Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists.
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SUBMITTER: Russell LB
PROVIDER: S-EPMC7772275 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
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Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists.
Russell Louise B LB Norton Laurie A LA Pagnotti David D Sevinc Christianne C Anderson Sophia S Finnerty Bigelow Darra D Iannotte Lauren G LG Josephs Michael M McGilloway Ryan R Barankay Iwan I Putt Mary E ME Reese Peter P PP Asch David A DA Goldberg Lee R LR Mehta Shivan J SJ Tanna Monique S MS Troxel Andrea B AB Volpp Kevin G KG
Medical decision making : an international journal of the Society for Medical Decision Making 20201120 1
Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are pote ...[more]
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Project description:ObjectiveTo investigate clinical trialists' opinions and experiences of sharing of clinical trial data with investigators who are not directly collaborating with the research team.Design and settingCross sectional, web based survey.ParticipantsClinical trialists who were corresponding authors of clinical trials published in 2010 or 2011 in one of six general medical journals with the highest impact factor in 2011.Main outcome measuresSupport for and prevalence of data sharing through data repositories and in response to individual requests, concerns with data sharing through repositories, and reasons for granting or denying requests.ResultsOf 683 potential respondents, 317 completed the survey (response rate 46%). In principle, 236 (74%) thought that sharing de-identified data through data repositories should be required, and 229 (72%) thought that investigators should be required to share de-identified data in response to individual requests. In practice, only 56 (18%) indicated that they were required by the trial funder to deposit the trial data in a repository; of these 32 (57%) had done so. In all, 149 respondents (47%) had received an individual request to share their clinical trial data; of these, 115 (77%) had granted and 56 (38%) had denied at least one request. Respondents' most common concerns about data sharing were related to appropriate data use, investigator or funder interests, and protection of research subjects.ConclusionsWe found strong support for sharing clinical trial data among corresponding authors of recently published trials in high impact general medical journals who responded to our survey, including a willingness to share data, although several practical concerns were identified.
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Project description:The data presented in this study were obtained from a novel approach to estimate a comprehensive loan acquisition cost. The latter includes commute costs and wage losses in addition to the monthly installment payments. These cost estimates represent the monetary value (in U.S. dollars) of the costs of driving to and from the installment lender storefront and that of the potential hourly wage losses, that is, wage loss from the driving time and the time spent at the loan office filling out the required paperwork to obtain the loan. Borrowers only get the net loan proceeds, that is, the original loan amount minus the comprehensive loan acquisition costs. The study area has 160 counties. It was created from the ESRI ArcGIS Map (a mapping software) using the spatial data from the U.S. Census, Topologically Integrated Geographic Encoding and Referencing (TIGER) Cartographic boundary files representing the geographies of states and counties. Using the U.S. road networks, the origin of the trip is a county seat in Arkansas and the destination of the trip is a county seat in a surrounding state of Tennessee, Mississippi, Louisiana, Texas, Oklahoma, and Missouri. The transportation networks were established using Google Earth/Directions to efficiently measure the travel time (distance). The average cost of a trip of 17 cents (U.S. dollar) was calculated based on the U.S. Department of Transportation Survey data, which identify important attributes of a typical vehicle used in a county such as model make, age of the vehicle, fuel consumption, etc. There are 10 occupational industry sectors where a typical borrower has a job. To estimate wage loss, the data were gathered from the U.S. Department of Labor, Bureau of Labor Statistics, namely, the Occupational Employment Statistics. Putting the missing pieces together, the data contain in this study improve our understanding of extra costs borne by borrowers located in the "loan desert" area. As expected, interior counties post high loan acquisition costs compared with border counties. The data from this study are useful to the public, businesses, policymakers, and researchers working on consumer finance.
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Project description:BackgroundQuality improvement (QI) evaluations rarely consider how a successful intervention can be sustained long term, nor how to spread or scale to other locations. A survey of authors of randomized trials of diabetes QI interventions included in an ongoing systematic review found that 78% of trials reported improved quality of care, but 40% of these trials were not sustained. This study explores why and how the effective interventions were sustained, spread, or scaled.MethodsA qualitative approach was used, focusing on case examples. Diabetes QI program trial authors were purposefully sampled and recruited for telephone interviews. Authors were eligible if they had completed the author survey, agreed to follow-up, and had a completed a diabetes QI trial they deemed "effective." Snowball sampling was used if the participant identified someone who could provide a different perspective on the same trial. Interviews were transcribed verbatim. Inductive thematic analysis was conducted to identify barriers and facilitators to sustainability, spread, and/or scale of the QI program, using case examples to show trajectories across projects and people.ResultsEleven of 44 eligible trialists participated in an interview. Four reported that the intervention was "sustained" and nine were "spread," however, interviews highlighted that these terms were interpreted differently over time and between participants. Participant stories highlighted the varied trajectories of how projects evolved and how some research careers adapted to increase impact. Three interacting themes, termed the "3C's," helped explain the variation in sustainability, spread, and scale: (i) understanding the concepts of implementation, sustainability, sustainment, spread, and scale; (ii) having the appropriate competencies; and (iii) the need for individual, organizational, and system capacity.ConclusionsChallenges in defining sustainability, spread and scale make it difficult to fully understand impact. However, it is clear that from the beginning of intervention design, trialists need to understand the concepts and have the competency and capacity to plan for feasible and sustainable interventions that have potential to be sustained, spread and/or scaled if found to be effective.
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Project description:The advancement of translational behavioral medicine will require that we discover new methods of managing large volumes of data from disparate sources such as disease surveillance systems, public health systems, and health information systems containing patient-centered data informed by behavioral and social sciences. The term "liquidity," when applied to data, refers to its availability and free flow throughout human/computer interactions. In seeking to achieve liquidity, the focus is not on creating a single, comprehensive database or set of coordinated datasets, nor is it solely on developing the electronic health record as the "one-stop shopping" source of health-related data. Rather, attention is on ensuring the availability of secure data through the various methods of collecting and storing data currently existent or under development-so that these components of the health information infrastructure together support a liquid data system. The value of accessible, interoperable, high-volume, reliable, secure, and contextually appropriate data is becoming apparent in many areas of the healthcare system, and health information liquidity is currently viewed as an important component of a patient-centered healthcare system. The translation from research interventions to behavioral and psychosocial indicators challenges the designers of healthcare systems to include this new set of data in the correct context. With the intention of advancing translational behavioral medicine at the local level, "on the ground" in the clinical office and research institution, this commentary discusses data liquidity from the patient's and clinician's perspective, requirements for a liquid healthcare data system, and the ways in which data liquidity can support translational behavioral medicine.
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Project description:The ethical dilemma (ED) of whether autonomous vehicles (AVs) should protect the passengers or pedestrians when harm is unavoidable has been widely researched and debated. Several behavioral scientists have sought public opinion on this issue, based on the premise that EDs are critical to resolve for AV adoption. However, many scholars and industry participants have downplayed the importance of these edge cases. Policy makers also advocate a focus on higher level ethical principles rather than on a specific solution to EDs. But conspicuously absent from this debate is the view of the consumers or potential adopters, who will be instrumental to the success of AVs. The current research investigated this issue both from a theoretical standpoint and through empirical research. The literature on innovation adoption and risk perception suggests that EDs will be heavily weighted by potential adopters of AVs. Two studies conducted with a broad sample of consumers verified this assertion. The results from these studies showed that people associated EDs with the highest risk and considered EDs as the most important issue to address as compared to the other technical, legal and ethical issues facing AVs. As such, EDs need to be addressed to ensure robustness in the design of AVs and to assure consumers of the safety of this promising technology. Some preliminary evidence is provided about interventions to resolve the social dilemma in EDs and about the ethical preferences of prospective early adopters of AVs.Supplementary informationThe online version contains supplementary material available at 10.1007/s10676-021-09605-y.
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Project description:Animal tracking and biologging devices record large amounts of data on individual movement behaviors in natural environments. In these data, movement ecologists often view unexplained variation around the mean as "noise" when studying patterns at the population level. In the field of behavioral ecology, however, focus has shifted from population means to the biological underpinnings of variation around means. Specifically, behavioral ecologists use repeated measures of individual behavior to partition behavioral variability into intrinsic among-individual variation and reversible behavioral plasticity and to quantify: a) individual variation in behavioral types (i.e. different average behavioral expression), b) individual variation in behavioral plasticity (i.e. different responsiveness of individuals to environmental gradients), c) individual variation in behavioral predictability (i.e. different residual within-individual variability of behavior around the mean), and d) correlations among these components and correlations in suites of behaviors, called 'behavioral syndromes'. We here suggest that partitioning behavioral variability in animal movements will further the integration of movement ecology with other fields of behavioral ecology. We provide a literature review illustrating that individual differences in movement behaviors are insightful for wildlife and conservation studies and give recommendations regarding the data required for addressing such questions. In the accompanying R tutorial we provide a guide to the statistical approaches quantifying the different aspects of among-individual variation. We use movement data from 35 African elephants and show that elephants differ in a) their average behavior for three common movement behaviors, b) the rate at which they adjusted movement over a temporal gradient, and c) their behavioral predictability (ranging from more to less predictable individuals). Finally, two of the three movement behaviors were correlated into a behavioral syndrome (d), with farther moving individuals having shorter mean residence times. Though not explicitly tested here, individual differences in movement and predictability can affect an individual's risk to be hunted or poached and could therefore open new avenues for conservation biologists to assess population viability. We hope that this review, tutorial, and worked example will encourage movement ecologists to examine the biology of individual variation in animal movements hidden behind the population mean.
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Project description:IntroductionDespite intense interest in trial registration, there is a wide gap between theoretical postulates on trial registration and its implementation worldwide.ObjectiveWe aimed to evaluate trialists views about current international guidelines on trial registration, including the World Health Organization's (WHO) International Clinical Trials Registry Platform (ICTRP) policies and the Ottawa Statement, as well as their intention to register any future clinical trials they conduct.MethodsWe identified all 40,158 PUBMED-indexed clinical trials published from May 2005 to May 2006 using an advanced search strategy. From a random sample of 500 confirmed clinical trials, corresponding authors with e-mail contact addresses were surveyed.ResultsA total of 275 (60%) questionnaires from 45 countries were completed. 31% of the respondents had received only nonindustry funding during the past ten years, while 5% and 61% had received only industry or mixed funding respectively. Approximately two third of participants supported registration of all 20 WHO Data Set items, and endorsed the Ottawa Statement part 1 and part 2. Delayed public disclosure of some essential data in instances where they may be considered sensitive for competitive commercial reasons was supported by 30% of the participants, whereas immediate disclosure was supported by 53%. Only 21% of participants had registered all of their ongoing trials since 2005, while 47% stated that they would provide the 20 WHO Data Set items to a publicly accessible register for all their future clinical trials; a significantly higher proportion of participants who received only nonindustry funding (62%) was found among those who would always provide the 20 WHO items for future trials, compared to 42% of participants who received mixed or only industry funding. Among those who were undecided about endorsing registration. One third of participants expressed a lack of sufficient knowledge as the primary reason.ConclusionAlthough disagreement was apparent on certain issues, our findings illustrate that trial registration is gradually becoming part of the current research paradigm internationally. Our results also suggest that researchers require more knowledge to inform their decision to comply with the International standards at this early stage of voluntary trial registration.
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Project description:BackgroundPatient-reported outcomes (PROs), such as health-related quality of life (HRQL) are increasingly used to evaluate treatment effectiveness in clinical trials, are valued by patients, and may inform important decisions in the clinical setting. It is of concern, therefore, that preliminary evidence, gained from group discussions at UK-wide Medical Research Council (MRC) quality of life training days, suggests there are inconsistent standards of HRQL data collection in trials and appropriate training and education is often lacking. Our objective was to investigate these reports, to determine if they represented isolated experiences, or were indicative of a potentially wider problem.Methods and findingsWe undertook a qualitative study, conducting 26 semi-structured interviews with research nurses, data managers, trial coordinators and research facilitators involved in the collection and entry of HRQL data in clinical trials, across one primary care NHS trust, two secondary care NHS trusts and two clinical trials units in the UK. We used conventional content analysis to analyze and interpret our data. Our study participants reported (1) inconsistent standards in HRQL measurement, both between, and within, trials, which appeared to risk the introduction of bias; (2), difficulties in dealing with HRQL data that raised concern for the well-being of the trial participant, which in some instances led to the delivery of non-protocol driven co-interventions, (3), a frequent lack of HRQL protocol content and appropriate training and education of trial staff, and (4) that HRQL data collection could be associated with emotional and/or ethical burden.ConclusionsOur findings suggest there are inconsistencies in the standards of HRQL data collection in some trials resulting from a general lack of HRQL-specific protocol content, training and education. These inconsistencies could lead to biased HRQL trial results. Future research should aim to develop HRQL guidelines and training programmes aimed at supporting researchers to carry out high quality data collection.
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Project description:BackgroundClinicians frequently prescribe antibiotics inappropriately for acute respiratory infections (ARIs). Our objective was to test information technology-enabled behavioral interventions to reduce inappropriate antibiotic prescribing for ARIs in a randomized controlled pilot test trial.MethodsPrimary care clinicians were randomized in a 2 × 2 × 2 factorial experiment with 3 interventions: 1) Accountable Justifications; 2) Suggested Alternatives; and 3) Peer Comparison. Beforehand, participants completed an educational module. Measures included: rates of antibiotic prescribing for: non-antibiotic-appropriate ARI diagnoses, acute sinusitis/pharyngitis, all other diagnoses/symptoms of respiratory infection, and all three ARI categories combined.ResultsWe examined 3,276 visits in the pre-intervention year and 3,099 in the intervention year. The antibiotic prescribing rate fell for non-antibiotic-appropriate ARIs (24.7 % in the pre-intervention year to 5.2 % in the intervention year); sinusitis/pharyngitis (50.3 to 44.7 %); all other diagnoses/symptoms of respiratory infection (40.2 to 25.3 %); and all categories combined (38.7 to 24.2 %; all p < 0.001). There were no significant relationships between any intervention and antibiotic prescribing for non-antibiotic-appropriate ARI diagnoses or sinusitis/pharyngitis. Suggested Alternatives was associated with reduced antibiotic prescribing for other diagnoses or symptoms of respiratory infection (odds ratio [OR], 0.62; 95 % confidence interval [CI], 0.44-0.89) and for all ARI categories combined (OR, 0.72; 95 % CI, 0.54-0.96). Peer Comparison was associated with reduced prescribing for all ARI categories combined (OR, 0.73; 95 % CI, 0.53-0.995).ConclusionsWe observed large reductions in antibiotic prescribing regardless of whether or not study participants received an intervention, suggesting an overriding Hawthorne effect or possibly clinician-to-clinician contamination. Low baseline inappropriate prescribing may have led to floor effects.Trial registrationClinicalTrials.gov: NCT01454960 .
| S-EPMC4975897 | biostudies-literature