503. The Misappropriation of COVID-19 Related TV Ad Expenditures

Abstract Background With nearly three-fourths of the U.S. population isolated in their homes between early March and the end of May, almost all of whom regularly watch television (TV), it was no surprise that companies began to purchase airtime on major television networks to advertise (ad) their brands and showcase their empathy with the populace. But how would the coronavirus disease 2019 (COVID-19) epidemic curve have changed had these same dollars been allocated to proven preventive interventions? Methods Performance and activity metrics on all COVID-19 related TV ads that have aired in the U.S. between February 26th and June 7th, 2020, were provided by iSpot.tv, Inc., including expenditures. COVID-19 incidence and mortality data were collected from the Centers for Disease Control and Prevention (CDC). Descriptive statistics were performed to calculate total TV ad expenditures and other performance metrics across industry categories. Leveraging a previously published stochastic agent-based model that was used to assess the cost-effectiveness of non-pharmaceutical interventions to control COVID-19, the number of cases that would have been prevented had these same dollars been used for preventive interventions was calculated using cost-effectiveness ratios (CERs), the cost divided by cases prevented. Results A total of 1,513 companies purchased TV airtime during the study period, totaling approximately 1.1 million airings, 215.5 billion impressions, and $2.7 billion in expenditures; most of the expenditures were spent by the restaurant (15.9%), electronics and communications (15.4%), and vehicle (13.7%) industries. The CERs for PPE and social distancing measures were $13,856 and $29,552, respectively; therefore, had all of these TV ad dollars instead been allocated to PPE or social distancing measures, approximately 194,908 and 91,386 cases of COVID-19 may have been prevented by the end of the study period, respectively. Figure 2. COVID-19 cases prevented had TV ad expenditures been reallocated for interventions. Conclusion Americans were inundated with COVID-19 related TV ads during the early months of the pandemic and companies are now showing some signs to relent. In times of disaster, however, it is paramount that the private sector go beyond showcasing their empathy and truly become socially responsible by allocating their funds to proven prevention and control measures. Disclosures All Authors: No reported disclosures

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A versatile web app for identifying the drivers of COVID-19 epidemics

Journal of Translational Medicine ◽

10.1186/s12967-021-02736-2 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wayne M. Getz ◽

Richard Salter ◽

Ludovica Luisa Vissat ◽

Nir Horvitz

Keyword(s):

Model Fitting ◽

Likelihood Estimation ◽

Stochastic Simulations ◽

Social Distancing ◽

Epidemic Curve ◽

Policies And Measures ◽

Incidence And Mortality ◽

Credible Intervals ◽

Web App ◽

Second Wave

Abstract Background No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. Methods Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. Results We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. Conclusion Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

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Cost effectiveness of DPYD genotyping to screen for dihydropyrimidine dehydrogenase (DPD) deficiency prior to adjuvant chemotherapy for colon cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.3_suppl.55 ◽

2021 ◽

Vol 39 (3_suppl) ◽

pp. 55-55

Author(s):

Gabriel A. Brooks ◽

Stephanie Tapp ◽

Allan T. Daly ◽

Jonathan Busam ◽

Anna N.A. Tosteson

Keyword(s):

Colon Cancer ◽

Cost Effectiveness ◽

Adjuvant Chemotherapy ◽

Transition Probabilities ◽

Dihydropyrimidine Dehydrogenase ◽

Sensitivity Analyses ◽

Dpyd Gene ◽

Cancer Screen ◽

The Cost ◽

The U.S

55 Background: Fluoropyrimidine chemotherapy agents, including 5-fluorouracil and capecitabine, are the backbone of adjuvant treatment for colon cancer, and adjuvant chemotherapy substantially reduces recurrence and mortality after surgical resection of stage 3 colon cancer. While fluoropyrimidine chemotherapy is generally safe, the risk of severe, potentially fatal chemotherapy toxicity is substantially increased for the 2-3% of U.S. patients with DPD deficiency caused by pathogenic variants in the DPYD gene. DPYD genotype testing is readily available in the U.S. but has not been widely adopted. We evaluated the cost effectiveness of DPYD genotyping prior to adjuvant chemotherapy for colon cancer in the U.S. Methods: We constructed a Markov model to simulate screening for DPD deficiency with DPYD genotyping (versus no screening) among patients receiving fluoropyrimidine-based adjuvant chemotherapy for stage 3 colon cancer. Screen-positive patients were modeled to receive dose-reduced fluoropyrimidine chemotherapy. Model transition probabilities for treatment-related toxicities were derived from published clinical trial data with annotation of DPYD genotype and chemotherapy dosing strategy. Our analysis is from the healthcare perspective, with a time horizon of five years and an annual discount rate of 3% for future costs and benefits. Direct healthcare costs and health utilities were estimated from published sources and converted to 2020 US dollars, and post-treatment survival was modeled from SEER data. The primary outcome was the incremental cost-effectiveness ratio (ICER), defined as dollars per quality-adjusted life year (QALY). We used a value of $100,000/QALY as the cost-effectiveness threshold. One-way sensitivity analyses were used to examine model uncertainty. Results: Compared with no screening, screening for DPD deficiency with DPYD genotyping increased per-patient costs by $106 and improved quality-adjusted survival by 0.0028 QALYs, leading to an ICER of $37,300/QALY. In one-way sensitivity analyses, the ICER exceeded $100,000/QALY when the carrier frequency of pathogenic DPYD gene variants was less than 1.17%, and when the specificity of DPYD genotyping was less than 98.9%. Cost-effectiveness estimates were not sensitive to the cost of DPYD genotyping, the cost of toxicity-related hospitalizations, or the health utility associated with grade 3-4 toxicity. Conclusions: Among patients receiving adjuvant chemotherapy for stage 3 colon cancer, screening for DPD deficiency with DPYD genotyping is a cost-effective strategy for preventing infrequent but severe, sometimes fatal toxicities of fluoropyrimidine chemotherapy.

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Infant Hospitalizations and Fatalities Averted by the Maternal Pertussis Vaccination Program in England, 2012–2017: Post-implementation Economic Evaluation

Clinical Infectious Diseases ◽

10.1093/cid/ciaa165 ◽

2020 ◽

Vol 71 (8) ◽

pp. 1984-1987 ◽

Cited By ~ 3

Author(s):

Frank Sandmann ◽

Mark Jit ◽

Nick Andrews ◽

Hannah L Buckley ◽

Helen Campbell ◽

...

Keyword(s):

Economic Evaluation ◽

Cost Effectiveness ◽

Vaccination Program ◽

Incidence And Mortality ◽

The Cost

Abstract In October 2012, a maternal pertussis vaccination program was implemented in England following an increased incidence and mortality in infants. We evaluated the cost-effectiveness of the program by comparing pertussis-related infant hospitalizations and deaths in 2012–2017 with nonvaccination scenarios. Despite considerable uncertainties, findings support the cost-effectiveness of the program.

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Cost-effectiveness of influenza control measures: a dynamic transmission model-based analysis

Epidemiology and Infection ◽

10.1017/s0950268813000423 ◽

2013 ◽

Vol 141 (12) ◽

pp. 2581-2594 ◽

Cited By ~ 7

Author(s):

S.-C. CHEN ◽

C.-M. LIAO

Keyword(s):

Cost Effectiveness ◽

Natural Ventilation ◽

Seasonal Influenza ◽

School Setting ◽

Control Measures ◽

Transmission Model ◽

Model Based ◽

Dynamic Transmission Model ◽

Influenza Transmission ◽

The Cost

SUMMARYWe investigated the cost-effectiveness of different influenza control strategies in a school setting in Taiwan. A susceptible-exposure-infected-recovery (SEIR) model was used to simulate influenza transmission and we used a basic reproduction number (R0)–asymptomatic proportion (θ) control scheme to develop a cost-effectiveness model. Based on our dynamic transmission model and economic evaluation, this study indicated that the optimal cost-effective strategy for all modelling scenarios was a combination of natural ventilation and respiratory masking. The estimated costs were US$10/year per person in winter for one kindergarten student. The cost for hand washing was estimated to be US$32/year per person, which was much lower than that of isolation (US$55/year per person) and vaccination (US$86/year per person) in containing seasonal influenza. Transmission model-based, cost-effectiveness analysis can be a useful tool for providing insight into the impacts of economic factors and health benefits on certain strategies for controlling seasonal influenza.

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Daily Almond Consumption in Cardiovascular Disease Prevention via LDL-C Change in the U.S. Population: a Cost-Effectiveness Analysis

10.21203/rs.2.11042/v2 ◽

2019 ◽

Author(s):

Jifan Wang(New Corresponding Author) ◽

Michelle A. Lee Bravatti ◽

Elizabeth J. Johnson ◽

Gowri Raman(Former Corresponding Author)

Keyword(s):

Cardiovascular Disease ◽

Cost Effectiveness ◽

Willingness To Pay ◽

Cost Effective ◽

Dietary Factors ◽

Sensitivity Analyses ◽

Healthcare Sector ◽

Cvd Prevention ◽

The Cost ◽

The U.S

Abstract Objectives Heart disease is the leading cause of death in the United States. The U.S. Food and Drug Administration approved the health claim that 1.5 ounces (42.5 grams) of nut intake may reduce the risk of cardiovascular disease (CVD). Previous studies have focused on the cost-effectiveness of other foods or dietary factors on primary CVD prevention, yet not in almond consumption. This study aimed to examine the cost-effectiveness of almond consumption in CVD primary prevention. Perspective & Setting This study assessed the cost-effectiveness of consuming 42.5 grams of almond from the U.S. healthcare sector perspective. Methods A decision model was developed for 42.5 grams of almond per day versus no almond consumption and CVD in the U.S. population. Parameters in the model were derived from the literature, which included the probabilities of increasing LDL-C, developing acute myocardial infarction (MI) and stroke, treating MI, dying from the disease and surgery, as well as the costs of the disease and procedures in the U.S. population, and the quality-adjusted life years (QALY). The cost of almonds was based on the current price in the U.S. market. Sensitivity analyses were conducted for different levels of willingness-to-pay, the probabilistic sensitivity analysis, ten-year risk prevention, different costs of procedures and almond prices, and patients with or without CVD. Results The almond strategy had $363 lower cost and 0.02 higher QALY gain compared to the non-almond strategy in the base-case model. The annual net monetary benefit (NMB) of almond consumption was $1,421 higher per person than no almond consumption, when the willingness to pay threshold was set at $50,000 for annual health care expenditure. Almond was more cost-effective than non-almond in CVD prevention in all the sensitivity analyses. Conclusion Consuming 42.5 grams of almonds per day is a cost-effective approach to prevent CVD in the short term and potentially in the long term.

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A Contact-Explicit Covid-19 Epidemic and Response Assessment Model

10.1101/2020.07.16.20155812 ◽

2020 ◽

Author(s):

Wayne M Getz ◽

Ludovica Luisa-Vissat ◽

Richard Salter

Keyword(s):

South Africa ◽

Latent Class ◽

Assessment Model ◽

Mortality Data ◽

Actual Number ◽

Social Distancing ◽

Pathogen Invasion ◽

Incidence And Mortality ◽

Negative Constant ◽

Web App

We formulate a refined SEIR epidemic model that explicitly includes a contact class C that either thwarts pathogen invasion and returns to the susceptible class S or progresses successively through a latent class L, a presymptomatic/asymptomatic class A, and a symptomatic class I. Individuals in both A and I may go directly to an immune class V, and in I to a dead class D. Upon this SCLAIV formulation we impose a set of drivers that can be used to develop policy to manage current Covid-19 and similar type disease outbreaks. These drivers include surveillance, social distancing (rate and efficacy), social relaxation, quarantining (linked to contact tracing), patient treatment/isolation and vaccination processes that can either be a non-negative constant or an s-shaped switching curve. The latter are defined in terms of onset and switching times, initial and final values, and abruptness of switching. We built a Covid-19 NMB-DASA web app to generate both deterministic and stochastic solutions to our SCLAIV and drivers model and use incidence and mortality data to provide both maximum-likelihood frequentist and Bayesian fitting of parameters. In the context of South African and English Covid-19 incidence data we demonstrate how to both identify and evaluate the role of drivers in ongoing outbreaks. In particular, we show that early social distancing in South Africa likely averted around 80,000 observed cases (actual number is double if only half the case are observed) during the months of June and July. We also demonstrated that incidence rates in South Africa will increase to between a conservative estimate of 15 and 30 thousand observed cases per day (again, actual number considerably higher) by the end of August if stronger social distancing measures are not effected during July and August, 2020. On different a note, we show that comparably good local optimal fits of the English data using surveillance, social distancing and social relaxation drivers can represent very different kinds of outbreaks---one with close to 90% and another with under 8% immune individuals. This latter result provides a cautionary tale of why fitting SEIR-like models to incidence or prevalence data can be extremely problematic when not anchored by other critical measures, such as levels of immunity in the population. Our presentation illustrates how our Covid-19 web app can be used by individuals without any programming skills to carry out forensic and scenario analyses in spatially contained populations such as small countries or metropolitan areas.

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Racial disparities in COVID-19 mortality are driven by unequal infection risks.

10.1101/2020.09.10.20192369 ◽

2020 ◽

Cited By ~ 3

Author(s):

Jon Zelner ◽

Rob Trangucci ◽

Ramya Naraharisetti ◽

Alex Cao ◽

Ryan Malosh ◽

...

Keyword(s):

Racial Disparities ◽

Data Science ◽

Age Groups ◽

Case Fatality ◽

The United States ◽

Mortality Rates ◽

Bayesian Regression ◽

Mortality Data ◽

Incidence And Mortality ◽

The U.S

Background. As of August 5, 2020, there were more than 4.8M confirmed and probable cases and 159K deaths attributable to SARS-CoV-2 in the United States, with these numbers undoubtedly reflecting a significant underestimate of the true toll. Geographic, racial-ethnic, age and socioeconomic disparities in exposure and mortality are key features of the first and second wave of the U.S. COVID-19 epidemic. Methods. We used individual-level COVID-19 incidence and mortality data from the U.S. state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. Findings. In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than Whites for all groups other than Native Americans. Of these, Blacks experienced the greatest burden of confirmed and probable COVID-19 infection (Age- standardized incidence = 1,644/100,000 population) and mortality (age-standardized mortality rate 251/100,000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.6 (95% CI = 5.5, 5.7) and 6.9 (6.5, 7.3) times higher than Whites, respectively. We also found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. Interpretation. This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as the U.S. state of Michigan, are driven primarily by variation in household, community and workplace exposure rather than case-fatality rates. Funding. This work was supported by a COVID-PODS grant from the Michigan Institute for Data Science (MIDAS) at the University of Michigan. The funding source had no role in the preparation of this manuscript.

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Estimating the incidence and mortality of cancer in Colombia: What are the best data for public policies?

Colombia Medica ◽

10.25100/cm.v47i2.2381 ◽

2016 ◽

pp. 71-73 ◽

Cited By ~ 1

Author(s):

Luis Eduardo Bravo

Keyword(s):

Information System ◽

National Cancer Institute ◽

Cancer Registries ◽

Population Based ◽

Control Measures ◽

Mortality Data ◽

Economic Costs ◽

Incidence And Mortality ◽

Colombian Government ◽

National Information

Colombia simultaneously faces the challenge of controlling both the communicable and non-communicable diseases. Information provided by the population-based cancer registries (PBCRs) of Colombia1-4 indicates that cancer is a major cause of morbidity in our region. Based on the information provided by the PBCRs of Colombia and taking into account mortality data from cancer, the Colombian National Cancer Institute (NCI) estimates that, in Colombia, there are about 63,000 new cases and 33,000 deaths by cancer each year5. The number of people living with the disease is unknown.The possibility of reducing cancer mortality and preventing 30% of malignancies has encouraged the Colombian government to design a Cancer National Information System (Cancer-NIS)6 and to formulate a ten-year plan for its control7. Having good quality statistics on the cancer burden is essential to make an accurate diagnostic of the cancer problem and to design, implement and monitor control measures. Otherwise, the human, social and economic costs produced by this group of diseases will continue to increase, and they will exceed the country’s capacity to fight them.

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