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Therrell BL, Padilla CD, Borrajo GJC, Khneisser I, Schielen PCJI, Knight-Madden J, Malherbe HL, Kase M. Current Status of Newborn Bloodspot Screening Worldwide 2024: A Comprehensive Review of Recent Activities (2020-2023). Int J Neonatal Screen 2024; 10:38. [PMID: 38920845 PMCID: PMC11203842 DOI: 10.3390/ijns10020038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 06/27/2024] Open
Abstract
Newborn bloodspot screening (NBS) began in the early 1960s based on the work of Dr. Robert "Bob" Guthrie in Buffalo, NY, USA. His development of a screening test for phenylketonuria on blood absorbed onto a special filter paper and transported to a remote testing laboratory began it all. Expansion of NBS to large numbers of asymptomatic congenital conditions flourishes in many settings while it has not yet been realized in others. The need for NBS as an efficient and effective public health prevention strategy that contributes to lowered morbidity and mortality wherever it is sustained is well known in the medical field but not necessarily by political policy makers. Acknowledging the value of national NBS reports published in 2007, the authors collaborated to create a worldwide NBS update in 2015. In a continuing attempt to review the progress of NBS globally, and to move towards a more harmonized and equitable screening system, we have updated our 2015 report with information available at the beginning of 2024. Reports on sub-Saharan Africa and the Caribbean, missing in 2015, have been included. Tables popular in the previous report have been updated with an eye towards harmonized comparisons. To emphasize areas needing attention globally, we have used regional tables containing similar listings of conditions screened, numbers of screening laboratories, and time at which specimen collection is recommended. Discussions are limited to bloodspot screening.
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Affiliation(s)
- Bradford L. Therrell
- Department of Pediatrics, University of Texas Health Science Center San Antonio, San Antonio, TX 78229, USA
- National Newborn Screening and Global Resource Center, Austin, TX 78759, USA
| | - Carmencita D. Padilla
- Department of Pediatrics, College of Medicine, University of the Philippines Manila, Manila 1000, Philippines;
| | - Gustavo J. C. Borrajo
- Detección de Errores Congénitos—Fundación Bioquímica Argentina, La Plata 1908, Argentina;
| | - Issam Khneisser
- Jacques LOISELET Genetic and Genomic Medical Center, Faculty of Medicine, Saint Joseph University, Beirut 1104 2020, Lebanon;
| | - Peter C. J. I. Schielen
- Office of the International Society for Neonatal Screening, Reigerskamp 273, 3607 HP Maarssen, The Netherlands;
| | - Jennifer Knight-Madden
- Caribbean Institute for Health Research—Sickle Cell Unit, The University of the West Indies, Mona, Kingston 7, Jamaica;
| | - Helen L. Malherbe
- Centre for Human Metabolomics, North-West University, Potchefstroom 2531, South Africa;
- Rare Diseases South Africa NPC, The Station Office, Bryanston, Sandton 2021, South Africa
| | - Marika Kase
- Strategic Initiatives Reproductive Health, Revvity, PL10, 10101 Turku, Finland;
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Bradburn E, Conde-Agudelo A, Roberts NW, Villar J, Papageorghiou AT. Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis. EClinicalMedicine 2024; 70:102498. [PMID: 38495518 PMCID: PMC10940947 DOI: 10.1016/j.eclinm.2024.102498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/21/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
Background Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Methods Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Findings Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. Interpretation We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. Funding The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.
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Affiliation(s)
- Elizabeth Bradburn
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
| | - Agustin Conde-Agudelo
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Nia W. Roberts
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Jose Villar
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Aris T. Papageorghiou
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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Zhu Y, Zhang J, Li Q, Lin M. Association between gestational weight gain and preterm birth and post-term birth: a longitudinal study from the National Vital Statistics System database. BMC Pediatr 2023; 23:127. [PMID: 36941673 PMCID: PMC10026488 DOI: 10.1186/s12887-023-03951-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 03/10/2023] [Indexed: 03/22/2023] Open
Abstract
BACKGROUND To evaluate the association between gestational weight gain (GWG) and preterm birth and post-term birth. METHODS This longitudinal-based research studied singleton pregnant women from the National Vital Statistics System (NVSS) (2019). Total GWG (kg) was converted to gestational age-standardized z scores. The z-scores of GWG were divided into four categories according to the quartile of GWG, and the quantile 2 interval was used as the reference for the analysis. Univariate and multivariate logistic regression analyses were performed to investigate the association between GWG and preterm birth, post-term birth, and total adverse outcome (preterm birth + post-term birth). Subgroup analysis stratified by pre-pregnancy body mass index (BMI) was used to estimate associations between z-scores and outcomes. RESULTS Of the 3,100,122 women, preterm birth occurred in 9.45% (292,857) population, with post-term birth accounting for 4.54% (140,851). The results demonstrated that low GWG z-score [odds ratio (OR): 1.04, 95% confidence interval (CI): 1.03 to 1.05, P < 0.001], and higher GWG z-scores (quantile 3: OR: 1.42, 95% CI: 1.41 to 1.44, P < 0.001; quantile 4: OR: 2.79, 95% CI: 2.76 to 2.82, P < 0.001) were positively associated with preterm birth. Low GWG z-score (OR: 1.18, 95% CI: 1.16 to 1.19, P < 0.001) was positively associated with an increased risk of post-term birth. However, higher GWG z-scores (quantile 3: OR: 0.84, 95% CI: 0.83 to 0.85, P < 0.001; quantile 4: 0.59, 95% CI: 0.58 to 0.60, P < 0.001) was associated with a decreased risk of post-term birth. In addition, low GWG z-score and higher GWG z-scores were related to total adverse outcome. A subgroup analysis demonstrated that pre-pregnancy BMI, low GWG z-score was associated with a decreased risk of preterm birth among BMI-obesity women (OR: 0.96, 95% CI: 0.94 to 0.98, P < 0.001). CONCLUSION Our result suggests that the management of GWG may be an important strategy to reduce the number of preterm birth and post-term birth.
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Affiliation(s)
- Yifang Zhu
- Department of Pediatrics, The Second Affiliated Hospital of Fujian Medical University, No.34 Zhongshan North Road, Licheng District, Quanzhou, 362000, P.R. China.
| | - Jiani Zhang
- Department of Pediatrics, The Second Affiliated Hospital of Fujian Medical University, No.34 Zhongshan North Road, Licheng District, Quanzhou, 362000, P.R. China
| | - Qiaoyu Li
- Department of Pediatrics, The Second Affiliated Hospital of Fujian Medical University, No.34 Zhongshan North Road, Licheng District, Quanzhou, 362000, P.R. China
| | - Min Lin
- Department of Pediatrics, The Second Affiliated Hospital of Fujian Medical University, No.34 Zhongshan North Road, Licheng District, Quanzhou, 362000, P.R. China
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Hawken S, Olibris B, Ducharme R, Bota AB, Murray JC, Potter BK, Walker M, Chakraborty P, Wilson K. Validation of gestational age determination from ultrasound or a metabolic gestational age algorithm using exact date of conception in a cohort of newborns conceived using assisted reproduction technologies. AJOG GLOBAL REPORTS 2022; 2:100091. [DOI: 10.1016/j.xagr.2022.100091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Zhuang C, Shi H, Jia Y, Chen J, Yang H, Chen X. Effects of Yoga exercise on anxiety and fetus growth in pregnant women with small for gestational age fetus. Am J Transl Res 2022; 14:5685-5692. [PMID: 36105014 PMCID: PMC9452333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/27/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To investigate the effect of yoga on anxiety and fetal weight of pregnant women carrying fetus small for gestational age (SGA). METHODS In this retrospective analysis, a total of 186 pregnant women with SGA fetus in our hospital from January 2015 to December 2017 were enrolled in this study. Among them, 90 patients received routine check-up were included in the control group, and the other 96 patients who had professional yoga exercise were included in the observation group. The differences of anxiety scale scores and fetal weight between the two groups before and after intervention were compared. RESULTS There was no significant difference in scores of anxiety scale (SAS) between the two groups before intervention. After intervention, the SAS score of pregnant women in intervention group was (46.48±3.79) was significantly lower than that in control group (60.13±4.25). There was also significant difference in fetal growth trajectory between the two groups, with a significant increase of 1021.36 g in the intervention group compared with 795.62 g in the control group (P<0.05). Furthermore, single regression analysis showed that average gestational weeks (r=0.064. P=0.011), yoga exercise (r=0.043, P<0.001), forceps use (r=0.338, P<0.001) and conversion to cesarean section (r=0.431, P<0.001) showed a significant correlation with anxiety and fetus growth in pregnant women carrying SGA fetus. Multiple regression analysis showed that yoga exercise (P<0.001) was selected as independent variables in the multiple regression model of anxiety and fetus growth in pregnant women with SGA fetus. CONCLUSION Yoga can effectively reduce the anxiety of pregnant women with small gestational age fetus and good for the growth and development of the fetus.
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Affiliation(s)
- Chunyu Zhuang
- Department of Children’s Health, Haikou Maternal and Child Health HospitalHaikoi 570203, China
| | - Huiling Shi
- Department of Women’s Health, Haikou Maternal and Child Health HospitalHaikoi 570203, China
| | - Yanping Jia
- Department of Neonatology, Haikou Maternal and Child Health HospitalHaikoi 570203, China
| | - Jiacheng Chen
- Department of Hepatobiliary Surgery, Hainan General HospitalHaikoi 570203, China
| | - Hui Yang
- Department of Obstetrics and Gynecology, Hainan Hospital of Traditional Chinese MedicineHaikoi 570203, China
| | - Xiaojing Chen
- Department of Obstetrics and Gynecology, Haikou Maternal and Child Health HospitalHaikoi 570203, China
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Hawken S, Ward V, Bota AB, Lamoureux M, Ducharme R, Wilson LA, Otieno N, Munga S, Nyawanda BO, Atito R, Stevenson DK, Chakraborty P, Darmstadt GL, Wilson K. Real world external validation of metabolic gestational age assessment in Kenya. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000652. [PMID: 36962760 PMCID: PMC10021775 DOI: 10.1371/journal.pgph.0000652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/20/2022] [Indexed: 11/29/2022]
Abstract
Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Victoria Ward
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nancy Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Bryan O Nyawanda
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Raphael Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - David K Stevenson
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
- Departments of Pediatrics, and of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Canada
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Ottawa, Canada
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Wilson K, Ward V, Chakraborty P, Darmstadt GL. A novel way of determining gestational age upon the birth of a child. J Glob Health 2021; 11:03078. [PMID: 34552714 PMCID: PMC8442512 DOI: 10.7189/jogh.11.03078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Affiliation(s)
- Kumanan Wilson
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Bruyère and Hospital Research Institutes, Ottawa Ontario, Canada
| | - Victoria Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Pranesh Chakraborty
- Department of Pediatrics, Children’s Hospital of Eastern Ontario and University of Ottawa, Ottawa, Ontario, Canada
- Newborn Screening Ontario, Ottawa, Ontario, Canada
| | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
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Bota AB, Ward V, Hawken S, Wilson LA, Lamoureux M, Ducharme R, Murphy MSQ, Denize KM, Henderson M, Saha SK, Akther S, Otieno NA, Munga S, Atito RO, Stringer JSA, Mwape H, Price JT, Mujuru HA, Chimhini G, Magwali T, Mudawarima L, Chakraborty P, Darmstadt GL, Wilson K. Metabolic gestational age assessment in low resource settings: a validation protocol. Gates Open Res 2021; 4:150. [PMID: 33501414 PMCID: PMC7801859 DOI: 10.12688/gatesopenres.13155.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/22/2021] [Indexed: 11/20/2022] Open
Abstract
Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.
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Affiliation(s)
- A. Brianne Bota
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Victoria Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen Hawken
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Lindsay A. Wilson
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Malia S. Q. Murphy
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Kathryn M. Denize
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Matthew Henderson
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Samir K. Saha
- Child Health Research Foundation, Mizapur, Bangladesh
| | - Salma Akther
- Child Health Research Foundation, Mizapur, Bangladesh
| | - Nancy A. Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Raphael O. Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | | | | | - Joan T. Price
- Department of Obstetrics and Gynecology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Hilda Angela Mujuru
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Gwendoline Chimhini
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Thulani Magwali
- Department of Obstetrics and Gynaecology, University of Zimbabwe, Avondale, Zimbabwe
| | - Louisa Mudawarima
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Gary L. Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Otttawa, Canada
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