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      Global, regional, and national estimates and trends in stillbirths from 2000 to 2019: a systematic assessment

      research-article
      , MA a , * , , PhD a , , PhD b , , PhD a , , MA c , , PhD d , , Prof, PhD d , , PhD e , , PhD f , , PhD g , , PhD h , , Prof, DipMathStat b , , PhD i , , MA j , , Prof, PhD k , * , , Prof, PhD l , m , , PhD n , , PhD o , , Prof, FRCOG p , , MA q , , Prof, PhD r , , Prof, DSc s , , PhD c , UN Inter-agency Group for Child Mortality Estimation and its Core Stillbirth Estimation Group
      Lancet (London, England)
      Elsevier

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          Summary

          Background

          Stillbirths are a major public health issue and a sensitive marker of the quality of care around pregnancy and birth. The UN Global Strategy for Women's, Children's and Adolescents’ Health (2016–30) and the Every Newborn Action Plan (led by UNICEF and WHO) call for an end to preventable stillbirths. A first step to prevent stillbirths is obtaining standardised measurement of stillbirth rates across countries. We estimated stillbirth rates and their trends for 195 countries from 2000 to 2019 and assessed progress over time.

          Methods

          For a systematic assessment, we created a dataset of 2833 country-year datapoints from 171 countries relevant to stillbirth rates, including data from registration and health information systems, household-based surveys, and population-based studies. After data quality assessment and exclusions, we used 1531 datapoints to estimate country-specific stillbirth rates for 195 countries from 2000 to 2019 using a Bayesian hierarchical temporal sparse regression model, according to a definition of stillbirth of at least 28 weeks’ gestational age. Our model combined covariates with a temporal smoothing process such that estimates were informed by data for country-periods with high quality data, while being based on covariates for country-periods with little or no data on stillbirth rates. Bias and additional uncertainty associated with observations based on alternative stillbirth definitions and source types, and observations that were subject to non-sampling errors, were included in the model. We compared the estimated stillbirth rates and trends to previously reported mortality estimates in children younger than 5 years.

          Findings

          Globally in 2019, an estimated 2·0 million babies (90% uncertainty interval [UI] 1·9–2·2) were stillborn at 28 weeks or more of gestation, with a global stillbirth rate of 13·9 stillbirths (90% UI 13·5–15·4) per 1000 total births. Stillbirth rates in 2019 varied widely across regions, from 22·8 stillbirths (19·8–27·7) per 1000 total births in west and central Africa to 2·9 (2·7–3·0) in western Europe. After west and central Africa, eastern and southern Africa and south Asia had the second and third highest stillbirth rates in 2019. The global annual rate of reduction in stillbirth rate was estimated at 2·3% (90% UI 1·7–2·7) from 2000 to 2019, which was lower than the 2·9% (2·5–3·2) annual rate of reduction in neonatal mortality rate (for neonates aged <28 days) and the 4·3% (3·8–4·7) annual rate of reduction in mortality rate among children aged 1–59 months during the same period. Based on the lower bound of the 90% UIs, 114 countries had an estimated decrease in stillbirth rate since 2000, with four countries having a decrease of at least 50·0%, 28 having a decrease of 25·0–49·9%, 50 having a decrease of 10·0–24·9%, and 32 having a decrease of less than 10·0%. For the remaining 81 countries, we found no decrease in stillbirth rate since 2000. Of these countries, 34 were in sub-Saharan Africa, 16 were in east Asia and the Pacific, and 15 were in Latin America and the Caribbean.

          Interpretation

          Progress in reducing the rate of stillbirths has been slow compared with decreases in the mortality rate of children younger than 5 years. Accelerated improvements are most needed in the regions and countries with high stillbirth rates, particularly in sub-Saharan Africa. Future prevention of stillbirths needs increased efforts to raise public awareness, improve data collection, assess progress, and understand public health priorities locally, all of which require investment.

          Funding

          Bill & Melinda Gates Foundation and the UK Foreign, Commonwealth and Development Office.

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          Most cited references32

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          Stan: A Probabilistic Programming Language

          Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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            Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016

            Summary Background Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. Methods We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15–60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. Findings Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5–24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates—a measure of relative inequality—increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7–87·2), and for men in Singapore, at 81·3 years (78·8–83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, and the gap between male and female life expectancy increased with progression to higher levels of SDI. Some countries with exceptional health performance in 1990 in terms of the difference in observed to expected life expectancy at birth had slower progress on the same measure in 2016. Interpretation Globally, mortality rates have decreased across all age groups over the past five decades, with the largest improvements occurring among children younger than 5 years. However, at the national level, considerable heterogeneity remains in terms of both level and rate of changes in age-specific mortality; increases in mortality for certain age groups occurred in some locations. We found evidence that the absolute gap between countries in age-specific death rates has declined, although the relative gap for some age-sex groups increased. Countries that now lead in terms of having higher observed life expectancy than that expected on the basis of development alone, or locations that have either increased this advantage or rapidly decreased the deficit from expected levels, could provide insight into the means to accelerate progress in nations where progress has stalled. Funding Bill & Melinda Gates Foundation, and the National Institute on Aging and the National Institute of Mental Health of the National Institutes of Health.
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              Maternal and fetal risk factors for stillbirth: population based study

              Objective To assess the main risk factors associated with stillbirth in a multiethnic English maternity population. Design Cohort study. Setting National Health Service region in England. Population 92 218 normally formed singletons including 389 stillbirths from 24 weeks of gestation, delivered during 2009-11. Main outcome measure Risk of stillbirth. Results Multivariable analysis identified a significant risk of stillbirth for parity (para 0 and para ≥3), ethnicity (African, African-Caribbean, Indian, and Pakistani), maternal obesity (body mass index ≥30), smoking, pre-existing diabetes, and history of mental health problems, antepartum haemorrhage, and fetal growth restriction (birth weight below 10th customised birthweight centile). As potentially modifiable risk factors, maternal obesity, smoking in pregnancy, and fetal growth restriction together accounted for 56.1% of the stillbirths. Presence of fetal growth restriction constituted the highest risk, and this applied to pregnancies where mothers did not smoke (adjusted relative risk 7.8, 95% confidence interval 6.6 to 10.9), did smoke (5.7, 3.6 to 10.9), and were exposed to passive smoke only (10.0, 6.6 to 15.8). Fetal growth restriction also had the largest population attributable risk for stillbirth and was fivefold greater if it was not detected antenatally than when it was (32.0% v 6.2%). In total, 195 of the 389 stillbirths in this cohort had fetal growth restriction, but in 160 (82%) it had not been detected antenatally. Antenatal recognition of fetal growth restriction resulted in delivery 10 days earlier than when it was not detected: median 270 (interquartile range 261-279) days v 280 (interquartile range 273-287) days. The overall stillbirth rate (per 1000 births) was 4.2, but only 2.4 in pregnancies without fetal growth restriction, increasing to 9.7 with antenatally detected fetal growth restriction and 19.8 when it was not detected. Conclusion Most normally formed singleton stillbirths are potentially avoidable. The single largest risk factor is unrecognised fetal growth restriction, and preventive strategies need to focus on improving antenatal detection.
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                Author and article information

                Contributors
                Journal
                Lancet
                Lancet
                Lancet (London, England)
                Elsevier
                0140-6736
                1474-547X
                28 August 2021
                28 August 2021
                : 398
                : 10302
                : 772-785
                Affiliations
                [a ]Division of Data, Analytics, Planning and Monitoring, UNICEF, New York, NY, USA
                [b ]Maternal, Adolescent, Reproductive & Child Health Centre, London School of Hygiene & Tropical Medicine, London, UK
                [c ]Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
                [d ]University of Washington, Seattle, WA, USA
                [e ]Department of Maternal, Newborn, Child and Adolescent Health, WHO, Geneva, Switzerland
                [f ]UN Population Division, New York, NY, USA
                [g ]Development Data Group, World Bank, Washington, DC, USA
                [h ]Centers for Disease Control and Prevention, Atlanta, GA, USA
                [i ]Johns Hopkins University, Baltimore, MD, USA
                [j ]The Demographic and Health Surveys Program, ICF, Rockville, MD, USA
                [k ]Stanton-Hill Research, Moultonborough, NH, USA
                [l ]University of British Columbia, Vancouver, BC, Canada
                [m ]Children's and Women's Hospital and Health Centre of British Columbia, Vancouver, BC, Canada
                [n ]University of Cape Town, Cape Town, South Africa
                [o ]RTI International, Research Triangle Park, NC, USA
                [p ]SAMRC/UP Maternal and Infant Health Care Strategies Unit, Department of Obstetrics and Gynaecology, University of Pretoria, Pretoria, South Africa
                [q ]Mikro, Oslo, Norway
                [r ]Department of Health Sciences, University of Leicester, Leicester, UK
                [s ]Inserm UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team, Center for Epidemiology and Statistics Sorbonne Paris Cité, DHU Risks in Pregnancy, Paris Descartes University, Paris, France
                Author notes
                [* ]Correspondence to: Lucia Hug, Division of Data, Analytics, Planning and Monitoring, UNICEF, New York, NY 10016, USA lhug@ 123456unicef.org
                [*]

                Retired in June, 2013

                Article
                S0140-6736(21)01112-0
                10.1016/S0140-6736(21)01112-0
                8417352
                34454675
                64cc4840-415d-44e2-a413-9cd91994029e
                © 2021 World Health Organization

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

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