Methodologic and Reporting Quality of Economic Evaluations in Hand and Wrist Surgery: A Systematic Review.

BACKGROUND: Economic evaluations can inform decision-making; however, previous publications have identified poor quality of economic evaluations in surgical specialties. METHODS: Study periods were from January 1, 2006, to April 20, 2020 (methodologic quality) and January 1, 2014, to April 20, 2020 (reporting quality). Primary outcomes were methodologic quality [Guidelines for Authors and Peer Reviewers of Economic Submissions to The BMJ (Drummond's checklist), 33 points; Quality of Health Economic Studies (QHES), 100 points; Consensus on Health Economic Criteria (CHEC), 19 points] and reporting quality (Consolidated Health Economic Evaluation Standards (CHEERS) statement, 24 points). RESULTS: Forty-seven hand economic evaluations were included. Partial economic analyses (i.e., cost analysis) were the most common (n = 34; 72 percent). Average scores of full economic evaluations (i.e., cost-utility analysis and cost-effectiveness analysis) were: Drummond's checklist, 27.08 of 33 (82.05 percent); QHES, 79.76 of 100 (79.76 percent); CHEC, 15.54 of 19 (81.78 percent); and CHEERS, 20.25 of 24 (84.38 percent). Cost utility analyses had the highest methodologic and reporting quality scores: Drummond's checklist, 28.89 of 35 (82.54 percent); QHES, 86.56 of 100 (86.56 percent); CHEC, 16.78 of 19 (88.30 percent); and CHEERS, 20.8 of 24 (86.67 percent). The association (multiple R) between CHEC and CHEERS was strongest: CHEC, 0.953; Drummond's checklist, 0.907; and QHES, 0.909. CONCLUSIONS: Partial economic evaluations in hand surgery are prevalent but not very useful. The Consensus on Health Economic Criteria and Consolidated Health Economic Evaluation Standards should be used in tandem when undertaking and evaluating economic evaluation in hand surgery.

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations.

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations.

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.

Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Explanation and Elaboration: A Report of the ISPOR CHEERS II Good Practices Task Force.

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces the previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, and the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as healthcare, public health, education, and social care). This Explanation and Elaboration Report presents the new CHEERS 2022 28-item checklist with recommendations and explanation and examples for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer-reviewed journals and the peer reviewers and editors assessing them for publication. Nevertheless, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, given that there is an increasing emphasis on transparency in decision making.

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Statement: Updated Reporting Guidance for Health Economic Evaluations.

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.

Cost-Effectiveness of COVID-19 Policy Measures: A Systematic Review.

OBJECTIVES: The COVID-19 pandemic has had a major impact on our society, with drastic policy restrictions being implemented to contain the spread of the severe acute respiratory syndrome coronavirus 2. This study aimed to provide an overview of the available evidence on the cost-effectiveness of various coronavirus disease 2019 policy measures. METHODS: A systematic literature search was conducted in PubMed, Embase, and Web of Science. Health economic evaluations considering both costs and outcomes were included. Their quality was comprehensively assessed using the Consensus Health Economic Criteria checklist. Next, the quality of the epidemiological models was evaluated. RESULTS: A total of 3688 articles were identified (March 2021), of which 23 were included. The studies were heterogeneous with regard to methodological quality, contextual factors, strategies' content, adopted perspective, applied models, and outcomes used. Overall, testing/screening, social distancing, personal protective equipment, quarantine/isolation, and hygienic measures were found to be cost-effective. Furthermore, the most optimal choice and combination of strategies depended on the reproduction number and context. With a rising reproduction number, extending the testing strategy and early implementation of combined multiple restriction measures are most efficient. CONCLUSIONS: The quality assessment highlighted numerous flaws and limitations in the study approaches; hence, their results should be interpreted with caution because the specific context (country, target group, etc) is a key driver for cost-effectiveness. Finally, including a societal perspective in future evaluations is key because this pandemic has an indirect impact on the onset and treatment of other conditions and on our global economy.

Estimation of the Width of Uncertainty in Care Consumption and Costs When Using Common Data Collection Tools in Economic Evaluations: A Benchmark for Sensitivity Analyses.

OBJECTIVES: This study aimed to evaluate the uncertainty related to the use of common collection tools to assess costs in economic evaluations compared with an exhaustive administrative database. METHODS: A pragmatic study was performed using preexisting cost-effectiveness studies. Patients were probabilistically matched with themselves in the French National Health Data System (Système National des Données de Santé [SNDS]), and all their reimbursed hospital and ambulatory care data during the study were extracted. Outcomes included the ratio of the number of each type of resources consumed using trial data (case report forms for ambulatory care and local hospital data for hospital care) versus the SNDS and the ratio of corresponding costs. Mean ratios and 95% confidence intervals (CIs) were calculated using bootstrapping. The impact of the collection tool on the result of the economic evaluation was calculated with the difference in costs between the 2 treatment arms with both collection methods. RESULTS: Five cost-effectiveness studies were included in the analysis. A total of 397 patients had the SNDS hospital data, and 321 had ambulatory care data. Common collection tools underestimated hospital admissions by 13% (95% CI 8-20), corresponding costs by 5% (95% CI 2-14), and ambulatory acts by 41% (95% CI 33-51), with large variations in costs depending on the study. There was no change in the economic conclusion in any study. CONCLUSIONS: The use of common collection tools underestimates healthcare resource consumption and its associated costs, particularly for ambulatory care. Our results could provide useful evidence-based estimates to inform sensitivity analyses' parameters in future cost-effectiveness analyses.

Good Days and Bad Days: Measuring Health-Related Quality of Life in People With Epilepsy.

OBJECTIVES: Cost-effectiveness analyses typically require measurement of health-related quality of life (HRQoL) to estimate quality-adjusted life-years. Challenges with measuring HRQoL arise in the context of episodic conditions if patients are less likely-or even unable-to complete surveys when having disease symptoms. This article explored whether HRQoL measured at regular time intervals adequately reflects the HRQoL of people with epilepsy (PWE). METHODS: Follow-up data from the Epilepsy Support Dog Evaluation study on the (cost-)effectiveness of seizure dogs were used in which HRQoL is measured in 25 PWE with the EQ-5D at baseline and every 3 months thereafter. Seizure count is recorded daily using a seizure diary. Regression models were employed to explore whether PWE were more likely to complete the HRQoL survey on a good day (ie, when seizures are absent or low in frequency compared with other days) and to provide an estimate of the impact of reporting HRQoL on a good day on EQ-5D utility scores. RESULTS: A total of 111 HRQoL measurements were included in the analyses. Regression analyses indicated that the day of reporting HRQoL was associated with a lower seizure count (P<.05) and that a lower seizure count was associated with a higher EQ-5D utility score (P<.05). CONCLUSIONS: When HRQoL is measured at regular time intervals, PWE seem more likely to complete these surveys on good days. Consequently, HRQoL might be overestimated in this population. This could lead to underestimation of the effectiveness of treatment and to biased estimates of cost-effectiveness.

Deriving a Preference-Based Measure for People With Duchenne Muscular Dystrophy From the DMD-QoL.

OBJECTIVES: This study generates a preference-based measure for capturing the quality of life of people with Duchenne muscular dystrophy (DMD) from a new measure of quality of life, DMD-QoL. METHODS: A health state classification system was derived from the DMD-QoL based on psychometric performance of items, factor analysis, and item response theory analysis. Preferences for health states described by the classification system were elicited using an online discrete choice experiment survey with life years as an additional attribute, from members of the UK general population (n = 1043). Discrete choice experiment data was modeled using a conditional fixed-effects logit model and utility estimates were directly anchored on the 1 to 0 full health-dead scale. RESULTS: The health state classification system has 8 dimensions: mobility, difficulty using hands, difficulty breathing, pain, tiredness, worry, participation, and feeling good about yourself. The standard model had mostly statistically significant coefficients and reflected the instrument's monotonic structure. However, 2 dimensions had inconsistent coefficients (where utility increased as health worsened) and a consistent model was estimated that merged adjacent inconsistent severity levels. The best state defined by the classification system has a value of 1 and the worst state has a value of -0.559. CONCLUSION: The modeled results enable DMD-QoL-8D utility values to be generated using DMD-QoL or DMD-QoL-8D data to generate QALYs for people with DMD. QALYs can then be used to inform economic models of the cost-effectiveness of interventions in DMD. Future research comparing the psychometric performance of DMD-QoL-8D to existing generic preference-based measures, including EQ-5D-5L, is recommended.

Systematic review of reporting quality of economic evaluations in plastic surgery based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement.

BACKGROUND: Economic evaluations in healthcare are designed to inform decisions by the estimation of cost and effect trade-off of two or more interventions. This review identified and appraised the quality of reporting of economic evaluations in plastic surgery based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. METHODS: Electronic databases were searched: MEDLINE, EMBASE, The Cochrane Library, Ovid Health Star, and Business Source Complete from January 1, 2012 to November 30, 2019. Data extracted included: the type of economic evaluation (i.e., cost-utility analysis (CUA), cost-effectiveness analysis (CEA), cost-benefit analysis (CBA), cost-minimization analysis (CMA)), domain of plastic surgery, journal, year, and country of publication. The CHEERS checklist (with 24 items) was used to appraise the quality of reporting. RESULTS: Ninety-two economic evaluations were identified; CUA (10%), CEA (31%), CBA (4%), and CMA (50%). Breast surgery was the top domain (48%). Most were conducted in the USA (61%) and published in Plastic and Reconstructive Surgery journal (28%). One-third were published in the last two years. The average CHEERS checklist compliance score was 15 (63%). The average CHEERS checklist compliance score per type of evaluation was 19 (77%) for CUA, 17 (70%) for CEA, 13 (52%) for CBA, and 14 (57%) for CMA. The least reported CHEERS checklist items included: time horizon (15%), discount rate (18%), and assessment of heterogeneity (15%). Thirty-two percent of studies were inappropriately titled (i.e., methodologically incorrect). CONCLUSION: Quality of reporting of economic evaluations is suboptimal. The CHEERS checklist should be consulted when performing and reporting economic evaluations in plastic surgery.

Impact of assumptions on future costs, disutility and mortality in cost-effectiveness analysis; a model exploration.

INTRODUCTION: In cost-effectiveness analyses, the future costs, disutility and mortality from alternative causes of morbidity are often not completely taken into account. We explored the impact of different assumed values for each of these factors on the cost-effectiveness of screening for colorectal cancer (CRC) and esophageal adenocarcinoma (EAC). METHODS: Twenty different CRC screening strategies and two EAC screening strategies were evaluated using microsimulation. Average health-related expenses, disutility and mortality by age for the U.S. general population were estimated using surveys and lifetables. First, we evaluated strategies under default assumptions, with average mortality, and no accounting for health-related costs and disutility. Then, we varied costs, disutility and mortality between 100% and 150% of the estimated population averages, with 125% as the best estimate. Primary outcome was the incremental cost per quality-adjusted life-year (QALY) gained among efficient strategies. RESULTS: The set of efficient strategies was robust to assumptions on future costs, disutility and mortality from other causes of morbidity. However, the incremental cost per QALY gained increased with higher assumed values. For example, for CRC, the ratio for the recommended strategy increased from $15,600 with default assumptions, to $32,600 with average assumption levels, $61,100 with 25% increased levels, and $111,100 with 50% increased levels. Similarly, for EAC, the incremental costs per QALY gained for the recommended EAC screening strategy increased from $106,300 with default assumptions to $198,300 with 50% increased assumptions. In sensitivity analyses without discounting or including only above-average expenses, the impact of assumptions was relatively smaller, but best estimates of the cost per QALY gained remained substantially higher than default estimates. CONCLUSIONS: Assumptions on future costs, utility and mortality from other causes of morbidity substantially impact cost-effectiveness outcomes of cancer screening. More empiric evidence and consensus are needed to guide assumptions in future analyses.

Current State of Health Economic Analyses for Low-Grade Glioma Management: A Systematic Review.

BACKGROUND: Health economic analyses help determine the value of a medical intervention by assessing the costs and outcomes associated with it. The objective of this study was to assess the level of evidence in economic evaluations for low-grade glioma (LGG) management. METHODS: Following the PRISMA guidelines, we conducted a systematic review of English articles in Medline, Embase, The Central Registration Depository, EconPapers, and EconLit. The results were screened, and data were extracted by 2 independent reviewers for studies reporting economic evaluations for LGG. The quality of each study was evaluated using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) checklist, the hierarchy scale developed by Cooper et al. (2005), and the Quality of Health Economic Studies instrument. RESULTS: Three studies met our inclusion criteria. The adjusted incremental cost-effectiveness ratio (ICER) values for the included studies ranged from $3934 to $9936, but each evaluated a different aspect of LGG management. All had a good quality of reporting per the CHEERS checklist. Based on the Cooper et al. hierarchy scale, the quality of data use was lacking most for utilities. The quality of study design was scored as 82, 92, and 100 for each study using the Quality of Health Economic Studies instrument. CONCLUSIONS: Although a limited number of economic evaluations were identified, the studies evaluated here were well designed. The interventions assessed were all considered cost-effective, but pooled analysis was not possible because of heterogeneity in the interventions assessed. Given the importance of value and cost-effectiveness in medical care, more evidence is needed in this area.

Rapid COVID-19 testing: Speed, quality and cost. Can you have all three?

KEY MESSAGES.

QALYs for COVID-19: A Comparison of US EQ-5D-5L Value Sets.

BACKGROUND: In economic evaluations, quality-adjusted life-years (QALYs) can serve as a unit of measurement for disease burden. Obtaining QALY values for COVID-19 presents a challenge owing to the availability of two US EQ-5D-5L value sets and the potentially asymptomatic presentation of the disease. The first value set was completed allowing for the discounting of future health outcomes while the second value set is undiscounted. OBJECTIVE: The objective of this study was to compare the distribution of QALY values using a national survey and the two published value sets; and to estimate the association between COVID-19 outcomes and QALY losses. METHODS: Between 9 and 11 November, 2020, 1153 US adults completed the EQ-5D-5L instrument (five items and a visual analog scale) as well as self-reported their demographics, COVID-19 symptoms, and memberships to populations that are at risk of COVID-19 infection. The two US value sets were applied to the EQ-5D-5L responses to produce QALY values. We estimated the mean QALYs by visual analog scale decile and a generalized linear model of COVID-19 outcomes. RESULTS: The discounted values are higher than the undiscounted values for each visual analog scale decile owing to methodological differences. Persons at increased risk, with a fever in the past day, and with one or more other symptoms have significantly greater QALY losses (p < 0.01). Overall, non-institutionalized individuals at risk of symptomatic clinical COVID-19 equal 0.68 for the 2016 value set (95% confidence interval 0.49-0.87) and 0.10 for the 2017 value set (95% confidence interval - 0.31 to 0.51) QALYs. CONCLUSIONS: Multiple studies have shown that decision makers discount future health outcomes, which increase QALY values. This study confronts the practical implications of these methodological advances for use in COVID-19 economic evaluations. Health economists will be able to use the QALY values in this study to better evaluate health interventions against COVID-19.

Accuracy Requirements for Cost-effective Suicide Risk Prediction Among Primary Care Patients in the US.

Importance: Several statistical models for predicting suicide risk have been developed, but how accurate such models must be to warrant implementation in clinical practice is not known. Objective: To identify threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals. Design, Setting, and Participants: This economic evaluation incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions into a novel decision analytic model. The model projected suicide-related health economic outcomes over a lifetime horizon among a population of US adults with a primary care physician. Data analysis was performed from September 19, 2019, to July 5, 2020. Interventions: Two possible interventions were delivered to individuals at high predicted risk: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual health care cost, $1088). Main Outcomes and Measures: Fatal and nonfatal suicide attempts, quality-adjusted life-years (QALYs), health care sector costs and societal costs (in 2016 US dollars), and incremental cost-effectiveness ratios (ICERs) (with ICERs ≤$150 000 per QALY designated cost-effective). Results: With a specificity of 95% and a sensitivity of 25%, primary care-based suicide risk prediction could reduce suicide death rates by 0.5 per 100 000 person-years (if used to target ACF) or 1.6 per 100 000 person-years (if used to target CBT) from a baseline of 15.3 per 100 000 person-years. To be cost-effective from a health care sector perspective at a specificity of 95%, a risk prediction method would need to have a sensitivity of 17.0% or greater (95% CI, 7.4%-37.3%) if used to target ACF and 35.7% or greater (95% CI, 23.1%-60.3%) if used to target CBT. To achieve cost-effectiveness, ACF required positive predictive values of 0.8% for predicting suicide attempt and 0.07% for predicting suicide death; CBT required values of 1.7% for suicide attempt and 0.2% for suicide death. Conclusions and Relevance: These findings suggest that with sufficient accuracy, statistical suicide risk prediction models can provide good health economic value in the US. Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in US health care systems.

Health-Related Quality-of-Life and Cost Utility Analyses in Critical Care: A Systematic Review.

OBJECTIVES: Cost utility analyses compare the costs and health outcome of interventions, with a denominator of quality-adjusted life year, a generic health utility measure combining both quality and quantity of life. Cost utility analyses are difficult to compare when methods are not standardized. It is unclear how cost utility analyses are measured/reported in critical care and what methodologic challenges cost utility analyses pose in this setting. This may lead to differences precluding cost utility analyses comparisons. Therefore, we performed a systematic review of cost utility analyses conducted in critical care. Our objectives were to understand: 1) methodologic characteristics, 2) how health-related quality-of-life was measured/reported, and 3) what costs were reported/measured. DESIGN: Systematic review. DATA SOURCES: We systematically searched for cost utility analyses in critical care in MEDLINE, Embase, American College of Physicians Journal Club, CENTRAL, Evidence-Based Medicine Reviews' selected subset of archived versions of UK National Health Service Economic Evaluation Database, Database of Abstracts of Reviews of Effects, and American Economic Association electronic databases from inception to April 30, 2020. SETTING: Adult ICUs. PATIENTS: Adult critically ill patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 8,926 citations, 80 cost utility analyse studies were eligible. The time horizon most commonly reported was lifetime (59%). For health utility reporting, health-related quality-of-life was infrequently measured (29% reported), with only 5% of studies reporting baseline health-related quality-of-life. Indirect utility measures (generic, preference-based health utility measurement tools) were reported in 85% of studies (majority Euro-quality-of-life-5 Domains, 52%). Methods of estimating health-related quality-of-life were seldom used when the patient was incapacitated: imputation (19%), assigning fixed utilities for incapacitation (19%), and surrogates reporting on behalf of incapacitated patients (5%). For cost utility reporting transparency, separate incremental costs and quality-adjusted life years were both reported in only 76% of studies. Disaggregated quality-adjusted life years (reporting separate health utility and life years) were described in only 34% of studies. CONCLUSIONS: We identified deficiencies which warrant recommendations (standardized measurement/reporting of resource use/unit costs/health-related quality-of-life/methodological preferences) for improved design, conduct, and reporting of future cost utility analyses in critical care.

Evaluation of N95 respirators, modified snorkel masks and low-cost powered air-purifying respirators: a prospective observational cohort study in healthcare workers.

Disposable N95 respirator masks are the current standard for healthcare worker respiratory protection in the COVID-19 pandemic. In addition to shortages, qualitative fit testing can have low sensitivity for detecting poor fit, leading to inconsistent protection. Multiple groups have developed alternative solutions such as modified snorkel masks to overcome these limitations, but validation of these solutions has been lacking. We sought to determine if N95s and snorkel masks with attached high-efficiency filters provide consistent protection levels in healthcare workers and if the addition of positive pressure via an inexpensive powered-air purifying respirator to the snorkel mask would provide enhanced protection. Fifty-one healthcare workers who were qualitatively fitted with N95 masks underwent quantitative mask fit testing according to a simulated workplace exercise protocol. N95, snorkel masks with high-efficiency filters and snorkel masks with powered-air purifying respirators were tested. Respiratory filtration ratios were collected for each step and averaged to obtain an overall workplace protocol fit factor. Failure was defined as either an individual filtration ratio or an overall fit factor below 100. N95s and snorkel masks with high-efficiency filters failed one or more testing steps in 59% and 20% of participants, respectively, and 24% and 12% failed overall fit factors, respectively. The snorkel masks with powered-air purifying respirators had zero individual or overall failures. N95 and snorkel masks with high-efficiency filter respirators were found to provide inconsistent respiratory protection in healthcare workers.