Forecasting the Early Impact of COVID-19 on Physician Supply in EU Countries.

BACKGROUND: Many countries faced health workforce challenges even before the pandemic, such as impending retirements, negative population growth, or sub-optimal allocation of resources across health sectors. Current quantitative models are often of limited use, either because they require extensive individual-level data to be properly calibrated, or (in the absence of such data) because they are too simplistic to capture important demographic changes or disruptive epidemiological shocks such as the SARS-CoV-2 pandemic. Method: We propose a population-dynamic and stock-flow-consistent approach to physician supply forecasting that is complex enough to account for dynamically changing behaviour, while requiring only publicly available time-series data for full calibration. We demonstrate the utility of this model by applying it to 21 European countries to forecast the supply of generalist and specialist physicians to 2040, and the impact of increased health care utilisation due to Covid on this supply. RESULTS: Compared with the workforce needed to maintain physician density at 2019 levels, we find that in many countries there is indeed a significant trend towards decreasing generalist density at the expense of increasing specialist density. The trends for specialists are exacerbated by expectations of negative population growth in many Southern and Eastern European countries. Compared to the expected demographic changes in the population and the health workforce, we expect a limited impact of Covid on these trends, even under conservative modelling assumptions. Finally, we generalise the approach to a multi-professional, multi-regional and multi-sectoral model for Austria, where we find an additional suboptimal distribution in the supply of contracted versus non-contracted (private) physicians. CONCLUSION: It is therefore vital to develop tools for decision-makers to influence the allocation and supply of doctors across specialties and sectors to address these imbalances.

Systematic population-wide ecological analysis of regional variability in disease prevalence.

The prevalence of diseases often varies substantially from region to region. Besides basic demographic properties, the factors that drive the variability of each prevalence are to a large extent unknown. Here we show how regional prevalence variations in 115 different diseases relate to demographic, socio-economic, environmental factors and migratory background, as well as access to different types of health services such as primary, specialized and hospital healthcare. We have collected regional data for these risk factors at different levels of resolution; from large regions of care (Versorgungsregion) down to a 250 by 250 m square grid. Using multivariate regression analysis, we quantify the explanatory power of each independent variable in relation to the regional variation of the disease prevalence. We find that for certain diseases, such as acute heart conditions, diseases of the inner ear, mental and behavioral disorders due to substance abuse, up to 80% of the variance can be explained with these risk factors. For other diagnostic blocks, such as blood related diseases, injuries and poisoning however, the explanatory power is close to zero. We find that the time needed to travel from the inhabited center to the relevant hospital ward often contributes significantly to the disease risk, in particular for diabetes mellitus. Our results show that variations in disease burden across different regions can for many diseases be related to variations in demographic and socio-economic factors. Furthermore, our results highlight the relative importance of access to health care facilities in the treatment of chronic diseases like diabetes.

High-risk multimorbidity patterns on the road to cardiovascular mortality.

BACKGROUND: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS: Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS: In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS: Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.

Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients.

Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.

Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment.

Aim: The aim of this project is to describe a causal (counterfactual) approach for analyzing when to start statin treatment to prevent cardiovascular disease using real-world evidence. Methods: We use directed acyclic graphs to operationalize and visualize the causal research question considering selection bias, potential time-independent and time-dependent confounding. We provide a study protocol following the 'target trial' approach and describe the data structure needed for the causal assessment. Conclusion: The study protocol can be applied to real-world data, in general. However, the structure and quality of the database play an essential role for the validity of the results, and database-specific potential for bias needs to be explicitly considered.

[Outpatient cardiac phase III rehabilitation at a Viennese institution - retrospective cohort study]. / Ambulante kardiologische Phase-III-Rehabilitation - retrospektive Kohortenstudie zu einer Wiener Einrichtung.

BACKGROUND: This retrospective cohort study analyses effectiveness and sustainability of the current cardiac Phase III (Ph-III) rehabilitation program, provided by the Centre for Outpatient Rehabilitation (ZAR). METHODS: We analysed routine data of 451 intervention group patients (IG, with Ph-III) and 781 control group patients (KG, without Ph-III). RESULTS: In a median observation period of 2.73 years we found 30% less cases of death in the IG based on the mortality risk observed in the KG (rr = 0.70; p = 0.108). However, we registered more re-events, mainly stent implantations in the IG (rr = 1.34; p = 0.095). Groups differed in some baseline characteristics. CONCLUSIONS: The lower mortality risk by trend might be explained by the close-meshed care, the IG patients' more health conscious behaviour or a selection bias of the KG (e.g. more severe underlying disease). The causality of potential positive effects cannot be confirmed by this study because of the study design.