PUBLIC HEALTH INTERVENTIONS IN MANAGEMENT OF CARDIOVASCULAR DISEASES IN THE FEDERATION OF BOSNIA AND HERZEGOVINA IN THE LIGHT OF WHO HEARTS STANDARDS AS A MODEL OF GOOD PRACTICE

2018 ◽  
Author(s):  
Aida Ramić-Čatak
Keyword(s):  
Public Health ◽  
Cardiovascular Diseases ◽  
Bosnia And Herzegovina ◽  
Good Practice ◽  
Health Interventions ◽  
Public Health Interventions

Applying methods of economic evaluation to public health: Contemporary solutions to traditional challenges

2019 ◽  
pp. 53-86
Author(s):  
Rhiannon T. Edwards ◽  
Emma McIntosh
Keyword(s):  
Public Health ◽  
Economic Evaluation ◽  
Study Design ◽  
Controlled Trial ◽  
Good Practice ◽  
Health Sector ◽  
Complex Interventions ◽  
Health Interventions ◽  
Natural Experiments ◽  
Public Health Interventions

Chapter 3 opens with a discussion of the role of study design, the gold standard traditionally being a randomized controlled trial, and widens this to consider other types of study design such as cohort studies and natural experiments. Readers are introduced to the idea that many public health interventions are ‘complex interventions’ and there is a need for a ‘systems-based approach’ to understanding their potential effectiveness and cost-effectiveness. The chapter highlights the relevance of behavioural economics to the evaluation of public health interventions. This chapter goes on to summarize a range of challenges faced by economists, used to evaluate healthcare technologies in a healthcare setting, when they start evaluating public health interventions, which are often delivered outside the health sector in, for example, schools and workplaces. UK guidance from NICE is presented on good practice in economic evaluation of public health interventions along with ideas about how such evaluations are best reported in the literature.

Identification, measurement, and valuation of resource use in economic evaluations of public health interventions

2019 ◽  
pp. 107-130 ◽  
Author(s):  
Carys Jones ◽  
Joanna M. Charles ◽  
Rhiannon T. Edwards
Keyword(s):  
Public Health ◽  
Economic Evaluation ◽  
Good Practice ◽  
Public Health Intervention ◽  
Health Interventions ◽  
Economic Evaluations ◽  
Public Health Interventions ◽  
Cost Information ◽  
Worked Example ◽  
Unit Costs

Chapter 5 provides a practical guide to the types of costs relevant to an economic evaluation of a public health intervention, perspective of analysis, and sources of unit costs spanning a range of sectors including health, social care, education, and transport. The chapter covers the important issues of time horizon and discounting in the economic evaluation of public health interventions. Taking account of sources of uncertainty, the chapter sets out good practice in reporting cost information in published economic studies. The chapter concludes with a worked example of a micro-costing of a parenting programme delivered in the community.

Synthesizing evidence for economic evaluations of public health interventions

2019 ◽  
pp. 87-106
Author(s):  
Olivia Wu ◽  
Joanna M. Charles ◽  
Nathan Bray
Keyword(s):  
Public Health ◽  
Economic Evaluation ◽  
Evidence Synthesis ◽  
Good Practice ◽  
Relevant Literature ◽  
Health Interventions ◽  
Economic Evaluations ◽  
Narrative Synthesis ◽  
Public Health Interventions

Reviewing and synthesizing evidence is an important component of the toolkit of methods for the economic evaluation of PHIs. Chapter 4 provides readers with information about good practice in identifying relevant literature, judging the quality of relevant literature, and synthesizing evidence for economic evaluations of PHIs. Narrative synthesis has become a key focus in synthesizing complex PHIs. Readers are also introduced to the idea that logic (conceptual) models can be helpful in describing processes and hence outcomes. The chapter goes on to describe mixed-method reviews, realist synthesis, other forms of evidence synthesis, and equity considerations.

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.

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