International Journal of Population Data Science最新文献

{"title":"Birth Outcomes after Prenatal Exposure to Maternal Stress due to Sudden Loss of Close Relatives: A Nationwide Population-Based Study in the Netherlands.","authors":"Yael Broos, Thijs van den Broek, Jasper Been, Loes Bertens, Anna Nieboer","doi":"10.23889/ijpds.v11i5.3609","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3609","url":null,"abstract":"<p><p>Sudden bereavement has a pronounced effect on the mental health of those left behind, yet less is known about intergenerational health effects if such a shock occurs during a pregnancy. We account for socioeconomic differences that influence both exposure to maternal loss and offspring birth outcomes, providing novel causal evidence on the effects of maternal loss during pregnancy. We employ data on all registered births (2001-2024) from the Dutch Perinatal Registry to obtain pregnancy trajectories and perinatal outcomes (pre-term birth and small-for-gestational age). We link this to data from Statistics Netherlands, including family and cause of death registers. With this linkage, we identify pregnancies that take place around the sudden loss of a mothers' family member. We exploit differences in the timing of maternal losses and compare perinatal outcomes of offspring exposed to a sudden maternal loss in utero (treatment group) and of their counterparts exposed shortly after birth (control group) to shed light on long-term mental health effects of exposure in utero. We are unable to present any results just yet, but will be able to do so at the time of the conference. However, because of various biological pathways that may transfer maternal stress to the fetus, we expect that the group exposed in utero will have worse perinatal outcomes than the group exposed shortly after birth. One of these pathways that causes this hypothesis is the through extensive exposure of cortisol, which may inhibit fetal growth and may active childbirth processes earlier than usual.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3609"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654071","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":"Harmonizing French and German administrative DADS-EDP and SIAB data - A practical guide on how to create comparable data sets on maternal employment.","authors":"Andreas Filser, Pascal Achard, Inga Amend, Corinna Frodermann, Ida Gaede, Sander Wagner","doi":"10.23889/ijpds.v11i5.3717","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3717","url":null,"abstract":"<p><p>Administrative data is increasingly used in social science research, including for studying the labour market consequences of motherhood. Yet despite the growing use of such data within countries, cross-national comparative research remains limited because administrative data require substantial harmonisation before meaningful comparisons are possible. In this paper, we harmonise two rich administrative data sets suitable for comparative research on maternal employment: the French DADS-EDP and the German SIAB. Both data sets are based on social security records, span several decades, include detailed employment histories linked to firm-level data, and are accessible to the scientific community. France and Germany present a compelling case for comparison on maternal employment, given that both countries have followed different policy paths regarding childcare and norms around maternal employment. We illustrate the potential of the harmonised data by showing how maternal employment trajectories evolve over time in France, West Germany, and East Germany. Our results reveal sharp employment declines after childbirth in all three regions, but with significant variation in magnitude and recovery. French mothers experience relatively modest declines, while German mothers, especially in West Germany, face steeper short-term drops and slower recoveries. Over successive cohorts, employment penalties shrink in all three regions, with particularly marked decreases in Germany following the 2007 parental leave reform - notwithstanding continued differences between East and West Germany. These findings highlight the value of harmonised administrative data for understanding maternal employment in a comparative perspective against the background of policy reforms and enduring regional differences.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3717"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654247","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":"Characterising Residential Environments for Children: A Nationwide Linked Data Approach to Mapping Built and Natural Features.","authors":"Jo Davies, Rowena Bailey, Amy Mizen, Dora Pouliou, Rebecca Pedrick-Case, Rich Fry, Gareth Stratton, Lucy Griffiths","doi":"10.23889/ijpds.v11i5.3558","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3558","url":null,"abstract":"<p><strong>Background: </strong>The environments where children live play a critical role in shaping health trajectories, yet comprehensive population-level descriptions of these environments remain limited. Understanding residential environmental characteristics is essential for informing urban planning and public health strategies aimed at creating child-friendly neighbourhoods.</p><p><strong>Methods: </strong>We linked nationwide geographic information systems (GIS) data to residential addresses for children in Wales, enabling detailed characterisation of local environments. Indicators included housing type, garden size, neighbourhood greenness (Enhanced Vegetation Index from Landsat 8 imagery), walkability, access to recreational spaces, and food outlet density. Using these measures, we classified neighbourhoods into distinct \"environmental phenotypes\" representing combinations of built and natural features. Spatial analyses explored variation in these phenotypes across urban and rural areas and by area-level deprivation.</p><p><strong>Results: </strong>Five discrete environmental phenotypes emerged, ranging from high-density housing with limited greenspace and high fast-food outlet density to low-density, walkable neighbourhoods with private gardens and abundant greenspace. These classifications reveal substantial geographic variation and highlight clusters of potentially obesogenic environments.</p><p><strong>Conclusion: </strong>This work demonstrates the feasibility of using linked GIS and administrative data to generate robust, scalable measures of residential environments for children. Such classifications provide a foundation for future research examining environmental influences on health and for guiding policy interventions to promote healthier, child-friendly communities.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3558"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654333","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":"Healthy Controls versus General Controls in Matched Cohort Studies - Comparison from a Post-Stroke Dementia Cohort Study in Ontario, Canada.","authors":"Jiming Fang, Peter Austin, Raed Joundi, Moira Kapral","doi":"10.23889/ijpds.v11i5.3545","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3545","url":null,"abstract":"<p><p>To match disease cohorts with the general population, researchers often exclude individuals with the disease (\"healthy controls\"). Alternatively, they may include those disease-free at baseline who may develop it later (\"general controls\"). Few studies have compared these methods. We examined how control selection affects cause-specific hazard ratios (csHR) for post-stroke dementia among stroke survivors and matched controls. Using Ontario's population-based Discharge Abstract Database (2002-2022), we identified adults hospitalized with stroke. Each stroke survivor was hard-matched 1:1 to residents without prior stroke on age, sex, rural residence, neighborhood deprivation, and vascular comorbidities. Matching was conducted separately for healthy and general control groups. Cause-specific hazard models, accounting for death as a competing risk (from the linked Registered Persons Database), estimated associations between stroke and incident dementia. General controls who subsequently experienced stroke were censored at stroke onset. Of 175,980 stroke survivors, 174,787 (99.3%) and 174,817 (99.3%) were successfully matched to healthy and general controls, respectively. At 20 years of follow-up, the cumulative incidence of dementia among stroke survivors was similar across both match types (0.302 vs. 0.302). However, healthy controls had a higher incidence than general controls (0.249 [95% CI: 0.244-0.253] vs. 0.238 [95% CI: 0.234-0.242]). The csHR comparing strokes with healthy controls (1.694 [95% CI: 1.668-1.720]) was significantly lower than that with general controls (1.759 [95% CI: 1.731-1.787]). Using healthy controls may underestimate post-stroke dementia risk. General controls more accurately represent the population at risk and are recommended for matched cohort studies.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3545"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654338","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":"Severe Maternal Mental Disoders and Adverse Perinatal Outcomes: A Cross-Jursdicational Population Data Linkage study in Australia.","authors":"Amanuel Gebremedhin, Bereket Duko, Damien Foo, Gizachew Tessema, Gavin Pereira","doi":"10.23889/ijpds.v11i5.3565","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3565","url":null,"abstract":"<p><strong>Objective: </strong>To examine the association between severe maternal mental disorders and adverse perinatal outcomes using population-based linked data from Western Australia (WA) and New South Wales (NSW), Australia.</p><p><strong>Approach: </strong>We conducted a population-based retrospective cohort study using perinatal datasets from WA and NSW, including births (n=1.76 million) between 1995 and 2015. Maternal mental health disorders (exposure) were identified using ICD-10-AM diagnosis codes if a primary or additional diagnosis was recorded within one year preceding the index birth. Outcomes evaluated included preterm birth, small for gestational age (SGA), low birth weight (LBW), and perinatal death. A generalised linear model with log link (binomial or Poisson) was employed to examine the associations.</p><p><strong>Results: </strong>The analysed included 3,817 births exposed to severe maternal mental disorders within one year before birth. After adjusting for a range of potential confounders, pregnancies exposed to severe maternal mental disorders in the one year before the birth were associated with increased risks of preterm birth with risk ratios and 95% CI of 1.82 (1.69, 1.97), low birth weight 1.74 (1.60, 1.89), small for gestational age 1.11 (1.03, 1.20) and perinatal death 1.53 (1.17, 2.02). Similar but elevated risk association patterns were observed across severe maternal mental disorder categories, including schizophrenia, bipolar disorder, and substance-induced psychotic disorders and adverse perinatal outcomes.</p><p><strong>Conclusions and implication: </strong>Severe maternal mental disorders pre-pregnancy are associated with increased risk of multiple adverse perinatal outcomes. Linked population data provide critical evidence to inform preconception and perinatal care strategies for women with severe mental illness.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3565"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654356","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":"CBS Microdata.","authors":"T J Slager","doi":"10.23889/ijpds.v11i5.3712","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3712","url":null,"abstract":"<p><p>CBS microdata is a valuable resource that provides researchers secure access to nearly all individual-level data available within Statistics Netherlands (CBS). The data is originated from administrative registers (e.g., Dutch Tax Authority, Personal records database (BRP)) and statistical surveys conducted by CBS. As such, CBS microdata includes information on individuals, households, businesses, and more, making it highly relevant for demographic, sociological and economic research. One of the key strengths of CBS microdata is its ability to link diverse datasets, enabling researchers to connect individuals to households, jobs to individuals, companies to jobs, and many other relationships. Furthermore, data can be linked across time, allowing for in-depth longitudinal studies. Researchers can also upload their own datasets, which can be pseudonymized and linked with CBS microdata to create even richer data analyses. CBS places a high priority on data security. Access to CBS microdata is strictly controlled and granted only to authorized institutions. Universities, scientific organizations, and public policy institutes in EEA countries can request authorization. All research is carried out within a secure CBS microdata environment, with privacy safeguards in place to ensure no individual-level data leaves this environment. Thanks to the vast amount of data, it's detailed level, the ability to link datasets, and robust data security, CBS microdata provides valuable insights into complex societal trends and relationships and therefore plays a vital role in supporting scientific or statistical research. More information can be found here: https://www.cbs.nl/en-gb/our-services/customised-services-microdata/microdata-conducting-your-own-research.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3712"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654406","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":"The nuance of public trust: How personal experiences shape and influence trust in Maritime health data use.","authors":"Amy Grant, Ted McDonald, Samuel A Stewart, Mary-Ann Standing, Elizabeth Lappin, Claire Keenan, Katie Aubrecht, Dorothy Barnard, Marc-André Bouchard, Sarah Bridges","doi":"10.23889/ijpds.v11i5.3541","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3541","url":null,"abstract":"<p><strong>Background: </strong>Provincial data repositories in Canada provide secure access to de-identified administrative health data. Social license is implied, yet local evidence around public support is lacking. This study explored public knowledge and perceptions around health data use and conditions that would support acceptance.</p><p><strong>Methods: </strong>In collaboration with public partners, this qualitative descriptive study involved residents in two Maritime provinces. Focus groups were held, with level of support for health data use cases measured using Fist to Five Voting (a real time 5-point voting activity) before and after group discussion. Transcripts were analyzed using inductive thematic analysis.</p><p><strong>Results: </strong>Seven focus groups were held with 32 participants (75% White; 72% female; 44% urban; 50% 60+). Participants' level of support often shifted after group discussion, increasing for some use cases but decreasing for others, depending on perceived risks and benefits. Participants generally supported data being used by health care practitioners, governments, healthcare facilities/administrators, and university-based researchers to improve the health system or understand disease drivers. Participants were less supportive of research by private companies even if analyzed locally or reported in aggregate, but support increased when they perceived greater public benefit. Support frequently changed after hearing others' narratives, knowledge, and experience.</p><p><strong>Conclusion: </strong>We found broad but conditional support of health data use for research. People's views on acceptable uses/users of health data are shaped by lived realities, and the experiences and perspectives of others. Communication on non-standard uses must acknowledge nuance - and speak directly to the conditions under which support changes.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3541"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654427","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":"Enhanced Depression Phenotyping Using Machine Learning and Electronic Medical Records for Disease Surveillance: A Validation Study in Inpatient Settings.","authors":"Na Lin, Elliot A Martin, Seungwon Lee, Allison Grothman, Na Li, Kamala Adhikari, Kiarash Riazi, Cathy A Eastwood, Danielle A Southern, Hude Quan","doi":"10.23889/ijpds.v11i5.3729","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3729","url":null,"abstract":"<p><strong>Background: </strong>Accurate detection of inpatient depression is essential for surveillance and resource allocation. Traditional International Classification of Diseases (ICD) codes suffer from low sensitivity and delayed availability.</p><p><strong>Objective: </strong>To develop and validate machine learning (ML) phenotyping algorithms for depression using unstructured inpatient clinical notes from electronic medical records (EMRs), compared with ICD-10-CA codes and manual chart review.</p><p><strong>Methods: </strong>This retrospective cohort study included a derivation cohort (N = 3032 adults, Jan-June 2015) and a temporally independent external validation cohort (N = 10659 adults, 2017-2022) from Calgary acute care hospitals. Both cohorts were deterministically linked to DAD and EMR databases. Manual chart reviews defined the reference standard for depression status. We compared ICD-10-CA codes against three EMR-based algorithms: Keyword Search, Concept Model and Document-Concept Model. The latter two employed a supervised ML classifier (XGBoost) utilizing clinical concepts extracted via the cTAKES natural language processing (NLP) pipeline. Primary measures were prevalence, sensitivity, positive predictive value (PPV), specificity, negative predictive value (NPV), and F1 score.</p><p><strong>Results: </strong>In derivation cohort (prevalence: 18.1%), the Concept Model achieved 76.4% sensitivity and 76.7% F1 score, significantly outperforming Keyword Search (F1: 66.7%), Document-Concept Model (F1: 73.7%), and ICD-10-CA codes (sensitivity: 20.0%; F1: 32.6%). In external validation (prevalence: 13.1%), the Concept Model retained robust performance (sensitivity: 92.3%; F1: 80.1%), compared with ICD-10-CA (sensitivity: 6.1%; F1: 11.4%).</p><p><strong>Conclusion: </strong>An EMR-based Concept Model using NLP and ML markedly improves depression case detection over ICD coding in inpatient settings. The methods could potentially facilitate psychiatric surveillance and epidemiological research using real-world clinical data.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3729"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654460","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":"Fuel Poverty Interventions and Healthcare Usage: Insights from Warm Wales.","authors":"Manar Alshams, Laura Bentley, Jerlyn Peh, Giles Greene, Alisha Davies, Ashley Akbari, Claire Newman, Owen Davies, Emma Taylor-Collins, Joanna Dundon","doi":"10.23889/ijpds.v11i5.3525","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3525","url":null,"abstract":"<p><p>Warm Wales (WW) is a community-based programme addressing fuel poverty and its broader impact on health and well-being. Interventions combine tailored energy advice, practical support, and education with wellbeing and social prescribing projects to improve health outcomes. Fuel poverty is closely linked to poorer health, making evaluating such programmes using housing and health linked data crucial. To describe the demographic, socioeconomic, health, and housing characteristics of individuals supported by WW and examine the programme's impact on primary care interactions. Methodology: An observational longitudinal cohort study linking WW service data with routinely collected electronic health record and administrative data in the Secure Anonymised Information Linkage (SAIL) Databank. Descriptive statistics summarised the WW supported population. Difference in Differences (DiD) compared primary care interactions rates between cases and Coarsened Exact Matched controls. WW supported 4,083 individuals living across 1,317 properties (January 2021-December 2023). Most WW-supported properties were older (65% built before 1966 vs 60.7% in Welsh housing stock) and socially rented (62.3% vs 19.2% in Welsh housing stock 2021). The cohort mirrored Welsh population sex and ethnicity distributions but was notably younger (aged 18 and under 40.1% vs 25.8%), in the most deprived quintile (40.9% vs 24.0%), and predominantly urban (81.5% vs 73.1%). Health characteristics and DiD results are pending approvals. Warm Wales supports younger individuals living in most deprived areas and older housing. These characteristics are consistent with groups at risk of fuel poverty. DiD analyses are ongoing and will assess the intervention's impact on healthcare utilisation.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3525"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654471","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":"Generating realistic synthetic data.","authors":"Zoe White","doi":"10.23889/ijpds.v11i5.3554","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3554","url":null,"abstract":"<p><p>Synthetic data has many different uses such as testing pipelines and training people for using new methods and technologies, which could be used within data linkage. The synthetic data generators project objectives are to create high fidelity synthetic data. To have high fidelity data, the synthetic data generator would need to include all variables, e.g. forename, surname and date of birth, and to include realistic errors, for example the frequency and types of errors. This ensures the synthetic data behaves similarly to the real underlying data. An initial workshop with users gained the requirements for; variables, data size, realistic errors and bias. For example, swapped forename and middle names for error, or missing children in census sized data. We also carried out research into different synthetic data generators in Python and Pyspark, as well as meeting our user requirements. Our final approach was to create the functions and documentation for running the generators with errors and guidance on how to run the generator. Results, Conclusions and Implementation The synthetic data generator will be run end to end with user specifying where errors and biases are for a particular dataframe and compared to the genuine sensitive data. The usability of the code is highly important for ability to create synthetic data for developing code or in the training of new team members. Therefore, user testing of the synthetic data generator will be carried out to gather feedback, which will inform version 2 development or implemented within the code.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3554"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654496","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}