Preterm birth is the leading cause of infant death worldwide, but the causes of preterm birth are largely unknown. During the early COVID-19 lockdowns, dramatic reductions in preterm birth were reported; however, these trends may be offset by increases in stillbirth rates. It is important to study these trends globally as the pandemic continues, and to understand the underlying cause(s). Lockdowns have dramatically impacted maternal workload, access to healthcare, hygiene practices, and air pollution - all of which could impact perinatal outcomes and might affect pregnant women differently in different regions of the world. In the international Perinatal Outcomes in the Pandemic (iPOP) Study, we will seize the unique opportunity offered by the COVID-19 pandemic to answer urgent questions about perinatal health. In the first two study phases, we will use population-based aggregate data and standardized outcome definitions to: 1) Determine rates of preterm birth, low birth weight, and stillbirth and describe changes during lockdowns; and assess if these changes are consistent globally, or differ by region and income setting, 2) Determine if the magnitude of changes in adverse perinatal outcomes during lockdown are modified by regional differences in COVID-19 infection rates, lockdown stringency, adherence to lockdown measures, air quality, or other social and economic markers, obtained from publicly available datasets. We will undertake an interrupted time series analysis covering births from January 2015 through July 2020. The iPOP Study will involve at least 121 researchers in 37 countries, including obstetricians, neonatologists, epidemiologists, public health researchers, environmental scientists, and policymakers. We will leverage the most disruptive and widespread “natural experiment” of our lifetime to make rapid discoveries about preterm birth. Whether the COVID-19 pandemic is worsening or unexpectedly improving perinatal outcomes, our research will provide critical new information to shape prenatal care strategies throughout (and well beyond) the pandemic. Pandemic response measures may have profound societal impacts owing to the combination of constrained supply, reduced resources, suppressed human interaction, and worsening socio-economic inequality. Projections already suggest about a 45% increase in child deaths and 39% increase in maternal deaths across low- and middle-income countries related to the pandemic.The COVID-19 pandemic and response measures taken to mitigate the spread of infection have dramatically impacted health and health systems across the globe. Maternal and child health is at high risk, especially in low- and middle-income countries where resources for health care are already limitedreductions in preterm births (up to 90% inDenmark and 23% in theNetherlands) and births classified as very low birth weight (70% inIreland) following the COVID-19 lockdowns. At the same time, reports fromNepal andIndia show an alarming increase in stillbirths and preterm births related to changes in maternity care. Increases in stillbirth have also been seen in theUK andItaly (Lazio region). InCalifornia, preterm birth rates seem largely unchanged during the pandemic period, except for a modest increase (11%) in very preterm birth, driven primarily by the Hispanic/Latinx population.Unexpectedly, recent evidence from some high-income countries suggests unprecedented, access to healthcare, hygiene practices, air pollution, nutrition, and non-SARS-CoV-2 infection, each of which may have affected maternal and perinatal outcomes disproportionately in different socio-economic and regional settings. It is plausible that changes in exposures to inflammatory triggers, such as infections and air pollution, may be partly responsible for changes to some perinatal outcomes, such as spontaneous preterm birth. We will therefore seize the unique opportunity resulting from the global COVID-19 pandemic to answer pressing questions on pandemic lockdowns and perinatal health on a global scale.It is critical to evaluate these seemingly contrasting trends and to understand the underlying mechanisms. The pandemic mitigation measures have substantially impacted maternal workloadInternational COVID-19 Data Alliance  to establish an inclusive international research programme that will collaborate to focus on key questions relevant to countries across the globe of all income levels. The initial focus of iPOP will be on the impact of COVID‐19 pandemic lockdowns on perinatal outcomes, including preterm birth, low birth weight, and stillbirth.To address the impact of the pandemic response measures on perinatal health, the international Perinatal Outcomes in the Pandemic (iPOP) study, is working in partnership with theThe overall objective of iPOP is to determine the impact of pandemic lockdowns on perinatal outcomes worldwide, and to investigate potential mechanisms underlying these effects.The overarching goal of iPOP is to:•    Investigate the impact of COVID-19 lockdowns on perinatal outcomes ;•    Compare the impact of COVID-19 lockdown on perinatal outcomes by country income setting: low-income countries (LICs), lower-middle-income countries (LMICs), upper-middle-income countries (UMICs) and high-income countries (HICs);•    Explore the underlying societal and etiological factors that are associated with between-country differences in the impact of COVID-19 lockdown on perinatal outcomes.The conceptual framework  for the WP1 - Describe global trends and regional differences in adverse perinatal outcomes during COVID-19 pandemic lockdowns, using population-based aggregate data and standardized outcome definitions: Report rates of preterm birth, low birth weight, and stillbirth and describe changes during the pandemic lockdown. Determine if these changes are consistent globally, or if they differ between or within LIC, LMIC, UMIC and HIC settings.WP2 - Address contextual influences and mechanisms for changes in preterm birth, stillbirth, and low birth weight during the COVID-19 pandemic, using population-based aggregate and publicly available data: determine if the magnitude of regional changes in adverse perinatal outcomes during lockdown are potentiallymodified by regional differences in COVID-19 infection rates, lockdown stringency, adherence to lockdown measures, air quality, and other social and economic markers available from public datasets.Possible mechanisms driving the association between pandemic lockdown measures and perinatal outcomes are represented in a directed acyclic graph (DAG) inIn WP1, we will estimate the impact of pandemic lockdowns on global incidence of preterm birth, low birth weight, and stillbirth using population-based data. Specifically, we will answer the following questions: 1. Has implementation of COVID-19 pandemic lockdowns been associated with a change in preterm birth rate (<37 weeks gestation)? 2. Does the association vary:a)spontaneous preterm birth (preterm birth preceded by spontaneous contractions and/or preterm prelabour rupture of membranes)?When the outcome is restricted tob)early preterm birth (<32 weeks gestation)?When the outcome is restricted to3. Does any association with preterm birth remain when analyses are restricted to live births only (i.e. exclusion of stillbirths)?4. Has implementation of COVID-19 pandemic lockdown been associated with a change in low birth weight rate (<2500g)?5. Has implementation of COVID-19 pandemic lockdown been associated with a change in stillbirth rate?6. Has implementation of COVID-19 pandemic lockdown been associated with a change in post term birth rate (≥42 weeks gestation)?7. Do any observed associations with preterm birth, low birth weight, and stillbirth vary by country income setting ?World Bank income grouping.Our primary hypothesis is that the rate of spontaneous preterm birth, low birth weight and/or stillbirth is changed during pandemic lockdowns worldwide. Our secondary hypothesis is that the magnitude and/or direction of the change in the spontaneous preterm birth, low birth weight, and/or stillbirth varies by country income setting as classified by theIn WP2 we will build directly from WP1 with the addition of national/regional characteristics derived from publicly available datasets to explore the influence of the association between lockdown measures and adverse perinatal outcomes.Specifically, we will address the following questions:8. Are the direction and magnitude of any changes in preterm birth, low birth weight and/or stillbirth rates observed in WP1 modified by factors such as:a.Exposures below)Lockdown stringency index .We will analyse aggregate population-based data provided by collaborators from different national/regional sites. Our primary method of analysis will be an interrupted time series analysis (ITSA) and we will consider alternative quasi-experimental approaches as appropriate.Results from each contributing site will be meta-analysed, if appropriate. We will classify data into one of three tiers  based on the nature of the datasets in terms of population coverage, quality and completeness, and availability of required variables . StandarA single contributing dataset may be categorised in different tiers for different analyses, e.g. a dataset with low completeness on gestational age but high completeness on birth weight might be categorised as investigative for analyses of preterm birth and as enhanced for analyses of low birth weight.. To allow meaningful comparison of international data, in our main analyses definitions we have used a gestational age threshold of 28 weeks, and birth weight threshold or 1000g for inclusion (identified by the extension “_m”). This is in recognition that inclusion of extreme preterm and extremely low birthweight births can disrupt the validity of such comparisons. However, these births will be included in definitions for the enhanced analyses (identified by the extension “_e”).Wherever possible we have aligned our definitions with those of the World Health Organization (WHO)We have chosen to use a denominator of total births for our primary outcome of preterm birth.all births (live and stillbirth) from 28+0 to 44+6 weeks gestation inclusive; or above ≥1000g birth weight. We also aim to capture additional data onall births (live and stillbirth) from 22+0 to 27+6 weeks gestation, or between 500g and 999g. These data will be included in enhanced analyses.Our aim is to capture, at a minimum, data onThe main analysis study period is January 1, 2015 to July 31, 2020, covering the first lockdown period (in 2020) and the previous five calendar years. We will include data from January 1, 2018 to July 31, 2020 in investigative analysis if earlier data is not available. We will request the most recent data available to allow enhanced analyses covering a wider time period.Oxford COVID-19 Government Response Tracker. The Oxford COVID-19 Government Response Tracker provides a systematic cross-national, cross-temporal measure to understand how government responses have evolved over the full period of the disease’s spread. It collects information on different policies and interventions that governments have instituted in response to the COVID-19 pandemic and using standardized series of indicators creates a suite of composites indices to measure the extent of these responses. The indicators cover information on containment and closure policies (e.g. school closures and restrictions in movement) (C1-C8); economic policies (e.g. income support to citizens or provision of foreign aid) (E1-E4); and record health system policies  (H1-H5). Thelockdown stringency index is calculated using only the policy indicators C1-C8 and H1. The value of the index on any given day is the average of nine sub-indices pertaining to the individual policy indicators, each taking a value between 0 and 100. If the most stringent policy is only present in a limited area or region, a binary flag variable denotes limited scope. Thecodebook for the stringency index is publicly available.The primary exposure will be a binary variable for lockdown based on the stringency index. We will use the stringency index from theWe will define lockdown as a score of ≥50 on the Oxford COVID-19 Government Response Tracker stringency index. The decision on this arbitrary cut off has been influenced by scoping of stringency index data in high income settings and comparison of stringency indexes in settings which have and have not implemented lockdown measures. For example, Sweden (which has not had a ‘lockdown’) never implemented measures during the study period that added up to higher than 50 on the stringency index, compared to neighbouring Denmark, which scored above 50 throughout the study period in 2020. We will record timing of reaching a score ≥50 separately for each country/region. Our primary analysis will focus on the start date of pandemic lockdown defined as the first date when a country/region’s stringency exceeded 49 (i.e. as a stringency score of ≥50).Subsequent analyses may include the:•    Time period of pandemic lockdown: defined as a continuous calendar period during which a country/region has a stringency score of ≥50•    Total duration of pandemic lockdown: defined as the sum of all calendar periods during which a country/region has a stringency score of ≥50Note: The beginning and length of lockdown may vary by country/regionlockdown stringency index in each country/region. The exact comparator time period may vary by country/region.Births during the 2020 lockdown periods will be compared with births occurring before the first date when a country/region’s stringency exceeded 49 (i.e. as a stringency score of ≥50), defined byPrimary outcome+0- 36+6 weeks gestation; denominator total births ≥28+0 weeks).•    Preterm birth rate_m .•    Preterm birth rate_e .•    Early preterm birth rate_m .•    Early preterm birth rate_e .•    Extreme preterm birth rate_e .•    Spontaneous preterm birth rate_e .•    Spontaneous preterm birth rate_e .•    Post term birth rate_m ; denominator total births ≥28+0 weeks (or ≥1000g if gestation not available).•    Stillbirth rate_m ; denominator total births ≥22+0 weeks (or ≥500g if gestation not available).•    Stillbirth rate_e .•    Low birth weight rate_e .•    Very low birth weight rate_m .•    Very low birth weight rate_e .•    Extremely low birth weight rate_e .Potential confounders/effect modifiers for the entire iPOP study are represented in a DAG . We recoWorld Bank  as a proxy for wider social security and healthcare system.National/regional level societal characteristics that we are interested in exploring include mediating and moderating factors obtained from publicly available datasets as described in the section below. Country classification by income as defined by thest 2020 are shown inWe have extended invitations for national, regional and institutional data custodians of birth data to participate through formal and informal networks, social media, lay and scientific media. Participating countries as of December 1We will request aggregate data from each data provider using an excel spreadsheet template, which includes details on levels of missing data. We will classify data provided to iPOP as Standard, Enhanced, or Investigative, based on the characteristics described inWe will also ask for completion of a questionnaire regarding the source of data including, country of origin, region(s) covered and size of population covered. To assist data providers on which template to use to capture their data, we have constructed a data flow diagram .For WP2 we will use the following publicly available data sources:Lockdown stringency: Using thestringency index (see sectionExposures) andCOVID-19: Containment and Health Index defined as a continuous (0–100) or categorical measures.•    Socioeconomic status: Measured byOrganisation for Economic Co-operation and Development (OECD) better life index.•    Ambient air quality: Estimated using the Data Integration Model for Air Quality (DIMAQ), which uses input data from a variety of public sources including:Open Air Quality, NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) global modelling initiative, satellite imagery data from the Multiangle Implementation of Atmospheric Correction (MAIAC), and global population density from theNASA/Columbia University Socioeconomic Data and Applications Center.•    Adherence to lockdown indicated by traffic and movement trends: Obtained from publicly availableGoogle mobility data.•    COVID-19 rates: Nationally available viaJohn Hopkins COVID-19 infection rates•    Parental leave policy: Measured byWorld Bank Data .•    Other country-level characteristics: Measured byWorld Bank Data , TheGlobal Gender Gap Index, TheGlobal Hunger Index andPolitical stability index.•    Secure Anonymised Information Linkage (SAIL) Databank, Swansea Wales, to store all data provided to iPOP. Upon completion of a Data Contribution Agreement between each iPOP data provider and the SAIL Databank, each data providers will either:We will use thei)upload aggregated data directly to the SAIL central repository, orii)transfer their data to the University of Edinburgh (RM), who will upload these to SAIL on their behalf.here.Data will be transferred into SAIL using the “Split-file” process with the support of the Informatics Service, National Health Services (NHS) of Wales. Person-level demographics are translated to an Anonymous Linking Field (ALF). Additional information on the SAIL File Structure & Data Transfer processes can be foundHDR UK website.iPOP Team Members  will access data stored within SAIL via a remote access and conduct data analyses remotely on the International COVID-19 Data Alliance (ICODA) Workbench, via a federated approach. ICODA is a new data platform that allows scientists and researchers across the globe to discover, access and analyse multi-dimensional datasets in a confidential and secure environment. More information can be found on theTo ensure outputs are confidential and safe, all statistical outputs will be checked using Statistical Disclosure Control (SDC) procedures before being exported out of the virtual environment. We will use SDC guiding principles from the Handbook on SDC for Outputs by the UK Data Service. This will prevent the identity of a birth from being revealed or inferred from outputs.A catalogue on the data variables captured will be recorded alongside relevant metadata. These high-level summaries will be made publicly available.STROBE).All analyses will be fully specified in a comprehensive Statistical Analysis Plan. We will adhere to relevant reporting guidance for example the Strengthening the Reporting of Observational studies in Epidemiology  to describe, explore and compare the national/regional data.Statistical modellingWe will undertake population-based ITSA for main analyses of primary and secondary outcomes. We will use time-series techniques to capture any underlying temporal trends and seasonality in the data before the implementation of lockdown measures. We will consider both linear and more flexible trends. We will use these time-series regression models to forecast (or predict) the expected trends and will compare these to the observed trends seen after the lockdown measures. This will capture both immediate (i.e. step) changes and gradual (i.e. slope) changes in the outcome in relation to implementation of lockdown measures in our models. All analyses will be prespecified in a Statistical Analyses Plan before analysis.Meta-analysis2 test.We will undertake a meta-analysis of national/regional results, on the step-change and the difference between the forecast and observed outcomes at different time points after the implementation of lockdown measures. We will also stratify by country income setting as a dichotomous variable (LIC+LMIC vs UMIC+HIC), since existing data suggests differing effects in these groups. Statistical heterogeneity will be assessed using IFor WP2, we will use these pooled estimates from WP1 in meta-regression analyses. These will incorporate the moderator/mediator variables as potential mechanisms at a national/regional level. This will measure the influence of these mechanisms on the association between lockdown measures and adverse perinatal outcomes.Sensitivity analysesWhere enhanced datasets are available for an outcome, we will perform similar modelling techniques to those described above with these enhanced data as sensitivity analyses to test the robustness of the main analyses in different populations. These analyses will be specified further in a comprehensive Statistical Analysis Plan. Predefined examples include:•    Sensitivity analyses restricting the denominator for our main outcomes of interest (excluding outcomes on spontaneous preterm birth) from all births to only live births. These analyses will be informative for the appropriateness of using datasets which only include information on live births.stringency index to test the robustness of assigning ≥50 as the primary cut-off point. These analyses will also allow inclusion of countries with less strict lockdown measures, such as Sweden, and inform whether/to what extent the observed associations might vary by lockdown stringency.•    Sensitivity analyses with varying cut-off points for our lockdown definition (i.e. above and below 50) from theWe will conduct supplementary analyses in investigative datasets.Output confidentialityHandbook on SDC for Outputs by the UK Data Service.All outputs will be checked for any potential disclosure and confidentiality breaches, using guidance from the. Further, public and patient involvement helps to identify research outcomes that are meaningful and pragmatic to knowledge users.Public and patient involvement early in study design and development ensures research studies are responsive to input, guidance and advice, and can help identify and mitigate potential challenges early in the research processThe iPOP team has engaged parents as patient partners early in the study design and have built a working group to capture and integrate patient involvement in the iPOP study as it moves forward. Meeting monthly, patient partners will be involved in developing effective and meaningful knowledge translation and communication strategies for disseminating iPOP findings. Specific to WP2, patient partners will work with researchers to examine mechanistic effects of the pandemic lockdown on perinatal outcomes. Patient partners will also work with researchers to develop knowledge translation strategies to ensure effective and meaningful dissemination of findings to knowledge users.To ensure transparent, equitable, and meaningful engagement, we have developed Guiding Principles that outline the terms of agreement for study leads and collaborators who are involved in the iPOP Study. Each member of the iPOP Study must read and sign the guiding principles document in order to collaborate on the study. While not legally binding, this document provides guidance and parameters around authorship, roles and responsibilities, research integrity, communication and Team Science guidelines.The iPOP Study ensures confidentiality and security of the processing of data for electronic files. Data will be safeguarded by an appropriate level of security, technical and organisational measures to prevent unauthorized disclosure or access, accidental or unlawful destruction, accidental loss or alteration, and unlawful forms of processing. WP1 and WP2 will be based on de-identified aggregate data only.It will be assumed that any Team member sharing data within the iPOP Study does so in accordance with relevant and applicable legal and regulatory standards and obligations including but not limited to, confidentiality, data protection and intellectual property, and access governance agreements. iPOP collaborators must adhere to these policies and processes.All collaborators must respect the iPOP principles of data protection and processing, which include the following:All contributed to iPOP data must be•    Processed fairly and lawfully•    Collected for specified and legitimate purposes•    Accurate•    Absent of personal identifiers •    Stored not longer than necessary•    Processed under the responsibility and liability of the data Controller for the provided data set•    Handled according to the EU GDPR rules (when hosted in the UK)Spanning 37 countries , the iPOThe initial focus of iPOP will be on the impact of COVID‐19 pandemic lockdowns on perinatal outcomes, including preterm birth, low birth weight, and stillbirth. Determining the worldwide extent of changes in perinatal outcomes during the COVID-19 pandemic will advance current understanding of preventable causes of these pervasive perinatal outcomes.Building on the first two phases of the iPOP Study described in this protocol, iPOP further aims to investigate mechanisms for any observed changes in perinatal outcomes during the COVID-19 pandemic, using individual-level and setting-specific data. In the next study phase (WP3), we aim to examine the impact of maternal comorbidities , COVID-19 and non-COVID-19 infections, socio-economic factors, prenatal care, and birth practices on any associations between pandemic lockdowns and perinatal outcomes.Results of the iPOP Study will be rapidly translated through our network of local and international stakeholders to inform further research and testable interventions for improving perinatal healthcare and social support systems during (and well beyond) the COVID-19 pandemic.No data are associated with this article. This paper describes an important and ambitious global study investigating how the COVID pandemic and lockdowns may have affected rates of preterm birth, stillbirth and low birth weight.  The vast multidisciplinary team of 121 researchers in 37 countries have come together to examine variations in COVID-19 rates and lockdown stringency, as well as other factors such as pollution levels, social deprivation and access to maternity care. Findings from the study may lead to a deeper understanding of these factors and what might be done to improve care and outcomes in the future. The team’s hypothesis is that the rate of spontaneous preterm birth, low birth weight and/or stillbirth is changed during lockdowns, and that the magnitude and/or direction of change varies between low, low-middle, upper-middle and high income countries. The research aims and questions are spelt out clearly and there is detailed explanation of how each dataset will be managed. The figures make a complex protocol easier to understand. The authors acknowledge some significant challenges, including the gathering of unreliable and incomplete data and propose methods to account for this, along with the use of internationally recognised definitions. I am not, however, completely convinced it will be possible to unpick spontaneous preterm birth from all preterm births, as I know how unreliable the UK data relating to this can be. This is an important and very interesting project; I wish the team all the best and look forward to reading the findings.Is the study design appropriate for the research question?YesIs the rationale for, and objectives of, the study clearly described?YesAre sufficient details of the methods provided to allow replication by others?YesAre the datasets clearly presented in a useable and accessible format?Not applicableReviewer Expertise:preterm birth, health data science, risk assessmentI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for the opportunity to review this study protocol. The investigators propose a 5-year multi-country study to assess the impact of the COVID-19 pandemic on perinatal outcomes, including rates of preterm birth, low birth weight, and stillbirths, using an interrupted time series analysis approach. The study design is appropriate for the described research questions, and the investigators clearly articulate their intended methodological approach. It may be difficult to assess the impact of the implementation of COVID-19 related lockdowns versus the impact of the pandemic (and infection in pregnancy) itself. I agree with the investigators’ decision to focus on gestational age 28+ weeks given the heterogeneity of international data and approaches to classifying viability; the impact of the pandemic/lockdowns on earlier gestational ages will be assessed in a separate analysis . Classifying stillbirth may be challenging, given the differences in definitions and reporting internationally.Please standardize use of either “health care” or “healthcare” – both terms are currently used in the manuscript.Last paragraph of introduction (page 5): Please delete comma after “study” in line 3 of the paragraph.Last paragraph of introduction (page 5): Please change “COVID 19” to “COVID-19” in second to last line of the paragraph.Figure 1 (page 6): Change “birthweight” to “birth weight” to match remainder of manuscript.Figure 2 (page 6): Change “birthweight” to “birth weight” to match remainder of manuscript. Font size is quite small, making DAG difficult to read. If possible, resize figure to fit width of page and increase font size. Current DAG only lists limited number of confounders, though many more could be explored.Figure 3 (page 3): Stillbirths are by definition always antenatal – consider using “antepartum” to distinguish from “intrapartum” instead. Strict classification of ante- versus intrapartum stillbirth may be difficult, depending on data sources.Outcomes (page 10): Current birth weight categories and numerators/denominators do not quite make sense. Would follow standard WHO or VON categories for defining and calculating these. What about neonates with birth weight less than 500g? Growth-restricted neonates may weigh less than 500 grams but still survive.Figure 5: Change “birthweight” to “birth weight” to match remainder of manuscript. Minor additional comments:Is the study design appropriate for the research question?YesIs the rationale for, and objectives of, the study clearly described?YesAre sufficient details of the methods provided to allow replication by others?YesAre the datasets clearly presented in a useable and accessible format?Not applicableReviewer Expertise:Neonatology, global health, infection prevention.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Aiming at the problem that the measured accuracy of the electric field intensity which is affected by the coupling interference by sensor output signal from the component of a three dimensional electric field, the causes of the coupling error was analyzed, and a decoupled calibration method based on support vector regression algorithm for three-dimensional electric field sensor is proposed. The solution of the decoupled calibration matrix was regarded as a multi-objective optimization process, and the optimal decoupling calibration matrix was obtained by the ν-SVR algorithm. The complex inverse calculation of the matrix was avoided, and the calculation error was reduced. A rotary calibration device was designed to accurately measure the angle between the induction electrode of the sensor and the electric-field vector, and an accurate calculation model of the theoretical electric field was established. The experimental results showed that the error between the calculated and theoretical values of the electric-field components obtained by the proposed method were smaller than those obtained by the traditional inverse matrix calibration method, the accuracy of the calibration was improved, the rationality of the calibration method was proven, and the accuracy of the three-dimensional electric-field intensity measurements was further improved. X, Y, Z, measuring [With the development of aerospace industry, lightning protection of aircraft has also become a research hot spot. In order to protect the aircraft from the interference of strong atmospheric electric field and damage of lightning during tropospheric flight, Atmospheric electric field intensity has become one of the important parameters of aircraft flight . Therefoeasuring . At preseasuring , airborneasuring . Distribeasuring . Three geasuring . Model, easuring , a reduceasuring . HoweverX, Y, and Z) induction electrodes, as shown in The commonly used field-mill-type three-dimensional electric-field sensor contains three mutually orthogonal  is a machine learning method developed on the basis of statistical theory and optimization theory , SVR is Spatial electric fields usually exist as three-dimensional vectors. Three-dimensional electric fields in any direction of space can be decomposed into three mutually perpendicular electric-field components, which are expressed in a Cartesian coordinate system as follows:X-direction induction electrode of the three-dimensional electric-field sensor under the influence of X-direction induction electrode to the Y-direction electric-field component. The input and output model of three-dimensional electric field sensor is shown in In the process of obtaining three-dimensional electric-field measurements, the electric-field intensity is reflected by the voltage output from the induction electrode of the electric-field sensor, and the relative position relationship between the three-dimensional electric-field vector and the sensor’s induction electrode is reflected by the voltage. The alternating current (AC) voltage signal output by any induction electrode of the three-dimensional electric-field sensor is affected by the three components of the three-dimensional electric field in the LCS coordinates. The relationships are expressed as follows:Linear Equations (2)–(4) can be written in a matrix form, as follows:X-direction, it will affect the voltage values of the induction electrodes in the Y- and Z-directions. Therefore, the voltage signals generated by the three induction electrodes can be expressed as follows:i in the electric field, and X-, Y-, and Z-directions, respectively, as the sensor changed its position. The output voltage signals and the electric-field components of the 3D electric-field sensor with the three induction electrodes that were perpendicular to each other can be expressed as follows:The components of the three-dimensional (3D) electric-field sensor are coupled in reality. The electric-field component acting in a direction of an induction electrode that is perpendicular to the electric-field sensor has an impact on the output voltages of the induction electrodes in the other two directions. The measured voltage data of the induction electrodes are represented as follows:In the actual calibration process, the three electric-field components, The precision of matrix C determines the accuracy of the electric-field intensity to be measured, which is demodulated by the sensor output signals. Therefore, to minimize the coupling interference and realize the accurate measurement of each component of the 3D electric field, a decoupled calibration is needed for the 3D electric-field sensor. The nature of the calibration is to solve the optimal decoupled calibration matrix C based on the known output voltage data of the electric field and sensor.x and y are the input and output of the training data set, respectively, and m and n are the dimensions of the input and output, respectively. In this model, the training data set was divided into n subspaces according to the dimensions. Each subspace was composed of m-dimensional inputs and a 1D output. An SVR was constructed for each subspace to estimate the output characteristics of the subspace by regression. When the model was applied to the 3D electric-field sensor, x was input into the corresponding model of the voltage components of the three passages. The components of the electric-field intensity in the three directions in the Cartesian coordinate system corresponded to y, m = n = 3. The f(x) was shown as follows,The 3D electric-field sensor is a multi-input and multi-output system. Based on the input and output models of the 3D electric-field sensor proposed in The combination of the SVR components in the three subspaces were the full-range output characteristics of the sensor.A typical SVR algorithm is the standard ε-SVR algorithm ; howeverl is the number of training sample points, a and b are the dimensions of the input and output variables, respectively.(1) The training set is selected, which is denoted as (2) An appropriate number of support vectors ν, the penalty factor C, and an appropriate kernel function (3) The following optimization problem is solved:The optimal solution is represented as follows:(4) The regression parameters Two components (5) A regression decision function is constructed, as follows:The regression bias, which is treated as the solution of the optimization problem, is as follows:In this study, the method of spatially oriented decomposition of a 3D electric field was studied, and a computational model for the theoretical value of the electric field was established. A measurement calibration device was also designed, which could measure the direction angle between the sensor’s electrode and the 3D electric field. The 3D model of the calibration device is shown in To ensure the accuracy of the decoupled calibration algorithm, a standard uniform electric field was generated using an electric-field box. The induced voltage generated by the induction electrode of the 3D electric-field sensor that rotated in the uniform electric field was measured. The inter-pole coupling coefficient was measured by testing the amplitudes of the induced voltage signals in different directions.X-, Y-, and Z-axes of the local coordinate system were in the directions of the sensor electrodes S1, S2, and S3, respectively. One-dimensional electric field was applied by electrostatic filed instrument, three groups of induction electrodes  of the three-dimensional electric field sensor are placed in the direction parallel to the one-dimensional electric field direction for three times respectively. By changing the DC voltage applied to the electrostatic filed instrument, the intensity of uniform electric field generated by the electric field meter was changed, the amplitudes of voltage generated by the three groups of induction electrodes were measured and obtained. These voltage data are applied to the traditional inverse matrix method and the method proposed in this paper, the decoupling calibration matrix was calculated. The output voltage amplitude of the induction electrode in X, Y and Z directions obtained by three measurements are shown in A 3D electric-field sensor containing three mutually orthogonal distributed induction electrodes was selected. Since the sensor only had three sensitive units, its decoupled calibration matrix C was a 3 × 3 matrix. A local coordinate system was established based on the sensor, and the X direction of the three-dimensional electric field sensor is parallel to the electric field direction, the induction electrode in the X direction is greatly affected by the electric field, while the induction voltage amplitude generated by the induction electrode in the Y and Z directions is much smaller than that generated by the induction electrode in the X direction. In fact, since the applied electric field is parallel to the X direction, and the theoretical value of the electric field intensity in the Y and Z directions is 0, the voltage generated by the sensor in the Y and Z directions are essentially formed by the coupling effect of the electric field in the X direction on the Y and Z directions. At the same time, whether parallel or perpendicular to the direction of the electric field, when the applied voltage of the parallel plate is increased, the electric field intensity is increased, the amplitude of the induced voltage signal is increased, and the relationship is approximately linear. Similarly, in Y and Z direction induction electrodes of the three-dimensional electric field sensor are placed parallel to the electric field direction respectively. The amplitude of the induced voltage parallel to the electric field is large, and that of the other two induced electrodes affected by the electric field coupling effect is small.As shown in X, Y and Z directions, the output voltage amplitude of the induction electrode is the largest, and the output voltage signal amplitude of the other two induction electrodes is lower. The relationship between the sensor input (electric-field intensity), the sensor output , and the sensitivity coefficient matrix obtained through fitting with Equation (9) is expressed as follows:As shown in The decoupled calibration matrix can be expressed as follows:The process of solving the decoupled calibration matrix C is actually the process of least-squares fitting of multivariate functions. To obtain a more accurate calibration matrix, the number of required calibration points should be much greater than the number of dimensions of the 3D electric-field sensor. With the traditional method, the following decoupled calibration matrix was obtained:The calibration method for the data along the other two axes was similar to the method described above, i.e., only the position of the induction electrode of the electric-field sensor in the equations was changed. According to the three calibration equations, the amplitudes of the 3D electric-field components can be obtained as follows:In the method proposed in this paper, the ν-SVR method described in X-, Y-, and Z-directions.(1) The training and test sets are established. The inputs of the training and test sets are the amplitudes of the output voltage signals of the 3D electric-field sensor in three different directions, and the outputs are the electric-field intensities of the 3D electric field in the (2) According to the output dimension of the model, the three-dimensional electric field sensor dimension is a three-input three-output model. The training data set is divided into three subspaces, and parameter optimization is conducted for each subspace. According to the training data set, the penalty factor, number of support vectors, and other parameters are obtained, and the kernel function K adopts the radial basis function. Regression decision function model was shown as Equation (25).(3) Regression analysis is conducted with the training data according to P and the prediction data to R. The regression matrix Q is solved according to PQ = R.(4) The coefficient matrix is solved, and the test set data is assigned to the matrix. The coefficient matrix is solved, assigning the test set data to (5) According to the test data in (6) Regression model and algorithm evaluation were shown in X, Y and Z in the three graphs is different. The induction coefficient of the induction electrode in each direction of the 3D electric field sensor is different. The induction coefficients in the X and Y directions are very close, and the induction coefficient in Z direction is quite different from that of X and Y. This is because the X and Y sensors have the same electrode structure, which are a quarter of a cylinder, but the z-direction sensor electrode is shaped like a quarter of circle. It is very different from X and Y directions, and the coupling coefficient is different from other directions. According to Equations (23) and (26), in the two different 3 × 3 matrixes, the theoretical values of matrix elements a11 and a22 should be the same, but because in the two different directions, the direct distance between the inductive electrode and the shielding electrode is different, and the thickness of the induction electrode is different. As a result, the values of a11 and a22 on the main diagonal of the matrix are different. This is related to the fabrication and installation error of the sensor’s induction electrode.Ideally, the coefficient matrix of the sensor is diagonal matrix. However, due to the coupling effect, according to To test the validity of the proposed decoupled calibration method based on the SVR for the 3D electric-field sensor, the sensor measurement data were taken as the training data set. The decoupled algorithm based on the traditional least-squares method and the proposed method were both used, and the errors were compared.A rotation table used for calibration was designed, as shown in The method of solving the inverse matrix is referred to as Method A, and the proposed method is referred to as Method B. The measurement results of the two decoupled calibration matrices obtained by these two methods were compared.X axis and rotated in the YOZ plane, θ is the rotation Angle, and the theoretical values of the electric field intensity in the X, Y and Z directions are shown in the Equation (27).A uniform intensity electric field of 12 kV/m was applied by electrostatic filed instrument, the electric field sensor is placed in a plane perpendicular to the X direction is 0, the electric field intensity in Y and Z directions was changed by the angle θ between the induction electrode and the actual electric field direction. With the rotation of the three-dimensional electric field sensor in the electric field changed, the rotation angle θ and output voltage data of the output sensor were recorded. The electric field intensity in different directions is calculated by voltage data and two decoupled calibration matrices obtained. When the electric field sensor rotated in a plane perpendicular to the X axis, the amplitude of the voltage generated by the three induction electrodes, and the obtained induced voltage amplitude are respectively used in the traditional inverse matrix (Method A) and the proposed method in this paper (method B). By substituting matrix (23) and matrix (26) into formula (24), the calculated values of the electric field components are obtained by calculation respectively. According to Equation (27), the theoretical value of the electric field intensity in the Y- and Z-directions, and the two methods both could effectively eliminate the coupling interference. According to Equation (27), as the electric field direction is perpendicular to the X direction, the sensor rotates in the YOZ plane, the theoretical value of electric field intensity in the X direction is 0. Therefore, in X direction is 0. In the X, Y, and Z directions, the decoupling calibration matrix obtained by the proposed method in this paper, compared with the traditional least square solution to obtain the decoupling matrix, the electric-field component calculated is closer to the theoretical value of electric field intensity.θ, with the largest errors at θ = 0 and θ = θ ≈ θ. Therefore, the proposed method can effectively avoid the errors caused by the angle between the electric-field direction and the direction perpendicular to the induction electrode during the measurement process.The relative errors between the calculated value of the total electric field and the theoretical value obtained by the two decoupled calibration matrices are shown in The intensity of the three-dimensional total electric field is calculated by the Equation (28).The actual amplitude of the total electric field is 12 kV/m, the calculated and theoretical values of the total electric field obtained by the two decoupled calibration matrices are shown in θ. The theoretical values of the electric-field components obtained by the two decoupled calibration methods both could effectively eliminate the coupling interference after synthesis. However, the relative errors between the calculated and theoretical values of the electric-field intensity obtained by the proposed method were smaller and were not affected by the rotation angle The decoupled calibration method for the 3D electric-field sensor based on the multi-output SVR was proposed to solve the measurement accuracy problem of the electric-field intensity caused by the coupling interference of the electric-field sensor given by the 3D electric-field components. The proposed method could replace the traditional method of solving the inverse matrix using the least-squares method. The decoupled calibration process for the 3D electric-field sensor was designed. A measurement calibration device was constructed to realize the angle between the sensor and the electric-field vector. The computational model of the theoretical electric-field value was established to obtain the 3D decoupled calibration matrix. The following conclusions were drawn.(1) Although the traditional decoupled calibration method can reduce the coupling interference by inverse matrix computation, the calculation errors are large, and it can easily miss the optimal solution due to the complex computations of the inverse matrix. Moreover, the traditional method can easily generate an ill-conditioned matrix. All these problems lead to large errors and cannot satisfy the measurement accuracy requirements of the 3D electric-field intensity. This paper proposes a decoupled calibration method to directly search for the global optimal solution in the solution space. The proposed method can avoid the ill-conditioned matrix and hence has high reliability and good decoupling accuracy.X-, Y-, and Z-directions given by the two decoupled calibration methods were compared. The test results showed that the proposed method yielded better consistency between the calculated and theoretical values than the traditional method.(2) The test data of the electric-field intensity components in the (3) The test data of the total electric-field intensity given by the two decoupled calibration methods were compared. The test results showed that compared to the traditional method, the proposed method reduced the maximum relative error from 16.3% to 4.58% and reduced the average relative error from 5.87% to 2.72%. Such reductions effectively reduced the coupling interference of the 3D electric-field components to the electric-field sensor, improved the calibration accuracy of the 3D electric-field sensor, and achieved accurate measurement of the 3D electric-field intensity.X and Y directions, which leading to the same elements a11 and a22 of coupling coefficient matrix in theory, but because in the two different directions, the direct distance between the inductive electrode and the shielding electrode is different, and the thickness of the induction electrode is different, there are differences between these two elements. Through calculation and analysis of coupling coefficient matrix, necessary data support is provided for the manufacturing, installation and calibration of sensor induction electrode.(4) Ideally, the coefficient matrix of sensor is diagonal matrix. However, due to the coupling effect, non-main-diagonal elements of the matrix are not 0. The structure of induction electrode is the same in


Congenital disorders (CDs), defined as abnormalities in structure or function present at birth, are an important contributor to the disease burden in developing countries. The size and extent of the problem in South Africa (SA) are unknown due to the lack of recent, reliable, observed data on CDs. To address this empirical data gap, this study aimed to measure the birth prevalence of congenital anomalies (a sub-set of CDs) and to describe the pattern of these anomalies at a regional hospital in KwaZulu Natal (KZN), SA. A retrospective, observational, descriptive review of congenital anomalies diagnosed within the neonatal service at Edendale Hospital (EDH), KZN was undertaken between January and December 2018. All EDH in-house live births diagnosed and notified with congenital anomalies by discharge were included. Stillbirths, other pregnancy losses and out-born neonates were excluded. Data were actively collected from the birth register, neonatal admission register, and the individual paper-based surveillance tool developed by the National Department of Health. The in-facility birth prevalence rate for congenital anomalies was 15.57 per 1 000 live births. The most observed system was musculoskeletal (32%) followed by circulatory system anomalies (19%). When the observed birth prevalence rates of key congenital anomalies were compared with previously published, modelled South African data, no significant difference was found. This study responds to the paucity of birth prevalence data on CDs overall and offers evidence that obvious, structural CDs  need to be addressed in the SA public health system. Congenital disorders (CDs) are defined as structural or functional abnormalities of prenatal origin which are present at birth . While tmost congenital anomalies—a distinct sub-set of CDs including obvious, structural malformations only [While CDs affect all populations worldwide, the scale of the burden varies between populations. True differences in these rates may be due to varying maternal age distribution for chromosomal disorders, consanguinity practices affecting recessive, single-gene disorders and localised environmental factors (teratogens) . The birons only , remainsons only –8. The gons only .Quantifying the CD burden of disease has been underway for decades in many high-income countries (HIC) using empiric datasets collected through established surveillance systems, such as the European Registration of Congenital Anomalies (EUROCAT) . AnalyseTo fill the gap in empiric CD data in the country, and confirm the estimated CD disease burden this study aimed to: 1) Measure the birth prevalence of congenital anomalies among live births—a sub-set of CDs as categorised in Chapter XVII: Congenital Malformations, Deformations, and Chromosomal Abnormalities in the International Classification of Diseases and Related Health Problems (ICD-10) ; and 2) This study was a retrospective, observational, descriptive review of congenital anomalies diagnosed at birth within the neonatal service at Edendale Hospital (EDH) in KZN Province, SA. The study period was from 1st January 2018 to 31st December 2018.EDH is a regional (secondary level) healthcare facility located in the uMgungundlovu District in the city of Pietermaritzburg. It serves a predominantly urban population of around 1.4 million mainly indigenous, Zulu-speaking African people. It has well-organized obstetric services and a 62-bed Neonatal Care Unit (NCU) catering for an average of 600 in-facility deliveries a month and referrals from the surrounding state-run healthcare clinics. This includes normal and complicated deliveries, with newborn care for well-babies and those requiring intensive care. This arrangement of a single, referral facility providing healthcare services to a relatively unchanging catchment population provided a relevant setting to meet the objectives of this study.As part of routine care, all in-house live births at EDH underwent a comprehensive neonatal examination within 24 hours of birth. Those identified with congenital anomalies were offered appropriate care and the BDNT was completed. These clinical records were included in the scope of this study, regardless of the gestational age of the baby. Stillbirths and other pregnancy losses  were excluded as little routine clinical data were collected for these cases. Neonates with identified congenital anomalies born elsewhere and referred to EDH after birth were also excluded from the study to avoid inflating the birth prevalence rate as the number of deliveries at referral sites was not accessible within the study.congenital anomalies only (a sub-set of CDs) were recorded. Congenital anomalies are defined as structural or anatomical abnormalities detected at birth (by discharge in this study) and classified according to Chapter XVII of ICD-10 [For this study, f ICD-10 . Based oCongenital anomalies identified in the study were categorised into major and minor conditions. Major or serious anomalies are those that may result in death, limited life expectancy or lifelong disability, particularly in the absence of care ; whereasRelevant cases for inclusion in this study were actively ascertained by the study team. Details of all deliveries at EDH were recorded in the birth register maintained by the Obstetric team. All live-born neonates were examined within 24 hours of delivery before discharge by a midwife or a doctor in the NCU team as a part of routine clinical care. For the study duration, the medical team comprised full-time neonatologists and general paediatricians, with the daily care team also including rotating paediatric registrars, medical officers (3-monthly) and interns (monthly). Following this assessment, healthy neonates and those diagnosed with minor congenital anomalies not requiring further care remained with their mothers in the post-natal units. Neonates and mothers with no complications were discharged as early as six hours post-delivery. Others may have been observed and routinely discharged within 24–48 hours of delivery (longer for weekend deliveries) when they were deemed fit. Sick neonates and those with serious congenital anomalies were admitted to the NCU for holistic management. This included a clinical assessment to ascertain the extent of the abnormality, to undertake appropriate diagnostic testing, clinical care, genetic counselling for parents and notification using the BDNT. These admissions were recorded in the neonatal admission register. All routine activities were managed by the NCU team. For complex cases, the team had access to a general paediatrician with a special interest and some training in clinical genetics for assistance with diagnosis, care and/or genetic counselling. A gatekeeping system in place for appropriate genetic testing ensured referral to and/or consultation with relevant genetic specialists at the tertiary hospital.Data for this retrospective study were extracted from the birth register, neonatal admission register and the individual BDNT forms . Incomplete BDNT forms or more complex cases requiring a dysmorphology evaluation and syndrome identification  required the researcher to review individual clinical records and update the BDNT forms. All original BDNT forms were routinely kept on file within the NCU. As reported by Lebese et al , and durThe in-facility birth prevalence rate was calculated in 2018 using the number of live births with congenital anomalies as the numerator and the total number of live births at EDH as the denominator, multiplied by 1 000 to report the rate per 1 000 live births as per convention. The number, percentage and birth prevalence rates of major, minor  and isolated congenital anomalies in diagnostic sub-categories were reported separately. To prevent double-counting, neonates with syndromes and multiple anomalies were counted only once in the overall live birth prevalence in the relevant sub-category and not for each specific anomaly. For example, a neonate with Trisomy 21 with a ventricular septal defect and a trachea-oesophageal fistula was counted under Down syndrome only and not separately (again) under both circulatory and digestive systems.p-values) were calculated using Chi-squared statistical test and confidence intervals were calculated with the “test-based method” [www.scistat.com).Statistical analysis was undertaken to compare birth prevalence rates for selected congenital anomalies observed with modelled birth prevalence rates for SA published previously ,21. Diff method”  using thEthical clearance was granted for this study by the University of KwaZulu-Natal Biomedical Research Ethics Committee (Ref No. BE409/18). Gatekeeper permission to conduct the study was obtained from the Chief Executive Officer at EDH and the study was registered on the National Health Research Database. All data were collected retrospectively in March 2019 from routine care records or registers during the study period and were anonymized at the point of collection with a sequential study number allocated to protect all patients’ identities and personal records. The data collection tool used was an anonymized copy of the BDNT. There was no direct patient contact in this study therefore individual patient consent was not required for ethical clearance. Collected data was stored electronically on password-protected drives and computers.Over the 12-month study period, 117 neonates were diagnosed and notified with congenital anomalies from the 7 516 live births examined at EDH. The birth prevalence  for this period was 15.57 per 1 000 live births. This equates to 1 in every 64 live births affected by a congenital anomaly at EDH in 2018. Excluding polydactyly—a minor congenital anomaly—the birth prevalence decreased to 13.44 per 1 000 live births, equivalent to 1 in 74 births.The demographic characteristics of the affected births are outlined in Congenital anomalies were more prevalent in male, term neonates with normal birth weights (>2500g). Twenty (17%) of the 117 affected births were recorded to mothers of advanced maternal age (AMA) defined as equal to or greater than 35 years of age. However, in 29 affected cases (25%) maternal age was not recorded in the Obstetric births register.Reported congenital anomalies categorised according to the ICD-10 classification are detailed in Polydactyly was the most common individual condition observed, accounting for 13.7% of total congenital anomalies identified. The other frequently reported conditions were Down syndrome  Congenital Talipes Equinovarus (9.4%) and anomalies of the nervous system (7.7%), including isolated Neural Tube Defects . Equal numbers of isolated Cleft Palate (CP) were reported as for isolated cleft lip/cleft lip and palate (collectively 3.4%).Other than chromosomal disorders, the aetiology of congenital anomalies observed in this study were mainly malformations due to multifactorial or unknown reasons. Some were less visible, internal malformations, including congenital heart defects (CHDs). Some single gene disorders with obvious dysmorphic phenotypes were also reported.Eight (9.4%) of the affected neonates were diagnosed with multiple congenital anomalies. In some cases, these were recognisable, dysmorphic syndromes caused by teratogen exposure during pregnancy , known genetic mutations (Cornelia De Lange syndrome) or due to associated congenital malformations including Vertebral-Anorectal-Cardiac-Tracheo-Esophageal-Renal-Limb (VACTERL) association and Pentalogy of Cantrell, due to unknown causes.The proportion of affected neonates that underwent investigations are summarised in This study measured the birth prevalence of congenital anomalies among live births at EDH in KZN, SA from January to December 2018. Examination of all new-borns and recording of anomalies identified at birth (by discharge) were reported using the enhanced BDNT for notification to the NDoH. Data collected were described and compared with existing published data for congenital anomalies, including historic research studies and modelled estimates for SA .Birth prevalence studies for congenital anomalies may be useful to establish baseline rates and monitor trends, explore possible causes, develop prevention strategies, and review changes and response to interventions for specific anomalies over time.congenital anomalies. This may be due to differences in the scope and case definitions used in these studies. Poorer case identification at the study site for reasons highlighted as study challenges and limitations may also be a factor.The main aim of this study was achieved with the measurement of the birth prevalence of congenital anomalies that were easily recognisable by discharge after birth as a subset of total CDs possible during all life stages. When compared with previously published birth prevalence rates for congenital anomalies globally; averaging at 20–30 per 1 000 live births –26, bothThe observed birth prevalence rate in this study is very similar to another SA study by Venter et al (14.97 per 1 000 live births in 1995), published over 25 years earlier . This oftotal CD-related birth prevalence for the study site may be approximated to around 59.88 per 1 000 live births. This is again within the range quoted for total CD-related birth prevalence globally of between 40 to 80 per 1 000 live births [If a congenital anomalies birth prevalence of 15.57 per 1 000 live births represents the 26% that are easily recognisable at birth [e births . This suThe pattern of congenital anomalies observed in this study aligned with those reported globally. The most frequently reported conditions are all defined as priority conditions in SA by the NDoH ,14. AddiThe high variation in birth prevalence of NTDs between the studies may be attributed to the introduction of mandatory folate fortification of staple crops in 2003 ,30. SimiBased on previously reported birth prevalence rates by similar studies for oculocutaneous albinism; one of the commonest single-gene disorders in SA, at least 2 affected births would have been expected in this study –26. ThisIn While there was apparent variation between birth prevalence rates for the conditions compared via the observed and modelled data , statistThis study, one of only a few undertaken following the improved management of the HIV/AIDS crisis, confirms congenital anomalies as a significant healthcare burden for SA to address. This was the first study conducted in KZN to determine the birth prevalence of congenital anomalies, with all previous published studies undertaken in other provinces. This empiric evidence highlights the re-emergence of congenital anomalies (and CDs) in KZN and the need for similar studies in other parts of the country.This facility-based study, incorporating active surveillance, is more feasible for large, diverse populations, particularly in LMICs such as SA where healthcare resources  are constrained. While this type of study may be subject to referral bias due to births by non-residents and referrals from outlying clinics before birth, it offers the advantage of obtaining high-quality data on key conditions at sentinel sites. The alternative, population-based approach is unaffected by this type of bias as it includes all (home and facility-based) deliveries. However, these studies are more suited for HIC, and smaller populations or regions or by sub-national systems due to the higher cost and infrastructure required, as has been demonstrated by poor data resulting from the population-based BDNT implemented in SA .The collection of accurate data is essential for precise surveillance. Before the study, a passive surveillance approach yielded approximately 3–6 congenital anomalies per 600 deliveries on average per month at the study facility . In this study, an active surveillance approach supported by a clinical champion resulted in a higher rate of congenital anomalies being detected, averaging at 10 per 600 deliveries per month. This simple and effective quality improvement measure enhanced reporting accuracy and offers a feasible methodology for sites in other LMIC contexts. Good quality data is more useful when comparing with other studies.Before a congenital anomaly can be reported it must first be accurately and timeously diagnosed. This requires appropriately trained healthcare professionals (HCPs) able to diagnose congenital anomalies and comply with reporting requirements of a surveillance system as a part of routine clinical care. Genetics content included in both medical school and nursing college curricula is insufficient –40, and In SA, specialised medical genetic services are inadequate. There were only 12 practising clinical medical geneticists and a similar number of genetic counsellors countrywide in 2015, equating to 1 per 5 million and 1 per 8.4 million of the population respectively . This isIn this study , complex cases were referred to a paediatrician with a genetics interest in place of a medical geneticist. This individual has received training in the Medical Genetics Education Program (MGEP) and five years of experience running a specialist level referral clinical genetics clinic at the nearby tertiary hospital in Pietermaritzburg. The team of neonatologists have received no formal genetics training other than their sub-speciality training, which included access to a foeto-maternal anomaly clinic at the training site. The remainder of the medical and nursing teams lack specialised genetics training. While the gatekeeping system involving the referral of patients to genetic specialists at tertiary hospitals in the region ensures appropriate tests are requested, it places additional stress on an already poorly resourced system.The use of paper-based systems, including the BDNT, continues to impact surveillance compliance and data quality even when integrated as part of routine clinical care. Within this study, the active, champion-based surveillance effectively improved data quality. However, a sustainable, long-term solution is needed, through the investment of relevant resources into integrating the notification of congenital anomalies as part of an effective health information system and electronic health record. By default, this would fill data gaps experienced in this study, such as missing maternal age data to establish the true extent of AMA as a risk factor for Trisomies. Such data systems are already in place in most private facilities and billing through medical aid (insurance) schemes (including ICD-10 coded diagnosis) making this smaller sector  appealing as a feasible and additional source of reliable CD data, despite not currently contributing to the BDNT.Some blood samples of the study population were lost by the testing laboratory. Anecdotal evidence suggests this is a common challenge in the province and largely systemic, with loss and leakage of samples occurring during transport from satellite to main laboratory sites. The additional cost implications, delayed diagnosis and patient trauma due to repeated testing highlight the need for these challenges to be addressed.These challenges may have contributed to the lower birth prevalence observed and highlight the urgent need for capacity building at all levels, together with appropriate resource investment to improve the surveillance, care and prevention for those affected by all CDs.This study was limited to recording live births affected by congenital anomalies and excluded affected births (live births and stillbirths) occurring outside EDH, from both public and private facilities. The proportion of ‘missed’ congenital anomalies resulting in stillbirths and early pregnancy losses, including termination of pregnancy, remains an unquantified element of the CD burden in this setting.total CD-related burden [The focus of the study on congenital anomalies only, a sub-set of CDs, excluded a significant proportion included elsewhere in the ICD-10 system such as inborn errors of metabolism and other single gene disorders. This prevented the measurement of the d burden ,18. A fuThis study on congenital anomalies at birth is the first published from KZN and the most recent in SA after the successful implementation of the PMTCT program. It quantified the birth prevalence of live births affected by congenital anomalies at EDH in 2018, demonstrating the ability of this facility-based method to collect high quality, accurate data on these conditions. The study responded to the paucity of available birth prevalence data on congenital anomalies in SA and has demonstrated the impact of championed, quality improvement efforts addressing diagnosis and notification challenges. It also offers reassurance that this approach may be replicated in similar contexts. The observed study rates are in line with published data locally and globally and serve as a baseline for the comparison of trends over time. The rates are also consistent with modelled estimates, indicating the further application of MGDb and other modelling approaches in under-served areas that lack resources to measure accurate data.This study offers additional evidence on the health burden represented by congenital anomalies in SA and the need to prioritise these conditions, their surveillance, care and prevention, as a healthcare priority. To respond appropriately to the proportionately growing health burden of congenital anomalies as infectious diseases are better controlled in SA, further studies of this nature are required to offer policy-makers reliable evidence for informed data-based decision making around essential healthcare services and value-based allocation of available limited funding. This should be undertaken in tandem with investment in electronic surveillance systems if SA is to respond appropriately to specific local, regional, and national health needs to prevent people with CDs from being excluded.Further research is recommended on:Congenital anomalies among stillbirth, pregnancy losses and termination of pregnancy at the study site.Affected births from referring facilities within the catchment area to enable a birth prevalence rate closer to the true value.Follow up population prevalence studies to incorporate other life-course stages including neonates, infants and children affected by congenital anomalies and other CDs beyond the birth period to improve quantification of the total CD burden of disease.Commonly missed functional CDs in the ICD-10 system in addition to structural disorders.Similar studies in other regions of SA to enable comparison to identify regional and demographic similarities or differences.The private healthcare setting using existing information systems to complete the picture for the entire SA population or looking at access and outcomes in the different sectors (insured vs uninsured).Looking at clinical care and outcomes of patients affected by CDs treated at hospitals, and how to improve quality through interventions such as early antenatal identification and planning of services before birth.Increased scope of study to include:Capacity building for HCPs on the clinical and genetic diagnosis of congenital anomalies and other CDs and reporting strategies, including the BDNT, to promote improved diagnosis and more accurate, comprehensive reporting.Long-term options for improving congenital anomaly and wider CD reporting i. e., the inclusion of congenital anomalies on the neonatal dashboard as a sentinel group of disorders and electronic health records as part of an integrated health information system, to evaluate the return on investment enabled through better diagnosis and care.S1 File(XLSX)Click here for additional data file.


Undernutrition contributes to almost half of all under-five deaths. Fetal malnutrition (FM) has been implicated in both short- and long-term consequences among fetal, neonatal, and adult life. Currently, Ethiopia lacks information on the prevalence of fetal malnutrition and its associated factors. This study, therefore, is aimed at assessing the prevalence of FM at birth and its associated factors in South Gondar zone hospitals, northwest Ethiopia.  A cross-sectional study was carried out from November 1, 2019, to April 30, 2020, among four hospitals of South Gondar zone. All women with their live newborns who met the eligibility criteria were included. Clinical assessment of nutritional status including other anthropometric measurements was done immediately after delivery. The newborn was declared as fetal malnourished if the clinical assessment of nutritional status cut-off point is less than 25. Data were collected by trained clinical midwives. Besides bivariate regression analysis, a multivariable logistic regression analysis was done to identify associations.  A total of 1592 mothers with their live newborns participated in this study. The prevalence of fetal malnutrition was 21.7% (95% CI: 19.7-23.9). Intimate partner violence , placental weight less than 512 grams , and small for gestational age  were significantly associated with fetal malnutrition.  The prevalence of fetal malnutrition was a public health problem in this study. Intimate partner violence, placental weight, and small for gestational age were found the most significant variables. To avert fetal malnutrition, positive family relation and additional or balanced nutritional supplementation during pregnancy are critical. We recommend researchers do clinical follow-up research which comprises a detailed investigation of placental, maternal, and fetal factors including genes. Fetal malnutrition (FM) is defined as intrauterine loss or failure to acquire a normal amount of subcutaneous fat and muscles . Fetal mPerinatal complications and/or central nervous system sequelae occurred primarily among malnourished babies whether of appropriate for gestational age (AGA) or small for gestational age (SGA), but not those who were simply SGA , 6, 9. PThe incidence of fetal malnutrition among newborns using clinical assessments of nutritional status (CAN score) was 84.2% among small for gestational age babies, 12.9% among appropriate for gestational age babies , and 54.Risk factors for FM included maternal factors such as advanced maternal age, parity, race, low educational status, poor wealth status, anemia, malaria, and chronic disease (like pregnancy-induced hypertension and diabetes mellitus) , 12, 15.Several complications during childhood are associated with the nutritional status of infants at birth , 20. TheThis study was carried out among the four hospitals of the South Gondar zone . South Gondar is a zone in Ethiopian Amhara region . In EthiThe zone has 8 hospitals, 96 health centers, 394 health posts, and 118 private health institutions. All of the hospitals give similar services except Debre Tabor General Hospital that serves as a referral center for private and governmental institutions. Averagely, 138, 336, 122, and 133 deliveries were conducted in two months among Nefas-Mewicha, Debre Tabor, Mekane Eyesus, and Addis Zemene Hospitals, respectively.Hospital-based cross-sectional study was conducted from November 1, 2019, to April 30, 2020.In this study, all women with the gestational age of 37-42 weeks and who have given singleton live birth among four hospitals  from November 1, 2019, to April 30, 2020, were included.Women who could not answer the intended questions because of illness and mental problems, mothers who died because of complications of labor, mothers referred to other higher health institutions, new birth with incomplete placenta, newborns with a congenital abnormality, and those born to mothers with known gestational diabetes mellitus were excluded.All women who gave live birth from November 1, 2019, to April 30, 2020, among the four hospitals and fulfilled the eligibility criteria were included under this study. Hence, the sample size for the current study consisted of 1592 (Debre Tabor (916), Nefas-Mewicha (241), Addis Zemene (211), and Mekane-Eyesus (224)) mothers with their live singleton newborns.Outcome (dependent) variable is fetal malnutrition.Independent variables are sociodemographic characteristics of the mothers and husband (intimate partner), newborn characteristics, and maternal obstetric factors.Clinical assessment of nutrition status (CAN score) is a scoring system based on nine superficial readily detectable signs of malnutrition among the newborn baby [orn baby , 19.Rapid assessment of gestational age at birth is the gestational ages derived from the total scores of skin texture (4 items), skin color (4 items), breast size (4 items), and ear firmness (4 items) [4 items) . But forSmall for gestational age (SGA) is defined as birth weight less than the 10th percentile for gestational age using weight percentile chart [le chart .Household wealth status is computed by principal component analysis from different variables such as the presence of own farmland, own toilet facility, bank account, mobile phone, electricity, the roof of the house with corrugated iron sheets, and number of cows/oxen, horses/mules/donkeys, goats/sheep, and chicken [ chicken .Chronic disease is defined as women who had bronchial asthma, heart diseases, hypertensive disorder , diabetes, and chronic renal disease through history or from their client chart.Violence was assessed as exposure to physical violence (6 items) such as slapping, hitting, kicking, and beating; sexual violence (3 items) including forced sexual intercourse and other forms of sexual coercion; and emotional  abuse (6 items) such as insults, belittling and intimidation, and controlling behaviors (7 items) including isolating a person from family and friends, monitoring their movement, and restricting accesses to financial resources. Women were asked to indicate whether they had experienced any of the violent acts during the current pregnancy and classified based on World Health Organization (WHO) classification [fication .Questionnaires were developed after reviewing relevant literature , 19, 24 Anthropometric measurements were carried out on babies using standard methods as soon as possible after birth and within 24 hours. The nutritional status of babies was assessed using Metcoff's clinical assessment of nutritional status score (CAN score) chart . The chaTo ensure the quality of data, training was given for all data collectors at each hospital for one day on the overall procedure of data collection by the principal investigator and pediatricians supported with video . The quep value ≤ 0.25 were a candidate to consider into a multivariable logistic regression analysis to avoid confounders. Hosmer and Lemeshow's goodness-of-fit test was done to check the appropriateness of the data for multivariable logistic regression analysis. Crude and adjusted odds ratio together with their corresponding 95% confidence interval were computed to see the strength of association between independent and dependent variables. For all statistical significant tests, p value < 0.05 was used as a cut-off point.All the interview questionnaires were checked manually for completeness and consistency before entry. Then, the data were entered, coded, and cleaned using EPI INFO Windows version 7 statistical software. The entered data were exported into the Statistical Package for the Social Sciences (SPSS) version 20 for further analysis. Descriptive statistics such as frequency distribution and percentage were performed. Bivariate logistic regression was performed for each independent variable with the outcome variable. All variables whose bivariate test had a Of all, 1592 women have participated in this study. Around two-fifth  of the mothers were found in the age category of 25-29 and one out of three (33.7%) of the mothers was able to read and write. Almost all  of them were living in union (with their husband). The majority (88.8%) of the mothers lived with a total family size of six or less. One out of five (20.8%) neonates was delivered with a weight less than 2500 grams (low birth weight). The majority (84%) of the mother's height was ≥150 cm .One in five (20.6) of the women's pregnancy intervals was less than 24 months. Of those who had a short interpregnancy interval, 84 (5.3%) had a very short interpregnancy interval (<12 months). Besides, 967 (60.7%) and 297 (18.7%) of the participants had an interpregnancy interval of 24-59 months and more than 60 months, respectively. The majority (91.6%) of the women attended at least one ANC follow-up. Of those who had at least one ANC follow-up, 477 (30%) and 568 (35.7%) of the participants have attained for third and fourth times, respectively. Around two-fifth of their placental weight was below 512 grams. Greater than one-third (35.7%) of the mothers were exposed to intimate partner violence .th percentile (SGA) were 2 times more likely exposed to fetal malnutrition than those born normal for its gestational age , and fetuses delivered at a placental weight less than 512 grams were 3 times more likely exposed to FM than those delivered at a placental weight less than 512 grams  . Newborns delivered at less than the 1013-3.57) .Clinical evidence of wasting in a newborn, known as fetal malnutrition (FM), is regarded as an index of intrauterine difficulties in the baby . Accuratth percentile of birth weights were missed about 10% of appropriate for gestational age cases of malnourished neonates [th percentile of birth weight measurements [This study was consistent with other studies conducted in Nigeria 18.8%)  and in Ineonates . Likewisurements , 14. Thuurements , 14. Nevurements , in Hydeurements , and in urements . The lowurements  and in Burements . The disurements , becauseurements . An infaurements . Since turements . Gestati8.8%  andurements , 7.To our knowledge, even though this study was the first study in the country, the prevalence of fetal malnutrition was a major public health concern. Intimate partner violence, placental weight less than 512 grams, and small for gestational age were found to be the most significant variables. To avert fetal malnutrition, effort in raising sensitization on intimate partner violence and its consequence through health extension workers and health professionals and screening, counseling, treating, and follow-up of abused women are needed. Primary prevention at the perinatal period by involving the couple decreases the risk of FM related to intimate partner violence. Increasing appropriate for gestational age and placental weight by additional and/or balanced nutritional supplementation during pregnancy is critical. We recommend researchers do further research which comprises a detailed investigation of placental, maternal, and fetal factors including genes.For this study, there might be a recall and social desirability bias especially for intimate partner violence and marital status. There might be also gestational age estimation bias because of the recall of the last menstrual period, and there was also a limitation during ultrasound estimation and rapid gestational age assessment. There may be a subjective nature, like all other scoring methods used in the evaluation of neonates.