scholarly journals Responsibility as an Ethical Framework for Public Health Interventions

2009 ◽  
Vol 99 (7) ◽  
pp. 1197-1202 ◽  
Author(s):  
Fabrizio Turoldo
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Ethical Framework ◽  
Public Health Interventions

scholarly journals Etica e Sanità Pubblica: massimizzare la relazione

2015 ◽  
Vol 64 (4) ◽  
Author(s):  
Massimiliano Colucci
Keyword(s):  
Public Health ◽  
Clinical Medicine ◽  
Health Interventions ◽  
Ethical Framework ◽  
Individual Autonomy ◽  
Public Health Interventions ◽  
The Public ◽  
The Social ◽  
The Individual ◽  
The Relationship

A causa del maggior sviluppo della bioetica negli ambiti della clinica e della sperimentazione biomedica, e per la difficoltà di definire la stessa sanità pubblica, quest’ultima manca ancora di un quadro etico di riferimento. Dopo un breve profilo storico e semantico, si esamina perciò l’antitesi, in letteratura, tra bioetica ed etica di sanità pubblica. Quindi si rileggono e sfatano le tre principali dicotomie su cui viene costruita tale antitesi – pazienti vs. assistiti, individuo vs. popolazione, paternalismo vs. autonomia. Si può affermare che la salute individuale e la salute collettiva sono fini simultanei e inseparabili degli interventi di sanità pubblica. Inoltre, l’autonomia relazionale è l’unica alternativa all’autonomia d’impronta liberale. L’autonomia individuale, infatti, si sviluppa attraverso l’influenza di legami umani e la giustizia sociale. La relazione – come capacità di promuovere la partecipazione e di mantenere la fiducia – è la sostanza della sanità pubblica, e fonte assiologica della sua etica. È cioé il primo valore e il principale criterio per indirizzare gli interventi di sanità pubblica, che saranno tanto più etici quanto più saranno in grado di massimizzare la relazione nel contesto in cui vengono attuati. ---------- Owing to a greater development of bioethics in the fields of clinical medicine and biomedical research, and because of the difficulty to define the public health itself, the latter still lacks an ethical framework. Therefore, after a brief historical and semantic outline, we examine the antithesis, as proposed in the literature, between bioethics and public health ethics. Then, we reread and debunk the three main dichotomies on which such an antithesis is built – patients vs. healthcare users, individual vs. population, paternalism vs. autonomy. We may state that the individual health and the collective health are simultaneous and inseparable purposes of public health interventions. Moreover, the relational autonomy it is the only alternative to the liberal-shaped autonomy. Indeed, the individual autonomy develops through the influence of human bonds and the social justice. The relationship – as the capability to promote the engagement and to maintain trust – is the substance of public health, and the axiological source of its ethics. In other words, it is the first value and the main criterion to address public health interventions; these will be ethical as much as they will be able to maximize the relationship in the context of their fulfilment.

Novel conceptual framework and toolset for country-wide assessments of opportunities and challenges for public health interventions

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Scott McNabb ◽  
Joseph Wamala ◽  
Anila Naz ◽  
Anna Hartrampf ◽  
Dan Samoly ◽  
...  
Keyword(s):  
Public Health ◽  
Conceptual Framework ◽  
Health Interventions ◽  
Public Health Interventions

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas J. Barrett ◽  
Karen C. Patterson ◽  
Timothy M. James ◽  
Peter Krüger
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Chronic Phase ◽  
Health Interventions ◽  
Resource Utilisation ◽  
Policy Planning ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Metropolitan Regions ◽  
Epidemic Dynamics

AbstractAs we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

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