Decreased Incidence of Oncology Admissions in 75 Helios Hospitals in Germany during the COVID-19 Pandemic

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Abstract

Background

The COVID-19 pandemic lead to a massive shutdown of social life in Germany starting in March 2020. Elective medical treatment was substantially reduced but urgent diagnostics and treatment including cancer care should not have been affected.

Materials and Methods

We analyzed the number of oncology admissions to 75 German Helios hospitals during 2 time periods in 2020 and compared the data with the respective periods in 2019. The study included nearly 69,000 admissions in total.

Results

A highly significant reduction in overall cancer admissions was seen for the early lockdown period from 13 March to 28 April 2020 compared to the same period in 2019. After an official communication advising the health system to return to normal practice on 29 April 2020, we again found a highly significant difference in admissions compared to the respective time in the previous year. Subgroup analysis shows a significant impact of age >75 years, high hospital volume, and intermediate or high COVID-19 case volume in the federal states. Gender had no impact on admission numbers. The effects and significance levels were comparable in nearly all different diagnostic subgroups according to the ICD codes.

Conclusions

For cancer diagnosis and treatment, we found a statistically significant decrease in hospital admissions in the range of 10–20% for both study periods in comparison to the previous year.

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Most cited references 7

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Fitting Linear Mixed-Effects Models Usinglme4

Steve Walker, Martin Mächler, Ben Bolker … (2022)

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Fitting Linear Mixed-Effects Models Using lme4

Douglas M Bates, Martin Mächler, Ben Bolker … (2015)

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660

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The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study

Camille Maringe, James Spicer, Melanie Morris … (2020)

Summary Background Since a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types. Methods In this national population-based modelling study, we used linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data. Findings We collected data for 32 583 patients with breast cancer, 24 975 with colorectal cancer, 6744 with oesophageal cancer, and 29 305 with lung cancer. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9–9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266–295) and 344 (329–358) additional deaths. For colorectal cancer, we estimate 1445 (1392–1591) to 1563 (1534–1592) additional deaths, a 15·3–16·6% increase; for lung cancer, 1235 (1220–1254) to 1372 (1343–1401) additional deaths, a 4·8–5·3% increase; and for oesophageal cancer, 330 (324–335) to 342 (336–348) additional deaths, 5·8–6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291–3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204–63 229 years. Interpretation Substantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer. Funding UK Research and Innovation Economic and Social Research Council.

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Author and article information

Journal

Journal ID (nlm-ta): Oncol Res Treat

Journal ID (iso-abbrev): Oncol Res Treat

Journal ID (publisher-id): ORT

Title: Oncology Research and Treatment

Publisher: S. Karger GmbH (Wilhelmstrasse 20A, P.O. Box · Postfach · Case postale, D–79095, Freiburg, Germany · Deutschland · Allemagne, Phone: +49 761 45 20 70, Fax: +49 761 4 52 07 14, information@karger.de )

ISSN (Print): 2296-5270

ISSN (Electronic): 2296-5262

Publication date (Electronic): 17 December 2020

Pages: 1-4

Affiliations

[1 ] ^aDepartment of Oncology and Palliative Care, Helios Klinikum Berlin-Buch, Berlin, Germany

[2 ] ^bHeart Center Leipzig and Leipzig Heart Institute, Leipzig, Germany

[3 ] ^cBerlin Cancer Institute, Berlin, Germany

[4 ] ^dHelios Health, Berlin, Germany

Author notes

*Peter Reichardt, Department of Oncology and Palliative Care, Helios Klinikum Berlin-Buch, Schwanebecker Chaussee 50, DE–13125 Berlin (Germany), peter.reichardt@ 123456helios-gesundheit.de

Article

Publisher ID: ort-0001

DOI: 10.1159/000512935

PMC ID: 7801981

PubMed ID: 33333506

SO-VID: 2ab159ea-e2ea-4040-ac11-f576e5276769

License:

This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

History

Date received : 26 August 2020

Date accepted : 5 November 2020

Page count

Tables: 2, References: 7, Pages: 4

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Decreased Incidence of Oncology Admissions in 75 Helios Hospitals in Germany during the COVID-19 Pandemic

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Abstract

Background

Materials and Methods

Results

Conclusions

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Karger: Coronavirus / COVID-19

Most cited references 7

Fitting Linear Mixed-Effects Models Usinglme4

Fitting Linear Mixed-Effects Models Using lme4

The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study

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Cited by 18

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