International Journal of Population Data Science最新文献

Introduction: Priority setting with patients, public and professionals is essential for research utilising routinely collected data, as this ensures data are being used in the public interest. However, it is challenging to identify research priorities that are relevant to a wide range of local stakeholders and can be addressed with routinely collected data.

Objectives: To describe and present the results of a priority setting exercise aiming to identify research priorities for Born in Bradford for All (BiB4All), a routine data linkage cohort of mothers and babies born in Bradford, a city in the north of England.

Methods: We developed a two-hour online workshop to engage a range of stakeholders across Bradford, including parents, early years practitioners, commissioners, and service providers. The workshop method combined elements of existing priority setting approaches to ensure priorities were identified in an inclusive, timely and deliberative way, and supported stakeholders to develop their understanding of using linked routine data for research.

Results: The workshop identified seventeen important and urgent research priorities around child and maternal health for research with locally linked routine data. Key topic areas included maternal and infant mental health, the long-term impact of the Covid-19 pandemic on maternal and child health outcomes, inequalities in access to services, and infant feeding experiences.

Conclusions: The identified research priorities have been shared widely amongst interested networks and have shaped the BiB4All research agenda, demonstrating the feasibility of the stakeholder engagement method. They also have important implications for policy and practice. For policy, they provide an understanding of the key issues faced by local communities, which can steer policy priorities and investment in evidence generation. For practice, involvement in the workshop has generated a greater understanding of how local service data can be used for research and to inform improvements to service delivery.

Introduction: Prescribing data has been collected electronically in Scotland for many years; however, data are collated in individual, non-overlapping datasets based on the origin of the prescription (e.g., primary or secondary care). The vision was to create a unified view of all prescribing data to provide a longitudinal dataset of medicines use for patients treated by the National Health Services (NHS) Scotland, irrespective of where or how that care was provided.

Methods: The Scottish Combined Medicines Dataset (SCoMeD) is, in essence, a data virtualisation tool collating information from three previously available prescribing datasets: the Prescribing Information System (PIS); the Hospital Electronic Prescribing and Medicines Administration (HEPMA) national dataset; and the Homecare Medicines (HCM) dataset. This allows the creation of study cohorts (patient groups of interest) that meet specified criteria across all prescribing settings and facilitates the retrieval of the prescribing history for individuals pre-identified from other datasets. Records contain a unique patient identifier (Community Health Index number) which is used to identify patients for inclusion in the dataset and also enables linkage to other routinely collected data, including hospital admission episodes and death records.

Results: SCoMeD contains details on the patient (age, sex, geographical information) and on the medication prescribed. Medication-related information includes what was received and when; strength and dose information are also available. The earliest date of data availability depends on the source (PIS, 01/2010; HEPMA, 07/2022; HCM, 01/2019). Data is held by Public Health Scotland.

Conclusion: SCoMeD facilitates a range of different studies, including cross-sectional/point-prevalence studies and drug utilisation studies as well as longitudinal studies, e.g., cohort and case-control studies. With the possibility to link to other relevant datasets, additional areas of interest may include health policy evaluations and health economics studies. Access to data is subject to approval; researchers need to contact the electronic Data Research and Innovation Service in the first instance.

Background: Household substance misuse (SM) is associated with child deprivation and worse physical and mental health. This study utilised linked healthcare, justice, and children's social care data in Wales for the first time, to create a reusable cohort of households that experience substance misuse (SMHH).

Methods: Using the SAIL Databank, a population-scale retrospective electronic cohort (e-cohort) was created to perform a cross-sectional analysis of SM-related health and criminal justice events during 2011-2019 for adults and children in SMHH, which were compared with the rest of the population using period prevalence ratios (PR) and 95% confidence intervals (CI). Other variables included demographics, children's social care, healthcare, and SM-related criminal court cases.

Results: There were 776,366 children and 1,032,088 adults, where 83,558 children (11%) lived in SMHH, and 48,398 (5%) of adults who lived with a child had a SM event. Children in SMHH had a 133% higher prevalence of referral to SM treatment (PR = 2.33, CI: 2.23-2.43), and a SM-related criminal case was 42% more prevalent (PR = 1.42, CI: 1.30-1.55) during the period. Notably, the prevalence of SMHH children receiving care and support was 300% higher (PR = 4.00, CI: 3.92-4.08), and self-harm was 78% more prevalent (PR = 1.78, CI: 1.71-1.86).

Conclusion: SMHH children experience significant disparities, including higher deprivation, adverse birth outcomes, mental health issues, social care involvement, and SM-related criminal justice prosecutions. Evidence-based interventions and policy are needed to support adults and children in SMHH to mitigate the intergenerational impact.

Introduction: Children in contact with the children's social care (CSC) system are a vulnerable group likely to have experienced one or multiple forms of childhood adversity. Understanding the characteristics and social care pathways of these children and their health and social outcomes across the life course is important for informing policy and practice. The Social Services Client Administration and Retrieval Environment (SOSCARE) dataset holds routinely collected CSC data in Northern Ireland (NI). The aim of this data resource profile is to provide an overview of the three key modules in the SOSCARE dataset to act as a guide for researchers.

Methods: This paper reports selected data contained in the SOSCARE data modules relating to Children in Need, Child Protection Registrations and Children in Care between 1995 and 2015. Information on how to access the data and the strengths and limitations are discussed.

Results: The SOSCARE dataset is available to approved researchers via the Health and Social Care Honest Broker Service (HSC HBS) in NI and allows researchers to examine population-level interactions with key statutory thresholds of CSC. Between 1^st January 1995 and 31^st December 2015, the Children in Need module contains data for 148,862 unique children, and the Child Protection Registration and Children in Care modules contain data for 20,355 and 12,335 children respectively. While there are several methodological limitations, the data is a unique and rich resource to examine prevalence and patterns of CSC activity in NI. There is great potential for linkage to other health and administrative datasets to examine predictors of social care involvement and a range of health and social outcomes in childhood and adulthood.

Conclusion: The SOSCARE data provides detailed case level information on all children in contact with CSC in NI. Research using this data can make an important contribution to evidence-informed policy and practice.

Public engagement is an important mechanism for ensuring that the voices of the public are integrated into study design and data use. The commissioning of a new UK-wide birth cohort study by the UKRI Economic and Social Research Council (ESRC), the Early Life Cohort Feasibility Study (ELC-FS), necessitated renewed dialogue with the public about the acceptability of conducting a large-scale study of this kind. The ELC-FS recruited several thousand children in their first year of life, using an administrative data sampling frame, an 'opt-out' recruitment approach, and embedded linkages to education, health and social care administrative data. The study faced many complexities and challenges to achieve this: the sampling frame had not been used for this purpose before, required negotiation with different data holders in the four UK nations, and the study needed to ensure transparency around how participants' administrative and survey data would be used. Conducting public engagement projects with parents of young children prior to the study's fieldwork was essential to understanding more about the public acceptability of data use in ELC-FS. Evidence from these projects was used to support negotiations with data holders, as well as in guiding best practice for informing participants about their data use and data linkage. This paper summarises the evidence from these public engagement projects relating to data transparency and enacting participant choice and control of the use of their data in the study. We describe how this evidence was implemented in three key study design areas: sampling and recruitment, the collection and use of survey data, and seeking participant consent to link administrative records to individual-level survey data. We also present evidence from the study's fieldwork about participants' acceptability of the survey design and transparency around data use, from recruitment to data collection and processing.

Introduction: Up-to-date, high-quality estimates of population and households are essential for planning the provision of local and central infrastructure.

Objectives: We aimed to derive estimates of population size, and household numbers and size on Census date (21/03/2021) using north-east London primary care Electronic Health Records (EHR) and calculate levels of their agreement with the publicly available official Census 2021 estimates to assess if health data have the potential to be used to create reliable statistics.

Methods: We compared EHR and Census population estimates by sex, age, local authority, and IMD quintile, and EHR and Census household estimates by number, size, and local authority. We estimated 95% Limits of Agreement between EHR and Census household and population estimates using the Bland and Altman method. In sensitivity analyses, we excluded people with no General Practice encounter within 12 months and compared the adjusted population's size to Census estimate.We compared EHR and administrative Statistical Population Dataset (SPD) to Census population estimates by sex and age, and EHR and Admin-based Occupied Address Dataset (ABOAD) to Census household estimates by local authority and household size.

Results: EHR population estimate was 2,130,965, i.e. 7.1% higher than Census of 1,990,087. EHR household estimate was 658,264, i.e. 9.1% lower than Census of 724,045. The estimate of population with recent GP encounter was 11.6% lower than the Census estimate.Compared to Census, both SPD and EHR overcounted population of males (10.7%, 7.9% respectively) and females (3.6%, 2.7% respectively). Both ABOAD and EHR had undercounted households compared to Census (-7.3%; -9.1% respectively).

Conclusions: Reliable, up-to-date populations and households estimates can be derived from health records. High residential mobility increases the complexity of deriving these estimates. Excluding people without GP encounters does not improve agreement with Census. Future work will focus on comparing Census and EHR estimates using individual-level data.

Introduction: Monitoring and addressing health inequalities is important. However, socioeconomic variables are usually unavailable within health datasets. Area deprivation measures provide access to open-source reliable socioeconomic data within low/middle-income countries and can contribute to the monitoring of the Sustainable Development Goals and assessing the growing burden of health inequalities.

Objective: To create a small-area deprivation measure for the whole of Brazil - the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP).

Methods: Using Census Sector data (mean population size=615) from the most recently available Brazilian Demographic Census (2010), variables measuring literacy, household income and housing conditions were standardised using z-scores and summed into a single measure. The IBP was validated using regional small-area measures of vulnerability: Belo Horizonte's Health Vulnerability Index (IVS) and São Paulo's Social Vulnerability Index (IPVS). Mortality data from Minas Gerais were used to estimate age-standardised mortality rates (ASMR) by ill-defined causes across IBP deprivation quintiles.

Results: The IBP was created for 303,218 (97.8%) census sectors (99.7% population). Substantial regional variation in deprivation was found using the IBP measure, with higher deprivation in rural than urban areas. The IBP was correlated with the other indicators used for validation: the IVS (r = 0.96) and the IPVS (r = 0.68). We found gradients across the ill-defined causes ASMR, in Minas Gerais mortality was 2.6 higher in the most deprived quintile of IBP, compared with the least deprived. Main challenges in creating a deprivation measure for LMICs and possible solutions are demonstrated.

Conclusion: A small area deprivation index was created for Brazil, a large and highly diverse middle-income country. The IBP improves our understanding and monitoring of inequalities, serving as a valuable tool for informing targeted public policies. Although the index is based on Brazil's specific context, the challenges faced, and the strategies implemented to tackle them are relevant for other low- and middle-income countries aiming to develop similar tools.

Introduction: Childhood exposure to and duration of poverty can affect several individual characteristics related to intellectual development.

Objectives: This paper examines the implications of movement in and out of childhood poverty using a unique linkable database from the Canadian province of Manitoba. Differences in measurement of poverty and intellectual development are explored.

Methods: Almost 90,000 children were followed using two definitions of poverty - neighborhood and household poverty. The large database permitted exploring the role of another variable - maternal mental health.

Results: The association of household poverty with poorer intellectual outcomes has been shown to be stronger than the association of neighborhood poverty with such outcomes. This was true using various outcome measures appropriate across childhood (from age 5 to age 17). Comparisons with the role of maternal mental health were made and further analyses suggested.

Conclusion: The richness of the data has facilitated the study of childhood intellectual development. Household poverty appears to play an important role; neighborhood poverty and maternal mental health also seem to influence such development, but less strongly.

Introduction: Psychotic disorders are associated with high levels of disability and poor clinical outcomes but little is known about the regional incidence of psychosis in Ontario.

Objective: This study aimed to understand regional incidence variation and demographic and regional characteristics of individuals who may be suitable for receiving early psychosis intervention (EPI) services, as well as evaluate post-diagnosis healthcare utilisation.

Methods: A population-based retrospective cohort study captured incident affective and non-affective psychosis cases among Ontario, Canada residents aged 12-50 from 2017-2021. The sociodemographic characteristics of the cohort were described, including Ontario Health region of residence. Incident cases were followed for 6-months post-diagnosis to capture health service utilisation. Logistic regression was used to model post-diagnosis hospitalisations and Poisson regression to model outpatient psychiatrist visits.

Results: The cohort contained 44,188 individuals (41,257 non-affective psychosis; 3,058 affective psychosis). We observed substantial regional variation in incidence rates, which were higher in the North Western region for non-affective psychosis (167.44/100,000) and North Eastern region for affective psychosis (14.23/100,000) compared to the provincial average (92.24; 6.84/100,000, respectively). Compared to the Toronto region, post-diagnosis hospitalisations were significantly higher in the North East (non-affective psychosis aOR 1.14, 95%CI 1.01-1.30; affective psychosis aOR 1.69, 95%CI 1.13-2.54). Among those with non-affective psychosis, outpatient psychiatrist visits were significantly lower in all regions compared to Toronto (e.g., East aRR 0.61, 95%CI 0.60-0.62; North West aRR 0.34, 95%CI 0.32-0.36).

Conclusions: There is considerable regional variation in incident psychosis and inverse relationships between hospitalisations and outpatient care. To successfully plan for future EPI programs in Ontario, it is essential to understand regional needs using a systematic, population-based approach.