International Journal of Population Data Science最新文献

{"title":"Defining a low-risk birth cohort: a cohort study comparing two perinatal data sets in Ontario, Canada.","authors":"Elizabeth Kathleen Darling, Olivia Marquez, Alison L Park","doi":"10.23889/ijpds.v9i1.2364","DOIUrl":"https://doi.org/10.23889/ijpds.v9i1.2364","url":null,"abstract":"<p><strong>Introduction: </strong>There are two main data sources for perinatal data in Ontario, Canada: the BORN BIS and CIHI-DAD. Such databases are used for perinatal health surveillance and research, and to guide health care related decisions.</p><p><strong>Objectives: </strong>Our primary objective was to examine the level of agreement between the BIS and CIHI-DAD. Our secondary objectives were to identify the differences between the data sources when identifying a low-risk birth (LRB) cohort and to understand their implications.</p><p><strong>Methods: </strong>We conducted a population-based cohort study comparing characteristics and clinical outcomes of all linkable births in BIS and CIHI-DAD between 1^st April 2012 and 31^st March 2018. We excluded out-of-hospital births, those with invalid healthcare numbers, non-Ontario residents and gestational age <20 weeks. We compared the portion of the cohort that met the criteria of a provincial definition of LRB based on each data source and compared clinical outcomes between the groups.</p><p><strong>Results: </strong>During the study period, 779,979 eligible births were linkable between the two data sources. After applying the LRB exclusions, there were 129,908 cases in the BIS and 136,184 cases in CIHI-DAD. Most exclusion criteria had almost perfect, substantial or moderate agreement. The agreement for non-cephalic presentation and BMI ≥ 40 kg/m² (kappa coefficients 0.409 and 0.256, respectively) was fair. Comparison between the two LRB cohorts identified differences in the prevalence of cesarean (14.3% BIS versus 12.0% CIHI-DAD) and NICU admission (8.7% BIS versus 7.5% CIHI-DAD) and only 0.01% difference in the prevalence of ICU admission.</p><p><strong>Conclusions: </strong>Overall, we found high levels of agreement between the BIS and CIHI-DAD. Identifying a LRB cohort in either database may be appropriate, with the caveat of appropriate understanding of the collection, coding and definition of certain outcomes. The decision for selecting a database may depend on which variables are most important in a particular analysis.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 1","pages":"2364"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10949111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176883","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: nutrition data in the VA million veteran program.","authors":"Xuan-Mai T Nguyen, Yanping Li, Kerry L Ivey, Stacey B Whitbourne, Walter C Willett, Frank B Hu, Kelly Cho, Michael Gaziano, Luc Djousse","doi":"10.23889/ijpds.v8i6.2366","DOIUrl":"10.23889/ijpds.v8i6.2366","url":null,"abstract":"<p><strong>Introduction: </strong>The Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ).</p><p><strong>Methods: </strong>Estimates of dietary energy, nutrient, and non-nutritive food components intakes data were derived from an extensively validated SFFQ, which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration.</p><p><strong>Results: </strong>Complete nutrition data was available for 353,418 MVP participants as of 30^th September 2021. Overall, 91.5% of MVP participants with nutrition data were male with an average age of 65.7 years at enrollment. Participants who completed the SFFQ were primarily White (82.5%), and Blacks accounted for 13.2% of the responders. Mean ± SD energy intake for 353, 418 MVP participants was 1428 ± 616 kcal/day, which was 1434 ± 617 kcal/day for males and 1364 ± 601 kcal/day for females. Energy intake and information on 322 nutrients and non-nutritive food components is available through contact with MVP for research collaborations at www.research.va.gov/mvp.</p><p><strong>Conclusions: </strong>The energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records, and genomic data, MVP nutrition data may be used to assess nutritional status and related risk factors, disease prevalence, and determinants of health that can provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and general population.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 6","pages":"2366"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10930149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140111687","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":"Deprivation effects on length of stay and death of hospitalised COVID-19 patients in Greater Manchester.","authors":"Jen Murphy, Mark Elliot, Rathi Ravidrarajah, William Whittaker","doi":"10.23889/ijpds.v5i4.1770","DOIUrl":"10.23889/ijpds.v5i4.1770","url":null,"abstract":"<p><strong>Introduction: </strong>The World Health Organisation declared a global pandemic in March 2020. The impact of COVID-19 has not been felt equally by all regions and sections of society. The extent to which socio-demographic and deprivation factors have adversely impacted on outcomes is of concern to those looking to 'level-up' and decrease widening health inequalities.</p><p><strong>Objectives: </strong>In this paper we investigate the impact of deprivation on the outcomes for hospitalised COVID-19 patients in Greater Manchester during the first wave of the pandemic in the UK (30/12/19-2/1/21), controlling for proven risk factors from elsewhere in the literature.</p><p><strong>Methods: </strong>We fitted Negative Binomial and logistic regression models to NHS administrative data to investigate death from COVID in hospital and length of stay for surviving patients in a sample of adult patients admitted within Greater Manchester (N = 10,372, spell admission start dates from 30/12/2019 to 02/01/2021 inclusive).</p><p><strong>Results: </strong>Deprivation was associated with death risk for hospitalised patients but not with length of stay. Male sex, co-morbidities and older age was associated with higher death risk. Male sex and co-morbidities were associated with increased length of stay. Black and other ethnicities stayed longer in hospital than White and Asian patients. Period effects were detected in both models with death risk reducing over time, but the length of stay increasing.</p><p><strong>Conclusion: </strong>Deprivation is important for death risk; however, the picture is complex, and the results of this analysis suggest that the reported COVID related mortality and deprivation linked reductions in life expectancy, may have occurred in the community, rather than in acute settings.</p><p><strong>Highlights: </strong>Older age and male sex are predictive of longer hospital stays and higher death risk for hospitalised cases in this analysis.Deprivation is associated with death risk but not length of stay for hospitalised patients.Ethnicity is associated with length of stay, but not with death risk.There is a social gradient in health, but these data would suggest that once in the care of an NHS hospital in an acute health episode, outcomes are more equal.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 1","pages":"1770"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10929766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140111688","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":"Variation in colorectal cancer treatment and outcomes in Scotland: real world evidence from national linked administrative health data.","authors":"Elizabeth Lemmon, Catherine Hanna, Katharina Diernberger, Hugh M Paterson, Sarah H Wild, Holly Ennis, Peter S Hall","doi":"10.23889/ijpds.v6i1.2179","DOIUrl":"10.23889/ijpds.v6i1.2179","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is the fourth most common type of cancer in the United Kingdom and the second leading cause of cancer death. Despite improvements in CRC survival over time, Scotland lags behind its UK and European counterparts. In this study, we carry out an exploratory analysis which aims to provide contemporary, population level evidence on CRC treatment and survival in Scotland.</p><p><strong>Methods: </strong>We conducted a retrospective population-based analysis of adults with incident CRC registered on the Scottish Cancer Registry (Scottish Morbidity Record 06 (SMR06)) between January 2006 and December 2018. The CRC cohort was linked to hospital inpatient (SMR01) and National Records of Scotland (NRS) deaths records allowing a description of their demographic, diagnostic and treatment characteristics. Cox proportional hazards regression models were used to explore the demographic and clinical factors associated with all-cause mortality and CRC specific mortality after adjusting for patient and tumour characteristics among people identified as early-stage and treated with surgery.</p><p><strong>Results: </strong>Overall, 32,691 (73%) and 12,184 (27%) patients had a diagnosis of colon and rectal cancer respectively, of whom 55% and 53% were early-stage and treated with surgery. Five year overall survival (CRC specific survival) within this cohort was 72% (82%) and 76% (84%) for patients with colon and rectal cancer respectively. Cox proportional hazards models revealed significant variation in mortality by sex, area-based deprivation and geographic location.</p><p><strong>Conclusions: </strong>In a Scottish population of patients with early-stage CRC treated with surgery, there was significant variation in risk of death, even after accounting for clinical factors and patient characteristics.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"9 1","pages":"2179"},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10929767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140111689","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":"Examining the quality and population representativeness of linked survey and administrative data: guidance and illustration using linked 1958 National Child Development Study and Hospital Episode Statistics data","authors":"Richard Silverwood, Nasir Rajah, Lisa Calderwood, Bianca De Stavola, Katie Harron, George Ploubidis","doi":"10.23889/ijpds.v9i1.2137","DOIUrl":"https://doi.org/10.23889/ijpds.v9i1.2137","url":null,"abstract":"IntroductionRecent years have seen an increase in linkages between survey and administrative data. It is important to evaluate the quality of such data linkages to discern the likely reliability of ensuing research. Evaluation of linkage quality and bias can be conducted using different approaches, but many of these are not possible when there is a separation of processes for linkage and analysis to help preserve privacy, as is typically the case in the UK (and elsewhere).\u0000ObjectivesWe aimed to describe a suite of generalisable methods to evaluate linkage quality and population representativeness of linked survey and administrative data which remain tractable when users of the linked data are not party to the linkage process itself. We emphasise issues particular to longitudinal survey data throughout.\u0000MethodsOur proposed approaches cover several areas: i) Linkage rates, ii) Selection into response, linkage consent and successful linkage, iii) Linkage quality, and iv) Linked data population representativeness. We illustrate these methods using a recent linkage between the 1958 National Child Development Study (NCDS; a cohort following an initial 17,415 people born in Great Britain in a single week of 1958) and Hospital Episode Statistics (HES) databases (containing important information regarding admissions, accident and emergency attendances and outpatient appointments at NHS hospitals in England).\u0000ResultsOur illustrative analyses suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness with respect to the single dimension we assessed.\u0000ConclusionsThrough this work we hope to encourage providers and users of linked data resources to undertake and publish thorough evaluations. We further hope that providing illustrative analyses using linked NCDS-HES data will improve the quality and transparency of research using this particular linked data resource.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"50 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139441975","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":"Data resource profile: the Edinburgh Child Protection Dataset - a new linked administrative data source of children referred to Child Protection paediatric services in Edinburgh, Scotland","authors":"Louise Marryat, Jacqueline Stephen, Jacqueline Mok, Sharon Vincent, Charlotte Kirk, Lindsay Logie, John Devaney, Rachael Wood","doi":"10.23889/ijpds.v8i6.2173","DOIUrl":"https://doi.org/10.23889/ijpds.v8i6.2173","url":null,"abstract":"IntroductionChild maltreatment affects a substantial number of children. However current evidence relies on either longitudinal studies, which are complex and resource-intensive, or linked data studies based on social services data, which is arguably the tip of the iceberg in terms of children who are maltreated. Reliable, linked, population-level data on children referred to services due to suspected abuse or neglect will increase our ability to examine risk factors for, and outcomes following, abuse and neglect.\u0000ObjectiveThe objective of this project was to create a linkable population level dataset, The Edinburgh Child Protection Dataset (ECPD), comprising all children referred to the Edinburgh Child Protection Paediatric healthcare team due to a concern about their welfare between 1995 and 2015.\u0000MethodsThe paper presents the process for creating the dataset. The analyses provide examples of available data from the main referrals dataset between 1995 and 2011 (where data quality was highest).\u0000Results19,969 referrals were captured, relating to 11,653 children. Of the 19,969 referrals, a higher proportion were girls (54%), although boys were referred for physical abuse more often than girls (41% versus 30%). Younger children were more likely to be referred for physical abuse (35% of 0-4 year olds vs. 27% 15+): older children were more likely to be referred for sexual abuse (48% of 15+ years vs. 18% of 0-4 years). Most referrals came from social workers (46%) or police (31%).\u0000ConclusionsThe ECPD offers a unique insight into the characteristics of referrals to child protection paediatric services over a key period in the history of child protection in Scotland. It is hoped that by making these data available to researchers, and able to be easily linked with both mother and child current and future health records, evidence will be created to better support maltreated children and monitor changes over time.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138972918","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":"Machine learning models in trusted research environments -- understanding operational risks","authors":"F. Ritchie, Amy Tilbrook, Christian Cole, Emily Jefferson, Susan Krueger, Esma Mansouri-Bensassi, Simon Rogers, Jim Q. Smith","doi":"10.23889/ijpds.v8i1.2165","DOIUrl":"https://doi.org/10.23889/ijpds.v8i1.2165","url":null,"abstract":"IntroductionTrusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale.\u0000ObjectivesAs part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.\u0000MethodsWe demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling.\u0000ResultsWe show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs.\u0000ConclusionsAt this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"261 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138972997","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":"De-identification of Free Text Data containing Personal Health Information: A Scoping Review of Reviews","authors":"Bekelu Negash, Alan Katz, Christine J. Neilson, Moniruzzaman Moni, Marc Nesca, Alexander Singer, J. Enns","doi":"10.23889/ijpds.v8i1.2153","DOIUrl":"https://doi.org/10.23889/ijpds.v8i1.2153","url":null,"abstract":"IntroductionUsing data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data.\u0000MethodsWe adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence.\u0000ResultsThe initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning.\u0000ConclusionOur review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"63 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009912","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":"Four questions to guide decision-making for data sharing and integration.","authors":"Amy Hawn Nelson, Sharon Zanti","doi":"10.23889/ijpds.v8i2.2159","DOIUrl":"10.23889/ijpds.v8i2.2159","url":null,"abstract":"<p><strong>Introduction: </strong>This paper presents a Four Question Framework to guide data integration partners in building a strong governance and legal foundation to support ethical data use.</p><p><strong>Objectives: </strong>While this framework was developed based on work in the United States that routinely integrates public data, it is meant to be a simple, digestible tool that can be adapted to any context.</p><p><strong>Methods: </strong>The framework was developed through a series of public deliberation workgroups and 15 years of field experience working with a diversity of data integration efforts across the United States.</p><p><strong>Results: </strong>The Four Questions-<i>Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)?</i>-should be considered within an established data governance framework and alongside core partners to determine whether and how to move forward when building an Integrated Data System (IDS) and also at each stage of a specific data project. We discuss these questions in depth, with a particular focus on the role of governance in establishing legal and ethical data use. In addition, we provide example data governance structures from two IDS sites and hypothetical scenarios that illustrate key considerations for the Four Question Framework.</p><p><strong>Conclusions: </strong>A robust governance process is essential for determining whether data sharing and integration is legal, ethical, and a good idea within the local context. This process is iterative and as relational as it is technical, which means authentic collaboration across partners should be prioritized at each stage of a data use project. The Four Questions serve as a guide for determining whether to undertake data sharing and integration and should be regularly revisited throughout the life of a project.</p><p><strong>Highlights: </strong>Strong data governance has five qualities: it is purpose-, value-, and principle-driven; strategically located; collaborative; iterative; and transparent.Through a series of public deliberation workgroups and 15 years of field experience, we developed a Four Question Framework to determine whether and how to move forward with building an IDS and at each stage of a data sharing and integration project.The Four Questions-<i>Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)?</i>-should be carefully considered within established data governance processes and among core partners.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 4","pages":"2159"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10900076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139991374","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":"Seasonal purchase of antihistamines and ovarian cancer risk in the Cancer Loyalty Card Study (CLOCS): results from an observational case-control study","authors":"H. Brewer, Q. Jiang, S. Sundar, Y. Hirst, J. Flanagan","doi":"10.1101/2023.05.30.23290729","DOIUrl":"https://doi.org/10.1101/2023.05.30.23290729","url":null,"abstract":"Objective: Antihistamine use has previously been associated with a reduction in incidence of ovarian cancer, particularly in premenopausal women. Herein, we investigate antihistamine exposure in relation to ovarian cancer risk using a novel data resource by examining purchase histories from retailer loyalty card data. Study Design: A subset of participants from the Cancer Loyalty Card Study (CLOCS) for which purchase histories were available were analysed in this study. Cases (n=153) were women in the UK with a first diagnosis of ovarian cancer between Jan 2018 to Jan 2022. Controls (n=120) were women in the UK without a diagnosis of ovarian cancer. Up to 6 years of purchase history was retrieved from two participating high street retailers from 2014 to 2022. Main outcome measures: Logistic regression was used to estimate the odds ratio (OR) and 95% confidence intervals (CIs) for ovarian cancer associated with antihistamine purchases, ever versus never, adjusting for age and oral contraceptive use. The association was stratified by season of purchase, age over and under 50 years, ovarian cancer histology, and family history. Results: Ever purchasing antihistamines was not significantly associated with ovarian cancer overall in this small study (OR:0.68, 95% CI: 0.39,1.19). However, antihistamine purchases were significantly associated with reduced ovarian cancer risk when purchased only in spring and/or summer (OR: 0.37, 95% CI: 0.17,0.82) compared with purchasing all year (OR: 0.99, 95% CI: 0.51,1.92). In the stratified analysis, the association was strongest in non-serous ovarian cancer (OR: 0.41, 95% CI:0.18,0.93). Conclusions: Antihistamine purchase is associated with reduced ovarian cancer risk when purchased seasonally in spring and summer. However, larger studies and more research is required to understand the mechanisms of reduced ovarian cancer risk related to seasonal purchases of antihistamines and allergies.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43794449","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}