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References.bib
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References.bib
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@article{Box1,
author = {Box, George E. P.},
title = {Science and Statistics},
journal = {Journal of the American Statistical Association},
volume = {71},
number = {356},
pages = {791-799},
year = {1976},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.1976.10480949},
URL = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1976.10480949}
}
@misc{Wyatt1,
author = {Wyatt, Steven},
title = {Strategies to reduce inequalities in access to planned hospital procedures},
institution = {The Strategy Unit},
year = {2022},
URL = {https://www.midlandsdecisionsupport.nhs.uk/wp-content/uploads/2022/05/Strategies-to-reduce-inequalities-in-access-to-planned-hospital-procedures_20220429iv.pdf}
}
@article{jarmanExplainingDifferencesEnglish1999,
title = {Explaining differences in {English} hospital death rates using routinely collected data},
volume = {318},
issn = {0959-8138 1468-5833},
doi = {10/fkkfm9},
abstract = {Objectives: To ascertain hospital inpatient mortality in England and to determine which factors best explain variation in standardised hospital death ratios. Design: Weighted linear regression analysis of routinely collected data over four years, with hospital standardised mortality ratios as the dependent variable. Setting: England. Subjects:Eight million discharges from NHS hospitals when the primary diagnosis was one of the diagnoses accounting for 80\% of inpatient deaths. Main outcome measures: Hospital standardised mortality ratios and predictors of variations in these ratios. Results: The four year crude death rates varied across hospitals from 3.4\% to 13.6\% (average for England 8.5\%), and standardised hospital mortality ratios ranged from 53 to 137 (average for England 100). The percentage of cases that were emergency admissions (60\% of total hospital admissions) was the best predictor of this variation in mortality, with the ratio of hospital doctors to beds and general practitioners to head of population the next best predictors. When analyses were restricted to emergency admissions (which covered 93\% of all patient deaths analysed) number of doctors per bed was the best predictor. Conclusion: Analysis of hospital episode statistics reveals wide variation in standardised hospital mortality ratios in England. The percentage of total admissions classified as emergencies is the most powerful predictor of variation in mortality. The ratios of doctors to head of population served, both in hospital and in general practice, seem to be critical determinants of standardised hospital death rates; the higher these ratios, the lower the death rates in both cases.\%U http://www.bmj.com/content/bmj/318/7197/1515.full.pdf},
number = {7197},
journal = {BMJ},
author = {Jarman, B. and Gault, S. and Alves, B. and Hider, A. and Dolan, S. and Cook, A. and Hurwitz, B. and Iezzoni, L. I.},
year = {1999},
pages = {1515--1520},
}
@article{campbellDevelopingSummaryHospital2012,
title = {Developing a summary hospital mortality index: retrospective analysis in {English} hospitals over five years},
volume = {344},
doi = {10/gb3r9t},
abstract = {Objectives To develop a transparent and reproducible measure for hospitals that can indicate when deaths in hospital or within 30 days of discharge are high relative to other hospitals, given the characteristics of the patients in that hospital, and to investigate those factors that have the greatest effect in changing the rank of a hospital, whether interactions exist between those factors, and the stability of the measure over time.Design Retrospective cross sectional study of admissions to English hospitals.Setting Hospital episode statistics for England from 1 April 2005 to 30 September 2010, with linked mortality data from the Office for National Statistics.Participants 36.5 million completed hospital admissions in 146 general and 72 specialist trusts.Main outcome measures Deaths within hospital or within 30 days of discharge from hospital.Results The predictors that were used in the final model comprised admission diagnosis, age, sex, type of admission, and comorbidity. The percentage of people admitted who died in hospital or within 30 days of discharge was 4.2\% for males and 4.5\% for females. Emergency admissions comprised 75\% of all admissions and 5.5\% died, in contrast to 0.8\% who died after an elective admission. The percentage who died with a Charlson comorbidity score of 0 was 2\% in contrast with 15\% who died with a score greater than 5. Given these variables, the relative standardised mortality rates of the hospitals were not noticeably changed by adjusting for the area level deprivation and number of previous emergency visits to hospital. There was little evidence that including interaction terms changed the relative values by any great amount. Using these predictors the summary hospital mortality index (SHMI) was derived. For 2007/8 the model had a C statistic of 0.911 and accounted for 81\% of the variability of between hospital mortality. A random effects funnel plot was used to identify outlying hospitals. The outliers from the SHMI over the period 2005-10 have previously been identified using other mortality indicators. Conclusion The SHMI is a relatively simple tool that can be used in conjunction with other information to identify hospitals that may need further investigation.},
journal = {BMJ},
author = {Campbell, Michael J. and Jacques, Richard M. and Fotheringham, James and Maheswaran, Ravi and Nicholl, Jon},
year = {2012},
pages = {e1001},
}
@article{charlsonNewMethodClassifying1987,
title = {A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation},
author = {Charlson, M. E. and Pompei, P. and Ales, K. L. and MacKenzie, C. R.},
date = {1987},
journaltitle = {Journal of Chronic Diseases},
shortjournal = {J. Chronic Dis.},
volume = {40},
number = {5},
pages = {373--83},
issn = {0021-9681 (Print) 0021-9681 (Linking)},
doi = {10/c4rqj6},
abstract = {The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12\% (181); "1-2", 26\% (225); "3-4", 52\% (71); and "greater than or equal to 5", 85\% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8\% (588); "1", 25\% (54); "2", 48\% (25); "greater than or equal to 3", 59\% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.},
langid = {english},
keywords = {*Epidemiologic Methods,*Longitudinal Studies,*Morbidity,Actuarial Analysis,Age Factors,Breast Neoplasms/epidemiology,Female,Follow-Up Studies,Humans,New York City,Prognosis,Prospective Studies,Risk}
}
@article{carrEvaluationImprovementNational2021,
title = {Evaluation and Improvement of the {{National Early Warning Score}} ({{NEWS2}}) for {{COVID-19}}: A Multi-Hospital Study},
author = {Carr, Ewan and Bendayan, Rebecca and Bean, Daniel and Stammers, Matt and Wang, Wenjuan and Zhang, Huayu and Searle, Thomas and Kraljevic, Zeljko and Shek, Anthony and Phan, Hang T. T. and Muruet, Walter and Gupta, Rishi K. and Shinton, Anthony J. and Wyatt, Mike and Shi, Ting and Zhang, Xin and Pickles, Andrew and Stahl, Daniel and Zakeri, Rosita and Noursadeghi, Mahdad and O’Gallagher, Kevin and Rogers, Matt and Folarin, Amos and Karwath, Andreas and Wickstrøm, Kristin E. and Köhn-Luque, Alvaro and Slater, Luke and Cardoso, Victor Roth and Bourdeaux, Christopher and Holten, Aleksander Rygh and Ball, Simon and McWilliams, Chris and Roguski, Lukasz and Borca, Florina and Batchelor, James and Amundsen, Erik Koldberg and Wu, Xiaodong and Gkoutos, Georgios V. and Sun, Jiaxing and Pinto, Ashwin and Guthrie, Bruce and Breen, Cormac and Douiri, Abdel and Wu, Honghan and Curcin, Vasa and Teo, James T. and Shah, Ajay M. and Dobson, Richard J. B.},
date = {2021-01-21},
journaltitle = {BMC Medicine},
shortjournal = {BMC Medicine},
volume = {19},
number = {1},
pages = {23},
issn = {1741-7015},
doi = {10.1186/s12916-020-01893-3},
url = {https://doi.org/10.1186/s12916-020-01893-3},
abstract = {The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.}
}
@article{vanaltenReweightingUKBiobank2022,
title = {Reweighting the {{UK Biobank}} to Reflect Its Underlying Sampling Population Substantially Reduces Pervasive Selection Bias Due to Volunteering},
author = {van Alten, Sjoerd and Domingue, Benjamin W. and Galama, Titus and Marees, Andries T.},
options = {useprefix=true},
date = {2022},
journaltitle = {medRxiv},
publisher = {{Cold Spring Harbor Laboratory Press}},
doi = {10.1101/2022.05.16.22275048},
url = {https://www.medrxiv.org/content/early/2022/05/16/2022.05.16.22275048},
abstract = {The UK Biobank (UKB) is a large cohort study of considerable empirical importance to fields such as medicine, epidemiology, statistical genetics, and the social sciences, due to its very large size (∼ 500,000 individuals) and its wide availability of variables. However, the UKB is not representative of its underlying sampling population. Selection bias due to volunteering (volunteer bias) is a known source of confounding. Individuals entering the UKB are more likely to be older, to be female, and of higher socioeconomic status. Using representative microdata from the UK Census as a reference, we document significant bias in estimated associations due to non-random selection into the UKB. For some associations, volunteer bias in the UKB is so severe that estimates have the opposite sign. E.g., older individuals in the UKB tend to be in better health. To aid researchers in correcting for volunteer bias in the UKB, we construct inverse probability weights based on UK census microdata. The use of these weights in weighted regressions reduces 78\% of volunteer bias on average. Our inverse probability weights will be made available.Competing Interest StatementThe authors have declared no competing interest.Funding StatementResearch reported in this publication was supported by the National Institute On Aging of the National Institutes of Health (RF1055654 and R56AG058726), the Dutch National Science Foundation (016.VIDI.185.044), and the Jacobs Foundation.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Ethics committee of Vrije Universiteit Amsterdam gave ethical approval for this workI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe present study uses UK Census safeguarded microdata and UK Biobank data. UK Census safeguarded microdata data and UKB data can be accessed upon request for research projects that have obtained necessary approval. These requests can be submitted through https://ukdataservice.ac.uk/ and https://www.ukbiobank.ac.uk/ respectively. https://www.ukbiobank.ac.uk/ https://ukdataservice.ac.uk/},
annotation = {\_eprint: https://www.medrxiv.org/content/early/2022/05/16/2022.05.16.22275048.full.pdf}
}