Literature DB >> 28882128

Institutional maternal and perinatal deaths: a review of 40 low and middle income countries.

Patricia E Bailey1,2, Wasihun Andualem3, Michel Brun4, Lynn Freedman5, Sourou Gbangbade6, Malick Kante5, Emily Keyes7,5, Edwin Libamba8, Allisyn C Moran9, Halima Mouniri10, Dahada Ould El Joud11, Kavita Singh12,13.   

Abstract

BACKGROUND: Understanding the magnitude and clinical causes of maternal and perinatal mortality are basic requirements for positive change. Facility-based information offers a contextualized resource for clinical and organizational quality improvement. We describe the magnitude of institutional maternal mortality, causes of death and cause-specific case fatality rates, as well as stillbirth and pre-discharge neonatal death rates.
METHODS: This paper draws on secondary data from 40 low and middle income countries that conducted emergency obstetric and newborn care assessments over the last 10 years. We reviewed 6.5 million deliveries, surveyed in 15,411 facilities. Most of the data were extracted from reports and aggregated with excel.
RESULTS: Hemorrhage and hypertensive diseases contributed to about one third of institutional maternal deaths and indirect causes contributed another third (given the overrepresentation of sub-Saharan African countries with large proportions of indirect causes). The most lethal obstetric complication, across all regions, was ruptured uterus, followed by sepsis in Latin America and the Caribbean and sub-Saharan Africa. Stillbirth rates exceeded pre-discharge neonatal death rates in nearly all countries, possibly because women and their newborns were discharged soon after birth.
CONCLUSIONS: To a large extent, facility-based findings mirror what population-based systematic reviews have also documented. As coverage of a skilled attendant at birth increases, proportionally more deaths will occur in facilities, making improvements in record-keeping and health management information systems, especially for stillbirths and early neonatal deaths, all the more critical.

Entities:  

Keywords:  Cause of maternal death; Cause specific case fatality rate; Direct and indirect deaths; Early neonatal death rate; Perinatal mortality; Stillbirth rate

Mesh:

Year:  2017        PMID: 28882128      PMCID: PMC5590194          DOI: 10.1186/s12884-017-1479-1

Source DB:  PubMed          Journal:  BMC Pregnancy Childbirth        ISSN: 1471-2393            Impact factor:   3.007


Background

Post Millennium Development Goal global action agendas such as the Sustainable Development Goals (SDGs), Every Newborn Action Plan (ENAP) and Ending Preventable Maternal Mortality continue to measure global progress to reduce the maternal mortality ratio (MMR), the neonatal mortality rate, and now (under ENAP guidance) the stillbirth rate [1-3]. Understanding the magnitude and clinical causes of maternal and perinatal mortality are basic requirements for policy setting, program design, innovation testing, and the implementation of evidence-based interventions. Understanding maternal and newborn outcomes captured at health facilities presents an opportunity for health care staff and decision-makers to reflect on what they could do better. High quality data on how many maternal and newborn deaths occur and their causes are notoriously difficult to come by and global estimates come from complex models based on multiple sources: vital registration data, confidential enquiries, large household surveys, reproductive-age mortality studies, research reports, surveillance data, and verbal autopsies [4-8]. Over the last few decades the Maternal Mortality Estimation Inter-Agency Group produced a series of maternal mortality estimates [7, 9, 10], the Global Burden of Disease Studies contributed important systematic analyses of trends, projections, and causes of maternal and child mortality [6, 11, 12], while the World Health Organization (WHO) produced two systematic analyses of the global causes of maternal death [13, 14]. The authors of these comprehensive systematic reviews shy away from using routine health facility data because of inherent selection bias. However, in the 2003–2009 WHO systematic analysis, the authors consulted health facility data when the institutional delivery rate in that country was 50% or greater during the period reviewed [14]. Globally, the coverage of skilled attendant at birth increased from 59% in 1990 to 71% in 2015 [15]. More countries are adopting a 100% institutional delivery policy and institutional delivery rates are rising. This shift means that proportionately more individuals with peripartum and perinatal complications will access treatment, and mortality events, when they happen, are more likely to occur in facilities than at home. In low and middle income countries facility-based maternal and perinatal mortality figures do not yet substitute population-derived estimates as they reflect only those women and newborns who succeed in accessing facility-based care. Facility-based events are highly specific to local contextualized conditions, and thus, are well-suited to inform local policy makers, clinicians and programs to target specific health system strengthening efforts. Most importantly, they can be used to improve service quality. As the SDGs bring a renewed global focus on improving the quality of routine health management information systems, through the Health Data Collaborative and other initiatives, and as they include the use of new technologies, data quality and availability will increase and the cost of collecting data will decrease. This paper reviews data from up to 40 low and middle income countries and describes the magnitude of institutional maternal mortality, causes of maternal death and cause-specific case fatality rates, as well as institutional stillbirth and early or pre-discharge neonatal death rates, in most cases, at the national level. This analysis draws on reports produced over the last decade.

Methods

This secondary data analysis is based on a review of cross-sectional health facility surveys known as emergency obstetric and newborn care (EmONC) assessments, which focus on routine intrapartum care for women and their newborns as well as more complicated births. These assessments have been driven by the United Nations Fund for Population (UNFPA), the United Nations Children’s Fund (UNICEF), the WHO, and the Averting Maternal Death and Disability (AMDD) program at Columbia University. The methods have been described elsewhere, but a summary follows [16, 17].

Sampling

Most EmONC assessments were national in scope and targeted facilities providing childbirth services. As a rule, all hospitals were selected, and if a “census” of childbirth sites was not possible, hospitals were supplemented by either a random sample of mid-level facilities (health centers, clinics), or a “restricted census” of higher volume mid-level facilities that attended more than a specified number of monthly deliveries. Usually, both private and public sector facilities were included. Table 1 shows the number of hospitals and other facilities surveyed in each country and the population size covered by the facilities visited.
Table 1

EmONC assessment characteristics and facility-based rates and ratios (40 countries)

Region, country and year of data collectionPopulation coveredNo. of health facilities providing delivery servicesNo. of hospitalsNo. of other facilitiesInstitu-tional deliveries (12 mo.s)Expected births (12 mo.s)Institu-tional delivery ratePercentage of deliveries with obstetric complica-tionse Institutional maternal deathsf (12 mo.s)Institu-tional MMRMMR (2015)
LAC
 Ecuador 2006a 690,0499908863NRNR30.0%77964
 Guyana 2010751,22351321912,80314,72487.0%7.1%19148229
 Haiti 20099,761,927120596165,731294,03422.4%20.8%170259359
 Nicaragua 20065,626,4931672014794,136176,40953.4%7.2%4144150
 Panama 20072078,4461911830,81152,31058.9%7.5%216894
Western Africa
 Benin 20108,497,82741734383203,412349,85658.1%11.8%483237405
 Burkina Faso 201015,224,7811626611565499,753697,29571.7%9.6%676135371
 Cote d’Ivoire 201021,693,1851419861333342,936790,77643.4%13.7%1416413645
 Gambia 20121,839,4479889051,51869,89973.7%10.7%169328706
 Ghana 201024,232,4311268285983434,508751,20557.8%20.1%840193319
 Guinea 201111,211,22350249453141,724438,29332.3%8.3%459324679
 Liberia 20103,709,8503042727746,841159,52429.4%11.7%335715725
 Mauritania 20113,297,0002541823683,409159,53352.3%5.0%132158602
 Niger 201015,203,82250336467152,415677,35222.5%11.7%1165970553
 Senegal 201312,873,60156029531237,494496,92147.8%5.0%1020429315
 Sierra Leone 2008a 5,532,0001453810725,447254,47210.0%11.4%2128331360
 Togo 20126,191,15586446818133,119202,45165.8%10.7%225169368
Eastern Africa
 Burundi 20108,246,87827448226231,293323,27871.5%4.5%22095712
 Djibouti 2005636,540167911,63622,28952.2%24.4%22189229
 Eritrea 2008b 3,543,5781181810025,000NR26.0%22.3%41164501
 Ethiopia 2008–973,918,505751112639174,5612,638,8916.6%13.9%685392353
 Madagascar 200919,378,009294147147118,774647,22618.4%17.4%357301353
 Malawi 2014c 15,805,23936587278476,272790,26260.3%8.4%586123634
 Mozambique 2012g 23,569,90894756891647,944895,65672.3%4.3%1840284489
 Rwanda 2007a,h 8,934,21540739368207,738384,17154.1%3.4%294142290
 South Sudan 201310,864,3574076334452,842456,30311.6%10.2%497941789
 Zanzibar 2012a 1,460,98779433627,10254,05750.1%9.3%62229NR
 Zambia 2014-15c 15,023,315384118266475,646644,50073.8%5.0%759160224
Central Africa
 Angola 200718,176,68540084316248,187872,48128.4%12.5%1410568477
 Cameroon 201015,544,387607123484282,486615,55845.9%11.4%744263596
 Chad 201111,679,974139578249,202520,9279.4%9.3%10482130856
 Congo 20124,085,4222403220885,038170,36249.9%5.1%246289442
 Dem Rep Congo 201115,421,73126669197158,546616,86925.7%10.8%282178693
 São Tomé & P 2013178,7396155455616688.5%3.2%6110156
Southern Africa
 Lesotho 20151,954,9061602213843,16558,19774.2%8.1%66153487
 Namibia 20052,028,238100415944,59262,87570.9%6.7%57128265
Asia
 Afghanistan 200923,500,00078699192,627993,84019.4%12.9%258134396
 Bangladesh 2012d 43,667,450846234612253,728NRNR17.3%377149176
 Cambodia 201413,388,9101809189119,931382,83031.3%8.2%5748161
 Mongolia 20091,139,4622121031,01227,448113.0%28.3%103244

NR not reported, LAC Latin America and the Caribbean, MMR maternal mortality ratio, mo months, MMR (2015) see reference [7]

aEcuador, Democratic Republic of Congo, Rwanda, São Tomé and Príncipe, Sierra Leone and Zanzibar reported only direct maternal deaths

bEritrea: 25,000 deliveries are live births, based on 2006 data, not EmONC assessment; institutional delivery rate also not based on EmONC assessment

cMalawi and Zambia: deaths and deliveries weighted; in Malawi unweighted deliveries = 367,738 and unwt deaths = 557; in Zambia, unweighted deliveries = 254,790 and unwt deaths = 673

dBangladesh health facilities that performed cesareans were considered hospitals; if not, considered “other”. Sample included 24 districts

eComplications included only major direct obstetric complications (hemorrhage, severe pre-eclampsia/eclampsia, sepsis, prolonged/obstructed labor, severe abortion complications, ruptured uterus, ectopic pregnancy)

fMaternal deaths include all maternal deaths (direct, indirect and unknown causes)

gMozambique: Based on 3 months of data, multiplied by 4 to show 12 months, for consistency across countries

hRwanda: Based on 6 mo.s of data for facility births, complications and deaths, multiplied by 2 to show 12 months of data, for consistency across countries

EmONC assessment characteristics and facility-based rates and ratios (40 countries) NR not reported, LAC Latin America and the Caribbean, MMR maternal mortality ratio, mo months, MMR (2015) see reference [7] aEcuador, Democratic Republic of Congo, Rwanda, São Tomé and Príncipe, Sierra Leone and Zanzibar reported only direct maternal deaths bEritrea: 25,000 deliveries are live births, based on 2006 data, not EmONC assessment; institutional delivery rate also not based on EmONC assessment cMalawi and Zambia: deaths and deliveries weighted; in Malawi unweighted deliveries = 367,738 and unwt deaths = 557; in Zambia, unweighted deliveries = 254,790 and unwt deaths = 673 dBangladesh health facilities that performed cesareans were considered hospitals; if not, considered “other”. Sample included 24 districts eComplications included only major direct obstetric complications (hemorrhage, severe pre-eclampsia/eclampsia, sepsis, prolonged/obstructed labor, severe abortion complications, ruptured uterus, ectopic pregnancy) fMaternal deaths include all maternal deaths (direct, indirect and unknown causes) gMozambique: Based on 3 months of data, multiplied by 4 to show 12 months, for consistency across countries hRwanda: Based on 6 mo.s of data for facility births, complications and deaths, multiplied by 2 to show 12 months of data, for consistency across countries

Primary data collection instruments

In each country, a core team adapted a set of standardized instruments that covered the availability and status of infrastructure, human resources, drugs, equipment, and supplies, and service statistics, in addition to a provider interview and chart reviews [18]. Most relevant to this paper was the 12-month retrospective summary of service statistics that included the number of deliveries, women experiencing obstetric and non-obstetric complications by type of complication, maternal deaths by cause, and birth outcomes. Data collectors extracted data from logbooks in labor and delivery wards, maternity wards, operating theatres, and newborn care units in each facility. When any doubt or clarification was required, data collectors turned to the staff on duty. Definitions of causes of maternal death were informed by the international statistical classification of diseases and related health problems, 10th edition (ICD-10) and its application to deaths during pregnancy, childbirth and the puerperium (ICD-MM). Obstetric complications were elaborated upon to distinguish between antepartum and postpartum hemorrhage and retained placenta. Prolonged and obstructed labor were included, sometimes joined as one category. Ruptured uterus and ectopic pregnancy along with postpartum sepsis, severe pre-eclampsia and eclampsia were the final “major direct complications” listed on the instrument. Indirect complications included malaria, HIV/AIDS, severe anemia, and less commonly, hepatitis and diabetes. In each case, the form included a category for “other” direct complications and “other” indirect complications. Causes of death mirrored the listing of complications. Finally, space permitted the reporting of unspecified/unknown causes of maternal death. For the 12-month summary of maternal deaths, the data collectors were guided by the primary sources they located on the wards or with the staff. Where maternal death audits or reviews took place, those records were also accessed, but generally no subsequent recoding was performed. The 12-month retrospective compilation of service statistics was also designed to test the intrapartum and early neonatal death rate as an indicator of intrapartum care quality [19]. Data extraction from maternity or delivery registers captured the number of antepartum (macerated) and intrapartum (fresh) stillbirths, defined by 28 weeks of gestation or more. Intrapartum stillbirths and live births were divided between those weighing above and below 2500 g. Early neonatal deaths were defined as those occurring before discharge or within the first 24 h, whichever came first. Countries varied widely as to level of detail captured, and thus, categories were added for unspecified stillbirths and birth weights when the timing of death or birth weight was not recorded, and for live births and early neonatal deaths when birth weight was not recorded. These categories for maternal and newborn outcomes were standardized across countries. Data collectors were trained to use a manual with the same definitions for each obstetric complication, type of stillbirth and early neonatal death.

Secondary analysis

EmONC assessment final reports were the source of most of the data compiled in this paper; we had access to primary data in nine countries, but only in two or three situations did we access those data. Because reporting was largely driven by country interests, not all reports contained the same information nor was it presented in a standardized fashion. Consequently, the number of countries in each table differs. For example, some countries did not report the major obstetric complications by type of complication, making it impossible to calculate cause specific case fatality rates. One report candidly reported that the number of maternal deaths was grossly underreported and was not included. Other countries presented the intrapartum and pre-discharge neonatal death rate as recommended, restricting the numerator and denominator to babies weighing 2500 g or more, but they failed to report all stillbirths, nor did they report the number for which birth weight or stillbirth timing was unspecified; these data were not included in the paper. A small number of countries reported only direct maternal deaths, omitting the number of unspecified/unknown maternal deaths or indirect deaths; these reports were retained. Some countries distinguished between antepartum hemorrhage and postpartum hemorrhage, while others reported the two together. About 10 of the 40 countries had conducted more than one EmONC assessment. In all cases, we extracted information from the most recent report except for Ethiopia, whose final report for their most recent assessment was not yet available. Based on numbers drawn from the reports, we calculated the percentage of deliveries with obstetric complications and the institutional maternal mortality ratio, using 100,000 deliveries rather than live births since some countries only counted deliveries. We also calculated any regional aggregations, newborn mortality rates, and the ratio of stillbirths to early neonatal deaths. The case fatality rate was calculated by dividing the number of maternal deaths due to a specific complication by the number of complications treated. The stillbirth rate was estimated by dividing the total number of stillbirths by all deliveries (multiplied by 1000); the pre-discharge neonatal mortality rate was similar but we removed the deliveries resulting in a stillbirth from the denominator.

Data collection and management

Ministries of Health provided oversight to all EmONC assessments and were usually supported by a technical steering committee. Public or private research institutions, universities, or central statistical offices were the most common implementing bodies for the assessments. Data collection teams usually consisted of four data collectors, generally having a health background. Data collectors participated in a weeklong training that included a review of each questionnaire, role plays, and exercises to familiarize themselves with the questionnaires and the data collectors’ manual. Each training included a one-day field activity in local hospitals and health centers where teams completed the questionnaires under supervision. Generally, quality assurance teams closely monitored the first week or two of field activities. Teams usually required one to two days to complete a hospital and half a day to complete a health center. Data collection was paper-based for all countries but one, and data entry performed with CSPro. Report analyses were produced with statistical software such as STATA, SPSS or sometimes excel. When mid-level facilities were sampled, the data were weighted based on selection probability. Weighting is required to account for the non-uniform selection probabilities that would affect how data from selected facilities represent all facilities, including those not selected. Technical support was provided by consultants to the AMDD program. Countries varied by the intensity of support – from no direct AMDD support (Ecuador, Panama, Cote d’Ivoire, Eritrea), to minimal remote support (Mongolia, Cambodia, Afghanistan), to most countries with more intensive support. UNFPA and UNICEF were the predominant supporters for EmONC assessments but bilateral partners and foundations also played important roles depending on the country.

Ethical concerns

Names of women or other identifying information were never included in the primary data collection. Countries followed the guidance of their ministries of health and when required, approval of the protocols and data collection instruments from local institutional review boards was obtained. No additional approval was sought for this paper since the primary source of the data were reports in the public domain.

Results

Up to 40 country reports (including Zanzibar) provided the number of maternal deaths that took place within health care institutions, 31 from sub-Saharan Africa, and the remaining nine from Latin America and the Caribbean and Asia (Table 1). The scope of EmONC assessments ranged from all nine hospitals in the province of Azuay, Ecuador to 1626 facilities in Burkina Faso, inclusive of all facilities with at least one delivery in the past 12 months. The total number of facilities (15,411) registered 6.5 million deliveries and 17,314 maternal deaths. To contextualize the number of institutional maternal deaths and associated MMR, we included the institutional delivery rate and the percentage of institutional deliveries with a major direct obstetric complication. Both indicators were derived from EmONC assessment data. The final column includes the 2015 population-based MMR estimated by the Maternal Mortality Estimation Inter-Agency Group [7], also included for context, although most assessments occurred before 2015. Institutional delivery rates ranged from 7% in Ethiopia in 2008–9 to 113% in Mongolia (likely explained by a non-standard sampling strategy of 21 hospitals and their catchment areas). The next highest institutional delivery rate was 88% in São Tomé & Príncipe. Institutional MMRs ranged from 2130 maternal deaths per 100,000 deliveries in Chad to 32 in Mongolia. Countries with relatively low coverage of institutional deliveries such as Haiti, Niger, Sierra Leone, Ethiopia, South Sudan, Angola, and Chad tended to have high institutional MMRs, suggesting that a disproportionate number of women delivering in facilities experienced serious complications. To some extent, the percentage of deliveries with major obstetric complications supports this pattern where high percentages were found in countries with high institutional MMRs. However, countries such as the Democratic Republic of Congo or Afghanistan also exhibited relatively low institutional delivery rates, 10% or more of deliveries with complications, and had institutional MMRs of less than 200, making it difficult to discern any robust pattern. A high percentage of complicated deliveries could also reflect the type of facility surveyed, e.g. Ecuador (30%) and Mongolia (28%), where only hospitals were assessed.

Causes of institutional maternal deaths and cause specific case fatality rates

Figure 1 shows the distribution of all reported causes of maternal death for 38 countries. In 20 countries, hemorrhage and hypertensive diseases (severe pre-eclampsia/eclampsia) approached or exceeded 40% of maternal deaths. Similarly, 10 countries reported similar levels of indirect causes of maternal death.
Fig. 1

Distribution of causes of maternal death (38 countries). São Tomé & Príncipe, Sierra Leone, Rwanda and Ecuador reported only direct causes of maternal death; HEM=hemmorrhage; PEE=pre-eclampsia, eclampsia; OBSTR=obstructed/prolonged labor; RU=ruptured uterus; SEP=sepsis; AB=abortion; EC=ectopic pregnancy; OTH DIR=other direct causes of death; IND=indirect causes of death; UNSPEC=unspecified/unknown cause of death

Distribution of causes of maternal death (38 countries). São Tomé & Príncipe, Sierra Leone, Rwanda and Ecuador reported only direct causes of maternal death; HEM=hemmorrhage; PEE=pre-eclampsia, eclampsia; OBSTR=obstructed/prolonged labor; RU=ruptured uterus; SEP=sepsis; AB=abortion; EC=ectopic pregnancy; OTH DIR=other direct causes of death; IND=indirect causes of death; UNSPEC=unspecified/unknown cause of death Only 33 countries reported the number of women with major obstetric complications by type of complication, found in the regional summaries of Table 2 (upper panel). In the Latin America and Caribbean region, hypertensive disorders ranked first (41% of direct maternal deaths), while hemorrhage ranked first in sub-Saharan Africa (33%) and Asia (42%). The lower panel shows that in sub-Saharan Africa 61% of maternal deaths were attributable to direct causes, 35% to indirect causes and 4% were unspecified or unknown, while Latin America and the Caribbean and Asian regions were weighted towards a larger proportion of direct causes of death.
Table 2

Numeric and percent distribution of direct obstetric complications and all maternal deaths by cause and region (33 countries)

All regionsLatin America & the Caribbeana Sub-Saharan Africab Asiac
ComplicationsDeathsCFRComplicationsDeathsCFRComplicationsDeathsComplicationsDeathsCFR
n % n % n % n % n % n %CFR n % n %
Direct complications/causes
 Hemorrhage183,23226%290233%1.6%520211%5023%1.0%149,09328%258833%1.7%28,93726%26442%0.9%
 Hypertensive disease71,65810%168919%2.4%944119%9041%1.0%47,9939%147019%3.1%14,22413%12921%0.9%
 Abortion65,5949%6538%1.0%38378%209%0.5%42,5488%5988%1.4%19,20917%356%0.2%
 Postpartum sepsis16,3632%8159%5.0%9692%115%1.1%13,9943%79310%5.7%14001%112%0.8%
 Obstr/prolong labor154,64822%7739%0.5%603712%73%0.1%128,09924%7229%0.6%20,51218%447%0.2%
 Ectopic pregnancy13,7322%2112%1.5%7271%63%0.8%10,7562%2003%1.9%22492%51%0.2%
 Ruptured uterus87271%7198%8.2%1220.2%31%2.5%77861%6959%8.9%8191%213%2.6%
 Other185,24126%92911%0.5%22,63946%3014%0.1%136,90725%78610%0.6%25,69523%11318%0.4%
 Total699,195100%8691100%1.2%48,974100%217100%0.4%537,176100%7852100%1.5%113,045100%622100%0.6%
 Direct causes869163%21782%785261%62289%
 Indirect causes466034%3313%455635%7110%
 Unspecified causes5394%125%5184%91%
 TOTAL13,890100%262100%12,926100%702100%

Note: CFR Case fatality rate; “other” direct complications included premature rupture of membranes, malpresentation, preterm labor, post-term labor, previous cesarean delivery, cord prolapse, multiple gestations and others. “Other” direct deaths include embolism, anesthesia complications, and others

aIncludes Ecuador, Guyana, Haiti, Nicaragua and Panama

bIncludes Benin, Gambia, Ghana, Guinea, Liberia, Mauritania, Niger, Senegal, Togo, Burundi, Djibouti, Eritrea, Ethiopia, Malawi, Mozambique, South Sudan, Zambia, Angola, Cameroon, Chad, Congo, São Tomé & Príncipe, Lesotho and Namibia

cIncludes Afghanistan, Bangladesh, Cambodia and Mongolia

Numeric and percent distribution of direct obstetric complications and all maternal deaths by cause and region (33 countries) Note: CFR Case fatality rate; “other” direct complications included premature rupture of membranes, malpresentation, preterm labor, post-term labor, previous cesarean delivery, cord prolapse, multiple gestations and others. “Other” direct deaths include embolism, anesthesia complications, and others aIncludes Ecuador, Guyana, Haiti, Nicaragua and Panama bIncludes Benin, Gambia, Ghana, Guinea, Liberia, Mauritania, Niger, Senegal, Togo, Burundi, Djibouti, Eritrea, Ethiopia, Malawi, Mozambique, South Sudan, Zambia, Angola, Cameroon, Chad, Congo, São Tomé & Príncipe, Lesotho and Namibia cIncludes Afghanistan, Bangladesh, Cambodia and Mongolia Ruptured uterus had the highest cause-specific case fatality rate in each region, ranging from 8.9% in sub-Saharan Africa to 2.5% in the Latin America and Caribbean region. In other words, for every 100 women with a ruptured uterus in sub-Saharan Africa, 9 will die. The second highest specific cause of death was postpartum sepsis in sub-Saharan Africa (5.7%) and in Latin America and the Caribbean (1.1%), while in Asia, hypertensive diseases and hemorrhage tied for second (0.9%). Seventeen sub-Saharan African countries reported the number of indirect complications and deaths due to malaria in pregnancy, HIV/AIDS, severe anemia and other indirect causes of death. A few countries reported sickle cell anemia, hepatitis and diabetes, but most countries placed these women in the category of “other” indirect complications; 68% of indirect complications were malaria-related, 13% to HIV/AIDS, 7% to anemia, and 12% to “other indirect” complications. Less than 1% of indirect complications reported were cases of sickle cell anemia, hepatitis or diabetes, but more than a third of pregnant or recently delivered women with hepatitis or diabetes died before discharge (underreporting of survivors was likely). The case fatality rate for anemia was 2.3%, 1.0% for HIV/AIDS, 0.5% for malaria, and 1.6% for “other” indirect complications (data not shown). Figure 2 depicts the cause-specific case fatality rates for direct obstetric complications, organized by countries within regions. Hashed bars indicate a case fatality rate based on small numbers. Angola, Chad, Congo Brazzaville, Guinea, Mauritania, Senegal, and South Sudan experienced high rates (≥4%) across three or more complications.
Fig. 2

Cause-specific case fatality rates by region and country (33 countries). Hashed bars represent rates based on very small numbers; HEM=hemmorrhage; OBL=obstructed/prolonged labor; RU=ruptured uterus; SEP=sepsis; PEE=pre-eclampsia, eclampsia; AB=abortion; ECT=ectopic pregnancy; LAC=Latin America & the Caribbean; Maurit=Mauritania; Mozam=Mozambique; Ecua=Ecuador; Guya=Guyana; Nica=Nicaragua; Panam=Panama; Afghan=Afghanistan; Bangla=Bangladesh; Camb=Cambodia; Mongol=Mongolia; STP=São Tomé e Príncipe

Cause-specific case fatality rates by region and country (33 countries). Hashed bars represent rates based on very small numbers; HEM=hemmorrhage; OBL=obstructed/prolonged labor; RU=ruptured uterus; SEP=sepsis; PEE=pre-eclampsia, eclampsia; AB=abortion; ECT=ectopic pregnancy; LAC=Latin America & the Caribbean; Maurit=Mauritania; Mozam=Mozambique; Ecua=Ecuador; Guya=Guyana; Nica=Nicaragua; Panam=Panama; Afghan=Afghanistan; Bangla=Bangladesh; Camb=Cambodia; Mongol=Mongolia; STP=São Tomé e Príncipe

Institutional stillbirth and pre-discharge early neonatal mortality rates

Twenty-three countries calculated stillbirth and pre-discharge early neonatal death rates, nine of which did not distinguish between antepartum and intrapartum stillbirths, while two countries calculated only a pre-discharge perinatal mortality rate (Table 3 and Fig. 3). Stillbirth rates ranged from 5.8 per 1000 deliveries in Mongolia, to 116.5 in Madagascar. Pre-discharge neonatal death rates were often much smaller than the stillbirth rates except for Mongolia. Early neonatal death rates ranged from 1.8 in Guinea to 21 in Bangladesh. The ratio of stillbirths to early neonatal deaths varied widely across countries, ranging from the outlier ratios of 26 and 25 stillbirths to 1 pre-discharge neonatal death in Madagascar and Guinea to 0.7 to 1 in Mongolia. Mongolia had the lowest institutional and population-based MMR and the lowest stillbirth rate, but its pre-discharge early neonatal death rate was similar to that of many countries, and begs for an explanation – a question of sampling or quality of newborn care?
Table 3

Institutional stillbirth and pre-discharge early neonatal mortality rates (23 countries)

Region, country and year of data collectionInstitu-tional deliveriesAnte-partum SBsIntra-partum SBsUnspe-cified SBsTotal SBsSB rate per 1000 deliveriespNDspND rate per 1000 live birthsSB:pND ratio
LAC
 Guyana 201012,80370678922617.7655.23.5
 Nicaragua 200694,136NRNRNR121012.98899.61.4
Western Africa
 Gambia 201251,518102394466203339.54338.84.7
 Ghana 2010434,508398946851223989722.822015.24.5
 Guinea 2011141,724194414573639604042.62421.825.0
 Niger 2010152,415117141051072634841.65453.711.6
 Senegal 2013237,494376133452078918438.714396.36.4
 Togo 2012133,11997417281150385228.96344.96.1
Eastern Africa
 Eritrea 200825,000NRNRNR93337.31857.75.0
 Ethiopia 2008–9174,561NRNRNR736642.25223.114.1
 Madagascar 2009118,774NRNRNR13,832116.55275.026.2
 Malawi 2014476,27236324403NR803516.9502810.71.6
 Mozambique 2012a 647,9448283440820012,46819.213802.29.0
 Rwanda 2007a 207,73817,4565618NR23,07411.194325.12.4
 South Sudan 201352,842208541373112221.294818.31.2
 Zambia 2014–15475,646NRNRNR11,23323.619804.35.7
Central Africa
 Chad 201149,202274814NR215543.82395.19.0
 Congo 201285,038657856219173220.42643.26.6
 Dem Rep Congo 2011156,546NRNRNR594938.012718.44.7
Asia
 Afghanistan 2009192,627NRNRNR417721.714227.52.9
 Bangladesh 2012253,728NRNRNR811932.0515821.01.6
 Cambodia 2014119,93192715NR8076.74794.01.7
 Mongolia 200930,131NRNRNR1755.82428.10.7

NR not reported, SB stillbirth, pND pre-discharge early neonatal death, dying before discharge or within the first 24 h, whichever came first

aMozambique and Rwanda adjusted to reflect 12 months of information

Fig. 3

Institutional stillbirth and pre-discharge neonatal death (pND) rates (25 countries)

Institutional stillbirth and pre-discharge early neonatal mortality rates (23 countries) NR not reported, SB stillbirth, pND pre-discharge early neonatal death, dying before discharge or within the first 24 h, whichever came first aMozambique and Rwanda adjusted to reflect 12 months of information Institutional stillbirth and pre-discharge neonatal death (pND) rates (25 countries)

Discussion

This institutional assessment compiles data from many countries where every country set out with similar objectives, used a similar methodology and data collection instrument, and had common indicators. By design, each assessment captured a complete recording of all maternal deaths by cause and common perinatal outcomes. This is a strength that other multi-country studies have not shared. In this overview, we gathered service statistics from more than 15,400 health care facilities that mirror findings from more complex modeling exercises, regardless of differences in methodology and reference populations. We saw hypertensive diseases as the predominant cause of institutional maternal death in the Latin America and Caribbean region and hemorrhage highlighted in Asian countries, despite the small number of surveys in each of those regions. Meanwhile, hemorrhage was the predominant cause of institutional maternal deaths in sub-Saharan African countries, a region also distinguished by its large proportion of indirect maternal deaths. In Table 4 below we compare the overall distribution of institutional causes of 14,785 deaths from 26 sub-Saharan African countries with the population-based distribution found in the WHO 2003–2009 systematic review (in both cases, unknown causes of death were excluded). Cases of ruptured uterus and obstructed labor were included in “other direct causes” for the EmONC Assessments while these cases were likely assigned to hemorrhage or sepsis in the WHO study [14]. The degree of similitude in the distribution of causes is both validating and reassuring but may also point to possible data quality issues and/or differences between all deaths versus just those occurring in facilities.
Table 4

Comparison of causes of maternal mortality in sub-Saharan countries by different sources

For sub-Saharan African countriesEmONC Assessments (institution-based)WHO 2003–2009 Review (population-based)
Hemorrhage21.0%24.5%
Abortion + ectopic pregnancy7.2%9.6%
Sepsis6.0%10.3%
Hypertensive diseases10.5%16.0%
Other direct causes17.8%11.1%
Indirect causes37.5%28.6%
Comparison of causes of maternal mortality in sub-Saharan countries by different sources The larger proportion of indirect causes found in this paper is noteworthy but it also may be underreported especially where comorbidities were common. During the training, data collectors were instructed to classify a maternal death as direct if there was evidence of both direct and indirect causes. For programmatic purposes, indirect causes of maternal mortality require a greater focus of attention, not just for purposes of reporting but also for health service delivery organization to intervene early to prevent these deaths. We also observed that institutional stillbirth rates tended to be substantially higher than early neonatal death rates, and that countries with high institutional stillbirth rates also tended to exhibit high institutional MMRs. According to other studies, we might have expected the ratio of stillbirths to early neonatal deaths to be approximately 1.3 to 1, but these institutional data suggest a lower ratio, i.e., more stillbirths than expected [20, 21]. Unfortunately, given the uneven reporting of whether the stillbirth was macerated or intrapartum, the often-cited ratio of 1 intrapartum stillbirth to 3 macerated stillbirths could not be assessed [22]. The 2030 ENAP target for the stillbirth rate is 12/1000 total births and the target neonatal death rate is the same, but among live births. At this time, most countries in this overview are far from reaching the stillbirth target and many countries would fail to reach the neonatal target of 12, although this is more difficult to ascertain given the censoring of data since so many women and their newborns are discharged within 12 h of delivery. A recent six-country study of early neonatal mortality showed that neonatal deaths in the first six and 24 h account for one-third and 46%, respectively, of all neonatal deaths [23]. Therefore, a doubling or tripling of the early neonatal deaths observed in this overview might provide a rough estimate of the actual neonatal death rate. But like the MMR, it is unclear whether institutional rates and ratios are likely to be higher or lower than the population-based rates. Nevertheless, high stillbirth rates observed in the EmONC assessments give pause; the global stillbirth rate for 2015 was 18.4 per 1000 births, while the rate for sub-Saharan Africa was 28.7 [4]. According to the authors of recent trend data for stillbirth rates, when compared to high quality vital registration data, facility data tend to overestimate the stillbirth rate due to selection bias [4, 5]. Despite evidence for reductions in maternal and perinatal mortality over the last two decades, this multi-country overview leads to recommendations for clinical practice and policy if we are to move towards the goal of ending preventable maternal and newborn deaths. From the clinical perspective, although fewer in absolute numbers than hemorrhage or hypertensive disorders, uterine rupture and maternal sepsis were the most lethal complications. The literature consistently shows the elevated risk of mortality from ruptured uterus [24-27]. High case fatality rates for uterine rupture suggest poor diagnostic skills, inadequate patient monitoring after admission and delays in appropriate treatment [28], or perhaps inappropriate or overuse of augmentation or induction. Several studies point to high rates of rupture after admission [25, 29]. Ruptured uterus is also an indication that women with obstructed labor or at risk of rupture, e.g. having a previous uterine scar, still experience difficulties in accessing surgical care in a timely manner. Considerable international investment has focused on reducing deaths due to hemorrhage and hypertensive disorders, given how many deaths are attributable to these complications. Both have well-known pharmacological solutions as well as effective preventative measures with active management of the third stage of labor and the potential to detect high blood pressure and proteinuria during antenatal care. Ruptured uterus might be viewed as requiring more complex multi-sectoral fixes – improved road networks, better communication and transportation options, as well as the human resources who can and will monitor the progression of labor, follow protocol, and perform cesarean delivery. Sepsis may require more of a professional culture change towards infection prevention, more accessible water, sanitation and hygiene infrastructure as well as antenatal screening. To optimize the investment of an EmONC assessment, it should be followed by multilevel planning and implementation phases. In 2016, only six of the countries mentioned in this publication have set up such processes that include maternal and newborn care monitoring in EmONC facilities (Burkina Faso, Cambodia, Haiti, Madagascar, Niger and Togo). However, this number is likely to increase significantly in 2017. The production, analysis and utilization of data by providers with the support of coaches also contribute to improve quality of care.

Limitations

Without access to the original data, we could not standardize reporting nor could we stratify by level of facility or management authority, which would have allowed a deeper understanding of which deaths occurred where and how many. There may also have been bias in how causes of death were ascertained across countries although training guidelines were the same across most countries. It is possible that some countries were more comfortable than others using ICD-MM. Going forward, EmONC assessments should better align the cause of death categories with ICD-MM, thus making these data more attractive as an additional source for global estimates. Systematic documentation of stillbirths is at an early stage in many low and middle income countries and the differentiation between antepartum and intrapartum stillbirths is not yet standard practice across or within countries. Like maternal deaths, stillbirth rates and early neonatal death rates are susceptible to errors of omission and misclassification [30]. Especially critical may be widespread misclassification of early neonatal deaths as intrapartum stillbirths due to lack of diagnostic skill, environmental pressure or convenience. Countries such as Madagascar, Guinea and Ethiopia that exhibited an extreme ratio of stillbirths to early neonatal deaths should investigate these rates to understand possible contributory clinical and reporting practices. Caregivers need access to simple equipment to measure the presence of fetal heart beats on admission, training to make accurate assessments and the paper or electronic tools that encourage reporting whether the fetal death was antepartum or intrapartum [31]. As long as large numbers of stillbirths and birth weights remain unspecified, the use of the intrapartum and early neonatal death rate as an indicator for quality of intrapartum care will be compromised or relegated to the status of a special study. The recording of maternal deaths is likely to be incomplete given the primary sources of the statistics – routine paper-based logbooks – the extended coverage of 12 months, and for unintentional and intentional reasons. Obstetric complications are also likely to be undercounted as they are rarely collected by routine health management information systems. Specific case fatality rates suggest inconsistent reporting and recording across facilities and countries. For example, the case fatality rate of 1% for HIV in sub-Saharan Africa was surprisingly low as were the case fatality rates of 0% for hemorrhage in Ecuador, and 0% for obstructed labor in Ghana, Togo and the Gambia. Nevertheless, by supporting the EmONC assessments we have learned that registers and logbooks tend to be more complete than facility reports of aggregated data. We also observed that where maternal death surveillance and response (MDSR) initiatives were well entrenched, the quality of the maternal death data in the EmONC assessments appeared to be of higher quality than where MDSR efforts were in their early stages. As countries adopt Making Every Baby Count: Audit and Review of Stillbirths and Neonatal Deaths, routine data on newborn outcomes are likely to improve in quality as will our understanding of why deaths occur and how to intervene.

Conclusions

As skilled delivery coverage increases and maternal mortality declines, women who die in facilities may no longer represent the tip of an iceberg, but most maternal deaths. With appropriate reflection, institutional stillbirth and early neonatal death rates, causes of maternal death and case fatality rates can guide management on how to improve health workers’ capacity to meet the demand for emergency care, including record-keeping, and identify hotspots of where and what is needed to reduce delays in seeking, reaching and receiving care. Facility-level data will become all the more important and thus efforts to improve data quality are crucial.
  23 in total

1.  No cry at birth: global estimates of intrapartum stillbirths and intrapartum-related neonatal deaths.

Authors:  Joy Lawn; Kenji Shibuya; Claudia Stein
Journal:  Bull World Health Organ       Date:  2005-06-17       Impact factor: 9.408

2.  Stillbirth and early neonatal mortality in rural Central Africa.

Authors:  Cyril Engmann; Richard Matendo; Rinko Kinoshita; John Ditekemena; Janet Moore; Robert L Goldenberg; Antoinette Tshefu; Waldemar A Carlo; Elizabeth M McClure; Carl Bose; Linda L Wright
Journal:  Int J Gynaecol Obstet       Date:  2009-02-07       Impact factor: 3.561

3.  Global, regional, and national levels of maternal mortality, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.

Authors: 
Journal:  Lancet       Date:  2016-10-08       Impact factor: 79.321

4.  Global, regional, and national levels of neonatal, infant, and under-5 mortality during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

Authors:  Haidong Wang; Chelsea A Liddell; Matthew M Coates; Meghan D Mooney; Carly E Levitz; Austin E Schumacher; Henry Apfel; Marissa Iannarone; Bryan Phillips; Katherine T Lofgren; Logan Sandar; Rob E Dorrington; Ivo Rakovac; Troy A Jacobs; Xiaofeng Liang; Maigeng Zhou; Jun Zhu; Gonghuan Yang; Yanping Wang; Shiwei Liu; Yichong Li; Ayse Abbasoglu Ozgoren; Semaw Ferede Abera; Ibrahim Abubakar; Tom Achoki; Ademola Adelekan; Zanfina Ademi; Zewdie Aderaw Alemu; Peter J Allen; Mohammad AbdulAziz AlMazroa; Elena Alvarez; Adansi A Amankwaa; Azmeraw T Amare; Walid Ammar; Palwasha Anwari; Solveig Argeseanu Cunningham; Majed Masoud Asad; Reza Assadi; Amitava Banerjee; Sanjay Basu; Neeraj Bedi; Tolesa Bekele; Michelle L Bell; Zulfiqar Bhutta; Jed D Blore; Berrak Bora Basara; Soufiane Boufous; Nicholas Breitborde; Nigel G Bruce; Linh Ngoc Bui; Jonathan R Carapetis; Rosario Cárdenas; David O Carpenter; Valeria Caso; Ruben Estanislao Castro; Ferrán Catalá-Lopéz; Alanur Cavlin; Xuan Che; Peggy Pei-Chia Chiang; Rajiv Chowdhury; Costas A Christophi; Ting-Wu Chuang; Massimo Cirillo; Iuri da Costa Leite; Karen J Courville; Lalit Dandona; Rakhi Dandona; Adrian Davis; Anand Dayama; Kebede Deribe; Samath D Dharmaratne; Mukesh K Dherani; Uğur Dilmen; Eric L Ding; Karen M Edmond; Sergei Petrovich Ermakov; Farshad Farzadfar; Seyed-Mohammad Fereshtehnejad; Daniel Obadare Fijabi; Nataliya Foigt; Mohammad H Forouzanfar; Ana C Garcia; Johanna M Geleijnse; Bradford D Gessner; Ketevan Goginashvili; Philimon Gona; Atsushi Goto; Hebe N Gouda; Mark A Green; Karen Fern Greenwell; Harish Chander Gugnani; Rahul Gupta; Randah Ribhi Hamadeh; Mouhanad Hammami; Hilda L Harb; Simon Hay; Mohammad T Hedayati; H Dean Hosgood; Damian G Hoy; Bulat T Idrisov; Farhad Islami; Samaya Ismayilova; Vivekanand Jha; Guohong Jiang; Jost B Jonas; Knud Juel; Edmond Kato Kabagambe; Dhruv S Kazi; Andre Pascal Kengne; Maia Kereselidze; Yousef Saleh Khader; Shams Eldin Ali Hassan Khalifa; Young-Ho Khang; Daniel Kim; Yohannes Kinfu; Jonas M Kinge; Yoshihiro Kokubo; Soewarta Kosen; Barthelemy Kuate Defo; G Anil Kumar; Kaushalendra Kumar; Ravi B Kumar; Taavi Lai; Qing Lan; Anders Larsson; Jong-Tae Lee; Mall Leinsalu; Stephen S Lim; Steven E Lipshultz; Giancarlo Logroscino; Paulo A Lotufo; Raimundas Lunevicius; Ronan Anthony Lyons; Stefan Ma; Abbas Ali Mahdi; Melvin Barrientos Marzan; Mohammad Taufiq Mashal; Tasara T Mazorodze; John J McGrath; Ziad A Memish; Walter Mendoza; George A Mensah; Atte Meretoja; Ted R Miller; Edward J Mills; Karzan Abdulmuhsin Mohammad; Ali H Mokdad; Lorenzo Monasta; Marcella Montico; Ami R Moore; Joanna Moschandreas; William T Msemburi; Ulrich O Mueller; Magdalena M Muszynska; Mohsen Naghavi; Kovin S Naidoo; K M Venkat Narayan; Chakib Nejjari; Marie Ng; Jean de Dieu Ngirabega; Mark J Nieuwenhuijsen; Luke Nyakarahuka; Takayoshi Ohkubo; Saad B Omer; Angel J Paternina Caicedo; Victoria Pillay-van Wyk; Dan Pope; Farshad Pourmalek; Dorairaj Prabhakaran; Sajjad U R Rahman; Saleem M Rana; Robert Quentin Reilly; David Rojas-Rueda; Luca Ronfani; Lesley Rushton; Mohammad Yahya Saeedi; Joshua A Salomon; Uchechukwu Sampson; Itamar S Santos; Monika Sawhney; Jürgen C Schmidt; Marina Shakh-Nazarova; Jun She; Sara Sheikhbahaei; Kenji Shibuya; Hwashin Hyun Shin; Kawkab Shishani; Ivy Shiue; Inga Dora Sigfusdottir; Jasvinder A Singh; Vegard Skirbekk; Karen Sliwa; Sergey S Soshnikov; Luciano A Sposato; Vasiliki Kalliopi Stathopoulou; Konstantinos Stroumpoulis; Karen M Tabb; Roberto Tchio Talongwa; Carolina Maria Teixeira; Abdullah Sulieman Terkawi; Alan J Thomson; Andrew L Thorne-Lyman; Hideaki Toyoshima; Zacharie Tsala Dimbuene; Parfait Uwaliraye; Selen Begüm Uzun; Tommi J Vasankari; Ana Maria Nogales Vasconcelos; Vasiliy Victorovich Vlassov; Stein Emil Vollset; Stephen Waller; Xia Wan; Scott Weichenthal; Elisabete Weiderpass; Robert G Weintraub; Ronny Westerman; James D Wilkinson; Hywel C Williams; Yang C Yang; Gokalp Kadri Yentur; Paul Yip; Naohiro Yonemoto; Mustafa Younis; Chuanhua Yu; Kim Yun Jin; Maysaa El Sayed Zaki; Shankuan Zhu; Theo Vos; Alan D Lopez; Christopher J L Murray
Journal:  Lancet       Date:  2014-05-02       Impact factor: 79.321

5.  Global, regional, and national levels and causes of maternal mortality during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

Authors:  Nicholas J Kassebaum; Amelia Bertozzi-Villa; Megan S Coggeshall; Katya A Shackelford; Caitlyn Steiner; Kyle R Heuton; Diego Gonzalez-Medina; Ryan Barber; Chantal Huynh; Daniel Dicker; Tara Templin; Timothy M Wolock; Ayse Abbasoglu Ozgoren; Foad Abd-Allah; Semaw Ferede Abera; Ibrahim Abubakar; Tom Achoki; Ademola Adelekan; Zanfina Ademi; Arsène Kouablan Adou; José C Adsuar; Emilie E Agardh; Dickens Akena; Deena Alasfoor; Zewdie Aderaw Alemu; Rafael Alfonso-Cristancho; Samia Alhabib; Raghib Ali; Mazin J Al Kahbouri; François Alla; Peter J Allen; Mohammad A AlMazroa; Ubai Alsharif; Elena Alvarez; Nelson Alvis-Guzmán; Adansi A Amankwaa; Azmeraw T Amare; Hassan Amini; Walid Ammar; Carl A T Antonio; Palwasha Anwari; Johan Arnlöv; Valentina S Arsic Arsenijevic; Ali Artaman; Majed Masoud Asad; Rana J Asghar; Reza Assadi; Lydia S Atkins; Alaa Badawi; Kalpana Balakrishnan; Arindam Basu; Sanjay Basu; Justin Beardsley; Neeraj Bedi; Tolesa Bekele; Michelle L Bell; Eduardo Bernabe; Tariku J Beyene; Zulfiqar Bhutta; Aref Bin Abdulhak; Jed D Blore; Berrak Bora Basara; Dipan Bose; Nicholas Breitborde; Rosario Cárdenas; Carlos A Castañeda-Orjuela; Ruben Estanislao Castro; Ferrán Catalá-López; Alanur Cavlin; Jung-Chen Chang; Xuan Che; Costas A Christophi; Sumeet S Chugh; Massimo Cirillo; Samantha M Colquhoun; Leslie Trumbull Cooper; Cyrus Cooper; Iuri da Costa Leite; Lalit Dandona; Rakhi Dandona; Adrian Davis; Anand Dayama; Louisa Degenhardt; Diego De Leo; Borja del Pozo-Cruz; Kebede Deribe; Muluken Dessalegn; Gabrielle A deVeber; Samath D Dharmaratne; Uğur Dilmen; Eric L Ding; Rob E Dorrington; Tim R Driscoll; Sergei Petrovich Ermakov; Alireza Esteghamati; Emerito Jose A Faraon; Farshad Farzadfar; Manuela Mendonca Felicio; Seyed-Mohammad Fereshtehnejad; Graça Maria Ferreira de Lima; Mohammad H Forouzanfar; Elisabeth B França; Lynne Gaffikin; Ketevan Gambashidze; Fortuné Gbètoho Gankpé; Ana C Garcia; Johanna M Geleijnse; Katherine B Gibney; Maurice Giroud; Elizabeth L Glaser; Ketevan Goginashvili; Philimon Gona; Dinorah González-Castell; Atsushi Goto; Hebe N Gouda; Harish Chander Gugnani; Rahul Gupta; Rajeev Gupta; Nima Hafezi-Nejad; Randah Ribhi Hamadeh; Mouhanad Hammami; Graeme J Hankey; Hilda L Harb; Rasmus Havmoeller; Simon I Hay; Ileana B Heredia Pi; Hans W Hoek; H Dean Hosgood; Damian G Hoy; Abdullatif Husseini; Bulat T Idrisov; Kaire Innos; Manami Inoue; Kathryn H Jacobsen; Eiman Jahangir; Sun Ha Jee; Paul N Jensen; Vivekanand Jha; Guohong Jiang; Jost B Jonas; Knud Juel; Edmond Kato Kabagambe; Haidong Kan; Nadim E Karam; André Karch; Corine Kakizi Karema; Anil Kaul; Norito Kawakami; Konstantin Kazanjan; Dhruv S Kazi; Andrew H Kemp; Andre Pascal Kengne; Maia Kereselidze; Yousef Saleh Khader; Shams Eldin Ali Hassan Khalifa; Ejaz Ahmed Khan; Young-Ho Khang; Luke Knibbs; Yoshihiro Kokubo; Soewarta Kosen; Barthelemy Kuate Defo; Chanda Kulkarni; Veena S Kulkarni; G Anil Kumar; Kaushalendra Kumar; Ravi B Kumar; Gene Kwan; Taavi Lai; Ratilal Lalloo; Hilton Lam; Van C Lansingh; Anders Larsson; Jong-Tae Lee; James Leigh; Mall Leinsalu; Ricky Leung; Xiaohong Li; Yichong Li; Yongmei Li; Juan Liang; Xiaofeng Liang; Stephen S Lim; Hsien-Ho Lin; Steven E Lipshultz; Shiwei Liu; Yang Liu; Belinda K Lloyd; Stephanie J London; Paulo A Lotufo; Jixiang Ma; Stefan Ma; Vasco Manuel Pedro Machado; Nana Kwaku Mainoo; Marek Majdan; Christopher Chabila Mapoma; Wagner Marcenes; Melvin Barrientos Marzan; Amanda J Mason-Jones; Man Mohan Mehndiratta; Fabiola Mejia-Rodriguez; Ziad A Memish; Walter Mendoza; Ted R Miller; Edward J Mills; Ali H Mokdad; Glen Liddell Mola; Lorenzo Monasta; Jonathan de la Cruz Monis; Julio Cesar Montañez Hernandez; Ami R Moore; Maziar Moradi-Lakeh; Rintaro Mori; Ulrich O Mueller; Mitsuru Mukaigawara; Aliya Naheed; Kovin S Naidoo; Devina Nand; Vinay Nangia; Denis Nash; Chakib Nejjari; Robert G Nelson; Sudan Prasad Neupane; Charles R Newton; Marie Ng; Mark J Nieuwenhuijsen; Muhammad Imran Nisar; Sandra Nolte; Ole F Norheim; Luke Nyakarahuka; In-Hwan Oh; Takayoshi Ohkubo; Bolajoko O Olusanya; Saad B Omer; John Nelson Opio; Orish Ebere Orisakwe; Jeyaraj D Pandian; Christina Papachristou; Jae-Hyun Park; Angel J Paternina Caicedo; Scott B Patten; Vinod K Paul; Boris Igor Pavlin; Neil Pearce; David M Pereira; Konrad Pesudovs; Max Petzold; Dan Poenaru; Guilherme V Polanczyk; Suzanne Polinder; Dan Pope; Farshad Pourmalek; Dima Qato; D Alex Quistberg; Anwar Rafay; Kazem Rahimi; Vafa Rahimi-Movaghar; Sajjad ur Rahman; Murugesan Raju; Saleem M Rana; Amany Refaat; Luca Ronfani; Nobhojit Roy; Tania Georgina Sánchez Pimienta; Mohammad Ali Sahraian; Joshua A Salomon; Uchechukwu Sampson; Itamar S Santos; Monika Sawhney; Felix Sayinzoga; Ione J C Schneider; Austin Schumacher; David C Schwebel; Soraya Seedat; Sadaf G Sepanlou; Edson E Servan-Mori; Marina Shakh-Nazarova; Sara Sheikhbahaei; Kenji Shibuya; Hwashin Hyun Shin; Ivy Shiue; Inga Dora Sigfusdottir; Donald H Silberberg; Andrea P Silva; Jasvinder A Singh; Vegard Skirbekk; Karen Sliwa; Sergey S Soshnikov; Luciano A Sposato; Chandrashekhar T Sreeramareddy; Konstantinos Stroumpoulis; Lela Sturua; Bryan L Sykes; Karen M Tabb; Roberto Tchio Talongwa; Feng Tan; Carolina Maria Teixeira; Eric Yeboah Tenkorang; Abdullah Sulieman Terkawi; Andrew L Thorne-Lyman; David L Tirschwell; Jeffrey A Towbin; Bach X Tran; Miltiadis Tsilimbaris; Uche S Uchendu; Kingsley N Ukwaja; Eduardo A Undurraga; Selen Begüm Uzun; Andrew J Vallely; Coen H van Gool; Tommi J Vasankari; Monica S Vavilala; N Venketasubramanian; Salvador Villalpando; Francesco S Violante; Vasiliy Victorovich Vlassov; Theo Vos; Stephen Waller; Haidong Wang; Linhong Wang; XiaoRong Wang; Yanping Wang; Scott Weichenthal; Elisabete Weiderpass; Robert G Weintraub; Ronny Westerman; James D Wilkinson; Solomon Meseret Woldeyohannes; John Q Wong; Muluemebet Abera Wordofa; Gelin Xu; Yang C Yang; Yuichiro Yano; Gokalp Kadri Yentur; Paul Yip; Naohiro Yonemoto; Seok-Jun Yoon; Mustafa Z Younis; Chuanhua Yu; Kim Yun Jin; Maysaa El Sayed Zaki; Yong Zhao; Yingfeng Zheng; Maigeng Zhou; Jun Zhu; Xiao Nong Zou; Alan D Lopez; Mohsen Naghavi; Christopher J L Murray; Rafael Lozano
Journal:  Lancet       Date:  2014-05-02       Impact factor: 79.321

6.  Count every newborn; a measurement improvement roadmap for coverage data.

Authors:  Sarah G Moxon; Harriet Ruysen; Kate J Kerber; Agbessi Amouzou; Suzanne Fournier; John Grove; Allisyn C Moran; Lara M E Vaz; Hannah Blencowe; Niall Conroy; A Gülmezoglu; Joshua P Vogel; Barbara Rawlins; Rubayet Sayed; Kathleen Hill; Donna Vivio; Shamim A Qazi; Deborah Sitrin; Anna C Seale; Steve Wall; Troy Jacobs; Juan Ruiz Peláez; Tanya Guenther; Patricia S Coffey; Penny Dawson; Tanya Marchant; Peter Waiswa; Ashok Deorari; Christabel Enweronu-Laryea; Shams Arifeen; Anne C C Lee; Matthews Mathai; Joy E Lawn
Journal:  BMC Pregnancy Childbirth       Date:  2015-09-11       Impact factor: 3.007

7.  Neonatal mortality within 24 hours of birth in six low- and lower-middle-income countries.

Authors:  Abdullah H Baqui; Dipak K Mitra; Nazma Begum; Lisa Hurt; Seyi Soremekun; Karen Edmond; Betty Kirkwood; Nita Bhandari; Sunita Taneja; Sarmila Mazumder; Muhammad Imran Nisar; Fyezah Jehan; Muhammad Ilyas; Murtaza Ali; Imran Ahmed; Shabina Ariff; Sajid B Soofi; Sunil Sazawal; Usha Dhingra; Arup Dutta; Said M Ali; Shaali M Ame; Katherine Semrau; Fern M Hamomba; Caroline Grogan; Davidson H Hamer; Rajiv Bahl; Sachiyo Yoshida; Alexander Manu
Journal:  Bull World Health Organ       Date:  2016-08-30       Impact factor: 9.408

8.  Stillbirths and quality of care during labour at the low resource referral hospital of Zanzibar: a case-control study.

Authors:  Nanna Maaløe; Natasha Housseine; Ib Christian Bygbjerg; Tarek Meguid; Rashid Saleh Khamis; Ali Gharib Mohamed; Birgitte Bruun Nielsen; Jos van Roosmalen
Journal:  BMC Pregnancy Childbirth       Date:  2016-11-10       Impact factor: 3.007

9.  Measuring maternal mortality: a systematic review of methods used to obtain estimates of the maternal mortality ratio (MMR) in low- and middle-income countries.

Authors:  Florence Mgawadere; Terry Kana; Nynke van den Broek
Journal:  Br Med Bull       Date:  2017-06-01       Impact factor: 4.291

10.  Maternal near misses from two referral hospitals in Uganda: a prospective cohort study on incidence, determinants and prognostic factors.

Authors:  Annettee Nakimuli; Sarah Nakubulwa; Othman Kakaire; Michael O Osinde; Scovia N Mbalinda; Rose C Nabirye; Nelson Kakande; Dan K Kaye
Journal:  BMC Pregnancy Childbirth       Date:  2016-01-28       Impact factor: 3.007

View more
  24 in total

1.  Determinants of obstructed labour and its adverse outcomes among women who gave birth in Hawassa University referral Hospital: A case-control study.

Authors:  Melaku Desta; Zenebe Mekonen; Addisu Alehegn Alemu; Minychil Demelash; Temesgen Getaneh; Yibelu Bazezew; Getachew Mullu Kassa; Negash Wakgari
Journal:  PLoS One       Date:  2022-06-24       Impact factor: 3.752

2.  SEPSIS project: a protocol for studying biomarkers of neonatal sepsis and immune responses of infants in a malaria-endemic region.

Authors:  Nadine Fievet; Sem Ezinmegnon; Gino Agbota; Darius Sossou; Rodolphe Ladekpo; Komi Gbedande; Valerie Briand; Gilles Cottrell; Laurence Vachot; Javier Yugueros Marcos; Alexandre Pachot; Julien Textoris; Sophie Blein; Ulrik Lausten-Thomsen; Achille Massougbodji; Lehila Bagnan; Nicole Tchiakpe; Marceline d'Almeida; Jules Alao; Ida Dossou-Dagba; Pierre Tissieres
Journal:  BMJ Open       Date:  2020-07-23       Impact factor: 2.692

3.  Progress in Mozambique: Changes in the availability, use, and quality of emergency obstetric and newborn care between 2007 and 2012.

Authors:  Orvalho Augusto; Emily E Keyes; Tavares Madede; Fátima Abacassamo; Pilar de la Corte; Baltazar Chilundo; Patricia E Bailey
Journal:  PLoS One       Date:  2018-07-18       Impact factor: 3.240

4.  Pregnancy outcomes among HIV-infected women who conceived on antiretroviral therapy.

Authors:  Elizabeth M Stringer; Michelle A Kendall; Shahin Lockman; Thomas B Campbell; Karin Nielsen-Saines; Fred Sawe; Susan Cu-Uvin; Xingye Wu; Judith S Currier
Journal:  PLoS One       Date:  2018-07-18       Impact factor: 3.240

5.  Health care professionals' knowledge and awareness of the ICD-10 coding system for assigning the cause of perinatal deaths in Jordanian hospitals.

Authors:  Mohammad S Alyahya; Yousef S Khader
Journal:  J Multidiscip Healthc       Date:  2019-02-14

Review 6.  Quality improvement in maternal and newborn healthcare: lessons from programmes supported by the German development organisation in Africa and Asia.

Authors:  Sophie Goyet; Valerie Broch-Alvarez; Cornelia Becker
Journal:  BMJ Glob Health       Date:  2019-09-06

7.  Readiness to treat and factors associated with survival of newborns with breathing difficulties in Ethiopia.

Authors:  Wasihun Andualem Gobezie; Patricia Bailey; Emily Keyes; Ana Lorena Ruano; Habtamu Teklie
Journal:  BMC Health Serv Res       Date:  2019-08-07       Impact factor: 2.655

Review 8.  Triangulating data sources for further learning from and about the MDSR in Ethiopia: a cross-sectional review of facility based maternal death data from EmONC assessment and MDSR system.

Authors:  Azmach Hadush; Ftalew Dagnaw; Theodros Getachew; Patricia E Bailey; Ruth Lawley; Ana Lorena Ruano
Journal:  BMC Pregnancy Childbirth       Date:  2020-04-09       Impact factor: 3.007

9.  Maternal Death Review at a Tertiary Hospital in Ethiopia.

Authors:  Matiyas Asrat Shiferaw; Delayehu Bekele; Feiruz Surur; Bethel Dereje; Lemi Belay Tolu
Journal:  Ethiop J Health Sci       Date:  2021-01

10.  Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania.

Authors:  Innocent B Mboya; Michael J Mahande; Joseph Obure; Henry G Mwambi
Journal:  PLoS One       Date:  2020-04-16       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.