International Journal of Population Data Science最新文献

{"title":"Public sector health analytics capacity before and after Covid-19: A case study of manager perspectives in New Brunswick, Canada","authors":"James Ayles, Maria Lima, Neeru Gupta","doi":"10.23889/ijpds.v9i1.2370","DOIUrl":"https://doi.org/10.23889/ijpds.v9i1.2370","url":null,"abstract":"BackgroundDemand for health data and analytics to support research, policy, and practice continues to rise, accelerated by the Covid-19 pandemic. Despite the importance of the government analytics workforce in driving academic-based data sharing and linkage platforms, little is known about how public sector managers assess capacity in health analytics. This case study describes findings from consultations among middle managers of analytics services in a Canadian provincial health ministry.\u0000MethodsData collection involved a mixed-questions survey to gauge the functional perspective of managers on organisational and human resource analytics capacity within the New Brunswick Department of Health. The repeated cross-sectional survey was implemented in two rounds, with a baseline collected before the Covid-19 global outbreak (in 2016) and a follow-up after the pandemic emergency response (in 2022).\u0000ResultsThe post-pandemic period was associated with perceptions of a growing role for public service personnel in handling analytics. Recruitment and retention of skilled analytics professionals emerged as the top priority for capacity building, including needs-based planning, competitive compensation packages to address skills shortages, professional development and promotion opportunities, and tracking key performance indicators for employee satisfaction.\u0000ConclusionsGovernment health analytics professionals play a critical role in advancing administrative data use and re-use. Enhanced knowledge sharing is needed on best practices in supply--demand monitoring for analytics professionals and planning for human resources surge capacity in the public service, lest significant innovation potential for health system improvement be left untapped.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":"15 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816583","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: Scottish Linked Pregnancy and Baby Dataset (SLiPBD)","authors":"Laura Lindsay, Kate Mark, Emily Moore, J. Carruthers, L. Hopkins, Denise Jennings, R. Wood","doi":"10.23889/ijpds.v9i2.2390","DOIUrl":"https://doi.org/10.23889/ijpds.v9i2.2390","url":null,"abstract":"IntroductionHere we present the Scottish Linked Pregnancy and Baby Dataset (SLiPBD), a new national data resource held by Public Health Scotland (PHS).\u0000MethodsSLiPBD comprises a population-based e-cohort of all fetuses and births (babies) from pregnancies to women in Scotland from 2000 onwards. It is updated monthly by linking and reconciling the following national datasets: antenatal booking records; general and maternity hospital discharge records; termination of pregnancy notifications; and statutory live and stillbirth registrations.\u0000ResultsKey information included on all babies in SLiPBD includes estimated date of conception, end of pregnancy date, gestation, multiple pregnancy status, pregnancy outcome, and maternal sociodemographic characteristics. For live births, additional information on the birth, the baby's sociodemographic characteristics, and subsequent infant deaths is included.\u0000Following the cohort refresh in January 2024, SLiPBD contained 1,770,226 babies from 1,750,830 pregnancies to 898,161 women. Of the 1,770,226 babies, 1,284,461 (73%) were live births, 5,731 (0.3%) stillbirths, and 316,897 (18%) and 114,840 (6%) came from a pregnancy ending a termination or early spontaneous loss respectively. 22,414 (1%) had an unknown pregnancy outcome, and for 25,883 (1%) the pregnancy was still ongoing. Data completeness for key sociodemographic characteristics except for ethnicity was very high, and variables showed expected patterns. Ethnicity data completeness is poor on historical records but improving over time. Completeness of unique patient identifiers was very high. External validation to source datasets was reassuring.\u0000ConclusionSLiPBD can be analysed standalone or linked to other national vital event and health datasets held by PHS. It supports longitudinal and intergenerational analyses, enabling epidemiological and health service surveillance and research on maternal and child health. Researchers interested in accessing pseudonymised extracts of SLiPBD through the Scottish NHS safe haven facility should contact Research Data Scotland. PHS will continue to refine SLiPBD as source datasets improve.\u0000Key Features\u0000\u0000The Scottish Linked Pregnancy and Baby Dataset (SLiPBD) is a new national data resource created and maintained by Public Health Scotland to facilitate epidemiological and health service analyses focused on maternal and child health.\u0000SLiPBD comprises a population-based e-cohort of all fetuses and births (babies) from pregnancies to women in Scotland from 2000 onwards. At least 68,000 babies (of which at least 46,000 are live births) are included annually.\u0000SLiPBD is updated on a monthly basis by linking and reconciling records relating to ongoing and completed pregnancies from the following existing national datasets: antenatal booking records; general and maternity hospital discharge records; termination of pregnancy notifications; and statutory live and stillbirth registrations.\u0000Key information included on all babies in","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":" 93","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825338","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":"Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts","authors":"Lisa Kent, Meabh McGirr, K. Eastwood","doi":"10.23889/ijpds.v9i2.2401","DOIUrl":"https://doi.org/10.23889/ijpds.v9i2.2401","url":null,"abstract":"Pregnant women with obesity are at greater risk of complications during pregnancy, peripartum and post-partum, compared to women with healthy BMI. Worldwide data demonstrating the changes in trends of maternal overweight and obesity prevalence informs service development to address maternal obesity, while directing resources to areas of greatest need. This systematic review and meta-analysis of population level data sought to evaluate global temporal changes in prevalence of maternal obesity and overweight/obesity, and compare trends between regions.\u0000Pooled prevalence of obesity and overweight/obesity was estimated using random effects meta-analysis. Temporal and geographical trends in prevalence of obesity and overweight/obesity were examined using linear regression.\u0000From 11,684 publications, 94 met inclusion criteria representing 121 study cohorts (Europe n = 71; North America n = 23; Australia/Oceania n = 10; Asia n = 5; South America n = 12), totalling 49,009,168 pregnancies. No studies from Africa met the inclusion criteria. Eighty studies (85.1%) were evaluated as having a low risk of bias and 14 studies (14.9%) moderate. In the most recent full decade (2010-2019), global prevalence of maternal obesity was estimated as 16.3% (95% confidence interval (CI): 15.1-17.5%), or approximately one in six pregnancies. Combined overweight/obesity in pregnancy had a pooled prevalence of 43.8% (95%CI: 42.2-45.4%), approaching half of all pregnancies. In each continent, an upward trend similar to the global trend was observed. North America demonstrated the highest prevalence (obesity: 18.7% (95%CI: 15.0-23.2%)); overweight/obesity: 47.0% (95%CI: 45.7-48.3%)) and Asia demonstrated the lowest prevalence (obesity: 10.8% (95%CI: 7.0-16.5%)); overweight/obesity: 28.5% (95%CI: 18.3-41.5%)). Both maternal obesity and combined overweight/obesity prevalence increased annually by 0.34% and 0.64% (p < 0.001), respectively. Our linear regression model estimates current global prevalence of maternal obesity as 20.9% (95%CI 18.6-23.1%) and projects that this will increase to 23.3% (95%CI 20.3-26.2%) by 2030.\u0000Globally, maternal obesity and overweight/obesity prevalence is high and increasing, but varies greatly between regions, being highest in North America and lower in Asia. Maternity services across the globe should be adequately resourced to cope with the complexity of needs of pregnant women living with obesity. Future public health interventions should focus on reversing the high prevalence of maternal obesity observed across the globe. The availability of population-level data and research varies between regions, with more data required to understand the needs of maternal populations in the continents of Africa and Asia. Globally, there is a need for improved harmonisation and publication of data for monitoring and improvement of maternal inequalities.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":"26 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645565","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":"Maternal disability and newborn discharge to social services: a population-based study","authors":"Claire Grant, Y. Lunsky, A. Guttmann, Simone Vigod, Isobel Sharpe, K. Fung, Hilary Brown","doi":"10.23889/ijpds.v9i2.2396","DOIUrl":"https://doi.org/10.23889/ijpds.v9i2.2396","url":null,"abstract":"IntroductionRemoving a child from their family is the option of last resort for social services. However, decisions to place children into care are occurring more frequently and earlier in children's lives, with newborn discharge to social services being a particular concern due to the effects of mother-newborn separations on child development. Women with disabilities face negative assumptions about their parenting capacity, but little is known about their rates of newborn discharge to social services.\u0000ObjectivesTo examine the risk of discharge to social services among newborns of women with and without disabilities.\u0000MethodsWe conducted a population-based cohort study of singleton livebirths in Ontario, Canada, 2008-2019. We used modified Poisson regression to estimate the relative risk (RR) of discharge to social services immediately after the birth hospital stay, comparing newborns of women with physical (n = 114,685), sensory (n = 38,268), intellectual/developmental (n = 2,094), and multiple disabilities (n = 8,075) to newborns of women without a disability (n = 1,221,765). Within each group, we also examined maternal sociodemographic, health, health care, and pregnancy-related characteristics associated with the outcome.\u0000ResultsCompared to newborns of women without disabilities (0.2%), newborns of women with physical (0.5%; aRR 1.53, 95% CI 1.39-1.69), sensory (0.4%; aRR 1.34, 95% CI 1.12-1.59), intellectual/developmental (5.6%; aRR 5.34, 95% CI 4.36-6.53), and multiple disabilities (1.7%; aRR 3.09, 95% CI 2.56-3.72) had increased risk of being discharged to social services after the birth hospital stay. Within each group, the strongest predictors of the outcome were young maternal age, low income quintile, social assistance, maternal mental illness and substance use disorders, inadequate prenatal care, and neonatal morbidity.\u0000ConclusionsNewborns of women with disabilities are at increased risk of being discharged to social services after the birth hospital stay. These findings can be used to inform the development of tailored supports for new mothers with disabilities and their infants.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":" 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678954","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":"Generating synthetic identifiers to support development and evaluation of data linkage methods","authors":"Joseph Lam, Andy Boyd, Robin Linacre, Ruth Blackburn, Katie Harron","doi":"10.23889/ijpds.v9i1.2389","DOIUrl":"https://doi.org/10.23889/ijpds.v9i1.2389","url":null,"abstract":"IntroductionCareful development and evaluation of data linkage methods is limited by researcher access to personal identifiers. One solution is to generate synthetic identifiers, which do not pose equivalent privacy concerns, but can form a 'gold-standard' linkage algorithm training dataset. Such data could help inform choices about appropriate linkage strategies in different settings.\u0000ObjectivesWe aimed to develop and demonstrate a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error.\u0000MethodsWe determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We then generated synthetic versions of a large UK cohort study (the Avon Longitudinal Study of Parents and Children; ALSPAC), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches).\u0000ResultsComparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility.\u0000ConclusionsWe show that replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":"372 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707970","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":"Understanding Vulnerability to the Poverty Premium: An Analysis of Factors Influencing Use of High-Cost Credit Among Low-Income Individuals","authors":"Fiona Rasanga, Tina Harrison, Raffaella Calabrese","doi":"10.23889/ijpds.v9i4.2431","DOIUrl":"https://doi.org/10.23889/ijpds.v9i4.2431","url":null,"abstract":"Introduction & BackgroundAccess to affordable credit is essential for individuals living on low incomes to participate in society fully. However, due to their limited credit history and volatile incomes, many cannot access mainstream sources of credit, such as credit cards and personal loans.\u0000As a result, they often must rely on more expensive sources of credit such as payday loans, doorstep loans or rent-to-own loans. This leads to them paying more for credit, also referred to as the poverty premium.\u0000Incurring the poverty premium exacerbates the financial challenges faced by already vulnerable individuals and can lead to a cycle of financial distress. Identifying the behaviours and factors that lead to a need for high-cost credit can help in identifying individuals who are most vulnerable to incurring the poverty premium.\u0000Objectives & ApproachTo achieve this, we rely on anonymized Open Banking transaction data from 100,000 individuals provided by a UK-based social lender.Given the latent nature of vulnerability, we identify indicators of vulnerability to poverty premium which include frequency of overdraft use, previous debt problems, low financial resilience, and indebtedness. We use a copula-based approach to create an index of vulnerability to poverty premium.\u0000This is based on weighting the individual indicators using their Spearman rank correlation coefficient. We use a fixed effects model to identify the factors that contribute to this vulnerability, where the index of vulnerability is the dependent variable.\u0000Relevance to Digital FootprintsWe use a rich and granular dataset on individual financial transactions to address a key social issue. The findings from this study can inform policy and industry efforts to promote greater credit affordability for these vulnerable individuals. This is particularly important due to the renewed concerns regarding the increased use of high-cost credit by individuals living on low incomes due to COVID-19 and the increased cost of living.\u0000ResultsOur findings show that variables related to the financial profile of an individual are important driving factors of the vulnerability to poverty premium. These include the number of salary sources, frequency of salary receipt, benefit receipt and savings frequency. Other variables related to spending behaviour such as gambling, volatility in fixed expenses and high transaction counts all have positive relationships with this vulnerability.\u0000Conclusions & ImplicationsThis study is a first step towards examining the determinants of vulnerability to poverty premium by analyzing an Open Banking transaction data set. The innovative feature of this work is the creation of an index of vulnerability to poverty premiums based on various indicators of financial distress and high-cost credit use.\u0000Our findings on the relationships between the individual's financial profile and the vulnerability to poverty premium suggest that policymakers should consider targeting interventions fo","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":"104 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361308","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":"Exploring Digital Biomarkers for Depression Using Mobile Technology","authors":"Yuezhou Zhang, A. Folarin, R. Dobson","doi":"10.23889/ijpds.v9i4.2422","DOIUrl":"https://doi.org/10.23889/ijpds.v9i4.2422","url":null,"abstract":"Introduction & BackgroundWith the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms. \u0000Objectives & ApproachThe present study summarizes findings from our five recent investigations that explored the relationships between depression severity and digital biomarkers captured by wearables and smartphones. These studies analyzed data from RADAR-MDD, a multinational mobile health program, involving 623 participants and tracked for up to two years. Participants' depression severity was measured biweekly using the PHQ-8 questionnaire conducted via smartphones. Concurrently, participants’ Fitbit and smartphone data were also collected. Given the longitudinal nature and repeated measurements for each participant, multilevel modeling techniques were employed to analyze the data. \u0000Relevance to Digital FootprintsOur approach involved extracting features from passive data that reflect various aspects of daily behavior—such as sleep quality, social interaction, physical activity, and walking patterns—akin to digital footprints. \u0000ResultsWe found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams). \u0000Conclusions & ImplicationsLeveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363886","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":"Utilising User Data from a Food-Sharing App to Evidence the \"Heat-or-Eat\" Dilemma","authors":"T. Semple, John Harvey, Lucelia Rodrigues, M. Gillott, Grazziela Figueredo, Georgiana Nica-Avram","doi":"10.23889/ijpds.v9i4.2424","DOIUrl":"https://doi.org/10.23889/ijpds.v9i4.2424","url":null,"abstract":"Introduction & BackgroundPrevious literature has found that financially vulnerable households often make involuntary spending trade-offs between necessities, particularly energy and food. This effect is especially pronounced during winter, when homes require greater energy expenditure to maintain an adequate temperature. Despite frequent colloquial and journalistic references to the \"heat-or-eat dilemma”, there remains limited recent empirical evidence of this phenomenon in the UK. This is a considerable knowledge gap, given recent economic hardship and rising energy costs. \u0000Objectives & ApproachThis study uses survey data (n=2877), collected during winter 2022 in London, UK, to analyse the sociodemographic and behavioural characteristics of respondents affecting self-reported heat-or-eat trade-offs. The survey was deployed via users of the food-sharing app, OLIO, and quota restraints were enforced to ensure the socioeconomic representativeness of the sample (based on Index of Multiple Deprivation). The survey question of interest (i.e., the dependent variable) was \"\"in the past year, how frequently did your household reduce or forego expenses for basic household necessities, such as medicine or food, in order to pay an energy bill?\"\" and responses were recorded using a discrete, ordinal scale: never; 1-2 months; some months but not every month; almost every month. \u0000Given the nature of the dependent variable, the Random Parameters Ordered Probit (RPOP) model, a statistical modelling framework used in the case of discrete, ordered outcomes, was considered suitable. The RPOP approach allows the effect of various independent variables to be explored, which in this case, are sociodemographic and behavioural characteristics of respondents. \u0000Relevance to Digital FootprintsThe relevance to the digital footprints theme is embedded in the study’s aim: to draw insights into social issues through the analysis of sociodemographic and behavioural data retrieved from the users of a mobile app. \u0000ResultsInitial results show that a considerable proportion (~37%) of the sample made heat-or-eat trade-offs at least one month of the year. Interestingly, this is several times higher than the official rate of fuel poverty in London (11.9%), suggesting that the government’s fuel poverty metric fails to capture many homes that display signs of energy unaffordability. The RPOP model estimation results show that a broad range of sociodemographic variables (including features of household composition and disability), as well as several behavioural features derived from the respondents’ use of the OLIO app, including the frequency of app usage and food requests, significantly affected the likelihood of heat-or-eat trade-offs. \u0000Conclusions & ImplicationsOur results can be used to guide remedial food and fuel poverty policies. It may be particularly useful to focus on the sociodemographic variables that lead to heat-or-eat trade-offs, given that the English fuel poverty metric","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":"124 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362688","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":"Who donates food purchase data for research? Results from two nationwide data collection efforts in Finland","authors":"Anna-Leena Vuorinen, H. Vepsäläinen, Jelena Meinilä, E. Lehto, H. Saarijärvi, M. Erkkola, Mikael Fogelholm, J. Nevalainen","doi":"10.23889/ijpds.v9i4.2428","DOIUrl":"https://doi.org/10.23889/ijpds.v9i4.2428","url":null,"abstract":"Introduction & BackgroundFood retailers’ transaction data are increasingly used for research. Unlike many other digital footprints, the representativeness of automatically accumulating food purchase data as such is less biased as food is consumed by all individuals. However, the process of obtaining individual/household level data requires consents from the consumers and, thus, may create selection bias. \u0000Objectives & ApproachThe aim of this work is to describe the recruitment process and participant characteristics in the Finnish LoCard study to evaluate the selection mechanism. \u0000The Finnish LoCard study comprises two cohorts collected in 2018 and 2023 with 47,066 and 42,340 participants, respectively, who have consented to release their food purchase data for research. The study collaborates with S group, the leading retailer in Finland with 47% market share. Members from their loyalty card program were invited to consent to their purchase data being used for research and voluntarily respond to a background questionnaire. \u0000Relevance to Digital FootprintsOur analyses may provide further insights into the selectivity of consumers who are willing to share their purchase data for research purposes. \u0000ResultsFor both LoCard cohorts, all loyalty card holders (n~2.3M) were considered. In LoCard I, the invitations were sent to 1.1M primary card holders with confirmed email addresses, of whom 47,066 (3.9%) participated and consented to their purchase data being used for research. In this cohort, women, middle-aged individuals, individuals with higher education, and employed individuals were overrepresented whereas the retired individuals, those with lower education and individuals with children were underrepresented. In the LoCard II, loyalty card holders (n ~2.24M) with an email address were invited. Of these, 852,009 (37.7%) opened the invitation link, and a further 42,340 provided the consent, resulting in response rate of 1.9% from the original population and 4.9% from those reacting to the email invitation. Data on the characteristics of LoCard II participants are not available yet but will be presented in the conference presentation. \u0000Conclusions & ImplicationsThis work investigates selection mechanisms in the Finnish LoCard study and evaluates the feasibility of reaching underrepresented groups in health studies, such as socioeconomically disadvantaged groups or young men, through a combination of loyalty card program and email invitations.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":"113 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361212","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":"The role of inequalities in managing symptoms of menstruation: harnessing shopping data to innovate female reproductive health research","authors":"Poppy Taylor, A. Skatova, Laura D Howe, Abigail Fraser, Hannah Knight","doi":"10.23889/ijpds.v9i4.2416","DOIUrl":"https://doi.org/10.23889/ijpds.v9i4.2416","url":null,"abstract":"Introduction & BackgroundMenstruation affects half the population, yet its patterns and management are greatly under-researched. A regular and functioning menstrual cycle is considered an important vital sign and menstrual-related issues can be strong indicators of both reproductive and wider health issues. This project explores a novel health data source - shopping data history - to study how individuals manage menstrual symptoms such as pain, intensity of flow, mental health, and other issues, and explore potential social inequalities in these management strategies. \u0000Objectives & ApproachWe present a conceptual framework of studying management of menstruation symptoms using shopping data. The core objectives of this research are to enhance our understanding of menstrual management strategies and potential inequalities in these whilst evaluating the utility and acceptability of shopping data for future female reproductive health research. Our research will focus on harnessing loyalty card data from UK supermarkets and pharmaceutical retailers to provide insights into the management of menstrual symptoms at a national level. We will study retail data to identify products and patterns of purchasing which may be relevant to menstrual management and conduct surveys for linkage with shopping data. The public will be consulted to investigate attitudes towards shopping data for health research and inform interpretations of patterns in the data. \u0000Relevance to Digital FootprintsThis project contributes to advancing of understanding of using digital data for health research on an important societal challenge. We investigate the practical applications for menstrual health and other female reproductive health issues, with scope to enact meaningful change. \u0000Conclusions & ImplicationsBy analysing shopping behaviour, combined with survey data and area-level socioeconomic data, we aim to identify regions of the UK and individual characteristics which influence the risk of experiencing menstrual symptoms and ability to manage these within a high-income context. Our research will contribute to understanding of menstrual management strategies for women and people who menstruate, and associated inequalities.","PeriodicalId":507952,"journal":{"name":"International Journal of Population Data Science","volume":" 84","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141365225","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}