International Journal of Population Data Science最新文献

{"title":"Estimated cumulative incidence of intervention by children's social care services to age 18: a whole-of-England administrative data cohort study using the child in need census.","authors":"Matthew A Jay, Patricio Troncoso, Andy Bilson, Dave Thomson, Richard Dorsett, Rachel Pearson, Bianca De Stavola, Ruth Gilbert","doi":"10.23889/ijpds.v10i1.2454","DOIUrl":"10.23889/ijpds.v10i1.2454","url":null,"abstract":"<p><strong>Introduction: </strong>Each year, children's social care (CSC) recognises around 3% of all children as children in need (CiN) of intervention, including those who receive a child protection plan due to risks of substantial harm and those who become looked after in state care. A previous cumulative estimate of the incidence of becoming CiN of 14% to age 5 indicates that the childhood lifetime incidence is likely very high.</p><p><strong>Objectives: </strong>We aimed to estimate the cumulative incidence of referrals, social work assessments, being recognised as a CiN or made subject to a child protection plan (CPP) before age 18 in England.</p><p><strong>Methods: </strong>The annual CiN census contains all-of-England longitudinal records of CSC referrals. Data collection began in 2008, meaning there is no cohort that can be followed up from birth to age 17 (i.e., before 18^th birthday). Analyses revealed data quality issues before 2011/12. We estimated the above cumulative incidences in three cohorts and combined them, adjusting numerators to account for left-censoring. The three cohorts were children born in: (a) 2012/13, followed to age 5; (b) 2005/06, followed from age 6 age to 12; and (c) 2000/01, followed from age 13 to 17. We carried out sensitivity analyses to address possible bias induced by linkage error using one of two encrypted identifiers in the dataset.</p><p><strong>Results: </strong>Of all children living in England, before turning 18, 35.4% were referred, 32.3% were assessed, 25.3% were recorded as CiN and 6.9% were subject to a CPP (37.5%, 34.6%, 26.0% and 7.1%, respectively, in sensitivity analyses).</p><p><strong>Conclusions: </strong>By age 18, an estimated 1 in 4 children are identified by CSC as needing support at some point. Government should monitor the cumulative incidence of ever receiving CSC support with a view to addressing upstream health and social determinants.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"10 1","pages":"2454"},"PeriodicalIF":1.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimum elements for reporting a multi-jurisdiction feasibility assessment of algorithms based on routinely collected health data: Health Data Research Network Canada recommendations.","authors":"Naomi C Hamm, Sharon Bartholomew, Yinshan Zhao, Sandra Peterson, Saeed Al-Azazi, Kimberlyn McGrail, Lisa M Lix","doi":"10.23889/ijpds.v10i2.2466","DOIUrl":"10.23889/ijpds.v10i2.2466","url":null,"abstract":"<p><strong>Background: </strong>Research and surveillance using routinely collected health data rely on algorithms or definitions to ascertain disease cases or health measures. Whenever algorithm validation studies are not possible due to the unavailability of a reference standard, algorithm feasibility studies can be used to create and assess algorithms for use in more than one population or jurisdiction. Publication of the methods used to conduct feasibility studies is critical for reproducibility and transparency. Existing guidelines applicable to feasibility studies include the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and REporting of studies Conducted using Observational Routinely collected health Data (RECORD) guidelines. These guidelines may benefit from additional elements that capture aspects particular to multi-jurisdiction algorithm feasibility studies and ensure their reproducibility. The aim of this paper is to identify the minimum elements for reporting feasibility studies to ensure reproducibility and transparency.</p><p><strong>Methods: </strong>A subcommittee of four individuals with expertise in routinely collected health data, multi-jurisdiction health research, and algorithm development and implementation was formed from Health Data Research Network (HDRN) Canada's Algorithms and Harmonized Data Working Group (AHD-WG). The subcommittee reviewed items within the STROBE and RECORD guidelines and evaluated these items against published feasibility studies. Items to ensure transparent reporting of feasibility studies not contained within STROBE or RECORD guidelines were identified through consensus by subcommittee members using the Nominal Group Technique. The AHD-WG reviewed and approved these additional recommended elements.</p><p><strong>Results: </strong>Eleven new recommended elements were identified: one element for the title and abstract, one for the introduction, five for the methods, and four for the results sections. Recommended elements primarily addressed reporting jurisdictional data variabilities, data harmonization methods, and algorithm implementation techniques.</p><p><strong>Significance: </strong>Implementation of these recommended elements, alongside the RECORD guidelines, is intended to encourage consistent publication of methods that support reproducibility, as well as increase comparability of algorithms and their use in national and international studies.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"10 2","pages":"2466"},"PeriodicalIF":1.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inequalities in overcrowding in households with children in an ethnically diverse urban population: a cross-sectional study using linked health and housing records.","authors":"Marta Wilk, Gill Harper, Silvia Liverani, Nicola Firman, Paul Simon, Carol Dezateux","doi":"10.23889/ijpds.v10i2.2408","DOIUrl":"10.23889/ijpds.v10i2.2408","url":null,"abstract":"<p><strong>Introduction: </strong>Household overcrowding is an important determinant of health and is associated with adverse child health, educational and social outcomes.</p><p><strong>Objectives: </strong>We aimed to determine whether households with dependent children were more likely to be overcrowded after taking into account household ethnicity and housing tenure in an urban, ethnically diverse, and disadvantaged London population by pseudonymously linking health and property data.</p><p><strong>Methods: </strong>We used pseudonymised Unique Property Reference Numbers to link electronic health records to Energy Performance Certificate property data in north-east London and identified 332,473 households comprising 1,093,047 people. Our primary outcomes were overcrowding measures based on a bedroom standard and a space standard (space per person; m²). We examined household level associations of overcrowding with presence of children in the household before and after adjusting for household ethnicity and tenure. We used multivariable logistic regression to estimate the adjusted odds (aOR) and 95% Confidence Intervals (CI) of bedroom standard overcrowding and linear regression to estimate effects (95% CI) on space per person.</p><p><strong>Results: </strong>Overall, 42.8% (142,401/332,473) of households included children, 54.5% were of White household ethnicity, and 58.4% in private or social rented accommodation. 22.5% (32,075/142,401) and 45.9% (65,388/142,401) of households with children were overcrowded by the bedroom and space standards respectively compared with 4.7% (8,953/190,072) and 9.6% (18,229/190,072) without children. After adjusting for household ethnicity and housing tenure, households with children were more likely to be overcrowded (aOR [95% CI] 5.54 [5.40-5.68] and had 22.61m² (95%CI: -22.75,-22.46) less space per person than those without children.</p><p><strong>Conclusions: </strong>Up-to-date estimates of household overcrowding measured by bedroom and space standards can be derived from linked housing and health records. Our findings highlight the inequalities in overcrowding experienced by households with children and enable future work using linked data to evaluate impacts of overcrowding on children's health.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"10 2","pages":"2408"},"PeriodicalIF":1.6,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Resource Profile: The Disability Statistics - Estimates Database (DS-E Database). An innovative database of internationally comparable statistics on disability inequalities.","authors":"Bradley Carpenter, Sureshkumar Kamalakannan, Kaviyarasan Patchaiappan, Katherine Theiss, Jaclyn Yap, Jill Hanass-Hancock, Gvs Murthy, Monica Pinilla-Roncancio, Minerva Rivas Velarde, Sophie Mitra","doi":"10.23889/ijpds.v8i6.2478","DOIUrl":"10.23889/ijpds.v8i6.2478","url":null,"abstract":"<p><strong>Introduction: </strong>The Disability Statistics (DS) Database provides internationally comparable statistics to monitor the rights of persons with disabilities. The Disability Statistics - Estimates (DS-E) Database includes national and subnational descriptive statistics based on the analysis and disaggregation of national population and housing censuses and household surveys. The database can inform policies and programs to advance the rights of persons with disabilities.</p><p><strong>Methods: </strong>As of 2024, the DS-E Database includes estimates for 29 indicators providing information on the prevalence of disability and associations with education, personal activities, health, standards of living, insecurity, and multidimensional poverty. Estimates are based on 53 national datasets, including 23 population and housing censuses and 30 household surveys for 40 countries. The results were disaggregated by type and severity for adults and population subgroups (women, men, rural and urban residents, age groups 15 to 29, 30 to 44, 45 to 64, 65 and older). Estimates are also available at the first subnational level for all countries and at the second subnational level for 17 countries.</p><p><strong>Results: </strong>At the time of publication, the DS-E Database includes 40 countries and 6,584 subnational locations, with more than 4.3 million estimates of indicators by disability status for adults and population subgroups. Results are in an interactive platform and in downloadable tables where both means and standard errors are available. The DS-E Database results indicate consistent inequalities within and across countries that show that persons with disabilities are more likely to experience deprivations and multidimensional poverty.</p><p><strong>Conclusion: </strong>The DS-E Database provides statistics on the disparities people with disabilities experience, which can be used to support advocacy for disability-inclusive policy and practice. It provides statistics on outcomes such as education, health, employment. Outcomes can be matched with environmental, service delivery and other datasets to provide insights into, for example, where people with disabilities are left behind and where services are needed.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 6","pages":"2478"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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.","authors":"Bradley Carpenter, Sureshkumar Kamalakannan, Pavani Saikam, David Vicente Alvarez, Jill Hanass-Hancock, Gvs Murthy, Monica Pinilla-Roncancio, Minerva Rivas Velarde, Douglas Teodoro, Sophie Mitra","doi":"10.23889/ijpds.v8i6.2477","DOIUrl":"10.23889/ijpds.v8i6.2477","url":null,"abstract":"<p><strong>Introduction: </strong>The 2030 Sustainable Development Agenda and the United Nations Convention on the Rights of Persons with Disabilities (CRPD) aspire to leave no one behind and call for the inclusion of persons with disabilities in all spheres of life. To monitor this goal of inclusion, CRPD's Article 31 requires state parties to collect data about the situation of persons with disabilities. The Disability Statistics - Questionnaire Review Database (DS-QR Database) reports on whether population and housing censuses and household surveys include internationally recommended disability questions for adults ages 15 and older.</p><p><strong>Methods: </strong>The Disability Data Initiative (DDI), an international consortium of researchers, regularly retrieves and analyses a list of surveys and censuses from international catalogs, libraries and websites of national statistics offices. Questionnaires are reviewed to identify if they include internationally recommended questions on functional difficulties (e.g. difficulty seeing), more specifically (i) the Washington Group Short Set (WG-SS) or (ii) questions that meet at least the United Nations 2017 guidelines for disability measurement in censuses (other functional difficulty questions thereafter).</p><p><strong>Results: </strong>The DS-QR Database includes the review results for the questionnaires of 3027 population censuses and surveys from 199 countries and territories collected from 2009 to 2023. The review has information on whether each dataset has the WG-SS or other functional difficulty questions and overall results per country, region, type of dataset and over time.</p><p><strong>Conclusion: </strong>By identifying countries that collect internationally comparable disability data, the DS-QR Database can help researchers, policymakers and advocates determine whether countries fulfill their obligations as per CRPD Article 31. It can also assist in identifying which datasets use functional difficulty questions and can be used to research and monitor disability rights over time and across countries. The DS-QR Database is in a Supplementary file and will be accessible on a website upon publication of this article.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 6","pages":"2477"},"PeriodicalIF":1.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neonates With In-Utero SSRI Exposure (NeoWISE): a retrospective cohort study examining the effect of newborn feeding method on newborn withdrawal.","authors":"Christina Cantin, Wenbin Li, Erna Snelgrove-Clarke, Daniel Corsi, Cindy-Lee Dennis, Amanda Ross-White, Susan Brogly, Laura Gaudet","doi":"10.23889/ijpds.v9i2.2458","DOIUrl":"10.23889/ijpds.v9i2.2458","url":null,"abstract":"<p><strong>Introduction: </strong>Up to 30% of newborns with in-utero selective serotonin reuptake inhibitor (SSRI) exposure experience withdrawal symptoms. The impact of newborn feeding method on alleviating withdrawal has not been investigated. We examined the effect of newborn feeding method (breastfeeding versus formula) among a cohort of <b>Neo</b>nates <b>W</b>ith <b>I</b>n-utero <b>S</b>SRI <b>E</b>xposure (NeoWISE).</p><p><strong>Methods: </strong>This population-based retrospective cohort study included newborns born in Ontario hospitals between April 1, 2012, and March 31, 2020 to Ontario Drug Benefit Program beneficiaries who filled at least one SSRI prenatal prescription. Linked administrative health and registry data were used. Method of newborn feeding was available from birth to hospital discharge. The primary outcome was newborn withdrawal. The secondary outcome was transfer to the Neonatal Intensive Care Unit (NICU). Adjusted risk ratios (adjRR) in breast- versus formula-fed newborns and our outcomes were estimated using generalized linear models. Propensity scores based on antepartum and intrapartum characteristics and inverse probability of treatment weighting were used to balance differences in maternal-newborn characteristics by treatment.</p><p><strong>Results: </strong>Overall, 5,079 newborns were included in the NeoWISE Cohort, with 3,321 (65.4%) exclusively breastfeeding from birth to hospital discharge. Among the breastfed newborns, 50 (1.5%) had neonatal withdrawal versus 41 (2.3%) in the formula-fed newborns. There was no difference in risk of withdrawal in breast versus formula-fed newborns (adjRR 0.86, 95% CI 0.56, 1.34). Breastfed newborns had a reduced risk of transfer to the NICU compared to formula-fed newborns (adjRR 0.80, 95% CI 0.66, 0.97); however, this finding did not persist in sensitivity analysis.</p><p><strong>Conclusion: </strong>The rate of newborn withdrawal was low in this cohort of SSRI-exposed neonates and was not associated with feeding method in the hospital. The results of this study inform shared decision-making around newborn feeding for perinatal women who take SSRI medications.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 2","pages":"2458"},"PeriodicalIF":1.6,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A maternal and child health administrative cohort in Scotland: the utility of linked administrative data for understanding early years' outcomes and inequalities.","authors":"Paul Henery, Ruth Dundas, Srinivasa Vittal Katikireddi, Alastair H Leyland, Lynda Fenton, Sonya Scott, Claire Cameron, Anna Pearce","doi":"10.23889/ijpds.v9i2.2402","DOIUrl":"10.23889/ijpds.v9i2.2402","url":null,"abstract":"<p><strong>Introduction: </strong>The early years are considered one of the most impactful points in the life course to intervene to improve population health and reduce health inequalities because, for example, both ill health and social disadvantage can track into adulthood. Scotland's outstanding systems for data linkage offer untapped potential to further our understanding of when and why inequalities in child health, development and wellbeing emerge. This understanding is vital for the consideration of policy options for their reduction.</p><p><strong>Methods: </strong>Birth registrations, hospital episodes, dispensed community prescriptions, child health reviews and immunisation records were linked for 198,483 mother-child pairs for babies born in Scotland from October 2009 to the end of March 2013, followed up until April 2018 (average age 6 years).</p><p><strong>Results: </strong>Outcomes include birthweight and newborn health, dispensed prescriptions for mental health medications, tobacco smoke exposure, infant feeding, immunisations, hospitalisation for unintentional injuries, socio-emotional, cognitive and motor development, and overweight and obesity. Several measures are repeated throughout childhood allowing examination of timing, change and persistence. Socio-economic circumstances (SECs) include neighbourhood deprivation, relationship status of the parents, and occupational status. Descriptive analyses highlight large inequalities across all outcomes. Inequalities are greater when measured by family-level as opposed to area-level, aspects of socio-economic circumstances and for persistent or more severe outcomes. For example, 41.4% of the most disadvantaged children (living with a lone, economically inactive mother in the most deprived fifth of areas) were exposed to tobacco smoke in utero and in infancy/toddlerhood compared to <1% in the least disadvantaged children (living with a married, managerial/professional mother in the least deprived quintile of areas).</p><p><strong>Conclusion: </strong>This novel linkage provides a longitudinal picture of health throughout the early years and how this varies according to family- and area-level measures of SECs. Future linkages could include other family members (e.g. siblings, grandmothers) and other sectors (e.g. education, social care). The creation of additional cohorts would allow for long-term and efficient evaluation of policies as natural experiments.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 2","pages":"2402"},"PeriodicalIF":1.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"I-SIRch: AI-powered concept annotation tool for equitable extraction and analysis of safety insights from maternity investigations.","authors":"Mohit Kumar Singh, Georgina Cosma, Patrick Waterson, Jonathan Back, Gyuchan Thomas Jun","doi":"10.23889/ijpds.v9i2.2439","DOIUrl":"https://doi.org/10.23889/ijpds.v9i2.2439","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Maternity care is a complex system involving treatments and interactions between patients, healthcare providers, and the care environment. To enhance patient safety and outcomes, it is crucial to understand the human factors (e.g. individuals' decisions, local facilities) influencing healthcare. However, most current tools for analysing healthcare data focus only on biomedical concepts (e.g. health conditions, procedures and tests), overlooking the importance of human factors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a new approach called I-SIRch, using artificial intelligence to automatically identify and label human factors concepts in maternity investigation reports describing adverse maternity incidents produced by England's Healthcare Safety Investigation Branch (HSIB). These incident investigation reports aim to identify opportunities for learning and improving maternal safety across the entire healthcare system. Unlike existing clinical annotation tools that extract solely biomedical insights, I-SIRch is uniquely designed to capture the socio-technical dimensions of patient safety incidents. This innovation enables a more comprehensive analysis of the complex systemic issues underlying adverse events in maternity care, providing insights that were previously difficult to obtain at scale. Importantly, I-SIRch employs a hybrid approach, incorporating human expertise to validate and refine the AI-generated annotations, ensuring the highest quality of analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;I-SIRch was trained using real data and tested on both real and synthetic data to evaluate its performance in identifying human factors concepts. When applied to real reports, the model achieved a high level of accuracy, correctly identifying relevant concepts in 90% of the sentences from 97 reports (Balanced Accuracy of 90% ± 18% (Recall 93% ± 18%, Precision 87% ± 34%, F-score 96% ± 10%). Applying I-SIRch to analyse these reports revealed that certain human factors disproportionately affected mothers from different ethnic groups. In particular, gaps in risk assessment were more prevalent for minority mothers, whilst communication issues were common across all groups but potentially more for minorities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;Our work demonstrates the potential of using automated tools to identify human factors concepts in maternity incident investigation reports, rather than focusing solely on biomedical concepts. This approach opens up new possibilities for understanding the complex interplay between social, technical and organisational factors influencing maternal safety and population health outcomes. By taking a more comprehensive view of maternal healthcare delivery, we can develop targeted interventions to address disparities and improve maternal outcomes. Targeted interventions to address these disparities could include culturally sensitive risk assessment protocols, enhanced language support, a","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 2","pages":"2439"},"PeriodicalIF":1.6,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11986904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secondary use of routinely collected administrative health data for epidemiologic research: Answering research questions using data collected for a different purpose.","authors":"Scott D Emerson, Taylor McLinden, Paul Sereda, Amanda M Yonkman, Jason Trigg, Sandra Peterson, Robert S Hogg, Kate A Salters, Viviane D Lima, Rolando Barrios","doi":"10.23889/ijpds.v9i1.2407","DOIUrl":"https://doi.org/10.23889/ijpds.v9i1.2407","url":null,"abstract":"<p><p>The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) <i>Know the data and their primary use</i> (understand their scope and limitations); 2) <i>Understand classification and coding systems</i> (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) <i>Transform data into meaningful forms</i> (process data and apply identification algorithms, when necessary); 4) <i>Recognize the importance of validity when defining analytic variables</i> (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 1","pages":"2407"},"PeriodicalIF":1.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A statewide system for maternal-infant linked longitudinal surveillance: Indiana's model for improving maternal and child health.","authors":"Jill Inderstrodt, Daniel P Riggins, Acatia Greenwell, John P Price, Jennifer L Williams, Eden Bezy, Allison Forkner, Elizabeth Bowman, Summer D Miller, Titus K L Schleyer, Shaun J Grannis, Brian E Dixon","doi":"10.23889/ijpds.v9i2.2395","DOIUrl":"https://doi.org/10.23889/ijpds.v9i2.2395","url":null,"abstract":"<p><p>Indiana, located in the Midwest region of the United States, faces significant challenges with respect to health, especially maternal and child health (MCH). These challenges include high rates of stillbirth, neonatal abstinence syndrome (NAS) and congenital syphilis (CS). Not only are these often-fatal conditions underreported, but it can also be difficult to track them longitudinally, as mothers and infants are not routinely linked through electronic health records (EHRs). This paper describes the process, structure and planned outcomes of a partnership between Indiana University, Regenstrief Institute and public health partners in support of the U.S. Centers for Disease Control and Prevention's Pregnant People-Infant Linked Longitudinal Surveillance (PILLARS) program. Together, academic, clinical and public health organisations are collaboratively developing an infrastructure and deploying novel methods to surveil stillbirth, CS and NAS longitudinally. The infrastructure includes: (a) deploying deterministic and probabilistic algorithms to link mothers and their infants using multiple, linked data sources; (b) creating and maintaining a registry of maternal-infant dyads; (c) using the registry to perform longitudinal surveillance in collaboration with Indiana public health authorities on stillbirth, NAS and CS and (d) translating information from surveillance activities into action by collaborating with public health and community-based organisations to improve and implement prevention activities in vulnerable Indiana communities. Our long-term goal is to improve outcomes for these conditions and other priority MCH outcomes by expanding our work to additional MCH use cases.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 2","pages":"2395"},"PeriodicalIF":1.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12076274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}