Model-based economic evaluation of ice cleat distribution programmes for the prevention of outdoor falls among adults from a Swedish societal perspective

BackgroundSlipping on snow or ice poses a significant health risk among older adults in Sweden. To combat this problem, about 80 Swedish municipalities have distributed ice cleats to older citizens (65+ years old) over the last decade. This paper details a cost–benefit analysis of such programmes.Materials and methodsWe developed a decision-analytical model to estimate the costs and benefits of ice cleat programmes in Swedish municipalities compared with a business-as-usual scenario. The modelled benefits of the programme were based on effect estimates from previous research, data from population and healthcare registers and a survey of attitudes to and actual ice cleat use. The modelled costs of the programme were based on resource use data collected from 34 municipalities with existing ice cleat programmes. We assessed heterogeneity in the potential impact and benefit-to-cost ratios across all Swedish municipalities as a function of the average number of days with snow cover per year. Uncertainty in the cost–benefit results was assessed using deterministic and probabilistic sensitivity analyses.ResultsThe average benefit-to-cost ratio was 87, ranging from about 40 in low-risk municipalities to 140 in high-risk municipalities, implying that the potential benefits of ice cleat programmes greatly outweigh their costs. Probabilistic and deterministic sensitivity analyses support the robustness of this conclusion to parameter uncertainty and large changes in assumptions about the magnitude of the impact on ice cleat use and injuries.ConclusionThe benefits of distributing ice cleats to older adults appear to outweigh the costs from a Swedish societal perspective.

Cost-utility of two minimally-invasive surgical techniques for operable oropharyngeal cancer: Transoral robotic surgery versus transoral laser microsurgery

ImportanceTransoral robotic surgery (TORS) and transoral laser micro-surgery (TLM) are two different but competing minimally invasive techniques to surgically remove operable oropharyngeal squamous cell cancers (OPSCC). As of now, no comparative analysis as to the cost-utility of these techniques exists.ObjectiveRecent population-level data suggest for TORS and TLM equivalent tumor control, but different total costs, need for adjuvant chemoradiation, and learning curves. Therefore, the objective of this study was to compare TORS and TLM from the cost-utility (C/U) point of view using a decision-analytical model from a Swiss hospital perspective.DesignOur decision-analytical model combines decision trees and a Markov model to compare TORS and TLM strategies. Model parameters were quantified using available literature, original cost data from two Swiss university tertiary referral centers, and utilities elicited directly from a Swiss population sample using standard gamble. C/U and sensitivity analyses were used to generate results and gauge model robustness.SettingSwiss hospital perspectiveInterventionCost-utility analysisMain outcome measureComparative cost-utility data from TLM and TORSResultsIn the base case analysis TLM dominates TORS. This advantage remains robust, even if the costs for TORS would reduce by up to 25%. TORS begins to dominate TLM, if less than 59,7% patients require adjuvant treatment (pTorsAlone>0.407), whereby in an interval between 55%-62% (pTorsAlone 0.38-0.45) cost effectiveness of TORS is sensitive to the prescription of adjuvant CRT. Also, exceeding 29% of TLM patients requiring a re-operation for inadequate margins renders TORS more cost-effective.ConclusionTLM is more cost-effective than TORS. However, this advantage is sensitive to various parameters i.e. the number of re-operations and adjuvant treatment.Key pointsQuestionCompare cost-utility of TORS versus TLMFindingsIn the base case analysis TLM dominates TORS, even if the costs for TORS would reduce by up to 25%. TORS begins to dominate TLM, if less than 59,7% patients require adjuvant treatment, whereby in an interval between 55%-62% cost effectiveness of TORS is sensitive to the prescription of adjuvant CRT. Exceeding 29% of TLM patients requiring a re-operation for inadequate margins renders TORS more cost-effective.MeaningTLM is more cost-effective than TORS. However, this advantage is sensitive to the number of re-operations and adjuvant treatment

Lower Diagnostic Costs Associated with Early Deployment of Capsule Endoscopy in Non-Hematemesis Gastrointestinal Bleeding: a Cost-Utilization Analysis

Introduction: Often, the diagnostic workup of patients presenting with non-hematemesis gastrointestinal bleeding (NHGIB) is inconclusive. Consequently, the diagnostic evaluation may incur unnecessary health care costs and diagnostic times. The use of a cost decision-analytical model of the current diagnostic management strategy applied to patients presenting with NHGIB may reveal alternative strategies for the evaluation of NHGIB.Methods: Cost decision-analytical model that retrospectively follows the diagnostic course of 231 consecutive patients presenting with NHGIB to the emergency department (ED) of a tertiary medical center. We measured the effect (cost and relative times) of selecting a specific procedure, plus the effect of pursuing secondary procedures after non-diagnostic primary procedures. Results: A primary VCE had a diagnostic rate of 68% vs. 45% and 48% for a primary EGD and COLO, respectively. Combining the diagnostic rates for each primary procedure with the cost of performing subsequent procedures (after non-diagnostic primary procedures), demonstrates the primary use of VCE (n=9) results in a total cost of $12,146 vs. $12,746 and $13,162 for a primary EGD (n=47) and COLO (n=33), respectively. Similarly, the use of VCE as a primary diagnostic procedure in NHGIB patients admitted to the floor would take 74 unit hours to reach a diagnosis compared to 104 and 131 for EGD and COLO, respectively. Conclusion: Our model suggests initial use of VCE for the diagnosis of acute NHGIB, may reduce time to diagnosis and management costs.

A Decision Analytical Model Investigating Cost-Effectiveness of Erlotinib

Background: A decision analytical model investigating cost-effectiveness of Erlotinib was submitted to the UK NICE (National Institute for Health and Care Excellence), which was not based on actual health-state transition probabilities, leading to structural uncertainty in the model. The study adopted a Markov state-transition model for investigating the cost-effectiveness of Erlotinib versus Best Supportive Care (BSC) as a maintenance therapy for patients with non-small cell lung cancer (NSCLC). Methods: Unlike manufacturer submission (MS), the Markov model was governed by transition probabilities, and allowed a negative post-progression survival (PPS) estimate to appear in later cycle. Using published summary survival data, the study employs three fixed- and time-varying approaches to estimate state transition probabilities that are used in a restructured model. Results: Post-progression probabilities and probabilities of death for Erlotinib were different than fixed-transition approaches. The best fitting curves are achieved for both PPS and probability of death across the time for which data were available, but the curves start diverging towards the end of this period. The Markov model which extrapolates the curves forward in time suggests that this difference between a time-varying and fixed-transition becomes even greater. Our models produce an ICER of £54k -£66k per QALY gain, which is comparable to an ICER presented in the MS (£55k/QALY gain). Conclusions: Results from restructured Markov models show robust cost-effectiveness results for Erlotinib vs BSC. Although these are comparable to manufacturer submissions, in terms of magnitude, they vary, and which are crucial for interventions falling near a threshold value. The study will further explore the cost-effectiveness of therapies for NSCLC in Qatar.

Lower Diagnostic Costs Associated With Early Deployment of Capsule Endoscopy in Non-hematemesis Gastrointestinal Bleeding: A Cost-utilization Analysis

Introduction: Often, the diagnostic workup of patients presenting with non-hematemesis gastrointestinal bleeding (NHGIB) is inconclusive. Consequently, the diagnostic evaluation may incur unnecessary health care costs and diagnostic times. The use of a cost decision-analytical model of the current diagnostic management strategy applied to patients presenting with NHGIB may reveal alternative strategies for the evaluation of NHGIB.Methods: Cost decision-analytical model that retrospectively follows the diagnostic course of 231 consecutive patients presenting with NHGIB to the emergency department (ED) of a tertiary medical center. We measured the effect (cost and relative times) of selecting a specific procedure, plus the effect of pursuing secondary procedures after non-diagnostic primary procedures.Results: A primary VCE had a diagnostic rate of 68% vs. 45% and 48% for a primary EGD and COLO, respectively. Combining the diagnostic rates for each primary procedure with the cost of performing subsequent procedures (after non-diagnostic primary procedures), demonstrates the primary use of VCE (n=9) results in a total cost of $12,146 vs. $12,746 and $13,162 for a primary EGD (n=47) and COLO (n=33), respectively. Similarly, the use of VCE as a primary diagnostic procedure in NHGIB patients admitted to the floor would take 74 unit hours to reach a diagnosis compared to 104 and 131 for EGD and COLO, respectively.Conclusion: Our model suggests initial use of VCE for the diagnosis of acute NHGIB, may reduce time to diagnosis and management costs.

Early Stage Prediction of US County Vulnerability to the COVID-19 Pandemic

ImportanceThe rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to rapidly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread.ObjectiveDeveloping county level prediction around near future disease movement for COVID-19 occurrences using publicly available data.DesignOriginal Investigation; Decision Analytical Model Study for County Level COVID-19 occurrences using data from March 14-31, 2020.SettingDisease spread prediction for US counties.ParticipantsAll US county level granularity based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features.Exposure(s) (for observational studies)Daily county level reported COVID-19 occurrences from March 14-31, 2020.Main Outcome(s) and Measure(s)We developed a 3-stage model to quantify, firstly the probability of COVID-19 occurrence for unaffected counties using XGBoost classifier and secondly, the number of potential occurrences of a county via XGBoost regression. Thirdly, these results are combined to compute the county level risk. This risk is then used as an estimated after-five-day-vulnerability of the county.ResultsUsing data from March 14-31, 2020, the model shows a sensitivity over 71.5% and specificity over 94%.Conclusions and RelevanceWe found that population, population density, percentage of people aged 70 or greater and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We found a positive association between affected and urban counties as well as less vulnerable and rural counties. The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduces significant variation in reported cases and produces a bias in the model.Trial RegistrationNot ApplicableKey PointsQuestionWhat are key factors that define the vulnerability of counties in the US to cases of the COVID-19 virus?FindingsIn this epidemiological study based on publicly available data, we develop a model that predicts vulnerability to COVID-19 for each US county in terms of likelihood of going from no documented cases to at least one case within five days and in terms of number of occurrences of the virus.MeaningPredicting county vulnerability to COVID-19 can assist health organizations to better plan for resource and workforce needs.