Deep clinical and biological phenotyping of the preterm birth and small for gestational age syndromes: The interbio-21 st newborn case-control study protocol

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Study Justification:
The INTERBIO-21st study aims to improve the understanding of preterm birth and small for gestational age (SGA) syndromes by investigating the mechanisms responsible for these conditions. By deep phenotyping of clinical, growth, and epidemiological data, as well as collecting biological samples, the study seeks to identify how environmental exposures, clinical conditions, and nutrition influence human growth and neurodevelopment. This study is justified because it will enhance medical knowledge and provide new insights into the environmental influences on human growth and neurodevelopment.
Study Highlights:
– The study is part of the larger INTERGROWTH-21st Project, a research initiative involving nearly 70,000 mothers and babies worldwide.
– Phase I of the project, conducted from 2008 to 2015, generated international standards for monitoring growth and neurodevelopment.
– Phase II aims to improve the functional classification of preterm birth and SGA syndromes through a better understanding of environmental influences.
– The study was conducted at seven sites in Brazil, Kenya, Pakistan, South Africa, Thailand, and the UK.
– The study collected longitudinal clinical data and biological samples, including maternal blood, umbilical cord blood, placental tissue, maternal feces, and infant buccal swabs.
Recommendations for Lay Reader:
The INTERBIO-21st study aims to improve our understanding of preterm birth and small for gestational age syndromes. By studying the effects of environmental exposures, clinical conditions, and nutrition on human growth and neurodevelopment, this research will enhance medical knowledge and provide new insights into how these factors influence our health. The study was conducted at seven sites around the world and collected data and samples from mothers and babies. The findings from this study will help healthcare professionals better understand and manage preterm birth and SGA syndromes.
Recommendations for Policy Maker:
The INTERBIO-21st study provides valuable insights into the mechanisms responsible for preterm birth and small for gestational age syndromes. By investigating the effects of environmental exposures, clinical conditions, and nutrition on human growth and neurodevelopment, this research will contribute to the development of evidence-based policies and interventions to prevent and manage these conditions. The study was conducted at multiple sites worldwide, representing diverse populations. The findings from this study will inform policy decisions and strategies aimed at improving maternal and child health outcomes.
Key Role Players:
– Healthcare professionals and scientists from 35 institutions in 21 countries
– Researchers and staff at the study sites in Brazil, Kenya, Pakistan, South Africa, Thailand, and the UK
– Oxford University and the INTERGROWTH-21st Project Coordinating Unit
– Global Alliance to Prevent Prematurity and Stillbirth (GAPPS)
– Bill & Melinda Gates Foundation
Cost Items for Planning Recommendations:
– Research staff salaries and training
– Data management systems and software
– Ultrasound machines and equipment
– Laboratory supplies and kits for sample collection and processing
– Storage facilities for biological samples
– Quality control measures and monitoring
– Travel and logistics for site visits and coordination
– Communication and collaboration tools (e.g., Skype)
– Statistical analysis and data interpretation
Please note that the provided cost items are general suggestions and may vary depending on the specific needs and requirements of the study.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a large-scale, population-based research initiative involving nearly 70,000 mothers and babies worldwide. The study design is comprehensive, incorporating deep phenotyping of clinical, growth, and epidemiological data, as well as various biological samples. The study sites are geographically diverse, providing a unique resource for analyzing the influence of environmental exposures on human growth and neurodevelopment. To improve the evidence, the abstract could provide more specific details about the methodology and standardization procedures used in the study.

Background: INTERBIO-21st is Phase II of the INTERGROWTH-21st Project, the population-based, research initiative involving nearly 70,000 mothers and babies worldwide coordinated by Oxford University and performed by a multidisciplinary network of more than 400 healthcare professionals and scientists from 35 institutions in 21 countries worldwide. Phase I, conducted 2008-2015, consisted of nine complementary studies designed to describe optimal human growth and neurodevelopment, based conceptually on the WHO prescriptive approach. The studies generated a set of international standards for monitoring growth and neurodevelopment, which complement the existing WHO Child Growth Standards. Phase II aims to improve the functional classification of the highly heterogenous preterm birth and fetal growth restriction syndromes through a better understanding of how environmental exposures, clinical conditions and nutrition influence patterns of human growth from conception to childhood, as well as specific neurodevelopmental domains and associated behaviors at 2 years of age. Methods: In the INTERBIO-21st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles. Here we describe the study sites, population characteristics, study design, methodology and standardization procedures for the collection of longitudinal clinical data and biological samples (maternal blood, umbilical cord blood, placental tissue, maternal feces and infant buccal swabs) for the study that was conducted between 2012 and 2018 in Brazil, Kenya, Pakistan, South Africa, Thailand and the UK. Discussion: Our study provides a unique resource for the planned analyses given the range of potentially disadvantageous exposures (including poor nutrition, pregnancy complications and infections) in geographically diverse populations worldwide. The study should enhance current medical knowledge and provide new insights into environmental influences on human growth and neurodevelopment.

The INTERBIO-21 st Study was conducted between February 2012 and June 2018 at seven sites: Pelotas (Brazil), Kilifi (Kenya), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand) and Oxford (UK), of which two (Kilifi and Mae Sot) were rural and the others urban. The sites in Pelotas, Nairobi and Oxford also participated in Phase I of the INTERGROWTH-21 st Project 40– 42. Pelotas (Brazil): The middle-income city of Pelotas, in the southernmost region of the country where the richer Brazilian states are located, was also the Latin American site for Phase I 40. Pelotas is the third most populous city in the state of Rio Grande do Sul, with 350,000 inhabitants (92% living in urban areas) and 4,000 births per year. More than 99% of these births take place in the city’s four maternity hospitals. In 2007, Pelotas had a per capita gross domestic product (GDP) of R$8248 (US$4933). A total of 47% of women in Pelotas receive >9 years of formal education, with 21% receiving more than 12 years. Data from the Pelotas 2004 birth cohort study indicate that the LBW and FGR rates are 10% and 12%, respectively, and that the mean birth weight of new-borns is 3150 g 43. The city has an Epidemiology Research Centre based at the Federal University of Pelotas, which has been conducting epidemiological research on maternal and child health nutrition for more than 30 years and is also a WHO Collaborating Centre in the field of nutrition. The same research team also participated in the WHO Multicentre Growth Reference Study (MGRS), which generated the WHO Child Growth Standards 44. Kilifi (Kenya): Kilifi County Hospital (KCH) is located in a rural, malaria endemic, coastal area, 55 km north of Mombasa, which is the second poorest county in Kenya. The hospital, has a catchment area of approximately 280,000 people with 3,000 births per year. The antenatal HIV prevalence is 7.9% 45. KCH hosts the Kenyan Medical Research Institute (KEMRI)/Wellcome Trust Research Programme, a partnership with the University of Oxford that, since 1989, has pioneered work on laboratory-based, epidemiological and clinical research. In the late 1990s, an antenatal ultrasound service was established facilitating pregnancy-related research. The research program has a computerized, Health and Demographic Surveillance System (KHDSS) that catalogues a sub-population of people living in KCH’s predominant catchment area 46. Each individual is given a personal identification number and births, deaths, in- and out-migration are recorded at 4-monthly household visits. The KHDSS provides a means to encourage attendance at antenatal clinics, particularly for booking early in pregnancy. Nairobi (Kenya): The relatively wealthy Parklands suburb of Nairobi, Kenya, was the sub-Saharan site for Phase I 41. Almost all births (>4,000) in this geographically delimited urban area, which mostly houses affluent Kenyan families, take place in three hospitals, the largest of which, The Aga Khan University Hospital (AKUH), participated in the study. AKUH is a private, not-for-profit, tertiary care institution, which is accessed predominantly by the middle and high socio‐economic sectors. These women are able to access the hospital services either through medical insurance cover or direct payment; thus, the pregnant population served is at relatively low risk of FGR. In 2008, the mean birth weight was 3101 g; the low birth weight and perinatal mortality rates were 11% and 1.7%, respectively. Nairobi is a non-endemic malaria area but HIV remains a significant problem in the city with a prevalence of 10% in the female population although much lower at 1% among women attending this hospital for antenatal care. Thanks to wide access to drugs and other aspects of care, during the period of this study almost all women living with HIV or newly diagnosed during antenatal screening had commenced antiretroviral therapy. Karachi (Pakistan): The Aga Khan University Hospital (AKUH) is a philanthropic, not-for-profit teaching institution, which caters to a range of socio-economic groups through an effective patient welfare program. The hospital is the most advanced private-sector tertiary care institution in Karachi, the largest city in Pakistan with an estimated population of about 20 million people. The AKUH is also affiliated with four secondary care hospitals (combined total approximately 15,000 births per year) from which high-risk cases are referred as needed: three in Karachi and one about 100 miles away. The main AKUH has 5,800 births per year, a third of which are high-risk. The per capita income in Karachi is approximately $1,427. At AKUH, the first antenatal visit is usually in the first trimester, at which time a dating scan is performed 47. Nuchal translucency screening is offered to high-risk women and a fetal anomaly scan is offered between 19–23 weeks’ gestation. Soweto (South Africa): Chris Hani Baragwanath Academic Hospital (CHBAH), which is attached to the University of the Witwatersrand, Johannesburg, South Africa, is the only government hospital serving two sub-districts in southwestern Johannesburg, which includes the Greater Soweto area, with a population of approximately 1.3 million. CHBAH provides district hospital referral services for seven community health centers in Greater Soweto, in which midwives conduct low-risk births; approximately 5% of births are tertiary referrals from other districts and provinces. The hospital has approximately 17,000 births per year, 75% of which are medium-to-high risk; this represents 53% of all births in Greater Soweto 48. The HIV prevalence is 29% 49. The population is urbanized, indigenous African, and working-class. The average annual income is substantially less than the national average, at approximately US$4,000. CHBAH is linked to the Soweto First 1000 Days Study (S1000) at the MRC/Wits Departmental Pathways for Health Research Unit (DPHRU) 50. Mae Sot (Thailand): The Shoklo Malaria Research Unit (SMRU), located on the Thailand-Myanmar border, is a field station of the Mahidol-Oxford Research Unit (MORU) of Mahidol University, Bangkok. SMRU was established in 1986 as a research centre for studying the epidemiology, prevention and treatment of resistant malaria (including in pregnancy) among the 130,000 people living in refugee camps along the border. As there is no safe and effective drug for preventing Plasmodium falciparum and Plasmodium vivax in pregnancy, women are encouraged to attend the antenatal clinic at SMRU every 2 weeks from as early as possible in the first trimester, and they are systematically screened for malaria at each antenatal consultation and treated if positive 51. In addition, since 2008, local health workers with limited education have been trained at SMRU to take fetal growth measurements with great accuracy 52. Three of its field sites, with a total of approximately 2,100 births per year, participated in the study. In 2008, in newborns >28 weeks’ gestation, the mean birth weight was 2908g; the LBW and perinatal mortality rates were 15.6% and 3.4%, respectively. Of the sites, two (Wang Pha and Mawker Thai) are clinics for migrants; the third, the Maela camp, is the largest refugee camp along the border. Both populations remain marginalized: the educational level is low and most of available income is obtained through intermittent work paid below the minimum wage and some women in the camp receive a supplement of refugee food rations. In 2012–13, HIV prevalence was low in refugees and migrants: 0.27% and 0.61%, respectively 53. Oxford (UK): The John Radcliffe Hospital, Oxford was one of the two sites in Europe that participated in Phase I 42. Oxfordshire has a population of more than 650,000 people, which includes a large proportion of young, middle‐class, well‐educated, professional families. A total of 37% of the Oxfordshire population hold a university degree, 16% higher than the national average. The hospital covers approximately 75% of more than 8,000 pregnancies that occur annually in this county. The general pregnant population served is at low risk of FGR. In 2008, the mean birth weight was 3334 g; the LBW and perinatal mortality rates were 6% and 0.5%, respectively. In addition, 99% of mothers delivering in the unit have completed secondary school or university level education. The hospital also houses the University of Oxford’s Nuffield Department of Women’s & Reproductive Health, which is where the INTERGROWTH‐21 st Project Coordinating Unit is located. These sites contributed cases and controls to the INTERBIO-21 st Newborn Case-Control Study, selected using the definitions described below. The INTERBIO-21 st Newborn Case-Control Study consisted of two components evaluating pregnancy characteristics, birth outcomes, neurodevelopment and biological markers associated with the preterm birth and SGA syndromes. The first component aimed to compare preterm phenotypic cases to term newborn controls. Preterm cases were singleton, naturally conceived babies, live-born at 23 +0 to 37 +6 weeks’ gestation 2, 3, whose mothers were ≥18 years of age and resided in the hospital’s catchment area (to avoid recruiting women referred for tertiary care from another geographical region), and whose gestational age was estimated by ultrasound measurement of either crown-rump length <14 +0 weeks’ gestation or head circumference <24 +0 weeks’ gestation 54. For the planned analyses, we will stratify the cases by gestational age (defined a priori) into those born <37 +0 weeks, and those born ≥37 +0 but <38 +0 weeks’ gestation, and according to the previously described phenotypes to explore interaction effects 11. Cases include groups A and C ( Table 1). For the case/non-case analyses, controls for preterm cases were singleton, naturally conceived babies, live-born at 38 +0 to 41 +6 weeks’ gestation, and appropriately grown for gestational age (AGA), i.e. with a birth weight for gestational age/sex ≥10 th centile of the INTERGROWTH-21 st Newborn Size Standards 22, whose mothers were ≥18 years of age and resided in the hospital’s catchment area, and whose gestational age was estimated by ultrasound measurement of either crown-rump length <14 +0 weeks’ gestation or head circumference <24 +0 weeks’ gestation 54. These controls include groups D and B. For group B, the sample will be down-weighted to represent their actual occurrence in the population. A cut-off of 37 +6 weeks instead of 36 +6 weeks’ gestation was used to define a preterm case because of the evidence of a small but nevertheless increased risk of respiratory and other adverse neonatal outcomes (including mechanical ventilation, sepsis, hypoglycemia, NICU admission, and hospitalization for 5 days or more) in those ‘term’ babies born between 37 +0 and 37 +6 weeks’ gestation 55. The second component aimed to compare SGA phenotypic cases to term newborn controls. SGA cases were singleton, naturally conceived babies, live-born at 23 +0 to 41 +6 weeks’ gestation, with a birth weight for gestational age/sex <10 th centile of the INTERGROWTH-21 st Newborn Size Standards 22, whose mothers were ≥18 years of age and resided in the hospital’s catchment area, and whose gestational age was estimated by ultrasound measurement of either crown-rump length <14 +0 weeks’ gestation or head circumference <24 +0 weeks’ gestation 54. Cases include groups B and C ( Table 1). For the case/non-case analyses, controls for SGA cases were singleton, naturally conceived babies, live-born at 38 +0 to 41 +6 weeks’ gestation, and AGA, i.e. with a birth weight for gestational age/sex ≥10 th centile of the INTERGROWTH-21 st Newborn Size Standards 22, whose mothers were ≥18 years of age and resided in the hospital’s catchment area, and whose gestational age was estimated by ultrasound measurement of either crown-rump length <14 +0 weeks’ gestation or head circumference <24 +0 weeks’ gestation 54. These controls include groups D and A. For group A, the sample will be down-weighted to represent their actual occurrence in the population. For the second analytical approach—the case-base analyses—controls will include newborns from all four groups. For groups A, B and C, the samples will be down-weighted to represent their actual occurrence in the population. Cases were recruited consecutively and one newborn control was recruited immediately after each preterm case was recruited; similarly, another newborn control was recruited immediately after each SGA case was recruited. Both sets of newborn controls will be pooled to create a control group for use in the comparative analyses with both preterm and SGA cases separately (as well as those cases born preterm and SGA), resulting in two newborn controls per case and a considerable increase in statistical power. At all sites, trained, dedicated research staff screened all women presenting for delivery on a daily basis using a tablet (iPad, Apple, USA)-based interface with the data management system ( https://doi.org/10.5281/zenodo.1442668 56). The software (available on request), that was specially written for the study, selected the correct proportion of preterm and SGA cases and corresponding controls according to birth weight and gestational age. Thus, each newborn recruited fell into one of the four groups shown in Table 1. The software selected a higher proportion of newborns with earlier gestational ages (for preterm cases) and lower birth weights, i.e. <3 rd centile (for SGA cases), using the sampling fractions shown in Table 2 so as to avoid recruiting excessive numbers just below the cut-offs that represent the majority of SGA and preterm newborns, i.e. moderate SGA and late preterm newborns. Oversampling cases at the lower end of the gestational age and birth weight distributions was important to have a large enough sample size to study the highest risk sub-groups; it was also expected to increase the statistical power of the study by producing a higher proportion of exposures and adverse neonatal outcomes. GA, gestational age; BW, birth weight. *Change occurred in November 2012. #Change occurred in November 2013. Slight changes in the sampling fractions (see arrows in Table 2) were recommended by the study’s epidemiological advisors and introduced for preterm cases in November 2012 and for SGA cases in November 2013 to reach the recruitment rates initially planned. These changes were anticipated because the actual recruitment rate of cases was difficult to predict. The adjustments were facilitated by the tablet software. We aimed to recruit at least 2,000 cases and 2,000 controls in total from the study sites. However, we recognised then that power calculations are a great challenge in any field-study of this magnitude and even more difficult when exploring risk factors with relatively unknown degrees of association and prevalence in such populations. The key issue is to reach a balance between logistical demands, including the need to maintain data quality in these populations, and power calculations especially for the planned genetic and epigenetic studies. In addition, when the study was designed in 2012, it was extremely difficult to provide reliable power calculations for epigenetic studies: the field was too new and very few relevant studies had been conducted. The compromise was to use experience gained from genome-wide association studies to facilitate sample size estimations. Thus, 1,500 cases and 1,500 controls (ratio 1:1) would be required, assuming a methylation proportion of 0.3 and 0.2 in cases and controls, respectively, to detect an odds ratio of 1.7 (population attributable fraction of 0.12) with a significance threshold alpha of 5.0 × 10 -7 and 90% power. These calculations included a continuity correction allowing for normal approximation of the binomial distribution. The methods used to estimate gestational age, as well as the training, standardization and quality control processes are described elsewhere 57– 59. In brief, crown-rump length measurements were taken <14 +0 weeks’ gestation in a mid-sagittal view of the horizontal fetus in a neutral position, with an angle of insonation as close as possible to 90°. The image could not fill less than 30% of the monitor screen. The callipers were placed on the outer borders of the head and rump, and gestational age was estimated using the INTERGROWTH-21 st standards for pregnancy dating 16. Head measurements were taken 12 but <24 h of delivery, samples were collected for histology only. Two photographs were taken of the placenta showing the whole placenta and umbilical cord with the ‘fetal’ and ‘maternal’ sides uppermost. A metric ruler was placed at right angles next to the tissue to indicate the size of the placenta. The weight of the placenta, trimmed of the cord and all its membranes, was recorded. Maternal feces: A sample of maternal feces (approximately 5 g), if passed at delivery, was collected and stored at -80°C. Buccal swabs: DNA was collected from the infants at 1 and 2 years of age using a buccal swab collection kit (MAWI DNA Technologies, CA 94545, USA). Up to four swabs were gently rubbed against the inside of the infant’s cheek and placed into a single vial of buffer. After collection, the swabs were discarded and the cloudy buffer containing the DNA was stored and transported at ambient temperature. All clinical data were managed in a system very similar to the one used in Phase I of the INTERGROWTH-21 st Project 67. In brief, the data were initially collected on paper forms capturing information relating to ultrasound estimation of gestational age, pregnancy & delivery, and any fetal/neonatal abnormalities; these forms were securely stored. The data were then entered at the local level into an on-line data management system, based on the one developed specifically for the INTERGROWTH-21 st Project (MedSciNet, London, UK). This on-line system, which resides on a secure MedSciNet server, facilitated quality control, correction of errors or missing values, and the initiation of data analysis soon after completion of data collection. A review process within the system, which involved weekly queries to each site via Skype if necessary, ensured that all key data were complete. Blinded data from the ultrasound machines were transferred directly to the database in Oxford. All sample-related data were collected on an electronic form (e-form) and the data were uploaded onto a separate data management system (Sapphire, Labvantage Solutions Ltd, High Wycombe, UK) that was specifically modified for the study. This system, which resides on a secure University server, allows samples to be tracked from the time of collection through processing, storage at the study sites, and transport to the central storage facility in Oxford. Each participant was given a unique identifier number, which was used to link the clinical and sample databases. Individual aliquots of each sample type were also given a unique number. All the electronically stored data were stripped of personal identifiers, which are held separately and securely on site. The anonymised databases are only accessible to designated personnel, including the Bill & Melinda Gates Foundation as part of a data sharing agreement. Users from each study site can only view their own data at present and a limited number of global administrators can see all the data on a secure server. These systems provided the Data Management Unit in Oxford with a detailed daily record of patient enrolment and data entry, at both individual and institutional levels, to monitor progress. Corresponding actions, such as telephone calls, web conferences and site visits took place within a week of detecting a problem at a study site to ensure that appropriate corrective measures were taken.

The INTERBIO-21st Study is a research initiative aimed at improving the functional classification of preterm birth and small for gestational age (SGA) syndromes. The study involves deep phenotyping of clinical, growth, and epidemiological data, as well as associated biological samples such as maternal blood, umbilical cord blood, placental tissue, maternal feces, and infant buccal swabs. The study was conducted at seven sites in Brazil, Kenya, Pakistan, South Africa, Thailand, and the UK.

Some potential innovations that can improve access to maternal health based on the study include:

1. Mobile health (mHealth) applications: Develop mobile applications that provide access to maternal health information, resources, and support. These apps can provide personalized guidance, reminders for prenatal care visits, nutrition advice, and educational materials.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls. This can improve access to prenatal care and enable early detection of potential complications.

3. Community health workers: Train and deploy community health workers to provide maternal health education, support, and basic healthcare services in underserved communities. These workers can help bridge the gap between healthcare facilities and pregnant women who may face barriers to accessing care.

4. Health financing schemes: Develop innovative health financing schemes, such as microinsurance or conditional cash transfer programs, to improve financial access to maternal health services. These schemes can help reduce out-of-pocket expenses and ensure that pregnant women can afford necessary care.

5. Digital health records: Implement electronic health record systems that allow for seamless sharing of patient information between healthcare providers. This can improve continuity of care, reduce duplication of tests and procedures, and enable better coordination of maternal health services.

6. Maternal health awareness campaigns: Launch targeted awareness campaigns to educate communities about the importance of prenatal care, nutrition, and early detection of complications. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience.

7. Maternal health clinics: Establish dedicated maternal health clinics in underserved areas to provide comprehensive prenatal care, including regular check-ups, screenings, and counseling. These clinics can also offer family planning services and postnatal care to ensure continuity of care throughout the reproductive cycle.

These innovations can help improve access to maternal health services, enhance early detection and management of complications, and ultimately contribute to better maternal and child health outcomes.
AI Innovations Description
The recommendation to improve access to maternal health based on the INTERBIO-21st newborn case-control study protocol is to utilize the findings and insights from the study to develop innovative solutions. This can include:

1. Implementing evidence-based interventions: Use the knowledge gained from the study to develop and implement evidence-based interventions that address the specific challenges related to preterm birth and small for gestational age syndromes. These interventions can focus on improving antenatal care, nutrition, and overall maternal health.

2. Strengthening healthcare systems: Use the findings from the study to identify gaps in healthcare systems and develop strategies to strengthen them. This can involve improving access to quality healthcare services, training healthcare professionals, and ensuring the availability of necessary resources and infrastructure.

3. Enhancing community engagement: Engage with communities to raise awareness about the importance of maternal health and the findings from the study. This can involve community education programs, outreach initiatives, and partnerships with local organizations to promote maternal health and encourage early and regular prenatal care.

4. Leveraging technology: Utilize technology to improve access to maternal health services. This can include telemedicine programs, mobile health applications, and remote monitoring systems to provide prenatal care and support to women in remote or underserved areas.

5. Collaboration and knowledge sharing: Foster collaboration among healthcare professionals, researchers, policymakers, and other stakeholders to share knowledge, best practices, and lessons learned. This can help accelerate the implementation of innovative solutions and improve access to maternal health globally.

By implementing these recommendations, it is possible to translate the findings from the INTERBIO-21st study into tangible innovations that can improve access to maternal health and ultimately reduce the burden of preterm birth and small for gestational age syndromes.
AI Innovations Methodology
The INTERBIO-21st Study is a research initiative aimed at improving the understanding of preterm birth and small for gestational age (SGA) syndromes. The study involves deep phenotyping of clinical, growth, and epidemiological data, as well as associated biological samples, to investigate the mechanisms responsible for these conditions.

To improve access to maternal health, the following innovations could be considered:

1. Telemedicine: Implementing telemedicine technologies can provide remote access to healthcare professionals, allowing pregnant women in remote or underserved areas to receive prenatal care and consultations without the need for travel.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take an active role in their own healthcare and improve access to maternal health services.

3. Community-based interventions: Establishing community-based programs that provide comprehensive maternal health services, including prenatal care, nutrition support, and education, can increase access for women who face barriers to accessing traditional healthcare facilities.

4. Task-shifting: Training and empowering non-medical healthcare workers, such as community health workers or midwives, to provide basic prenatal care and screenings can help alleviate the shortage of healthcare professionals in underserved areas.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific population or geographic area where the recommendations will be implemented.

2. Collect baseline data: Gather data on the current state of maternal health access in the target population, including metrics such as the number of prenatal care visits, distance to healthcare facilities, and availability of healthcare professionals.

3. Implement the recommendations: Introduce the recommended innovations, such as telemedicine, mHealth applications, community-based interventions, or task-shifting programs, in the target population.

4. Monitor and evaluate: Track the implementation of the recommendations and collect data on key indicators, such as the number of telemedicine consultations, usage of mHealth applications, participation in community-based programs, or the number of trained non-medical healthcare workers.

5. Analyze the impact: Compare the data collected after implementing the recommendations to the baseline data to assess the impact on access to maternal health. This could include measuring changes in the number of prenatal care visits, improvements in healthcare provider availability, or reductions in travel distances for pregnant women.

6. Adjust and refine: Based on the findings, make adjustments and refinements to the recommendations to further improve access to maternal health.

By following this methodology, stakeholders can gain insights into the potential impact of these innovations on improving access to maternal health and make informed decisions on implementing and scaling up effective interventions.

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