Data saves lives: bottom-up, professionally-led endorsement would increase the chance of success

Lusignan, Simon de; Leston, Meredith; Ikpoh, Margaret; Howsam, Gary

doi:10.3399/bjgp22x720965

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…It is now one of the data collections available through the NHS England Secure Data Environment (SDE) [45]. SDE was created as part of NHS England's Data Saves Lives policy, following the Goldacre report [46].…”

Section: Interpretations and Implicationsmentioning

confidence: 99%

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Kar,

Taylor,

Joy

et al. 2024

Journal of Medical Internet Research

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Background: No single multimorbidity measure is validated for use in NHS England's General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care dataset created for coronavirus disease 19 (COVID-19) pandemic research. A single morbidity measure is advantageous when there is a need to adjust for multimorbidity, such as modelling the effectiveness of vaccinations against COVID-19, as including multiple individual morbidities is challenging. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk. However, the number of Systematised Nomenclature of Medicine clinical terms (SNOMED CT) for the GDPPR dataset is limited and does not define all the conditions used to calculate the CMMS Objective: To develop and validate a modified version of CMMS using the clinical terms available for the GDPPR. Methods:We used pseudonymised data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), which has a more extensive SNOMED CT list. From the 37 conditions used in the original CMMS model, we selected conditions either with: (a) high prevalence ratio (? 85%), calculated as the prevalence in the RSC data set as defined by the GDPPR set of SNOMED CT codes, divided by the prevalence as defined by the RSC set of SNOMED CT codes, or (b) conditions with lower prevalence ratio but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development dataset (n=300,000) and construct a new CMMS model, following the original CMMS, with variable reduction and parsimony, achieved by backward elimination and Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous dataset (n=150,000) and 1-year and 5-year mortality estimates for an asynchronous dataset (n=150,000).Results: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of a modified CMMS, which we previously developed using RSC data and the more extensive RSC SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation datasets (Harrell's C=0.92), and 5-year mortality was slightly lower (Harrell's C= 0.90), and the calibration was reasonable following an adjustment for over-fitting. The performance was similar to that of both the original and previous modified CMMS models.

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Section: Interpretations and Implicationsmentioning

confidence: 99%

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Kar,

Taylor,

Joy

et al. 2024

Journal of Medical Internet Research

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“…"Data Saves Lives" new post-pandemic policy document, recognises need for IMS. Stresses the need for accessible data for research and care [34]. Not all NHS information strategies have been a success [35].…”

Section: Englandmentioning

confidence: 99%

Integrated Management Systems (IMS) to Support and Sustain Quality One Health Services: International Lessons from the COVID-19 Pandemic by the IMIA Primary Care Working Group

et al. 2023

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Objectives: One Health considers human, animal and environment health as a continuum. The COVID-19 pandemic started with the leap of a virus from animals to humans. Integrated management systems (IMS) should provide a coherent management framework, to meet reporting requirements and support care delivery. We report IMS deployment during, and retention post the COVID-19 pandemic, and exemplar One Health use cases. Methods: Six volunteer members of the International Medical Association’s (IMIA) Primary Care Working Group provided data about any IMS and One Health use to support the COVID-19 pandemic initiatives. We explored how IMS were: (1) Integrated with organisational strategy; (2) Utilised standardised processes, and (3) Met reporting requirements, including public health. Selected contributors provided Unified Modelling Language (UML) use case diagram for a One Health exemplar. Results: There was weak evidence of synergy between IMS and health system strategy to the COVID-19 pandemic. However, there were rapid pragmatic responses to COVID-19, not citing IMS. All health systems implemented IMS to link COVID test results, vaccine uptake and outcomes, particularly mortality and to provide patients access to test results and vaccination certification. Neither proportion of gross domestic product alone, nor vaccine uptake determined outcome. One Health exemplars demonstrated that animal, human and environmental specialists could collaborate. Conclusions: IMS use improved the pandemic response. However, IMS use was pragmatic rather than utilising an international standard, with some of their benefits lost post-pandemic. Health systems should incorporate IMS that enables One Health approaches as part of their post COVID-19 pandemic preparedness.

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“…GPs should be the orchestra I thank Dr Mummery for writing this article 1 and I hope it is taken on board by those who are driving the changes in primary care. May I though reflect that one unspoken concern is that nobody polices primary care as we are independent contractors, and whether a practice provides extra services (for example, ear syringing) or works more sessions for its income is not checked.…”

Section: Medical Musingsmentioning

confidence: 99%

“…I too was interested in the extraordinary outcome of this single-centre trial, 1,2 and it has unquestionably influenced my practice since its publication. Thankfully, and ethics notwithstanding, this same question HAS been studied again in a UK population.…”

mentioning

confidence: 99%

TIME to take antihypertensives

Kelly

2022

Br J Gen Pract

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Data saves lives: bottom-up, professionally-led endorsement would increase the chance of success

Cited by 7 publications

References 6 publications

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Creating a Modified Version of the Cambridge Multimorbidity Score (CMMS) to Predict Mortality in People Over 16 Years in the English Nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) Dataset: Model Development and Validation (Preprint)

Integrated Management Systems (IMS) to Support and Sustain Quality One Health Services: International Lessons from the COVID-19 Pandemic by the IMIA Primary Care Working Group

TIME to take antihypertensives

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