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Article ; Online: Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: a cohort study of 23.5 million people in England.

Green, Mark Alan / McKee, Martin / Massey, Jon / Mackenna, Brian / Mehrkar, Amir / Bacon, Seb / Macleod, John / Sheikh, Aziz / Shah, Syed Ahmar / Katikireddi, Srinivasa Vittal

BMJ open

2024 Volume 14, Issue 1, Page(s) e077948

Abstract: Objective: To determine whether periods of disruption were associated with increased 'avoidable' hospital admissions and wider social inequalities in England.: Design: Observational repeated cross-sectional study.: Setting: England (January 2019 ... ...

Abstract	Objective: To determine whether periods of disruption were associated with increased 'avoidable' hospital admissions and wider social inequalities in England. Design: Observational repeated cross-sectional study. Setting: England (January 2019 to March 2022). Participants: With the approval of NHS England we used individual-level electronic health records from OpenSAFELY, which covered ~40% of general practices in England (mean monthly population size 23.5 million people). Primary and secondary outcome measures: We estimated crude and directly age-standardised rates for potentially preventable unplanned hospital admissions: ambulatory care sensitive conditions and urgent emergency sensitive conditions. We considered how trends in these outcomes varied by three measures of social and spatial inequality: neighbourhood socioeconomic deprivation, ethnicity and geographical region. Results: There were large declines in avoidable hospitalisations during the first national lockdown (March to May 2020). Trends increased post-lockdown but never reached 2019 levels. The exception to these trends was for vaccine-preventable ambulatory care sensitive admissions which remained low throughout 2020-2021. While trends were consistent by each measure of inequality, absolute levels of inequalities narrowed across levels of neighbourhood socioeconomic deprivation, Asian ethnicity (compared with white ethnicity) and geographical region (especially in northern regions). Conclusions: We found no evidence that periods of healthcare disruption from the COVID-19 pandemic resulted in more avoidable hospitalisations. Falling avoidable hospital admissions has coincided with declining inequalities most strongly by level of deprivation, but also for Asian ethnic groups and northern regions of England.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Cohort Studies ; Communicable Disease Control ; Cross-Sectional Studies ; Pandemics ; England/epidemiology ; Hospitalization
Language	English
Publishing date	2024-01-08
Publishing country	England
Document type	Observational Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2023-077948
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England.

Hopcroft, Lisa Em / Massey, Jon / Curtis, Helen J / Mackenna, Brian / Croker, Richard / Brown, Andrew D / O'Dwyer, Thomas / Macdonald, Orla / Evans, David / Inglesby, Peter / Bacon, Sebastian Cj / Goldacre, Ben / Walker, Alex J

JMIR medical informatics

2023 Volume 11, Page(s) e44237

Abstract: Background: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. ...

Abstract	Background: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. Objective: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. Methods: Here we report a new data-driven approach to quantify how "unusual" the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. Results: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. Conclusions: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance.
Language	English
Publishing date	2023-04-19
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/44237
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impact of first UK COVID-19 lockdown on hospital admissions: Interrupted time series study of 32 million people.

Shah, Syed Ahmar / Brophy, Sinead / Kennedy, John / Fisher, Louis / Walker, Alex / Mackenna, Brian / Curtis, Helen / Inglesby, Peter / Davy, Simon / Bacon, Seb / Goldacre, Ben / Agrawal, Utkarsh / Moore, Emily / Simpson, Colin R / Macleod, John / Cooksey, Roxane / Sheikh, Aziz / Katikireddi, Srinivasa Vittal

EClinicalMedicine

2022 Volume 49, Page(s) 101462

Abstract: Background: Uncontrolled infection and lockdown measures introduced in response have resulted in an unprecedented challenge for health systems internationally. Whether such unprecedented impact was due to lockdown itself and recedes when such measures ... ...

Abstract	Background: Uncontrolled infection and lockdown measures introduced in response have resulted in an unprecedented challenge for health systems internationally. Whether such unprecedented impact was due to lockdown itself and recedes when such measures are lifted is unclear. We assessed the short- and medium-term impacts of the first lockdown measures on hospital care for tracer non-COVID-19 conditions in England, Scotland and Wales across diseases, sexes, and socioeconomic and ethnic groups. Methods: We used OpenSAFELY (for England), EAVEII (Scotland), and SAIL Databank (Wales) to extract weekly hospital admission rates for cancer, cardiovascular and respiratory conditions (excluding COVID-19) from the pre-pandemic period until 25/10/2020 and conducted a controlled interrupted time series analysis. We undertook stratified analyses and assessed admission rates over seven months during which lockdown restrictions were gradually lifted. Findings: Our combined dataset included 32 million people who contributed over 74 million person-years. Admission rates for all three conditions fell by 34.2% (Confidence Interval (CI): -43.0, -25.3) in England, 20.9% (CI: -27.8, -14.1) in Scotland, and 24.7% (CI: -36.7, -12.7) in Wales, with falls across every stratum considered. In all three nations, cancer-related admissions fell the most while respiratory-related admissions fell the least (e.g., rates fell by 40.5% (CI: -47.4, -33.6), 21.9% (CI: -35.4, -8.4), and 19.0% (CI: -30.6, -7.4) in England for cancer, cardiovascular-related, and respiratory-related admissions respectively). Unscheduled admissions rates fell more in the most than the least deprived quintile across all three nations. Some ethnic minority groups experienced greater falls in admissions (e.g., in England, unscheduled admissions fell by 9.5% (CI: -20.2, 1.2) for Whites, but 44.3% (CI: -71.0, -17.6), 34.6% (CI: -63.8, -5.3), and 25.6% (CI: -45.0, -6.3) for Mixed, Other and Black ethnic groups respectively). Despite easing of restrictions, the overall admission rates remained lower in England, Scotland, and Wales by 20.8%, 21.6%, and 22.0%, respectively when compared to the same period (August-September) during the pre-pandemic years. This corresponds to a reduction of 26.2, 23.8 and 30.2 admissions per 100,000 people in England, Scotland, and Wales respectively. Interpretation: Hospital care for non-COVID diseases fell substantially across England, Scotland, and Wales during the first lockdown, with reductions persisting for at least six months. The most deprived and minority ethnic groups were impacted more severely. Funding: This work was funded by the Medical Research Council as part of the Lifelong Health and Wellbeing study as part of National Core Studies (MC_PC_20030). SVK acknowledges funding from the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE - The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. BG has received research funding from the NHS National Institute for Health Research (NIHR), the Wellcome Trust, Health Data Research UK, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme.
Language	English
Publishing date	2022-05-20
Publishing country	England
Document type	Journal Article
ISSN	2589-5370
ISSN (online)	2589-5370
DOI	10.1016/j.eclinm.2022.101462
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections

Fahmi, Ali / Palin, Victoria / Zhong, Xiaomin / Yang, Ya-Ting / Watts, Simon / Ashcroft, Darren M / Goldacre, Ben / Mackenna, Brian / Fisher, Louis / Massey, Jon / Mehrkar, Amir / Bacon, Seb / the OpenSAFELY collaborative / Hand, Kieran / van Staa, Tjeerd

medRxiv

Abstract: Background: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The COVID-19 pandemic impacted antibiotic prescribing for common bacterial infections. This highlights the need to ... ...

Abstract	Background: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The COVID-19 pandemic impacted antibiotic prescribing for common bacterial infections. This highlights the need to examine risk of hospital admissions related to common infections, excluding COVID-19 infections during the pandemic. Methods: With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from January 2019 to August 2022. We excluded patients with a COVID-19 record 90 days before to 30 days after the infection diagnosis. Using Cox proportional-hazard regression models, we predicted risk of infection-related hospital admission in 30 days follow-up period after the diagnosis. Results: We found 12,745,165 infection diagnoses from January 2019 to August 2022. Of them, 80,395 (2.05%) cases were admitted to hospital in the follow-up period. Counts of hospital admission for infections dropped during COVID-19, e.g., LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted odds ratio (OR) of 0.35; 95% CI, 0.35-0.36) and UTI (adjusted OR 0.45; 95% CI, 0.44-0.46), compared to URTI (adjusted OR 1.04; 95% CI, 1.03-1.06). Conclusion: Large effectiveness of antibiotics in preventing complications related to LRTI and UTI can support better targeting of antibiotics to patients with higher complication risks.
Keywords	covid19
Language	English
Publishing date	2023-07-18
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.07.16.23292723
Database	COVID19

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Article ; Online: Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections

Fahmi, Ali / Palin, Victoria / Zhong, Xiaomin / Yang, Ya-Ting / Watts, Simon / Ashcroft, Darren M / Goldacre, Ben / Mackenna, Brian / Fisher, Louis / Massey, Jon / Mehrkar, Amir / Bacon, Seb / OpenSAFELY collaborative / Hand, Kieran / Staa, Tjeerd Pieter van

medRxiv

Abstract	Background: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The COVID-19 pandemic impacted antibiotic prescribing for common bacterial infections. This highlights the need to examine risk of hospital admissions related to common infections, excluding COVID-19 infections during the pandemic. Methods: With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from January 2019 to August 2022. We excluded patients with a COVID-19 record 90 days before to 30 days after the infection diagnosis. Using Cox proportional-hazard regression models, we predicted risk of infection-related hospital admission in 30 days follow-up period after the diagnosis. Results: We found 12,745,165 infection diagnoses from January 2019 to August 2022. Of them, 80,395 (2.05%) cases were admitted to hospital in the follow-up period. Counts of hospital admission for infections dropped during COVID-19, e.g., LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted odds ratio (OR) of 0.35; 95% CI, 0.35-0.36) and UTI (adjusted OR 0.45; 95% CI, 0.44-0.46), compared to URTI (adjusted OR 1.04; 95% CI, 1.03-1.06). Conclusion: Large effectiveness of antibiotics in preventing complications related to LRTI and UTI can support better targeting of antibiotics to patients with higher complication risks.
Keywords	covid19
Language	English
Publishing date	2023-07-18
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.07.16.23292723
Database	COVID19

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Article ; Online: Impact of the COVID-19 pandemic on antipsychotic prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: a federated analysis of 59 million patients' primary care records in situ using OpenSAFELY.

Macdonald, Orla / Green, Amelia / Walker, Alex / Curtis, Helen / Croker, Richard / Brown, Andrew / Butler-Cole, Ben / Andrews, Colm / Massey, Jon / Inglesby, Peter / Morton, Caroline / Fisher, Louis / Morley, Jessica / Mehrkar, Amir / Bacon, Sebastian / Davy, Simon / Evans, David / Dillingham, Iain / Ward, Tom /

Hulme, William / Bates, Chris / Cockburn, Jonathan / Parry, John / Hester, Frank / Harper, Sam / O'Hanlon, Shaun / Eavis, Alex / Jarvis, Richard / Avramov, Dima / Parkes, Nasreen / Wood, Ian / Goldacre, Ben / Mackenna, Brian

BMJ mental health

2023 Volume 26, Issue 1

Abstract: Background: The COVID-19 pandemic affected how care was delivered to vulnerable patients, such as those with dementia or learning disability.: Objective: To explore whether this affected antipsychotic prescribing in at-risk populations.: Methods: ... ...

Abstract	Background: The COVID-19 pandemic affected how care was delivered to vulnerable patients, such as those with dementia or learning disability. Objective: To explore whether this affected antipsychotic prescribing in at-risk populations. Methods: With the approval of NHS England, we completed a retrospective cohort study, using the OpenSAFELY platform to explore primary care data of 59 million patients. We identified patients in five at-risk groups: autism, dementia, learning disability, serious mental illness and care home residents. We calculated the monthly prevalence of antipsychotic prescribing in these groups, as well as the incidence of new prescriptions in each month. Findings: The average monthly rate of antipsychotic prescribing increased in dementia from 82.75 patients prescribed an antipsychotic per 1000 patients (95% CI 82.30 to 83.19) in January-March 2019 to 90.1 (95% CI 89.68 to 90.60) in October-December 2021 and from 154.61 (95% CI 153.79 to 155.43) to 166.95 (95% CI 166.23 to 167.67) in care homes. There were notable spikes in the rate of new prescriptions issued to patients with dementia and in care homes. In learning disability and autism groups, the rate of prescribing per 1000 decreased from 122.97 (95% CI 122.29 to 123.66) to 119.29 (95% CI 118.68 to 119.91) and from 54.91 (95% CI 54.52 to 55.29) to 51.04 (95% CI 50.74 to 51.35), respectively. Conclusion and implications: We observed a spike in antipsychotic prescribing in the dementia and care home groups, which correlated with lockdowns and was likely due to prescribing of antipsychotics for palliative care. We observed gradual increases in antipsychotic use in dementia and care home patients and decreases in their use in patients with learning disability or autism.
MeSH term(s)	Humans ; COVID-19 ; Antipsychotic Agents/therapeutic use ; Autistic Disorder/drug therapy ; Pandemics ; Retrospective Studies ; Communicable Disease Control ; Learning Disabilities/drug therapy ; Primary Health Care ; Dementia/drug therapy
Chemical Substances	Antipsychotic Agents
Language	English
Publishing date	2023-09-15
Publishing country	England
Document type	Journal Article
ISSN	2755-9734
ISSN (online)	2755-9734
DOI	10.1136/bmjment-2023-300775
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: a cohort study of 23.5 million people in England.

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Article: Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England.

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Order via subito

Article ; Online: Impact of first UK COVID-19 lockdown on hospital admissions: Interrupted time series study of 32 million people.

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Order via subito

Inter-library loan at ZB MED

Article ; Online: Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections

Full text online

Kategorien

Inter-library loan at ZB MED

Article ; Online: Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections

Full text online

Kategorien

Inter-library loan at ZB MED

Article ; Online: Impact of the COVID-19 pandemic on antipsychotic prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: a federated analysis of 59 million patients' primary care records in situ using OpenSAFELY.

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Kategorien

Order via subito

Inter-library loan at ZB MED