Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) causes significant morbidity and mortality worldwide. Estimation of incidence, prevalence and disease burden through routine insurance data is challenging because of under-diagnosis and under-treatment, particularly for early stage disease in health care systems where outpatient International Classification of Diseases (ICD) diagnoses are not collected. This poses the question of which criteria are commonly applied to identify COPD patients in claims datasets in the absence of ICD diagnoses, and which information can be used as a substitute. The aim of this systematic review is to summarize previously reported methodological approaches for the identification of COPD patients through routine data and to compile potential criteria for the identification of COPD patients if ICD codes are not available. METHODS: A systematic literature review was performed in Medline via PubMed and Google Scholar from January 2000 through October 2018, followed by a manual review of the included studies by at least two independent raters. Study characteristics and all identifying criteria used in the studies were systematically extracted from the publications, categorized, and compiled in evidence tables. RESULTS: In total, the systematic search yielded 151 publications. After title and abstract screening, 38 publications were included into the systematic assessment. In these studies, the most frequently used (22/38) criteria set to identify COPD patients included ICD codes, hospitalization, and ambulatory visits. Only four out of 38 studies used methods other than ICD coding. In a significant proportion of studies, the age range of the target population (33/38) and hospitalization (30/38) were provided. Ambulatory data were included in 24, physician claims in 22, and pharmaceutical data in 18 studies. Only five studies used spirometry, two used surgery and one used oxygen therapy. CONCLUSIONS: A variety of different criteria is used for the identification of COPD from routine data. The most promising criteria set in data environments where ambulatory diagnosis codes are lacking is the consideration of additional illness-related information with special attention to pharmacotherapy data. Further health services research should focus on the application of more systematic internal and/or external validation approaches.

Statistical methods and modelling techniques for analysing hospital readmission of discharged psychiatric patients: a systematic literature review.

BACKGROUND: Psychiatric services have undergone profound changes over the last decades. CEPHOS-LINK is an EU-funded study project with the aim to compare readmission of patients discharged with psychiatric diagnoses using a registry-based observational record linkage study design and to analyse differences in the findings for five different countries. A range of different approaches is available for analysis of the available data. Although there are some studies that compare selected methods for evaluating questions on readmission, there are to our knowledge no published systematic literature reviews on commonly used methods and their comparison. This work shall therefore provide an overview of the methods in use, their evolution throughout history and new developments which can further improve the research quality in this area. METHODS: Based on systematic literature reviews realized in the course of the CEPHOS-LINK study, this work is a systematic evaluation of mathematical (statistical and modelling) methods used in studies examining psychiatric readmission. The starting point were 502 papers, of which 407 were analysed in detail; Methods used were assigned to one of five categories with subcategories and analysed accordingly. Our particular interest next to survival analysis and regression models is modelling and simulation. RESULTS: As population sizes and follow-up times in the included studies varied widely, a range of methods was applied. Studies with bigger sample sizes conducted survival and regression analysis more often than studies with fewer patients did. These latter relied more on classical statistical tests (e.g. t-tests and Student Newman Keuls). Statistical strategies were often insufficiently described, posing a major problem for the evaluation. Almost all cases failed to provide and explanation of the rationale behind using certain methods. CONCLUSION: There is a discernible trend from classical parametric/nonparametric tests in older studies towards regression and survival analyses in more recent ones. Modelling and simulation were under-represented despite their high usability, as has been identified in other health applications and comparable research areas.

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data.

Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose in this paper an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of relative stochasticity in time series of reported case numbers using a specially crafted statistical model for reproduction. This allows to detect potential transitions from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be a decisive turning point in the progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.

Time Dynamics of the Spread of Virus Mutants with Increased Infectiousness in Austria.

In spring 2021, it became eminent that the emergence of higher infectious virus mutants of SARS-CoV-2 is an unpredictable and omnipresent threat for fighting the pandemic and has wide-ranging implications on containment policies and herd immunity goals. To quantify the risk related to a more infectious virus variant, extensive surveillance and proper data analysis are required. Key observable of the analysis is the excess infectiousness defined as the quotient between the effective reproduction rate of the new and the previous variants. A proper estimate of this parameter allows forecasts for the epidemic situation after the new variant has taken over and enables estimates by how much the new variant will increase the herd immunity threshold. Here, we present and analyse methods to estimate this crucial parameter based on surveillance data. We specifically focus on the time dynamics of the ratio of mutant infections among the new confirmed cases and discuss, how the excess infectiousness can be estimated based on surveillance data for this ratio. We apply a modified susceptible-infectious-recovered approach and derive formulas which can be used to estimate this parameter. We will provide adaptations of the formulas which are able to cope with imported cases and different generation-times of mutant and previous variants and furthermore fit the formulas to surveillance data from Austria. We conclude that the derived methods are well capable of estimating the excess infectiousness, even in early phases of the replacement process. Yet, a high ratio of imported cases from regions with higher variant prevalence may cause a major overestimation of the excess infectiousness, if not considered. Consequently, the analysis of Austrian data allowed a proper estimate for the Alpha variant, but results for the Delta variant are inconclusive.

An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply.

BACKGROUND AND OBJECTIVE: The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply. METHODS: We present an optimization meta-heuristic which can be used in a classic simulation-optimization setting with a simulation model in a feedback loop. The meta-heuristic can be applied in combination with any epidemiological simulation model capable of depicting the effects of vaccine distribution to the modeled population, accepts a vaccine prioritization plan in a certain notation as input, and generates decision making relevant variables such as COVID-19 caused deaths or hospitalizations as output. We finally demonstrate the mechanics of the algorithm presenting the results of a case study performed with an epidemiological agent-based model. RESULTS: We show that the developed method generates a highly robust vaccination prioritization plan which is proven to fulfill an elegant supremacy criterion: the plan is equally optimal for any quantity of vaccine doses available. The algorithm was tested on a case study in the Austrian context and it generated a vaccination plan prioritization favoring individuals age 65+, followed by vulnerable groups, to minimize COVID-19 related burden. DISCUSSION: The results of the case study coincide with the international policy recommendations which strengthen the applicability of the approach. We conclude that the path-dependent optimum optimum provided by the algorithm is well suited for real world applications, in which decision makers need to develop strategies upfront under high levels of uncertainty.

Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects.

Several systemic factors indicate that worldwide herd immunity against COVID-19 will probably not be achieved in 2021. On the one hand, vaccination programs are limited by availability of doses and on the other hand, the number of people already infected is still too low to have a disease preventing impact and new emerging variants of the virus seem to partially neglect developed antibodies from previous infections. Nevertheless, by February 2021 after one year of observing high numbers of reported COVID-19 cases in most European countries, we might expect that the immunization level should have an impact on the spread of SARS-CoV-2. Here we present an approach for estimating the immunization of the Austrian population and discuss potential consequences on herd immunity effects. To estimate immunization we use a calibrated agent-based simulation model that reproduces the actual COVID-19 pandemic in Austria. From the resulting synthetic individual-based data we can extract the number of immunized persons. We then extrapolate the progression of the epidemic by varying the obtained level of immunization in simulations of an hypothetical uncontrolled epidemic wave indicating potential effects on the effective reproduction number. We compared our theoretical findings with results derived from a classic differential equation SIR-model. As of February 2021, [Formula: see text] of the Austrian population has been affected by a SARS-CoV-2 infection which causes a [Formula: see text] reduction of the effective reproduction number and a [Formula: see text] reduction of the prevalence peak compared to a fully susceptible population. This estimation is now recomputed on a regular basis to publish model based analysis of immunization level in Austria also including the fast growing effects of vaccination programs. This provides substantial information for decision makers to evaluate the necessity of non pharmaceutical intervention measures based on the estimated impact of natural and vaccinated immunization.

Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale.

SARS-CoV-2 surveillance by wastewater-based epidemiology is poised to provide a complementary approach to sequencing individual cases. However, robust quantification of variants and de novo detection of emerging variants remains challenging for existing strategies. We deep sequenced 3,413 wastewater samples representing 94 municipal catchments, covering >59% of the population of Austria, from December 2020 to February 2022. Our system of variant quantification in sewage pipeline designed for robustness (termed VaQuERo) enabled us to deduce the spatiotemporal abundance of predefined variants from complex wastewater samples. These results were validated against epidemiological records of >311,000 individual cases. Furthermore, we describe elevated viral genetic diversity during the Delta variant period, provide a framework to predict emerging variants and measure the reproductive advantage of variants of concern by calculating variant-specific reproduction numbers from wastewater. Together, this study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without extensive individual monitoring.

Evaluation of Contact-Tracing Policies against the Spread of SARS-CoV-2 in Austria: An Agent-Based Simulation.

BACKGROUND: Many countries have already gone through several infection waves and mostly managed to successfully stop the exponential spread of SARS-CoV-2 through bundles of restrictive measures. Still, the danger of further waves of infections is omnipresent, and it is apparent that every containment policy must be carefully evaluated and possibly replaced by a different, less restrictive policy before it can be lifted. Tracing of contacts and consequential breaking of infection chains is a promising strategy to help contain the disease, although its precise impact on the epidemic is unknown. OBJECTIVE: In this work, we aim to quantify the impact of tracing on the containment of the disease and investigate the dynamic effects involved. DESIGN: We developed an agent-based model that validly depicts the spread of the disease and allows for exploratory analysis of containment policies. We applied this model to quantify the impact of different approaches of contact tracing in Austria to derive general conclusions on contract tracing. RESULTS: The study displays that strict tracing complements other intervention strategies. For the containment of the disease, the number of secondary infections must be reduced by about 75%. Implementing the proposed tracing strategy supplements measures worth about 5%. Evaluation of the number of preventively quarantined persons shows that household quarantine is the most effective in terms of avoided cases per quarantined person. LIMITATIONS: The results are limited by the validity of the modeling assumptions, model parameter estimates, and the quality of the parametrization data. CONCLUSIONS: The study shows that tracing is indeed an efficient measure to keep case numbers low but comes at a high price if the disease is not well contained. Therefore, contact tracing must be executed strictly, and adherence within the population must be held up to prevent uncontrolled outbreaks of the disease.

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model.

In 2020, the ongoing COVID-19 pandemic caused major limitations for any aspect of social life and in specific for all events that require a gathering of people. While most events of this kind can be postponed or cancelled, democratic elections are key elements of any democratic regime and should be upheld if at all possible. Consequently, proper planning is required to establish the highest possible level of safety to both voters and scrutineers. In this paper, we present the novel and innovative way how the municipal council and district council elections in Vienna were planned and conducted using an discrete event simulation model. Key target of this process was to avoid queues in front of polling stations to reduce the risk of related infection clusters. In cooperation with a hygiene expert, we defined necessary precautions that should be met during the election in order to avoid the spread of COVID-19. In a next step, a simulation model was established and parametrized and validated using data from previous elections. Furthermore, the planned conditions were simulated to see whether excessive queues in front of any polling stations could form, as these could on the one hand act as an infection herd, and on the other hand, turn voters away. Our simulation identified some polling stations where long queues could emerge. However, splitting up these electoral branches resulted in a smooth election across all of Vienna. Looking back, the election did not lead to a significant increase of COVID-19 incidences. Therefore, it can be concluded that careful planning led to a safe election, despite the pandemic.

Budget Impact Analysis of Cancer Screening: A Methodological Review.

BACKGROUND: Budget impact analyses (BIAs) describe changes in intervention- and disease-related costs of new technologies. Evidence on the quality of BIAs for cancer screening is lacking. OBJECTIVES: We systematically reviewed the literature and methods to assess how closely BIA guidelines are followed when BIAs are performed for cancer-screening programs. DATA SOURCES: Systematic searches were conducted in MEDLINE, EMBASE, EconLit, CRD (Centre for Reviews and Dissemination, University of York), and CEA registry of the Tufts Medical Center. STUDY ELIGIBILITY CRITERIA: Eligible studies were BIAs evaluating cancer-screening programs published in English, 2010-2018. SYNTHESIS METHODS: Standardized evidence tables were generated to extract and compare study characteristics outlined by the ISPOR BIA Task Force. RESULTS: Nineteen studies were identified evaluating screening for breast (5), colorectal (6), cervical (3), lung (1), prostate (3), and skin (1) cancers. Model designs included decision-analytic models (13) and simple cost calculators (6). From all studies, only 53% reported costs for a minimum of 3 years, 58% compared to a mix of screening options, 42% reported model validation, and 37% reported uncertainty analysis for participation rates. The quality of studies appeared to be independent of cancer site. LIMITATIONS: "Gray" literature was not searched, misinterpretation is possible due to limited information in publications, and focus was on international methodological guidelines rather than regional guidelines. CONCLUSIONS: Our review highlights considerable variability in the extent to which BIAs evaluating cancer-screening programs followed recommended guidelines. The annual budget impact at least over the next 3-5 years should be estimated. Validation and uncertainty analysis should always be conducted. Continued dissemination efforts of existing best-practice guidelines are necessary to ensure high-quality analyses.

Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness.

Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alternative matrix transformation methods (Eigenvalue method [E], Schure-Padé method [SP]) to method C applied in an STM of 3 different treatment strategies for women with breast cancer. We convert the given annual transition matrix into a monthly-cycle matrix and evaluate induced transformation errors for the transition matrices and the long-term outcomes: life years, quality-adjusted life-years, costs and incremental cost-effectiveness ratios, and the performance related to the decisions. In addition, we applied these transformation methods to randomly generated annual transition matrices with 4, 7, 10, and 20 health states. Results. In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation-induced discrepancies for generated annual transition matrices for 2 treatment strategies. E showed slightly smaller discrepancies than SP in the strategy, where one of the direct transitions between health states was excluded. For long-term outcomes, the largest discrepancy occurred for estimated costs applying method C. For higher dimensional models, E performs best. Conclusions. In our modeling examples, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should therefore be applied in sensitivity analyses.

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.

A Web-Based Tool to Evaluate Data Quality of Reused Health Data Assets.

BACKGROUND: Data from the health care domain is often reused to create and parameterize simulation models for example to support life science business in the evaluation of new products. Data quality assessments play an important part to help model users in interpreting simulation results by showing deficiencies in the raw data used in the model building and offers model builders a comparison of data quality amongst the used data assets. OBJECTIVES: Assess data quality in raw data prior to creating simulation models and prepare results for model users. METHODS: Using a literature review and documentation of previous models created, we searched data quality criteria. For eligible criteria we formulated questions and viable answers to be used in a questionnaire to assess data quality of a data asset. RESULTS: We developed a web tool to evaluate data assets using a generic data model. Percentage results are visualized using a radar chart. CONCLUSION: Data quality assessment with questionnaires offers model builders a framework to critically analyse raw data and to detect deficiencies early in the modelling process. The summarized results can help model users to better interpret simulation results.