International Journal of Population Data Science最新文献

{"title":"Record linkage as a vital key player for the COVID-19 syndemic - The call for legal harmonization to overcome research challenges.","authors":"Julia Nadine Doetsch, Eero Kajantie, Vasco Dias, Marit S Indredavik, Randi Kallar Devold, Raquel Teixeira, Jarkko Reittu, Henrique Barros","doi":"10.23889/ijpds.v8i1.2131","DOIUrl":"https://doi.org/10.23889/ijpds.v8i1.2131","url":null,"abstract":"Key messages:\u0000\u0000Chronicity and social context influence COVID-19 risk highlighting its syndemic dimension\u0000Record Linkage advances knowledge on COVID-19, associated chronic diseases, and social indicators\u0000Further harmonization of data protection requirements for scientific research may create multilevel public health measures\u0000As a multidimensional tool, it optimizes integrated strategies and fosters solidarity on Health in All Policies (HiAP)","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 1","pages":"2131"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/8f/ijpds-08-2131.PMC10476633.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10225315","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":"Developing a linked electronic health record derived data platform to support research into healthy ageing.","authors":"Nadine E Andrew,&nbsp;Richard Beare,&nbsp;Tanya Ravipati,&nbsp;Emily Parker,&nbsp;David Snowdon,&nbsp;Kim Naude,&nbsp;Velandai Srikanth","doi":"10.23889/ijpds.v8i1.2129","DOIUrl":"https://doi.org/10.23889/ijpds.v8i1.2129","url":null,"abstract":"<p><strong>Introduction: </strong>Digitalisation of Electronic Health Record (EHR) data has created unique opportunities for research. However, these data are routinely collected for operational purposes and so are not curated to the standard required for research. Harnessing such routine data at large scale allows efficient and long-term epidemiological and health services research.</p><p><strong>Objectives: </strong>To describe the establishment a linked EHR derived data platform in the National Centre for Healthy Ageing, Melbourne, Australia, aimed at enabling research targeting national health priority areas in ageing.</p><p><strong>Methods: </strong>Our approach incorporated: data validation, curation and warehousing to ensure quality and completeness; end-user engagement and consensus on the platform content; implementation of an artificial intelligence (AI) pipeline for extraction of text-based data items; early consumer involvement; and implementation of routine collection of patient reported outcome measures, in a multisite public health service.</p><p><strong>Results: </strong>Data for a cohort of >800,000 patients collected over a 10-year period have been curated within the platform's research data warehouse. So far 117 items have been identified as suitable for inclusion, from 11 research relevant datasets held within the health service EHR systems. Data access, extraction and release processes, guided by the Five Safes Framework, are being tested through project use-cases. A natural language processing (NLP) pipeline has been implemented and a framework for the routine collection and incorporation of patient reported outcome measures developed.</p><p><strong>Conclusions: </strong>We highlight the importance of establishing comprehensive processes for the foundations of a data platform utilising routine data not collected for research purposes. These robust foundations will facilitate future expansion through linkages to other datasets for the efficient and cost-effective study of health related to ageing at a large scale.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 1","pages":"2129"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/f0/ijpds-08-2129.PMC10476553.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10170165","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":"Study protocol: Generation Victoria (GenV) special care nursery registry.","authors":"Jing Wang,&nbsp;Yanhong Jessika Hu,&nbsp;Lana Collins,&nbsp;Anna Fedyukova,&nbsp;Varnika Aggarwal,&nbsp;Fiona Mensah,&nbsp;Jeanie L Y Cheong,&nbsp;Melissa Wake","doi":"10.23889/ijpds.v8i1.2139","DOIUrl":"https://doi.org/10.23889/ijpds.v8i1.2139","url":null,"abstract":"<p><strong>Introduction: </strong>Newborn babies who require admission for specialist care can experience immediate and sometimes lasting impacts. For babies admitted to special care nurseries (SCN), there is no dataset comparable to that of the Australian and New Zealand Neonatal Network (ANZNN), which has helped improve the quality and consistency of neonatal intensive care through standardised data collection.</p><p><strong>Objectives: </strong>We aim to establish a proof-of-concept, Victoria-wide registry of babies admitted to SCN, embedded within the whole-of-Victoria Generation Victoria (GenV) cohort.</p><p><strong>Methods: </strong>This prototype registry is a depth sub-cohort nested within GenV, targeting all babies born in Victoria from Oct-2021 to Oct-2023. Infants admitted to SCN are eligible. The minimum dataset will be harmonised with ANZNN for common constructs but also include SCN-only items, and will cover maternal, antenatal, newborn, respiratory/respiratory support, cardiac, infection, nutrition, feeding, cerebral and other items. As well as the dataset, this protocol outlines the anticipated cohort, timeline for this registry, and how this will serve as a resource for longitudinal research through its integration with the GenV longitudinal cohort and linked datasets.</p><p><strong>Conclusion: </strong>The registry will provide the opportunity to better understand the health and future outcomes of the large and growing cohort of children that require specialist care after birth. The data would generate translatable evidence and could lay the groundwork for a stand-alone ongoing clinical quality registry post-GenV.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 1","pages":"2139"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e4/6a/ijpds-08-2139.PMC10476699.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10225321","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 scoping review of preprocessing methods for unstructured text data to assess data quality.","authors":"Marcello Nesca, Alan Katz, Carson K Leung, Lisa M Lix","doi":"10.23889/ijpds.v6i1.1757","DOIUrl":"10.23889/ijpds.v6i1.1757","url":null,"abstract":"<p><strong>Introduction: </strong>Unstructured text data (UTD) are increasingly found in many databases that were never intended to be used for research, including electronic medical record (EMR) databases. Data quality can impact the usefulness of UTD for research. UTD are typically prepared for analysis (i.e., preprocessed) and analyzed using natural language processing (NLP) techniques. Different NLP methods are used to preprocess UTD and may affect data quality.</p><p><strong>Objective: </strong>Our objective was to systematically document current research and practices about NLP preprocessing methods to describe or improve the quality of UTD, including UTD found in EMR databases.</p><p><strong>Methods: </strong>A scoping review was undertaken of peer-reviewed studies published between December 2002 and January 2021. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature relevant to the study objective. Information extracted from the studies included article characteristics (i.e., year of publication, journal discipline), data characteristics, types of preprocessing methods, and data quality topics. Study data were presented using a narrative synthesis.</p><p><strong>Results: </strong>A total of 41 articles were included in the scoping review; over 50% were published between 2016 and 2021. Almost 20% of the articles were published in health science journals. Common preprocessing methods included removal of extraneous text elements such as stop words, punctuation, and numbers, word tokenization, and parts of speech tagging. Data quality topics for articles about EMR data included misspelled words, security (i.e., de-identification), word variability, sources of noise, quality of annotations, and ambiguity of abbreviations.</p><p><strong>Conclusions: </strong>Multiple NLP techniques have been proposed to preprocess UTD, with some differences in techniques applied to EMR data. There are similarities in the data quality dimensions used to characterize structured data and UTD. While a few general-purpose measures of data quality that do not require external data; most of these focus on the measurement of noise.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"7 1","pages":"1757"},"PeriodicalIF":1.6,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a2/2f/ijpds-07-1757.PMC10476151.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10522446","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":"Evaluating pregnancy reporting in Siaya Health and Demographic Surveillance System through record linkage with ANC clinics.","authors":"Hallie Eilerts-Spinelli, Julio Romero Prieto, Julie Ambia, Sammy Khagayi, Chodziwadziwa Kabudula, Jeffrey W Eaton, Georges Reniers","doi":"10.23889/ijpds.v7i4.1762","DOIUrl":"10.23889/ijpds.v7i4.1762","url":null,"abstract":"<p><strong>Introduction: </strong>Health and Demographic Surveillance Systems (HDSS) are important sources of population health data in sub-Saharan Africa, but the recording of pregnancies, pregnancy outcomes, and early mortality is often incomplete.</p><p><strong>Objective: </strong>This study assessed HDSS pregnancy reporting completeness and identified predictors of unreported pregnancies that likely ended in adverse outcomes.</p><p><strong>Methods: </strong>The analysis utilized individually-linked HDSS and antenatal care (ANC) data from Siaya, Kenya for pregnancies in 2018-2020. We cross-checked ANC records with HDSS pregnancy registrations and outcomes. Pregnancies observed in the ANC that were missing reports in the HDSS despite a data collection round following the expected delivery date were identified as likely adverse outcomes, and we investigated the characteristics of such individuals. Clinical data were used to investigate the timing of HDSS pregnancy registration relative to care seeking and gestational age, and examine misclassification of miscarriages and stillbirths.</p><p><strong>Results: </strong>From an analytical sample of 2,475 pregnancies observed in the ANC registers, 46% had pregnancy registrations in the HDSS, and 89% had retrospectively reported pregnancy outcomes. 1% of registered pregnancies were missing outcomes, compared to 10% of those lacking registration. Registered pregnancies had higher rates of stillbirth and perinatal mortality than those lacking registration. In 77% of cases, women accessed ANC prior to registering the pregnancy in the HDSS. Half of reported miscarriages were misclassified stillbirths. We identified 141 unreported pregnancies that likely ended in adverse outcomes. Such cases were more common among those who visited ANC clinics during the first trimester, made fewer overall visits, were HIV-positive, and outside of formal union.</p><p><strong>Conclusions: </strong>Record linkage with ANC clinics revealed pregnancy underreporting in HDSS, resulting in biased measurement of perinatal mortality. Integrating records of ANC usage into routine data collection can augment HDSS pregnancy surveillance and improve monitoring of adverse pregnancy outcomes and early mortality.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"7 4","pages":"1762"},"PeriodicalIF":1.6,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7b/84/ijpds-07-1762.PMC10167572.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9845524","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":"Anticholinergic burden in middle and older age is associated with reduced cognitive function, but not with brain atrophy","authors":"J. Mur, R. Marioni, T. Russ, G. Muniz-Terrera, S. R. Cox","doi":"10.1101/2022.09.04.22279576","DOIUrl":"https://doi.org/10.1101/2022.09.04.22279576","url":null,"abstract":"Anticholinergic drugs block muscarinic receptors in the body. They are commonly prescribed for a variety of indications and their use has previously been associated with dementia and cognitive decline. In UK Biobank participants with linked health-care records (n=163,043, aged 40-71 at baseline), for about 17,000 of which MRI data was available, we calculated the total anticholinergic drug burden according to 15 different anticholinergic scales and due to different classes of drugs. We then used linear regression to explore the associations between anticholinergic burden and various measures of cognition and structural MRI, including general cognitive ability, 9 separate cognitive domains, brain atrophy, volumes of 68 cortical and 14 subcortical areas, and fractional anisotropy and median diffusivity of 25 white-matter tracts. Anticholinergic burden was modestly associated with poorer cognition across most anticholinergic scales and cognitive tests (7/9 FDR-adjusted significant associations, standardised betas ({beta}) range: -0.039, -0.003). When using the anticholinergic scale exhibiting the strongest association with cognitive functions, anticholinergic burden due to most classes of drugs exhibited negative associations with cognitive function, with {beta}-lactam antibiotics ({beta}=-0.035, pFDR<0.001) and opioids ({beta}=-0.026, pFDR<0.001) exhibiting the strongest effects. Anticholinergic burden was not associated with any measure of brain macro- or microstructure (pFDR>0.08). Anticholinergic burden is weakly associated with poorer cognition, but there is little evidence for associations with brain structure. Future studies might focus more broadly on polypharmacy or more narrowly on distinct drug classes, instead of using purported anticholinergic action to study the effects of drugs on cognitive ability.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62350694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defying Expectations: Can We Identify Neighbourhoods with “Other Than Expected” Developmental Outcomes?","authors":"E. Duku, B. Forer, Molly M. Pottruff, M. Guhn, M. Janus","doi":"10.23889/ijpds.v7i3.1814","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1814","url":null,"abstract":"ObjectivesTo contribute to the evidence on the association between neighbourhood-level child development in Kindergarten and neighbourhood SES, our objective was to quantify the sociodemographic and child development characteristics of the neighbourhoods that “defy expectations”: high SES neighbourhoods with much-worse-than-expected child outcomes, and low SES neighbourhoods with much-better-than-expected child outcomes. \u0000ApproachUsing exploratory and model-based Latent Profile Analysis (LPA), we identified homogenous profile groups of 2038 customized Canadian neighbourhoods using ten SES indicators. We identified the most parsimonious number of profile groups and validated and characterized the derived groups of neighbourhoods using neighbourhood and aggregated child characteristics. Next, as our outcome, we created quartile groups for developmental vulnerability risk, measured with the Early Development Instrument (EDI), to match the number of derived neighbourhood profile groups. Last, we used contingency table analysis to identify neighbourhoods that defy expectations, and then characterized these neighbourhoods using descriptive statistics and correlational analysis. \u0000ResultsThe LPA identified four neighbourhood SES groups which we labelled “Low” (31.6%), “Low-moderate” (12.7%), “High-moderate” (38.4%) and “High” (17.4%). These four SES groups were cross-tabulated with quartile groups of EDI vulnerability risk. Inspection of the resulting 4-by-4 contingency table showed that within the “Low” SES profile group 57 (8.9%) neighbourhoods had much-better-than-expected developmental vulnerability risk. Conversely, within the “High” SES profile group, 12 (3.4%) neighbourhood had much-worse-than-expected developmental vulnerability risk. Additionally, these analyses identified large provincial differences in the proportion of neighbourhoods that defy expectation. In 12 provinces and territories in the study, the proportion of neighbourhoods that defied expectations within each province ranged from zero to 50%. \u0000ConclusionThe identification of neighbourhoods that defy expectations contributes to our understanding of neighbourhood factors influencing child development. Using mixed-methods approaches, these neighbourhoods can be compared to nearby neighbourhoods from the same SES profile group that do not defy expectations, in an effort to identify contextual factors that differentiate them.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45777702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A population-based approach to assessing diabetes management during COVID-19: insights from population data in Ontario, Canada.","authors":"W. Wodchis, Yuqing Bai, L. Mondor, R. Hall","doi":"10.23889/ijpds.v7i3.2091","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2091","url":null,"abstract":"ObjectiveDiabetes management requires ongoing monitoring of diabetes care from primary care, specialist care and laboratory testing. COVID-19 led to changes in access to in-person care. The purpose of this research was to assess changes in the management of diabetes during the COVID-19 pandemic using population-linked datasets and population segmentation. \u0000ApproachWe identified over 1.4 Million Ontarians with diabetes (approximately 10% of the population) with valid health insurance as of April 1, 2019 and April 1, 2020. We measured 11 indicators of diabetes management including laboratory testing for HbA1c and LDL (highlighted in this abstract). With screening indicators, we examined changes in the proportion of the population up-to-date with screening at the end of each fiscal year (March 31, 2020 and March 31, 2021) overall and according to population segments created using linked health data from primary care, home care, long term care and hospitals. \u0000ResultsOverall screening rates that required laboratory testing for HbA1c and LDL fell substantially from 54% to 40% and 68% to 59% overall. Comparing across population segments, residents in Long Term Care facilities had the smallest changes in screening rates; individuals with low, medium and high complexity chronic conditions and end-of-life conditions had the largest changes; maternity, cancer, mental health and frail populations were in the middle. Differences according the primary care enrolment models (capitation vs fee-for-service) were relatively minor but patients who were not rostered to a primary care physician had the largest reductions in laboratory screening. Results for all 11 indicators will be shared in the presentation. \u0000ConclusionCOVID-19 was associated with substantial reductions in laboratory-based diabetes screening. Poor diabetes management is one of the strongest risk-factors for adverse outcomes in COVID-19. Rates of diabetes management declined most for at risk patient populations amplifying the need to differentially connect with patients to ensure ongoing care during the pandemic.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44036690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-creating a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups and a creative workshop.","authors":"Elizabeth Ford, Kathryn Stanley, M. Rees-Roberts, A. Madzvamuse, Jocelyn W. Armes, Sarah Giles","doi":"10.23889/ijpds.v7i3.2109","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2109","url":null,"abstract":"ObjectivesIn Kent, Surrey and Sussex (KSS), linked health and social care datasets are in set-up phase in NHS integrated care systems (ICS), and governance models for using data for planning and research are under development. This represented an exceptional opportunity to consult with KSS citizens and work together to identify how ICSs in KSS can secure a social licence for data-linkage and data uses. \u0000MethodsWe held online deliberative discussion focus groups asking KSS citizens to discuss the perceived benefits and risks of data-linkage for planning and research; to describe safeguards they expected around the data, and to describe how the public should be involved in, and communicated with, regarding governance and uses of datasets. We held one creative workshop in which participants artistically depicted their support or concerns around data. \u0000Results79 KSS citizens took part in 5 focus groups, and 7 participants attended the creative workshop. There was widespread support for data-linkage to improve efficiency of services and information flows, with the expectation that this would improve patient experience. Proposed ICS governance models were acceptable, but participants identified four key values to ensure appropriate use: acknowledging experience of stigma and discrimination; public voices being heard; holding people to account; and keeping data trails and audits. Participants gave a range of suggestions for ensuring public involvement and communication would be accessible and reach a diverse audience, such as using community champions to ensure a range of contributors, using plain language, giving concise information, building trust through mutually respectful relationships, and valuing public contributions through appropriate payment. \u0000ConclusionSocial licence theory describes expectations that organisations go beyond requirements of formal regulation and ensure transparent values of reciprocity, non-exploitation and service to the public good. Following findings from this project, ICSs in KSS are now in a good position to deliver social licence values, together with a strong public voice, to inform and determine governance arrangements for linked datasets in the region.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44237369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AD|ARC: Construction of a research ready dataset to better understand farmers and farming households.","authors":"J. Hampton, Nick Webster, Sophie Jordan, S. Morrison-Rees, O. Bateson, James Watson, S. McFarlane, Alastair McAlpine, L. Cavin","doi":"10.23889/ijpds.v7i3.1905","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1905","url":null,"abstract":"ObjectivesThe AD|ARC Administrative Data: Agriculture Research Collection is an ambitious and original linkage project, bringing together information about farmers and farming households from several sources. When complete, this research-ready dataset will assist in addressing three broad themes: health and well-being, prosperity and resilience, and engagement with agri-environment. \u0000ApproachThe dataset is being constructed from information drawn from survey, census, and administrative sources. Necessarily, this includes working across government departments to ensure comprehensive coverage of farm, business, education, and health data. Similarly, data owners, processors, and researchers are working closely to ensure the resultant dataset meets expectations. Alongside this cross-sectoral aspect, the work is also cross-jurisdictional, with the intention being for the data to capture information about farms, farmers and farming households from across the UK. \u0000ResultsRather than focus on the detail of the substantive research that AD|ARC will enable, this paper discusses some of the challenges and successes of this linkage project to date. Drawing on the experience of the teams from across the UK (England, Northern Ireland, Scotland, and Wales), the first part will discuss challenges faced in linkage of this multi-faceted project, alongside how the population census is being utilised to better understand farming communities, through the identification of both farming households and workers. Secondly, a broader discussion of the challenges and sensitivities of working across government departments and administrations will be presented, alongside ways of working developed to recognise and overcome these. \u0000ConclusionThe AD|ARC project will result in an invaluable resource to better understand the farming community, which in turn will help to better inform policy debate and decision making. Alongside this, the process of creating the dataset has offered opportunities for learning and insight across a range of issues.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48988975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}