scholarly journals The assembly effect: the connectedness between populations is a double‐edged sword for public health interventions

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Sai Thein Than Tun ◽  
Daniel M. Parker ◽  
Ricardo Aguas ◽  
Lisa J. White
Keyword(s):  
Public Health ◽  
Spatial Scales ◽  
Public Health Intervention ◽  
Human Movement ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Malaria Intervention ◽  
Disease Epidemiology ◽  
Intervention Measures ◽  
The Impact

Abstract Background Many public health interventions lead to disruption or decrease of transmission, providing a beneficial effect for people in the population regardless of whether or not they individually participate in the intervention. This protective benefit has been referred to as a herd or community effect and is dependent on sufficient population participation. In practice, public health interventions are implemented at different spatial scales (i.e., at the village, district, or provincial level). Populations, however defined (i.e., neighbourhoods, villages, districts) are frequently connected to other populations through human movement or travel, and this connectedness can influence potential herd effects. Methods The impact of a public health intervention (mass drug administration for malaria) was modelled, for different levels of connectedness between populations that have similar disease epidemiology (e.g., two nearby villages which have similar baseline malaria incidences and similar malaria intervention measures), or between populations of varying disease epidemiology (e.g., two nearby villages which have different baseline malaria incidences and/or malaria intervention measures). Results The overall impact of the interventions deployed could be influenced either positively (adding value to the intervention) or negatively (reducing the impact of the intervention) by how much the intervention units are connected with each other (e.g., how frequent people go to the other village or town) and how different the disease intensity between them are. This phenomenon is termed the “assembly effect”, and it is a meta-population version of the more commonly understood “herd effect”. Conclusions The connectedness of intervention units or populations is an important factor to be considered to achieve success in public health interventions that could provide herd effects. Appreciating the assembly effect can improve the cost-effective strategies for global disease elimination projects.

scholarly journals The assembly effect: the connectedness between populations is a double-edged sword for public health interventions

2020 ◽  
Author(s):  
Sai Thein Than Tun ◽  
Daniel M. Parker ◽  
Ricardo Aguas ◽  
Lisa J. White
Keyword(s):  
Public Health ◽  
Spatial Scales ◽  
Public Health Intervention ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Herd Effect ◽  
Intervention Coverage ◽  
The Impact ◽  
The Village ◽  
Different Levels

Many public health interventions lead to disruption or decrease of transmission, providing a beneficial effect for people in the population regardless of whether or not they individually participate in the intervention. This protective benefit has been referred to as a herd or community effect and is dependent on sufficient population participation. In practice, public health interventions are implemented at different spatial scales (i.e. at the village, district, or provincial level). Populations, however defined, are frequently connected to other populations and this connectedness can influence potential herd effects. In this research we model the impact of a public health intervention (mass drug administration for malaria), given different levels of connectedness between similar populations and between populations of varying epidemiology (i.e. baseline transmission levels and intervention coverage). We show that the way such intervention units are connected to each other may influence the impact of the focal interventions deployed in both positive (adding value to the intervention) and negative (reducing the impact of the intervention) ways. We term this phenomenon the "assembly effect" which is a meta-population version of the more commonly understood "herd effect". We conclude that public health interventions should consider the connectedness of intervention units or populations in order to achieve success.

scholarly journals Electrophysiology in the time of coronavirus: coping with the great wave

2020 ◽  
Author(s):  
Jia Li ◽  
Patrizio Mazzone ◽  
Lisa WM Leung ◽  
Weiqian Lin ◽  
Giuseppe D’Angelo ◽  
...  
Keyword(s):  
Public Health ◽  
Public Health Intervention ◽  
Normal Activity ◽  
Health Intervention ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Interventional Electrophysiology ◽  
Ethical Approval ◽  
The Impact ◽  
Nationwide Program

ABSTRACTAimsTo chart the effect of the COVID-19 pandemic on the activity of interventional electrophysiology services in affected regions.MethodsWe reviewed the electrophysiology laboratory records in 3 affected cities: Wenzhou in China, Milan in Italy and London, United Kingdom. We interviewed electrophysiologists in each centre to gather information on the impact of the pandemic on working patterns and on the health of staff members.ResultsThere was a striking decline in interventional electrophysiology activity in each of the centres. The decline occurred within a week of the recognition of widespread community transmission of the virus in each region and shows a striking correlation with the national figures for new diagnoses of COVID-19 in each case. During the period of restriction, work-flow dropped to <5% of normal, consisting of emergency cases only. In 2 of 3 centres, electrophysiologists were redeployed to perform emergency work outside electrophysiology. Among the centres studied, only Wenzhou has seen a recovery from the restrictions in activity. Following an intense nationwide program of public health interventions, local transmission of COVID-19 ceased to be detectable after February 18th allowing the electrophysiology service to resume with a strict testing regime for all patients.ConclusionInterventional electrophysiology is vulnerable to closure in times of great social difficulty including the COVID-19 pandemic. Intense public health intervention can permit suppression of local disease transmission allowing resumption of some normal activity.CONDENSED ABSTRACTCOVID-19 has affected every aspect of life worldwide. In the electrophysiology labs of Wenzhou, Milan and London, activity was suspended as the disease took hold. Only Wenzhou has resumed normal services, facilitated by a monumental nationwide program of public health interventions and supported by stringent testing protocols.WHAT’S NEWWe describe the impact of the COVID-19 pandemic on interventional electrophysiology units in 3 cities: Wenzhou, Milan and London.In all cases, the routine work of the electrophysiology was virtually suspended within a week of the recognition of widespread virus transmission in the area.During the period of restricted activity imposed by the pandemic, centres have dealt with a small number of emergency ablations only, a balanced mix of atrial, ventricular and junctional arrhythmias.In 2 of the 3 centres, electrophysiologists were redeployed to perform other medical duties including in COVID-19 wards.COVID-19 infection occurred in medical and nursaing staff in 2 of the 3 centres.Only in the cases of Wenzhou, China, has a resumption of normal activity been possible; this follows intensive public health intervention and is protected by stringent testing.FUNDINGNoneETHICAL APPROVALNone required from the Research Ethics Committee (REC) London according to the type of study. Institutional ethical approval obtained at the centres of: St. George’s Hospital NHS Foundation Trust, London, UK; Local Health Authority Ethical Approval was obtained in: The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University in Wenzhou, PR China and San Raffaele in Milan, Italy.CONSENTInformed consent was obtained from all participants/interviewees who took part in this study.

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.

The Impact of Public Health Interventions on Delaying and Mitigating against Replacement by SARS-CoV-2 Variants of Concern

2021 ◽  
Author(s):  
Jianhong Wu ◽  
Francesca Scarabel ◽  
Bushra Majeed ◽  
Nicola Luigi Bragazzi ◽  
James Orbinski
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Public Health Interventions ◽  
The Impact

Syphilis epidemiology and public health interventions in Western Australia from 1991 to 2009

Sexual Health ◽  
2012 ◽  
Vol 9 (3) ◽  
pp. 272 ◽  
Author(s):  
Kellie S. H. Kwan ◽  
Carolien M. Giele ◽  
Heath S. Greville ◽  
Carole A. Reeve ◽  
P. Heather Lyttle ◽  
...  
Keyword(s):  
Public Health ◽  
Western Australia ◽  
Aboriginal People ◽  
Congenital Syphilis ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Sex With Men ◽  
Notification Data ◽  
The Impact

Objectives To describe the epidemiology of congenital and infectious syphilis during 1991–2009, examine the impact of public health interventions and discuss the feasibility of syphilis elimination among Aboriginal people in Western Australia (WA). Methods: WA congenital and infectious syphilis notification data in 1991–2009 and national infectious syphilis notification data in 2005–2009 were analysed by Aboriginality, region of residence, and demographic and behavioural characteristics. Syphilis public health interventions in WA from 1991–2009 were also reviewed. Results: During 1991–2009, there were six notifications of congenital syphilis (50% Aboriginal) and 1441 infectious syphilis notifications (61% Aboriginal). During 1991–2005, 88% of notifications were Aboriginal, with several outbreaks identified in remote WA. During 2006–2009, 62% of notifications were non-Aboriginal, with an outbreak in metropolitan men who have sex with men. The Aboriginal : non-Aboriginal rate ratio decreased from 173 : 1 (1991–2005) to 15 : 1 (2006–2009). Conclusions: These data demonstrate that although the epidemiology of syphilis in WA has changed over time, the infection has remained endemic among Aboriginal people in non-metropolitan areas. Given the continued public health interventions targeted at this population, the limited success in eliminating syphilis in the United States and the unique geographical and socioeconomic features of WA, the elimination of syphilis seems unlikely in this state.

scholarly journals Leveraging Rich Data and Machine Learning to Facilitate Policy Making on Public Health Interventions in Fighting to Contain the COVID-19 Pandemic

2020 ◽  
Author(s):  
Robin Qiu
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Public Health Intervention ◽  
Research Direction ◽  
Health Interventions ◽  
Mitigation Measures ◽  
Public Health Interventions ◽  
Short Article ◽  
The World ◽  
Rich Data

<p>This is a short article, focusing on promoting more study on SEIR modeling by leveraging rich data and machine learning. We believe that this is extremely critical as many regions at the country or state/provincial levels have been struggling with their public health intervention policies on fighting the COVID-19 pandemic. Some recent published papers on mitigation measures show promising SEIR modeling results, which could shred the light for other policymakers at different community levels. We present our perspective on this research direction. Hopefully, we can stimulate more studies and help the world win this “war” against the invisible enemy “coronavirus” sooner rather than later. </p>

The Economics of Infectious Diseases

2018 ◽  
Author(s):  
Katharina Hauck
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Disease Transmission ◽  
Health Interventions ◽  
Health System Strengthening ◽  
Economic Research ◽  
Economic Evaluations ◽  
Research Needs ◽  
Public Health Interventions ◽  
The Impact

Economics can make immensely valuable contributions to our understanding of infectious disease transmission and the design of effective policy responses. The one unique characteristic of infectious diseases makes it also particularly complicated to analyze: the fact that it is transmitted from person to person. It explains why individuals’ behavior and externalities are a central topic for the economics of infectious diseases. Many public health interventions are built on the assumption that individuals are altruistic and consider the benefits and costs of their actions to others. This would imply that even infected individuals demand prevention, which stands in conflict with the economic theory of rational behavior. Empirical evidence is conflicting for infected individuals. For healthy individuals, evidence suggests that the demand for prevention is affected by real or perceived risk of infection. However, studies are plagued by underreporting of preventive behavior and non-random selection into testing. Some empirical studies have shown that the impact of prevention interventions could be far greater than one case prevented, resulting in significant externalities. Therefore, economic evaluations need to build on dynamic transmission models in order to correctly estimate these externalities. Future research needs are significant. Economic research needs to improve our understanding of the role of human behavior in disease transmission; support the better integration of economic and epidemiological modeling, evaluation of large-scale public health interventions with quasi-experimental methods, design of optimal subsidies for tackling the global threat of antimicrobial resistance, refocusing the research agenda toward underresearched diseases; and most importantly to assure that progress translates into saved lives on the ground by advising on effective health system strengthening.

scholarly journals How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sam McCrabb ◽  
Kaitlin Mooney ◽  
Benjamin Elton ◽  
Alice Grady ◽  
Sze Lin Yoong ◽  
...  
Keyword(s):  
Public Health ◽  
Scoping Review ◽  
Implementation Strategy ◽  
Public Health Intervention ◽  
Health Intervention ◽  
Health Interventions ◽  
Problem Identification ◽  
Key Concepts ◽  
The Common ◽  
The Impact

Abstract Background Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints. This study aimed to identify frameworks used to optimise the impact of health interventions and/or their implementation, and characterise the key concepts, steps or processes of identified frameworks. Methods A scoping review of MEDLINE, CINAL, PsycINFO, and ProQuest Nursing & Allied Health Source databases was undertaken. Two reviewers independently coded the key concepts, steps or processes involved in each frameworks, and identified if it was a framework aimed to optimise interventions or their implementation. Two review authors then identified the common steps across included frameworks. Results Twenty optimisation frameworks were identified. Eight frameworks were for optimising interventions, 11 for optimising implementation and one covered both intervention and implementation optimisation. The mean number of steps within the frameworks was six (range 3–9). Almost half (n = 8) could be classified as both linear and cyclic frameworks, indicating that some steps may occur multiple times in a single framework. Two meta-frameworks are proposed, one for intervention optimisation and one for implementation strategy optimisation. Steps for intervention optimisation are: Problem identification; Preparation; Theoretical/Literature base; Pilot/Feasibility testing; Optimisation; Evaluation; and Long-term implementation. Steps for implementation strategy optimisation are: Problem identification; Collaborate; Plan/design; Pilot; Do/change; Study/evaluate/check; Act; Sustain/endure; and Disseminate/extend. Conclusions This review provides a useful summary of the common steps followed to optimise a public health intervention or its implementation according to established frameworks. Further opportunities to study and/or validate such frameworks and their impact on improving outcomes exist.

scholarly journals Impact of Public Health Interventions on Seasonal Influenza Activity During the COVID-19 Outbreak in Korea

2020 ◽  
Author(s):  
Hyunju Lee ◽  
Heeyoung Lee ◽  
Kyoung-Ho Song ◽  
Eu Suk Kim ◽  
Jeong Su Park ◽  
...  
Keyword(s):  
Public Health ◽  
Seasonal Influenza ◽  
Health Interventions ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Public Health Interventions ◽  
National Response ◽  
Influenza Activity ◽  
High Level ◽  
The Impact

Abstract Background Coronavirus disease 2019 (COVID-19) was introduced in Korea early with a large outbreak in mid-February. We reviewed the public health interventions used during the COVID-19 outbreak and describe the impact on seasonal influenza activity in Korea. Methods National response strategies, public health interventions and daily COVID-19–confirmed cases in Korea were reviewed during the pandemic. National influenza surveillance data were compared between 7 sequential seasons. Characteristics of each season, including rate of influenza-like illness (ILI), duration of epidemic, date of termination of epidemic, distribution of influenza virus strain, and hospitalization, were analyzed. Results After various public health interventions including enforced public education on hand hygiene, cough etiquette, staying at home with respiratory symptoms, universal mask use in public places, refrain from nonessential social activities, and school closures the duration of the influenza epidemic in 2019/2020 decreased by 6–12 weeks and the influenza activity peak rated 49.8 ILIs/1000 visits compared to 71.9–86.2 ILIs/1000 visits in previous seasons. During the period of enforced social distancing from weeks 9–17 of 2020, influenza hospitalization cases were 11.9–26.9-fold lower compared with previous seasons. During the 2019/2020 season, influenza B accounted for only 4%, in contrast to previous seasons in which influenza B accounted for 26.6–54.9% of all cases. Conclusions Efforts to activate a high-level national response not only led to a decrease in COVID-19 but also a substantial decrease in seasonal influenza activity. Interventions applied to control COVID-19 may serve as useful strategies for prevention and control of influenza in upcoming seasons.

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