scholarly journals Data as promise: Reconfiguring Danish public health through personalized medicine

2019 ◽  
Vol 49 (4) ◽  
pp. 531-555 ◽  
Author(s):  
Klaus Hoeyer
Keyword(s):  
Public Health ◽  
Personalized Medicine ◽  
Population Based ◽  
Data Driven ◽  
Health Policies ◽  
Public Health Policies ◽  
Roles And Responsibilities ◽  
Population Science ◽  
The Individual ◽  
Existing Data

‘Personalized medicine’ might sound like the very antithesis of population science and public health, with the individual taking the place of the population. However, in practice, personalized medicine generates heavy investments in the population sciences – particularly in data-sourcing initiatives. Intensified data sourcing implies new roles and responsibilities for patients and health professionals, who become responsible not only for data contributions, but also for responding to new uses of data in personalized prevention, drawing upon detailed mapping of risk distribution in the population. Although this population-based ‘personalization’ of prevention and treatment is said to be about making the health services ‘data-driven’, the policies and plans themselves use existing data and evidence in a very selective manner. It is as if data-driven decision-making is a promise for an unspecified future, not a demand on its planning in the present. I therefore suggest interrogating how ‘promissory data’ interact with ideas about accountability in public health policies, and also with the data initiatives that the promises bring about. Intensified data collection might not just be interesting for what it allows authorities to do and know, but also for how its promises of future evidence can be used to postpone action and sidestep uncomfortable knowledge in the present.

scholarly journals NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Natália Martins Arruda ◽  
Cátia Sepetauskas ◽  
Everton Silva ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Neonatal Mortality ◽  
Regression Models ◽  
Mortality Rates ◽  
Data Driven ◽  
Health Policies ◽  
Neonatal Mortality Rate ◽  
Policy Makers ◽  
Public Health Policies

ABSTRACTReduction in child mortality is one of the United Nations Sustainable Development Goals for 2030. In Brazil, despite recent reduction in child mortality in the last decades, the neonatal mortality is a persistent problem and it is associated with the quality of prenatal, childbirth care and social-environmental factors. In a proper health system, the effect of some of these factors could be minimized by the appropriate number of newborn intensive care units, number of health care units, number of neonatal incubators and even by the correct level of instruction of mothers, which can lead to a proper care along the prenatal period. With the intent of providing knowledge resources for planning public health policies focused on neonatal mortality reduction, we propose a new data-driven machine leaning method for Neonatal Mortality Rate forecasting called NeMoR, which predicts neonatal mortality rates for 4 months ahead, using NeoDeathForecast, a monthly base time series dataset composed by these factors and by neonatal mortality rates history (2006-2016), having 57,816 samples, for all 438 Brazilian administrative health regions. In order to build the model, Extra-Tree, XGBoost Regressor, Gradient Boosting Regressor and Lasso machine learning regression models were evaluated and a hyperparameters search was also performed as a fine tune step. The method has been validated using São Paulo city data, mainly because of data quality. On the better configuration the method predicted the neonatal mortality rates with a Mean Square Error lower than 0.18. Besides that, the forecast results may be useful as it provides a way for policy makers to anticipate trends on neonatal mortality rates curves, an important resource for planning public health policies.Graphical AbstractHighlightsProposition of a new data-driven approach for neonatal mortality rate forecast, which provides a way for policy-makers to anticipate trends on neonatal mortality rates curves, making a better planning of health policies focused on NMR reduction possible;a method for NMR forecasting with a MSE lower than 0.18;an extensive evaluation of different Machine Learning (ML) regression models, as well as hyperparameters search, which accounts for the last stage in NeMoR;a new time series database for NMR prediction problems;a new features projection space for NMR forecasting problems, which considerably reduces errors in NRM prediction.

scholarly journals Tracking the dynamics and allocating tests for COVID-19 in real-time: An acceleration index with an application to French age groups and départements

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252443
Author(s):  
Christelle Baunez ◽  
Mickael Degoulet ◽  
Stéphane Luchini ◽  
Patrick A. Pintus ◽  
Miriam Teschl
Keyword(s):  
Public Health ◽  
Real Time ◽  
Age Groups ◽  
Population Based ◽  
National Average ◽  
Health Policies ◽  
Age Group ◽  
Actual Distribution ◽  
Public Health Policies ◽  
Using Data

An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs—May 13 to October 25, 2020—our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration was stronger than national average for the [59–68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19–28] age group was the lowest and is about half that of the [69–78]. In addition, we propose an algorithm to allocate tests among French “départements” (roughly counties), based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France might possibly enter a third lock-down period with indeterminate duration.

scholarly journals Agency and Health Policies

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Rodrigo Lopez Barreda
Keyword(s):  
Public Health ◽  
Clinical Practice ◽  
Health Status ◽  
Medical Ethics ◽  
Current Medical ◽  
Health Policies ◽  
Health Issues ◽  
Public Health Policies ◽  
Collective Levels ◽  
The Individual

In the current medical ethics literature, the concept of agency is receiving growing attention. Nevertheless, many of those definitions are narrow in scope. This article intends to provide a deeper understanding of this concept, allowing for its use in clinical practice and public health policies. First, it revises the current concept of agency and some of its shortcomings. Then, the article presents two philosophical accounts of agency, identifying three relevant features, namely time-extended organised planfulness, endorsement of their own actions, and identification with the activity. Lastly, the article depicts how those features may help in the application of agency to the analysis of health issues by means of a number of examples at the individual and collective levels. When analysing health issues, the health status is a key component, but the process that brought about the outcome must be examined; agency informs about this procedural dimension.

Wir müssen abwägen – aber wie sollen wir abwägen?

2021 ◽  
Vol 69 (1) ◽  
pp. 29-66
Author(s):  
Lutz Wingert
Keyword(s):  
Public Health ◽  
Civil Liberties ◽  
Health Policies ◽  
Public Health Policies ◽  
Individual Health ◽  
Health Measures ◽  
Wide Range ◽  
The Individual ◽  
Prospect Of Success ◽  
Moral Questions

Abstract The global Covid-19 crisis raises at least three moral questions, which my contribution answers as follows: (1) Which patient should get treatment according to triage criteria? The patient whose treatment has the best prospect of success. (2) How should we resolve the conflict between public health measures and economic needs? Public health should have priority, but reaches its limits where the individual right to stay afloat through one’s own work is violated. (3) How should we resolve the conflict between public health measures and civil liberties? Public health should have priority, but reaches its limits where the restriction of freedom violates the integrity of individual health and personal freedom. The answers and the arguments behind these are developed through the discussion of a wide range of current public health policies, concrete measures, and competing approaches to moral questions in the Covid-19 pandemic.

scholarly journals Tracking the Dynamics and Allocating Tests for COVID-19 in Real-Time: an Acceleration Index with an Application to French Age Groups and Départements*

2020 ◽  
Author(s):  
Christelle Baunez ◽  
Mickael Degoulet ◽  
Stéphane Luchini ◽  
Patrick A. Pintus ◽  
Miriam Teschl
Keyword(s):  
Public Health ◽  
Real Time ◽  
Age Groups ◽  
Population Based ◽  
Jel Classification ◽  
National Average ◽  
Health Policies ◽  
Age Group ◽  
Actual Distribution ◽  
Public Health Policies

AbstractAn acceleration index is proposed as a novel indicator to track the dynamics of the COVID-19 in real-time. Using French data on cases and tests for the period following the first lock-down - from May 13, 2020, onwards - our acceleration index shows that the ongoing pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration has been stronger than national average for the [59 − 68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19 − 28] age group is the lowest and is about half that of the [69 − 78], as of October 25. In addition, we propose an algorithm to allocate tests among French départements, based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France enters a second lock-down period with indeterminate duration.JEL Classification NumbersI18; H12

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