System factors to explain H1N1 state vaccination rates for adults in US emergency response to pandemic

Supply, then demand? Health expenditure, political leanings, cost obstacles to care, and vaccine hesitancy predict state-level COVID-19 vaccination rates

OBJECTIVES

To examine predictors of state-level COVID-19 vaccination rates during the first nine months of 2021.

METHODS

Using publicly available data, we employ a robust, iteratively re-weighted least squares multivariable regression with state characteristics as the independent variables and vaccinations per capita as the outcome. We run this regression for each day between February 1 and September 21, the last day before vaccine booster rollout.

RESULTS

We identify associations between vaccination rates and several state characteristics, including health expenditure, vaccine hesitancy, cost obstacles to care, Democratic voting, and elderly population share. We show that the determinants of vaccination rates have evolved: while supply-side factors were most clearly associated with early vaccination uptake, demand-side factors have become increasingly salient over time. We find that our results are generally robust to a range of alternative specifications.

CONCLUSIONS

Both supply and demand-side factors relate to vaccination coverage and the determinants of success have changed over time.

POLICY IMPLICATIONS

Investing in health capacity may improve early vaccine distribution and administration, while overcoming vaccine hesitancy and cost obstacles to care may be crucial for later immunisation campaign stages.

COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program.

Ensuring Equitable Access To COVID-19 Vaccines In The US: Current System Challenges And Opportunities

There has been a worldwide effort to accelerate the development of safe and effective vaccines for severe acute respiratory syndrome coronavirus-2. When vaccines become licensed and available broadly to the public, the final hurdle is equitable distribution and access for all who are recommended for vaccination. Frameworks and existing systems for allocation, distribution, vaccination, and monitoring for safety and effectiveness are assets of the current immunization delivery system that should be leveraged to ensure the equitable distribution and broad uptake of licensed vaccines. The system should be strengthened to address gaps in access to immunization services and to modernize the public health infrastructure. We offer five recommendations as guideposts to ensure that policies and practices at the federal, state, local, and tribal levels support equity, transparency, accountability, availability, and access to coronavirus disease 2019 vaccines.

Adherence to Emergency Public Health Measures for Bioevents: Review of US Studies

The frequency of bioevents is increasing worldwide. In the United States, as elsewhere, control of contagion may require the cooperation of community members with emergency public health measures. The US general public is largely unfamiliar with these measures, and our understanding of factors that influence behaviors in this context is limited. The few previous reviews of research on this topic focused on non-US samples. For this review, we examined published research on the psychosocial influences of adherence in US sample populations. Of 153 articles identified, only 9 met the inclusion criteria. Adherence behaviors were categorized into 2 groups: self-protective behaviors (personal hygiene, social distancing, face mask use, seeking out health care advice, and vaccination) and protecting others (isolation, temperature screening, and quarantine). A lack of uniformity across studies regarding definitions and measures was noted. Only 5 of the 9 articles reported tests of association between adherence with emergency measures and psychosocial factors; perceived risk and perceived seriousness were found to be significantly associated with adherence or adherence intentions. Although it is well documented that psychosocial factors are important predictors of protective health behaviors in general, this has not been rigorously studied in the context of bioevents. (Disaster Med Public Health Preparedness. 2018;12:528-535).

Contextual generalized trust and immunization against the 2009 A(H1N1) pandemic in the American states: A multilevel approach

The aim of the study was to investigate the association between contextual generalized trust and individual-level 2009 A(H1N1) pandemic immunization acceptance. A second aim was to investigate whether knowledge about the A(H1N1) pandemic mediated the association between contextual generalized trust and A(H1N1) immunization acceptance. Data from the National 2009 H1N1 Flu Survey was used. To capture contextual generalized trust, data comes from an aggregation of surveys measuring generalized trust in the American states. To investigate the association between contextual generalized trust and immunization acceptance, while taking potential individual-level confounders into account, multilevel logistic regression was used. The investigation showed contextual generalized trust to be significantly associated with immunization acceptance. However, controlling for knowledge about the A(H1N1) pandemic did not substantially affect the association between contextual generalized trust and immunization acceptance. In conclusion, contextual state-level generalized trust was associated with A(H1N1) immunization, but knowledge about A(H1N1) was not mediating this association.

Pandemic Influenza

This chapter will describe pandemic threats and the steps necessary to reduce morbidity and mortality. An explanation is provided on genetic mutations leading to the emergence of novel influenza strains. Medical countermeasures include vaccination and antiviral medications. Public messaging is also important to promote flu shots, reinforce basic hygiene measures, and instruct those who may be ill to limit contacts. The World Health Organization (WHO) tracks seasonal influenza for anomalies indicating an emerging pandemic influenza strain. WHO updated the international pandemic alert system after the 2009–2010 H1N1 pandemic. With each declaration, temporary recommendations are provided, and added regulatory actions are taken by WHO member states. In addition, the Centers for Disease Control and Prevention (CDC) updated the U.S. framework for pandemic influenza preparedness and response. Each pandemic interval is accompanied by specific recommendations for state and local public health authorities. Effective influenza control includes both pharmaceutical and nonpharmaceutical recommendations. Other severe viral pandemic respiratory threats, including the “Middle East Respiratory Syndrome Coronavirus” (MERS-CoV), require similar resources and infrastructure to manage.

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Differentiate between seasonal and pandemic influenza.

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Identify proteins that change during genetic mutation of influenza viruses.

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Describe pharmaceutical options available to prevent influenza or reduce severity of disease.

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List the World Health Organization (WHO) phases of pandemic response.

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Explain the difference between the WHO pandemic phases and the US pandemic intervals.

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Describe how an outbreak peak curve can be compressed and what benefits are derived from compressing it.

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List what individuals can do to reduce the risk of influenza.

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Define nonpharmaceutical interventions and give examples.

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Summarize the types of activities local public health agencies should consider for each of the US pandemic intervals.

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Describe the limitations of a government response to a pandemic.

System factors to explain 2009 pandemic H1N1 state vaccination rates for children and high-risk adults in US emergency response to pandemic

INTRODUCTION

During the 2009-2010 H1N1 pandemic, children and high-risk adults had priority for vaccination. Vaccine in short supply was allocated to states pro-rata by population, but vaccination rates as of January 2010 varied among states from 21.3% to 84.7% for children and 10.4% to 47.2% for high-risk adults. States had different campaign processes and decisions.

OBJECTIVE

To determine program and system factors associated with higher state pandemic vaccination coverage for children and high-risk adults during an emergency response with short supply of vaccine.

METHODS

Regression analysis of factors predicting state-specific H1N1 vaccination coverage in children and high-risk adults, including state campaign information, demographics, preventive or health-seeking behavior, preparedness funding, providers, state characteristics, and surveillance data.

RESULTS

Our modeling explained variation in state-specific vaccination coverage with an adjusted R-squared of 0.82 for children and 0.78 for high-risk adults. We found that coverage of children was positively associated with programs focusing on school clinics and with a larger proportion of doses administered in public sites; negatively with the proportion of children in the population, and the proportion not visiting a doctor because of cost. The coverage for high-risk adults was positively associated with shipments of vaccine to "general access" locations, including pharmacy and retail, with the percentage of women with a Pap smear within the past 3 years and with past seasonal influenza vaccination. It was negatively associated with the expansion of vaccination to the general public by December 4, 2009. For children and high-risk adults, coverage was positively associated with the maximum number of ship-to-sites and negatively associated with the proportion of medically underserved population.

CONCLUSION

Findings suggest that distribution and system decisions such as vaccination venues and providers targeted can positively impact vaccination rates for children and high-risk adults. Additionally, existing health infrastructure, health-seeking behaviors, and access affected coverage.

FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations

BACKGROUND

Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels.

RESULTS

FRED (a Framework for Reconstructing Epidemic Dynamics) is a freely available open-source agent-based modeling system based closely on models used in previously published studies of pandemic influenza. This version of FRED uses open-access census-based synthetic populations that capture the demographic and geographic heterogeneities of the population, including realistic household, school, and workplace social networks. FRED epidemic models are currently available for every state and county in the United States, and for selected international locations.

CONCLUSIONS

State and county public health planners can use FRED to explore the effects of possible influenza epidemics in specific geographic regions of interest and to help evaluate the effect of interventions such as vaccination programs and school closure policies. FRED is available under a free open source license in order to contribute to the development of better modeling tools and to encourage open discussion of modeling tools being used to evaluate public health policies. We also welcome participation by other researchers in the further development of FRED.