Highlights: This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.
Background: Graphical representation formats (e.g., icon arrays) have been shown to lead to better understanding of the benefits and risks of treatments compared to numbers. We investigate the cognitive processes underlying the effects of format on understanding: how much cognitive effort is required to process numerical and graphical representations, how people process inconsistent representations, and how numeracy and graph literacy affect information processing.
Methods: In a preregistered between-participants experiment, 665 participants answered questions about the relative frequencies of benefits and side effects of 6 medications. First, we manipulated whether the medical information was represented numerically, graphically (as icon arrays), or inconsistently (numerically for 3 medications and graphically for the other 3). Second, to examine cognitive effort, we manipulated whether there was time pressure or not. In an additional intervention condition, participants translated graphical information into numerical information before answering questions. We also assessed numeracy and graph literacy.
Results: Processing icon arrays was more strongly affected by time pressure than processing numbers, suggesting that graphical formats required more cognitive effort. Understanding was lower when information was represented inconsistently (v. consistently) but not if there was a preceding intervention. Decisions based on inconsistent representations were biased toward graphically represented options. People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did. Graph literacy was not related to processing efficiency.
Limitations: Our study was conducted with a nonpatient sample, and the medical information was hypothetical.
Conclusions: Although graphical (v. numerical) formats have previously been found to lead to better understanding, they may require more cognitive effort. Therefore, the goal of risk communication may play an important role when choosing how to communicate medical information.
Highlights: This article investigates the cognitive processes underlying the effects of representation format on the understanding of statistical information and individual differences therein.Processing icon arrays required more cognitive effort than processing numbers did.When information was represented inconsistently (i.e., partly numerically and partly graphically), understanding was lower than with consistent representation, and decisions were biased toward the graphically represented options.People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did.
Objective: Naturalness preference can influence important health decisions. However, the literature lacks a reliable way to measure individual differences in naturalness preferences. We fill this gap by designing and validating a scale to measure individual differences in naturalness preference.
Methods: We conducted 3 studies among Amazon Mechanical Turk participants. In study 1 (N = 451), we created scale items through an iterative process that measured naturalness preference in hypothesized domains. We conducted exploratory factor analysis (EFA) to identify items that assess the naturalness preference construct. In study 2 (N = 448), we conducted confirmatory factor analysis (CFA) and tests of criterion, discriminant, convergent, and incremental validity. In study 3 (N = 607), we confirmed test-retest reliability of the scale and performed additional validity tests.
Results: EFA revealed 3 correlated factors consistent with naturalness preference in medicine, food, and household products. The CFA confirmed the 3-factor structure and led to the decision to drop reverse-coded items. The finalized Naturalness Preference Scale (NPS) consists of 20 items and 3 subscales: NPS-medicine, NPS-food, and NPS-household products. The NPS demonstrated good test-retest reliability, and subscales had good validity in their respective domains. The NPS-medicine subscale was predictive of the uptake of a hypothetical COVID-19 vaccine (r = -0.45) and belief in unproven natural COVID remedies and treatments (r = 0.29).
Conclusions: The NPS will allow researchers to better assess individual differences in naturalness preference and how they influence decision making and health behaviors.
Highlights: This research created and validated a scale to measure individual differences in naturalness preference in 3 domains: medicine, food, and household products.This study confirms that the strength of the naturalness preference differs in different domains.An important and timely finding is that higher scores in the naturalness preference medical subscale are associated with belief in COVID-19 misinformation and reluctance toward COVID-19 vaccination.
Background: In valuation studies of the EQ-5D-5L instrument, the composite time tradeoff method (cTTO) is often used to elicit preferences. In cTTO, some health states are considered worse than dead (WTD) and are assigned negative utility values. However, these negative values correlate poorly with state severity, which suggests that cTTO is insufficiently sensitive. A recent threshold explanation has been offered to account for the lack of correlation: because the severity threshold beyond which a state is considered WTD differs between respondents, the correlation should be studied for individual respondents clustered by the number of WTD states. The results obtained in such a threshold approach were interpreted to disprove the insensitivity of the cTTO method.
Aim: To scrutinize the threshold explanation and test whether it indeed refutes the insensitivity of cTTO.
Methods: The study uses data from the EQ-5D-5L Polish valuation study, which includes cTTO responses from 1,510 participants, each of whom evaluated 10 EQ-5D-5L states. The correlation analysis and threshold approach are repeated to confirm the results from previous studies. The data are then modified in 2 contrasting ways. First, negative utilities are randomly reshuffled to test whether the threshold approach can capture cTTO insensitivity. Second, individual-level regressions are used to simulate negative values to ensure they correlate with severity at the individual respondent level, verifying whether the overall severity-utility correlation should be observed.
Results: First, reshuffling negative utilities does not change the results of the threshold approach. Hence, the threshold explanation fails to prove cTTO sensitivity. Second, when sensitivity was introduced on an individual level, a significant overall correlation between severity and negative utility arose.
Conclusion: cTTO is insensitive to severity for WTD states.
Highlights: For the composite time tradeoff method, the utility values of health states worse than dead correlate poorly with state severity, which suggests that cTTO has insufficient sensitivity.Recently, a so-called threshold explanation was offered for the lack of correlation.I show why the threshold explanation fails and why the composite time tradeoff is indeed insensitive for worse-than-dead states.
Purpose: Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. We developed a general microsimulation framework to study therapy sequence and applied it to metastatic prostate cancer.
Methods: We constructed a discrete-time state transition model to study 2 lines of therapy. Using digitized published survival curves (progression-free survival, time to progression, and overall survival [OS]), we inferred event types (progression or death) and estimated transition probabilities using cumulative incidence functions with competing risks. We incorporated within-patient dependence over time; first-line therapy response informed subsequent event probabilities. Parameters governing within-patient dependence calibrated the model-based results to a target clinical trial. We applied these methods to 2 therapy sequences for metastatic prostate cancer, wherein both docetaxel (DCT) and abiraterone acetate (AA) are appropriate for either first- or second-line treatment. We assessed costs and quality-adjusted life-years (5-y QALYs) for 2 treatment strategies: DCT → AA versus AA → DCT.
Results: Models assuming within-patient independence overestimated OS time, which corrected with the calibration approach. With generic pricing, AA → DCT dominated DCT → AA, (higher 5-y QALYs and lower costs), consistent for all values of calibration parameters (including no correction). Model calibration increased the difference in 5-y QALYs between treatment strategies (0.07 uncorrected v. 0.15 with base-case correction). Applying the correction decreased the estimated difference in cost (-$5,360 uncorrected v. -$3,066 corrected). Results were strongly affected by the cost of AA. Under a lifetime horizon, AA → DCT was no longer dominant but still cost-effective (incremental cost-effectiveness ratio: $19,463).
Conclusions: We demonstrate a microsimulation approach to study the cost-effectiveness of therapy sequences for advanced prostate cancer, taking care to account for within-patient dependence.
Highlights: We developed a discrete-time state transition model for studying therapy sequence in advanced cancers.Results are sensitive to dependence within patients.A calibration approach can introduce dependence across lines of therapy and closely match simulation outcomes to target trial outcomes.
Background: Polio antiviral drugs (PAVDs) may provide a critical tool in the eradication endgame by stopping poliovirus infections in immunodeficient individuals who may not clear the virus without therapeutic intervention. Although prolonged/chronic poliovirus excreters are rare, they represent a source of poliovirus reintroduction into the general population. Prior studies that assumed the successful cessation of all oral poliovirus vaccine (OPV) use estimated the potential upper bound of the incremental net benefits (INBs) of resource investments in research and development of PAVDs. However, delays in polio eradication, OPV cessation, and the development of PAVDs necessitate an updated economic analysis to reevaluate the costs and benefits of further investments in PAVDs.
Methods: Using a global integrated model of polio transmission, immunity, vaccine dynamics, risks, and economics, we explore the risks of reintroduction of polio transmission due to immunodeficiency-related vaccine-derived poliovirus (iVDPV) excreters and reevaluate the upper bound of the INBs of PAVDs.
Results: Under the current conditions, for which the use of OPV will likely continue for the foreseeable future, even with successful eradication of type 1 wild poliovirus by the end of 2023 and continued use of Sabin OPV for outbreak response, we estimate an upper bound INB of 60 million US$2019. With >100 million US$2019 already invested in PAVD development and with the introduction of novel OPVs that are less likely to revert to neurovirulence, our analysis suggests the expected INBs of PAVDs would not offset their costs.
Conclusions: While PAVDs could play an important role in the polio endgame, their expected economic benefits drop with ongoing OPV use and poliovirus transmissions. However, stakeholders may pursue the development of PAVDs as a desired product regardless of their economic benefits.HighlightsWhile polio antiviral drugs could play an important role in the polio endgame, their expected economic benefits continue to drop with delays in polio eradication and the continued use of oral poliovirus vaccines.The incremental net benefits of investments in polio antiviral drug development and screening for immunodeficiency-related circulating polioviruses are small.Limited global resources are better spent on increasing global population immunity to polioviruses to stop and prevent poliovirus transmission.
Background: How health workers frame their communication about vaccines' probability of adverse side effects could play an important role in people's intentions to be vaccinated (e.g., positive frame: side effects are unlikely v. negative frame: there is a chance of side effects). Based on the pragmatic account of framing as implicit advice, we expected that participants would report greater vaccination intentions when a trustworthy physician framed the risks positively (v. negatively), but we expected this effect would be reduced or reversed when the physician was untrustworthy.
Design: In 4 online experiments (n = 191, snowball sampling and n = 453, 451, and 464 UK residents via Prolific; Mage≈ 34 y, 70% women, 84% White British), we manipulated the trustworthiness of a physician and how they framed the risk of adverse side effects in a scenario (i.e., a chance v. unlikely adverse side effects). Participants reported their vaccination intention, their level of distrust in health care systems, and COVID-19 conspiracy beliefs.
Results: Physicians who were trustworthy (v. untrustworthy) consistently led to an increase in vaccination intention, but the way they described adverse side effects mattered too. A positive framing of the risks given by a trustworthy physician consistently led to increased vaccination intention relative to a negative framing, but framing had no effect or the opposite effect when given by an untrustworthy physician. The exception to this trend occurred in unvaccinated individuals in experiment 3, following serious concerns about one of the COVID vaccines. In that study, unvaccinated participants responded more favorably to the negative framing of the trustworthy physician.
Conclusions: Trusted sources should use positive framing to foster vaccination acceptance. However, in a situation of heightened fears, a negative framing-attracting more attention to the risks-might be more effective.
Highlights: How health workers frame their communication about a vaccine's probability of adverse side effects plays an important role in people's intentions to be vaccinated.In 4 experiments, we manipulated the trustworthiness of a physician and how the physician framed the risk of adverse side effects of a COVID vaccine.Positive framing given by a trustworthy physician promoted vaccination intention but had null effect or did backfire when given by an untrustworthy physician.The effect occurred over and above participants' attitude toward the health care system, risk perceptions, and beliefs in COVID misinformation.
Introduction: The Dental Caries Utility Index (DCUI) is a new oral health-specific health state classification system for adolescents, consisting of 5 domains: pain/discomfort, difficulty eating food/drinking, worried, ability to participate in activities, and appearance. Each domain has 4 response levels. This study aims to generate an Australian-specific utility algorithm for the DCUI.
Methods: An online survey was conducted using a representative sample of the adult Australian general population. The discrete choice experiment (DCE) was used to elicit the preferences on 5 domains. Then, the latent utilities were anchored onto the full health-dead scale using the visual analogue scale (VAS). DCE data were modeled using conditional logit, and 2 anchoring procedures were considered: anchor based on the worst health state and a mapping approach. The optimal anchoring procedure was selected based on the model parsimony and the mean absolute error (MAE).
Results: A total of 995 adults from the Australian general population completed the survey. The conditional logit estimates on 5 dimensions and levels were monotonic and statistically significant, except for the second level of the "worried" and "appearance" domains. The mapping approach was selected based on a smaller MAE between the 2 anchoring procedures. The Australian-specific tariff of DCUI ranges from 0.1681 to 1.
Conclusion: This study developed a utility algorithm for the DCUI. This value set will facilitate utility value calculations from the participants' responses for DCUI in economic evaluations of dental caries interventions targeted for adolescents.
Highlights: Preference-based quality-of-life measures (PBMs), which consist of a health state classification system and a set of utility values (a scoring algorithm), are used to generate utility weights for economic evaluations.This study is the first to develop an Australian utility value set for the Dental Caries Utility Index (DCUI), a new oral health-specific classification system for adolescents.The availability of a utility value set will enable using DCUI in economic evaluations of oral health interventions targeted for adolescents and may ultimately lead to more effective and efficient planning of oral health care services.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：