Efficacy,safety and immunogenicity of hexavalent rotavirus vaccine in Chinese infants

A randomized, double-blind, placebo-controlled multicenter trial was conducted in healthy Chinese infants to assess the efficacy and safety of a hexavalent live human-bovine reassortant rotavirus vaccine (HRV) against rotavirus gastroenteritis (RVGE). A total of 6400 participants aged 6–12 weeks were enrolled and randomly assigned to either HRV (n = 3200) or placebo (n = 3200) group. All the subjects received three oral doses of vaccine four weeks apart. The vaccine efficacy (VE) against RVGE caused by rotavirus serotypes contained in HRV was evaluated from 14 days after three doses of administration up until the end of the second rotavirus season. VE against severe RVGE, VE against RVGE hospitalization caused by serotypes contained in HRV, and VE against RVGE, severe RVGE, and RVGE hospitalization caused by natural infection of any serotype of rotavirus were also investigated. All adverse events (AEs) were collected for 30 days after each dose. Serious AEs (SAEs) and intussusception cases were collected during the entire study. Our data showed that VE against RVGE caused by serotypes contained in HRV was 69.21% (95%CI: 53.31–79.69). VE against severe RVGE and RVGE hospitalization caused by serotypes contained in HRV were 91.36% (95%CI: 78.45–96.53) and 89.21% (95%CI: 64.51–96.72) respectively. VE against RVGE, severe RVGE, and RVGE hospitalization caused by natural infection of any serotype of rotavirus were 62.88% (95%CI: 49.11–72.92), 85.51% (95%CI: 72.74–92.30) and 83.68% (95%CI: 61.34–93.11). Incidences of AEs from the first dose to one month post the third dose in HRV and placebo groups were comparable. There was no significant difference in incidences of SAEs in HRV and placebo groups. This study shows that this hexavalent reassortant rotavirus vaccine is an effective, well-tolerated, and safe vaccine for Chinese infants.     

Estimating vaccine effects on transmission of infection from household outbreak data

This article is concerned with a method for making inferences about various measures of vaccine efficacy. These measures describe reductions in susceptibility and in the potential to transmit infection. The method uses data on household outbreaks; it is based on a model that allows for transmission of infection both from within a household and from the outside. The use of household data is motivated by the hope that these are informative about vaccine-induced reduction of the potential to transmit infection, as household outbreaks contain some information about the possible source of infection. For illustration, the method is applied to observed data on household outbreaks of smallpox. These data are of the form needed and the number of households is of a size that can be managed in a vaccine trial. It is found that vaccine effects, such as the mean reduction in susceptibility and the mean reduction in the potential to infect others, per infectious contact, can be estimated with precision. However, a more specific parameter reflecting the reduction in infectivity for individuals partially responding to vaccination is not estimated well in the application. An evaluation of the method using artificial data shows that this parameter can be estimated with greater precision when we have outbreak data on a large number of small households.     

Estimating and interpreting secondary attack risk: Binomial considered biased

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be neglected safely when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.     

Sensitivity analyses comparing outcomes only existing in a subset selected post-randomization, conditional on covariates, with application to HIV vaccine trials

In many experiments, researchers would like to compare between treatments and outcome that only exists in a subset of participants selected after randomization. For example, in preventive HIV vaccine efficacy trials it is of interest to determine whether randomization to vaccine causes lower HIV viral load, a quantity that only exists in participants who acquire HIV. To make a causal comparison and account for potential selection bias we propose a sensitivity analysis following the principal stratification framework set forth by Frangakis and Rubin (2002, Biometrics58, 21-29). Our goal is to assess the average causal effect of treatment assignment on viral load at a given baseline covariate level in the always infected principal stratum (those who would have been infected whether they had been assigned to vaccine or placebo). We assume stable unit treatment values (SUTVA), randomization, and that subjects randomized to the vaccine arm who became infected would also have become infected if randomized to the placebo arm (monotonicity). It is not known which of those subjects infected in the placebo arm are in the always infected principal stratum, but this can be modeled conditional on covariates, the observed viral load, and a specified sensitivity parameter. Under parametric regression models for viral load, we obtain maximum likelihood estimates of the average causal effect conditional on covariates and the sensitivity parameter. We apply our methods to the world's first phase III HIV vaccine trial.     

On estimation of vaccine efficacy using validation samples with selection bias

Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini, 2001; Halloran and others, 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random (MAR). However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenza-like illness as part of routine influenza surveillance. VE estimates based on such non-MAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating VE in the presence of validation bias. Our work builds on the ideas of Rotnitzky and others (1998, 2001), Scharfstein and others (1999, 2003), and Robins and others (2000). Our methods require expert opinion about the nature of the validation selection bias. In a re-analysis of an influenza vaccine study, we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the non-specific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.     

Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics 58, 21-29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.     

Influenza transmission in a community during a seasonal influenza A(H3N2) outbreak (2010-2011) in Mongolia: a community-based prospective cohort study

Background

Knowledge of how influenza viruses spread in a community is important for planning and implementation of effective interventions, including social distancing measures. Households and schools are implicated as the major sites for influenza virus transmission. However, the overall picture of community transmission is not well defined during actual outbreaks. We conducted a community-based prospective cohort study to describe the transmission characteristics of influenza in Mongolia.

Methods and Findings

A total of 5,655 residents in 1,343 households were included in this cohort study. An active search for cases of influenza-like illness (ILI) was performed between October 2010 and April 2011. Data collected during a community outbreak of influenza A(H3N2) were analyzed. Total 282 ILI cases occurred during this period, and 73% of the subjects were aged <15 years. The highest attack rate (20.4%) was in those aged 1–4 years, whereas the attack rate in those aged 5–9 years was 10.8%. Fifty-one secondary cases occurred among 900 household contacts from 43 households (43 index cases), giving an overall crude household secondary attack rate (SAR) of 5.7%. SAR was significantly higher in younger household contacts (relative risk for those aged <1 year: 9.90, 1–4 years: 5.59, and 5–9 years: 6.43). We analyzed the transmission patterns among households and a community and repeated transmissions were detected between households, preschools, and schools. Children aged 1–4 years played an important role in influenza transmission in households and in the community at large. Working-age adults were also a source of influenza in households, whereas elderly cases (aged ≥65 years) had no link with household transmission.

Conclusions

Repeated transmissions between households, preschools, and schools were observed during an influenza A(H3N2) outbreak period in Mongolia, where subjects aged 1–4 years played an important role in influenza transmission.     

Modelling the proportion of influenza infections within households during pandemic and non-pandemic years

Background

The key epidemiological difference between pandemic and seasonal influenza is that the population is largely susceptible during a pandemic, whereas, during non-pandemic seasons a level of immunity exists. The population-level efficacy of household-based mitigation strategies depends on the proportion of infections that occur within households. In general, mitigation measures such as isolation and quarantine are more effective at the population level if the proportion of household transmission is low.

Methods/Results

We calculated the proportion of infections within households during pandemic years compared with non-pandemic years using a deterministic model of household transmission in which all combinations of household size and individual infection states were enumerated explicitly. We found that the proportion of infections that occur within households was only partially influenced by the hazard h of infection within household relative to the hazard of infection outside the household, especially for small basic reproductive numbers. During pandemics, the number of within-household infections was lower than one might expect for a given because many of the susceptible individuals were infected from the community and the number of susceptible individuals within household was thus depleted rapidly. In addition, we found that for the value of at which 30% of infections occur within households during non-pandemic years, a similar 31% of infections occur within households during pandemic years.

Interpretation

We suggest that a trade off between the community force of infection and the number of susceptible individuals in a household explains an apparent invariance in the proportion of infections that occur in households in our model. During a pandemic, although there are more susceptible individuals in a household, the community force of infection is very high. However, during non-pandemic years, the force of infection is much lower but there are fewer susceptible individuals within the household.     

On vaccine efficacy and reproduction numbers

We consider the impact of a vaccination programme on the transmission potential of the infection in large populations. We define a measure of vaccine efficacy against transmission which combines the possibly random effect of the vaccine on individual susceptibility and infectiousness. This definition extends some previous work in this area to arbitrarily heterogeneous populations with one level of mixing, but leads us to question the usefulness of the concept of vaccine efficacy against infectiousness. We derive relationships between vaccine efficacy against transmission, vaccine coverage and reproduction numbers, which generalize existing results. In particular we show that the projected reproduction number RV does not depend on the details of the vaccine model, only on its overall effect on transmission. Explicit expressions for RV and the basic reproduction number R0 are obtained in a variety of settings. We define a measure of projected effectiveness of a vaccination programme PE=1-(RV/R0) and investigate its relationship with efficacy against transmission and vaccine coverage. We also study the effective reproduction number Re(t) at time t. Monitoring Re(t) over time is an important aspect of programme surveillance. Programme effectiveness PE is less sensitive than RV or the critical vaccination threshold to model assumptions. On the other hand Re(t) depends on the details of the vaccine model.     

Failure time analysis of HIV vaccine effects on viral load and antiretroviral therapy initiation

The world's first efficacy trial of a preventive HIV vaccine was completed in 2003. Study participants who became HIV infected were followed for 2 years and monitored for HIV viral load and initiation of antiretroviral therapy (ART). In order to determine if vaccination may have altered HIV progression in persons who acquired HIV, a pre-specified objective was to compare the time until a composite endpoint between the vaccine and placebo arms, where the composite endpoint is the first event of ART initiation or viral failure (HIV viral load exceeds a threshold x(vl) copies/ml). Specifically, with vaccine efficacy, VE(tau, x(vl)), defined as one minus the ratio (vaccine/placebo) of the cumulative probability of the composite endpoint (with failure threshold x(vl)) occurring by tau months, the aim was to estimate the four parameters {VE(tau, x(vl)): x(vl) is an element of {1500, 10,000, 20,000, 55,000} copies/ml} with simultaneous 95% confidence bands. A Gaussian multipliers simulation method is devised for constructing confidence bands for VE(tau, x(vl)) with x(vl) spanning multiple discrete values or a continuous range. The new method is evaluated in simulations and is applied to the vaccine trial data set.     

Estimating individual and household reproduction numbers in an emerging epidemic

Reproduction numbers, defined as averages of the number of people infected by a typical case, play a central role in tracking infectious disease outbreaks. The aim of this paper is to develop methods for estimating reproduction numbers which are simple enough that they could be applied with limited data or in real time during an outbreak. I present a new estimator for the individual reproduction number, which describes the state of the epidemic at a point in time rather than tracking individuals over time, and discuss some potential benefits. Then, to capture more of the detail that micro-simulations have shown is important in outbreak dynamics, I analyse a model of transmission within and between households, and develop a method to estimate the household reproduction number, defined as the number of households infected by each infected household. This method is validated by numerical simulations of the spread of influenza and measles using historical data, and estimates are obtained for would-be emerging epidemics of these viruses. I argue that the household reproduction number is useful in assessing the impact of measures that target the household for isolation, quarantine, vaccination or prophylactic treatment, and measures such as social distancing and school or workplace closures which limit between-household transmission, all of which play a key role in current thinking on future infectious disease mitigation.     

Reproductive numbers, epidemic spread and control in a community of households

Many of the studies on emerging epidemics (such as SARS and pandemic flu) use mass action models to estimate reproductive numbers and the needed control measures. In reality, transmission patterns are more complex due to the presence of various social networks. One level of complexity can be accommodated by considering a community of households. Our study of transmission dynamics in a community of households emphasizes five types of reproductive numbers for the epidemic spread: household-to-household reproductive number, leaky vaccine-associated reproductive numbers, perfect vaccine reproductive number, growth rate reproductive number, and the individual reproductive number. Each of those carries different information about the transmission dynamics and the required control measures, and often some of those can be estimated from the data while others cannot. Simulations have shown that under certain scenarios there is an ordering for those reproductive numbers. We have proven a number of ordering inequalities under general assumptions about the individual infectiousness profiles. Those inequalities allow, for instance, to estimate the needed vaccine coverage and other control measures without knowing the various transmission parameters in the models. Along the way, we have also shown that in choosing between increasing vaccine efficacy and increasing coverage levels by the same factor, preference should go to efficacy.     

Estimation of Vaccine Efficacy in Non-Randomly Mixing Populations

A deterministic model for the transmission of an acute infectious disease in a heterogeneous, nonrandomly mixing population is developed. This model facilitates the estimation of transmission probabilities from the observed attack rates. If some of the members of the population are vaccinated, then the vaccine efficacy (VE), defined as the relative reduction in the transmission probability due to vaccination, can be estimated. We provide several estimators of VE, depending on the amount of information available on the mixing pattern and on the action of the vaccine. We show that if vaccinated persons increase the frequency of their contacts with infectious persons, then estimators ignoring this change in behavior may substantially underestimate the VE.     

Environmental Contamination as a Risk Factor for Intra-Household Staphylococcus aureus Transmission

Background

The household is a recognized community reservoir for Staphylococcus aureus. This study investigated potential risk factors for intra-household S. aureus transmission, including the contribution of environmental contamination.

Methods

We investigated intra-household S. aureus transmission using a sample of multiple member households from a community-based case-control study examining risk factors for CA-MRSA infection conducted in Northern Manhattan. During a home visit, index subjects completed a questionnaire. All consenting household members were swabbed, as were standardized environmental household items. Swabs were cultured for S. aureus. Positive isolates underwent further molecular characterization. Intra-household transmission was defined as having identical strains among two or more household members. Multiple logistic regression was used to identify independent risk factors for transmission.

Results

We enrolled 291 households: 146 index cases, 145 index controls and 687 of their household contacts. The majority of indexes were Hispanic (85%), low income (74%), and female (67%), with a mean age of 31 (range 1–79). The average size of case and control households was 4 people. S. aureus colonized individuals in 62% of households and contaminated the environment in 54% of households. USA300 was the predominant clinical infection, colonizing and environmental strain. Eighty-one households had evidence of intra-household transmission: 55 (38%) case and 26 (18%) control households (P<.01). Environmental contamination with a colonizing or clinical infection strain (aOR: 5.4 [2.9–10.3] P<.01) and the presence of a child under 5 (aOR: 2.3 [1.2–4.5] P = .02) were independently associated with transmission. In separate multivariable models, environmental contamination was associated with transmission among case (aOR 3.3, p<.01) and control households (aOR 27.2, p<.01).

Conclusions

Environmental contamination with a colonizing or clinical infection strain was significantly and independently associated with transmission in a large community-based sample. Environmental contamination should be considered when treating S. aureus infections, particularly among households with multiple infected members.     

The time to extinction for a stochastic SIS-household-epidemic model

We analyse a Markovian SIS epidemic amongst a finite population partitioned into households. Since the population is finite, the epidemic will eventually go extinct, i.e., have no more infectives in the population. We study the effects of population size and within household transmission upon the time to extinction. This is done through two approximations. The first approximation is suitable for all levels of within household transmission and is based upon an Ornstein-Uhlenbeck process approximation for the diseases fluctuations about an endemic level relying on a large population. The second approximation is suitable for high levels of within household transmission and approximates the number of infectious households by a simple homogeneously mixing SIS model with the households replaced by individuals. The analysis, supported by a simulation study, shows that the mean time to extinction is minimized by moderate levels of within household transmission.     

Staphylococcus aureus in the Community: Colonization Versus Infection

Background

Antibiotic-resistant Staphylococcus aureus infections have increased dramatically in the community, yet S. aureus nasal colonization has remained stable. The objectives of this study were to determine if S. aureus colonization is a useful proxy measure to study disease transmission and infection in community settings, and to identify potential community reservoirs.

Methodology/Principal Findings

Randomly selected households in Northern Manhattan, completed a structured social network questionnaire and provided nasal swabs that were typed by pulsed field gel electrophoresis to identify S. aureus colonizing strains. The main outcome measures were: 1) colonization with S. aureus; and 2) recent serious skin infection. Risk factor analyses were conducted at both the individual and the household levels; logistic regression models identified independent risks for household colonization and infection.

Results

321 surveyed households contained 914 members. The S. aureus prevalence was 25% and MRSA was 0.4%. More than 40% of households were colonized. Recent antibiotic use was the only significant correlate for household colonization (p = .002). Seventy-eight (24%) households reported serious skin infection. In contrast with colonization, five of the six risk factors that increased the risk of skin infection in the household at the univariate level remained independently significant in multivariable analysis: international travel, sports participation, surgery, antibiotic use and towel sharing. S. aureus colonization was not significantly associated with serious skin infection in any analysis. Among multiperson households with more than one person colonized, 50% carried the same strain.

Conclusions/Significance

The lack of association between S. aureus nasal colonization and serious skin infection underscores the need to explore alternative venues or body sites that may be crucial to transmission. Moreover, the magnitude of colonization and infection within the household suggests that households are an underappreciated and substantial community reservoir.     

Efficacy of live attenuated and inactivated influenza vaccines among children in rural India: A 2-year,randomized, triple-blind,placebo-controlled trial

BackgroundInfluenza is a cause of febrile acute respiratory infection (FARI) in India; however, few influenza vaccine trials have been conducted in India. We assessed absolute and relative efficacy of live attenuated influenza vaccine (LAIV) and inactivated influenza vaccine (IIV) among children aged 2 to 10 years in rural India through a randomized, triple-blind, placebo-controlled trial conducted over 2 years.Methods and findingsIn June 2015, children were randomly allocated to LAIV, IIV, intranasal placebo, or inactivated polio vaccine (IPV) in a 2:2:1:1 ratio. In June 2016, vaccination was repeated per original allocation. Overall, 3,041 children received LAIV (n = 1,015), IIV (n = 1,010), nasal placebo (n = 507), or IPV (n = 509). Mean age of children was 6.5 years with 20% aged 9 to 10 years.Through weekly home visits, nasal and throat swabs were collected from children with FARI and tested for influenza virus by polymerase chain reaction. The primary outcome was laboratory-confirmed influenza-associated FARI; vaccine efficacy (VE) was calculated using modified intention-to-treat (mITT) analysis by Cox proportional hazards model (PH) for each year.In Year 1, VE was 40.0% (95% confidence interval (CI) 25.2 to 51.9) for LAIV and 59.0% (95% CI 47.8 to 67.9) for IIV compared with controls; relative efficacy of LAIV compared with IIV was −46.2% (95% CI −88.9 to −13.1). In Year 2, VE was 51.9% (95% CI 42.0 to 60.1) for LAIV and 49.9% (95% CI 39.2 to 58.7) for IIV; relative efficacy of LAIV compared with IIV was 4.2% (95% CI −19.9 to 23.5). No serious adverse vaccine-attributable events were reported. Study limitations include differing dosage requirements for children between nasal and injectable vaccines (single dose of LAIV versus 2 doses of IIV) in Year 1 and the fact that immunogenicity studies were not conducted.ConclusionsIn this study, we found that LAIV and IIV vaccines were safe and moderately efficacious against influenza virus infection among Indian children.Trial registrationClinical Trials Registry of India CTRI/2015/06/005902.

Anand Krishnan and co-workers study the efficacy and safety of influenza vaccines for children in India.     

Comprehensive Sieve Analysis of Breakthrough HIV-1 Sequences in the RV144 Vaccine Efficacy Trial

The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through sieve analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or “signatures” and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.     

Impact of Intensive Handwashing Promotion on Secondary Household Influenza-Like Illness in Rural Bangladesh: Findings from a Randomized Controlled Trial

Rationale

There is little evidence for the efficacy of handwashing for prevention of influenza transmission in resource-poor settings. We tested the impact of intensive handwashing promotion on household transmission of influenza-like illness and influenza in rural Bangladesh.

Methods

In 2009–10, we identified index case-patients with influenza-like illness (fever with cough or sore throat) who were the only symptomatic person in their household. Household compounds of index case-patients were randomized to control or intervention (soap and daily handwashing promotion). We conducted daily surveillance and collected oropharyngeal specimens. Secondary attack ratios (SAR) were calculated for influenza and ILI in each arm. Among controls, we investigated individual risk factors for ILI among household contacts of index case-patients.

Results

Among 377 index case-patients, the mean number of days between fever onset and study enrollment was 2.1 (SD 1.7) among the 184 controls and 2.6 (SD 2.9) among 193 intervention case-patients. Influenza infection was confirmed in 20% of controls and 12% of intervention index case-patients. The SAR for influenza-like illness among household contacts was 9.5% among intervention (158/1661) and 7.7% among control households (115/1498) (SAR ratio 1.24, 95% CI 0.92–1.65). The SAR ratio for influenza was 2.40 (95% CI 0.68–8.47). In the control arm, susceptible contacts <2 years old (RR_adj 5.51, 95% CI 3.43–8.85), those living with an index case-patient enrolled ≤24 hours after symptom onset (RR_adj 1.91, 95% CI 1.18–3.10), and those who reported multiple daily interactions with the index case-patient (RR_adj 1.94, 95% CI 1.71–3.26) were at increased risk of influenza-like illness.

Discussion

Handwashing promotion initiated after illness onset in a household member did not protect against influenza-like illness or influenza. Behavior may not have changed rapidly enough to curb transmission between household members. A reactive approach to reduce household influenza transmission through handwashing promotion may be ineffective in the context of rural Bangladesh.

Trial Registration

ClinicalTrials.gov NCT00880659     

Property,Power, and the Political Economy of Farming Households in Costa Rica

The relationship between size of landholdings and household economic status is fairly clear, particularly in societies where agricultural exports dominate the economy. Less clear is the effect of differential access to and control of productive property within households and the ways in which it affects the economic opportunities of individual household members. This paper examines property holdings and inheritance patterns among coffeeproducing households in Costa Rica. I show that while cultural norms regulatin g labor contributions do affect the balance of authority within households, de facto property rights can significantly enhance an individual's decision-making power both within households and between generationally-related households. Unless new opportunities arise, as population increases, coffee production expands, and lands become increasingly scarce, we shall likely see increased stratification both within households—as women inherit less land—and among households, as some sons inherit at the expense of other sons and daughters.