Postvaccination Serum Antirotavirus Immunoglobulin A as a Correlate of Protection Against Rotavirus Gastroenteritis Across Settings.

BACKGROUND: A correlate of protection for rotavirus gastroenteritis would facilitate rapid assessment of vaccination strategies and the next generation of rotavirus vaccines. We aimed to quantify a threshold of postvaccine serum antirotavirus immunoglobulin A (IgA) as an individual-level immune correlate of protection against rotavirus gastroenteritis. METHODS: Individual-level data on 5074 infants in 9 GlaxoSmithKline Rotarix Phase 2/3 clinical trials from 16 countries were pooled. Cox proportional hazard models were fit to estimate hazard ratios (HRs) describing the relationship between IgA thresholds and occurrence of rotavirus gastroenteritis. RESULTS: Seroconversion (IgA ≥ 20 U/mL) conferred substantial protection against any and severe rotavirus gastroenteritis to age 1 year. In low child mortality settings, seroconversion provided near perfect protection against severe rotavirus gastroenteritis (HR, 0.04; 95% confidence interval [CI], .01-.31). In high child mortality settings, seroconversion dramatically reduced the risk of severe rotavirus gastroenteritis (HR, 0.46; 95% CI, .25-.86). As IgA threshold increased, risk of rotavirus gastroenteritis generally decreased. A given IgA threshold provided better protection in low compared to high child mortality settings. DISCUSSION: Postvaccination antirotavirus IgA is a valuable correlate of protection against rotavirus gastroenteritis to age 1 year. Seroconversion provides an informative threshold for assessing rotavirus vaccine performance.

Antirotavirus IgA seroconversion rates in children who receive concomitant oral poliovirus vaccine: A secondary, pooled analysis of Phase II and III trial data from 33 countries.

BACKGROUND: Despite the success of rotavirus vaccines over the last decade, rotavirus remains a leading cause of severe diarrheal disease among young children. Further progress in reducing the burden of disease is inhibited, in part, by vaccine underperformance in certain settings. Early trials suggested that oral poliovirus vaccine (OPV), when administered concomitantly with rotavirus vaccine, reduces rotavirus seroconversion rates after the first rotavirus dose with modest or nonsignificant interference after completion of the full rotavirus vaccine course. Our study aimed to identify a range of individual-level characteristics, including concomitant receipt of OPV, that affect rotavirus vaccine immunogenicity in high- and low-child-mortality settings, controlling for individual- and country-level factors. Our central hypothesis was that OPV administered concomitantly with rotavirus vaccine reduced rotavirus vaccine immunogenicity. METHODS AND FINDINGS: Pooled, individual-level data from GlaxoSmithKline's Phase II and III clinical trials of the monovalent rotavirus vaccine (RV1), Rotarix, were analyzed, including 7,280 vaccinated infants (5-17 weeks of age at first vaccine dose) from 22 trials and 33 countries/territories (5 countries/territories with high, 13 with moderately low, and 15 with very low child mortality). Two standard markers for immune response were examined including antirotavirus immunoglobulin A (IgA) seroconversion (defined as the appearance of serum antirotavirus IgA antibodies in subjects initially seronegative) and serum antirotavirus IgA titer, both collected approximately 4-12 weeks after administration of the last rotavirus vaccine dose. Mixed-effect logistic regression and mixed-effect linear regression of log-transformed data were used to identify individual- and country-level predictors of seroconversion (dichotomous) and antibody titer (continuous), respectively. Infants in high-child-mortality settings had lower odds of seroconverting compared with infants in low-child-mortality settings (odds ratio [OR] = 0.48, 95% confidence interval [CI] 0.43-0.53, p < 0.001). Similarly, among those who seroconverted, infants in high-child-mortality settings had lower IgA titers compared with infants in low-child-mortality settings (mean difference [ß] = 0.83, 95% CI 0.77-0.90, p < 0.001). Infants who received OPV concomitantly with both their first and their second doses of rotavirus vaccine had 0.63 times the odds of seroconverting (OR = 0.63, 95% CI 0.47-0.84, p = 0.002) compared with infants who received OPV but not concomitantly with either dose. In contrast, among infants who seroconverted, OPV concomitantly administered with both the first and second rotavirus vaccine doses was found to be positively associated with antirotavirus IgA titer (ß = 1.28, 95% CI 1.07-1.53, p = 0.009). Our findings may have some limitations in terms of generalizability to routine use of rotavirus vaccine because the analysis was limited to healthy infants receiving RV1 in clinical trial settings. CONCLUSIONS: Our findings suggest that OPV given concomitantly with RV1 was a substantial contributor to reduced antirotavirus IgA seroconversion, and this interference was apparent after the second vaccine dose of RV1, as with the original clinical trials that our reanalysis is based on. However, our findings do suggest that the forthcoming withdrawal of OPV from the infant immunization schedule globally has the potential to improve RV1 performance.

Longer-term Direct and Indirect Effects of Infant Rotavirus Vaccination Across All Ages in the United States in 2000-2013: Analysis of a Large Hospital Discharge Data Set.

BACKGROUND: Rotavirus disease rates dramatically declined among children <5 years of age since the rotavirus vaccine was introduced in 2006; population-level impacts remain to be fully elucidated. METHODS: Data from the Healthcare Cost and Utilization Project State Inpatient Databases were used to conduct a time-series analysis of monthly hospital discharges across age groups for acute gastroenteritis and rotavirus from 2000 to 2013. Rate ratios were calculated comparing prevaccine and postvaccine eras. RESULTS: Following vaccine introduction, a decrease in rotavirus hospitalizations occurred with a shift toward biennial patterns across all ages. The 0-4-year age group experienced the largest decrease in rotavirus hospitalizations (rate ratio, 0.14; 95% confidence interval, .09-.23). The 5-19-year and 20-59-year age groups experienced significant declines in rotavirus hospitalization rates overall; the even postvaccine calendar years were characterized by progressively lower rates, and the odd postvaccine years were associated with reductions in rates that diminished over time. Those aged ≥60 years experienced the smallest change in rotavirus hospitalization rates overall, with significant reductions in even postvaccine years compared with prevaccine years (rate ratio, 0.51; 95% confidence interval, .39-.66). CONCLUSIONS: Indirect impacts of infant rotavirus vaccination are apparent in the emergence of biennial patterns in rotavirus hospitalizations that extend to all age groups ineligible for vaccination. These observations are consistent with the notion that young children are of primary importance in disease transmission and that the initial postvaccine period of dramatic population-wide impacts will be followed by more complex incidence patterns across the age range in the long term.

Maximum likelihood estimation of influenza vaccine effectiveness against transmission from the household and from the community.

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.

The case test-negative design for studies of the effectiveness of influenza vaccine in inpatient settings.

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.

Frequency and determinants of disagreement and error in gleason scores: a population-based study of prostate cancer.

BACKGROUND: To examine factors that affect accuracy and reliability of prostate cancer grade we compared Gleason scores documented in pathology reports and those assigned by urologic pathologists in a population-based study. METHODS: A stratified random sample of 318 prostate cancer cases was selected to ensure representation of whites and African-Americans and to include facilities of various types. The slides borrowed from reporting facilities were scanned and the resulting digital images were re-reviewed by two urologic pathologists. If the two urologic pathologists disagreed, a third urologic pathologist was asked to help arrive at a final "gold standard" result. The agreements between reviewers and between the pathology reports and the "gold standard" were examined by calculating kappa statistics. The determinants of discordance in Gleason scores were evaluated using multivariate models with results expressed as odds ratios (OR) and 95% confidence intervals (CI). RESULTS: The kappa values (95% CI) reflecting agreement between the pathology reports and the "gold standard," were 0.61 (95% CI: 0.54, 0.68) for biopsies, and 0.37 (0.23, 0.51) for prostatectomies. Sixty three percent of discordant biopsies and 72% of discordant prostatectomies showed only minimal differences. Using freestanding laboratories as reference, the likelihood of discordance between pathology reports and expert-assigned biopsy Gleason scores was particularly elevated for small community hospitals (OR = 2.98; 95% CI: 1.73, 5.14). CONCLUSIONS: The level of agreement between pathology reports and expert review depends on the type of diagnosing facility, but may also depend on the level of expertise and specialization of individual pathologists.

Use of the concordance correlation coefficient when examining agreement in dyadic research.

BACKGROUND: The statistical measure used to quantify the degree of agreement or congruence between two research subjects has been the intraclass or the Pearson correlation coefficient; however, the concordance correlation coefficient (CCC) is another measure of agreement used to examine agreement between two observers or raters. OBJECTIVES: The aims of this study were to (a) highlight the differences among three statistical measures used to quantify the degree of agreement or congruence, (b) demonstrate the use of the CCC in examining agreement between heart failure (HF) patients and their family members, and (c) provide nurse researchers another method for evaluating agreement. METHODS: Symptom evaluation scores obtained from HF patients and their family members were used in the analysis of this study. To explain the use of the CCC in this analysis, a distinction between Pearson correlation coefficient and intraclass correlation coefficient is discussed. The CCC calculation is then described in detail. RESULTS: The HF patients in this sample were 71 +/- 9.6 years in age, 40% male, and 41.4% African American. Most (75%) family members were female. There were several different categories of family members, but most were spouses. The CCC results indicated that no symptom achieved good agreement, and 8 of 14 symptoms were in moderate agreement (.4

An overview on assessing agreement with continuous measurements.

Reliable and accurate measurements serve as the basis for evaluation in many scientific disciplines. Issues related to reliable and accurate measurement have evolved over many decades, dating back to the nineteenth century and the pioneering work of Galton (1886), Pearson (1896, 1899, 1901), and Fisher (1925). Requiring a new measurement to be identical to the truth is often impractical, either because (1) we are willing to accept a measurement up to some tolerable (or acceptable) error, or (2) the truth is simply not available to us, either because it is not measurable or is only measurable with some degree of error. To deal with issues related to both (1) and (2), a number of concepts, methods, and theories have been developed in various disciplines. Some of these concepts have been used across disciplines, while others have been limited to a particular field but may have potential uses in other disciplines. In this paper, we elucidate and contrast fundamental concepts employed in different disciplines and unite these concepts into one common theme: assessing closeness (agreement) of observations. We focus on assessing agreement with continuous measurements and classify different statistical approaches as (1) descriptive tools; (2) unscaled summary indices based on absolute differences of measurements; and (3) scaled summary indices attaining values between -1 and 1 for various data structures, and for cases with and without a reference. We also identify gaps that require further research and discuss future directions in assessing agreement.

Assessing individual agreement.

Evaluating agreement between measurement methods or between observers is important in method comparison studies and in reliability studies. Often we are interested in whether a new method can replace an existing invasive or expensive method, or whether multiple methods or multiple observers can be used interchangeably. Ideally, interchangeability is established only if individual measurements from different methods are similar to replicated measurements from the same method. This is the concept of individual equivalence. Interchangeability between methods is similar to bioequivalence between drugs in bioequivalence studies. Following the FDA guidelines on individual bioequivalence, we propose to assess individual agreement among multiple methods via individual equivalence using the moment criteria. In the case where there is a reference method, we extend the individual bioequivalence criteria to individual equivalence criteria and propose to use individual equivalence coefficient (IEC) to compare multiple methods to one or multiple references. In the case where there is no reference method available, we propose a new IEC to assess individual agreement between multiple methods. Furthermore, we propose a coefficient of individual agreement (CIA) that links the IEC with two recent agreement indices. A method of moments is used for estimation, where one can utilize output from ANOVA models. The nonparametric and bootstrap approaches are used for inference. Five examples are used for illustration.

Effectiveness of interventions to reduce contact rates during a simulated influenza pandemic.

Measures to decrease contact between persons during an influenza pandemic have been included in pandemic response plans. We used stochastic simulation models to explore the effects of school closings, voluntary confinements of ill persons and their household contacts, and reductions in contacts among long-term care facility (LTCF) residents on pandemic-related illness and deaths. Our findings suggest that school closings would not have a substantial effect on pandemic-related outcomes in the absence of measures to reduce out-of-school contacts. However, if persons with influenzalike symptoms and their household contacts were encouraged to stay home, then rates of illness and death might be reduced by approximately 50%. By preventing ill LTCF residents from making contact with other residents, illness and deaths in this vulnerable population might be reduced by approximately 60%. Restricting the activities of infected persons early in a pandemic could decrease the pandemic's health effects.

Methodologic issues regarding the use of three observational study designs to assess influenza vaccine effectiveness.

BACKGROUND: Influenza causes substantial morbidity and annual vaccination is the most important prevention strategy. Accurately measuring vaccine effectiveness (VE) is difficult. The clinical syndrome most closely associated with influenza virus infection, influenza-like illness (ILI), is not specific. In addition, laboratory confirmation is infrequently done, and available rapid diagnostic tests are imperfect. The objective of this study was to estimate the joint impact of rapid diagnostic test sensitivity and specificity on VE for three types of study designs: a cohort study, a traditional case-control study, and a case-control study that used as controls individuals with ILI who tested negative for influenza virus infection. METHODS: We developed a mathematical model with five input parameters: true VE, attack rates (ARs) of influenza-ILI and non-influenza-ILI and the sensitivity and specificity of the diagnostic test. RESULTS: With imperfect specificity, estimates from all three designs tended to underestimate true VE, but were similar except if fairly extreme inputs were used. Only if test specificity was 95% or more or if influenza attack rates doubled that of background illness did the case-control method slightly overestimate VE. The case-control method usually produced the highest and most accurate estimates, followed by the test-negative design. The bias toward underestimating true VE introduced by low test specificity increased as the AR of influenza- relative to non-influenza-ILI decreases and, to a lesser degree, with lower test sensitivity. CONCLUSIONS: Demonstration of a high influenza VE using tests with imperfect sensitivity and specificity should provide reassurance that the program has been effective in reducing influenza illnesses, assuming adequate control of confounding factors.

Seasonal variation in the epidemiology of sepsis.

OBJECTIVE: We sought to investigate seasonal and regional variability in the epidemiology of sepsis and to identify underlying associations based on geography and seasonal viral infections. Understanding seasonal or regional variations may improve knowledge of sepsis epidemiology and pathophysiology and could affect healthcare planning and resource allocation. DESIGN: Retrospective cohort study using the National Hospital Discharge Survey to identify cases of sepsis, severe sepsis, influenza, and viral pneumonia using ICD-9-CM codes. Incidence rates are reported as mean cases frequencies per season per 100,000 as calculated by normalization to the 2000 U.S. Census. SETTING: Acute-care nonfederal U.S. hospitals. PATIENTS: Patients hospitalized between 1979 and 2003 in acute-care nonfederal U.S. hospitals with a diagnosis of sepsis or viral respiratory infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The seasonal incidence rate of sepsis increased 16.5% from a low of 41.7 in the fall to a high of 48.6 cases per 100,000 in the winter (p<.05). Similarly, seasonal rates for severe sepsis statistically increased 17.7% from fall to winter at 13.0 and 15.3 cases per 100,000, respectively. The greatest change in sepsis incidence occurred with respiratory sources, increasing 40% during the winter compared with the fall (p<.05). Seasonal variations in viral respiratory infections paralleled changes in sepsis incidence but did not fully account for the changes. The greatest seasonal change in sepsis rates occurred in the Northeast (+30%). Sepsis case-fatality rates were 13% greater in the winter compared with the summer (p<.05) despite similar severity of illness. CONCLUSIONS: The incidence and mortality of sepsis and severe sepsis are seasonal and consistently highest during the winter, predominantly related to respiratory sepsis. Seasonal changes in sepsis incidence vary according to geographic region. The mechanisms underlying these differences require further investigation.

Assessing intra, inter and total agreement with replicated readings.

In clinical studies, assessing agreement of multiple readings on the same subject plays an important role in the evaluation of continuous measurement scale. The multiple readings within a subject may be replicated readings by using the same method or/and readings by using several methods (e.g. different technologies or several raters). The traditional agreement data for a given subject often consist of either replicated readings from only one method or multiple readings from several methods where only one reading is taken from each of these methods. In the first case, only intra-method agreement can be evaluated. In the second case, traditional agreement indices such as intra-class correlation (ICC) or concordance correlation coefficient (CCC) is often reported as inter-method agreement. We argue that these indices are in fact measures of total agreement that contains both inter and intra agreement. Only if there are replicated readings from several methods for a given subject, then one can assess intra, inter and total agreement simultaneously. In this paper, we present new inter-method agreement index, inter-CCC, and total agreement index, total-CCC, for agreement data with replicated readings from several methods where the ICCs within methods are used to assess intra-method agreement for each of the several methods. The relationship of the total-CCC with the inter-CCC and the ICCs is investigated. We propose a generalized estimating equations approach for estimation and inference. Simulation studies are conducted to assess the performance of the proposed approach and data from a carotid stenosis screening study is used for illustration.

Does antimicrobial resistance cluster in individual hospitals?

Factors that affect the resistance rates for an organism-drug combination in a given hospital also might influence resistance rates for other organism-drug combinations. We examined correlations between resistance prevalence in non-intensive care inpatient areas of 41 hospitals participating in phase 3 (1998-1999) of Project ICARE (Intensive Care Antimicrobial Resistance Epidemiology). We focused on statistically significant (P<.05) Pearson correlation coefficients for methicillin-resistant Staphylococcus aureus, coagulase-negative staphylococci, vancomycin-resistant enterococci, and resistance to third-generation cephalosporins, imipenem, and fluoroquinolones in Escherichia coli, Klebsiella pneumoniae, Enterobacter species, and Pseudomonas aeruginosa. Resistance prevalence rates in individual hospitals were not strongly correlated among gram-positive organisms, and few correlations were seen between rates in gram-positive and gram-negative organisms. More frequent significant associations were found among resistance rates for gram-negative organisms. Resistance to third-generation cephalosporins in K. pneumoniae was significantly correlated with the majority of other sentinel antimicrobial-resistant organisms. High prevalence of this organism may serve as a marker for more generalized resistance problems in hospital inpatient areas.