International Journal of Population Data Science最新文献

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Introduction: 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.

Objectives: 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.

Methods: 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.

Results: The Four Questions-Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)?-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.

Conclusions: 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.

Highlights: 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-Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)?-should be carefully considered within established data governance processes and among core partners.

Introduction: "Big data" - including linked administrative data - can be exploited to evaluate interventions for maternal and child health, providing time- and cost-effective alternatives to randomised controlled trials. However, using these data to evaluate population-level interventions can be challenging.

Objectives: We aimed to inform future evaluations of complex interventions by describing sources of bias, lessons learned, and suggestions for improvements, based on two observational studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP) in England and Scotland.

Methods: We first considered how different sources of potential bias within the administrative data could affect results of the evaluations. We explored how each study design addressed these sources of bias using maternal confounders captured in the data. We then determined what additional information could be captured at each step of the complex intervention to enable analysts to minimise bias and maximise comparability between intervention and usual care groups, so that any observed differences can be attributed to the intervention.

Results: Lessons learned include the need for i) detailed data on intervention activity (dates/geography) and usual care; ii) improved information on data linkage quality to accurately characterise control groups; iii) more efficient provision of linked data to ensure timeliness of results; iv) better measurement of confounding characteristics affecting who is eligible, approached and enrolled.

Conclusions: Linked administrative data are a valuable resource for evaluations of the FNP national programme and other complex population-level interventions. However, information on local programme delivery and usual care are required to account for biases that characterise those who receive the intervention, and to inform understanding of mechanisms of effect. National, ongoing, robust evaluations of complex public health evaluations would be more achievable if programme implementation was integrated with improved national and local data collection, and robust quasi-experimental designs.

Introduction: Research to date has established that the COVID-19 pandemic has not impacted everyone equitably. Whether this unequitable impact was seen educationally with regards to educator reported barriers to distance learning, concerns and mental health is less clear.

Objective: The objective of this study was to explore the association between the neighbourhood composition of the school and kindergarten educator-reported barriers and concerns regarding children's learning during the first wave of COVID-19 related school closures in Ontario, Canada.

Methods: In the spring of 2020, we collected data from Ontario kindergarten educators (n = 2569; 74.2% kindergarten teachers, 25.8% early childhood educators; 97.6% female) using an online survey asking them about their experiences and challenges with online learning during the first round of school closures. We linked the educator responses to 2016 Canadian Census variables based on schools' postal codes. Bivariate correlations and Poisson regression analyses were used to determine if there was an association between neighbourhood composition and educator mental health, and the number of barriers and concerns reported by kindergarten educators.

Results: There were no significant findings with educator mental health and school neighbourhood characteristics. Educators who taught at schools in neighbourhoods with lower median income reported a greater number of barriers to online learning (e.g., parents/guardians not submitting assignments/providing updates on their child's learning) and concerns regarding the return to school in the fall of 2020 (e.g., students' readjustment to routines). There were no significant associations with educator reported barriers or concerns and any of the other Census neighbourhood variables (proportion of lone parent families, average household size, proportion of population that do no speak official language, proportion of population that are recent immigrants, or proportion of population ages 0-4).

Conclusions: Overall, our study suggests that the neighbourhood composition of the children's school location did not exacerbate the potential negative learning experiences of kindergarten students and educators during the COVID-19 pandemic, although we did find that educators teaching in schools in lower-SES neighbourhoods reported more barriers to online learning during this time. Taken together, our study suggests that remediation efforts should be focused on individual kindergarten children and their families as opposed to school location.