Seasonal influenza causes considerable morbidity and mortality across all age groups, and influenza vaccination was recommended in 2010 for all persons aged 6 months and above. We estimated the ...averted costs due to influenza vaccination, taking into account the seasonal economic burden of the disease.
We used recently published values for averted outcomes due to influenza vaccination for influenza seasons 2005-06, 2006-07, 2007-08, and 2008-09, and age cohorts 6 months-4 years, 5-19 years, 20-64 years, and 65 years and above. Costs were calculated according to a payer and societal perspective (in 2009 US$), and took into account medical costs and productivity losses.
When taking into account direct medical costs (payer perspective), influenza vaccination was cost saving only for the older age group (65≥) in seasons 2005-06 and 2007-08. Using the same perspective, influenza vaccination resulted in total costs of $US 1.7 billion (95%CI: $US 0.3-4.0 billion) in 2006-07 and $US 1.8 billion (95%CI: $US 0.1-4.1 billion) in 2008-09. When taking into account a societal perspective (and including the averted lost earnings due to premature death) averted deaths in the older age group influenced the results, resulting in cost savings for all ages combined in season 07-08.
Influenza vaccination was cost saving in the older age group (65≥) when taking into account productivity losses and, in some seasons, when taking into account medical costs only. Averted costs vary significantly per season; however, in seasons where the averted burden of deaths is high in the older age group, averted productivity losses due to premature death tilt overall seasonal results towards savings. Indirect vaccination effects and the possibility of diminished case severity due to influenza vaccination were not considered, thus the averted burden due to influenza vaccine may be even greater than reported.
The SARS-CoV-2 pandemic has underscored the need for field specimen collection and transport to diagnostic and public health laboratories. Self-collected nasal swabs transported without dependency on ...a cold chain have the potential to remove critical barriers to testing, expand testing capacity, and reduce opportunities for exposure of health professionals in the context of a pandemic.
We compared nasal swab collection by study participants from themselves and their children at home to collection by trained research staff.
Each adult participant collected 1 nasal swab, sampling both nares with the single swab, after which they collected 1 nasal swab from 1 child. After all the participant samples were collected for the household, the research staff member collected a separate single duplicate sample from each individual. Immediately after the sample collection, the adult participants completed a questionnaire about the acceptability of the sampling procedures. Swabs were placed in temperature-stable preservative and respiratory viruses were detected by shotgun RNA sequencing, enabling viral genome analysis.
In total, 21 households participated in the study, each with 1 adult and 1 child, yielding 42 individuals with paired samples. Study participants reported that self-collection was acceptable. Agreement between identified respiratory viruses in both swabs by RNA sequencing demonstrated that adequate collection technique was achieved by brief instructions.
Our results support the feasibility of a scalable and convenient means for the identification of respiratory viruses and implementation in pandemic preparedness for novel respiratory pathogens.
Purpose Concerns have been raised regarding possible racial-ethnic disparities in 2009 pandemic influenza A (H1N1) (pH1N1) illness severity and health consequences for U.S. minority populations. ...Methods Using data from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System, Emerging Infections Program Influenza-Associated Hospitalization Surveillance, and Influenza-Associated Pediatric Mortality Surveillance, we calculated race-ethnicity-specific, age-adjusted rates of self-reported influenza-like illness (ILI) and pH1N1-associated hospitalizations. We used χ2 tests to evaluate racial-ethnic disparities in ILI-associated health care-seeking behavior and pH1N1 hospitalization. To evaluate pediatric deaths, we compared racial-ethnic proportions of deaths against U.S. population distributions. Results Prevalence of self-reported ILI was lower among Hispanics (6.5%), higher among American Indians/Alaska Natives (16.2%), and similar among non-Hispanic blacks (7.7%) compared with non-Hispanic whites (8.5%). No racial-ethnic differences were identified in ILI-associated health care-seeking behavior. Age-adjusted pH1N1-associated Emerging Infections Program hospitalization rates were higher among all minority populations (range: 8.1–10.9/100,000 population) compared with non-Hispanic whites (3.0/100,000). The proportion of pH1N1-associated pediatric deaths was higher than expected among Hispanics (31%) and lower than expected among non-Hispanic whites (45%) given the proportions of the U.S. population they comprise (22% and 58%, respectively). Conclusions Racial-ethnic disparities in pH1N1-associated hospitalizations and pediatric deaths were identified. Vaccination remains the primary intervention for preventing influenza.
In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes ...the rich discussions that occurred during the workshop.
The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness.
Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.
•We sought to identify published epidemic forecasting and prediction reporting guidelines.•This systematic review confirms that no specific guidelines have been published.•Developing such reporting ...guidelines will be important to improve epidemic forecasting research.
High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications.
We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors.
A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies.
This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.
From April 2009 through March 2010, during the pandemic (H1N1) 2009 outbreak, ≈8.2 million prescriptions for influenza neuraminidase-inhibiting antiviral drugs were filled in the United States. We ...estimated the number of hospitalizations likely averted due to use of these antiviral medications. After adjusting for prescriptions that were used for prophylaxis and personal stockpiles, as well as for patients who did not complete their drug regimen, we estimated the filled prescriptions prevented ≈8,400-12,600 hospitalizations (on the basis of median values). Approximately 60% of these prevented hospitalizations were among adults 18-64 years of age, with the remainder almost equally divided between children 0-17 years of age and adults >65 years of age. Public health officials should consider these estimates an indication of success of treating patients during the 2009 pandemic and a warning of the need for renewed planning to cope with the next pandemic.
Abstract
Modeling complements surveillance data to inform coronavirus disease 2019 (COVID-19) public health decision making and policy development. This includes the use of modeling to improve ...situational awareness, assess epidemiological characteristics, and inform the evidence base for prevention strategies. To enhance modeling utility in future public health emergencies, the Centers for Disease Control and Prevention (CDC) launched the Infectious Disease Modeling and Analytics Initiative. The initiative objectives are to: (1) strengthen leadership in infectious disease modeling, epidemic forecasting, and advanced analytic work; (2) build and cultivate a community of skilled modeling and analytics practitioners and consumers across CDC; (3) strengthen and support internal and external applied modeling and analytic work; and (4) working with partners, coordinate government-wide advanced data modeling and analytics for infectious diseases. These efforts are critical to help prepare the CDC, the country, and the world to respond effectively to present and future infectious disease threats.
Modeling has informed public health decision making and policy development throughout the COVID-19 response. CDC has launched the Infectious Disease Modeling and Analytics Initiative to continue to enhance the use of modeling during public health emergencies.
Invasive plants can have substantial negative impacts on native flora and fauna. As a result, ecological restoration often involves removal of invasive species. We examined the effects of the removal ...of Hedera helix (English ivy) on regeneration of native vegetation in the Piedmont of Georgia. Ivy was removed by hand or by herbicide from five 5 m × 5 m plots for each treatment and half of each plot was seeded with native seeds. We then counted the number of seedlings present in each plot bimonthly over a 5-mo period. Ivy removal by pulling resulted in the greatest density and diversity of seedlings. Furthermore, these plots exhibited increased seedling density and diversity due to seed addition. Spraying was effective in removal of the ivy but significantly lowered seedling density and diversity and hindered any seed addition efforts. Control plots in which ivy was not removed had no seedlings germinate. Our results suggest that the method of exotic plant removal and the addition of native seed can have profound effects on the regeneration of native vegetation and should be a major consideration for future exotic plant removal projects.
Abstract
Assessments of influenza season severity can guide public health action. We used the moving epidemic method to develop intensity thresholds (ITs) for 3 US surveillance indicators from the ...2003–2004 through 2014–2015 influenza seasons (excluding the 2009 pandemic). The indicators were: 1) outpatient visits for influenza-like illness; 2) influenza-related hospitalizations; and 3) influenza- and pneumonia-related deaths. ITs were developed for the population overall and separately for children, adults, and older adults, and they were set at the upper limit of the 50% (IT50), 90% (IT90), and 98% (IT98) 1-sided confidence intervals of the geometric mean of each season’s 3 highest values. Severity was classified as low if ≥2 systems peaked below IT50, moderate if ≥2 peaked between IT50 and IT90, high if ≥2 peaked between IT90 and IT98, and very high if ≥2 peaked above IT98. We pilot-tested this method with the 2015–2016 season and the 2009 pandemic. Overall, 4 seasons were classified as low severity, 7 as moderate, 2 as high, and none as very high. Among the age groups, older adults had the most seasons (n = 3) classified as high, and children were the only group to have seasons (n = 2) classified as very high. We will apply this method to classify the severity of future seasons and inform pandemic response.
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