scholarly journals Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic

2021 ◽  
pp. 0272989X2199037
Author(s):  
Reza Yaesoubi ◽  
Joshua Havumaki ◽  
Melanie H. Chitwood ◽  
Nicolas A. Menzies ◽  
Gregg Gonsalves ◽  
...  
Keyword(s):  
Real Time ◽  
Decision Rules ◽  
Social Costs ◽  
Surveillance Data ◽  
Economic Costs ◽  
Decision Tool ◽  
Policy Makers ◽  
Decision Tools ◽  
Complex Models ◽  
Adaptive Policies

Policy makers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We describe a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions.

scholarly journals Adaptive policies for use of physical distancing interventions during the COVID-19 pandemic

2020 ◽  
Author(s):  
Reza Yaesoubi ◽  
Joshua Havumaki ◽  
Melanie Chitwood ◽  
Nicolas A Menzies ◽  
Gregg Gonsalves ◽  
...  
Keyword(s):  
Real Time ◽  
Decision Rules ◽  
Social Costs ◽  
Surveillance Data ◽  
Decision Tool ◽  
Decision Tools ◽  
Model Based ◽  
Complex Models ◽  
Adaptive Policies

Policymakers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We develop a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions.

Malaria elimination in India requires additional surveillance mechanisms

2021 ◽  
Author(s):  
Manju Rahi ◽  
Payal Das ◽  
Amit Sharma
Keyword(s):  
Real Time ◽  
Malaria Elimination ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Malaria Surveillance ◽  
Aggregated Data ◽  
Malaria Burden ◽  
The Core ◽  
Reporting Systems ◽  
Geographic Regions

Abstract Malaria surveillance is weak in high malaria burden countries. Surveillance is considered as one of the core interventions for malaria elimination. Impressive reductions in malaria-associated morbidity and mortality have been achieved across the globe, but sustained efforts need to be bolstered up to achieve malaria elimination in endemic countries like India. Poor surveillance data become a hindrance in assessing the progress achieved towards malaria elimination and in channelizing focused interventions to the hotspots. A major obstacle in strengthening India’s reporting systems is that the surveillance data are captured in a fragmented manner by multiple players, in silos, and is distributed across geographic regions. In addition, the data are not reported in near real-time. Furthermore, multiplicity of malaria data resources limits interoperability between them. Here, we deliberate on the acute need of updating India’s surveillance systems from the use of aggregated data to near real-time case-based surveillance. This will help in identifying the drivers of malaria transmission in any locale and therefore will facilitate formulation of appropriate interventional responses rapidly.

Good Data, Good Law

2021 ◽  
Vol 33 (4) ◽  
pp. 262-264
Author(s):  
John Tilley ◽  
Serena Chang ◽  
Richard J. Peay
Keyword(s):  
Real Time ◽  
Racial Disparity ◽  
Prison Population ◽  
Time Data ◽  
Policy Makers ◽  
Non Profit ◽  
Life Years ◽  
Mandatory Minimums ◽  
Visualization Tools ◽  
Data Driven Decisions

Real-time data is crucial in delivering actionable information, yet sparse in the criminal justice space. Often, practitioners and policy makers (“System Actors”), are forced to rely on information that is missing, incorrect, and/or outdated. Recidiviz, a nonpartisan tech non-profit, enables System Actors to make data-driven decisions as part of their regular workflows. This article describes Recidiviz’s work modeling the projected influence of eliminating mandatory minimums in Virginia, including state costs avoided, impact on the prison population, and number of life-years individuals would regain by avoiding incarceration. Recidiviz calculated that if Virginia eliminated mandatory minimums for drug sales offenses only, over the next five years, it could avoid a cumulative $11.6 million in incarceration costs, give 360 years of life back to people, and decrease the prison population’s racial disparity. If Virginia eliminated all mandatory minimums, as SB 5046 proposes, it could help the Commonwealth avoid $80.2 million in costs and give back 2,496 years of life over five years. In addition to policy impact modeling, Recidiviz’s core work is in partnering with state corrections and supervision departments to provide real-time feedback and data visualization tools.

Policy makers on the research team: real-time health policy improvements during mifepristone implementation research in Canada

Contraception ◽  
2018 ◽  
Vol 98 (4) ◽  
pp. 331 ◽  
Author(s):  
WV Norman ◽  
S Munro ◽  
C Devane ◽  
E Guilbert ◽  
M Brooks ◽  
...  
Keyword(s):  
Health Policy ◽  
Real Time ◽  
Implementation Research ◽  
Research Team ◽  
Policy Makers

scholarly journals The CNN effect: can the news media drive foreign policy?

1999 ◽  
Vol 25 (2) ◽  
pp. 301-309 ◽  
Author(s):  
PIERS ROBINSON
Keyword(s):  
Foreign Policy ◽  
Real Time ◽  
News Media ◽  
New Technologies ◽  
Berlin Wall ◽  
Policy Makers ◽  
Tiananmen Square ◽  
Time News ◽  
Tv News ◽  
The Cold War

During the 1980s the proliferation of new technologies transformed the potential of the news media to provide a constant flow of global real-time news. Tiananmen Square and the collapse of communism symbolised by the fall of the Berlin Wall became major media events communicated to Western audiences instantaneously via TV news media. By the end of the decade the question was being asked as to what extent this ‘media pervasiveness’ had impacted upon government – particularly the process of foreign policy making. The new technologies appeared to reduce the scope for calm deliberation over policy, forcing policy-makers to respond to whatever issue journalists focused on. This perception was in turn reinforced by the end of the bipolar order and what many viewed as the collapse of the old anti-communist consensus which – it was argued – had led to the creation of an ideological bond uniting policy makers and journalists. Released from the ‘prism of the Cold War’ journalists were, it was presumed, freer not just to cover the stories they wanted but to criticise US foreign policy as well. The phrase ‘CNN effect’ encapsulated the idea that real-time communications technology could provoke major responses from domestic audiences and political elites to global events.

scholarly journals Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

2018 ◽  
Author(s):  
Robert Moss ◽  
Alexander E Zarebski ◽  
Sandra J Carlson ◽  
James M McCaw
Keyword(s):  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Healthcare Seeking ◽  
Public Health Decision ◽  
Predictive Skill ◽  
Health Decision ◽  
Substantial Change

AbstractFor diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

scholarly journals Views of health care professionals and policy-makers on the use of surveillance data to combat antimicrobial resistance

2020 ◽  
Author(s):  
Mustafa Al-Haboubi ◽  
Andrew Trathen ◽  
Nick Black ◽  
Elizabeth Eastmure ◽  
Nicholas Mays
Keyword(s):  
Health Care ◽  
Antimicrobial Resistance ◽  
Health Care Professionals ◽  
Secondary Care ◽  
Local Level ◽  
Surveillance Data ◽  
Data Availability ◽  
Electronic Prescribing ◽  
Data Systems ◽  
Policy Makers

Abstract Background Providing healthcare professionals with health surveillance data aims to support professional and organisational behaviour change. The UK Five Year Antimicrobial Resistance (AMR) Strategy 2013 to 2018 identified better access to and use of surveillance data as a key component. Our aim was to determine the extent to which data on antimicrobial use and resistance met the perceived needs of health care professionals and policy-makers at national, regional and local levels, and how provision could be improved. Methods We conducted 41 semi-structured interviews with national policy makers in the four Devolved Administrations and 71 interviews with health care professionals in six locations across the United Kingdom selected to achieve maximum variation in terms of population and health system characteristics. Transcripts were analysed thematically using a mix of a priori reasoning guided by the main topics in the interview guide together with themes emerging inductively from the data. Views were considered at three levels - primary care, secondary care and national - and in terms of availability of data, current uses, benefits, gaps and potential improvements. Results Respondents described a range of uses for prescribing and resistance data. The principal gaps identified were prescribing in private practice, internet prescribing and secondary care (where some hospitals did not have electronic prescribing systems). Some respondents under-estimated the range of data available. There was a perception that the responsibility for collecting and analysing data often rests with a few individuals who may lack sufficient time and appropriate skills. Conclusions There is a need to raise awareness of data availability and the potential value of these data, and to ensure that data systems are more accessible. Any skills gap at local level in how to process and use data needs to be addressed. This requires an identification of the best methods to improve support and education relating to AMR data systems.

A simulation model-based clinical decision tool to guide personalized treatment based on individual characteristics: Does 21-gene recurrence score assay testing change decisions?

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12507-e12507
Author(s):  
Jinani Jayasekera ◽  
Joseph A. Sparano ◽  
Young Chandler ◽  
Claudine Isaacs ◽  
Allison W. Kurian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Simulation Model ◽  
Early Stage ◽  
Clinical Decision ◽  
Distant Recurrence ◽  
Decision Tool ◽  
Decision Tools ◽  
Model Based ◽  
Life Years ◽  
Size Grade

e12507 Background: There is a need for web-based decision tools that integrate clinicopathologic features and genomic information to guide breast cancer therapy for women with node-negative, hormone receptor positive, HER2 negative (“early-stage”) breast cancer. We developed a novel simulation model-based clinical decision tool that provides prognostic estimates of treatment outcomes based on age, tumor size, grade, and comorbidities with and without 21-gene recurrence scores (RS). Methods: We adapted an extant breast cancer simulation model developed within the NCI-funded Cancer Intervention and Surveillance Modeling Network (CISNET) to derive estimates for the 10-year risks of distant recurrence, breast cancer-specific mortality, other cause mortality and life-years gained with endocrine vs. chemo-endocrine therapy for individual women based on their age, tumor size, grade, and comorbidity-level with and without RS test results. The model used an empiric Bayesian analytical approach to combine information from clinical trials, registry and claims data to provide individual estimates. External validation of the model was performed by comparing model-based breast cancer mortality rates and observed rates in the Surveillance Epidemiology and End Results (SEER) registry. Results: Several exemplar profiles were selected to illustrate the clinical utility of the decision tool. For example, the absolute chemotherapy benefit for 10-year distant recurrence risk and life-years gained, without RS testing, and the outcomes if a woman got tested and had a RS 16-20 are provided below for a 40-44-year-old woman and a 65–69-year-old woman diagnosed with a small (≤2cm), intermediate grade tumor and mild comorbidities. Conclusions: Simulation modeling is useful for creating clinical decision tools to support shared decision making for early-stage breast cancer treatment.[Table: see text]

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