Small Molecule RNA Degraders.

RNA is a central molecule in life, involved in a plethora of biological processes and playing a key role in many diseases. Targeting RNA emerges as a significant endeavor in drug discovery, diverging from conventional protein-centric approaches to tackle various pathologies. Whilst identifying small molecules that bind to specific RNA regions is the first step, the abundance of non-functional RNA segments renders many interactions biologically inert. Consequently, small molecule binding does not necessarily meet stringent criteria for clinical translation, calling for solutions to push the field forward. Converting RNA-binders into RNA-degraders presents a promising avenue to enhance RNA-targeting. This mini-review outlines strategies and exemplars wherein simple small molecule RNA binders are reprogrammed into active degraders through the linkage of functional groups. These approaches encompass mechanisms that induce degradation via endogenous enzymes, termed RIBOTACs, as well as those with functional moieties acting autonomously to degrade RNA. Through this exploration, we aim to offer insights into advancing RNA-targeted therapeutic strategies.

The Performance of Kaizen Tasks Across Three Online Discrete Choice Experiment Surveys: An Evidence Synthesis.

BACKGROUND: Kaizen is a Japanese term for continuous improvement (kai ~ change, zen ~ good). In a kaizen task, a respondent makes sequential choices to improve an object's profile, revealing a preference path. Including kaizen tasks in a discrete choice experiment has the advantage of collecting greater preference evidence than pick-one tasks, such as paired comparisons. OBJECTIVE AND METHODS: So far, three online discrete choice experiments have included kaizen tasks: the 2020 US COVID-19 vaccination (CVP) study, the 2021 UK Children's Surgery Outcome Reporting (CSOR) study, and the 2023 US EQ-5D-Y-3L valuation (Y-3L) study. In this evidence synthesis, we describe the performance of the kaizen tasks in terms of response behaviors, conditional logit and Zermelo-Bradley-Terry (ZBT) estimates, and their standard errors in each of the surveys. RESULTS: Comparing the CVP and Y-3L, including hold-outs (i.e., attributes shared by all alternatives) seems to reduce positional behavior by half. The CVP tasks excluded multi-level improvements; therefore, we could not estimate logit main effects directly. In the CSOR, only 12 of the 21 logit estimates are significantly positive (p < 0.05), possibly due to the fixed attribute order. All Y-3L estimates are significantly positive, and their predictions are highly correlated (Pearson: logit 0.802, ZBT 0.882) and strongly agree (Lin: logit 0.744, ZBT 0.852) with the paired-comparison probabilities. CONCLUSIONS: These discrete choice experiments offer important lessons for future studies: (1) include warm-up tasks, hold-outs, and multi-level improvements; (2) randomize the attribute order (i.e., up-down) at the respondent level; and (3) recruit smaller samples of respondents than traditional discrete choice experiments with only pick-one tasks.

Scale and rate heterogeneity in the EQ-5D-5L valuation.

OBJECTIVES: To estimate values on a quality-adjusted life year (QALY) scale using individual preference evidence, choice analyses typically include ancillary parameters, such as scale factors and discount rates. These parameters potentially differ among respondents. In this study, we investigated how allowing heterogeneity in scale and rate affects the estimation of EQ-5D-5L values. METHODS: Using the first wave of the 2016 EQ-5D-5L valuation study (N = 1017), we estimated a conditional logit (CL) model and three mixed logit models: random scale, random rate, and bivariate. Prior to the exploratory study, we hypothesized that scale and rate are correlated and that allowing heterogeneity in both parameters decreases the number of insignificant incremental effects. We confirmed the exploratory findings by re-estimating these models using paired comparison responses from a second wave (N = 1229). RESULTS: Scale and rate exhibited significant heterogeneity and were positively correlated. As hypothesized, allowing this heterogeneity improved the face validity of the EQ-5D-5L value set by reducing the number of insignificant incremental effects (from 6 to 2 p-values > 0.05; out of 20). Nevertheless, the CL and bivariate mixed logit estimates are highly correlated and concordant (Pearson correlation coefficient of 0.897, Spearman correlation coefficient of 0.888, Lin's concordance coefficient of 0.763). CONCLUSIONS: Allowing this heterogeneity adds three parameters to the estimation (two variances and a correlation) and improves the face validity of the EQ-5D-5L values. This finding may influence experimental design and choice analysis in health valuation more generally.

Role Preferences in Medical Decision Making: Relevance and Implications for Health Preference Research.

Health preference research (HPR) is being increasingly conducted to better understand patient preferences for medical decisions. However, patients vary in their desire to play an active role in medical decisions. Until now, few studies have considered patients' preferred roles in decision making. In this opinion paper, we advocate for HPR researchers to assess and account for role preferences in their studies, to increase the relevance of their work for medical and shared decision making. We provide recommendations on how role preferences can be elicited and integrated with health preferences: (1) in formative research prior to a health preference study that aims to inform medical decisions or decision makers, (2a) in the development of health preference instruments, for instance by incorporating a role preference instrument and (2b) by clarifying the respondent's role in the decision prior to the preference elicitation task or by including role preferences as an attribute in the task itself, and (3) in statistical analysis by including random parameters or latent classes to raise awareness of heterogeneity in role preferences and how it relates to health preferences. Finally, we suggest redefining the decision process as a model that integrates the role and health preferences of the different parties that are involved. We believe that the field of HPR would benefit from learning more about the extent to which role preferences relate to health preferences, within the context of medical and shared decision making.

Child health valuation protocol for a discrete choice experiment comparing paired comparison and kaizen tasks and estimating US EQ-5D-Y-3L values on an experience scale.

INTRODUCTION: A decade ago, the first national valuation study of the EQ-5D-Y-3L (Y-3L) involved a discrete choice experiment (DCE) that asked 4155 US adult respondents to complete 40 paired comparisons, choosing between two dying children. Instead of choosing between dying children, the respondents in this novel protocol are asked whether 'being in a coma' is better or worse than experiencing 'health problems' (ie, experience scale) and how they would relieve health problems (ie, kaizen tasks). Our aims are to compare the preference evidence of the paired comparison and kaizen tasks and to conduct a DCE for the valuation of Y-3L profiles on an experience scale. METHODS AND ANALYSIS: Under this protocol, we will conduct an online survey that collects preference evidence from 600 US adult respondents on the health of a 10-year-old child for a week. Across all scenarios, each child will be described as either being 'in a coma' or having 'health problems', namely five three-level attributes (Y-3L). In this DCE, each respondent will be randomly assigned to one of four D-efficient blocks, including five coma comparisons (ie, Y-3L vs coma), 10 paired comparisons (Y-3L vs Y-3L) and 10 kaizen tasks (preference paths). In addition to comparing evidence by task (aim 2), the analysis plan includes the estimation of main-effects conditional logit models to create a Y-3L value set on an 'experience scale' where positive (negative) experiences have positive (negative) values (0 is 'being in a coma' and 1 is full health). ETHICS AND DISSEMINATION: The institutional review board (IRB) (Advarra) determined that this project (Pro00072276) is exempt from IRB oversight based on DHHS 45 CFR 46.104(d)(2) and is not subject to requirements for continuing review. The results will be prepared for publication in peer-reviewed journals and presented at scientific meetings. The data and code will be made available on reasonable request.

Preference heterogeneity in health valuation: a latent class analysis of the Peru EQ-5D-5L values.

BACKGROUND: Preference heterogeneity in health valuation has become a topic of greater discussion among health technology assessment agencies. To better understand heterogeneity within a national population, valuation studies may identify latent groups that place different absolute and relative importance (i.e., scale and taste parameters) on the attributes of health profiles. OBJECTIVE: Using discrete choice responses from a Peruvian valuation study, we estimated EQ-5D-5L values on a quality-adjusted life-year (QALY) scale accounting for latent heterogeneity in scale and taste, as well as controlling heteroskedasticity at task level variation. METHOD: We conducted a series of latent class analyses, each including the 20 main effects of the EQ-5D-5L and a power function that relaxes the constant proportionality assumption (i.e., discounting) between value and lifespan. Taste class membership was conditional on respondent-specific characteristics and their experience with the composite time trade-off (cTTO) tasks. Scale class membership was conditional on behavioral characteristics such as survey duration and self-stated difficulty level in understanding tasks. Each analysis allowed the scale factor to vary by task type and completion time (i.e., heteroskedasticity). RESULTS: The results indicated three taste classes: a quality-of-life oriented class (33.35%) that placed the highest value on levels of severity, a length-of-life oriented class (26.72%) that placed the highest value on lifespan, and a middle class (39.71%) with health attribute effects lower than the quality class and lifespan effect lower than the length-of-life oriented class. The EQ-5D-5L values ranged from - 2.11 to 0.86 (quality-of-life oriented class), from - 0.38 to 1.02 (middle class), and from 0.36 to 1.01 (length-of-life oriented class). The likelihood of being a member of the quality-of-life class was highly dependent on whether the respondent completed the cTTO tasks (p-value < 0.001), which indicated that the cTTO tasks might cause the Peru respondents to inflate the burden of health problems on a QALY scale compared to those who did not complete the cTTO tasks. The results also showed two scale classes as well as heteroskedasticity within each scale class. CONCLUSION: Accounting for taste and scale classes simultaneously improveds understanding of preference heterogeneity in health valuation. Future studies may confirm the differences in taste between classes in terms of the effect of quality of life and lifespan attributes. Furthermore, confirmatory evidence is needed on how behavioral variables captured within a study protocol may enhance analyses of preference heterogeneity.

Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review.

BACKGROUND: Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices. OBJECTIVES: Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare. METHODS: This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both. RESULTS: Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity. CONCLUSIONS: Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.

Using stated-preferences methods to develop a summary metric to determine successful treatment of children with a surgical condition: a study protocol.

INTRODUCTION: Wide variation in the management of key paediatric surgical conditions in the UK has likely resulted in outcomes for some children being worse than they could be. Consequently, it is important to reduce unwarranted variation. However, major barriers to this are the inability to detect differences between observed and expected hospital outcomes based on the casemix of the children they have treated, and the inability to detect variation in significant outcomes between hospitals. A stated-preference study has been designed to estimate the value key stakeholders place on different elements of the outcomes for a child with a surgical condition. This study proposes to develop a summary metric to determine what represents successful treatment of children with surgical conditions. METHODS AND ANALYSIS: Preferences from parents, individuals treated for surgical conditions as infants/children, healthcare professionals and members of the public will be elicited using paired comparisons and kaizen tasks. A descriptive framework consisting of seven attributes representing types of operations, infections treated in hospital, quality of life and survival was identified. An experimental design has been completed using a D-efficient design with overlap in three attributes and excluding implausible combinations. All participants will be presented with an additional choice task including a palliative scenario that will be used as an anchor. The survey will be administered online. Primary analysis will estimate a mixed multinomial logit model. A traffic light system to determine what combination of attributes and levels represent successful treatment will be created. ETHICS AND DISSEMINATION: Ethics approval to conduct this study has been obtained from the Medical Sciences Inter-Divisional Research Ethics Committee (IDREC) at the University of Oxford (R59631/RE001-05). We will disseminate all of our results in peer-review publications and scientific presentations. Findings will be additionally disseminated through relevant charities and support groups and professional organisations.

Exploring the importance of controlling heteroskedasticity and heterogeneity in health valuation: a case study on Dutch EQ-5D-5L.

BACKGROUND: Respondents in a health valuation study may have different sources of error (i.e., heteroskedasticity), tastes (differences in the relative effects of each attribute level), and scales (differences in the absolute effects of all attributes). Although prior studies have compared values by preference-elicitation tasks (e.g., paired comparison [PC] and best-worst scaling case 2 [BWS]), no study has yet controlled for heteroskedasticity and heterogeneity (taste and scale) simultaneously in health valuation. METHODS: Preferences on EQ-5D-5L profiles were elicited from a random sample of 380 adults from the general population of the Netherlands, using 24 PC and 25 BWS case 2 tasks. To control for heteroskedasticity and heterogeneity (taste and scale) simultaneously, we estimated Dutch EQ-5D-5L values using conditional, heteroskedastic, and scale-adjusted latent class (SALC) logit models by maximum likelihood. RESULTS: After controlling for heteroskedasticity, the PC and BWS values were highly correlated (Pearson's correlation: 0.9167, CI: 0.9109-0.9222) and largely agreed (Lin's concordance: 0.7658, CI: 0.7542-0.7769) on a pits scale. In terms of preference heterogeneity, some respondents (mostly young men) failed to account for any of the EQ-5D-5L attributes (i.e., garbage class), and others had a lower scale (59%; p-value: 0.123). Overall, the SALC model produced a consistent Dutch EQ-5D-5L value set on a pits scale, like the original study (Pearson's correlation:0.7295; Lin's concordance: 0.6904). CONCLUSIONS: This paper shows the merits of simultaneously controlling for heteroskedasticity and heterogeneity in health valuation. In this case, the SALC model dispensed with a garbage class automatically and adjusted the scale for those who failed the PC dominant task. Future analysis may include more behavioral variables to better control heteroskedasticity and heterogeneity in health valuation. HIGHLIGHTS: The Dutch EQ-5D-5L values based on paired comparison [PC] and best-worst scaling [BWS] responses were highly correlated and largely agreed after controlling for heteroskedasticity. Controlling for taste and scale heterogeneity simultaneously enhanced the Dutch EQ-5D-5Lvalues by automatically dispensing with a garbage class and adjusting the scale for those who failed the dominant task. After controlling for heteroskedasticity and heterogeneity, this study produced Dutch EQ-5D-5L values on a pits scale moderately concordant with the original values.

A Guide to Observable Differences in Stated Preference Evidence.

BACKGROUND AND OBJECTIVE: In health preference research, studies commonly hypothesize differences in parameters (i.e., differential or joint effects on attribute importance) and/or in choice predictions (marginal effects) by observable factors. Discrete choice experiments may be designed and conducted to test and estimate these observable differences. This guide covers how to explore and corroborate various observable differences in health preference evidence. METHODS: The analytical process has three steps: analyze the exploratory data, analyze the confirmatory data, and interpret and disseminate the evidence. In this guide, we demonstrate the process using dual samples (where exploratory and confirmatory samples were collected from different sources) on 2020 US COVID-19 vaccination preferences; however, investigators may apply the same approach using split samples (i.e., single source). RESULTS: The confirmatory analysis failed to reject ten of the 17 null hypotheses generated by the exploratory analysis (p < 0.05). Apart from demographic, socioeconomic, and geographic differences, political independents and persons who have never been vaccinated against influenza are among those least likely to be vaccinated (0.838 and 0.872, respectively). CONCLUSIONS: For all researchers in health preference research, it is essential to know how to identify and corroborate observable differences. Once mastered, this skill may lead to more complex analyses of latent differences (e.g., latent classes, random parameters). This guide concludes with six questions that researchers may ask themselves when conducting such analyses or reviewing published findings of observable differences.

Preference Paths and Their Kaizen Tasks for Small Samples.

BACKGROUND: Stated preference research currently lacks a form of evidence that is well suited for small samples. A preference path is a sequence of two or more choices showing the evolution of an object following an adaptive process. OBJECTIVES: The aims were to introduce preference paths and their kaizen tasks and to demonstrate how to analyze their evidence using a small sample. METHODS: Twenty respondents were assigned the same 16 profiles generated from an orthogonal array based on the five attributes of the EQ-5D-5L descriptive system. Each kaizen task began with an opt-out paired comparison (i.e., choosing between the initial 10-year profile and the opt-out "dying immediately"), followed by choosing three changes, and ended with a second paired comparison (final profile versus opt-out) if the respondent chose opt-out initially. By maximum likelihood with respondent clusters, we estimated the 20 main effects using conditional logit and Zermelo-Bradley-Terry (ZBT) specifications. RESULTS: Apart from demonstrating heterogeneity and profile effects, all main effect estimates were non-negative, and most were significant (15 for logit and all 20 for ZBT; p value < 0.05). Under the logit and ZBT specifications, the value of the worst EQ-5D-5L profile (55555) is - 0.920 quality-adjusted life years (QALYs) or - 1.478 QALYs, respectively. Furthermore, the findings illustrate a log-linear relationship between the logit and ZBT main effects. CONCLUSION: This paper demonstrates the feasibility of a stated-preference study that estimates 20 main effects using path evidence from 20 respondents (16 kaizen tasks, 15-min interviews). This approach shows promise for future application in stated-preference research, particularly in small samples.

Indoor chlorine gas release in a natatorium: A case study.

Pool chemicals are utilized in pools to inactivate pathogens, optimize pH, and increase water clarity. This is conducted to ensure public health and safety by reducing bacteria concentrations and allowing distressed swimmers to be detected underwater. In commercial recreational facilities, muriatic acid and gaseous CO2 are typically used to maintain pH. Chlorine, which can take the form of liquid or solid tablets of sodium hypochlorite or granular calcium hypochlorite, is used to sanitize pool water, and is the most used chemical treatment in the world for swimming pools disinfection. If chlorine is mixed with muriatic acid, chlorine gas is formed, which can lead to severe injuries and fatalities (SIFs) to exposed individuals. This work illustrates an incident that occurred as a result of the simultaneous injection of muriatic acid and liquid sodium hypochlorite into a recreational natatorium chemical feed line. This led to the release of chlorine gas in the indoor environment, which resulted in injuries to five patrons. Furthermore, strategies are proposed to prevent this from occurring and to reduce the likelihood of similar incidents in the future. These include the implementation of fail-safe logic to prevent the controller from malfunctioning and ensuring that controller program settings do not permit simultaneous chemical injection.

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.

United States COVID-19 Vaccination Preferences (CVP): 2020 Hindsight.

BACKGROUND: Shortly after the 2020 US election, initial evidence on first-generation COVID-19 vaccines showed 70-95% efficacy and minimal risks. Yet, many US adults expressed reluctance. AIMS: The aim of this study was to compare persons willing and unwilling to be vaccinated against COVID-19 and to estimate the effects of vaccination attributes on uptake: proof of vaccination, vaccination setting, effectiveness, duration of immunity, and risk of severe side effects. METHOD: Between 9 and 11 November 2020, 1153 US adults completed a discrete choice experiment (DCE) on Phase 2 of the CDC Vaccination Program (August 2021). Each of its eight choice tasks had three vaccination alternatives and "no vaccination for 6 months." An opt-out inflated logit model was estimated to test for respondent differences and attribute effects. RESULTS: Respondent demographics were unrelated to one's willingness to be vaccinated (p value 0.533), but those with less education were more likely to be unwilling (p < 0.001). Among those willing, uptake ranged from 61.70 to 97.75%, depending on the vaccination attributes. Effectiveness and safety had the largest effects. Offering proof of vaccination and a choice of setting increased uptake as much as increasing immunity from 3 to 6 months. CONCLUSIONS: To maximize uptake, the CDC Program should standardize proof of vaccination and offer a choice of setting, instead of a one-size-fits-all approach. If the first-generation vaccines are efficacious, widely available, and free, overall predicted uptake is 68.81% by the end of Phase 2 (August 2021), which is well below the 75-90% needed for herd immunity. Further health preference research is necessary to uncover and address unwillingness and reluctance to vaccinate against COVID-19.

Does Controlling for Scale Heterogeneity Better Explain Respondents' Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand.

Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents' randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

Analyzing the national fire incident reporting system to identify carbon monoxide incidents in the U.S. lodging industry.

Carbon Monoxide (CO) poisonings in the U.S. lodging industry have become a regular occurrence, however there is no current mandatory national reporting, tracking, or surveillance mechanism for CO incidents in the U.S. lodging industry. As such, the problem is largely invisible. The objective of this study was to utilize the National Fire Incident Reporting System (NFIRS) data to better understand the public health risk from Carbon Monoxide (CO) incidents in the U.S. lodging industry. The NFIRS datasets for years 1999 through 2018 were assessed to identify CO incidents occurring at U.S. hotels and motels. The results of the analysis identified 3405 incidents. Incidents were strongly correlated with increased fire department participation in NFIRS (R = 0.82). The number and frequency of CO incidents in the U.S. lodging industry are underreported. Previous efforts relying on news media identified only 10% of the incidents reported in the NFIRS data. This indicates a greater public health risk associated with CO exposure in the U.S. lodging industry than previously realized.

Corrigendum to "Analyzing the national fire incident reporting system to identify carbon monoxide incidents in the U.S. lodging industry" [Prev. Med. Rep. 24 (2021) 101531].

[This corrects the article DOI: 10.1016/j.pmedr.2021.101531.].

Peruvian Valuation of the EQ-5D-5L: A Direct Comparison of Time Trade-Off and Discrete Choice Experiments.

OBJECTIVES: (1) To produce Peruvian general population EQ-5D-5L value sets on a quality-adjusted life-year scale, (2) to investigate the feasibility of a "Lite" protocol less reliant on the composite time trade-off (cTTO), and (3) to compare cTTO and discrete choice experiment (DCE) value sets. METHODS: A random sample of adults (N = 1000) in Lima, Arequipa, and Iquitos did a home interview; 300 were randomly selected to complete 11 cTTOs first. All respondents completed a DCE, including 10 latent-scale pairs (A/B) with 5 EQ-5D-5L attributes, and 12 matched pairs (A/B and B/C) with 5 EQ-5D-5L and one lifespan attributes. We estimated a cTTO heteroscedastic tobit (N = 300) model and 3 DCE Zermelo-Bradley-Terry models (N = 300, 700, and 1000). RESULTS: Each model produced a consistent value set (20 positive incremental parameters). Nevertheless, their lowest quality-adjusted life-year values differed greatly (cTTO: -1.076 [N = 300]; DCE: -0.984 [300], 0.048 [700], -0.213 [1000]). Compared with the cTTO, the DCE (N = 300) produced different parameters (Pearson's correlation = 0.541), fewer insignificant parameters (0 vs 8), and fewer values less than 0 (26% vs 44%). Compared with the DCE (N = 300), the DCE (N = 700) produced higher values but similar parameters (Pearson's correlation = 0.800). CONCLUSIONS: Besides producing EQ-5D-5L value sets for Peru, the results casts doubt about the feasibility of a Lite protocol like the one in this study. Additionally, fundamental differences between cTTO and DCE-without the existence of a gold standard-need further clarification. The choice between the two rational value sets produced in the current study is a matter of judgment and may have substantial policy implications.

Mid- to long-wave infrared computational spectroscopy with a graphene metasurface modulator.

In recent years there has been much interest concerning the development of modulators in the mid- to long-wave infrared, based on emerging materials such as graphene. These have been frequently pursued for optical communications, though also for other specialized applications such as infrared scene projectors. Here we investigate a new application for graphene modulators in the mid- to long-wave infrared. We demonstrate, for the first time, computational spectroscopy in the mid- to long-wave infrared using a graphene-based metasurface modulator. Furthermore, our metasurface device operates at low gate voltage. To demonstrate computational spectroscopy, we provide our algorithm with the measured reflection spectra of the modulator at different gate voltages. We also provide it with the measured reflected light power as a function of the gate voltage. The algorithm then estimates the input spectrum. We show that the reconstructed spectrum is in good agreement with that measured directly by a Fourier transform infrared spectrometer, with a normalized mean-absolute-error (NMAE) of 0.021.

Reporting Formative Qualitative Research to Support the Development of Quantitative Preference Study Protocols and Corresponding Survey Instruments: Guidelines for Authors and Reviewers.

BACKGROUND: Formative qualitative research is foundational to the methodological development process of quantitative health preference research (HPR). Despite its ability to improve the validity of the quantitative evidence, formative qualitative research is underreported. OBJECTIVE: To improve the frequency and quality of reporting, we developed guidelines for reporting this type of research. The guidelines focus on formative qualitative research used to develop robust and acceptable quantitative study protocols and corresponding survey instruments in HPR. METHODS: In December 2018, a steering committee was formed as a means to accumulate the expertise of the HPR community on the reporting guidelines (21 members, seven countries, multiple settings and disciplines). Using existing guidelines and examples, the committee constructed, revised, and refined the guidelines. The guidelines underwent beta testing by three researchers, and further revisions to the guidelines were made based on their feedback as well as on comments from members of the International Academy of Health Preference Research (IAHPR) and the editorial board of The Patient: Patient-Centered Outcomes Research. RESULTS: The guidelines have five components: introductory material (4 domains), methods (12), results/findings (2), discussion (2), and other (2). They are concordant with existing guidelines, published examples, beta-testing results, and expert comments. CONCLUSIONS: Publishing formative qualitative research is a necessary step toward strengthening the foundation of any quantitative study, enhancing the relevance of its preference evidence. The guidelines should aid researchers, reviewers, and regulatory agencies and promote transparency within HPR more broadly.