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Background: Preterm birth is the leading cause of death in children younger than 5 years worldwide. Although preterm survival rates have increased in high-income countries, preterm newborns still die because of a lack of adequate newborn care in many low-income and middle-income countries. We estimated global, regional, and national rates of preterm birth in 2014, with trends over time for some selected countries. Methods: We systematically searched for data on preterm birth for 194 WHO Member States from 1990 to 2014 in databases of national civil registration and vital statistics (CRVS). We also searched for population-representative surveys and research studies for countries with no or limited CRVS data. For 38 countries with high-quality data for preterm births in 2014, data are reported directly. For countries with at least three data points between 1990 and 2014, we used a linear mixed regression model to estimate preterm birth rates. We also calculated regional and global estimates of preterm birth for 2014. Findings: We identified 1241 data points across 107 countries. The estimated global preterm birth rate for 2014 was 10·6% (uncertainty interval 9·0–12·0), equating to an estimated 14·84 million (12·65 million–16·73 million) live preterm births in 2014. 12· 0 million (81·1%) of these preterm births occurred in Asia and sub-Saharan Africa. Regional preterm birth rates for 2014 ranged from 13·4% (6·3–30·9) in North Africa to 8·7% (6·3–13·3) in Europe. India, China, Nigeria, Bangladesh, and Indonesia accounted for 57·9 million (41×4%) of 139·9 million livebirths and 6·6 million (44×6%) of preterm births globally in 2014. Of the 38 countries with high-quality data, preterm birth rates have increased since 2000 in 26 countries and decreased in 12 countries. Globally, we estimated that the preterm birth rate was 9×8% (8×3–10×9) in 2000, and 10×6% (9×0–12×0) in 2014. Interpretation: Preterm birth remains a crucial issue in child mortality and improving quality of maternal and newborn care. To better understand the epidemiology of preterm birth, the quality and volume of data needs to be improved, including standardisation of definitions, measurement, and reporting. Funding: WHO and the March of Dimes.
We did a systematic review for data on preterm birth in databases of national civil registration and vital statistics, supplemented with population-representative surveys and research studies. For countries without high-quality data on preterm birth for 2014, we used a regression model to estimate preterm birth rates. We also calculated regional and global estimates of preterm birth for 2014. Methodological details for this study are available in the protocol.22 The preterm birth rate (based on the WHO definition1) was the primary outcome, defined as the number of liveborn preterm births (ie, a singleton or multiple livebirth before 37 completed weeks of gestation) divided by the number of livebirths. We identified and extracted available data on preterm birth for the 194 Member States of WHO23 for 1990 to 2014 (inclusive). The preferred data source was civil registration and vital statistics (CRVS) data. However, data for preterm birth are often not available in national CRVS systems, and not all countries have a CRVS system.24 Thus, in countries where no or limited CRVS preterm birth data were reported, we searched for research studies (whether representative of the national population or not) and population-representative household surveys. We classified WHO Member States into three groups (A, B, or C) to guide data extraction, based on the availability of CRVS preterm birth data and the estimated coverage of births captured by the national CRVS system (appendix pp 5–7).25 Group A countries were those with an estimated CRVS birth registration coverage of more than 80%, and CRVS preterm birth data available for at least 50% of the years from 1990 to 2014. Group B countries were those with coverage from 60% to 80%, CRVS preterm birth data available for less than 50% of years, or both. Group C countries were those with less than 60% birth registration coverage (or unknown), no available CRVS data for preterm birth, or both. For group A countries, we used CRVS data only. For group B and C countries, we used any available CRVS data and sought additional data from research studies and population-representative household surveys. All searches were done without language restrictions. To identify CRVS data, we searched all online databases of national ministries of health and national statistical offices for all WHO Member States using search terms related to births or preterm birth in the relevant language.26 We did a first search in January, 2016, and an updated search in May, 2017. For group B and C countries, we also searched the online repositories of Demographic and Health Surveys27 and Reproductive Health Surveys28 for all survey reports with data for preterm birth. We also conducted a systematic review, searching MEDLINE, Embase, Popline, Global Index Medicus, CINAHL, PsychInfo, and the Cochrane Central Register of Controlled Trials for articles with data for preterm birth (appendix pp 8–17). Given the large population in China (where national data on preterm birth are not reported), we also searched Sinomed, a Chinese language database, restricted to the six most highly cited medical journals. We included reports providing preterm birth data for 1990 to 2014 with at least 500 births.22 Reports using definitions similar to our primary outcome (such as those reporting preterm birth in all births, in singletons only, or with minor exclusions for selected subpopulations) were also eligible. We included reports using livebirths or all births (ie, livebirths and stillbirths), regardless of the definition used. The Intergrowth-21st multicentre cohort study reported that 3% of healthy, low-risk women experienced a preterm birth and spontaneous onset of labour.29 Hence, we excluded data points in which less than 3% of births were preterm on the basis of biological implausibility. We excluded case-control studies, studies with insufficient information (such as conference abstracts), or those in which preterm birth data were available only for high-risk subpopulations (eg, women with specific medical or obstetric complications, women or newborn babies using specialty services, or selected socio-demographic subgroups). If multiple reports originated from the same dataset, we used the report providing the most comprehensive information. We excluded articles in which the midpoint of data collection was before 1990, or in which the year of data collection was not reported. We also excluded some reports for reasons that were not prespecified: reports in which gestational age was reported using months only, and several population-representative surveys that lacked a clear operational definition of preterm birth or that relied on maternal recall. We developed a study manual of operations to standardise all screening and extraction procedures, which was used to train all participating reviewers in an in-person workshop. An eligibility checklist was used to screen reports. Each CRVS report and population-representative survey report were reviewed independently for inclusion by two reviewers. For the systematic review, all recovered citations were merged and duplications removed, and all titles and abstracts were screened by two reviewers independently using the Covidence platform. Full texts of potentially eligible articles were reviewed independently for inclusion by two reviewers. Disagreements were resolved through discussion or a third reviewer. We developed a customised online database (OpenClinica) for data extraction. Extracted data included: country, data source, design, time period, range of gestational age used, method of assessment of gestational age, and data for preterm birth. We also extracted any data on prespecified gestational age subgroups (<28 weeks, 28<32 weeks, 32<37 weeks). CRVS data were extracted by two reviewers, with results compared and disagreements resolved by a third reviewer. Data from population-representative surveys and research studies were extracted by one reviewer, with a 10% random sample extracted by a second reviewer and compared for quality control. A midpoint year was assigned to each data point. Additional quality checks were done for on all outlier data points. There was considerable heterogeneity in how preterm birth was defined and measured; some reports provided multiple indicators (eg, preterm birth reported in livebirths; in livebirths and stillbirths; in singletons only; in multiples only). We extracted characteristics and data for all available preterm birth indicators and developed a hierarchy of indicators based on proximity to the WHO definition to assign a primary indicator for each data point (appendix p 18). We also created a database of candidate predictors of preterm birth to use in our models, based on potential risk factors and covariates identified in previous estimates of preterm births.22 Data were extracted from the most recent published national estimates of these predictors from the pertinent United Nations sources (appendix pp 19–20). In the event of missing years of data, interpolation was applied. We modelled preterm birth rates using a multilevel (region, country, and preterm birth rates) linear mixed regression model, with correlated random country-specific intercepts and time slopes; a regional random intercept was also applied. Inclusion of covariates was based on the deviance information criterion and significance and inspection at country level. Covariates included in the final model were: (1) Human Development Index, (2) low birthweight proportion, (3) definition of preterm used in data source, (4) birth population used (livebirths or all births), (5) whether singleton or multiple births were included, or both, and (6) data source. We did statistical regression analyses in Stata (version 14.2) based on the full dataset (1241 data points from 107 countries). We estimated the logit transform of the preterm birth data using a linear Bayesian model (bayesmh command in Stata): for the i:th year, j:th country, k:th region, and the l:th observation. The random component αj is the correlated country intercept and the random component βj is the time slopes, γk is the regional dummy intercept, δ1 is the Human Development Index (continuous) slope, δ2 is the low birthweight (continuous) proportion slope, δ3 is the coefficient of the categorical variable for preterm definition (0 if ≤36 weeks, 1 if <36 weeks, 2 if ≤37 weeks), δ4 is the coefficient of the categorical variable for population (0 if livebirths, 1 if all births, 2 if not defined), δ5 is the coefficient of the plurality dummy (0 if singleton and multiple, 1 if other or not defined), δ6 is the coefficient of the data source dummy (0 if CRVS, 1 if research study or household survey), and εijkl is a normally distributed residual. In the regression models, we used the United Nations Statistics Division (M49) regional groupings (merging Latin America and North America into one region, and sub-Saharan Africa and North Africa into one region).30 We did not apply weights for study sample size because of differences in sample sizes between studies and CRVS data. We used Markov Chain Monte Carlo to simulate the posterior distribution. We used three chains, each with a sample size of 10 000 draws, following a burn-in of 10 000 draws, for a total of 30 000 posterior draws. The technical details and syntax (including distribution of priors) are shown in the appendix (pp 21–24). To estimate preterm birth rates at global and regional level, we generated predictions for all country-years from the estimated model parameters. To properly reflect uncertainty around estimates for countries without data, random intercepts and slopes for these countries were simulated from the posterior means and covariance of country intercepts and slopes for countries with data. From this procedure, we had 30 000 sets of model parameter values for the model for each country. When predicting preterm birth rates from the model, the dummy variables, δ3, δ4, δ5, and δ6, were all set to 0, which was the preferred level for these four variables (appendix 21–24). The predicted preterm birth rates per year in each country were then weighted into global and regional estimates, based on the estimated total number of livebirths per country per year. Uncertainty intervals (UI) were constructed from the weighted 2nd and 98th percentiles of the posterior samples (30 000 replicates). The estimated numbers of preterm births were calculated as the product of the preterm birth rate and the yearly total livebirths.31 We used United Nations Development Programme estimates of livebirths (1990–2014) rather than country estimates because we needed comparable data across all countries. Consequently, we excluded 11 countries without a United Nations Development Programme estimate of livebirths from the analysis. Regional estimates were weighted to a global estimate based on the regional livebirth population. We also estimated the contribution of different preterm birth definitions (<28 weeks, 28 to <32 weeks, 32 to <37 weeks) and of multiple births to the overall preterm birth rates. We conducted an official country consultation with WHO Member States in July, 2017, for review of the methods and provision of potentially eligible additional data. Additional data were received from four countries that were eligible for inclusion. Global and regional estimates of preterm birth rate for 2014 are based on model predictions for 183 countries. For the 38 group A countries, the data for 2014 are reported directly without modelling and without UIs. If data for 2014 were not available, the predicted preterm birth rates for 2014 (extrapolated from available data) are provided. Trends in preterm birth rates from 2000 to 2014 were also estimated for group A countries. For group B and C countries with three or more data points (of any type) available for 1990 to 2014, we present the predicted preterm birth rates with UIs for 2014. For group B and group C countries with fewer than three data points, we do not present a country-specific estimate because of concerns regarding the reliability of national estimates for which no or few country data are available. In these countries, the corresponding regional estimate is provided to guide stakeholders. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the Article. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
