The MIDAS-GP Study

Multi-level Integrated Data for musculoskeletal health intelligence and ActionS

Summary

Reducing the burden of common musculoskeletal (MSK) conditions and addressing NHS priories requires better integration of data upstream of hospital care. Meaningful data collected from population level surveys, primary care electronic health records (EHR) and MSK community service providers have not been co-ordinated and linked together to inform public health and primary care policy making. In this project, we seek to overcome these barriers by developing a novel and scalable approach within North Staffordshire & Stoke-on-Trent.

 

Cheif Investigator: Professor George Peat
Associate Investigator: Dr Jonathan Hill
Trial Manager: Steff Garvin
Sponsor / Reference Number: Keele University / RG-0327-21
Funder / Reference Number: Nuffield Foundation / OBF/43990
UKCRN Study Portfolio Reference Number: TBC
Registration Reference Number: ISRCTN18132064
Start Date: 01 Oct 2020
End Date:    

This is an observational cohort study with 6-month follow-up for self-reported outcomes and 12-month follow-up for Electronic Health Record outcomes.

The overall aim of this ‘at-the-point-of-care' prospective cohort study is to collect and describe episodic MSK outcome data using a point-of-care survey in primary care capturing: a) patient reported outcome and experience measures, b) linked primary care electronic health record and NHS Digital datasets, including information about the care provided, and c) linked place-based information to understand relevant social determinants for MSD inequalities.

Primary objective: 

To estimate the magnitude of between-practice variation in case-mix-adjusted rates of primary care reconsultation, secondary care referral, opioid prescribing, and musculoskeletal imaging among adult patients presenting with painful MSDs.

Secondary objectives:

  • To estimate the magnitude of variation between primary care networks in case-mix-adjusted 6-month change in work productivity (WPAI), and general health utility (EQ5D5L)
  • To plot the flow of patients along different MSK care pathways and to define MSK service organisation characteristics for participating PCNs
  • To produce new benchmarked data at the PCN level and to explore ways to identify inequalities in musculoskeletal outcomes and care (treatment)
  • To provide new insights into the credibility, validity, and persuasiveness of new visualisations of this MSK health intelligence and present these for feedback from key stakeholders
  • To explore the potential reasons for identified inequalities using data on wider determinants
  • To test our case-mix adjustment model for identifying outliers for MSD outcomes
  • To evaluate patterns of non-response and non-participation and their implications for bias in the above estimates