Katherine A. Poehling

Background/Objectives: The COVID-19 pandemic disproportionately impacted rural areas across the United States, including rural North Carolina (NC). Consistent with national patterns, COVID-19 vaccination coverage as of December 2022 was higher for non-rural (72%) than rural (58%) NC counties. The role of trusted sources of vaccine information used by rural and non-rural residents is unknown. Methods: Using data from two surveys distributed by the COVID-19 Community Research Partnership from 8 June 2021 through 21 December 2021, we compared self-reported sources of trusted COVID-19 vaccine information by non-rural and rural counties and by county-level predominant political vote in the 2020 Presidential election. Results: While NC respondents were highly vaccinated (94%), fewer residents from rural counties self-reported COVID-19 vaccination than those from non-rural counties (91% versus 95%). The most common reported source of trusted vaccine information was federal health agencies. The proportion citing a federal health agency was higher for respondents from non-rural (80%) than rural (72%) counties and was higher for vaccinated (75%) than unvaccinated (42%) rural respondents. The next two most trusted sources of vaccine information were state/local health officials (48%) and health care providers (42%). Among trusted resources reported by 10–15% of respondents, those from rural counties were less likely to use hospital websites, employers, or news sources than those from non-rural counties. More respondents from counties with >60% vote for the 2020 Democratic Presidential candidate cited federal health agencies, state and local officials, and new sources than respondents from counties with >60% vote for the 2020 Republican Presidential candidate. Conclusions: By identifying the trusted sources of vaccine information for residents in non-rural and rural NC counties, future vaccine implementation efforts can tailor communication efforts to increase vaccine uptake and potentially reduce the rates of hospitalizations and death from vaccine-preventable diseases such as COVID-19 or other future pandemics.

BACKGROUND: Intensive blood pressure (BP) control in youth with chronic kidney disease (CKD) slows progression, delaying the need for kidney replacement therapy (KRT). Most youth with CKD have hypertension and BP control is difficult to achieve outside of controlled experimental settings. Implementing effective BP control strategies in this population may be cost-saving despite requiring additional resources. Our objective was to determine the economic and clinical impact of intensive versus usual care for BP management in youth with CKD in a microeconomic model. METHODS: We developed a decision tree from the US payer perspective to estimate the total costs and clinical effect of an intensified BP intervention over 5 years, modeled after the ESCAPE trial (Effect of Strict Blood Pressure Control and Angiotensin-Converting Enzyme [ACE] Inhibition on Progression of Chronic Renal Failure in Pediatric Patients) protocol. We compared this intervention to usual care in a hypothetical population of youth with mild-to-moderate CKD. Probabilities were informed by published literature; cost estimates were informed by publicly available data. Our outcomes were the net discounted cost of an intensive BP intervention, number needed to treat with the intervention to prevent 1 KRT episode, and incremental cost per KRT episode avoided. RESULTS: An intensive BP intervention, with a goal of an average 24-hour mean arterial pressure <50th percentile, improved outcomes with net cost savings of $9440 per participant over 5 years compared with usual care. To prevent 1 episode of KRT over 5 years, 13 participants need to receive intensive BP intervention. CONCLUSIONS: Routine use of the ESCAPE protocol for intensive BP control in youth with CKD could save overall costs for the payer and improve clinical outcomes.

As the world transitions from COVID-19 pandemic to endemic, influenza continues to adapt and challenge our immune systems and our public health initiatives. Influenza remains an important respiratory illness among all populations, including pregnant persons and infants, who both have increased susceptibility to severe disease. Notably, infants younger than 6 months have the highest hospitalization rate of all children.¹ Maternal influenza vaccination protects expectant persons from illness and its complications as well as infants in the first 6 months of life through transplacental transfer of antibodies. The US Centers for Disease Control and Prevention and American College of Obstetricians and Gynecologists recommend inactivated influenza vaccination be administered during the influenza season to all pregnant persons.² While influenza vaccines administered during pregnancy have been shown to reduce cases of influenza for both pregnant persons and their infants, the impact on neonatal outcomes since the 2009 novel influenza A (H1N1) pandemic and with the use of quadrivalent influenza vaccines is not well understood.^3,4

In the 2015–2016 season, quadrivalent live attenuated influenza vaccine (LAIV) and both trivalent and quadrivalent inactivated influenza vaccine (IIV) were available in the United States. This study, conducted according to a test-negative case-control design, enrolled children aged 2–17 years presenting to outpatient settings with fever and respiratory symptoms for <5 days at 8 sites across the United States between 30 November 2015 and 15 April 2016. A nasal swab was obtained for reverse-transcriptase polymerase chain reaction (RT-PCR) testing for influenza, and influenza vaccination was verified in the medical record or vaccine registry. Influenza vaccine effectiveness (VE) was estimated using a logistic regression model. Of 1012 children retained for analysis, most children (59%) were unvaccinated, 10% received LAIV, and 31% received IIV. Influenza A (predominantly antigenically similar to the A/California/7/2009 strain) was detected in 14% and influenza B (predominantly a B/Victoria lineage) in 10%. For all influenza, VE was 46% (95% confidence interval [CI], 7%–69%) for LAIV and 65% (48%–76%) for IIV. VE against influenza A(H1N1)pdm09 was 50% (95% CI, −2% to 75%) for LAIV and 71% (51%–82%) for IIV. The odds ratio for vaccine failure with RT-PCR–confirmed A(H1N1)pdm09 was 1.71 (95% CI, 0.78–3.73) in LAIV versus IIV recipients. LAIV and IIV demonstrated effectiveness against any influenza among children aged 2–17 years in 2015–2016. When compared to all unvaccinated children, VE against influenza A(H1N1)pdm09 was significant for IIV but not LAIV. NCT01997450

Objective. This study was performed to determine predictors of clinical influenza diagnosis among patients with laboratory-confirmed influenza. Methods. Prospective, laboratory-confirmed surveillance for influenza was conducted among patients of all ages who were hospitalized or presented to the emergency department with fever and respiratory symptoms during 2009–2013. We evaluated all enrolled persons who had influenza confirmed by viral culture and/or polymerase chain reaction and received any discharge diagnosis. The primary outcome, clinical influenza diagnosis, was defined as (1) a discharge diagnosis of influenza, (2) a prescription of neuraminidase inhibitor, or (3) a rapid test positive for influenza virus. Bivariate analyses and multiple logistic regression modeling were performed. Results. Influenza was diagnosed for 29% of 504 enrolled patients with laboratory-confirmed influenza and for 56% of 236 patients with high-risk conditions. Overall, clinical influenza diagnosis was predicted by race/ethnicity, insurance status, year, being hospitalized, having high-risk conditions, and receiving no diagnosis of bacterial infection. Being diagnosed with a bacterial infection reduced the odds of receiving an influenza diagnosis by >3-fold for all patients and for patients with high-risk conditions. Conclusions. Many influenza virus–positive patients, including those with high-risk conditions, do not receive a clinical diagnosis of influenza. The pattern of clinical diagnoses among influenza virus–positive patients suggests preferential consideration of bacterial diseases as a diagnosis.

Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.