International Journal of Population Data Science (IJPDS)

Rob French, lead of the ADR Wales Education research theme, highlights the opportunities/challenges of linking child health and education data for research – nationally and internationally. These will be explored in a new special edition of International Journal of Population Data Science (IJPDS). https://ow.ly/eGKc50UtQ1L

IJPDS will be operating on limited staff during the following dates: 𝟏𝟔𝐭𝐡 𝐃𝐞𝐜𝐞𝐦𝐛𝐞𝐫 - 𝟏𝟎𝐭𝐡 𝐉𝐚𝐧𝐮𝐚𝐫𝐲 We look forward to bringing you all the latest research in 2025!

𝐀𝐫𝐞 𝐏𝐞𝐫𝐬𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐃𝐢𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐋𝐞𝐟𝐭 𝐁𝐞𝐡𝐢𝐧𝐝? 𝐀𝐧 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐝𝐢𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐨𝐮𝐭 A new Disability Statistics – Estimates Database (DS-E Database) has been created by an international research team from Colombia, India, South Africa, Switzerland, and the USA. The Disability Data Initiative, or DDI, is an international and interdisciplinary research programme that provides analyses of disability data to help advance the rights of persons with disabilities and sustainable human development for all. Taking advantage of data from household surveys and population censuses with an internationally comparable short set of questions on disability, the DDI has produced statistics for 40 countries and 6,584 subnational locations. It includes disability statistics with vital information about education, personal activities, health, standards of living, economic insecurity and poverty for people living with a disability, and has revealed that about one in six adults have some type of disability. Given the complexity of defining and measuring disability, the DS-E Database uses different methods to breakdown the adult population into subgroups based on disability severity and type, allowing for deeper analysis on subgroups of the adult population based on sex, rural/urban residence and age groups as well as areas within countries. The current study, published in the International Journal of Population Data Science (IJPDS), revealed that for certain indicators, inequalities between persons with and without disabilities are consistently experienced across and within countries. This is particularly evident in areas like education and poverty. Overall, results suggest that persons with disabilities seem to be ‘left behind’ and that national and local policymakers must prioritise disability-inclusive approaches to address disparities both within and across countries. There are only five years left to achieve the 2030 Sustainable Development Agenda, which pledges to “leave no one behind”. Sustainable Development Goal (SDG) 10 states that “inequality within and among countries is a persistent cause for concern.” While the achievement of the 17 SDGs needs to be monitored for persons with disabilities, the lack of...   𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐞 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 https://lnkd.in/dStpxUXK or 𝐂𝐥𝐢𝐜𝐤 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐯𝐢𝐞𝐰 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 https://lnkd.in/dr7cq9zQ Carpenter, B., Kamalakannan, S., Patchaiappan, K., Theiss, K., Yap, J., Hanass-Hancock, J., Murthy, G., Pinilla-Roncancio, M., Rivas Velarde, M. and Mitra, S. (2024) “Data Resource Profile: The Disability Statistics -- Estimates Database (DS-E Database). An innovative database of internationally comparable statistics on disability inequalities”, International Journal of Population Data Science, 8(6). doi: 10.23889/ijpds.v8i6.2478. #DisabilityData

𝐍𝐞𝐰 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐖𝐢𝐥𝐥 𝐇𝐞𝐥𝐩 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐏𝐞𝐫𝐬𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐃𝐢𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 Disability is difficult to define and measure accurately. When a survey directly asks, "Do you have a disability?" respondents may be unclear about what "disability" means. In places where disability is stigmatised, people may be hesitant to answer or may say "no" even if they have disabilities. However, gathering reliable data on the human rights situation of persons with disabilities is crucial. This data helps track national and international laws, policies, and commitments, including the United Nations (UN) Convention on the Rights of Persons with Disabilities and the Sustainable Development Goals. Thanks to the work of researchers, national statistics offices, and especially the United Nations Washington Group (WG) on Disability Statistics, new questionnaires are now available to measure disability across different countries. These questionnaires can reveal both the prevalence of disability and the inequalities that persons with disabilities face. For example, the WG has developed a short set of six questions that ask about difficulties with seeing, hearing, walking or climbing stairs, self-care, concentrating or remembering, and communication. The Disability Data Initiative (DDI) has prepared a new database, the Disability Statistics - Questionnaire Review Database (DS-QR Database), to track the availability of these questions in population censuses and national surveys worldwide. The DDI is an international and interdisciplinary research programme that provides analyses of disability data to help advance the rights of persons with disabilities and sustainable human development for all. The DS-QR Database has reviewed 3,027 censuses and surveys from various countries and regions over time, indicating whether they include the WG’s six questions or similar ones. The data shows that the availability of disability-related questions is increasing. Currently, 101 countries were found to have at least one dataset that includes the WG’s short set of questions. However, there are regional differences. For example... 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐞 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 https://lnkd.in/dHsHED5z or 𝐂𝐥𝐢𝐜𝐤 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐯𝐢𝐞𝐰 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 https://lnkd.in/dDj3qppS Carpenter, B., Kamalakannan, S., Saikam, P., Alvarez, D. V., Hanass-Hancock, J., Murthy, G., Pinilla-Roncancio, M., Rivas Velarde, M., Teodoro, D. and Mitra, S. (2024) “Data resource profile: the disability statistics questionnaire review database (DS-QR Database): a database of population censuses and household surveys with internationally comparable disability questions”, International Journal of Population Data Science, 8(6). doi: 10.23889/ijpds.v8i6.2477. #Disability #HumanRights #HouseholdSurveys #PopulationCensus #HousingCensus #DisabilityData #DisabilityStatistics

𝐍𝐨 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐝𝐞𝐭𝐞𝐜𝐭𝐞𝐝 𝐢𝐧 𝐧𝐞𝐰𝐛𝐨𝐫𝐧 𝐰𝐢𝐭𝐡𝐝𝐫𝐚𝐰𝐚𝐥 𝐬𝐢𝐠𝐧𝐬 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐛𝐫𝐞𝐚𝐬𝐭𝐟𝐞𝐝 𝐚𝐧𝐝 𝐟𝐨𝐫𝐦𝐮𝐥𝐚-𝐟𝐞𝐝 𝐛𝐚𝐛𝐢𝐞𝐬 𝐨𝐟 𝐩𝐫𝐞𝐠𝐧𝐚𝐧𝐭 𝐦𝐨𝐦𝐬 𝐭𝐚𝐤𝐢𝐧𝐠 𝐚𝐧𝐭𝐢𝐝𝐞𝐩𝐫𝐞𝐬𝐬𝐚𝐧𝐭𝐬 Researchers from Queen’s University, Ontario have discovered no significant difference in the withdrawal risk between newborns who are breastfed* or formula-fed, when the mothers* take a selective serotonin reuptake inhibitor (SSRI) antidepressant during pregnancy. The findings, published in the International Journal of Population Data Science (IJPDS), add to the existing research into treatment of mood and anxiety disorders in pregnancy, and offer women who take SSRI medications during pregnancy support for their preferred newborn feeding method with the appropriate monitoring for newborn well-being. Maternal medication use in pregnancy and newborn feeding methods is an important research topic because mood and anxiety disorders are the most common pregnancy complication, affecting about 1 in 5 women. SSRIs are the most used antidepressant medication in the general population and in pregnant women. There is a small risk that a newborn will experience withdrawal signs after being exposed to SSRIs, including jitteriness, fussiness, crying, feeding problems, or changes in sleep. These signs will usually improve a few days after birth. The study looked at the outcomes of newborns whose mothers* took SSRIs during their pregnancy, and whether the feeding method after birth impacted the risk for newborn withdrawal or transfer to a Neonatal Intensive Care Unit (NICU). It revealed an overall low risk of newborn withdrawal, but also that there was no difference in risk among breastfed newborns compared with formula-fed newborns who were exposed to SSRIs. The study also showed a possible lower risk of transfer to the NICU in breastfed newborns. This research gives health care providers evidence-based information to share with pregnant women when making decisions about how they will feed their baby after birth and any support that they might need. Pregnant and breastfeeding families need information about potential signs of withdrawal in their baby and how best to provide comfort. Parents also need to know when to...   𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐞 𝐑𝐞𝐚𝐝𝐢𝐧𝐠... https://lnkd.in/eSdwQjEg 𝐂𝐥𝐢𝐜𝐤 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐯𝐢𝐞𝐰 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 https://lnkd.in/ehhT7zzY Christina Cantin, Faculty of Health Sciences, School of Nursing, Queen’s University, Kingston, Ontario; CHEO Research Institute, Ottawa, Ontario Cantin, C., Li, W., Snelgrove-Clarke, E., Corsi, D., Dennis, C.-L., Ross-White, A., Brogly, S. and Gaudet, L. (2024) “Neonates With In-Utero SSRI Exposure (NeoWISE): a retrospective cohort study examining the effect of newborn feeding method on newborn withdrawal”, International Journal of Population Data Science, 9(2). doi: 10.23889/ijpds.v9i2.24

𝐅𝐈𝐍𝐀𝐋 𝐂𝐀𝐋𝐋 𝐟𝐨𝐫 𝐒𝐔𝐁𝐌𝐈𝐒𝐒𝐈𝐎𝐍𝐒 to 𝐈𝐉𝐏𝐃𝐒 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲 𝐒𝐩𝐞𝐜𝐢𝐚𝐥 𝐈𝐬𝐬𝐮𝐞 Our Data Transparency Special Issue call is closing this Friday 13th December Do get in touch if you would like to submit a manuscript, but may miss the deadline. We will be happy to discuss a possible extension - contact@ijpds.org For details of this call visit https://lnkd.in/erWeYRRn #DataTransparency #OpenCall

📣 The call for abstracts for the ADR UK (Administrative Data Research UK) 2025 Conference hosted by ADR Wales is NOW OPEN. 💻 Head to the conference website to learn about the themes and how to submit abstracts: https://lnkd.in/ec5jWkyf. #ADRUKConf25 Swansea University Swansea University Medicine, Health and Life Science WISERD Datacise Open Learning Office for National Statistics Scottish Centre for Administrative Data Research (SCADR) #ADRC_NI

Linkage of health birth records deepens our understanding of child health inequalities in Scotland A novel data linkage for around 200,000 children born in Scotland, 2009-2013 and published in the International Journal of Population Data Science (IJPDS) has shown large social inequalities in health which start from the moment of birth. The linkages were carried out across a range of health record types, including prescribing data, hospital admissions, and child health checks, and children were followed up until the age of 6 years. Birth registrations provided information on area deprivation, family structure and parents’ social class. Researchers from the MRC/CSO Social and Public Health Sciences Unit at the University of Glasgow, found that the health differences between those from less, compared to more, advantaged families were largest for health conditions which persisted (e.g., experiencing overweight or obesity as a toddler and in primary school), or that were more severe (e.g. an injury which required a longer stay in hospital). Health differences also tended to be larger when looking at the social characteristics of families’ households (family structure and social class), as opposed to where they live. Furthermore, differences in health increased with every additional aspect of disadvantage experienced. For example, 41% of the most disadvantaged children (who were living in a lone parent household, with an economically inactive parent, and in the most deprived areas) were exposed to tobacco smoke during pregnancy and during the early years. This was far higher than children who were living with a married, managerial/professional mother, in the least deprived areas (of whom, less than 1% were exposed to tobacco smoke throughout pregnancy and the early years). Official reports, which monitor trends in health inequalities, normally rely on...   Continue Reading... https://lnkd.in/eDRrJtYw Go to full open access article... https://lnkd.in/eVxwa-7n Anna Pearce, Senior Research Fellow, MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow Henery, P., Dundas, R., Katikireddi , S. V., Leyland, A. H., Fenton, L., Scott, S., Cameron, C. and Pearce, A. (2024) “A maternal and child health administrative cohort in Scotland: the utility of linked administrative data for understanding early years’ outcomes and inequalities”, International Journal of Population Data Science, 9(2). doi: 10.23889/ijpds.v9i2.2402.

𝐅𝐢𝐧𝐚𝐥 𝐖𝐞𝐞𝐤: 𝐀𝐩𝐩𝐥𝐲 𝐟𝐨𝐫 𝐭𝐡𝐞 𝟐𝟎𝟐𝟓-𝟐𝟎𝟐𝟔 𝐈𝐏𝐃𝐋𝐍 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐂𝐨𝐦𝐦𝐢𝐭𝐭𝐞𝐞  Don’t miss your chance to apply for the 2025-2026 International Population Data Linkage Network Executive Committee! 𝐑𝐞𝐚𝐝 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐫𝐨𝐥𝐞 𝐡𝐞𝐫𝐞  https://lnkd.in/e5C6tHfp 𝐀𝐩𝐩𝐥𝐲 𝐡𝐞𝐫𝐞  https://lnkd.in/eJwXsB5X 𝐍𝐨𝐦𝐢𝐧𝐞𝐞𝐬 𝐦𝐮𝐬𝐭 𝐦𝐞𝐞𝐭 𝐭𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐜𝐫𝐢𝐭𝐞𝐫𝐢𝐚: Be a member of IPDLN (become a member here) and have demonstrated commitment to advancing the field of data linkage -OR- Be a current IPDLN Executive Committee Member who has served less than two terms and would like to continue Submission Requirements - Applicants must provide their: - Name - Title - Institutional affiliation(s) - Qualifications - A brief biography - A description (250 words or less) of previous and/or potential contributions to IPDLN The 𝐝𝐞𝐚𝐝𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐧𝐨𝐦𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐬 𝐍𝐨𝐯𝐞𝐦𝐛𝐞𝐫 𝟐𝟗, 𝟐𝟎𝟐𝟒. Email all submissions to info@ipdln.org. #IPLDN

𝐍𝐞𝐰 𝐀𝐈 𝐭𝐨𝐨𝐥 𝐨𝐟𝐟𝐞𝐫𝐬 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐬𝐚𝐟𝐞𝐭𝐲 𝐟𝐨𝐫 𝐦𝐨𝐭𝐡𝐞𝐫𝐬 𝐚𝐧𝐝 𝐛𝐚𝐛𝐢𝐞𝐬 𝐢𝐧 𝐦𝐚𝐭𝐞𝐫𝐧𝐢𝐭𝐲 𝐜𝐚𝐫𝐞 Loughborough University researchers have developed an artificial intelligence (AI) tool that identifies the key human factors influencing maternity care outcomes, supporting ongoing efforts to improve safety for mothers and babies. Developed by AI and data scientist Professor Georgina Cosma and Professor Patrick Waterson, an expert in human factors and complex systems, the new ‘I-SIRch’ tool analyses maternity incident reports to highlight human factors – such as communication, teamwork, and decision-making – that may have influenced care outcomes. When an adverse maternity incident occurs in England, detailed investigation reports are produced to identify opportunities for learning and enhancing safety. These reports provide valuable insights into clinical aspects that impacted care, such as health conditions, procedures, and tests. However, identifying the human factors involved is often more challenging, as they tend to be complex and nuanced. To extract human factor insights from incident reports, experts must carry out manual reviews. This process is resource-intensive, time-consuming, and relies on individual interpretation and expertise, which can lead to varying conclusions. The I-SIRch tool addresses these challenges by automatically identifying and categorising human factors within an incident report. The AI model was trained and tested on data from 188 real maternity incident reports. It successfully identified human factors in each report and analysed them collectively to highlight specific areas where additional support could be beneficial. "AI has transformed our analysis of maternity safety reports. We've uncovered crucial insights far quicker than manual methods," said Professor Cosma. “This has enabled us to gather a comprehensive understanding of where there are areas for improvement in maternity care, and these insights will help identify ways to enhance patient safety and improve outcomes for mothers and babies." 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐫𝐞𝐩𝐨𝐫𝐭𝐬 Teamwork and communication emerged as... 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐞 𝐑𝐞𝐚𝐝𝐢𝐧𝐠: https://lnkd.in/eH-tNYKk 𝐂𝐥𝐢𝐜𝐤 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐯𝐢𝐞𝐰 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/en5x-dTP The I-SIRch project was jointly funded by the Health Foundation and the NHS AI Lab at the NHS Transformation Directorate and supported by the National Institute for Health and Care Research. Singh, M. K., Cosma, G., Waterson, P., Back, J. and Jun, G. T. (2024) “I-SIRch: AI-powered concept annotation tool for equitable extraction and analysis of safety insights from maternity investigations”, International Journal of Population Data Science, 9(2). doi: 10.23889/ijpds.v9i2.2439.