skip to main content

This content will become publicly available on May 1, 2023

Title: Evaluating Risk-Stratified HPV Catch-up Vaccination Strategies: Should We Go beyond Age 26?
Background Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. HPV can cause genital warts and multiple types of cancers in females. HPV vaccination is recommended to youth age 11 or 12 years before sexual initiation to prevent onset of HPV-related diseases. For females who have not been vaccinated previously, catch-up vaccines are recommended through age 26. The extent to which catch-up vaccines are beneficial in terms of disease prevention and cost-effectiveness is questionable given that some women may have been exposed to HPV before receiving the catch-up vaccination. This study aims to examine whether the cutoff age of catch-up vaccination should be determined based on an individual woman’s risk characteristic instead of a one-size-fits-all age 26. Methods We developed a microsimulation model to evaluate multiple clinical outcomes of HPV vaccination for different women based on a number of personal attributes. We modeled the impact of HPV vaccination at different ages on every woman and tracked her course of life to estimate the clinical outcomes that resulted from receiving vaccines. As the simulation model is risk stratified, we used extreme gradient boosting to build an HPV risk model estimating every woman’s dynamic HPV risk over more » time for the lifetime simulation model. Results Our study shows that catch-up vaccines still benefit all women after age 26 from the perspective of clinical outcomes. Women facing high risk of HPV infection are expected to gain more health benefits compared with women with low HPV risk. Conclusions From a cancer prevention perspective, this study suggests that the catch-up vaccine after age 26 should be deliberately considered. « less
; ;
Award ID(s):
Publication Date:
Journal Name:
Medical Decision Making
Page Range or eLocation-ID:
524 to 537
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model basedmore »on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75 % ) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28 % ) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.« less
  2. The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations.more »Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.« less
  3. Background The novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation. Objective The aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainablemore »laboratory data. Methods We performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient’s hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients. Results Of the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients’ length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001). Conclusions Machine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.« less
  4. Read, Andrew Fraser (Ed.)
    Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmissionmore »to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8–29.7) infections, 0.69 (95% CrI: 0.52–0.97) hospitalizations, and 0.34 (95% CrI: 0.25–0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37–0.89) hospitalizations and 0.32 (95% CrI: 0.23–0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses.« less
  5. Controlling the spread of SARS-CoV-2 will require high vaccination coverage, but acceptance of the vaccine could be impacted by perceptions of vaccine safety and effectiveness. The aim of this study was to characterize how vaccine safety and effectiveness impact acceptance of a vaccine, and whether this impact varied over time or across socioeconomic and demographic groups. Repeated cross-sectional surveys of an opt-in internet sample were conducted in 2020 in the US, mainland China, Taiwan, Malaysia, Indonesia, and India. Individuals were randomized into receiving information about a hypothetical COVID-19 vaccine with different safety and effectiveness profiles (risk of fever 5% vs.more »20% and vaccine effectiveness 50% vs. 95%). We examined the effect of the vaccine profile on vaccine acceptance in a logistic regression model, and included interaction terms between vaccine profile and socioeconomic/demographic variables to examine the differences in sensitivity to the vaccine profile. In total, 12,915 participants were enrolled in the six-country study, including the US (4054), China (2797), Taiwan (1278), Malaysia (1497), Indonesia (1527), and India (1762). Across time and countries, respondents had stronger preferences for a safer and more effective vaccine. For example, in the US in November 2020, acceptance was 3.10 times higher for a 95% effective vaccine with a 5% risk of fever, vs a vaccine 50% effective, with a 20% risk of fever (95% CI: 2.07, 4.63). Across all countries, there was an increase in the effect of the vaccine profile over time (p < 0.0001), with stronger preferences for a more effective and safer vaccine in November 2020 compared to August 2020. Sensitivity to the vaccine profile was also stronger in August compared to November 2020, in younger age groups, among those with lower income; and in those that are vaccine hesitant. Uptake of COVID-19 vaccines could vary in a country based upon effectiveness and availability. Effective communication tools will need to be developed for certain sensitive groups, including young adults, those with lower income, and those more vaccine hesitant.« less