Covid-19

Einleitung / Introduction

Auswertungen des Statistischen Bundesamts belegen für den COVID-19-Pandemiezeitraum 2020 bis 2022 eine im Vergleich zu den Vorjahren deutlich erhöhte Sterblichkeit in Deutschland. In der jüngeren öffentlichen Diskussion wurden Studienergebnisse aufgegriffen, die einen Zusammenhang zwischen dieser Übersterblichkeit und COVID-19 in Frage stellen und stattdessen eine Verknüpfung mit COVID-19 Schutzimpfungen suggerieren. Diese Auswertungen basieren hierbei auf aggregierten Zeitreihendaten, die keinen direkten Rückschluss auf personenindividuelle Zusammenhänge zwischen Erkrankungen und Sterblichkeit erlauben. Zudem erfolgte keine Adjustierung der geschätzten Übersterblichkeit für weitere mortalitätsrelevante Erkrankungen, die einen Zusammenhang zwischen Übersterblichkeit und COVID-19 verwässern können.

Methode / Method

Die vorliegende Analyse adressiert diese Limitationen unter Nutzung von Routinedaten der gesetzlichen Krankenversicherung (GKV-Routinedaten). Diese Daten von rund 10,5 Mio. BARMER-Versicherten erlauben die Abbildung von Sterblichkeit, demografischen Merkmalen und Erkrankungen auf der Individualebene. Zur Schätzung der Übersterblichkeit der Jahre 2020-2022 wurden zwei statistische Modelle auf Basis der Daten des Zeitraums 2018-2019 geschätzt: 1) ein demografisches Modell, welches Alter und Geschlecht als Risikofaktoren für Sterblichkeit einschließt, 2) ein morbiditätsadjustiertes Modell, welches zusätzlich ausgewählte Erkrankungen (Herzinfarkt, Schlaganfall, Herzrhythmusstörungen, Influenza, Nierenversagen, Pneumonie, Polytrauma, Schlaganfall) berücksichtigt.

Ergebnisse / Results

Die Analyseergebnisse zeigen, dass eine Adjustierung für Morbidität insbesondere in hohen Altersgruppen einen starken Einfluss auf die geschätzte Übersterblichkeit der Jahre 2020 bis 2022 hat. Für den Zeitraum 2020 bis 2022 ergab sich eine geschätzte kumulierte Übersterblichkeit von ca. 166.000 Fällen in Deutschland, von denen rund 99% den Altersgruppen 60+ zuzuschreiben waren. Des Weiteren belegen die Auswertungen einen engen Zusammenhang zwischen dieser morbiditätsadjustierten Übersterblichkeit und COVID-19. So war mehr als 3/4 der geschätzten Übersterblichkeit mit vorangegangenen COVID-19-Diagnosen assoziiert, während dies auf lediglich 8% der beobachteten Sterbefälle zutraf.

Zusammenfassung / Conclusion

Diese Untersuchung stützt den Zusammenhang von Übersterblichkeit und COVID-19 im betrachteten Pandemiezeitraum und belegt zudem die Relevanz einer Adjustierung für Morbidität bei der Schätzung von Übersterblichkeit. Vor dem Hintergrund dieser Evidenz werden die Potenziale und Limitationen von GKV-Routinedaten diskutiert.

AutorInnen

Martin Rößler, BARMER Institut für Gesundheitssystemforschung (bifg)

Claudia Schulte, BARMER Institut für Gesundheitssystemforschung (bifg)

Dagmar Hertle, BARMER Institut für Gesundheitssystemforschung (bifg)

Uwe Repschläger, BARMER Institut für Gesundheitssystemforschung (bifg)

Danny Wende, BARMER Institut für Gesundheitssystemforschung (bifg)

Einleitung / Introduction

Establishing robust evidence on population health impact of novel diagnostics for infectious diseases is essential to inform resource allocation decisions. Mathematical models have proven useful in quantifying both direct and indirect epidemiological impact of diagnostic interventions at the population level. However, there are currently limited guidance or papers on best practices to guide the reporting and assessment of such models. We conducted a scoping review and methodological assessment of mathematical modelling studies of two exemplary infectious diseases, namely Tuberculosis (TB) and COVID-19 with an aim of informing specific guidance for modelling diagnostic impact for infectious diseases.

Methode / Method

We comprehensively searched for English articles using a combination of four key terms “TB or COVID-19” and “Diagnostics” and “Modelling” and “Population impact” on two major scientific electronic databases (PubMed and Web of Science) from 1st January 2010 until 15th March 2023 for TB. For COVID-19, we restricted the search starting from 1st December 2019 to 15th March 2023. In addition, we contacted modelling experts in our network and searched reference lists of included articles to identify additional relevant studies. We conducted the methodological assessment of the included models using the questionnaire to assess relevance and credibility of modelling studies by Caro et al. in 2014.

Ergebnisse / Results

We included 100 modelling studies (32 TB articles and 68 COVID-19 articles) for the final review and methodological assessment. The most common outcome evaluated in the included studies is reduction in transmission. A majority of included studies used dynamic transmission models. While most TB models were deterministic and compartmental, most COVID-19 models were stochastic and at individual-level. The availability of better empirical data explained the differences in the choice of more sophisticated model type and detailed structure of COVID-19 models compared to TB models. Calibration was reported in most models and often for two purposes, i.e. estimating unknown parameters and external validation. In contrast, validation was reported in a small number of models for both diseases.

Zusammenfassung / Conclusion

Our review found important similarities and differences in the approaches of modelling transmission dynamics and quantifying population health impact of diagnostic interventions across the two diseases. Our findings lay groundworks for development of the specific guidance for modelling diagnostic impact for infectious diseases in global health.

AutorInnen

Thi Hoa Nguyen, Heidelberg Institute of Global Health and Division of Infectious Disease and Tropical Medicine

Lisa Koeppel, Division of Infectious Disease and Tropical Medicine, University Hospital of Heidelberg, German Center for Infection Research, partner site Heidelberg, Germany

Florian Marx, Division of Infectious Disease and Tropical Medicine, University Hospital of Heidelberg, German Center for Infection Research, partner site Heidelberg, Germany

Claudia M. Denkinger, Division of Infectious Disease and Tropical Medicine, University Hospital of Heidelberg, German Center for Infection Research, partner site Heidelberg, Germany

Einleitung / Introduction

Subjective unmet health care needs have become an important public health concern amidst the COVID-19 pandemic. Their consequences have long-term implications for persons with chronic medical conditions and multi-morbidity, as well as for the non-chronic trajectories of many ill or injured people. I aim to provide a picture of the general population in a cross-European comparison rather than analysing specific sub-groups in singular countries, which nevertheless could provide greatly needed insights. Therein, this original research article is designed to shine as a spotlight and not to serve as a magnifying lens. Precisely, I enquire into the scale and the major drivers of unmet health care needs amidst the COVID-19 pandemic across the 27 European Union (EU) member states.

Methode / Method

This research article uses preliminary data of the Living, working and COVID-19 e-survey of spring 2021 and spring 2022, which cover 27 EU member states, to associate key predictors, like costs and waiting times with barriers to health care in various areas. Various pooled and comparative analyses of the survey data, propensity score estimation, and cluster analysis are employed.

Ergebnisse / Results

The results highlight the significant impact of the Coronavirus pandemic for accessibility. Approximately 16.78% of the study sample report unmet health care needs of some kind or another. These consist of 9.79% of the sample that state unmet needs in one area of health care and 6.99% report access restrictions in at least two spheres of health care. Altogether, the country dummies indicate that hospital and specialist care were Europe's most restricted types of health care amidst the pandemic. Some countries, such as Latvia, stand out with relatively low levels of unreported unmet health care needs. Noteworthy, this pattern could be particularly furthered by a low base of total reported needs in these countries. This could result in fewer unmet health care needs proportional to the population but not necessarily relative to overall needs. When comparing the periods of spring 2021 and 2022 by health care domain, one finds that unmet needs in preventive health care and surgeries, as well as hospital and specialist care, decreased. However, the five other types of health care produced higher unmet needs in 2022 than in 2021

Zusammenfassung / Conclusion

This study's findings regarding the impact of access restrictions and implicit rationing during the COVID-19 pandemic underscore the importance of maintaining essential health services. Public health strategies should prioritise the preservation of preventive and primary care interventions, as neglecting these areas may lead to exacerbation of chronic conditions and delayed treatment-seeking behaviour. Emphasising resilience in health care systems can enhance preparedness for future crises and reduce the burden on health care resources.

AutorInnen

Thomas Resch, Medical University of Vienna