Background. This study presents a new extraction fraction (EF) model based on physiological measures of invasive coronary flow reserve (CFR) and fractional flow reserve (FFR) in patients with suspected coronary artery disease (CAD) and normal index microcirculatory resistance (IMR). To ascertain the clinical relevance of the new EFs, flow measurements using the newly patient-determined EFs were compared to flow measurements using traditional animal-determined EFs. Methods. 39 patients were retrospectively selected that included a total of 91 vascular territories with invasive coronary angiography physiological measures. [N-13]-ammonia dynamic rest/adenosine-stress PET imaging was conducted in all patients and absolute myocardial flow was estimated using four published compartmental models. The extraction fraction during hyperemic flow was iteratively estimated by maximizing the agreement between invasive CFR and FFR with the non-invasive analogs myocardial flow reserve (MFR) and relative flow reserve (RFR) at similar physiological states, respectively. Results. Using the new patient-determined EFs, agreement between CFR vs MFR for Model 1 and 2 was moderate and poor for Model 3 and 4. All models showed moderate agreement for FFR vs RFR. When using published models of animal-determined EFs, agreement between CFR vs MFR remained moderate for Model 1 and 2, and poor for Model 3 and 4. Similarly, all models showed moderate agreement for FFR vs RFR using animal-determined EF values. None of the observed differences were statistically significant. Conclusions. Flow measurements using extraction fraction correction for [N-13]-ammonia based on calibration to invasive intracoronary angiography physiological measures in patients with CAD were not discordant from those reported in the literature. Either patient-determined or traditional animal-determined EF correction, when used with the appropriate flow model, yields moderate agreement with invasive measurements of coronary flow reserve and fractional flow reserve. (J Nucl Cardiol 2021)

Background: Using mathematical deterministic models of the epidemiology of hospital-acquired infections and antibiotic resistance, it has been shown that the rates of hospital-acquired bacterial infection and frequency of antibiotic infections can be reduced by (i) restricting the admission of patients colonized with resistant bacteria, (ii) increasing the rate of turnover of patients, (iii) reducing transmission by infection control measures, and (iv) the use of second-line drugs for which there is no resistance. In an effort to explore the generality and robustness of the predictions of these deterministic models to the real world of hospitals, where there is variation in all of the factors contributing to the incidence of infection, we developed and used a stochastic model of the epidemiology of hospital-acquired infections and resistance. In our analysis of the properties of this model we give particular consideration different regimes of using second-line drugs in this process. Methods: We developed a simple model that describes the transmission of drug-sensitive and drug-resistant bacteria in a small hospital. Colonized patients may be treated with a standard drug, for which there is some resistance, and with a second-line drug, for which there is no resistance. We then ran deterministic and stochastic simulation programs, based on this model, to predict the effectiveness of various treatment strategies. Results: The results of the analysis using our stochastic model support the predictions of the deterministic models; not only will the implementation of any of the above listed measures substantially reduce the incidences of hospital-acquired infections and the frequency of resistance, the effects of their implementation should be seen in months rather than the years or decades anticipated to control resistance in open communities. How effectively and how rapidly the application of second-line drugs will contribute to the decline in the frequency of resistance to the first-line drugs depends on how these drugs are administered. The earlier the switch to second-line drugs, the more effective this protocol will be. Switching to second-line drugs at random is more effective than switching after a defined period or only after there is direct evidence that the patient is colonized with bacteria resistant to the first antibiotic. Conclusions: The incidence of hospital-acquired bacterial infections and frequencies of antibiotic resistant bacteria can be markedly and rapidly reduced by different readily implemented procedures. The efficacy using second line drugs to achieve these ends depends on the protocol used for their administration.

BACKGROUND: As annual influenza vaccination is recommended for all U.S. persons aged 6 months or older, it is unethical to conduct randomized clinical trials to estimate influenza vaccine effectiveness (VE). Observational studies are being increasingly used to estimate VE. We developed a probability model for comparing the bias and the precision of VE estimates from two case-control designs: the traditional case-control (TCC) design and the test-negative (TN) design. In both study designs, acute respiratory illness (ARI) patients seeking medical care testing positive for influenza infection are considered cases. In the TN design, ARI patients seeking medical care who test negative serve as controls, while in the TCC design, controls are randomly selected individuals from the community who did not contract an ARI. METHODS: Our model assigns each study participant a covariate corresponding to the person's health status. The probabilities of vaccination and of contracting influenza and non-influenza ARI depend on health status. Hence, our model allows non-random vaccination and confounding. In addition, the probability of seeking care for ARI may depend on vaccination and health status. We consider two outcomes of interest: symptomatic influenza (SI) and medically-attended influenza (MAI). RESULTS: If vaccination does not affect the probability of non-influenza ARI, then VE estimates from TN studies usually have smaller bias than estimates from TCC studies. We also found that if vaccinated influenza ARI patients are less likely to seek medical care than unvaccinated patients because the vaccine reduces symptoms' severity, then estimates of VE from both types of studies may be severely biased when the outcome of interest is SI. The bias is not present when the outcome of interest is MAI. CONCLUSIONS: The TN design produces valid estimates of VE if (a) vaccination does not affect the probabilities of non-influenza ARI and of seeking care against influenza ARI, and (b) the confounding effects resulting from non-random vaccination are similar for influenza and non-influenza ARI. Since the bias of VE estimates depends on the outcome against which the vaccine is supposed to protect, it is important to specify the outcome of interest when evaluating the bias.

Purpose To identify predictors of human papillomavirus (HPV) vaccination among rural African American families. Design Cross-sectional descriptive study in schools in three rural counties in southeastern United States. The sample consisted of African American parents or caregivers with children 9 to 13 years of age who attended elementary or middle school in 2010–2011. Methods Using an anonymous, 26-item survey, we collected descriptive data during parent-teacher events from African American parents with children in elementary or middle school. The main outcome was measured as a response of “yes” to the statement “I have or will vaccinate my child with the HPV vaccine.” In addition, composite scores of knowledge and positive attitudes and beliefs were compared. No interventions were conducted. Findings We identified predictors of HPV vaccination and found that religious affiliation had a correlation with vaccinating or planning to vaccinate a child. Conclusions Results indicate a need for further research on the role of local culture, including religion and faith, in rural African Americans’ decisions about giving their children the HPV vaccination. Clinical Relevance This study emphasizes the importance of understanding rural African American parents’ knowledge, attitudes, and spiritual beliefs when designing health education programs and public health interventions to increase HPV vaccination uptake among African American boys and girls living in rural areas.

Background: The test-negative design (TND) to evaluate influenza vaccine effectiveness is based on patients seeking care for acute respiratory infection, with those who test positive for influenza as cases and the test-negatives serving as controls. This design has not been validated for the inpatient setting where selection bias might be different from an outpatient setting. Methods: We derived mathematical expressions for vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations and used numerical simulations to verify theoretical results exploring expected biases under various scenarios. We explored meaningful interpretations of VE estimates from inpatient TND studies. Results: VE estimates from inpatient TND studies capture the vaccine-mediated protection of the source population against laboratory-confirmed influenza hospitalizations. If vaccination does not modify disease severity, these estimates are equivalent to VE against influenza virus infection. If chronic cardiopulmonary individuals are enrolled because of non-infectious exacerbation, biased VE estimates (too high) will result. If chronic cardiopulmonary disease status is adjusted for accurately, the VE estimates will be unbiased. If chronic cardiopulmonary illness cannot be adequately be characterized, excluding these individuals may provide unbiased VE estimates. Conclusions: The inpatient TND offers logistic advantages and can provide valid estimates of influenza VE. If highly vaccinated patients with respiratory exacerbation of chronic cardiopulmonary conditions are eligible for study inclusion, biased VE estimates will result unless this group is well characterized and the analysis can adequately adjust for it. Otherwise, such groups of subjects should be excluded from the analysis.

Test-negative (TN) studies have become the most widely used study design for the estimation of influenza vaccine effectiveness (VE) and are easily incorporated into existing influenza surveillance networks. We seek to determine the bias of TN-based VE estimates during a pandemic using a dynamic probability model. The model is used to evaluate and compare the bias of VE estimates under various sources of bias when vaccination occurs after the beginning of an outbreak, such as during a pandemic. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing influenza and non-influenza acute respiratory illness (ARI), and seeking medical care. Specifically, we evaluate the bias of VE estimates when (1) vaccination affects the probability of developing a non-influenza ARI; (2) vaccination affects the probability of seeking medical care; (3) a covariate (e.g. health status) is related to both the probabilities of vaccination and developing an ARI; and (4) a covariate (e.g. health awareness) is related to both the probabilities of vaccination and of seeking medical care. We considered two outcomes against which the vaccine is supposed to protect: symptomatic influenza and medically-attended influenza. When vaccination begins during an outbreak, we found that the effect of delayed onset of vaccination is unpredictable. VE estimates from TN studies were biased regardless of the source of bias present. However, if the core assumption of the TN design is satisfied, that is, if vaccination does not affect the probability of non-influenza ARI, then TN-based VE estimates against medically-attended influenza will only suffer from small (<0.05) to moderate bias (≥0.05 and <0.10). These results suggest that if sources of bias listed above are ruled out, TN studies are a valid study design for the estimation of VE during a pandemic.

Influenza vaccination is recommended as the best way to protect against influenza infection and illness. Due to seasonal changes in influenza virus types and subtypes, a new vaccine must be produced, and vaccine effectiveness (VE) must be estimated, annually. Since 2010, influenza vaccination has been recommended universally in the United States, making randomized clinical trials unethical. Recent studies have used a monitored household cohort study design to determine separate VE estimates against influenza transmission from the household and community. We developed a probability model and accompanying maximum likelihood procedure to estimate vaccine-related protection against transmission of influenza from the household and the community. Using agent-based stochastic simulations, we validated that we can obtain maximum likelihood estimates of transmission parameters and VE close to their true values. Sensitivity analyses to examine the effect of deviations from our assumptions were conducted. We used our method to estimate transmission parameters and VE from data from a monitored household study in Michigan during the 2012-2013 influenza season and were able to detect a significant protective effect of influenza vaccination against community-acquired transmission.

As influenza vaccination is now widely recommended, randomized clinical trials are no longer ethical in many populations. Therefore, observational studies on patients seeking medical care for acute respiratory illnesses (ARIs) are a popular option for estimating influenza vaccine effectiveness (VE). We developed a probability model for evaluating and comparing bias and precision of estimates of VE against symptomatic influenza from two commonly used case-control study designs: the test-negative design and the traditional case-control design. We show that when vaccination does not affect the probability of developing non-influenza ARI then VE estimates from test-negative design studies are unbiased even if vaccinees and non-vaccinees have different probabilities of seeking medical care against ARI, as long as the ratio of these probabilities is the same for illnesses resulting from influenza and non-influenza infections. Our numerical results suggest that in general, estimates from the test-negative design have smaller bias compared to estimates from the traditional case-control design as long as the probability of non-influenza ARI is similar among vaccinated and unvaccinated individuals. We did not find consistent differences between the standard errors of the estimates from the two study designs.

Estimation of the effectiveness of rotavirus vaccines via the test-negative control study design has gained popularity over the past few years. In this study design, children with severe diarrhea who test positive for rotavirus infection are considered as cases, while children who test negative serve as controls. We use a simple probability model to evaluate and compare the test-negative control and the traditional case-control designs with respect to the bias of resulting estimates of rotavirus vaccine effectiveness (VE). Comparisons are performed under two scenarios, corresponding to studies performed in high-income and low-income countries. We consider two potential sources of bias: (a) misclassification bias resulting from imperfect sensitivity and specificity of the test used to diagnose rotavirus infection, and (b) selection bias associated with possible effect of rotavirus vaccination on the probability of contracting severe non-rotavirus diarrhea. Our results suggest that both sources of bias may produce VE estimates with substantial bias. Particularly, lack of perfect specificity is associated with severe negative bias. For example, if the specificity of the diagnostic test is 90% then VE estimates from both types of case-control studies may under-estimate the true VE by more than 20%. If the vaccine protects children against non-rotavirus diarrhea then VE estimates from test-negative control studies may be close to zero even though the true VE is 50%. However, the sensitivity and specificity of the enzyme immunoassay test currently used to diagnose rotavirus infections are both over 99%, and there is no solid evidence that the existing rotavirus vaccines affect the rates of non-rotavirus diarrhea. We therefore conclude that the test-negative control study design is a convenient and reliable alternative for estimation of rotavirus VE.

Introduction: Influenza vaccination is regarded as the most effective way to prevent influenza infection. Due to the rapid genetic changes that influenza viruses undergo, seasonal influenza vaccines must be reformulated and re-administered annually necessitating the evaluation of influenza vaccine effectiveness (VE) each year. The estimation of influenza VE presents numerous challenges. Areas Covered: This review aims to identify, discuss, and, where possible, offer suggestions for dealing with the following challenges in estimating influenza VE: different outcomes of interest against which VE is estimated, study designs used to assess VE, sources of bias and confounding, repeat vaccination, waning immunity, population level effects of vaccination, and VE in at-risk populations. Expert Opinion: The estimation of influenza VE has improved with surveillance networks, better understanding of sources of bias and confounding, and the implementation of advanced statistical methods. Future research should focus on better estimates of the indirect effects of vaccination, the biological effects of vaccination, and how vaccines interact with the immune system. Specifically, little is known about how influenza vaccination impacts an individual’s infectiousness, how vaccines wane over time, and the impact of repeated vaccination.