The overarching aim of this project is to identify and review the evidence based on approaches taken within the health and care system to improve the quality of data used for the identification and monitoring of health inequalities.
Background
Previous work has established that high data quality is a necessary criterion for understanding and developing policies to combat inequities in health outcomes. For example, where ethnicity data are missing or incorrect, it is not possible to determine whether a particular subgroup within a population is at risk of adverse outcomes in a given setting.
It is therefore essential to know as much as possible, including about protected characteristics, when aiming to determine population-level patterns in diagnosis, treatment, survival and quality of life. Without high-quality data, we risk ignoring populations at need of intervention — or exacerbating the inequalities we aim to reduce.
The aim of this project is to better understand how to improve the quality of data researchers and other professionals rely on for detecting and monitoring health inequalities. Here, we describe the project’s phases, our current state of work and our plans for disseminating our findings.
Previous phases
In Phase 1 of this work, the project team undertook a scoping review to identify evidence-based approaches for improving the quality of health inequality data based on published scientific and grey literature. The review was published in the International Journal of Environmental Research and Public Health (accessible here). Click here to find out more about the findings and the Phase 1 undertaking.
As part of Phase 2, we gathered qualitative data from professionals working in local health and care systems, public health and the third sector to gain insight into the most effective way to present the evidence collated from the scoping review. The objective of the interviews and focus groups was to gain insights into the types of resources that can aid in implementing mechanisms to improve data quality. Our research article based on this qualitative study is currently under review in an academic journal.
Project Aims and Activities
Our current work will take forward Phase 2 through an exploration and analysis of views on effective ways to communicate and transition identified evidence into practice. This knowledge will feed into the development of a resource (or resources) for enabling services to realise mechanisms for improving the quality of health inequalities data. The completion of this phase will allow us to adopt a co-production approach for developing a service-driven resource (or resources) for use in driving action to improve data on health inequalities.
Anticipated Impact
By working with those within services — and gaining insights that will help tailor the resources for their settings — we will aim to implement best practices for collecting, maintaining, and analysing data related to health inequalities. Dr. Louise Lafortune, the co–theme lead of the PEDS projects, will also present our current findings at the forthcoming ‘ARC East of England: Celebrating Research & Shaping Our Future Together’ Annual Showcase and Partners Event in Norwich.
The team is also working with the School for Public Health Research Health Inequalities programme to review available data and datasets (e.g. surveys, information routinely collected by services and organisations) for evaluating the impacts of levelling up/pandemic recovery initiatives. This includes looking at how to improve the type of information used and how it is used by describing biases in the data-to-decision–making pathways.
Papers and resources
This project can be viewed in a case study format for easy accessibility
Who is involved?
- Principle Investigator: Louise Lafortune (Co-Theme Lead for Population Evidence and Data Science, NIHR ARC East of England), University of Cambridge
- Sian Evans, Office for Health Improvement and Disparities
- Carol Brayne, University of Cambridge
- Emre Oguzman, Hertfordshire Partnership University NHS Foundation Trust
- Jonathan R. Goodman, University of Cambridge
- Aalia Karamat, University of Cambridge
Contact
Louise Lafortune, ll394@cam.ac.uk.
