scholarly journals Impact of bystander-focused public health interventions on cardiopulmonary resuscitation and survival: a cohort study

2020 ◽  
Vol 5 (8) ◽  
pp. e428-e436 ◽  
Author(s):  
Audrey L Blewer ◽  
Andrew Fu Wah Ho ◽  
Nur Shahidah ◽  
Alexander Elgin White ◽  
Pin Pin Pek ◽  
...  
Keyword(s):  
Public Health ◽  
Cohort Study ◽  
Cardiopulmonary Resuscitation ◽  
Health Interventions ◽  
Public Health Interventions

scholarly journals Impact of climate and public health interventions on the COVID-19 pandemic: a prospective cohort study

2020 ◽  
Vol 192 (21) ◽  
pp. E566-E573 ◽  
Author(s):  
Peter Jüni ◽  
Martina Rothenbühler ◽  
Pavlos Bobos ◽  
Kevin E. Thorpe ◽  
Bruno R. da Costa ◽  
...  
Keyword(s):  
Public Health ◽  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Health Interventions ◽  
Public Health Interventions

Abstract 314: The Impact of City-Level Public Health Interventions on Increased Bystander Cardiopulmonary Resuscitation

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Audrey L Blewer ◽  
Andrew F Ho ◽  
Nur Shahidah Binte Ahamad ◽  
Alexander E White ◽  
Pin Pin Pek ◽  
...  
Keyword(s):  
Public Health ◽  
Cardiopulmonary Resuscitation ◽  
Sudden Cardiac Arrest ◽  
Healthcare Facility ◽  
Health Interventions ◽  
Public Understanding ◽  
Public Health Interventions ◽  
The Public ◽  
Bystander Cardiopulmonary Resuscitation ◽  
The Impact

Introduction: Bystander cardiopulmonary resuscitation (B-CPR) may increase a victim’s chance of survival from sudden cardiac arrest (SCA), but B-CPR rates are low in many communities. Few studies have examined the association of city-wide public health interventions on B-CPR. Objectives: We sought to assess whether there is variation in B-CPR by intervention and location of arrest. We hypothesized that implementation of dispatch-assisted CPR (DA-CPR), CPR/AED training, and a first responder mobile application (myResponder) would increase B-CPR by two-fold. Methods: We conducted a retrospective study of adult, non-traumatic SCAs from the Singapore registry (4/2010-12/2016). Interventions included DA-CPR (7/2012 - present), CPR/AED training (04/2014 - present), and myResponder (4/2015 - present). Using logistic regression, we modeled the likelihood of receiving B-CPR by increased number of interventions over time. We examined these effects together, in the home, and public accounting for patient-level confounding. Results: From 2010-2016, the Singapore registry contained 12,546 SCA events. Excluding pediatric, EMS witnessed, and healthcare facility arrests, 7,476 were analyzed. Of these, mean age was 66±15 and 68% were male. B-CPR was administered in 45% of the events and varied by location (home: 43% v public: 52%). With implementation of DA-CPR, likelihood of B-CPR increased (OR: 3.5 (2.9-4.2) p<0.01) compared to no intervention; with implementation of CPR/AED training, likelihood of B-CPR increased compared to no intervention (OR: 5.8 (4.8-7.0), p<0.01). Lastly, implementation of myResponder resulted in a 7.09 increased likelihood of B-CPR compared to no intervention (OR: 7.1 (5.9-8.4), p<0.01). Variation was seen when examining likelihood of B-CPR by all interventions compared to no intervention, in the home (OR: 8.7 (7.0-10.7)) and the public (OR: 4.0 (2.9-5.6)). Survival increased, corresponding to the increase in B-CPR. Conclusion: City-level public health interventions increased the likelihood of layperson B-CPR, while variation was seen in the home and public. Understanding the impact of public health interventions may shed light on strategies to increase B-CPR and inform targeted initiatives to improve survival from SCA.

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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