scholarly journals A common policy framework for evidence generation on promising health technologies

2009 ◽  
Vol 25 (S2) ◽  
pp. 56-67 ◽  
Author(s):  
Cédric Carbonneil ◽  
Fabienne Quentin ◽  
Sun Hae Lee-Robin
Keyword(s):  
Critical Mass ◽  
Policy Framework ◽  
Quality Data ◽  
Marketing Approval ◽  
High Quality Data ◽  
Health Technologies ◽  
Current Implementation ◽  
Timely Access ◽  
New Evidence ◽  
Evidence Generation

Background: Generation of additional evidence may be necessary to access new promising technologies (marketing approval or coverage). Access with evidence generation (AEG) is a more recent concept with regard to coverage than to marketing approval.Objectives: One aim of Work Package 7 (WP7) Strand A of the European network for Health Technology Assessment (EUnetHTA) was to provide an overview of national AEG mechanisms associated with marketing approvals and funding or coverage decisions.Methods: A systematic literature review, surveys of WP7 Partners, and consultation of key people were used to obtain information on the AEG mechanisms used by twenty-three countries (twenty European countries, United States, Canada [Ontario], and Australia).Results: Interest in the implementation of AEG policies, particularly at the coverage decision stage, is growing. An overview of national experiences was used to draw up a generally applicable five-step policy framework for AEG mechanisms that comprised (i) a first assessment identifying knowledge gaps; (ii) a decision conditional to evidence generation; (iii) generation of the evidence requested; (iv) re-assessment integrating the new evidence; (v) a revised decision. The critical factors for success that were identified were coordination, methodological guidance, funding, and a regulatory framework. Countries were categorized on the basis of current implementation of the proposed policy framework.Conclusions: International collaboration is necessary to gather a critical mass of high-quality data quickly and to ensure timely access to new promising technologies. The overview produced by WP7A has led to development of tools to facilitate collaboration on evidence generation.

scholarly journals Redistribution Preferences, Inequality Information, and Partisan Motivated Reasoning in the United States

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 65
Author(s):  
Clem Brooks ◽  
Elijah Harter
Keyword(s):  
Party Identification ◽  
Motivated Reasoning ◽  
The United States ◽  
Quality Data ◽  
Historical Time ◽  
High Quality Data ◽  
Redistributive Policies ◽  
Election Studies ◽  
New Evidence ◽  
The U.S

In an era of rising inequality, the U.S. public’s relatively modest support for redistributive policies has been a puzzle for scholars. Deepening the paradox is recent evidence that presenting information about inequality increases subjects’ support for redistributive policies by only a small amount. What explains inequality information’s limited effects? We extend partisan motivated reasoning scholarship to investigate whether political party identification confounds individuals’ processing of inequality information. Our study considers a much larger number of redistribution preference measures (12) than past scholarship. We offer a second novelty by bringing the dimension of historical time into hypothesis testing. Analyzing high-quality data from four American National Election Studies surveys, we find new evidence that partisanship confounds the interrelationship of inequality information and redistribution preferences. Further, our analyses find the effects of partisanship on redistribution preferences grew in magnitude from 2004 through 2016. We discuss implications for scholarship on information, motivated reasoning, and attitudes towards redistribution.

scholarly journals Personalized analytics and wearable biosensor platform for early detection of COVID-19 decompensation (DeCODe: Detection of COVID-19 Decompensation): protocol for development of COVID-19 Decompensation Index (Preprint)

2021 ◽  
Author(s):  
Karen Larimer
Keyword(s):  
Data Collection ◽  
Early Detection ◽  
Digital Health ◽  
World Health ◽  
Quality Data ◽  
Ordinal Scale ◽  
High Quality ◽  
High Quality Data ◽  
Digital Platform ◽  
Health Technologies

BACKGROUND During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of the SARS CO-V2 disease, improve care delivery and produce better health outcomes. The National Institutes of Health called on digital health leaders in this space to contribute to a high-quality data repository that will support the work of researchers to make discoveries not possible through small, limited data sets. OBJECTIVE To this end, we seek to develop a COVID-19 biomarker that could provide early detection of a patient’s physiologic decompensation. As a contributing spoke in this model, we propose developing and validating a COVID-19 Decompensation Index (CDI) in a two-phased project that builds off existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous monitoring of physiology with wearable biosensors. This effort will achieve two primary objectives: 1) collect adequate data to enable the development of the CDI; and 2) collect rich deidentified clinical data correlative with outcomes and symptomology related to COVID-19 disease progression. Secondary objectives include evaluation of feasibility and usability of pinpointIQ™, the digital platform through which data is gathered, analyzed, and displayed. METHODS This study is a prospective, non-randomized, open-label, two-phase design. Phase I will involve data collection for the NIH digital data hub as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study. RESULTS Our target COVID-19 Decompensation Index (CDI) will be a binary classifier trained to distinguish between subjects decompensating and not decompensating. The primary performance metric for CDI will be ROC AUC with a minimum performance criterion of AUC ≥ 0.75 (significance α = 0.05 and power 1 – β = 0.80). Determination of sex/gender, race or ethnic characteristics that impact differences in the CDI performance, as well as lead time-time to predict decompensation and the relationship to ultimate severity of disease based on the World Health Organization COVID-19 Ordinal Scale will be explored. CONCLUSIONS Using machine learning techniques on a large data set of COVID-19 positive patients could produce valuable insights into the physiology of COVID-19 as well as a digital biomarker for COVID-19 decompensation. We plan, with this study, to develop a tool that can uniquely reflect the physiologic data of a diverse population and contribute to a trove of high-quality data that will help researchers better understand COVID-19. CLINICALTRIAL Trial Registration: ClinicalTrials.gov NCT NCT04575532

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Velocity Analysis Using Separated Diffractions for Lunar Penetrating Radar Obtained by Yutu-2 Rover

2021 ◽  
Vol 13 (7) ◽  
pp. 1387
Author(s):  
Chao Li ◽  
Jinhai Zhang
Keyword(s):  
Dielectric Permittivity ◽  
Lunar Regolith ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Quality Data ◽  
High Quality Data ◽  
Shallow Subsurface ◽  
Subsurface Structures ◽  
Ground Penetrating ◽  
Permittivity Model

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

‘Feels like I'm doing it on my own’: Examining the Synchronicity between Policy Responses and the Circumstances and Experiences of Mortgage Borrowers in Arrears

2011 ◽  
Vol 11 (1) ◽  
pp. 117-129 ◽  
Author(s):  
Alison Wallace
Keyword(s):  
Policy Framework ◽  
State Support ◽  
Safety Nets ◽  
Mortgage Debt ◽  
Policy Responses ◽  
The Uk ◽  
New Evidence

The previous administration introduced several measures to prevent mortgage possessions, some of which were modestly effective. However, these hastily introduced initiatives were insufficient to bridge the gap between a fragmented policy framework and borrowers’ circumstances and experiences of managing mortgage debt. The present restructuring of welfare and regulation represents a unique window to address these long-standing policy omissions in relation to sustainable homeownership in the UK. However, in the context of weakening state support, it is uncertain how or indeed whether, the opportunity to reform mortgage safety nets will be grasped. This article reflects upon the continuing misalignment of policy with borrowers’ circumstances and experiences of mortgage arrears using new evidence from this downturn.

Potential of existing online 24-hour dietary recall tools for national dietary surveys

2021 ◽  
pp. 1-62
Author(s):  
Rozenn Gazan ◽  
Florent Vieux ◽  
Ségolène Mora ◽  
Sabrina Havard ◽  
Carine Dubuisson
Keyword(s):  
Grey Literature ◽  
Quality Data ◽  
High Quality Data ◽  
Require Time ◽  
Dietary Recall ◽  
Dietary Surveys ◽  
Almost All ◽  
The Cost ◽  
Web Platform ◽  
Country Specific

Abstract Objective: To describe existing online 24-hour dietary recall (24hDR) tools in terms of functionalities and ability to tackle challenges encountered during national dietary surveys, such as maximizing response rates and collecting high-quality data from a representative sample of the population, while minimizing the cost and response burden. Design: A search (from 2000 to 2019) was conducted in peer-reviewed and grey literature. For each tool, information on functionalities, validation and user usability studies, and potential adaptability for integration into a new context was collected. Setting: Not country-specific Participants: General population Results: Eighteen online 24hDR tools were identified. Most were developed in Europe, for children ≥10 years old and/or for adults. Eight followed the five multiple-pass steps, but used various methodologies and features. Almost all tools (except three) validated their nutrient intake estimates, but with high heterogeneity in methodologies. User usability was not always assessed, and rarely by applying real-time methods. For researchers, eight tools developed a web platform to manage the survey and five appeared to be easily adaptable to a new context. Conclusions: Among the eighteen online 24hDR tools identified, the best candidates to be used in national dietary surveys should be those that were validated for their intake estimates, had confirmed user and researcher usability, and seemed sufficiently flexible to be adapted to new contexts. Regardless of the tool, adaptation to another context will still require time and funding, and this is probably the most challenging step.

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