Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021.

Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21-30 to 8-11 hours, while maintaining program effectiveness.

Estimated public health impact of concurrent mask mandate and vaccinate-or-test requirement in Illinois, October to December 2021.

BACKGROUND: Facing a surge of COVID-19 cases in late August 2021, the U.S. state of Illinois re-enacted its COVID-19 mask mandate for the general public and issued a requirement for workers in certain professions to be vaccinated against COVID-19 or undergo weekly testing. The mask mandate required any individual, regardless of their vaccination status, to wear a well-fitting mask in an indoor setting. METHODS: We used Illinois Department of Public Health's COVID-19 confirmed case and vaccination data and investigated scenarios where masking and vaccination would have been reduced to mimic what would have happened had the mask mandate or vaccine requirement not been put in place. The study examined a range of potential reductions in masking and vaccination mimicking potential scenarios had the mask mandate or vaccine requirement not been enacted. We estimated COVID-19 cases and hospitalizations averted by changes in masking and vaccination during the period covering October 20 to December 20, 2021. RESULTS: We find that the announcement and implementation of a mask mandate are likely to correlate with a strong protective effect at reducing COVID-19 burden and the announcement of a vaccinate-or-test requirement among frontline professionals is likely to correlate with a more modest protective effect at reducing COVID-19 burden. In our most conservative scenario, we estimated that from the period of October 20 to December 20, 2021, the mask mandate likely prevented approximately 58,000 cases and 1,175 hospitalizations, while the vaccinate-or-test requirement may have prevented at most approximately 24,000 cases and 475 hospitalizations. CONCLUSION: Our results indicate that mask mandates and vaccine-or-test requirements are vital in mitigating the burden of COVID-19 during surges of the virus.

Estimated Cases Averted by COVID-19 Digital Exposure Notification, Pennsylvania, USA, November 8, 2020-January 2, 2021.

We combined field-based data with mathematical modeling to estimate the effectiveness of smartphone-enabled COVID-19 exposure notification in Pennsylvania, USA. We estimated that digital notifications potentially averted 7-69 cases/1,000 notifications during November 8, 2020-January 2, 2021. Greater use and increased compliance could increase the effectiveness of digital notifications.

Public Health Response to Multistate Salmonella Typhimurium Outbreak Associated with Prepackaged Chicken Salad, United States, 2018.

Quantifying the effect of public health actions on population health is essential when justifying sustained public health investment. Using modeling, we conservatively estimated that rapid response to a multistate foodborne outbreak of Salmonella Typhimurium in the United States in 2018 potentially averted 94 reported cases and $633,181 in medical costs and productivity losses.

Economic Burden of Reported Lyme Disease in High-Incidence Areas, United States, 2014-2016.

Approximately 476,000 cases of Lyme disease are diagnosed in the United States annually, yet comprehensive economic evaluations are lacking. In a prospective study among reported cases in Lyme disease-endemic states, we estimated the total patient cost and total societal cost of the disease. In addition, we evaluated disease and demographic factors associated with total societal cost. Participants had a mean patient cost of ≈$1,200 (median $240) and a mean societal cost of ≈$2,000 (median $700). Patients with confirmed disseminated disease or probable disease had approximately double the societal cost of those with confirmed localized disease. The annual, aggregate cost of diagnosed Lyme disease could be $345-968 million (2016 US dollars) to US society. Our findings emphasize the importance of effective prevention and early diagnosis to reduce illness and associated costs. These results can be used in cost-effectiveness analyses of current and future prevention methods, such as a vaccine.

Modeling the Transmission of COVID-19: Impact of Mitigation Strategies in Prekindergarten-Grade 12 Public Schools, United States, 2021.

BACKGROUND: Schools are an integral part of the community; however, congregate settings facilitate transmission of SARS-CoV-2, presenting a challenge to school administrators to provide a safe, in-school environment for students and staff. METHODS: We adapted the Centers for Disease Control and Prevention's COVIDTracer Advanced tool to model the transmission of SARS-CoV-2 in a school of 596 individuals. We estimate possible reductions in cases and hospitalizations among this population using a scenario-based analysis that accounts for (a) the risk of importation of infection from the community; (b) adherence to key Centers for Disease Control and Prevention-recommended mitigation strategies: mask wearing, cleaning and disinfection, hand hygiene, and social distancing; and (c) the effectiveness of contact tracing interventions at limiting onward transmission. RESULTS: Low impact and effectiveness of mitigation strategies (net effectiveness: 27%) result in approximately 40% of exposed staff and students becoming COVID-19 cases. When the net effectiveness of mitigation strategies was 69% or greater, in-school transmission was mostly prevented, yet importation of cases from the surrounding community could result in nearly 20% of the school's population becoming infected within 180 days. The combined effects of mitigation strategies and contact tracing were able to prevent most onward transmission. Hospitalizations were low among children and adults (<0.5% of the school population) across all scenarios examined. CONCLUSIONS: Based on our model, layering mitigation strategies and contact tracing can limit the number of cases that may occur from transmission in schools. Schools in communities with substantial levels of community spread will need to be more vigilant to ensure adherence of mitigation strategies to minimize transmission. Our results show that for school administrators, teachers, and parents to provide the safest environment, it is important to utilize multiple mitigation strategies and contract tracing that reduce SARS CoV-2 transmission by at least 69%. This will require training, reinforcement, and vigilance to ensure that the highest level of adherence is maintained over the entire school term.

Estimates of Cases and Hospitalizations Averted by COVID-19 Case Investigation and Contact Tracing in 14 Health Jurisdictions in the United States.

CONTEXT: The implementation of case investigation and contact tracing (CICT) for controlling COVID-19 (caused by SARS-CoV-2 virus) has proven challenging due to varying levels of public acceptance and initially constrained resources, especially enough trained staff. Evaluating the impacts of CICT will aid efforts to improve such programs. OBJECTIVES: Estimate the number of COVID-19 cases and hospitalizations averted by CICT and identify CICT processes that could improve overall effectiveness. DESIGN: We used data on the proportion of cases interviewed, contacts notified or monitored, and days from testing to case and contact notification from 14 jurisdictions to model the impact of CICT on cumulative case counts and hospitalizations over a 60-day period. Using the Centers for Disease Control and Prevention's COVIDTracer Advanced tool, we estimated a range of impacts by assuming either contacts would quarantine only if monitored or would do so upon notification of potential exposure. We also varied the observed program metrics to assess their relative influence. RESULTS: Performance by jurisdictions varied widely. Jurisdictions isolated between 12% and 86% of cases (including contacts that became cases) within 6 to 10 days after infection. We estimated that CICT-related reductions in transmission ranged from 0.4% to 32%. For every 100 remaining cases after other nonpharmaceutical interventions were implemented, CICT averted between 4 and 97 additional cases. Reducing time to case isolation by 1 day increased averted case estimates by up to 15 percentage points. Increasing the proportion of cases interviewed or contacts notified by 20 percentage points each resulted in at most 3 or 6 percentage point improvements in averted cases. CONCLUSIONS: We estimated that CICT reduced the number of COVID-19 cases and hospitalizations among all jurisdictions studied. Reducing time to isolation produced the greatest improvements in impact of CICT.

Cost-Effectiveness and Impact of a Targeted Age- and Incidence-based West Nile Virus Vaccine Strategy.

BACKGROUND: West Nile virus (WNV) is the leading cause of arboviral disease in the United States and is associated with significant morbidity and mortality. A previous analysis found that a vaccination program targeting persons aged ≥60 years was more cost-effective than universal vaccination, but costs remained high. METHODS: We used a mathematical Markov model to evaluate cost-effectiveness of an age- and incidence-based WNV vaccination program. We grouped states and large counties (≥100 000 persons aged ≥60 years) by median annual WNV incidence rates from 2004 to 2017 for persons aged ≥60 years. We defined WNV incidence thresholds, in increments of 0.5 cases per 100 000 persons ≥60 years. We calculated potential cost per WNV vaccine-prevented case and per quality adjusted life-years (QALYs) saved. RESULTS: Vaccinating persons aged ≥60 years in states with an annual incidence of WNV neuroinvasive disease of ≥0.5 per 100 000 resulted in approximately half the cost per health outcome averted compared to vaccinating persons aged ≥60 years in the contiguous United States. This approach could potentially prevent 37% of all neuroinvasive disease cases and 63% of WNV-related deaths nationally. Employing such a threshold at a county level further improved cost-effectiveness ratios while preventing 19% and 30% of WNV-related neuroinvasive disease cases and deaths, respectively. CONCLUSIONS: An age- and incidence-based WNV vaccination program could be a more cost-effective strategy than an age-based program while still having a substantial impact on lowering WNV-related morbidity and mortality.

The CAPACITI Decision-Support Tool for National Immunization Programs.

OBJECTIVES: Immunization programs in low-income and middle-income countries (LMICs) are faced with an ever-growing number of vaccines of public health importance recommended by the World Health Organization, while also financing a greater proportion of the program through domestic resources. More than ever, national immunization programs must be equipped to contextualize global guidance and make choices that are best suited to their setting. The CAPACITI decision-support tool has been developed in collaboration with national immunization program decision makers in LMICs to structure and document an evidence-based, context-specific process for prioritizing or selecting among multiple vaccination products, services, or strategies. METHODS: The CAPACITI decision-support tool is based on multi-criteria decision analysis, as a structured way to incorporate multiple sources of evidence and stakeholder perspectives. The tool has been developed iteratively in consultation with 12 countries across Africa, Asia, and the Americas. RESULTS: The tool is flexible to existing country processes and can follow any type of multi-criteria decision analysis or a hybrid approach. It is structured into 5 sections: decision question, criteria for decision making, evidence assessment, appraisal, and recommendation. The Excel-based tool guides the user through the steps and document discussions in a transparent manner, with an emphasis on stakeholder engagement and country ownership. CONCLUSIONS: Pilot countries valued the CAPACITI decision-support tool as a means to consider multiple criteria and stakeholder perspectives and to evaluate trade-offs and the impact of data quality. With use, it is expected that LMICs will tailor steps to their context and streamline the tool for decision making.

Estimated Resource Costs for Implementation of CDC's Recommended COVID-19 Mitigation Strategies in Pre-Kindergarten through Grade 12 Public Schools - United States, 2020-21 School Year.

As school districts across the United States consider how to safely operate during the 2020-21 academic year, CDC recommends mitigation strategies that schools can adopt to reduce the risk for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1). To identify the resources and costs needed to implement school-based mitigation strategies and provide schools and jurisdictions with information to aid resource allocation, a microcosting methodology was employed to estimate costs in three categories: materials and consumables, additional custodial staff members, and potential additional transportation. National average estimates, using the national pre-kindergarten through grade 12 (preK-12) public enrollment of 50,685,567 students, range between a mean of $55 (materials and consumables only) to $442 (all three categories) per student. State-by-state estimates of additional funds needed as a percentage of fiscal year 2018 student expenditures (2) range from an additional 0.3% (materials and consumables only) to 7.1% (all three categories); however, only seven states had a maximum estimate above 4.2%. These estimates, although not exhaustive, highlight the level of resources needed to ensure that schools reopen and remain open in the safest possible manner and offer administrators at schools and school districts and other decision-makers the cost information necessary to budget and prioritize school resources during the COVID-19 pandemic.

Forecasting the 2014 West African Ebola Outbreak.

In 2014-2015, a large Ebola outbreak afflicted Liberia, Guinea, and Sierra Leone. We performed a systematic review of 26 manuscripts, published between 2014 and April 2015, that forecasted the West African Ebola outbreak while it was occurring, and we derived implications for how results could be interpreted by policymakers. Forecasted case counts varied widely. An important determinant of forecast accuracy for case counts was how far into the future predictions were made. Generally, forecasts for less than 2 months into the future tended to be more accurate than those made for more than 10 weeks into the future. The exceptions were parsimonious statistical models in which the decay of the rate of spread of the pathogen among susceptible individuals was dealt with explicitly. The most important lessons for policymakers regarding future outbreaks, when using similar modeling results, are: 1) uncertainty of forecasts will be greater in the beginning of the outbreak; 2) when data are limited, forecasts produced by models designed to inform specific decisions should be used complementarily for robust decision-making (e.g., 2 statistical models produced the most reliable case-counts forecasts for the studied Ebola outbreak but did not enable understanding of interventions' impact, whereas several compartmental models could estimate interventions' impact but required unavailable data); and 3) timely collection of essential data is necessary for optimal model use.

The PanVax Tool to Improve Pandemic Influenza Emergency Vaccination Program Readiness and Partnership.

Objectives. To show how the Centers for Disease Control and Prevention's Pandemic Vaccine Campaign Planning Tool (PanVax Tool) can help state and local public health emergency planners demonstrate and quantify how partnerships with community vaccine providers can improve their overall pandemic vaccination program readiness.Methods. The PanVax Tool helps planners compare different strategies to vaccinate their jurisdiction's population in a severe pandemic by allowing users to customize the underlying model inputs in real time, including their jurisdiction's size, community vaccine provider types, and how they allocate vaccine to these providers. In this report, we used a case study with hypothetical data to illustrate how jurisdictions can utilize the PanVax Tool for preparedness planning.Results. By using the tool, planners are able to understand the impact of engaging with different vaccine providers in a vaccination campaign.Conclusions. The PanVax Tool is a useful tool to help demonstrate the impact of community vaccine provider partnerships on pandemic vaccination readiness and identify areas for improved partnerships for pandemic response.

Evaluation of the impact of shigellosis exclusion policies in childcare settings upon detection of a shigellosis outbreak.

BACKGROUND: In the event of a shigellosis outbreak in a childcare setting, exclusion policies are typically applied to afflicted children to limit shigellosis transmission. However, there is scarce evidence of their impact. METHODS: We evaluated five exclusion policies: Children return to childcare after: i) two consecutive laboratory tests (either PCR or culture) do not detect Shigella, ii) a single negative laboratory test (PCR or culture) does not detect Shigella, iii) seven days after beginning antimicrobial treatment, iv) after being symptom-free for 24 h, or v) 14 days after symptom onset. We also included four treatments to assess the policy options: i) immediate, effective treatment; ii) effective treatment after laboratory diagnosis; iii) no treatment; iv) ineffective treatment. Relying on published data, we calculated the likelihood that a child reentering childcare would be infectious, and the number of childcare-days lost per policy. RESULTS: Requiring two consecutive negative PCR tests yielded a probability of onward transmission of < 1%, with up to 17 childcare-days lost for children receiving effective treatment, and 53 days lost for those receiving ineffective treatment. CONCLUSIONS: Of the policies analyzed, requiring negative PCR testing before returning to childcare was the most effective to reduce the risk of shigellosis transmission, with one PCR test being the most effective for the least childcare-days lost.

Antiviral treatment for outpatient use during an influenza pandemic: a decision tree model of outcomes averted and cost-effectiveness.

BACKGROUND: Many countries have acquired antiviral stockpiles for pandemic influenza mitigation and a significant part of the stockpile may be focussed towards community-based treatment. METHODS: We developed a spreadsheet-based, decision tree model to assess outcomes averted and cost-effectiveness of antiviral treatment for outpatient use from the perspective of the healthcare payer in the UK. We defined five pandemic scenarios-one based on the 2009 A(H1N1) pandemic and four hypothetical scenarios varying in measures of transmissibility and severity. RESULTS: Community-based antiviral treatment was estimated to avert 14-23% of hospitalizations in an overall population of 62.28 million. Higher proportions of averted outcomes were seen in patients with high-risk conditions, when compared to non-high-risk patients. We found that antiviral treatment was cost-saving across pandemic scenarios for high-risk population groups, and cost-saving for the overall population in higher severity influenza pandemics. Antiviral effectiveness had the greatest influence on both the number of hospitalizations averted and on cost-effectiveness. CONCLUSIONS: This analysis shows that across pandemic scenarios, antiviral treatment can be cost-saving for population groups at high risk of influenza-related complications.

How did Ebola information spread on twitter: broadcasting or viral spreading?

BACKGROUND: Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. METHODS: Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. RESULTS: On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. CONCLUSIONS: Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.

Assessment of age-related differences in smoking status and health-related quality of life (HRQoL): Findings from the 2016 Behavioral Risk Factor Surveillance System.

Despite significant declines in the use of cigarettes, a significant proportion of adults smoke. This study explores the association between smoking and health-related quality of life (HRQoL) by age. The 2016 Behavioral Risk Factor Surveillance System survey was administered to adults in 50 states and District of Columbia (n = 437,195). Physically unhealthy days (PUDs) and mentally unhealthy days (MUDs)) were regressed on age strata (18-24, 25-34, 35-44, 45-54, 55-64, ≥ 65 years) and smoking status (never, former, someday, and everyday) using negative binomial regression models with adjustment for sociodemographic covariates. For each age group, everyday smoking highly predicted PUDs and MUDs. Predicted PUDs increased with age; predicted MUDs decreased with age. Among adults aged 45-54 and 55-64 years, 3-day difference in PUDs was observed between never smokers and everyday smokers. Among young adults (18-24 years), a 4.3-day difference in MUDs was observed between everyday and never smokers. The discrepancies were nonlinear with age. The observed relationship between smoking and HRQoL provides novel information about the need to consider age when designing community-based interventions. Additional research can provide needed depth to understanding the relationship between smoking and HRQoL in specific age groups.

The Epidemiology of Foodborne Botulism Outbreaks: A Systematic Review.

Background: We performed a systematic review of foodborne botulism outbreaks to describe their clinical aspects and descriptive epidemiology in order to inform public health response strategies. Methods: We searched seven databases for reports of foodborne botulism outbreaks published in English from database inception to May 2015. We summarized descriptive characteristics and analyzed differences in exposure and toxin types by geographic region. We performed logistic regression to assess correlations between exposure source, implicated food, and outbreak size. Results: There were 197 outbreaks reported between 1920 and 2014. The median number of cases per outbreak was 3 (range 2-97). The majority of reported outbreaks (109; 55%) occurred in the United States. Toxin types A, B, E, and F were identified as the causative agent in 34%, 16%, 17%, and 1% of outbreaks, respectively. The median duration between exposure and symptom onset was approximately 1 day. The mean percentage of cases requiring mechanical ventilation per outbreak was 34%. Seventy percent of all outbreaks and 77% of small outbreaks (≤11 cases) originated from point source exposures, while commercial foods were significantly (odds ratio, 6.9; 95% confidence interval, 2.2-21.1) associated with large outbreaks (≥12 cases). Conclusions: Toxin type A accounted for half of outbreaks, and these outbreaks had a higher proportion of patient ventilatory failure. Most outbreaks were due to point source exposures, while outbreaks due to commercial food were larger. For effective responses to foodborne botulism outbreaks, these findings demonstrate the need for timely outbreak investigation and hospital surge capacity.

Modeling Tool for Decision Support during Early Days of an Anthrax Event.

Health officials lack field-implementable tools for forecasting the effects that a large-scale release of Bacillus anthracis spores would have on public health and hospitals. We created a modeling tool (combining inhalational anthrax caseload projections based on initial case reports, effects of variable postexposure prophylaxis campaigns, and healthcare facility surge capacity requirements) to project hospitalizations and casualties from a newly detected inhalation anthrax event, and we examined the consequences of intervention choices. With only 3 days of case counts, the model can predict final attack sizes for simulated Sverdlovsk-like events (1979 USSR) with sufficient accuracy for decision making and confirms the value of early postexposure prophylaxis initiation. According to a baseline scenario, hospital treatment volume peaks 15 days after exposure, deaths peak earlier (day 5), and recovery peaks later (day 23). This tool gives public health, hospital, and emergency planners scenario-specific information for developing quantitative response plans for this threat.

20 Years of Public Health Economics and Decision Sciences at the US Centers for Disease Control and Prevention: The CDC Steven M. Teutsch Prevention Effectiveness Fellowship, 1995-2015.

The CDC Steven M. Teutsch Prevention Effectiveness Fellowship was started in 1995 to provide postdoctoral training in public health economics. This article describes the origins and state of the fellowship and the practice of prevention effectiveness research at the Centers for Disease Control and Prevention. The fellowship can be seen as one successful example of a demand-driven public health innovation to develop crucial capacity for the contemporary health system. Nearly 150 individuals have been trained through the program since its inception.

CDC Grand Rounds: Modeling and Public Health Decision-Making.

Mathematical models incorporate various data sources and advanced computational techniques to portray real-world disease transmission and translate the basic science of infectious diseases into decision-support tools for public health. Unlike standard epidemiologic methods that rely on complete data, modeling is needed when there are gaps in data. By combining diverse data sources, models can fill gaps when critical decisions must be made using incomplete or limited information. They can be used to assess the effect and feasibility of different scenarios and provide insight into the emergence, spread, and control of disease. During the past decade, models have been used to predict the likelihood and magnitude of infectious disease outbreaks, inform emergency response activities in real time (1), and develop plans and preparedness strategies for future events, the latter of which proved invaluable during outbreaks such as severe acute respiratory syndrome and pandemic influenza (2-6). Ideally, modeling is a multistep process that involves communication between modelers and decision-makers, allowing them to gain a mutual understanding of the problem to be addressed, the type of estimates that can be reliably generated, and the limitations of the data. As models become more detailed and relevant to real-time threats, the importance of modeling in public health decision-making continues to grow.