Examining the spatiotemporal evolution of vaccine refusal: nonmedical exemptions from vaccination in California, 2000–2013

Abstract Background Vaccine hesitancy continues to be an issue throughout the United States, as numerous vaccine hesitant parents are choosing to exempt their children from school-entry vaccination requirements for nonmedical reasons, despite the safety and effectiveness of vaccines. We conduct an analysis of how vaccine refusal, measured by the use of nonmedical exemptions (based on personal or religious beliefs) from vaccination (NMEs), evolved across space and over time in California. Methods Using school-entry data from the California Department of Public Health, we examined NMEs for students entering kindergarten in California from 2000 to 2013. We conduct global and local spatial autocorrelation analysis to determine whether NME use became more geographically clustered over the study period and whether the location of local clusters of high use were temporally stable. We conducted a grouping analysis that identified the general temporal trends in NME use over the time period. Results The use of NMEs increased from 0.73% of all kindergarteners in 2000 to 3.09% in 2013 and became more geographically clustered over the study period. Local geographic clusters of high use were relatively isolated early in the study period, but expanded in size over time. The grouping analysis showed that regions with high NME use early in the study period were generally few (15% of all US Census tracts) and relatively isolated. Regions that had low initial NME use and moderate to large increases over the study period were located in close proximity to the initial high use regions. The grouping analysis also showed that roughly half of all tracts had 0% or very low NME use throughout the study period. Conclusions We found an observable spatial structure to vaccine refusal and NME use over time, which appeared to be a self-reinforcing process, as well as a spatially diffusive process. Importantly, we found evidence that use of NMEs in the initially isolated regions appeared to stimulate vaccine refusal in geographically proximal regions. Thus, our results suggest that efforts aimed at decreasing future NME use may be most effective if they target regions where NME use is already high, as well as the nearby regions

Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality

IMPORTANCE Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and COVID-19 morbidity and mortality. The relative importance of vaccination strategies and nonpharmaceutical interventions (NPIs) is not well understood. OBJECTIVE To assess the association of simulated COVID-19 vaccine efficacy and coverage scenarios with and without NPIs with infections, hospitalizations, and deaths. DESIGN, SETTING, AND PARTICIPANTS An established agent-based decision analytical model was used to simulate COVID-19 transmission and progression from March 24, 2020, to September 23, 2021. The model simulated COVID-19 spread in North Carolina, a US state of 10.5 million people. A network of 1 017 720 agents was constructed from US Census data to represent the statewide population. EXPOSURES Scenarios of vaccine efficacy (50% and 90%), vaccine coverage (25%, 50%, and 75% at the end of a 6-month distribution period), and NPIs (reduced mobility, school closings, and use of face masks) maintained and removed during vaccine distribution. MAIN OUTCOMES AND MEASURES Risks of infection from the start of vaccine distribution and risk differences comparing scenarios. Outcome means and SDs were calculated across replications. RESULTS In the worst-case vaccination scenario (50% efficacy, 25%coverage), a mean (SD) of 2 231 134 (117 867) new infections occurred after vaccination began with NPIs removed, and a mean (SD) of 799 949 (60 279) new infections occurred with NPIs maintained during 11 months. In contrast, in the best-case scenario (90% efficacy, 75%coverage), a mean (SD) of 527 409 (40 637) new infections occurred with NPIs removed and a mean (SD) of 450 575 (32 716) new infections occurred with NPIs maintained. With NPIs removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared with theworst-case scenario (mean [SD] absolute risk reduction, 13%[1%] and 8%[1%], respectively). CONCLUSIONS AND RELEVANCE Simulation outcomes suggest that removing NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared with more efficacious vaccines at lower coverage. These findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many prepandemic activities can be resumed

Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study

Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers.HighlightsOrganizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19.Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies.Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level

Can vaccine prioritization reduce disparities in COVID-19 burden for historically marginalized populations?

SARS-CoV-2 vaccination strategies were designed to reduce COVID-19 mortality, morbidity, and health inequities. To assess the impact of vaccination strategies on disparities in COVID-19 burden among historically marginalized populations (HMPs), e.g. Black race and Hispanic ethnicity, we used an agent-based simulation model, populated with census-tract data from North Carolina. We projected COVID-19 deaths, hospitalizations, and cases from 2020 July 1 to 2021 December 31, and estimated racial/ethnic disparities in COVID-19 outcomes. We modeled 2-stage vaccination prioritization scenarios applied to sub-groups including essential workers, older adults (65+), adults with high-risk health conditions, HMPs, or people in low-income tracts. Additionally, we estimated the effects of maximal uptake (100% for HMP vs. 100% for everyone), and distribution to only susceptible people. We found strategies prioritizing essential workers, then older adults led to the largest mortality and case reductions compared to no prioritization. Under baseline uptake scenarios, the age-adjusted mortality for HMPs was higher (e.g. 33.3%-34.1% higher for the Black population and 13.3%-17.0% for the Hispanic population) compared to the White population. The burden on HMPs decreased only when uptake was increased to 100% in HMPs; however, the Black population still had the highest relative mortality rate even when targeted distribution strategies were employed. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve. The vaccination strategies publicly outlined were insufficient, exacerbating disparities between racial and ethnic groups. Strategies targeted to increase vaccine uptake among HMPs are needed to ensure equitable distribution and minimize disparities in outcomes

Early Markers of Glycaemic Control in Children with Type 1 Diabetes Mellitus

Background: Type 1 diabetes mellitus (T1DM) may lead to severe long-term health consequences. In a longitudinal study, we aimed to identify factors present at diagnosis and 6 months later that were associated with glycosylated haemoglobin (HbA 1c) levels at 24 months after T1DM diagnosis, so that diabetic children at risk of poor glycaemic control may be identified. Methods: 229 children,15 years of age diagnosed with T1DM in the Auckland region were studied. Data collected at diagnosis were: age, sex, weight, height, ethnicity, family living arrangement, socio-economic status (SES), T1DM antibody titre, venous pH and bicarbonate. At 6 and 24 months after diagnosis we collected data on weight, height, HbA 1c level, and insulin dose. Results: Factors at diagnosis that were associated with higher HbA1c levels at 6 months: female sex (p,0.05), lower SES (p,0.01), non-European ethnicity (p,0.01) and younger age (p,0.05). At 24 months, higher HbA1c was associated with lower SES (p,0.001), Pacific Island ethnicity (p,0.001), not living with both biological parents (p,0.05), and greater BMI SDS (p,0.05). A regression equation to predict HbA1c at 24 months was consequently developed. Conclusions: Deterioration in glycaemic control shortly after diagnosis in diabetic children is particularly marked in Pacific Island children and in those not living with both biological parents. Clinicians need to be aware of factors associated wit

Kinase and phosphatase engagement is dissociated between memory formation and extinction

Associative long-term memories (LTMs) support long-lasting behavioural changes resulting from sensory experiences. Retrieval of a stable LTM by means of a large number of conditioned stimulus (CS) alone presentations produces inhibition of the original memory through extinction. Currently, there are two opposing hypotheses to account for the neural mechanisms supporting extinction. The unlearning hypothesis posits that extinction affects the original memory trace by reverting the synaptic changes supporting LTM. On the contrary, the new learning hypothesis proposes that extinction is simply the formation of a new associative memory that inhibits the expression of the original one. We propose that detailed analysis of extinction-associated molecular mechanisms could help distinguish between these hypotheses. Here we will review experimental evidence regarding the role of protein kinases and phosphatases on LTM formation and extinction. Even though kinases and phosphatases regulate both memory processes, their participation appears to be dissociated. LTM formation recruits kinases, but is constrained by phosphatases. Memory extinction presents a more diverse molecular landscape, requiring phosphatases and some kinases, but also being constrained by kinase activity. Based on the available evidence, we propose a new theoretical model for memory extinction: a neuronal segregation of kinases and phosphatases supports a combination of time-dependent reversible inhibition of the original memory (CS-US), with establishment of a new associative memory trace (CS-noUS)

Lawson criterion for ignition exceeded in an inertial fusion experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion

Legislative and administrative actions to increase vaccination coverage in Washington schools

Current outbreaks of vaccine-preventable diseases in the U.S. highlight the consequences of declining levels of vaccination coverage. Attempts to increase coverage by banning or restricting nonmedical exemptions from school-entry vaccination requirements disregard children not up to date on vaccination who already attend school and those who are not up to date for reasons other than vaccine hesitancy. We analyze the potential effects of legislative and administrative options to increase vaccination coverage in Washington schools. We constructed a grade-specific model of the detailed vaccination status for all required vaccines and the MMR vaccine specifically for all children in the state’s school system. We used scenario modeling to evaluate the effects of potential legislative and administrative actions on the percent of students up to date on all required vaccines and the MMR vaccine from 2018 to 2030. Our analysis shows that eliminating nonmedical exemptions may not be the optimal solution for reducing disease outbreak risk. Instead, focusing on children not up to date for reasons other than nonmedical exemption could have a larger impact and does not carry the controversy that accompanies attempts to ban or restrict nonmedical exemptions. Further, implementing a one-time catch-up period for all children not up to date would increase coverage promptly. Evidence-based policymaking is an essential component of efforts to reduce the risk of disease outbreaks in U.S. schools, and analysis of potential legislative and administrative actions complement these efforts