The authors have declared that no competing interests exist.
Conceived and designed the experiments: LAVO EWO MT SOS MML. Performed the experiments: LAVO EWO IT MK MML. Analyzed the data: LAVO EWO. Wrote the paper: LAVO EWO IT MK SOS MT MML.
Maternal immunization has gained traction as a strategy to diminish maternal and young infant mortality attributable to infectious diseases. Background rates of adverse pregnancy outcomes are crucial to interpret results of clinical trials in Sub-Saharan Africa.
We developed a mathematical model that calculates a clinical trial's expected number of neonatal and maternal deaths at an interim safety assessment based on the person-time observed during different risk windows. This model was compared to crude multiplication of the maternal mortality ratio and neonatal mortality rate by the number of live births. Systematic reviews of severe acute maternal morbidity (SAMM), low birth weight (LBW), prematurity, and major congenital malformations (MCM) in Sub-Saharan African countries were also performed.
Accounting for the person-time observed during different risk periods yields lower, more conservative estimates of expected maternal and neonatal deaths, particularly at an interim safety evaluation soon after a large number of deliveries. Median incidence of SAMM in 16 reports was 40.7 (IQR: 10.6–73.3) per 1,000 total births, and the most common causes were hemorrhage (34%), dystocia (22%), and severe hypertensive disorders of pregnancy (22%). Proportions of liveborn infants who were LBW (median 13.3%, IQR: 9.9–16.4) or premature (median 15.4%, IQR: 10.6–19.1) were similar across geographic region, study design, and institutional setting. The median incidence of MCM per 1,000 live births was 14.4 (IQR: 5.5–17.6), with the musculoskeletal system comprising 30%.
Some clinical trials assessing whether maternal immunization can improve pregnancy and young infant outcomes in the developing world have made ethics-based decisions not to use a pure placebo control. Consequently, reliable background rates of adverse pregnancy outcomes are necessary to distinguish between vaccine benefits and safety concerns. Local studies that quantify population-based background rates of adverse pregnancy outcomes will improve safety assessment of interventions during pregnancy.
Maternal and neonatal mortality in Sub-Saharan Africa present a major barrier to achievement of Millennium Development Goals 4 and 5. Sub-Saharan Africa accounts for 52-57% of all maternal deaths worldwide and contains 23 of the 27 countries with neonatal morality rates greater than 30 per 1000 live births
Vaccination to prevent tetanus, influenza, hepatitis B, and invasive meningococcal disease is currently recommended for pregnant women in high-income countries
This review seeks to assemble and synthesize available data on pregnancy outcomes in Sub-Saharan Africa to provide a context for assessing the safety of maternal immunization at interim stages during a clinical trial as well as after follow-up is complete. We present expected rates of maternal mortality, severe acute maternal morbidity (SAMM), neonatal mortality, low birth weight (LBW), and major congenital malformations (MCM) in a hypothetical cohort of pregnant women enrolled in a clinical trial in Sub-Saharan Africa.
We used a mathematical model to develop interim estimates of maternal and neonatal deaths over the course of a clinical trial. Maternal mortality was defined as death from direct or indirect obstetric causes during pregnancy or <42 days after pregnancy termination
In an interim analysis of vaccine safety by an independent DSMB during a clinical trial of maternal immunization, the expected number of maternal deaths depends on the person-time observed during different risk windows. We considered three periods: pregnancy, within 24 hours of delivery, and the first 42 days post-partum. Large prospective cohort studies in Sub-Saharan Africa were combined by random-effects meta-analysis to determine the proportions of maternal deaths occurring during each interval
Where
This equation makes the following three assumptions, all of which bias towards underestimating the expected background number of deaths and hence create more stringent thresholds for evaluating vaccine safety.
The risk of maternal death before delivery is evenly distributed throughout pregnancy.
The risk of maternal death between 24 hours after delivery and 42 days post-partum is also uniformly distributed.
The maternal mortality ratio multiplied by 1 minus the stillbirth rate approximates the ratio of maternal deaths per 100,000 total births. We also assume that each birth contributes 46 weeks of maternal person-time, yielding an incidence of maternal deaths per 100,000 * 46 maternal weeks. The calculated incidence is lower than the true incidence of maternal deaths because women who die before finishing 6 weeks post-partum contribute fewer than 46 maternal weeks of person-time.
Similarly, the expected number of stillbirths and neonatal deaths at an interim analysis would depend on the number of deliveries and the amount of person-time observed:
Where
We compared the observation-time model estimates of maternal and neonatal deaths to a crude calculation based on the number of live births:
Statistical calculations were performed in R Version 2.12.1, WinBUGS Version 1.4, and StatsDirect Version 2.7.8.
Systematic reviews were conducted on the following pregnancy outcomes in Sub-Saharan Africa: severe acute maternal morbidity (SAMM), low birth weight (LBW), small for gestational age (SGA), prematurity, and major congenital malformations (MCM). For each systematic review, we searched the Medline electronic database for English, French, and Portuguese language publications in peer-reviewed journals. Prospective cohorts, retrospective cohorts, and cross-sectional studies with defined catchment populations were included. Studies in which all data were collected before 1991 were excluded except in the review of congenital malformations. Studies with <100 live births in Sub-Saharan Africa were excluded. Regions of Sub-Saharan Africa were defined by the Global Burden of Disease Study
We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines
Literature search terms were “severe maternal morbidity OR near miss” along with the name of each country in Sub-Saharan Africa. The principal outcome measure was the incidence of severe acute maternal morbidity (SAMM) or near-miss cases, defined as direct or indirect obstetric complications that threaten the woman's survival but do not lead to her death
Literature search terms were “low birth weight OR prematurity OR small for gestational age,” along with the name of each country in Sub-Saharan Africa. LBW was defined as birth weight <2.5 kg measured by the study team within 7 days of life or extracted from official birth records. Prematurity was defined as estimated gestational age at delivery <37 weeks as determined by ultrasound, last menstrual period, or validated exam within 7 days of life
Literature search terms were “major congenital abnormalities OR congenital malformations [title] OR congenital anomalies [title]” along with the name of each country in Sub-Saharan Africa. The principal outcome measure was the incidence of MCM in liveborn infants, defined as structural defects of the body and/or the organs that affect viability or quality of life and require medical intervention
We compared the expected background rates of maternal and neonatal deaths over the course of a hypothetical clinical trial of 1000 pregnant women in Mali given by the observation-time model to a crude calculation based on live births (
Assuming a maternal mortality ratio of 418.8 (327.5–519.8) per 100,000 live births, stillbirth rate of 23 (18–42) per 1,000 total births, early neonatal mortality rate of 33.5 (28.1–39.0) per 1,000 live births, and late neonatal mortality rate of 12.4 (9.9–15.5) per 1,000 live births estimated for Mali by recently published systematic analyses
We screened 469 titles and abstracts and 37 full texts and included 16 studies of SAMM in Sub-Saharan Africa (
A. Severe Acute Maternal Morbidity. B. Low Birth Weight, Prematurity, and Small for Gestational Age. C. Congenital Malformations.
Study Author | Country | Years | SAMM Definition | MI (%) | Site of case detection | Denominator | Size | #SAMM per 1000 Births | SAMM by Cause (%) | |||||
Hem | Dyst | HTN | Anem | Infect | Other | |||||||||
Okong |
Uganda | 1999–2000 | Organ failure/management | 54 | Urban and rural hospitals | Hospital births | 55803 LB |
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42 | 21 | 7.9 | 0 | 18 | 11 |
Gandhi |
South Africa | — | Organ failure/management | — | Rural hospitals | Hospital and ancillary clinic births | 5728 TB |
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19 | 6.5 | 52 | 0 | 9.7 | 13 |
Cochet |
South Africa | 2000–2001 | Organ failure/management | 14 | Urban hospitals | Hospital births | 29832 TB |
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40 | 0 | 15 | 0 | 12 | 34 |
Van den Akker |
Malawi | 2007–2009 | Disease-Specific | 12 | Rural hospital | All facility deliveries in district | 33254 D |
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32 | 11 | 20 | 0 | 32 | 5.1 |
Mantel |
South Africa | 1996–1997 | Organ failure/management | 17 | Urban hospitals | All deliveries in region | 13429 D |
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26 | 0 | 26 | 0 | 20 | 29 |
Vandecruys |
South Africa | 1997–1999 |
Organ failure/management | 16 | Urban hospitals | Hospital births | 26152 TB |
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18 | 0 | 40 | 0 | 13 | 28 |
Ali |
Sudan | 2008–2010 | Disease-Specific | 16 | Urban hospital | Hospital births | 9578 TB |
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41 | 8 | 18 | 12 | 22 | 0 |
Mayi-Tsonga |
Gabon | 2006 | Disease-Specific | — | Urban hospital | Hospital births | 4350 TB |
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82 | 0 | 14 | 0 | 4.4 | 0 |
Nyamtema |
Tanzania | 2008–2010 | Disease-Specific | 9.9 | Rural hospital | Hospital births | 6572 TB |
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30 | 22 | 28 | 8.0 | 4.0 | 8.6 |
Prual |
6 countries (West Africa) | 1994–1996 | Disease-Specific | 3.7 | Predominantly urban communities | Pregnant women followed prospectively | 20326 D |
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50 | 34 | 10 | 0 | 1.0 | 4.0 |
Prual |
Niger | — | Disease-Specific | 8.3 | Urban hospitals | Hospital and ancillary clinic births | 4081 TB |
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13 | 56 | 18 | 0 | 3.4 | 8.6 |
Gessessew |
Ethiopia | 1993–2003 | Disease-Specific | 6.9 | Urban hospital | Hospital births | 7150 D |
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21 | 70 | 9.3 | 0 | 0 | 0 |
Lori |
Liberia | 2008 | Disease-Specific | 19 | Rural hospital | Hospital births | 1386 TB |
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42 | 5 | 11 | 21 | 14 | 4.0 |
Filippi |
Benin, Côte d'Ivoire | 1999–2001 | Disease-Specific | 6.5 | Predominantly urban hospitals | Hospital births | 27620 TB |
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34 | 15 | 26 | 19 | 5.4 | 0 |
Oladapo |
Nigeria | 1999–2004 | Disease-Specific | 16 | Urban hospital | Hospital deliveries | 2577 D |
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31 | 20 | 30 | 9.0 | 11 | 0 |
Cham |
Gambia | 2006 | Disease-Specific | 3.6 | Urban and rural hospitals | Hospital births | 3280 TB |
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20 | 26 | 26 | 17 | 1.4 | 10 |
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34 | 22 | 22 | 10.5 | 7.0 | 5.0 |
SAMM: Severe Acute Maternal Morbidity; MI: Mortality Index (# of maternal deaths divided by the sum of near-miss cases and maternal deaths) TB: Total births; D: Deliveries; Hem: Hemorrhage; Dyst: Dystocia (includes uterine rupture); HTN: Hypertensive diseases of pregnancy (severe pre-eclampsia and eclampsia); Anem: Anemia; Infect: Infection;
Data from the year 2000 published by Vandecruys et al
In Prual et al
We screened 1766 titles and abstracts and 245 full texts and included 104 studies in our review of LBW, prematurity, and SGA (
We screened 184 titles and abstracts and 30 full texts and included 11 studies in our review of MCM among liveborn infants in Sub-Saharan Africa (
Author | Country | Years | Study Design | Method of CM Detection | # LB | # Expected MCM per 1000 LB |
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MSK | CNS | GI | GU | Chrom | CV | HEENT | Resp | Multip | Other | |||||||
Ahuka |
Dem Rep Congo | 1993–2001 | Retrospective cohort, hospital based | Midwife exam at birth | 8824 |
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31 | 47 | 17 | 2.8 | 0 | 0 | 0 | 0 | 0 | 2.8 |
Sukhani |
Zambia | 1976 | Prospective cohort, hospital based | Physical exam at birth, imaging as indicated | 17030 |
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13 | 19 | 16 | 9.7 | 13 | 9.7 | 0 | 4.3 | 8.6 | 6.5 |
Embree |
Kenya | — | Prospective cohort, hospital based | Standardized exam at birth and 6 month intervals | 183 |
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— | — | — | — | — | — | — | — | — | — |
Shija |
Zimbabwe | 1984 | Prospective cohort, hospital based | Physical exam at birth or pediatric surgery clinic | 18033 |
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21 | 4.5 | 29 | 12 | 0 | 0 | 0 | 0 | 15 | 20 |
Delport |
South Africa | 1986–1989 | Prospective cohort, hospital based | Physician exam at birth | 17351 |
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18 | 19 | 9.2 | 7.8 | 14 | 15 | 1.0 | 0.5 | 3.9 | 11 |
Abudu |
Nigeria | 1982–1983 | Prospective cohort, hospital based | Physician exam at birth, autopsy | 2912 |
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— | — | — | — | — | — | — | — | — | — |
Venter |
South Africa | 1989–1992 | Prospective cohort, hospital based | Standardized exam by geneticist at birth, lab tests and imaging as indicated | 7617 |
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23 | 28 | 5.2 | 8.6 | 18 | 0 | 0.9 | 0 | 7.8 | 7.8 |
Stevenson |
South Africa | 1961–1964 | Prospective cohort, hospital based | Standardized exam at birth | 23675 |
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— | — | — | — | — | — | — | — | — | — |
Khan |
Zambia | 1974–1975 | Prospective cohort, hospital based | Physical exam at birth | 8508 |
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63 | 4.0 | 6.0 | 5.3 | 2.7 | 4.7 | 1.3 | 0 | 6.0 | 6.7 |
Gupta |
Nigeria | 1964 | Prospective cohort, hospital based | Standardized exam at birth | 4054 |
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39 | 14 | 19 | 5.5 | 0 | 10 | 7.3 | 0 | 0 | 5.5 |
Bakare |
Nigeria | 2003–2004 | Prospective cohort, outpatient delivery wards | Physical exam at birth | 624 |
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43 | 13 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 4.3 |
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CNS = Central Nervous System (ICD-10: Q00–Q07); Resp = Respiratory (ICD-10: Q30–Q34); CV = Cardiovascular (ICD-10: Q20–Q28); MSK = Musculoskeletal system (ICD-10: Q65–79); GI = Digestive system (ICD-10: Q35–Q45); GU = genital organs and urinary system (ICD-10: Q50–Q56, Q60–Q64); HEENT = Eye, ear, face, and neck (ICD-10: Q10–Q18); Chrom = Chromosomal abnormalities (ICD-10: Q90–Q99); Multip = Major congenital malformations in multiple systems.
Denominator given in total births.
Pregnancy outcomes including maternal deaths, SAMM, stillbirths, neonatal deaths, LBW, prematurity, and MCM are summarized for the 4 sub-regions of Sub-Saharan Africa (
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Central | 4.5 (3.7–5.3) | 31.5 (—) | 24.6 (12.3–52.4) | 25.4 (22.6–28.3) | 8.8 (3.1–5.0) | 12.4 (10.3–34.2) | 17.9 (2.3–21.3) | 0.41 (—) |
East | 4.0 (3.7–4.4) | 21.4 (10.2–59.4) | 24.8 (16.3–43.9) | 20.3 (19.1–21.5) | 6.9 (6.3–7.6) | 12.4 (6.3–37.1) | 11.6 (3.4–20.3) | 0.55 (0.53–1.76) |
Southern | 1.7 (1.4–2.0) | 6.8 (5.2–11.0) | 20.1 (13.4–32.7) | 13.1 (12.2–14.3) | 4.2 (3.7–4.9) | 14.1 (6.0–20.3) | 18.7 (17.3–20.1) | 1.36 (0.70–1.56) |
West | 4.6 (4.2–5.1) | 92.6 (56.8–242.7) | 33.3 (20.3–58.8) | 26.2 (24.1–28.4) | 9.7 (8.7–10.7) | 13.3 (5.5–29.0) | 13.4 (5.3–30.5) | 2.69 (1.44–3.69) |
SAMM = Severe Acute Maternal Morbidity; NND = Neonatal Deaths; LBW = Low Birth Weight; MCM = Major Congenital Malformations.
For Severe Maternal Morbidity, Low Birth Weight, Prematurity, and Major Congenital Malformations, the median and range of a systematic review is presented for the entire region. In Central Africa, only 1 data point was available for Severe Maternal Morbidity
Maternal and neonatal deaths were expressed as a fraction of total births by multiplying the maternal mortality ratio (maternal deaths/live births) and the early and late neonatal mortality ratios (neonatal deaths/live births) by 1 minus the stillbirth rate
Maternal deaths per 1000 Total Births |
Stillbirths per 1000 Total Births |
Early neonatal deaths per 1000 Total Births |
Late neonatal deaths per 1000 Total Births |
% LBW |
% <37 wks |
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Angola | 3.3(2.1–4.5) | 25.1(12–54) | 28.0(24–33) | 10.2(8.0–13) |
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Central African Republic | 6.7(5.0–8.6) | 24.2(12–49) | 31.5(27–37) | 12.9(11–16) |
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Congo (Brazzaville) | 12.4 (—) | 16.7 (—) | ||||
Congo (Dem Republic) | 4.7(3.6–5.9) | 25.6(14–55) | 24.4(21–28) | 8.2(6.7–9.7) | 20.0 (10.5–34.2) | 2.3 ( |
Equatorial Guinea | 2.1(1.3–3.2) | 16.9 (8–36) | 35.1(29–42) | 15.1(12–19) |
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Gabon | 4.2 (3.1–5.4) | 17.3 (9–38) | 22.4 (19–26) | 3.9 (3.1–5) | 10.5 (10.3–10.7) | 20.2 (19.1–21.3) |
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Burundi | 8.7(6.1–11) | 27.7(15–61) | 19.1(16–22) | 9.3(7.6–11) |
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Comoros | 2.6(1.9–3.7) | 27.0(14–59) | 21.3(19–24) | 8.4(7.1–9.7) |
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Djibouti | 3.5(2.5–4.8) | 33.9(15–55) | 17.4(15–20) | 5.4(4.4–6.7) |
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Eritrea | 10.6(8.1–13) | 21.2(11–49) | 17.0(15–20) | 4.6(3.6–5.9) |
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Ethiopia | 5.2(3.8–6.6) | 25.6(15–52) | 24.3(21–28) | 8.5(6.9–10) | 9.7 (6.3–20.3) | 13.5 (11.6–15.3) |
Kenya | 2.9(2.2–3.6) | 21.8(14–38) | 18.6(17–21) | 4.9(4.3–5.5) | 9.7 (7.9–18.0) | 11.3 (3.4–19.1) |
Madagascar | 4.2(3.3–5.2) | 20.6(15–36) | 14.0(13–16) | 4.8(4.3–5.4) |
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18.1 ( |
Malawi | 4.1(3.1–5.3) | 23.7(17–35) | 20.1(17–23) | 6.4(5.5–7.7) | 15.1 (13.3–18.3) | 17.5 (17.3–17.6) |
Mozambique | 5.0(3.7–6.5) | 28.4(17–51) | 27.1(24–31) | 10.5(9.0–12) | 16.2 ( |
15.4 ( |
Rwanda | 3.3(2.2–4.8) | 22.8(16–36) | 19.3(17–22) | 5.8(4.9–6.7) |
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Somalia | 4.6(3.2–6.3) | 30.1(15–64) | 16.5(14–19) | 9.5(7.8–12) |
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Sudan | 2.7(2.0–3.5) | 23.9(17–40) | 20.1(17–23) | 7.2(6.0–8.8) | 14.9 (8.3–18.0) | 5.7 ( |
Tanzania | 4.1(3.3–5.0) | 25.6(19–40) | 18.0(16–20) | 5.7(5.1–6.4) | 14.2 (8.6–22.4) | 8.3 (7.9–10.0) |
Uganda | 2.7(2.0–3.4) | 24.8(19–36) | 20.6(18–23) | 5.9(5.1–6.8) | 9.6 (6.4–37.1) | 20.3 ( |
Zambia | 2.9(2.2–3.8) | 25.5(18–40) | 17.2(15–19) | 8.5(7.4–9.6) |
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Botswana | 5.1(3.6–6.8) | 16.2 (9–37) | 14.8(12–18) | 3.4(2.4–4.6) | 13.0 ( |
20.1 ( |
Lesotho | 2.3(1.7–3.2) | 25.2(14–54) | 20.4(27–34) | 8.1(6.7–9.7) |
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Namibia | 1.3(1.0–1.8) | 15.0(11–35) | 18.5(16–21) | 4.1(3.2–5.3) |
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South Africa | 0.9(0.7–1.2) | 20.4(14–31) | 10.6(10–12) | 3.4(3.1–3.8) | 14.7 (13.8–16.3) |
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Swaziland | 2.8(2.0–3.7) | 18.2(11–36) | 18.1(15–21) | 5.0(3.8–6.6) |
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Zimbabwe | 3.2(2.3–4.6) | 20.0(13–35) | 16.2(14–19) | 5.9(4.5–7.6) | 14.1 (6.0–20.3) | 17.3 ( |
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Benin | 3.2(2.5–4.1) | 24.3(17–41) | 22.8(20–26) | 5.8(4.8–6.9) | 15.7 (10.0–17.8) |
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Burkina Faso | 3.4(2.8–4.1) | 26.2(19–40) | 24.8(21–30) | 13.1(10–16.5) | 12.2 (—) |
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Cameroon |
5.2(4.0–6.7) | 25.6(13–54) | 25.2(22–29) | 7.8(5.9–9.6) | 16.4 (9.6–20.3) | 20.3 (—) |
Cape Verde | 1.3(0.9–1.7) | 15.6 (8–34) | 10.8(9.0–13) | 3.0(2.4–3.8) | 8.2 (—) | 13.4 (—) |
Chad |
5.9(4.8–7.1) | 29.2(14–64) | 30.8(27–36) | 13.7(11–16) |
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Côte d'Ivoire | 4.4(3.3–5.8) | 27.4(14–45) | 26.2(22–30) | 10.3(7.9–13) | 10.6 (—) |
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The Gambia | 2.7(1.9–3.5) | 26.0(14–53) | 22.4(19–26) | 7.3(5.6–9.4) | 18.6 (13.3–23.9) | 21.4 (12.3–30.5) |
Ghana | 3.2(2.4–4.0) | 22.0(14–37) | 19.8(17–22) | 4.7(4.0–5.6) | 16.4 (13.3–20.3) | 14.1 (—) |
Guinea | 6.5(5.1–7.9) | 23.8(16–48) | 28.5(25–33) | 10.3(8.4–13) |
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Guinea-Bissau | 8.2(6.3–10) | 29.6(16–62) | 30.9(26–36) | 13.8(11–17) | 13.3 (11.8–14.7) |
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Liberia | 8.8(7.1–10) | 26.9(14–56) | 23.5(21–26) | 7.6(6.5–9.0) |
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Mali | 4.1(3.2–5.1) | 23.2(18–42) | 32.7(27–38) | 12.1(9.7–15) | 18.6 (—) | 5.3 (—) |
Mauritania | 5.4(4–7.0) | 27.4(17–51) | 24.7(21–29) | 6.3(4.8–8) |
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Niger | 5.1(4.1–6.2) | 22.9(17–41) | 20.0(17–24) | 11.6(9.6–14) |
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Nigeria | 4.7(3.8–5.6) | 41.7(25–72) | 27.5(24–32) | 9.9(8.3–11) | 12.2 (5.5–29) | 13.5 (10.6–19.4) |
Sao Tome and Principe | 2.6(3.3–3.3) | 21.0(11–48) | 16.7(15–19) | 4.1(3.5–4.7) |
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Senegal | 3.6(2.7–4.5) | 33.8(27–50) | 18.8(16–22) | 6.6(5.3–7.9) | 10.3 (9.5–18.8) |
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Sierra Leone | 6.0(4.7–7.4) | 30.0(16–66) | 26.3(23–30) | 9.0(7.5–11) |
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Togo | 3.9(2.6–5.5) | 25.0(13–54) | 26.7(23–31) | 6.9(5.6–8.7) |
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11.1 (—) |
Maternal and neonatal deaths were expressed as a fraction of total births by multiplying the maternal mortality ratio (maternal deaths/live births) and the neonatal mortality ratio (neonatal deaths/live birth) by 1 minus the stillbirth rate
For % LBW and % <37 wks, the median and range for all studies performed in the specified country are presented.
Note –UN Data classifies Cameroon and Chad as falling within the Middle Africa sub-region rather than the West Africa sub-region. However, in this table these countries are kept within the West Africa sub-region to maintain congruity with global burden of disease publications.
We present a comprehensive review of pregnancy outcomes in Sub-Saharan Africa intended to inform safety assessments of current and future clinical trials conducted in pregnant women or neonates. The observation-time model of maternal and neonatal mortality accounts for the logistical realities of variation in gestational age at recruitment, time to delivery, and follow-up time. This model yields lower mortality estimates than multiplying mortality rates by cumulative live births, especially if a safety evaluation is conducted immediately after a cluster of deliveries. Overall, our review noted that the incidences of maternal and neonatal mortality, severe acute maternal morbidity, stillbirths, and major congenital malformations are highest in West Africa, the least developed sub-region of the continent. However, relatively few detailed published studies of severe acute maternal morbidity and congenital malformations in Sub-Saharan Africa exist, especially in the community setting.
Of all adverse pregnancy outcomes, the incidences of SAMM and MCM were the most variable across sub-regions. Studies with more stringent organ failure/management-based definitions found lower incidences of SAMM. Many studies of major congenital malformations were not included because malformations among live- and stillborn infants could not be separated. The limited time frame and diagnostic tools to detect congenital malformations in many studies may have led to systematic underreporting of specific types of malformations not readily apparent by physical examination at birth, such as many cardiovascular anomalies.
This compilation of pregnancy outcomes relevant to a maternal immunization clinical trial is consistent with the few other published summaries of pregnancy outcomes in Sub-Saharan Africa. Kaye et al
Our systematic review of LBW and prematurity does not yield nationally representative populations as are sought out in Demographic and Health Surveys and Multiple Indicator Cluster Surveys
We recognize that our study has several limitations. First, we do not address differences in neonatal and maternal mortality rates among sub-populations within an individual country or across time that arise from geographic, socioeconomic, educational, and health access diversity. Thus, a DSMB should use its expertise and the best available data to select the most locally appropriate background rates for anticipated adverse event calculations. Second, pregnant women under the active surveillance of a clinical trial tend to have better access to medical care and therefore superior pregnancy outcomes relative to the general population. As in all mathematical models and systematic reviews, we are limited by the quality of the data informing our calculations. Many countries in Sub-Saharan Africa contribute incomplete vital registration data to systematic analyses of maternal mortality, stillbirths, and neonatal mortality
The global public health community awaits with great anticipation the results of several ongoing clinical trials in developing countries that explore whether maternal immunization improves pregnancy outcomes and enhances young infant survival. In some of these trials, investigators have chosen to avoid the use of pure placebo in the control group, instead providing a licensed vaccine with an independent potential benefit that does not influence the primary outcome. For example, in an ongoing clinical trial in Mali assessing the effectiveness of maternal influenza immunization against influenza in mothers and their infants, the pregnant women randomized to the control group receive quadrivalent meningococcal conjugate vaccine (NCT01430689). In such situations where for ethical reasons the study design does not include a true placebo group, reliable background rates of adverse pregnancy outcomes are invaluable to help distinguish between vaccine benefits and safety concerns. Further studies that clarify locally relevant, population-based background rates of adverse pregnancy outcomes will improve safety assessment of maternal interventions.
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We would like to thank the Centre pour le Développement des Vaccins, Mali (in particular, Dr. Fadima Haidara, Dr. Moussa Doumbia, Dr. Fatoumata Diallo, and Dr. Flanon Coulibaly) for their advice and support throughout this project. We would also like to thank the participants, communities, and study teams involved in an ongoing trial of maternal influenza immunization in Bamako, Mali, as well as the Mali Ministry of Health and staff at the Gabriel Touré Teaching Hospital.