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Phylogenies of highly genetically variable viruses such as HIV-1 are potentially informative of epidemiological dynamics. Several studies have demonstrated the presence of clusters of highly related HIV-1 sequences, particularly among recently HIV-infected individuals, which have been used to argue for a high transmission rate during acute infection. Using a large set of HIV-1 subtype B pol sequences collected from men who have sex with men, we demonstrate that virus from recent infections tend to be phylogenetically clustered at a greater rate than virus from patients with chronic infection ('excess clustering') and also tend to cluster with other recent HIV infections rather than chronic, established infections ('excess co-clustering'), consistent with previous reports. To determine the role that a higher infectivity during acute infection may play in excess clustering and co-clustering, we developed a simple model of HIV infection that incorporates an early period of intensified transmission, and explicitly considers the dynamics of phylogenetic clusters alongside the dynamics of acute and chronic infected cases. We explored the potential for clustering statistics to be used for inference of acute stage transmission rates and found that no single statistic explains very much variance in parameters controlling acute stage transmission rates. We demonstrate that high transmission rates during the acute stage is not the main cause of excess clustering of virus from patients with early/acute infection compared to chronic infection, which may simply reflect the shorter time since transmission in acute infection. Higher transmission during acute infection can result in excess co-clustering of sequences, while the extent of clustering observed is most sensitive to the fraction of infections sampled.

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.

Information on the dynamics of the effective population size over time can be obtained from the analysis of phylogenies, through the application of time-varying coalescent models. This approach has been used to study the dynamics of many different viruses, and has demonstrated a wide variety of patterns, which have been interpreted in the context of changes over time in the 'effective number of infections', a quantity proportional to the number of infected individuals. However, for infectious diseases, the rate of coalescence is driven primarily by new transmissions i.e. the incidence, and only indirectly by the number of infected individuals through sampling effects. Using commonly used epidemiological models, we show that the coalescence rate may indeed reflect the number of infected individuals during the initial phase of exponential growth when time is scaled by infectivity, but in general, a single change in time scale cannot be used to estimate the number of infected individuals. This has important implications when integrating phylogenetic data in the context of other epidemiological data.

Objective: To describe long-term temporal trends in patient characteristics, processes of care, and in-hospital outcomes among unselected admissions within the contemporary coronary care unit., Design: Hospital administrative database that records both payment and operation data., Setting: Coronary care unit of a large, academic, tertiary-care medical institution., Patients: A total of 29,275 patients admitted from January 1, 1989 through December 31, 2006., Interventions: Unadjusted time-trend plots were created for all variables of interest, and multivariable modeling of coronary care unit death was performed., Measurements and Main Results: Temporal trends in Coronary Care Unit and in-hospital mortality, length-of-stay, demographic characteristics, discharge diagnoses, Coronary Care Unit procedures, and Charlson comorbidity scores were evaluated. Admission severity increased significantly over time (p < .001), but hospital length-of-stay decreased (p < .001). The proportion of coronary care unit admissions with non-ST-segment elevation myocardial infarction increased (p < .001), whereas ST-segment elevation myocardial infarction decreased (p < .001). The prevalence of non-cardiovascular diagnoses increased, with the rate greatest for comorbid critical illnesses, including sepsis, acute kidney injury, and respiratory failure (all p < .001). The use of non-cardiac procedures, such as mechanical ventilation and central venous catheterization, also increased over time (p < .001). Unadjusted coronary care unit and in-hospital mortality did not change during the study period, although death did decrease in the adjusted setting., Conclusions: Substantial changes have occurred over time in patient characteristics, diagnoses, and procedures within the coronary care unit of a large, academic medical center. In particular, there have been significant increases in noncardiovascular critical illness, the results of which may be influencing patient outcomes. These findings underscore an existing need to clarify the role of the coronary care unit in contemporary cardiovascular care and to develop strategies for optimal training, staffing, and clinical investigation.

Background: Respondent-driven sampling (RDS) is a network-based method used to recruit hidden populations. Since it is respondent-driven, RDS is prone to bias. However, these biases could facilitate recruitment of high-risk networks. We examined recruitment patterns of human immunodeficiency virus (HIV)-positive injection drug users (IDUs) and identified factors associated with being recruited by an HIV-positive IDU in a RDS-based study., Methods: IDUs aged > or =18, who injected within the last month and resided in Tijuana, Mexico, were recruited using RDS and underwent interviews and testing for HIV, syphilis, and tuberculosis. Weighted logistic regression was used to identify predictors of being recruited by an HIV-positive IDU., Results: Of 1056 IDUs, HIV-positive subjects comprised 4.4% of the sample and generated 4.7% of recruits, indicating that recruitment effectiveness did not vary by HIV-status. However, 10% of the subjects recruited by HIV-positive recruiters were infected with HIV as compared to 4.1% of subjects recruited by HIV-negative recruiters, (P = 0.06), a difference that, after controlling for whether the recruiter and recruit injected drugs together, attained statistical significance (P = 0.04), indicating that recruitment patterns differed by HIV-status. Factors independently associated with being recruited by an HIV-positive IDU included lifetime syphilis infection, ever having sex with an HIV-positive person, knowing someone with HIV/AIDS, being recruited at a shooting gallery, having recently used the local needle exchange program, and having a larger number of recent arrests for track marks., Conclusion: HIV-positive IDUs have different recruitment patterns than HIV-negative IDUs, with HIV-positive IDUs tending to recruit other HIV-positive IDUs. Social and environmental factors along with risk behaviors were independently associated with being the recruit of an HIV-positive IDU in Tijuana. Although the goal of this study was not to recruit HIV+ or other high-risk persons, our results suggest that RDS has the potential to successfully be used in the identification of HIV+ or other high risk individuals.

Systemic and pulmonary congestion is a central aspect of both acute and chronic heart failure and directly leads to many of the clinical manifestations of these syndromes. Therefore, diuretic therapy to treat congestion plays a fundamental role in heart failure management. However, although diuretics are the most common drugs prescribed for heart failure, there is limited quality evidence to guide their use. Unlike other components of the heart failure armamentarium, such as beta-blockers and angiotensin-converting enzyme inhibitors, diuretics (with the exception of aldosterone antagonists) have not been shown to decrease heart failure progression or improve mortality. Additionally, some observational data suggest that diuretics may actually be harmful in heart failure, contributing to neurohormonal activation, renal dysfunction, and potentially mortality. Despite these concerns, diuretics remain ubiquitous in heart failure management because of the need to address symptoms of congestion and the lack of alternative strategies. Recently, the development of a variety of potential adjuncts or alternatives to diuretic therapy has suggested the need for an active reappraisal of diuretic therapy for heart failure. The main classes of diuretics are the loop diuretics, potassium-sparing diuretics, and thiazides. Loop diuretics, the mainstay of acute and chronic therapy for heart failure, are "threshold drugs"; therefore, an adequate dose to achieve a pharmacodynamic effect (ie, to increase urine output) must be prescribed for effective therapy. The minimum dose to achieve diuresis and manage congestion should be used to minimize adverse effects. For patients refractory to initial dosing of intravenous diuretics, options include dose escalation, use of continuous infusion rather than intermittent boluses, or combination therapy with the addition of a thiazide or thiazide-like diuretic (eg, metolazone). Management of chronic heart failure often includes patient-directed titration of diuretics based on changes in symptoms or body weight in an attempt to decrease hospitalizations, although the efficacy of this strategy has not been tested in well-designed trials. Aldosterone antagonists, which are used primarily as neurohormonal agents rather than for their diuretic effects, are indicated for patients with systolic failure and moderate to severe symptoms, as long as renal function and serum potassium are stable and monitored closely. All diuretic therapy requires careful monitoring of electrolytes and renal function. Whether newer modalities for managing congestion (vasopressin antagonists, adenosine A(1) antagonists, and ultrafiltration therapy) will be an improvement over diuretic therapy will be determined by the results of multiple ongoing clinical trials.

We present a formalism for unifying the inference of population size from genetic sequences and mathematical models of infectious disease in populations. Virus phylogenies have been used in many recent studies to infer properties of epidemics. These approaches rely on coalescent models that may not be appropriate for infectious diseases. We account for phylogenetic patterns of viruses in susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) models of infectious disease, and our approach may be a viable alternative to demographic models used to reconstruct epidemic dynamics. The method allows epidemiological parameters, such as the reproductive number, to be estimated directly from viral sequence data. We also describe patterns of phylogenetic clustering that are often construed as arising from a short chain of transmissions. Our model reproduces the moments of the distribution of phylogenetic cluster sizes and may therefore serve as a null hypothesis for cluster sizes under simple epidemiological models. We examine a small cross-sectional sample of human immunodeficiency (HIV)-1 sequences collected in the United States and compare our results to standard estimates of effective population size. Estimated prevalence is consistent with estimates of effective population size and the known history of the HIV epidemic. While our model accurately estimates prevalence during exponential growth, we find that periods of decline are harder to identify.