COVID-19 Vaccine Effectiveness by Product and Timing in New York State

Background: US population-based data on COVID-19 vaccine effectiveness (VE) for the 3 currently FDA- authorized products is limited. Whether declines in VE are due to waning immunity, the Delta variant, or other causes, is debated. Methods: We conducted a prospective study of 8,834,604 New York adults, comparing vaccine cohorts defined by product, age, and month of full-vaccination to age-specific unvaccinated cohorts, by linking statewide testing, hospital, and vaccine registry databases. VE was estimated from May 1, 2021 for incident laboratory-confirmed COVID-19 cases (weekly life-table hazard rates through September 3) and hospitalizations (monthly incidence rates through August 31). Results: 155,092 COVID-19 cases and 14,862 hospitalizations occurred. Estimated VE for cases declined contemporaneously across age, products, and time-cohorts, from high levels beginning May 1 (1.8% Delta variant prevalence), to a nadir around July 10 (85.3% Delta), with limited changes thereafter (>95% Delta). Decreases were greatest for Pfizer-BioNTech (-24.6%, -19.1%, -14.1% for 18-49, 50-64 years, and ≥65 years, respectively), and similar for Moderna (-18.0%, -11.6%, -9.0%, respectively) and Janssen (-19.2%, -10.8, -10.9%, respectively). VE for hospitalization for adults 18-64 years was >86% across cohorts, without time trend. Among persons ≥65 years, VE declined from May to August for Pfizer-BioNTech (95.0% to 89.2%) and Moderna (97.2% to 94.1%). VE was lower for Janssen, without trend, ranging 85.5%-82.8%. Conclusions: Declines in VE for cases may have been primarily driven by factors other than waning. VE for hospitalizations remained high, with modest declines limited to Pfizer-BioNTech and Moderna recipients ≥65 years, supporting targeted booster dosing recommendations.

Association of Health Status and Nicotine Consumption with SARS-CoV-2 positivity rates

Background Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. Methods Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. Results Compared to people with self-identified “excellent” or very good health status, those reporting “good” or “fair” health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. Conclusions In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Association of Health Status and Smoking Behaviors with SARS-CoV-2 Positivity Rates

Background Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or tobacco use is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. Methods Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n= 8,214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in April or June of 2020. Before testing, participants provided demographic information and self-reported health status and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco with SARS-CoV-2 positivity on either PCR or antibody tests.Results Compared to people with self-identified “excellent” or very good health status, those reporting “good” or “fair” health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. Conclusions In a statewide, community-based population drawn for seroprevalence studies, we find that overall health status is associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

The Sensitivity of Teacher Value-Added Scores to the Use of Fall or Spring Test Scores

Papay (2011) noticed that teacher value-added measures (VAMs) from a statistical model using the most common pre/post testing timeframe–current-year spring relative to previous spring (SS)–are essentially unrelated to those same teachers’ VAMs when instead using next-fall relative to current-fall (FF). This is concerning since this choice–made solely as an artifact of the timing of statewide testing–produces an entirely different ranking of teachers’ effectiveness. Since subsequent studies (grades K/1) have not replicated these findings, we revisit and extend Papay’s analyses in another Grade 3–8 setting. We find similarly low correlations (.13–.15) that persist across value-added specifications. We delineate and apply a literature-based framework for considering the role of summer learning loss in producing these low correlations.

Navigating the Evidence-Based Practice Maze

Factors such as the standards-based education movement, mandated participation in statewide testing, and inclusion have forced an increased focus on improving outcomes for students with disabilities. There are many determinants of postschool success for students with disabilities; however, teachers primarily have control over only one, teaching practices and programs. As a result, it is important that teachers choose and implement practices that have proven successful for secondary students with disabilities. This article guides teachers through the process of navigating the evidence-based practice maze to identify evidence-based practices and programs for secondary students with disabilities. Particularly, it addresses the need to (a) follow a research-based framework (i.e., Kohler’s Taxonomy), (b) use practices with the best available research evidence to support effectiveness, and (c) use data-based decision making to guide use of evidence-based practices.