Trivalent Influenza Vaccine

Information on individual-level health behaviors came from the 2015 Behavioral Risk Factor Surveillance Survey (BRFSS) SMART dataset. The 2015 BRFSS SMART dataset is a sub-sample of the 2015 state BRFSS surveys based on geographies defined as metropolitan statistical areas, micropolitan statistical areas, and metropolitan divisions (collectively called MMSAs) made publicly available to researchers. The 2015 BRFSS Smart dataset included 132 MMSAs where at least 500 BRFSS surveys were collected [18 ].
Individual health behaviors selected for this study were those identified by the Centers for Disease Control and Prevention (CDC) as “Winnable Battles”. Winnable Battles are health outcomes for which the CDC believes that public health can make significant progress in a relatively short time frame (i.e., within one to 4 years), have a large-scale public impact, and have evidenced-based interventions readily available for ease of implementation. From the “Winnable Battles” list, we included the following modifiable behaviors/conditions into our analysis: smoking, wearing a seatbelt, binge drinking, eating vegetables daily, eating fruit daily, general exercise in a month, vigorous exercise (300 min) in a week and being overweight or obese (based on self-reported height and weight). Additionally, while not designated by the CDC as a “Winnable Battle”, flu vaccinations are also an important measure of community health where LHDs and nonprofit hospitals may collaborate and was thus included in our analysis. While we did not have access to specific collaborative action strategies, many of the included measures are commonly identified in community health needs assessments (CHNAs) as being health needs in the community (healthy eating, physical activity, smoking, etc.) [19 , 20 ].
In addition to studying the impact of LHD-hospital collaboration on specific individual health behaviors, we also conducted analyses using two index measures of the health behaviors. We created one index for risky behaviors and another for healthy lifestyle behaviors (healthy eating and exercise).
The risky behaviors index included wearing a seatbelt, not smoking, not binge drinking and getting a flu shot. For each individual respondent, we assigned a score for this index based on the number of specific behaviors that the individual reported undertaking (or in the case of smoking and binge drinking reported not undertaking). Thus, if an individual did not report undertaking any of these behaviors, we assigned a score of zero. If an individual reported all of the behaviors, we assigned a score of four. To create the healthy lifestyle index, we combined the specific behaviors of eating vegetable(s) daily, eating fruit daily and vigorously exercising. We found a high correlation between general exercise and vigorous exercise and felt the latter was more representative of a healthy lifestyle, so we included that variable only. For this index, we also assigned a score to each individual respondent based on the number of behaviors that reportedly were undertaken. Thus, the healthy lifestyle index ranged from zero (if an individual did not report any of the variables) to three (if an individual reported eating fruit, vegetables and vigorously exercising). Index variables were analyzed as continuous outcomes.

Our primary measure of interest was participants’ stated intention to get the COVID-19 vaccine when it becomes available. We were also interested in their perceptions of COVID-19 threat, general vaccination attitudes, and intention to get the flu shot. For all measures, except flu shot intentions, we combined multiple items to create a composite measure (see S2 Table for specific questions and construct compositions). Questions designed to measure general vaccination attitudes were adapted from prior work [33 (link)].
Additional measures of interest were participants’ trust in broad institutions (media, local government, and federal government). These trust measures followed different trends from each other, and therefore were not combined. At the end of the survey, participants responded to demographic questions. We retained all questions used in wave 1 throughout all six waves (our survey included additional items not reported in this paper; see S2 and S3 Tables for a complete list of measured items).

All study participants gave informed consent, and protocols were approved by the Stanford Institutional Review Board. Five healthy humans aged 18–28 were vaccinated with the 2011–2012 seasonal trivalent inactivated influenza vaccine and gave blood 3 and 5 days before vaccination (D3 and D5), immediately before vaccination (D0), and 1, 4, 7, 9, and 11 days afterward (D1, D4, D7, D9, D11). Peripheral blood mononuclear cells were isolated, total RNA was extracted, and sequencing libraries were prepared from 500 ng of total RNA using isotype-specific IGH constant region primers for reverse transcription and IGH variable region primers for second-strand cDNA synthesis followed by PCR, following Vollmers et al. (17 (link)) and Horns et al. (18). Sequencing was performed for all libraries using the Illumina HiSeq 2500 or MiSeq platform with paired-end reads. Sequences were preprocessed using a custom informatics pipeline to perform consensus unique molecular identifier (UMI)-based error correction, annotation of V and J gene use and CDR3 length using IgBLAST (42 (link)), and isotype determination using BLASTN. Clonal lineages were identified by grouping sequences sharing the same V and J germline genes and CDR3 length, and then performing single-linkage clustering with a cutoff of 90% nucleotide identity across both the CDR3 and the rest of the variable region (18 (link)). SFSs were constructed based on somatic mutations relative to the germline V and J genes (excluding CDR3 polymorphisms because the ancestral state may not be known with high confidence in the CDR3) and then compared with simulations of evolutionary models using betatree (43 (link)) or custom software. Multiple sequence alignment was performed using a custom fast heuristic algorithm based on MUSCLE (44 (link)), and phylogenetic reconstruction was performed using FastTree (45 (link)). Selection on subclones was detected using a custom algorithm that performs greedy breadth-first search based on Fay and Wu’s H statistic (27 (link)) of subtrees. Fitness inference based on the local branching rate of a phylogeny was performed following Neher et al. (34 ).

Antibodies CH65, CH66, and CH67 were isolated from plasmablasts from donor TIV01 (Moody et al., 2011 (link)) after receiving the trivalent influenza vaccine in the 2007–2008 influenza season, which contained the A/Solomon Islands/3/2006 (SI06) H1N1 strain. The donor TIV01 was born in ~1990 and subsequent work identified that the inferred UCA of this lineage bound to the strain A/Massachusetts/1/1990 (MA90) circulating near the donor’s birth date and is suspected to be highly similar to the original immunogenic stimulus of this lineage (Schmidt et al., 2015a (link)). However, the UCA did not bind SI06, which escaped the UCA and I-2 of this lineage (Schmidt et al., 2015a (link)). To assess whether affinity maturation in this lineage is capable of accommodating for an escape mutation that abrogates binding to less mature variants, we drove viral escape from MA90 in vitro (see above) using the UCA and identified that matured variants of this lineage (e.g., CH65 and CH67) bound the escape variant (MA90-G189E) with high affinity. To understand how this antibody lineage evolved to compensate for viral escape mutations, we included MA90-G189E and SI06 in addition to MA90.

We used the adult Vaccine Hesitancy Scale (aVHS) to assess vaccine hesitancy [38 (link)]. Briefly, this is a 10-item scale, and the sum of each question’s responses is dichotomized at 25 to partition individuals into being hesitant or not. The face validity, concurrent validity, and reliability of this question are reported elsewhere [38 (link)].
For COVID-19 vaccination, participants were asked at the start of the questionnaire: “Have you personally received at least one dose of a COVID-19 vaccine or not?” Those who answered “No” are categorized as having not received the COVID-19 vaccine. Vaccine questions were based on survey items from the Kaiser Family Foundation COVID-19 Vaccine Monitor.
The non-receipt of the flu vaccine was obtained by asking participants: “Have you received a flu shot for the current flu season that began October 2021 or not?” Those who answered “No” are categorized as non-receipt of the flu vaccine.