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 Differential impact of non-pharmaceutical public health interventions on COVID-19 epidemics in the United States

2020 ◽  
Author(s):  
Xiaoshuang Liu ◽  
Xiao Xu ◽  
Guanqiao Li ◽  
Xian Xu ◽  
Yuyao Sun ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
The United States ◽  
School Closure ◽  
Health Interventions ◽  
Public Health Interventions ◽  
The Us ◽  
The Impact ◽  
Individualized Decision

Abstract The widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs) to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is in great need to assist in guiding the individualized decision making for adjustment of interventions in the US and around the world. However, the impact of these approaches remain uncertain. Based on the reported cases, the effective reproduction number of COVID-19 epidemic for 50 states in the US was estimated. The measurement on the effectiveness of eight different NPIs was conducted by assessing risk ratios (RRs) between and NPIs through a generalized linear model (GLM). Different NPIs were found to have led to different levels of reduction in. Stay-at-home contributed approximately 51% (95% CI 46%-57%), gathering ban (more than 10 people) 19% (14%-24%), non-essential business closure 16% (10%-21%), declaration of emergency 13% (8%-17%), interstate travel restriction 11% (5%-16%), school closure 10% (7%-13%), initial business closure 10% (6%-14%), and gathering ban (more than 50 people) 6% (2%-11%). This retrospective assessment of NPIs on has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.

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.

Recent Attitudes toward ADHD in the Broader Community: A Systematic Review

2021 ◽  
pp. 108705472110036
Author(s):  
Matthew Bisset ◽  
Leanne Winter ◽  
Christel M. Middeldorp ◽  
David Coghill ◽  
Nardia Zendarski ◽  
...  
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Social Distance ◽  
Mental Health Literacy ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Negative Attitudes ◽  
Community Attitudes ◽  
Standardized Protocol

Objective: This review aimed to understand the broader community’s attitudes toward ADHD, which could facilitate public health interventions to improve outcomes for individuals with ADHD. Methods: A standardized protocol identified peer-reviewed studies focusing on attitudes of broader community samples, published from January 2014 to February 2020 (inclusive). Results: A total of 1,318 articles were screened and 10 studies were included, examining attitudes of broader community samples from Australia, Sweden, Germany, Finland, Korea, Indonesia, and the United States. Findings revealed that broader community samples displayed varying degrees of ADHD-related knowledge, negative attitudes (that ADHD is over-diagnosed; that pharmacological treatment is not acceptable; that those with ADHD are more likely to exhibit poor behavior), and a desire for maintaining social distance from individuals with ADHD. Conclusion: Findings suggest that community attitudes are generally negative toward those with ADHD. Targeted mental health literacy could provide an important avenue for improving the broader community’s attitudes toward those with ADHD.

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

scholarly journals Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243622
Author(s):  
David S. Campo ◽  
Joseph W. Gussler ◽  
Amanda Sue ◽  
Pavel Skums ◽  
Yury Khudyakov
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Strong Association ◽  
The United States ◽  
Health Interventions ◽  
Morbidity And Mortality ◽  
Public Health Interventions ◽  
Mortality Trends ◽  
Spatiotemporal Mapping ◽  
Increased Risk

Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004–2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE’s error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005–2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.

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 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.

scholarly journals Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions

Epidemiology ◽  
2017 ◽  
Vol 28 (6) ◽  
pp. 889-897 ◽  
Author(s):  
Esra Kürüm ◽  
Joshua L. Warren ◽  
Cynthia Schuck-Paim ◽  
Roger Lustig ◽  
Joseph A. Lewnard ◽  
...  
Keyword(s):  
Public Health ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Health Interventions ◽  
Change Points ◽  
Public Health Interventions ◽  
The Impact
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