ObjectivesThis presentation will detail results from an initial study using linked administrative health data to explore the impact of COVID-19 lockdown measures on birth and neonatal outcomes in Scotland. It will further outline a funded longer-term follow-up study of child health and developmental outcomes using administrative health data.
ApproachTo combat the wide-spread transmission of COVID-19 Scotland imposed a nationwide lockdown. Little is known about how lockdown measures affected pregnant mothers and their offspring. Using routinely collected health data on pregnancy and birth in Scotland, the initial study compares births (N = 11220) between March and May 2020 to births in the same period in 2018 (N = 12428) to investigate the effects of lockdown measures, using descriptive statistics (Mann-Whitney U tests/Chi-squared tests). A 5-year follow-up study will track child health and developmental outcomes for the 99,000 children born in Scotland during the pandemic up to age five.
ResultsResults of the initial study indicated that mothers giving birth during the pandemic demonstrated significant differences in feeding methods on discharge (χ2(3) = 19.09, p <.001), and analgesia during labour and delivery (χ2(6) = 104.68, p <.001), and stayed in hospital for fewer days (Z = -10.90, p <.001) compared with women who gave birth in 2018. Post-hoc tests revealed that women were more likely to combine breastfeeding with formula-feeding than to exclusively breastfeed (P <.001) or exclusively formula-feed (P <.001). They were also more likely to require spinal anaesthetics compared to using no pain relief air (P =.035), gas and air (P <.001) or opioids (P < .001).
ConclusionFindings of the current study suggest that lockdown measures implemented in Scotland as a response to the COVID-19 pandemic had limited effects on maternal and neonatal outcomes. The CHILDS study will provide robust evidence on the longer-term impacts of the pandemic on child development, which may have long-lasting consequences for this generation.
AbstractPrevious studies have hinted at sex differences in developmental trajectories in ADHD symptoms; however, little is known about the nature or cause of these differences and their implications for clinical practice. We used growth mixture modelling in a community‐ascertained cohort of n = 1,571 participants to study sex differences in ADHD symptom developmental trajectories across the elementary and secondary school years. Participants were measured at ages 7, 8, 9, 10, 11, 12, 13, and 15. We found that females were more likely to show large symptom increases in early adolescence while males were more likely to show elevated symptoms from childhood. For both males and females, early adolescence represented a period of vulnerability characterized by relatively sudden symptom increases. Females affected by hyperactivity/impulsivity may be more likely to be excluded from diagnosis due to current age of onset criteria. More attention should be paid to early adolescence as a period of risk for hyperactivity/impulsivity symptom onset or worsening.
ObjectivesPrevious research suggests that prenatal maternal infections may be linked to later childhood neurodevelopmental outcomes and socioemotional difficulties. We exploited a large linked administrative health dataset to examine relationships between prenatal infections and early childhood development outcomes in Greater Glasgow & Clyde (GGC), Scotland.
MethodsWe used population data from birth records, hospital records, prescriptions and routine child health reviews for 55,856 children born in GGC, 2011-2015, and their mothers. Logistic regression models were used to examine the relationship between prenatal infections, measured as both hospital-diagnosed prenatal infections and receipt of infection-related prescription(s) during pregnancy, and having adverse childhood development outcomes identified by health visitors during 6-8 weeks/27-30 months routine child health reviews. Secondary analysis examined whether results varied by (a) specific development outcome types (i.e. gross-motor-skills, hearing-communication, vision-social-awareness, personal-social, emotional-behavioural-attention, and speech-language-communication development), and (b) the trimester(s) in which infections occurred.
ResultsAfter adjusting for confounders/covariates, hospital-diagnosed infections were associated with increased odds of having at least one adverse development outcome identified during child health reviews (OR: 1.30; 95% CI: 1.19-1.42). This relationship was consistent across almost all development outcome types, and appeared to be specifically linked to infections occurring in trimesters 2 (OR: 1.34; 95% CI: 1.07-1.67) and 3 (OR: 1.33; 95% CI: 1.21-1.47), i.e. the trimesters in which fetal brain myelination occurs. Infection-related prescriptions were not associated with a significant increase in odds of having at least one adverse development outcome after adjusting for confounders/covariates (OR: 1.03; 95% CI: 0.98-1.08), but were associated with slightly increased odds of adverse outcomes specifically related to personal-social (OR: 1.12; 95% CI: 1.03-1.22) and emotional-behavioural-attention (OR: 1.15; 95% CI: 1.08-1.22) development.
ConclusionPrenatal infections, particularly those which are hospital-diagnosed and therefore likely to be more severe, are associated with early childhood development outcomes. Our study highlights the usefulness of Scotland's administrative health data in measuring childhood development. Future research will examine the impact of COVID-19 prenatal infections and lockdown measures.
The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Any views expressed are those of the author(s) and not necessarily those of the funders.
Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n = 694; n = 249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45–62% of ASC adults show evidence for large impairments (Cohen's d = −1.03 to −11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals. ; This study was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England at Cambridgeshire and Peterborough NHS Foundation Trust. This study was also conducted in association with the European Autism Interventions—A Multicentre Study for Developing New Medications (EU-AIMS) consortium; EU-AIMS receives support from the Innovative Medicines Initiative Joint Undertaking under grant agreement number 115300, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013), EFPIA companies, and Autism Speaks. This study was also supported by grants from the UK Medical Research Council (MRC) (G0600977), the Wellcome Trust (091774/Z/10/Z), and the Autism Research Trust (ART). M-CL and AR received support from the William Binks Autism Neuroscience Fellowship at the University of Cambridge. M-CL received support from the O'Brien Scholars Program within the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto. ; This is the final version of the article. It first appeared from Nature Publishing Group via https://doi.org/10.1038/srep35333
Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n = 694; n = 249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen's d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals. ; This study was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England at Cambridgeshire and Peterborough NHS Foundation Trust. This study was also conducted in association with the European Autism Interventions—A Multicentre Study for Developing New Medications (EU-AIMS) consortium; EU-AIMS receives support from the Innovative Medicines Initiative Joint Undertaking under grant agreement number 115300, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013), EFPIA companies, and Autism Speaks. This study was also supported by grants from the UK Medical Research Council (MRC) (G0600977), the Wellcome Trust (091774/Z/10/Z), and the Autism Research Trust (ART). M-CL and AR received support from the William Binks Autism Neuroscience Fellowship at the University of Cambridge. M-CL received support from the O'Brien Scholars Program within the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto. ; This is the final version of the article. It first appeared from Nature Publishing Group via https://doi.org/10.1038/srep35333
Funding: COPS is a sub-study of EAVE II, which is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004; AS], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government DG Health and Social Care and the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation. COPS has received additional funding from Tommy's charity and support from Sands charity. SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z; SJS). SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02; SVK), the Medical Research Council (MC_UU_00022/2; SVK) and the Scottish Government Chief Scientist Office (SPHSU17; SVK). ; Population-level data on COVID-19 vaccine uptake in pregnancy and SARS-CoV-2 infection outcomes are lacking. We describe COVID-19 vaccine uptake and SARS-CoV-2 infection in pregnant women in Scotland, using whole population data from a national, prospective cohort. Between the start of COVID-19 vaccine programme in Scotland, on 8 December 2020, and 31 October 2021, 25,917 COVID-19 vaccinations were given to 18,457 pregnant women. Vaccine coverage was substantially lower in pregnant women than in the general female population 18-44 years: 32.3% of women giving birth in October 2021 had two doses of vaccine compared to 77.4% in all women. The extended perinatal mortality rate for women who gave birth within 28 days of a COVID-19 diagnosis was 22.6 per 1,000 births (95% CI 12.9-38.5; pandemic background rate 5.6 per 1,000 births (452/80,456; 95% CI 5.1-6.2). 77.4% (3,833/ 4,950; 95% CI 76.2-78.6) of SARS-CoV-2 infections, 90.9% (748/823; 95% CI 88.7-92.7) of SARS-CoV-2 associated with hospital admission, and ...
The 10Kin1day workshop was generously sponsored by the Neuroscience and Cognition program Utrecht (NCU) of the Utrecht University (https://www.uu.nl/en/research/neuroscience-and-cognition-utrecht), the ENIGMA consortium (http://enigma.ini.usc.edu), and personal grants: MvdH: NWO-VIDI (452-16-015), MQ Fellowship; SB-C: the Wellcome Trust; Medical Research Council UK; NIHR CLAHRC for Cambridgeshire and Peterborough Foundation National Health Services Trust; Autism Research Trust; LB: New Investigator Award, Canadian Institutes of Health Research; Dara Cannon: Health Research Board (HRB), Ireland (grant code HRA-POR-2013-324); SC: Research Grant Council (Hong Kong)-GRF 14101714; Eveline Crone: ERC-2010-StG-263234; UD: DFG, grant FOR2107 DA1151/5-1, DA1151/5-2, SFB-TRR58, Project C09, IZKF, grant Dan3/012/17; SD: MRC-RFA-UFSP-01-2013 (Shared Roots MRC Flagship grant); TF: Marie Curie Programme, International Training Programme, r'Birth; DG: National Science Centre (UMO-2011/02/A/NZ5/00329); BG: National Science Centre (UMO-2011/02/A/NZ5/00329); JH: Western Sydney University Postgraduate Research Award; LH: Science Foundation Ireland, ERC; HH: Research Grant Council (Hong Kong)-GRF 14101714; LJ: Velux Stiftung, grant 369 & UZH University Research Priority Program Dynamics of Healthy Aging; AJ: DFG, grant FOR2107 JA 1890/7-1; KJ: National Science Centre (UMO-2013/09/N/HS6/02634); VK: The Russian Foundation for Basic Research (grant code 15-06-05758 A); TK: DFG, grant FOR2107 KI 588/14-1, DFG, grant FOR2107 KI 588/15-1; AK: DFG, grant FOR2107 KO 4291/4-1, DFG, grant FOR2107 KO 4291/3-1; IL: The Russian Foundation for Basic Research (grant code 15-06-05758 A); EL: Health and Medical Research Fund - 11121271; SiL: NHMRC-ARC Dementia Fellowship 1110414, NHMRC Dementia Research Team Grant 1095127, NHMRC Project Grant 1062319; CL-J: 537-2011, 2014-849; AM: Wellcome Trust Strategic Award (104036/Z/14/Z), MRC Grant MC_PC_17209; CM: Heisenberg-Grant, German Research Foundation, DFG MO 2363/3-2; PM: Foundation for Science and Technology, Portugal - PDE/BDE/113601/2015; KN: National Science Centre (UMO-2011/02/A/NZ5/00329); PN: National Science Centre (UMO-2013/09/N/HS6/02634); JiP: NWO-Veni 451-10-007; PaR: PER and US would like to thank the Schizophrenia Research Institute and the Chief-Investigators of the Australian Schizophrenia Research Bank V. Carr, U. Schall, R. Scott, A. Jablensky, B. Mowry, P. Michie, S. Catts, F. Henskens, and C. Pantelis; AS: National Science Centre (UMO-2011/02/A/NZ5/00329); SS: European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 707730; CS-M: Carlos III Health Institute (PI13/01958), Carlos III Health Institute (PI16/00889), Carlos III Health Institute (CPII16/00048); ES: National Science Centre (UMO-2011/02/A/NZ5/00329); AT: The Russian Foundation for Basic Research (grant code 15-06-05758 A); DT-G: PI14/00918, PI14/00639; Leonardo Tozzi: Marie Curie Programme, International Training Programme, r'Birth; SV: IMPRS Neurocom stipend; TvE: National Center for Research Resources at the National Institutes of Health (grant numbers: NIH 1 U24 RR021992 (Function Biomedical Informatics Research Network), NIH 1 U24 RR025736-01 (Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org) and the NIH Big Data to Knowledge (BD2K) award (U54 EB020403 to Paul Thompson). NvH: NWO-VIDI (452-11-014); MW: National Science Centre (UMO-2011/02/A/NZ5/00329); Veronica O'Keane: Meath Foundation; AV and AW: CRC Obesity Mechanism (SFB 1052) Project A1 funded by DFG. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data ; We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain. Ongoing grand-scale projects like the European Human Brain Project (1), the US Brain Initiative (2), the Human Connectome Project (3), the Chinese Brainnetome (4) and exciting world-wide neuroimaging collaborations such as ENIGMA (5) herald the new era of big neuroscience. In conjunction with these major undertakings, there is an emerging trend for bottom-up initiatives, starting with small-scale projects built upon existing collaborations and infrastructures. As described by Mainen et al. (6), these initiatives are centralized around self-organized groups of researchers working on the same challenges and sharing interests and specialized expertise. These projects could scale and open up to a larger audience and other disciplines over time, eventually lining up and merging their findings with other programs to make the bigger picture.