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      Neonatal Mortality Risk Associated with Preterm Birth in East Africa, Adjusted by Weight for Gestational Age: Individual Participant Level Meta-Analysis

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          Abstract

          In an analysis of four datasets from East Africa, Tanya Marchant and colleagues investigate the neonatal mortality risk associated with preterm birth and how this changes with weight for gestational age.

          Abstract

          Background

          Low birth weight and prematurity are amongst the strongest predictors of neonatal death. However, the extent to which they act independently is poorly understood. Our objective was to estimate the neonatal mortality risk associated with preterm birth when stratified by weight for gestational age in the high mortality setting of East Africa.

          Methods and Findings

          Members and collaborators of the Malaria and the MARCH Centers, at the London School of Hygiene & Tropical Medicine, were contacted and protocols reviewed for East African studies that measured (1) birth weight, (2) gestational age at birth using antenatal ultrasound or neonatal assessment, and (3) neonatal mortality. Ten datasets were identified and four met the inclusion criteria. The four datasets (from Uganda, Kenya, and two from Tanzania) contained 5,727 births recorded between 1999–2010. 4,843 births had complete outcome data and were included in an individual participant level meta-analysis. 99% of 445 low birth weight (<2,500 g) babies were either preterm (<37 weeks gestation) or small for gestational age (below tenth percentile of weight for gestational age). 52% of 87 neonatal deaths occurred in preterm or small for gestational age babies. Babies born <34 weeks gestation had the highest odds of death compared to term babies (odds ratio [OR] 58.7 [95% CI 28.4–121.4]), with little difference when stratified by weight for gestational age. Babies born 34–36 weeks gestation with appropriate weight for gestational age had just three times the likelihood of neonatal death compared to babies born term, (OR 3.2 [95% CI 1.0–10.7]), but the likelihood for babies born 34–36 weeks who were also small for gestational age was 20 times higher (OR 19.8 [95% CI 8.3–47.4]). Only 1% of babies were born moderately premature and small for gestational age, but this group suffered 8% of deaths. Individual level data on newborns are scarce in East Africa; potential biases arising due to the non-systematic selection of the individual studies, or due to the methods applied for estimating gestational age, are discussed.

          Conclusions

          Moderately preterm babies who are also small for gestational age experience a considerably increased likelihood of neonatal death in East Africa.

          Please see later in the article for the Editors' Summary.

          Editors' Summary

          Background

          Worldwide, every year around 3.3 million babies die within their first month of life and the proportion of under-five child deaths that are now in the neonatal period (the first 28 days of life) has increased in all regions of the world and is currently estimated at 41%. Of these deaths, over 90% occur in low- and middle-income countries, and a third of all neonatal deaths occur in sub-Saharan Africa. Low birth weight (defined as <2,500 g) is one of the biggest risk factors associated with neonatal deaths but it is the causes of low birth weight, rather than the low weight itself that is thought to lead to neonatal deaths. The two main causes of low birth weight are preterm birth (delivery before 37 weeks gestation) and/or restricted growth in the womb (intra-uterine growth retardation), resulting in babies who are small for their dates (defined as being in the lowest 10% of weight expected for gestational age with reference to a US population).

          Why Was This Study Done?

          Despite growing international attention focused on neonatal mortality in recent years, the relative importance of low birth weight, small for gestational age, and preterm birth in causing newborn deaths remains unclear. So in this study, the researchers investigated these relationships by calculating the risk of neonatal mortality associated with preterm birth after adjusting for weight for gestational age by conducting a meta-analysis (synthesis of the data) using information from studies reporting neonatal mortality conducted in sub-Saharan Africa.

          What Did the Researchers Do and Find?

          The researchers identified potential African datasets and selected four out of a possible ten to include in their analysis as these studies included three essential birth outcomes: birth weight; gestational age measured using antenatal ultrasound, or neonatal assessment on the day of birth; and neonatal mortality. These four studies were conducted in Kenya, Tanzania, and Uganda, all in East Africa. The researchers analysed each study separately but also conducted a pooled statistical analysis on all four studies. To give a more detailed analysis, the researchers categorized babies into six groups taking into account whether the babies were moderately preterm (born at 34–36 weeks) or very preterm (born before 34 weeks) and whether their weight was appropriate for their gestational age.

          The researchers included a total of 4,843 live births in their analysis and found that overall, 9.2% of babies were low birth weight, 4.0% were preterm, and 20.4% were small for gestational age. Amongst low birth weight babies, 26.1% were preterm, 85.0% were small for gestational age, and 98.8% were either preterm or small for gestational age. In their detailed analysis, the researchers found that the odds (chance) of death in the first 28 days of life were seven times higher for babies born low birth weight compared to those with normal birth weight, with low birth weight infants experiencing a neonatal mortality rate of 80.9/1,000 live births. The odds of death were twice as high for babies born small for gestational age compared to those born appropriate for gestational age, giving a neonatal mortality rate of 29.3/1,000 live births. Furthermore, compared to those born at term, the odds of death were over six times higher for babies born moderately preterm and almost 60 times higher for babies born very preterm with almost half of all very preterm babies dying in the first 28 days of life, giving a neonatal mortality rate 473.6/1,000 live births. However, moderately preterm babies who were small for gestational age had a much greater odds of death than moderately preterm babies who were of the appropriate weight for their gestational age.

          What Do These Findings Mean?

          These findings from East Africa show that babies born either small for gestational age or preterm contributed 52% of neonatal deaths. The detailed analysis suggests that babies born preterm are at the greatest risk of death, but size for gestational age also plays an important role especially in moderately preterm babies. The results from this study emphasize the pressing need to find ways to prevent preterm delivery and intra-uterine growth retardation and also illustrate the importance of measuring and reporting outcomes of individual babies.

          Additional Information

          Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001292.

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          Most cited references28

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          Global report on preterm birth and stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data

          Introduction This is the first of seven articles from a preterm birth and stillbirth report. Presented here is an overview of the burden, an assessment of the quality of current estimates, review of trends, and recommendations to improve data. Preterm birth Few countries have reliable national preterm birth prevalence data. Globally, an estimated 13 million babies are born before 37 completed weeks of gestation annually. Rates are generally highest in low- and middle-income countries, and increasing in some middle- and high-income countries, particularly the Americas. Preterm birth is the leading direct cause of neonatal death (27%); more than one million preterm newborns die annually. Preterm birth is also the dominant risk factor for neonatal mortality, particularly for deaths due to infections. Long-term impairment is an increasing issue. Stillbirth Stillbirths are currently not included in Millennium Development Goal tracking and remain invisible in global policies. For international comparisons, stillbirths include late fetal deaths weighing more than 1000g or occurring after 28 weeks gestation. Only about 2% of all stillbirths are counted through vital registration and global estimates are based on household surveys or modelling. Two global estimation exercises reached a similar estimate of around three million annually; 99% occur in low- and middle-income countries. One million stillbirths occur during birth. Global stillbirth cause-of-death estimates are impeded by multiple, complex classification systems. Recommendations to improve data (1) increase the capture and quality of pregnancy outcome data through household surveys, the main data source for countries with 75% of the global burden; (2) increase compliance with standard definitions of gestational age and stillbirth in routine data collection systems; (3) strengthen existing data collection mechanisms—especially vital registration and facility data—by instituting a standard death certificate for stillbirth and neonatal death linked to revised International Classification of Diseases coding; (4) validate a simple, standardized classification system for stillbirth cause-of-death; and (5) improve systems and tools to capture acute morbidity and long-term impairment outcomes following preterm birth. Conclusion Lack of adequate data hampers visibility, effective policies, and research. Immediate opportunities exist to improve data tracking and reduce the burden of preterm birth and stillbirth.
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            Estimating the causes of 4 million neonatal deaths in the year 2000.

            Information on cause-of-death is lacking for 98% of the world's 4 million neonatal deaths that occur in countries with inadequate vital registration (VR). Our aim was to estimate, by country for the year 2000, the distribution of neonatal deaths across programme-relevant causes including: asphyxia, preterm birth, congenital abnormalities, sepsis/pneumonia, neonatal tetanus, diarrhoea, and 'other'. Two sources of neonatal cause-of-death data were examined: VR datasets for countries with high coverage (>90%), and published and unpublished studies identified through systematic searches. Multinomial regression was used to model the distribution of neonatal deaths. A VR-based model was used to estimate the distribution of causes of death for 37 low-mortality countries without national data. A study-based model was applied to obtain estimates for 111 high-mortality countries. Uncertainty estimates were derived using the jackknife approach. Data from 44 countries with VR (96 797 neonatal deaths) and from 56 studies (29 countries, 13 685 neonatal deaths) met inclusion criteria. The distribution of reported causes of death varied substantially between countries and across studies. Based on 193 countries, the major causes of neonatal death globally were estimated to be infections (sepsis/pneumonia, tetanus, and diarrhoea, 35%), preterm birth (28%), and asphyxia (23%). Regional variation is important. Substantial uncertainty surrounds these estimates. This exercise highlights the lack of reliable cause-of-death data in the settings in which most neonatal deaths occur. Complex statistical models are not a panacea. Representative data with comparable case definitions and consistent hierarchical cause-of-death attribution are required.
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              On the importance--and the unimportance--of birthweight.

              Birthweight is one of the most accessible and most misunderstood variables in epidemiology. A baby's weight at birth is strongly associated with mortality risk during the first year and, to a lesser degree, with developmental problems in childhood and the risk of various diseases in adulthood. Epidemiological analyses often regard birthweight as on the causal pathway to these health outcomes. Under this assumption of causality, birthweight is used to explain variations in infant mortality and later morbidity, and is also used as an intermediate health endpoint in itself. Evidence presented here suggests the link between birthweight and health outcomes may not be causal. Methods of analysis that assume causality are unreliable at best, and biased at worst. The category of 'low birthweight' in particular is uninformative and seldom justified. The main utility of the birthweight distribution is to provide an estimate of the proportion of small preterm births in a population (although even this requires special analytical methods). While the ordinary approaches to birthweight are not well grounded, the links between birthweight and a range of health outcomes may nonetheless reflect the workings of biological mechanisms with implications for human health.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                August 2012
                August 2012
                14 August 2012
                : 9
                : 8
                : e1001292
                Affiliations
                [1 ]Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [2 ]Malaria Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [3 ]Maternal Reproductive and Child Health Centre (MARCH), London School of Hygiene & Tropical Medicine, London, United Kingdom
                [4 ]Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [5 ]Department of International Health, Program in Global Disease Epidemiology and Control, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [6 ]Kenya Medical Research Institute/Centre for Global Health Research, Kisumu, Kenya
                [7 ]Centers for Disease Control and Prevention Kenya, Kisumu, Kenya
                [8 ]Child and Reproductive Health Group, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
                [9 ]National Institute Medical Research, Tanga, Tanzania
                [10 ]Vector Control Division, Ministry of Health, Uganda
                [11 ]Centre for Medical Parasitology, Department of International Health, Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
                [12 ]Department of Infectious Diseases Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark
                [13 ]Mwanza Intervention Trials Unit, National Institute for Medical Research, Mwanza, Tanzania
                Aga Khan University, Pakistan
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: TM BW JK JAS. Performed the experiments: TM BW. Analyzed the data: BW TM. Contributed reagents/materials/analysis tools: SC SK FtK JL RN CS DW-J. Wrote the first draft of the manuscript: TM. Contributed to the writing of the manuscript: TM BW JK SC SK FtK JL RN CS DW-J JAS. ICMJE criteria for authorship read and met: TM BW JK SC SK FtK JL RN CS DW-J JAS. Agree with manuscript results and conclusions: TM BW JK SC SK FtK JL RN CS DW-J JAS. Enrolled patients: SC SK FtK JL RN CS DW-J.

                Article
                PMEDICINE-D-11-03018
                10.1371/journal.pmed.1001292
                3419185
                22904691
                1f21671c-af67-4511-9b98-6be9072084f7
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 December 2011
                : 6 July 2012
                Page count
                Pages: 13
                Funding
                This analysis was funded by a sub-grant to LSHTM (London School of Hygiene & Tropical Medicine) from the CHERG (Child Health Epidemiology Research Group) under objective 5, MA8 of grant number 50140. The CHERG is funded by a grant from the Bill & Melinda Gates Foundation (810-2054), Seattle, Washington, USA via a partnership with the US Fund for UNICEF and receives ongoing financial support from WHO and UNICEF. The original studies were funded by the following: (1) USAID (United States Agency for International Development) (Asembo Bay cohort study, Kenya); (2) Wellcome Trust Training Fellowship in Clinical Tropical Medicine to DW-J (Syphilis screening study, Tanzania); (3) Gates Malaria Partnership (Kabale malaria study, Uganda); (4) European Union Framework 7 (STOPPAM, Tanzania) contract number 200889. The funders had no role in the design, data collection and analysis, decision to publish or preparation of the manuscript.
                Categories
                Research Article
                Medicine
                Obstetrics and Gynecology
                Pregnancy
                Preterm Labor
                Pediatrics
                Neonatology

                Medicine
                Medicine

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