Edward Goldstein

Genomic epidemiology of Neisseria gonorrhoeae with reduced susceptibility to cefixime in the USA: a retrospective observational study

Summary Background The emergence of Neisseria gonorrhoeae with decreased susceptibility to extended spectrum cephalosporins raises the prospect of untreatable gonorrhoea. In the absence of new treatments, efforts to slow the increasing incidence of resistant gonococcus require insight into the factors that contribute to its emergence and spread. We assessed the relatedness between isolates in the USA and reconstructed likely spread of lineages through different sexual networks. Methods We sequenced the genomes of 236 isolates of N gonorrhoeae collected by the Centers for Disease Control and Preventions Gonococcal Isolate Surveillance Project (GISP) from sentinel public sexually transmitted disease clinics in the USA, including 118 (97%) of the isolates from 2009–10 in GISP with reduced susceptibility to cefixime (cefRS) and 118 cefixime-susceptible isolates from GISP matched as closely as possible by location, collection date, and sexual orientation. We assessed the association between antimicrobial resistance genotype and phenotype and correlated phylogenetic clustering with location and sexual orientation. Findings Mosaic penA XXXIV had a high positive predictive value for cefRS. We found that two of the 118 cefRS isolates lacked a mosaic penA allele, and rechecking showed that these two were susceptible to cefixime. Of the 116 remaining cefRS isolates, 114 (98%) fell into two distinct lineages that have independently acquired mosaic penA allele XXXIV. A major lineage of cefRS strains spread eastward, predominantly through a sexual network of men who have sex with men. Eight of nine inferred transitions between sexual networks were introductions from men who have sex with men into the heterosexual population. Interpretation Genomic methods might aid efforts to slow the spread of antibiotic-resistant N gonorrhoeae through augmentation of gonococcal outbreak surveillance and identification of populations that could benefit from increased screening for aymptomatic infections. Funding American Sexually Transmitted Disease Association, Wellcome Trust, National Institute of General Medical Sciences, and National Institute of Allergy and Infectious Diseases, National Institutes of Health.

Absolute Humidity and Pandemic Versus Epidemic Influenza

Experimental and epidemiologic evidence indicates that variations of absolute humidity account for the onset and seasonal cycle of epidemic influenza in temperate regions. A role for absolute humidity in the transmission of pandemic influenza, such as 2009 A/H1N1, has yet to be demonstrated and, indeed, outbreaks of pandemic influenza during more humid spring, summer, and autumn months might appear to constitute evidence against an effect of humidity. However, here the authors show that variations of the basic and effective reproductive numbers for influenza, caused by seasonal changes in absolute humidity, are consistent with the general timing of pandemic influenza outbreaks observed for 2009 A/H1N1 in temperate regions, as well as wintertime transmission of epidemic influenza. Indeed, absolute humidity conditions correctly identify the region of the United States vulnerable to a third, wintertime wave of pandemic influenza. These findings suggest that the timing of pandemic influenza outbreaks is controlled by a combination of absolute humidity conditions, levels of susceptibility, and changes in population-mixing and contact rates.

Improving the estimation of influenza-related mortality over a seasonal baseline.

Background: Existing methods for estimation of mortality attributable to influenza are limited by methodological and data uncertainty. We have used proxies for disease incidence of the three influenza cocirculating subtypes (A/H3N2, A/H1N1, and B) that combine data on influenza-like illness consultations and respiratory specimen testing to estimate influenza-associated mortality in the United States between 1997 and 2007. Methods: Weekly mortality rate for several mortality causes potentially affected by influenza was regressed linearly against subtype-specific influenza incidence proxies, adjusting for temporal trend and seasonal baseline, modeled by periodic cubic splines. Results: Average annual influenza-associated mortality rates per 100,000 individuals were estimated for the following underlying causes of death: for pneumonia and influenza, 1.73 (95% confidence interval = 1.53–1.93); for chronic lower respiratory disease, 1.70 (1.48–1.93); for all respiratory causes, 3.58 (3.04–4.14); for myocardial infarctions, 1.02 (0.85–1.2); for ischemic heart disease, 2.7 (2.23–3.16); for heart disease, 3.82 (3.21–4.4); for cerebrovascular deaths, 0.65 (0.51–0.78); for all circulatory causes, 4.6 (3.79–5.39); for cancer, 0.87 (0.68–1.05); for diabetes, 0.33 (0.26–0.39); for renal disease, 0.19 (0.14–0.24); for Alzheimer disease, 0.41 (0.3–0.52); and for all causes, 11.92 (10.17–13.67). For several underlying causes of death, baseline mortality rates changed after the introduction of the pneumococcal conjugate vaccine. Conclusions: The proposed methodology establishes a linear relation between influenza incidence proxies and excess mortality, rendering temporally consistent model fits, and allowing for the assessment of related epidemiologic phenomena such as changes in mortality baselines.

Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method

Using weekly influenza surveillance data from the US CDC, Edward Goldstein and colleagues develop a statistical method to predict the sizes of epidemics caused by seasonal influenza strains. This method could inform decisions about the most appropriate vaccines or drugs needed early in the influenza season.

Excess Mortality Associated With Influenza A and B Virus in Hong Kong, 1998–2009

BACKGROUND Although deaths associated with laboratory-confirmed influenza virus infections are rare, the excess mortality burden of influenza estimated from statistical models may more reliably quantify the impact of influenza in a population. METHODS We applied age-specific multiple linear regression models to all-cause and cause-specific mortality rates in Hong Kong from 1998 through 2009. The differences between estimated mortality rates in the presence or absence of recorded influenza activity were used to estimate influenza-associated excess mortality. RESULTS The annual influenza-associated all-cause excess mortality rate was 11.1 (95% confidence interval [CI], 7.2-14.6) per 100,000 person-years. We estimated an average of 751 (95% CI, 488-990) excess deaths associated with influenza annually from 1998 through 2009, with 95% of the excess deaths occurring in persons aged ≥65 years. Most of the influenza-associated excess deaths were from respiratory (53%) and cardiovascular (18%) causes. Influenza A(H3N2) epidemics were associated with more excess deaths than influenza A(H1N1) or B during the study period. CONCLUSIONS Influenza was associated with a substantial number of excess deaths each year, mainly among the elderly, in Hong Kong in the past decade. The influenza-associated excess mortality rates were generally similar in Hong Kong and the United States.

Age- and Sex-related Risk Factors for Influenza-associated Mortality in the United States Between 1997–2007

Limited information on age- and sex-specific estimates of influenza-associated death with different underlying causes is currently available. We regressed weekly age- and sex-specific US mortality outcomes underlying several causes between 1997 and 2007 to incidence proxies for influenza A/H3N2, A/H1N1, and B that combine data on influenza-like illness consultations and respiratory specimen testing, adjusting for seasonal baselines and time trends. Adults older than 75 years of age had the highest average annual rate of influenza-associated mortality, with 141.15 deaths per 100,000 people (95% confidence interval (CI): 118.3, 163.9), whereas children under 18 had the lowest average mortality rate, with 0.41 deaths per 100,000 people (95% CI: 0.23, 0.60). In addition to respiratory and circulatory causes, mortality with underlying cancer, diabetes, renal disease, and Alzheimer disease had a contribution from influenza in adult age groups, whereas mortality with underlying septicemia had a contribution from influenza in children. For adults, within several age groups and for several underlying causes, the rate of influenza-associated mortality was somewhat higher in men than in women. Of note, in men 50-64 years of age, our estimate for the average annual rate of influenza-associated cancer mortality per 100,000 persons (1.90, 95% CI: 1.20, 2.62) is similar to the corresponding rate of influenza-associated respiratory deaths (1.81, 95% CI: 1.42, 2.21). Age, sex, and underlying health conditions should be considered when planning influenza vaccination and treatment strategies.

What is the mechanism for persistent coexistence of drug-susceptible and drug-resistant strains of Streptococcus pneumoniae?

The rise of antimicrobial resistance in many pathogens presents a major challenge to the treatment and control of infectious diseases. Furthermore, the observation that drug-resistant strains have risen to substantial prevalence but have not replaced drug-susceptible strains despite continuing (and even growing) selective pressure by antimicrobial use presents an important problem for those who study the dynamics of infectious diseases. While simple competition models predict the exclusion of one strain in favour of whichever is ‘fitter’, or has a higher reproduction number, we argue that in the case of Streptococcus pneumoniae there has been persistent coexistence of drug-sensitive and drug-resistant strains, with neither approaching 100 per cent prevalence. We have previously proposed that models seeking to understand the origins of coexistence should not incorporate implicit mechanisms that build in stable coexistence ‘for free’. Here, we construct a series of such ‘structurally neutral’ models that incorporate various features of bacterial spread and host heterogeneity that have been proposed as mechanisms that may promote coexistence. We ask to what extent coexistence is a typical outcome in each. We find that while coexistence is possible in each of the models we consider, it is relatively rare, with two exceptions: (i) allowing simultaneous dual transmission of sensitive and resistant strains lets coexistence become a typical outcome, as does (ii) modelling each strain as competing more strongly with itself than with the other strain, i.e. self-immunity greater than cross-immunity. We conclude that while treatment and contact heterogeneity can promote coexistence to some extent, the in-host interactions between strains, particularly the interplay between coinfection, multiple infection and immunity, play a crucial role in the long-term population dynamics of pathogens with drug resistance.

Distribution of vaccine/antivirals and the ‘least spread line’ in a stratified population

We describe a prioritization scheme for an allocation of a sizeable quantity of vaccine or antivirals in a stratified population. The scheme builds on an optimal strategy for reducing the epidemics initial growth rate in a stratified mass-action model. The strategy is tested on the EpiSims network describing interactions and influenza dynamics in the population of Utah, where the stratification we have chosen is by age (0–6, 7–13, 14–18, adults). No prior immunity information is available, thus everyone is assumed to be susceptible—this may be relevant, possibly with the exception of persons over 50, to the 2009 H1N1 influenza outbreak. We have found that the top priority in an allocation of a sizeable quantity of seasonal influenza vaccinations goes to young children (0–6), followed by teens (14–18), then children (7–13), with the adult share being quite low. These results, which rely on the structure of the EpiSims network, are compared with the current influenza vaccination coverage levels in the US population.

Infection Fatality Risk of the Pandemic A(H1N1)2009 Virus in Hong Kong

One measure of the severity of a pandemic influenza outbreak at the individual level is the risk of death among people infected by the new virus. However, there are complications in estimating both the numerator and denominator. Regarding the numerator, statistical estimates of the excess deaths associated with influenza virus infections tend to exceed the number of deaths associated with laboratory-confirmed infection. Regarding the denominator, few infections are laboratory confirmed, while differences in case definitions and approaches to case ascertainment can lead to wide variation in case fatality risk estimates. Serological surveillance can be used to estimate the cumulative incidence of infection as a denominator that is more comparable across studies. We estimated that the first wave of the influenza A(H1N1)pdm09 virus in 2009 was associated with approximately 232 (95% confidence interval: 136, 328) excess deaths of all ages in Hong Kong, mainly among the elderly. The point estimates of the risk of death on a per-infection basis increased substantially with age, from below 1 per 100,000 infections in children to 1,099 per 100,000 infections in those 60-69 years of age. Substantial variation in the age-specific infection fatality risk complicates comparison of the severity of different influenza strains.

Epidemiologic Inference From the Distribution of Tuberculosis Cases in Households in Lima, Peru

BACKGROUND Tuberculosis (TB) often occurs among household contacts of people with active TB. It is unclear whether clustering of cases represents household transmission or shared household risk factors for TB. METHODS We used cross-sectional data from 764 households in Lima, Peru, to estimate the relative contributions of household and community transmission, the average time between cases, and the immunity afforded by a previous TB infection. RESULTS The distribution of cases per household suggests that almost 7 of 10 nonindex household cases were infected in the community rather than in the household. The average interval between household cases was 3.5 years. We observed a saturation effect in the number of cases per household and estimated that protective immunity conferred up to 35% reduction in the risk of disease. CONCLUSIONS Cross-sectional household data can elucidate the natural history and transmission dynamics of TB. In this high-incidence setting, we found that the majority of cases were attributable to community transmission and that household contacts of case patients derive some immunity from household exposures. Screening of household contacts may be an effective method of detecting new TB cases if carried out over several years.