scholarly journals The current burden of Japanese encephalitis and the estimated impacts of vaccination: Combining estimates of the spatial distribution and transmission intensity of a zoonotic pathogen

2021 ◽  
Vol 15 (10) ◽  
pp. e0009385
Author(s):  
Sean M. Moore
Keyword(s):  
At Risk ◽  
Japanese Encephalitis ◽  
Vaccination Coverage ◽  
Susceptible Population ◽  
Incidence Data ◽  
Force Of Infection ◽  
Risk Areas ◽  
At Risk Populations ◽  
The Impact ◽  
Combining Estimates

Japanese encephalitis virus (JEV) is a major cause of neurological disability in Asia and causes thousands of severe encephalitis cases and deaths each year. Although Japanese encephalitis (JE) is a WHO reportable disease, cases and deaths are significantly underreported and the true burden of the disease is not well understood in most endemic countries. Here, we first conducted a spatial analysis of the risk factors associated with JE to identify the areas suitable for sustained JEV transmission and the size of the population living in at-risk areas. We then estimated the force of infection (FOI) for JE-endemic countries from age-specific incidence data. Estimates of the susceptible population size and the current FOI were then used to estimate the JE burden from 2010 to 2019, as well as the impact of vaccination. Overall, 1,543.1 million (range: 1,292.6-2,019.9 million) people were estimated to live in areas suitable for endemic JEV transmission, which represents only 37.7% (range: 31.6-53.5%) of the over four billion people living in countries with endemic JEV transmission. Based on the baseline number of people at risk of infection, there were an estimated 56,847 (95% CI: 18,003-184,525) JE cases and 20,642 (95% CI: 2,252-77,204) deaths in 2019. Estimated incidence declined from 81,258 (95% CI: 25,437-273,640) cases and 29,520 (95% CI: 3,334-112,498) deaths in 2010, largely due to increases in vaccination coverage which have prevented an estimated 314,793 (95% CI: 94,566-1,049,645) cases and 114,946 (95% CI: 11,421-431,224) deaths over the past decade. India had the largest estimated JE burden in 2019, followed by Bangladesh and China. From 2010-2019, we estimate that vaccination had the largest absolute impact in China, with 204,734 (95% CI: 74,419-664,871) cases and 74,893 (95% CI: 8,989-286,239) deaths prevented, while Taiwan (91.2%) and Malaysia (80.1%) had the largest percent reductions in JE burden due to vaccination. Our estimates of the size of at-risk populations and current JE incidence highlight countries where increasing vaccination coverage could have the largest impact on reducing their JE burden.

scholarly journals The current burden of Japanese encephalitis and the estimated impacts of vaccination: Combining estimates of the spatial distribution and transmission intensity of a zoonotic pathogen

2021 ◽  
Author(s):  
Sean M. Moore
Keyword(s):  
At Risk ◽  
Japanese Encephalitis ◽  
Vaccination Coverage ◽  
Susceptible Population ◽  
Incidence Data ◽  
Force Of Infection ◽  
Risk Areas ◽  
At Risk Populations ◽  
The Impact ◽  
Combining Estimates

AbstractJapanese encephalitis virus (JEV) is a major cause of neurological disability in Asia and causes thousands of severe encephalitis cases and deaths each year. Although Japanese encephalitis (JE) is a WHO reportable disease, cases and deaths are significantly underreported and the true burden of the disease is not well understood in most endemic countries. Here, we first conducted a spatial analysis of the risk factors associated with JE to identify the areas suitable for sustained JEV transmission and the size of the population living in at-risk areas. We then estimated the force of infection (FOI) for JE-endemic countries from age-specific incidence data. Estimates of the susceptible population size and the current FOI were then used to estimate the JE burden from 2010 to 2019, as well as the impact of vaccination. Overall, 1.15 billion (range: 982.1-1543.1 million) people were estimated to live in areas suitable for endemic JEV transmission, which represents 28.0% (range: 24.0-37.7%) of the over four billion people living in countries with endemic JEV transmission. Based on the baseline number of people at risk of infection, there were an estimated 45,017 (95% CI: 13,579-146,375) JE cases and 16,319 (95% CI: 1,804-60,041) deaths in 2019. Estimated incidence declined from 61,879 (95% CI: 18,377-200,406) cases and 22,448 (95% CI: 2,470-83,588) deaths in 2010, largely due to increases in vaccination coverage which have prevented an estimated 214,493 (95% CI: 75,905-729,009) cases and 78,544 (95% CI: 8,243-325,755) deaths over the past decade. India had the largest estimated JE burden in 2019, followed by Bangladesh and China. From 2010-2019, we estimate that vaccination had the largest absolute impact in China, with 142,471 (95% CI: 56,208-484,294) cases and 52,338 (95% CI: 6,421-185,285) deaths prevented, while Taiwan (91.1%) and Malaysia (80.5%) had the largest percent reductions in JE burden due to vaccination. Our estimates of the size of at-risk populations and current JE incidence highlight countries where increasing vaccination coverage could have the largest impact on reducing their JE burden.Author SummaryJapanese encephalitis is a vector-transmitted, zoonotic disease that is endemic throughout a large portion of Asia. Vaccination has significantly reduced the JE burden in several formerly high-burden countries, but vaccination coverage remains limited in several other countries with high JE burdens. A better understanding of both the spatial distribution and the magnitude of the burden in endemic countries is critical for future disease prevention efforts. To estimate the number of people living in areas within Asia suitable for JEV transmission we conducted a spatial analysis of the risk factors associated with JE. We estimate that over one billion people live in areas suitable for local JEV transmission. We then combined these population-at-risk estimates with estimates of the force of infection (FOI) to model the national-level burden of JE (annual cases and deaths) over the past decade. Increases in vaccination coverage have reduced JE incidence from over 60,000 cases in 2010 to 45,000 cases in 2019. We estimate that vaccination has prevented over 214,000 cases and 78,000 deaths in the past decade. Our results also call attention to the countries, and high-risk areas within countries, where increases in vaccination coverage are most needed.

scholarly journals Quantifying Model Form Uncertainty of Epidemic Forecasting Models from Incidence Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Nicholas Hengartner ◽  
Paul Fenimore
Keyword(s):  
Time Series ◽  
Model Misspecification ◽  
Disease Model ◽  
Parametric Models ◽  
Incidence Data ◽  
Force Of Infection ◽  
Model Form ◽  
Contagious Disease ◽  
The Impact ◽  
Non Parametric

ObjectiveWe present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. The resulting estimates allow to assess how well simpler models, such as SEIR, fit the observed time series of incidence data.IntroductionUncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a "universal model" to which more restrictive parametric models are compared too.MethodsThis talk presents a useful modeling framework for time series of incidence data from contagious diseases that enables one to identify and quantify the impact of model form uncertainty. Specifically, we propose to focus on estimating the timedependent force of infection. The latter is a universal parameters for all contagious disease model. Using a machine learning technique for estimating monotone functions, i.e., isotonic regression and its variants, one can estimate the force ofinfection without addtional assumptions. We note that most contagious disease model do satisfy this monotonicity assumption, due to a combination of factors: depletion of susceptibles, implementation of mitigation strategies, behavior change, etc. Comparing the resulting "non-parametric" estimate with parametric estimates, obtained by fitting an SEIR for example, can reveal model deficiencies and help quantify model form uncertainties.Finally, we discuss how ideas from "strict bound theory" can be used to develop upper and lower uncertainty bands for force of infection that acknowledge the intrinsic stochasticity in the data.ResultsWe demonstrate the application of the methodology to weekly Influenza Like Illness (ILI) incidence data from France andcompare the results to fitted SIR and SEIR models. This comparison can be seen as a nonparametric goodness of fit test, providing one with tools to do simple model selection.ConclusionsWe present a novel and flexible model to statistically describe the force of infection as a function of time. Comparing the fit to incidence data of that model with the fit of simpler parametric models enables the quantification of model form uncertainty and associated model selection.

The “Drugs-Crime Nexus”

2018 ◽  
Author(s):  
Dominique de Andrade
Keyword(s):  
At Risk ◽  
Drug Use ◽  
Harm Reduction ◽  
Good Evidence ◽  
People Who Use Drugs ◽  
Correctional Institutions ◽  
Reduction Strategies ◽  
At Risk Populations ◽  
Prison Populations ◽  
The Impact

The prioritization of imprisonment as a response to drug use in many countries has led to growing prison populations, with little impact on drug use, drug-related harm, or drug-related crime. There is increased international debate around how to best manage and respond to at-risk populations, with good evidence to suggest that embracing harm reduction strategies in the community and in prison can lead to reduced rates of imprisonment, infectious disease, and other preventable harms. Despite this, evidence-based treatment and harm reduction programs have largely failed to penetrate the walls of correctional institutions in most countries. This chapter provides an overview of major drug groups and explores the impact of drug policy on international imprisonment rates, and the diversity of responses to people who use drugs in the community and prison. The potential for corrections to play a significant therapeutic role in addressing the urgent treatment and harm reduction needs of at-risk, drug-using populations in prison and during their transition back to the community is highlighted.

scholarly journals Reducing Public Health Risk During Disasters: Identifying Social Vulnerabilities

2015 ◽  
Vol 12 (4) ◽  
pp. 809-822 ◽  
Author(s):  
Amy Wolkin ◽  
Jennifer Rees Patterson ◽  
Shelly Harris ◽  
Elena Soler ◽  
Sherry Burrer ◽  
...  
Keyword(s):  
Public Health ◽  
At Risk ◽  
Social Vulnerability ◽  
Emergency Managers ◽  
The Social ◽  
The Us ◽  
At Risk Populations ◽  
Negative Impacts ◽  
Social Vulnerabilities ◽  
The Impact

Abstract All regions of the US experience disasters which result in a number of negative public health consequences. Some populations have higher levels of social vulnerability and, thus, are more likely to experience negative impacts of disasters including emotional distress, loss of property, illness, and death. To mitigate the impact of disasters on at-risk populations, emergency managers must be aware of the social vulnerabilities within their community. This paper describes a qualitative study which aimed to understand how emergency managers identify social vulnerabilities, also referred to as at-risk populations, in their populations and barriers and facilitators to current approaches. Findings suggest that although public health tools have been developed to aid emergency managers in identifying at-risk populations, they are not being used consistently. Emergency managers requested more information on the availability of tools as well as guidance on how to increase ability to identify at-risk populations.

scholarly journals Using general practice data to monitor influenza vaccination coverage in the medically at risk: a data linkage study

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e031802
Author(s):  
Dauda Badmus ◽  
Robert Menzies
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
General Practice ◽  
Influenza Vaccination ◽  
Vaccination Coverage ◽  
Risk Groups ◽  
Medical Risk ◽  
Medical Risk Factors ◽  
Adult Vaccination ◽  
The Impact

ObjectiveTo examine the possibility of using data from a network of Australian General Practices (GPs) to estimate influenza vaccination coverage in Australians medically at risk.DesignData electronically extracted from a large national network of Australian GP clinics (MedicineInsight) was analysed for annual influenza vaccination coverage from 2008 to 2014. We compared the results with the 2009 and 2014 Adult Vaccination Survey. We adjusted for differences in the distribution of age, risk groups and provider types.SettingAll states in Australia.ParticipantsGPs participating in MedicineInsight programme.InterventionsNot applicable.Main outcome measuresAnnual vaccination coverage across risk groups as recorded in Adult Vaccination Survey in 2009 and 2014 were compared with vaccination coverage in MedicineInsight. The impact of National Immunisation Programme expansion of free vaccine in 2010 to cover patients aged <65 years with medical risk factors.ResultsThe proportion of MedicineInsight patients aged ≥18 years and diagnosed with medical risk factors was higher in 2014 (33.2%), compared with the AVS in 2009 (25%). In 2009, influenza vaccination coverage estimates for those aged 18–64 years with medical risk factors was lower for MedicineInsight patients compared with the AVS (26% vs 36%). There was no evidence of any change in coverage between 2008 and 2014, despite the vaccine being available free of charge to this group from 2010.ConclusionGeneral practice databases have the potential to help fill the gap in vaccination coverage data in patients with medical risk factors.

scholarly journals Measles elimination in Italy: projected impact of the National Elimination Plan

2004 ◽  
Vol 133 (1) ◽  
pp. 87-97 ◽  
Author(s):  
P. MANFREDI ◽  
J. R. WILLIAMS ◽  
M. L. CIOFI DEGLI ATTI ◽  
S. SALMASO
Keyword(s):  
Mathematical Model ◽  
Vaccination Coverage ◽  
Measles Vaccine ◽  
Demographic Situation ◽  
Force Of Infection ◽  
Measles Elimination ◽  
Catch Up ◽  
The Impact ◽  
Below Replacement Fertility

A mathematical model was used to evaluate the impact of the Italian Measles National Elimination Plan (NEP), and possible sources of failure in achieving its targets. The model considered two different estimates of force of infection, and the possible effect on measles transmission of the current Italian demographic situation, characterized by a below-replacement fertility. Results suggest that reaching all NEP targets will allow measles elimination to be achieved. In addition, the model suggests that achieving elimination by reaching a 95% first-dose coverage appears unlikely; and that conducting catch-up activities, reaching high vaccination coverage, could interrupt virus circulation, but could not prevent the infection re-emerging before 2020. Also, the introduction of the second dose of measles vaccine seems necessary for achieving and maintaining elimination. Furthermore, current Italian demography appears to be favourable for reaching elimination.

scholarly journals A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

2021 ◽  
Vol 10 (11) ◽  
pp. 781
Author(s):  
Gainbi Park
Keyword(s):  
At Risk ◽  
Big Data ◽  
Hurricane Damage ◽  
Long Span ◽  
Risk Areas ◽  
At Risk Populations ◽  
Spatial Boundary ◽  
Risk Zones ◽  
Hurricane Vulnerability ◽  
The U.S

(1) Background: Hurricane events are expected to increase as a consequence of climate change, increasing their intensity and severity. Destructive hurricane activities pose the greatest threat to coastal communities along the U.S. Gulf of Mexico and Atlantic Coasts in the conterminous United States. This study investigated the historical extent of hurricane-related damage, identifying the most at-risk areas of hurricanes using geospatial big data. As a supplement to analysis, this study further examined the overall population trend within the hurricane at-risk zones. (2) Methods: The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model and the HURRECON model were used to estimate the geographical extent of the storm surge inundation and wind damage of historical hurricanes from 1950 to 2018. The modeled results from every hurricane were then aggregated to a single unified spatial surface to examine the generalized hurricane patterns across the affected coastal counties. Based on this singular spatial boundary coupled with demographic datasets, zonal analysis was applied to explore the historical population at risk. (3) Results: A total of 777 counties were found to comprise the “hurricane-prone coastal counties” that have experienced at least one instance of hurricane damage over the study period. The overall demographic trends within the hurricane-prone coastal counties revealed that the coastal populations are growing at a faster pace than the national average, and this growth puts more people at greater risk of hurricane hazards. (4) Conclusions: This study is the first comprehensive investigation of hurricane vulnerability encompassing the Atlantic and Gulf Coasts stretching from Texas to Maine over a long span of time. The findings from this study can serve as a basis for understanding the exposure of at-risk populations to hurricane-related damage within the coastal counties at a national scale.

scholarly journals Quantifying population contact patterns in the United States during the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dennis M. Feehan ◽  
Ayesha S. Mahmud
Keyword(s):  
United States ◽  
At Risk ◽  
The United States ◽  
Interpersonal Contact ◽  
Demographic Groups ◽  
Contact Rates ◽  
Contact Patterns ◽  
The Us ◽  
At Risk Populations ◽  
The Impact

AbstractSARS-CoV-2 is transmitted primarily through close, person-to-person interactions. Physical distancing policies can control the spread of SARS-CoV-2 by reducing the amount of these interactions in a population. Here, we report results from four waves of contact surveys designed to quantify the impact of these policies during the COVID-19 pandemic in the United States. We surveyed 9,743 respondents between March 22 and September 26, 2020. We find that interpersonal contact has been dramatically reduced in the US, with an 82% (95%CI: 80%–83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. However, we find increases in contact rates over the subsequent waves. We also find that certain demographic groups, including people under 45 and males, have significantly higher contact rates than the rest of the population. Tracking these changes can provide rapid assessments of the impact of physical distancing policies and help to identify at-risk populations.

EASEing Students Into College: The Impact of Multidimensional Support for Underprepared Students

2018 ◽  
Vol 47 (7) ◽  
pp. 435-450 ◽  
Author(s):  
Di Xu ◽  
Sabrina Solanki ◽  
Peter McPartlan ◽  
Brian Sato
Keyword(s):  
At Risk ◽  
Learning Communities ◽  
Underprepared Students ◽  
Program Participation ◽  
College Level ◽  
Stem Learning ◽  
Quantitative Evidence ◽  
At Risk Populations ◽  
High Level ◽  
The Impact

Extensive theoretical literature and qualitative evidence nominate learning communities as a promising strategy to improve persistence and success among at-risk populations, such as students who are academically underprepared for college-level coursework. Yet rigorous quantitative evidence on the impacts of these programs is limited. This paper estimates the causal effects of a first-year STEM learning communities program on both cognitive and noncognitive outcomes at a large public 4-year institution. We use a regression discontinuity design based on the fact that students are assigned to the program if their math SAT score is below a threshold. Our results indicate that program participation increased the academic performance and sense of belonging for students around the cutoff. These results provide compelling evidence that learning communities can support at-risk populations when implemented with a high level of fidelity.

Age and marital status predictors of binge alcohol use among adults with major depressive episode

2020 ◽  
Author(s):  
Areen Omary
Keyword(s):  
At Risk ◽  
Alcohol Use ◽  
Marital Status ◽  
Depressive Episode ◽  
Major Depressive Episode ◽  
Age Groups ◽  
Future Research ◽  
Major Depressive ◽  
Never Married ◽  
At Risk Populations

Aims: This study aims to examine if age and marital status can predict the risk for binge alcohol use (BAU) among adults with a major depressive episode (MDE). Methods: Data from the Substance Abuse and Mental Health Services Administration’s (SAMHSA) 2018 National Survey for Drug Use and Health (NSDUH) were analyzed. The unweighted sample included 6,999 adults representing a weighted population size of 33,900,452.122 in the US. Results and Conclusions: The findings of this retrospective research confirmed that age and marital status significantly predicted BAU in the past month among adults with MDE. Adults with MDE at higher risk for BAU were adults under the age of 50, adults who were never married, and adults who were divorced/separated. Special attention must be paid to those in age groups under 50, never married, and have been separated/divorced who are particularly at-risk for future alcohol abuse. Future research should consider examining additional potential confounders for BAU among other at-risk populations.

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