence Urban ref ref Peri-urban 1.61 0.76 3.42 1.14 0.64 2.02 Rural 2.24* 1.02 4.95 1.30 0.75 2.26 Self-reported unintended pregnancy 1.57 0.92 2.65 - - - Likely induced abortion 1.03 0.66 1.62 - - - Self-reported intention to use family planning in the next 12 months 6.68** 3.10 13.99 4.94** 2.44 9.99 Facility and management characteristics Provider type OB/GYN or general physician ref ref Lower-level provider (nurse, midwife, physician’s assistant) 7.41** 2.40 22.89 3.76* 1.08 13.11 Facility Level Primary ref Secondary – health center 1.45 0.87 2.42 Secondary – county hospital 0.86 0.51 1.45 Facility Type Private ref Public 2.57 0.90 7.30 ♠ Weighted using facility-level weights *Significant at p < 0.05 ** Significant at p < 0.01 After accounting for the additional factors that were associated with postabortion contraceptive provision, the relationship between complication severity and the receipt of a contraceptive method remained statistically significant. Controlling for age, residence, future intentions to use, and provider type, women classified as having severe or near miss complications were approximately twice as likely (aIRR 1.98, 95% CI 1.18, 3.32) to receive a contraceptive method before completion of care than women classified as having mild severity. The difference between women with moderate and mild severity was not statistically significant. Discussion The results of this analysis reveal a high prevalence of the most severe postabortion complications in Liberia. We estimate that close to half of PAC patients in Liberia experience either severe complications or meet the criteria for maternal near miss, the later representing 11% of PAC cases. These rates are higher than recent studies in African contexts; a recent study that investigated abortion complication in 11 African countries found that only 1.9% of cases met the criteria for near miss.[ 24 ] In other recent studies in Zimbabwe and Malawi, approximately 22% of women seeking PAC had either severe or near miss complications.[ 6 , 25 ] Similar, a study conducted in Kinshasa reported that 16% of women seeking PAC has severe complications.[ 8 ] The elevated rates of severe and near miss complications revealed in this study are not surprising given Liberia’s high maternal mortality rate. We observed few associations between PAC patient characteristics and the severity of their postabortion complications. This finding suggests that vulnerability to adverse postabortion outcomes is shared across demographic profiles of women in Liberia. Our results did suggest that marriage acted as a protective factor, with married women being less likely to experience severe for near miss complications. In a recent systematic review and meta-analysis, marriage or male involvement in pregnancy care was associated with improved maternal health outcomes in developing countries, and often acts as a protective factor for severe maternal outcomes.[ 26 ] Having a partner may provide both financial and other support, help women to promptly identify health problems, and help navigate barriers to care and reduce delays accessing appropriate care. Conversely, it is also possible that stigma related to extramarital pregnancy and induced abortion may also be driving this result. Unmarried women who become pregnant may be more motivated to induce an abortion, leading them to be more likely to resort to harmful methods. Similarly, unmarried women may delay seeking care due to fear of experiencing provider stigma from providers at the health facility, thereby worsening their complications. More research should investigate whether community-level education programs aimed at destigmatizing abortion, emphasizing sexual and reproductive health rights, and encouraging male involvement in pregnancy could be helpful tools in reducing the severity of postabortion complications in Liberia. The only other patient-specific indicator associated with complication severity was our indicator for likely induced abortion. That said, it is important to highlight the limitations of this measure. Previous research has widely documented that women often do not report abortions when asked directly in surveys.[ 20 , 27 ] We attempted to partially address this concern by including women in our induced abortion indicator whose providers identified evidence of a foreign body or mechanical injury, which should only be present in the case of induced abortion. While this strategy helped to identify some additional induced abortions that women did not self-report, it may also have led us to over-estimate less safe abortions in our sample, as evidence of foreign body or mechanical injury would also indicate that a woman used a potentially harmful method to end her pregnancy. This concern is especially salient in the current context; previous research has documented the increasing access to medication abortion in legally restrictive settings over the past several years,[ 28 – 30 ] although little is known about the current state of this phenomenon in the context of Liberia. In light of these limitations, it is difficult to interpret the observed relationship between induced abortion and complication severity. While our method for identifying likely induced abortions may overestimate those conducted with unsafe methods, it is unlikely that our inclusion of clinically identified abortions is driving these results, as the relationship between induced abortion and complication severity persists when we remove women who were only classified as having an abortion by provider observation. An alternative explanation is that women who are experiencing more severe complications may be more likely to disclose that they had an induced abortion due to a concern for their health. In order to better understand the relationship between induced abortion and the severity of resulting complications in Liberia, future is needed to investigate the most common methods women are using to induce abortions in Liberia, the extent to which women are able to access medication abortion, and the subsequent care seeking patterns women take after inducing their abortions. This study also reveals important information about the management of postabortion care cases in Liberia. In line with recommendations from the WHO and Liberia CAC guideline, the vast majority of PAC cases in Liberia are being treated with either MVA or misoprostol. While we did find that D&C was still the main uterine evacuation method in approximately 8% of cases, this is lower than the average proportion documented in a recent cross-sectional study of 11 sub-Saharan African counties.[ 31 ] We also found that most women accessed PAC services in a public facility, and nearly 3 in 4 women accessed this care at primary-level health facilities. While public and primary-level facilities are the dominant access points for women seeking PAC services in most sub-Saharan African countries, these level facilities are often the least equipped with trained staff, essential equipment, and commodities, impeding access to quality PAC in a timely manner.[ 32 ] It is also notable that most patients were attended to by mid-level providers (nurses and midwives), including for those experiencing severe and near miss complications. This is consistent with the task shifting and task sharing strategies employed by most low-and-middle income countries to expand availability and access to PAC. Future research is also needed to understand the capacity of these facilities to provide high quality postabortion care services, the findings of which can also be used to better direct investment in the public health system. Our analysis also highlights some important areas where the provision of postabortion care contraceptives in Liberia can be improved. While providers reported that the majority of PAC patients received postabortion family planning counseling, we also estimated that only one-third of patients received a contraceptive method prior to discharge. Current WHO recommendations outline that all women should receive comprehensive family planning counseling as part of postabortion care and that facilities should provide access to a wide variety of methods.[ 33 ] However, these same guidelines explicitly do not set standards for the proportion of patients who should receive a contraceptive method after PAC, and instead note that patient desires as to whether or not to use contraception are paramount.[ 33 ] That said, the fact that so few women were discharged without a contraceptive method is not a concern in and of itself. Our unadjusted models indicated that women’s intention to use a contraceptive method in the next 12 months was highly correlated with receipt of a method at discharge. However, the fact that provider type was also strongly associated with receiving a contraceptive method suggests that other factors may also be influencing this outcome in Liberia. Future research should investigate this issue to determine whether low uptake of contraceptive methods is a reflection of women’s preferences, poor quality family planning counseling, commodity availability, or some combination of these factors. The more concerning finding regarding the provision of postabortion contraceptive methods is that this varied by complication severity; while half of patients with near miss or severe complications received a contraceptive method, this was only true for one in five women with mild complications. Again, this could be a reflection of women’s preferences, where women with more severe complications had a greater motivation to avoid pregnancy in the near future. However, the relationship between complication severity and receiving a contraceptive method persisted even after controlling for women’s intentions to use family planning in the future (along with other factors that were associated with receiving a method in the unadjusted models.) As such, it is possible that provider bias is driving these results. Several studies have documented the existence of provider bias in provision of family planning counseling and services, with evidence to suggest that providers limit access to contraceptive methods or narrow choices for patients based on factors such as patient age, marital status, or other demographic factors.[ 34 , 35 ] The dynamic suggested by this analysis is slightly different, where providers may be treating patients with more severe complications differently due to their perceived increased need to avoid pregnancy. More research is needed to understand the nature of any differential treatment before policy recommendations can be made. For example, if providers are spending less time with or providing lower quality counseling to patients with mild complications, guidelines and trainings should be revised to stress the importance of providing high quality counseling to all postabortion care patients, regardless of the nature of their postabortion complications. Conversely, if providers tend to use more coercive tactics with patients they view as higher risk, then guidelines and trainings should underline the importance of patient’s preferences and agency. Limitations This analysis has several limitations. First, classifying the severity of abortion complication requires the collection of detailed clinical detail, some of which was not available in this study. For example, the study instrument did not collect the number of units of blood that was provided in the case of blood transfusion, making it difficult to determine was a transfusion met the criteria for “massive”. In addition, indicators such as temperature or blood-pressure were not routinely collected in all study facilities. While stuff staff worked closely with sampled facilities to encourage collection of these indicators during the study period, missing data on for these indicators remained a concern and may have led to an underestimate of complication severity in this study. Further, the severity definition used for this study relied on some somewhat subjective indicators or clinical cut-offs. Examples of this include (but are not limited to) what constitutes “severe” vs. “non-severe” anemia, how to define a “hemorrhage” vs. severe bleeding, and what blood pressure level indicates the existence of shock. A recently published study has made progress in improving the measurement of complication severity by using more comprehensive severity categories and less subjective clinical indicators.[ 36 ] Unfortunately, the results and recommendations from that analysis was not available at the time this study’s instruments were being designed and fielded. Future research aiming to measuring the severity of postabortion complications should attempt to incorporate as many recommendations from Pasquier et al. (2023) as possible. As we discussed above, our measure of induced abortion in this study is biased in several ways. It is likely underestimating the true number of induced abortions in our sample, and it may be over-representing induced abortions that were conducted with more harmful methods. Given these concerns, the results indicating a relationship between induced abortion and more severe complications should be interpreted with caution. It is possible that there is no relationship between induced abortion and complication severity in Liberia, especially if women are able to easily access medication abortion, and more research is needed to understand this phenomenon. While the goal of this analysis was to produce nationally representative estimates of the severity of postabortion complications, it is possible that concerns in our sampling and data collection efforts may have resulted in biased estimates. First, we aimed to include all sampled health facilities in the PMS data collection effort, but 22% sampled facilities declined to participate. Our facility level weights accounted for this non-response based on the level of these facilities. However, if other factors related to non-participation, such as staff capacity to attend the study training or actively collect data during the study period, were also related to complication severity, our results may be biased. Further, we were unable to account for non-response at the individual level, which may also impact the representativeness of our sample. Conclusions The results from this analysis, along with the new estimates of abortion incidence for Liberia,[ 15 ] suggest that induced abortion is a common occurrence in Liberia. Further, our results indicate that postabortion complications are a major public health concern. Both of these findings provide evidence in support of the revised Public Health Law. If passed by the national legislature, this newly revised law would create a less restrictive legal context for induced abortion in Liberia as compared to the existing 1976 penal code. Our results also have important implications for the provision of postabortion care in Liberia. The large proportion of postabortion care patients in Liberia who are experiencing near miss or severe postabortion complications underscores the importance of providing high quality postabortion care, and future research is needed to document the current health system’s capacity to provide this care. Further, these results are a call to action for the government of Liberia to invest more in primary level facilities and strengthen their ability to manage postabortion complications. While several aspects of postabortion complication management were in line with recommended guidelines, we did find that provider attitudes and/or practices may be creating differences in family planning uptake based on the severity of women’s postabortion complications. Future investigation is needed to better understand the mechanisms behind this relationship, the results of which can be used to improve the provision of postabortion family planning services in Liberia. Declarations Ethics Approval and Consent to Participate Ethical approval for this study was received from 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; the Clinton Health Access Initiative’s internal Scientific and Ethical Review Committee (SERC); and the Institutional Review Board of the African Population and Health Research Center. All study investigators completed the human subjects’ protection training before engaging in the study. Consent for Publication – N/A Availability of Data and Materials All data and materials are available on request. According to the APHRC policies (the organization hosting the datasets), all deidentified datasets will be publicly available on the APHRC microdata portal after 7 years (https://aphrc.org/microdata-portal/). 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, for APHRC’s Challenging the Politics of Social Exclusion project. Authors’ Contributions BU, KJ and MM conceived of the idea for this study. BU, KJ, MM, LL, and VD oversaw data collection. MG and OO analyzed the data. VD, OO, BT, and MG interpreted the results and framed the discussion. MG drafted the manuscript. All authors provided critical review of the manuscript and have approved the final manuscript. Acknowledgements The study team wishes to thank the Ministry of Health and Social Welfare, Liberia for supporting the research. We express gratitude to the facility managers and the health care providers for the collaboration and support accorded during the study. Our appreciation also goes out to all the study participants: health providers, women and girls, and other professionals who agreed to participate in the research and furnished the data used in the report. References WHO et al. Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023. Available: https://www.who.int/publications-detail-redirect/9789240068759 . Geller SE, Cox SM, Callaghan WM, Berg CJ. Morbidity and mortality in pregnancy: laying the groundwork for safe motherhood. Women’s health issues. 2006;16:176–88. Kilpatrick SK, Ecker JL. American College of Obstetricians and Gynecologists. Severe maternal morbidity: screening and review. Am J Obstet Gynecol. 2016;215:B17–22. World Health Organization. Evaluating the quality of care for severe pregnancy complications: the WHO near-miss approach for maternal health. 2011. Calvert C, Owolabi OO, Yeung F, Pittrof R, Ganatra B, Tunçalp Ö, et al. The magnitude and severity of abortion-related morbidity in settings with limited access to abortion services: a systematic review and meta-regression. BMJ Glob Health. 2018;3:e000692. 10.1136/bmjgh-2017-000692 . Madziyire MG, Polis CB, Riley T, Sully EA, Owolabi O, Chipato T. Severity and management of postabortion complications among women in Zimbabwe, 2016: a cross-sectional study. BMJ Open. 2018;8. 10.1136/bmjopen-2017-019658 . Rees H, Katzenellenbogen J, Shabodien R, Jewkes R, Fawcus S, McIntyre J, et al. The epidemiology of incomplete abortion in South Africa. National Incomplete Abortion Reference Group. S Afr Med J. 1997;87:432–7. Bankole A, Kayembe P, Chae S, Owolabi O, Philbin J, Mabika C. The Severity and Management of Complications Among Postabortion Patients Treated in Kinshasa Health Facilities. Int Perspect Sex Reprod Health. 2018;44:1–9. 10.1363/44e5618 . Owolabi O, Riley T, Juma K, Mutua M, Pleasure ZH, Amo-Adjei J, et al. Incidence of maternal near-miss in Kenya in 2018: findings from a nationally representative cross-sectional study in 54 referral hospitals. Sci Rep. 2020;10:1–10. World Health O. Safe Abortion: technical and policy guidance for health systems. 2012;2nd edition. Ganatra B, Gerdts C, Rossier C, Johnson BR, Tunçalp Ö, Assifi A, et al. Global, regional, and subregional classification of abortions by safety, 2010–14: estimates from a Bayesian hierarchical model. Lancet. 2017;390:2372–81. 10.1016/S0140-6736(17)31794-4 . Bankole A, Remez L, Owolabi O, Philibin J, Williams P. From Unsafe to Safe Abortion in Sub-Saharan Africa: Slow but Steady Progress. 2020. Prada E, Bankole A, Oladapo OT, Awolude OA, Adewole IF, Onda T. Maternal Near-Miss Due to Unsafe Abortion and Associated Short-Term Health and Socio-Economic Consequences in Nigeria. Afr J Reprod Health. 2015;19:52–62. Konvitz MR. Liberian Code of Laws Revised. Cornell University Press; 1973. Ushie B, Juma K, Giorgio M, Philbin J. Estimating unintended pregnancy and induced abortion in Liberia: A nationally representative cross-sectional survey. Under Rev. 2024. Liberia Institute of Statistics and Geo-Information Services (LISGIS), Ministry of Health and Social Welfare [Liberia], National AIDS Control Program [Liberia], and ICF International. (2014). Liberia Demographic and Health Survey 2013. Monrovia, Liberia: Liberia Institute of Statistics and Geo-Information Services (LISGIS) and ICF International.; 2013. Odunvbun WO, Kollie JT. The burden of surgical complications from unsafe abortion treated at the John F. Kennedy Maternity Center (JFKMC), Monrovia, Liberia. Malawi Med J. 2022;34:43–8. Ndyanabangi B, Livingstone M, Gobeh W, Logan GG, Atukunda L. Unsafe abortion–A Risk factor for maternal mortality in Liberia-An analysis of the characteristics of unsafe abortion clients and risk factors for maternal morbidity and mortality. Afr J Reprod Health. 2021;25:43–50. Lindberg L, Kost K, Maddow-Zimet I, Desai S, Zolna M. Abortion Reporting in the United States: An Assessment of Three National Fertility Surveys. Demography. 2020 [cited 10 Jun 2020]. 10.1007/s13524-020-00886-4 . Giorgio M, Makumbi F, Kibira SPS, Shiferaw S, Seme A, Bell SO et al. Self-reported abortion experiences in Ethiopia and Uganda, new evidence from cross-sectional community-based surveys. PLOS Global Public Health. 2023;3: e0002340. Figa-Talamanca I, Sinnathuray TA, Yusof K, Fong CK, Palan VT, Adeeb N, et al. Illegal abortion: an attempt to assess its cost to the health services and its incidence in the community. Int J Health Serv. 1986;16:375–89. Kaye DK, Mirembe FM, Bantebya G, Johansson A, Ekstrom AM. Domestic violence as risk factor for unwanted pregnancy and induced abortion in Mulago Hospital, Kampala, Uganda. Tropical Med Int Health. 2006;11:90–101. Rossier C, Guiella G, Ou‚draogo A, Thi‚ba B. Estimating clandestine abortion with the confidants method–results from Ouagadougou, Burkina Faso. Soc Sci Med. 2006;62:254–66. Qureshi Z, Mehrtash H, Kouanda S, Griffin S. Understanding abortion-related complications in health facilities: results from WHO multicountry survey on abortion (MCS-A) across 11 sub-Saharan African countries. Kalilani-Phiri L, Gebreselassie H, Levandowski BA, Kuchingale E, Kachale F, Kangaude G. The severity of abortion complications in Malawi. Int J Gynecol Obstet. 2015;128:160–4. 10.1016/j.ijgo.2014.08.022 . Judith Yargawa, Jo Leonardi-Bee. Male involvement and maternal health outcomes: systematic review and meta-analysis. J Epidemiol Community Health. 2015;69:604. 10.1136/jech-2014-204784 . Jones RK, Kost K. Underreporting of induced and spontaneous abortion in the United States: an analysis of the 2002 National Survey of Family Growth. Stud Fam Plann. 2007;38:187–97. Awowole IO, Ijarotimi OA. Restrictive abortion laws, COVID-19, telehealth, and medication abortion in the SDG era. Lancet Global Health. 2022;10:e14–5. 10.1016/S2214-109X(21)00544-1 . Footman K, Keenan K, Reiss K, Reichwein B, Biswas P, Church K. Medical Abortion Provision by Pharmacies and Drug Sellers in Low- and Middle-Income Countries: A Systematic Review. Stud Fam Plann. 2018;49:57–70. 10.1111/sifp.12049 . Moseson H, Herold S, Filippa S, Barr-Walker J, Baum SE, Gerdts C. Self-managed abortion: a systematic scoping review. Best Pract Res Clin Obstet Gynecol. 2020;63:87–110. Qureshi Z, Mehrtash H, Kouanda S, Griffin S, Filippi V, Govule P, et al. Understanding abortion-related complications in health facilities: results from WHO multicountry survey on abortion (MCS-A) across 11 sub-Saharan African countries. BMJ Glob Health. 2021;6:e003702. 10.1136/bmjgh-2020-003702 . Owolabi OO, Biddlecom A, Whitehead HS. Health systems’ capacity to provide post-abortion care: a multicountry analysis using signal functions. Lancet Global Health. 2019;7:e110–8. 10.1016/S2214-109X(18)30404-2 . World Health Organization. Abortion care guideline. 2022. Available: https://www.who.int/publications-detail-redirect/9789240039483 . Sieverding M, Schatzkin E, Shen J, Liu J. Bias in Contraceptive Provision to Young Women Among Private Health Care Providers in South West Nigeria. Int Perspect Sex Reproductive Health. 2018;44:19–29. Solo J, Festin M. Provider Bias in Family Planning Services: A Review of Its Meaning and Manifestations. Global Health: Sci Pract. 2019;7:371–85. 10.9745/GHSP-D-19-00130 . Pasquier E, Owolabi OO, Fetters T, Ngbale RN, Adame Gbanzi MC, Williams T, et al. High severity of abortion complications in fragile and conflict-affected settings: a cross-sectional study in two referral hospitals in sub-Saharan Africa (AMoCo study). BMC Pregnancy Childbirth. 2023;23:143. 10.1186/s12884-023-05427-6 . Footnotes While we originally defined this measure as MVA or EVA, there was only 1 case in our sample where EVA was used. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4757559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331608485,"identity":"47d6519e-7582-4d60-b050-35a30df5adf0","order_by":0,"name":"Margaret M Giorgio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFACxgYgIcHAD+MbEK1FsgHIOkCcFpjhB4jVotu/uPFxxS8LOePza8wff8yxkzdn733A8HFPLU4tZjceNhue7ZMwNrvxxrDh4LZkw509xw0YZzw7jkfLwTbJxh6JxG03zoC0MCcY3EhjYOY5cIywls0zwFrqEwzuPyOg5Xxjm2TDD4nEDfw9IC2HgbawgbTU4LGFsdmwsUHCWOIGW+GMs9uOG244k8ZwcMaBA3hsOf7wYcOfOjn+/sMbPlRuq5Y3OH6M8cGHA3U4tTBIJABjsw3KgAGgFYdxa+EHueAPjIEAeGwZBaNgFIyCkQYAdJdlT41DkMoAAAAASUVORK5CYII=","orcid":"","institution":"Guttmacher Institute","correspondingAuthor":true,"prefix":"","firstName":"Margaret","middleName":"M","lastName":"Giorgio","suffix":""},{"id":331608491,"identity":"a8cd56c4-b3fa-4606-bc5f-38d268957bb8","order_by":1,"name":"Boniface Ayanbekongshie Ushie","email":"","orcid":"","institution":"Beshi King Development Services","correspondingAuthor":false,"prefix":"","firstName":"Boniface","middleName":"Ayanbekongshie","lastName":"Ushie","suffix":""},{"id":331608493,"identity":"56dc7199-c945-4444-a4f8-d308b541d237","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":331608497,"identity":"0e6b904e-888e-4f7a-89aa-fbd899bb237f","order_by":3,"name":"Moses BF Massaquoi","email":"","orcid":"","institution":"Clinton Health Services Initiative Liberia","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"BF","lastName":"Massaquoi","suffix":""},{"id":331608498,"identity":"058772a9-9907-4092-9a30-35d06e95b1af","order_by":4,"name":"Lily Lu","email":"","orcid":"","institution":"Clinton Health Services Initiative Liberia","correspondingAuthor":false,"prefix":"","firstName":"Lily","middleName":"","lastName":"Lu","suffix":""},{"id":331608501,"identity":"72d3a078-0863-4dc3-aba1-b278ec6adac3","order_by":5,"name":"Bentoe Zoogley Tehoungue","email":"","orcid":"","institution":"Ministry of Health and Social Welfare Liberia, Family Health Program","correspondingAuthor":false,"prefix":"","firstName":"Bentoe","middleName":"Zoogley","lastName":"Tehoungue","suffix":""},{"id":331608504,"identity":"3e909841-2520-415e-990c-9d91aaac02b6","order_by":6,"name":"Vekeh Donzo","email":"","orcid":"","institution":"Clinton Health Services Initiative Liberia","correspondingAuthor":false,"prefix":"","firstName":"Vekeh","middleName":"","lastName":"Donzo","suffix":""},{"id":331608505,"identity":"41da55da-140e-40c8-964b-00417044f49b","order_by":7,"name":"Onikepe Owolabi","email":"","orcid":"","institution":"Guttmacher Institute","correspondingAuthor":false,"prefix":"","firstName":"Onikepe","middleName":"","lastName":"Owolabi","suffix":""}],"badges":[],"createdAt":"2024-07-17 16:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4757559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4757559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82719888,"identity":"d5d12f2e-f569-4373-8fac-392493181047","added_by":"auto","created_at":"2025-05-14 12:53:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1156702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4757559/v1/e1d42ac0-10c0-4f55-b01c-a26ff08e71ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The severity and management of postabortion care complications in Liberia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eComplications that arise from pregnancy termination, either due to spontaneous abortion or induced abortion, are a major driver of maternal morbidity and mortality in many resource-limited settings. The World Health Organization (WHO) estimated that in 2020 the maternal mortality ratio (MMR) for sub-Saharan Africa (SSA) was 536 maternal deaths per 100,000 live births.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] While maternal death is the most severe outcome of pregnancy-related complications, women often experience less severe but significant health events that can have short or long-term consequences for their health and the well-being of their families.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] In 2009, the WHO introduced the concept of \u0026lsquo;maternal near miss\u0026rsquo;, defined as a woman who nearly died but survived a complication that occurred during pregnancy, childbirth, or within 42 days of termination of pregnancy.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] A recent review of the literature suggests that in settings where access to safe abortion care is limited, approximately 1 in 10 postabortion care hospital admissions were a near-miss event.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] In recent years, several studies have investigated a broad array of maternal outcomes, ranging from mild complications to near miss to death,[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and the evidence from this research has been a useful tool for understanding and improving the quality of postabortion care.\u003c/p\u003e \u003cp\u003eWhile induced abortions rarely result in medical complications when conducted in accordance with internationally accepted standards, [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] it is estimated that only 24% of abortions that occur across Africa would be classified as safe (i.e., performed by a trained provider using a recommended method).[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Abortion-related stigma, abortion under-reporting, and incomplete or unreliable medical record data make it difficult to generate reliable estimates of the relationship between unsafe abortion and maternal outcomes in many settings, particularly in those settings where abortion is highly legally restrictive. A recent report suggests that the SSA region has the highest rate of abortion-related deaths globally, at 185 deaths per 100,000 abortions, although this may be an underestimate.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Similar global or regional estimates for unsafe abortion\u0026rsquo;s impact on maternal morbidity are not available due to the measurement and documentation challenges described above. However, a small number of country-specific and sub-national studies have found a relationship between postabortion care patients who have likely had an induced abortion and more severe postabortion complications.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn the context of Liberia, maternal morbidity and mortality is a major public health concern. The maternal mortality ratio for 2020 is estimated to be 652 per 100,000 live births, representing one of the highest rates in the region.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] However, to date there are no national estimates for the severity of postabortion complications or the frequency of near-miss cases in Liberia. Women have limited access to safe abortion care in Liberia; the 1978 Offenses Against the Family Penal Law only allows abortion when there is a risk to the life, physical health or mental health of the pregnant woman, or if the pregnancy is a result of rape or incest, or in the case of fetal impairment.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Despite these legal restrictions, induced abortion is a common occurrence in Liberia; a recent study estimated a one-year abortion incidence rate of 30.7 per 1,000 women aged 15\u0026ndash;49 in 2021.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] According to the 2013 demographic and health survey in Liberia (LDHS), approximately 10% of maternal deaths in the country are due to unsafe abortion, and it is unknown if this estimate has changed in the past decade.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Further, while two recent studies have provided some evidence of the existence of unsafe abortion among postabortion care patients in Liberia, this data was only collected retrospectively in a small number of hospitals and was limited by incomplete documentation of medical records.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn light of these gaps in knowledge, the goal of this paper is to investigate the severity of postabortion complications among patients accessing postabortion care within nationally representative sample of health facilities in Liberia. In addition, we describe how these postabortion complications are managed by the Liberian health system. Finally, we investigate the likelihood that women seeking postabortion care in our sample did so as a result of an induced abortion.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling and data collection\u003c/h2\u003e \u003cp\u003eData from this analysis come from a larger study investigating the incidence of induced abortion and unintended pregnancy in Liberia in 2021.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] That study included a Health Facility Survey (HFS), which was administered among a nationally representative sample of health facilities capable of providing postabortion care (PAC) services. A detailed description of the sampling strategy is described elsewhere.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] In brief, the study team sampled a total of 132 facilities for the HFS, which represents 100% of hospitals that provide PAC in Liberia in 2021, 84% of all health centers, and 7.5% of all clinics. The response rate for the survey was 97%, which corresponded to a final sample of 128 facilities.\u003c/p\u003e \u003cp\u003e The Prospective Morbidity Survey (PMS), from which the data for this analysis are derived, aimed to include all 128 facilities that participated in the HFS. To participate in the PMS, each facility was required to send at least one health provider (typically a nurse, midwife or a physician assistant), to attend a week-long training in Monrovia, which covered topics such as the study aims and rationale, research ethics, how to conduct interviews, and the specific study instruments. Overall, 78% of the HFS facilities (n\u0026thinsp;=\u0026thinsp;100) met this eligibility criteria and participated in the PMS.\u003c/p\u003e \u003cp\u003eData were collected prospectively over 30 days in each participating facility. All patients who arrived at the facility during the study period seeking treatment for postabortion complications and whose pregnancy gestation was 28 weeks or lower were eligible to participate in the study, with the exception of women who presented with ectopic pregnancies, molar pregnancies, or blighted ovum. Study enumerators worked with the providers at each facility to determine whether each patient was eligible to participate.\u003c/p\u003e \u003cp\u003eThe PMS consisted of two surveys. The first was a survey of postabortion care patients that captured data on their socio-demographic characteristics, reproductive histories, experiences accessing and receiving care, as well as the circumstances surrounding the abortion (spontaneous or induced) that led them to seek care. The second survey was administered to the patient\u0026rsquo;s health provider and captured the clinical details of the patient\u0026rsquo;s case. After the study enumerator determined eligibility, PAC patients were taken through the informed consent process and were asked to consent separately for the patient survey as well as allowing their provider to be interviewed in the provider survey. In the event of a maternal death, the study team received permission from the ethical review board to interview the woman\u0026rsquo;s provider in order to capture her clinical details. During the study period, a total of 455 women who met the study eligibility criteria presented at one of the selected study facilities for PAC. Of these, 390 provided consent for both the patient and provider surveys (response rate\u0026thinsp;=\u0026thinsp;86%). An additional 11 patients agreed to participate in the patient survey but did not provide consent for the provider survey, resulting in a final patient sample of 401. Further, 40 patients declined the patient interviewed but allowed study staff to interview their provider, and there were 2 PAC patient deaths during the course of the study. This resulted in a total of 432 provider surveys. During the analysis phase, we identified 3 cases where the patient did not meet the study\u0026rsquo;s clinical eligibility criteria. As such, the final samples for this study are 387 for the patient survey and 429 for the provider survey. We created composite facility-level weights by multiplying the sampling weights by the facility- level non-response weights. This approach allowed us to appropriately adjust for the complexities associated with our sampling design and the potential biases introduced by non-response.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003efor this study was received from 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; the Clinton Health Access Initiative\u0026rsquo;s internal Scientific and Ethical Review Committee (SERC); and the Institutional Review Board of the African Population and Health Research Center. All study investigators completed the human subjects\u0026rsquo; protection training before engaging in the study.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003ePatient sociodemographic characteristics and reproductive health histories\u003c/h2\u003e \u003cp\u003eWe collected data on the patient\u0026rsquo;s age, marital status, highest level of education completed, and area of residence (urban, peri-urban, or rural). Women also provided information on the total number of pregnancies and live births they have experienced in their lifetime. We also collected data on pregnancy intentions before conceiving the pregnancy for which the patient was seeking postabortion care. Women were asked whether at the time they became pregnant, whether they wanted to be pregnant, they wanted to be pregnant at that time but then later changed their minds, or they did not want to be pregnant at all.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClinical indicators and the severity of postabortion complications\u003c/h2\u003e \u003cp\u003eTo measure the severity of abortion complications, we adapted a categorization approach that has been used in several prior studies on abortion-related complications.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Using data from the provider survey, each PAC patient\u0026rsquo;s clinical signs and symptoms, diagnosis, and interventions were examined to generate five hierarchically distinct categories of complications \u0026ndash; mild, moderate, severe, near-miss, or death. Box 1 describes the specific definitions used for this analysis. Women were classified as \u0026ldquo;near miss\u0026rdquo; if they experienced \u003cem\u003eany\u003c/em\u003e of the following: hemorrhagic or septic shock, generalized peritonitis, uterine perforation with an accompanying surgical repair or procedure, or system or organ failure. Previous studies have also included massive blood transfusion (defined as the provision of two or more units of blood) as part of the near miss definition.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] However, the PMS instrument in this study only measured whether a blood transfusion occurred and not the amount of blood given. As such, we were unable to include this criterion in our severity definition. Women with severe complications were those who experienced a least one of the following symptoms or diagnoses: temperature\u0026thinsp;\u0026ge;\u0026thinsp;39\u0026deg;C or \u0026le;\u0026thinsp;35\u0026deg;C and a clinical sign of infection, sepsis/septicemia with no signs of septic shock, pelvic abscess or pelvic peritonitis with no sign of shock, uterine perforation \u003cem\u003ewithout\u003c/em\u003e surgical repair or procedure, or anemia with the presence of hemorrhage and blood transfusion. Moderate severity is characterized by the experience of at least one of the following: elevated temperature (37.3\u0026deg;C-38.9\u0026deg;C), clinical signs of infection, or hemorrhage that did not require blood transfusion. Finally, women classified as having mild severity must meet all of the following criteria: temperature 35.1\u0026deg;C-38.9\u0026deg;C with no clinical signs of infection, systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90mm Hg, no hemorrhage OR hemorrhage not requiring blood transfusion, and having been discharged from care in a stable state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eManagement of postabortion patients\u003c/h2\u003e \u003cp\u003eWe investigated several indicators of PAC clinical management and treatment by the severity of postabortion complications. First, we determined methods of evacuation, which were either manual or electric vacuum aspiration (MVA/EVA), other non-surgical uterine evacuation (i.e. digital evacuation), the use of misoprostol, dilatation and curettage (D\u0026amp;C), or none. We also investigate the provider type (OBGYN or other general physician vs. lower-level providers such as nurses, midwives, and physician\u0026rsquo;s assistants), length of stay in the hospital, gestational age at the time the pregnancy was terminated, and whether the patient received contraceptive counseling and/or a contraceptive method before the completion of care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvidence of an induced abortion\u003c/h2\u003e \u003cp\u003eIt is widely documented that women often underreport experiences with induced abortion when asked directly.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] As such, solely relying on women\u0026rsquo;s direct reports would likely underestimate the true prevalence of induced abortion in our sample. To address this issue, several previous studies have employed a WHO developed algorithm to help determine the likelihood that PAC patients have experienced an induced abortion.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Using this approach, abortion definitions include certain induced abortion, probably induced abortion, possible induced abortion, and likely spontaneous abortion. Classification into one of these definitions depends on different combinations of the following indicators: evidence of foreign bodies or trauma to the vagina, evidence of sepsis or peritonitis, provider perceptions that the abortion was induced, patient reports that the abortion was induced, and the intendedness of the pregnancy.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] However, this approach could overestimate the existence of induced abortion for several reasons: provider perceptions may be inappropriately influenced by stereotypes about the demographic profile of women who induce abortions; many unintended pregnancies will result in miscarriage given the high prevalence of spontaneous abortion in pregnancy; and sepsis and peritonitis can commonly occur after a miscarriage if women experience delays in receiving care or receive poor quality care. Therefore, this study uses a more conservative approach and defines induced abortion in our sample using the WHO\u0026rsquo;s criteria for certain abortion: either the patient reported that she did something to intentionally end the pregnancy and/or the provider reported clinical evidence of a foreign body or mechanical injury (e.g., lacerated cervix).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eWe first present descriptive statistics for our study sample, as well as for the distribution of the abortion complication severity indicator. Next, we investigate whether any sociodemographic characteristics are associated with complication severity by calculating unadjusted incidence rate ratios (uIRRs) and 95% confidence intervals from Poisson regression models estimating the relationship between selected patient characteristics and experiencing severe or near miss complications (vs. experiencing moderate or mild complications.) We also present descriptive statistics for indicators describing the management of postabortion care and conduct bivariate tests of significance to determine whether PAC management practices vary by the level of complication severity.\u003c/p\u003e \u003cp\u003eOur final set of analyses focuses on a specific component of PAC management: the provision of postabortion care family planning. To understand whether the receipt of a family planning method varies by the level of a patient\u0026rsquo;s complication severity, we first identify which patient and facility characteristics are also associated with receiving a method at the end of care. We then construct Poisson regression models for the relationship between complication severity and family planning method receipt, controlling for patient and facility-level characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the unweighted descriptive statistics for women who responded to the patient survey. Approximately 1 in 5 (21.2%, n\u0026thinsp;=\u0026thinsp;82) PAC clients were adolescents between the ages of 12 and 19, and half of patients were between the ages of 20 and 29 (49.6%, n\u0026thinsp;=\u0026thinsp;192). The majority of patients were either currently married or cohabitating with a partner (74.2%, n\u0026thinsp;=\u0026thinsp;285). Approximately 20% of patients (n\u0026thinsp;=\u0026thinsp;79) had no formal education, 10.6% (n\u0026thinsp;=\u0026thinsp;41) had completed a level of education greater than high school, and only 27.4% (n\u0026thinsp;=\u0026thinsp;106) lived in a rural area. Approximately one quarter of PAC patients (24.3%, n\u0026thinsp;=\u0026thinsp;94) reported that the pregnancy that brought them to the facility was their first, and 30.5% (n\u0026thinsp;=\u0026thinsp;118) reported they had never given birth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eSociodemographic characteristics of the PMS sample and characteristics of the pregnancy that led the respondent to seek PAC (n\u0026thinsp;=\u0026thinsp;387)\u0026spades;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.2% (82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1% (101)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5% (91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.5% (56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7% (57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabitating, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.2% (285)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.4% (79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.1% (136)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.9% (131)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6% (41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.4% (168)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2% (113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.4% (106)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3% (94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.1% (132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.6% (161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever given birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.5% (118)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.4% (199)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026thinsp;+\u0026thinsp;children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.1% (70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of pregnancy that led respondent to seek PAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.3% (218)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWanted to get pregnant but then later changed my mind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4% (21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDid not want to get pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.2% (148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvidence of induced abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5% (161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSelf-reported induced abortion\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e26.6% (103)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEvidence of a foreign body **\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e17.5% (75)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003e\u0026spades;\u003c/b\u003eUnweighted n\u0026rsquo;s and percentages shown\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e**Denominator\u0026thinsp;=\u0026thinsp;429, as includes evidence from the provider survey\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eJust over half of women (56.3%, n\u0026thinsp;=\u0026thinsp;148) reported that their current pregnancy was intended, 5.4% (n\u0026thinsp;=\u0026thinsp;21) reported that they wanted to get pregnant at the time but later changed their mind, and 38.2% (n\u0026thinsp;=\u0026thinsp;148) reported that they did not want to get pregnant at the time of conception. One in four women (26.6%, n\u0026thinsp;=\u0026thinsp;103) reported having done something to intentionally end their pregnancy. Further, providers indicated that there was evidence of a foreign body or mechanical injury in 17.5% of cases for which there was clinical data (n\u0026thinsp;=\u0026thinsp;75 out of 429 total cases). After combining these indicators, we identified 161 cases (37.5%) that had at least one of these criteria and were therefore classified as postabortion care as a result of an induced abortion.\u003c/p\u003e \u003cp\u003eThe distribution of the severity of postabortion complications is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After applying facility-level weights, we estimate that approximately 34.7% (95% CI 26.1, 44.6) of postabortion care patients in Liberia are classified as having severe complications and 10.9% (95% CI 6.3, 18.2) are near miss. Two women in our sample died, corresponding to an estimated less than 0.1% of PAC patients nationally. Approximately 1 in 4 women (24.2%, 95% CI 15.3, 35.9) were classified as having moderate morbidity, and 30.1% (95% CI 18.2, 45.4) had mild morbidity, meaning that these patients presented with normal temperatures, no clinical signs of infection, normal blood pressure, bleeding that was not classified as hemorrhage or did not require any blood transfusion, and were discharged in stable state.\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\u003eSeverity of Postabortion Complications (n\u0026thinsp;=\u0026thinsp;429)\u0026spades;\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted proportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnweighted n\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNear miss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e\u0026spades;\u003c/b\u003eWeighted using facility-level weights, unweighted n\u0026rsquo;s shown\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays unadjusted incidence rate ratios for the relationship between key patient characteristics and the severity of their postabortion complications. The only sociodemographic factor that was significantly associated with complication severity was marital status; women who were married or currently cohabitating were less likely to experience the most severe complications, with an unadjusted incidence rate ratio of 0.69 (95% CI 0.48, 0.99). Further, women who were identified as likely having an induced abortion were at a higher risk of experiencing severe or near miss complications as compared to women who either did not self-report an induced abortion or whose providers did not indicate evidence of a foreign body or mechanical injury (uIRR 1.74, 95% CI 1.11, 2.74).\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\u003eUnadjusted incidence rate ratios (uIRRs) and 95% confidence intervals from Poisson regression models estimating the relationship between selected patient characteristics and experiencing severe or near miss complications\u0026spades;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSevere/near miss complications\u003c/p\u003e \u003cp\u003e(vs. moderate/mild)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003euIRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabitating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond trimester pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported unintended pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikely induced abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSelf-reported induced abortion\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.52*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEvidence of a foreign body **\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.62*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.70\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e\u0026spades;\u003c/b\u003eWeighted using facility-level weights\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e** Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIndicators for the management of postabortion cases are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The majority of PAC cases were not managed by an OB/GYN or other general physician but instead by a lower-level provider, such as nurses, midwives, or physician assistants (79.2%). Most women access PAC at primary level facilities in Liberia (76.7%), and more than half of women accessed services at public facilities (60.5%). A small proportion of women in Liberia sought PAC for a second trimester abortion (15%). Approximately half of patients were treated as outpatients (56.4%) and few spent more than 1 night admitted to a facility (17.7%). Overall, the most common uterine evacuation method was MVA\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e[1]\u003c/a\u003e, with approximately 67.5% of PAC cases treated with this method in Liberia. The second most common uterine evacuation method was the use of misoprostol, with approximately 16.3% of cases where this medication was used. Although dilation and curettage (D\u0026amp;C) is not currently recommended for uterine evacuation by the WHO, we estimate that it is used in approximately 8.4% of PAC cases in Liberia. Finally, we estimate that 6.3% of cases did not require uterine evacuation. This may be because either all products of conception were expelled without the need for medical intervention, or the patient had received an abortion procedure or medication prior to arriving at the facility for PAC. Almost all patients (87.5%) received pain medication during their treatment. While providers reported that most patients (89.7%) received contraceptive counseling before leaving, only 38.9% of PAC patients received a contraceptive method prior to or upon discharge. We We found no statistically significant differences for any of the care management indicators by the level of severity of postabortion complications, with the exception of receipt of a contraceptive method; while half of women (49.9%) classified as having severe or near miss complications received a contraceptive method, this proportion drops to 39.8% for women with moderate levels of severity and 21.6% for women with mild complications.\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\u003eManagement of PAC, overall and by severity of postabortion complications\u0026spades;\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere/ near miss\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod of Evacuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther uterine evacuation method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisoprostol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u0026amp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5%\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 \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvider type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOB/GYN or general physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower-level provider (nurse, midwife, physician\u0026rsquo;s assistant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary \u0026ndash; health center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary \u0026ndash; county hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.5%\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\u003eFacility ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient received pain medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age second trimester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 night or \u0026lt;\u0026thinsp;24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 nights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 or more nights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient received contraceptive counseling\u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient received a contraceptive method\u0026sect;*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e\u0026spades;\u003c/b\u003eWeighted using facility-level weights\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026sect;Proportion excludes individuals who died or were referred to higher level facilities to complete care\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results from the unadjusted Poisson regression models revealed that several other factors were associated with whether a patient received a contraceptive method before discharge (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As compared to the youngest patients (aged 12\u0026ndash;19), women of all ages were less likely to receive a contraceptive method, although this relationship was only statistically significant for women aged 20\u0026ndash;24 (uIRR 0.46, 95% CI 0.23, 0.94) and women aged 35 or older (uIRR 0.27, 95% CI 0.14, 0.52). Women living in rural areas were approximately twice as likely to receive a contraceptive method as compared to women in urban areas (uIRR 2.24, 95% CI 1.02, 4.95). Finally, the factors most strongly associated with whether a patient received a contraceptive method in the unadjusted models were the patient\u0026rsquo;s future contraceptive intentions and the provider type; the unadjusted incidence rate ratio for the relationship between a woman receiving a method and reporting that she planned to use a contraceptive method in the next 12 months was 6.68 (95% CI 3.10, 13.99), and women who received care from mid-level providers (i.e. nurse, midwife, physician\u0026rsquo;s assistant) were 7.41 (95% CI 2.40, 22.89) times more likely to receive a contraceptive method than those whose provider was an OBGYN or general physician.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted incidence rate ratios (IRRs) and 95% confidence intervals from Poisson regression models estimating the relationship between selected patient characteristics and receiving a family planning method before discharge\u0026spades;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted IRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted IRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSeverity of postabortion complications\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere or near miss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.98**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePatient characteristics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabitating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported unintended pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikely induced abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported intention to use family planning in the next 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFacility and management characteristics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvider type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOB/GYN or general physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower-level provider (nurse, midwife, physician\u0026rsquo;s assistant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.76*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary \u0026ndash; health center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary \u0026ndash; county hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003e\u0026spades;\u003c/b\u003eWeighted using facility-level weights\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e** Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter accounting for the additional factors that were associated with postabortion contraceptive provision, the relationship between complication severity and the receipt of a contraceptive method remained statistically significant. Controlling for age, residence, future intentions to use, and provider type, women classified as having severe or near miss complications were approximately twice as likely (aIRR 1.98, 95% CI 1.18, 3.32) to receive a contraceptive method before completion of care than women classified as having mild severity. The difference between women with moderate and mild severity was not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this analysis reveal a high prevalence of the most severe postabortion complications in Liberia. We estimate that close to half of PAC patients in Liberia experience either severe complications or meet the criteria for maternal near miss, the later representing 11% of PAC cases. These rates are higher than recent studies in African contexts; a recent study that investigated abortion complication in 11 African countries found that only 1.9% of cases met the criteria for near miss.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] In other recent studies in Zimbabwe and Malawi, approximately 22% of women seeking PAC had either severe or near miss complications.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Similar, a study conducted in Kinshasa reported that 16% of women seeking PAC has severe complications.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] The elevated rates of severe and near miss complications revealed in this study are not surprising given Liberia\u0026rsquo;s high maternal mortality rate.\u003c/p\u003e \u003cp\u003eWe observed few associations between PAC patient characteristics and the severity of their postabortion complications. This finding suggests that vulnerability to adverse postabortion outcomes is shared across demographic profiles of women in Liberia. Our results did suggest that marriage acted as a protective factor, with married women being less likely to experience severe for near miss complications. In a recent systematic review and meta-analysis, marriage or male involvement in pregnancy care was associated with improved maternal health outcomes in developing countries, and often acts as a protective factor for severe maternal outcomes.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Having a partner may provide both financial and other support, help women to promptly identify health problems, and help navigate barriers to care and reduce delays accessing appropriate care. Conversely, it is also possible that stigma related to extramarital pregnancy and induced abortion may also be driving this result. Unmarried women who become pregnant may be more motivated to induce an abortion, leading them to be more likely to resort to harmful methods. Similarly, unmarried women may delay seeking care due to fear of experiencing provider stigma from providers at the health facility, thereby worsening their complications. More research should investigate whether community-level education programs aimed at destigmatizing abortion, emphasizing sexual and reproductive health rights, and encouraging male involvement in pregnancy could be helpful tools in reducing the severity of postabortion complications in Liberia.\u003c/p\u003e \u003cp\u003eThe only other patient-specific indicator associated with complication severity was our indicator for likely induced abortion. That said, it is important to highlight the limitations of this measure. Previous research has widely documented that women often do not report abortions when asked directly in surveys.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] We attempted to partially address this concern by including women in our induced abortion indicator whose providers identified evidence of a foreign body or mechanical injury, which should only be present in the case of induced abortion. While this strategy helped to identify some additional induced abortions that women did not self-report, it may also have led us to over-estimate less safe abortions in our sample, as evidence of foreign body or mechanical injury would also indicate that a woman used a potentially harmful method to end her pregnancy. This concern is especially salient in the current context; previous research has documented the increasing access to medication abortion in legally restrictive settings over the past several years,[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] although little is known about the current state of this phenomenon in the context of Liberia. In light of these limitations, it is difficult to interpret the observed relationship between induced abortion and complication severity. While our method for identifying likely induced abortions may overestimate those conducted with unsafe methods, it is unlikely that our inclusion of clinically identified abortions is driving these results, as the relationship between induced abortion and complication severity persists when we remove women who were only classified as having an abortion by provider observation. An alternative explanation is that women who are experiencing more severe complications may be more likely to disclose that they had an induced abortion due to a concern for their health. In order to better understand the relationship between induced abortion and the severity of resulting complications in Liberia, future is needed to investigate the most common methods women are using to induce abortions in Liberia, the extent to which women are able to access medication abortion, and the subsequent care seeking patterns women take after inducing their abortions. This study also reveals important information about the management of postabortion care cases in Liberia. In line with recommendations from the WHO and Liberia CAC guideline, the vast majority of PAC cases in Liberia are being treated with either MVA or misoprostol. While we did find that D\u0026amp;C was still the main uterine evacuation method in approximately 8% of cases, this is lower than the average proportion documented in a recent cross-sectional study of 11 sub-Saharan African counties.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] We also found that most women accessed PAC services in a public facility, and nearly 3 in 4 women accessed this care at primary-level health facilities. While public and primary-level facilities are the dominant access points for women seeking PAC services in most sub-Saharan African countries, these level facilities are often the least equipped with trained staff, essential equipment, and commodities, impeding access to quality PAC in a timely manner.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] It is also notable that most patients were attended to by mid-level providers (nurses and midwives), including for those experiencing severe and near miss complications. This is consistent with the task shifting and task sharing strategies employed by most low-and-middle income countries to expand availability and access to PAC. Future research is also needed to understand the capacity of these facilities to provide high quality postabortion care services, the findings of which can also be used to better direct investment in the public health system.\u003c/p\u003e \u003cp\u003eOur analysis also highlights some important areas where the provision of postabortion care contraceptives in Liberia can be improved. While providers reported that the majority of PAC patients received postabortion family planning counseling, we also estimated that only one-third of patients received a contraceptive method prior to discharge. Current WHO recommendations outline that all women should receive comprehensive family planning counseling as part of postabortion care and that facilities should provide access to a wide variety of methods.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] However, these same guidelines explicitly do not set standards for the proportion of patients who should \u003cem\u003ereceive\u003c/em\u003e a contraceptive method after PAC, and instead note that patient desires as to whether or not to use contraception are paramount.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] That said, the fact that so few women were discharged without a contraceptive method is not a concern in and of itself. Our unadjusted models indicated that women\u0026rsquo;s intention to use a contraceptive method in the next 12 months was highly correlated with receipt of a method at discharge. However, the fact that provider type was also strongly associated with receiving a contraceptive method suggests that other factors may also be influencing this outcome in Liberia. Future research should investigate this issue to determine whether low uptake of contraceptive methods is a reflection of women\u0026rsquo;s preferences, poor quality family planning counseling, commodity availability, or some combination of these factors.\u003c/p\u003e \u003cp\u003eThe more concerning finding regarding the provision of postabortion contraceptive methods is that this varied by complication severity; while half of patients with near miss or severe complications received a contraceptive method, this was only true for one in five women with mild complications. Again, this could be a reflection of women\u0026rsquo;s preferences, where women with more severe complications had a greater motivation to avoid pregnancy in the near future. However, the relationship between complication severity and receiving a contraceptive method persisted even after controlling for women\u0026rsquo;s intentions to use family planning in the future (along with other factors that were associated with receiving a method in the unadjusted models.) As such, it is possible that provider bias is driving these results. Several studies have documented the existence of provider bias in provision of family planning counseling and services, with evidence to suggest that providers limit access to contraceptive methods or narrow choices for patients based on factors such as patient age, marital status, or other demographic factors.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] The dynamic suggested by this analysis is slightly different, where providers may be treating patients with more severe complications differently due to their perceived increased need to avoid pregnancy. More research is needed to understand the nature of any differential treatment before policy recommendations can be made. For example, if providers are spending less time with or providing lower quality counseling to patients with mild complications, guidelines and trainings should be revised to stress the importance of providing high quality counseling to all postabortion care patients, regardless of the nature of their postabortion complications. Conversely, if providers tend to use more coercive tactics with patients they view as higher risk, then guidelines and trainings should underline the importance of patient\u0026rsquo;s preferences and agency.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis analysis has several limitations. First, classifying the severity of abortion complication requires the collection of detailed clinical detail, some of which was not available in this study. For example, the study instrument did not collect the number of units of blood that was provided in the case of blood transfusion, making it difficult to determine was a transfusion met the criteria for \u0026ldquo;massive\u0026rdquo;. In addition, indicators such as temperature or blood-pressure were not routinely collected in all study facilities. While stuff staff worked closely with sampled facilities to encourage collection of these indicators during the study period, missing data on for these indicators remained a concern and may have led to an underestimate of complication severity in this study. Further, the severity definition used for this study relied on some somewhat subjective indicators or clinical cut-offs. Examples of this include (but are not limited to) what constitutes \u0026ldquo;severe\u0026rdquo; vs. \u0026ldquo;non-severe\u0026rdquo; anemia, how to define a \u0026ldquo;hemorrhage\u0026rdquo; vs. severe bleeding, and what blood pressure level indicates the existence of shock. A recently published study has made progress in improving the measurement of complication severity by using more comprehensive severity categories and less subjective clinical indicators.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Unfortunately, the results and recommendations from that analysis was not available at the time this study\u0026rsquo;s instruments were being designed and fielded. Future research aiming to measuring the severity of postabortion complications should attempt to incorporate as many recommendations from Pasquier et al. (2023) as possible.\u003c/p\u003e \u003cp\u003eAs we discussed above, our measure of induced abortion in this study is biased in several ways. It is likely underestimating the true number of induced abortions in our sample, and it may be over-representing induced abortions that were conducted with more harmful methods. Given these concerns, the results indicating a relationship between induced abortion and more severe complications should be interpreted with caution. It is possible that there is no relationship between induced abortion and complication severity in Liberia, especially if women are able to easily access medication abortion, and more research is needed to understand this phenomenon.\u003c/p\u003e \u003cp\u003eWhile the goal of this analysis was to produce nationally representative estimates of the severity of postabortion complications, it is possible that concerns in our sampling and data collection efforts may have resulted in biased estimates. First, we aimed to include all sampled health facilities in the PMS data collection effort, but 22% sampled facilities declined to participate. Our facility level weights accounted for this non-response based on the level of these facilities. However, if other factors related to non-participation, such as staff capacity to attend the study training or actively collect data during the study period, were also related to complication severity, our results may be biased. Further, we were unable to account for non-response at the individual level, which may also impact the representativeness of our sample.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results from this analysis, along with the new estimates of abortion incidence for Liberia,[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] suggest that induced abortion is a common occurrence in Liberia. Further, our results indicate that postabortion complications are a major public health concern. Both of these findings provide evidence in support of the revised Public Health Law. If passed by the national legislature, this newly revised law would create a less restrictive legal context for induced abortion in Liberia as compared to the existing 1976 penal code.\u003c/p\u003e \u003cp\u003eOur results also have important implications for the provision of postabortion care in Liberia. The large proportion of postabortion care patients in Liberia who are experiencing near miss or severe postabortion complications underscores the importance of providing high quality postabortion care, and future research is needed to document the current health system\u0026rsquo;s capacity to provide this care. Further, these results are a call to action for the government of Liberia to invest more in primary level facilities and strengthen their ability to manage postabortion complications. While several aspects of postabortion complication management were in line with recommended guidelines, we did find that provider attitudes and/or practices may be creating differences in family planning uptake based on the severity of women\u0026rsquo;s postabortion complications. Future investigation is needed to better understand the mechanisms behind this relationship, the results of which can be used to improve the provision of postabortion family planning services in Liberia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was received from 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; the Clinton Health Access Initiative\u0026rsquo;s internal Scientific and Ethical Review Committee (SERC); and the Institutional Review Board of the African Population and Health Research Center. \u0026nbsp;All study investigators completed the human subjects\u0026rsquo; protection training before engaging in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication \u0026ndash; N/A\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and materials are available on request. According to the APHRC policies (the organization hosting the datasets), all deidentified datasets will be publicly available on the APHRC microdata portal after 7 years (https://aphrc.org/microdata-portal/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by a grant from the African Regional Office of the Swedish International Development Cooperation Agency, Sida Contribution No. 12103, for APHRC\u0026rsquo;s Challenging the Politics of Social Exclusion project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBU, KJ and MM conceived of the idea for this study. BU, KJ, MM, LL, and VD oversaw data collection. MG and OO analyzed the data. VD, OO, BT, and MG interpreted the results and framed the discussion. MG drafted the manuscript. All authors provided critical review of the manuscript and have approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study team wishes to thank the Ministry of Health and Social Welfare, Liberia for supporting the research. We express gratitude to the facility managers and the health care providers for the collaboration and support accorded during the study. Our appreciation also goes out to all the study participants: health providers, women and girls, and other professionals who agreed to participate in the research and furnished the data used in the report.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO et al. Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications-detail-redirect/9789240068759\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications-detail-redirect/9789240068759\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeller SE, Cox SM, Callaghan WM, Berg CJ. Morbidity and mortality in pregnancy: laying the groundwork for safe motherhood. Women\u0026rsquo;s health issues. 2006;16:176\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKilpatrick SK, Ecker JL. American College of Obstetricians and Gynecologists. Severe maternal morbidity: screening and review. 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Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications-detail-redirect/9789240039483\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications-detail-redirect/9789240039483\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSieverding M, Schatzkin E, Shen J, Liu J. Bias in Contraceptive Provision to Young Women Among Private Health Care Providers in South West Nigeria. Int Perspect Sex Reproductive Health. 2018;44:19\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolo J, Festin M. Provider Bias in Family Planning Services: A Review of Its Meaning and Manifestations. Global Health: Sci Pract. 2019;7:371\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.9745/GHSP-D-19-00130\u003c/span\u003e\u003cspan address=\"10.9745/GHSP-D-19-00130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasquier E, Owolabi OO, Fetters T, Ngbale RN, Adame Gbanzi MC, Williams T, et al. High severity of abortion complications in fragile and conflict-affected settings: a cross-sectional study in two referral hospitals in sub-Saharan Africa (AMoCo study). BMC Pregnancy Childbirth. 2023;23:143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12884-023-05427-6\u003c/span\u003e\u003cspan address=\"10.1186/s12884-023-05427-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e While we originally defined this measure as MVA or EVA, there was only 1 case in our sample where EVA was used.\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":"Post-abortion care, induced abortion, complication severity, maternal morbidity and mortality, family planning services","lastPublishedDoi":"10.21203/rs.3.rs-4757559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4757559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Complications from unsafe abortion are a major contributor to maternal morbidity and mortality in resource poor settings. This study aims to assess the severity and management of abortion complications in Liberia.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Data were collected among a nationally representative sample of health facilities in Liberia (n=100). Study staff administered a survey to all postabortion care (PAC) patients and their health providers arriving at a study facility over the course of 30 days. A total of 387 patient surveys and 429 provider surveys were included in the final analysis. Postabortion complication severity was classified into five categories, ranging from mild to near miss. Likely induced abortions were identified though patient self-reports reports and provider reports of clinical evidence of a foreign body or mechanical injury. We conducted bivariate tests to determine whether PAC management practices varied by complication severity. Poisson regression models were used to assess the relationship between patient characteristics and complication severity, as well as between complication severity and receipt of a family planning method.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Overall, 10.9% of PAC patients were classified as near miss and 34.7% had severe complications. Likely induced abortions were identified in 38% of women. Having a more severe complication was associated with marital status (uIRR 0.69, 95% CI 0.48,0.99) and the indicator for likely induced abortion (uIRR 1.74, 95% CI 1.11,2.74). Most women accessed PAC at primary level facilities (76.7%). The most common uterine evacuation methods were MVA (67.5%) and misoprostol (16.3%). Only 38.9% of patients received a family planning method prior to discharge. Controlling for age, residence, future intentions to use, and provider type, women classified as having severe or near miss complications were approximately twice as likely (aIRR 1.98, 95% CI 1.18,3.32) to receive a contraceptive method.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Postabortion complications are a major public health concern in Liberia. Our results underscore the need for high quality postabortion care and greater access to safe abortion care. Liberia should invest in primary level facilities and strengthen their ability to manage postabortion complications. Future research is needed to understand how provider practices/attitudes shape the provision of postabortion family planning services.\u003c/p\u003e","manuscriptTitle":"The severity and management of postabortion care complications in Liberia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 10:42:11","doi":"10.21203/rs.3.rs-4757559/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":"29a0f6c6-a2a5-4a70-a846-b8c1d8ddc714","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-14T12:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 10:42:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4757559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4757559","identity":"rs-4757559","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}