In clinical data from 409 hospitals, sepsis was present in 6% of adult hospitalizations, and in contrast to claims-based analyses, neither the incidence of sepsis nor the combined outcome of death or discharge to hospice changed significantly between 2009-2014. The findings also suggest that EHR-based clinical data provide more objective estimates than claims-based data for sepsis surveillance.

Sociability as a disposition describes a tendency to affiliate with others (vs. be alone). Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting behavioral sociability tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To provide an assessment of individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 1078) to collect real-world data about the sensed social behaviors of young adults across four communication channels: conversations, phone calls, text messages, and messaging and social media application use. To examine individual differences, we first focused on establishing between-person variability in daily social behavior, examining stability of and relationships among daily sensed social behavior tendencies. To explore factors that may explain the observed individual differences in sensed social behavior, we then expanded our focus to include other time estimates (e.g., times of the day, days of the week) and personality traits. In doing so, we present the first large-scale descriptive portrait of behavioral sociability patterns, characterizing the degree of social behavior young adults typically engaged in and mapping behavioral to self-reported personality dispositions. Our discussion focuses on how the observed sociability patterns compare to previous research on young adults' social behavior. We conclude by pointing to areas for future research aimed at understanding sociability using mobile sensing and other naturalistic observation methods for the assessment of social behavior.

In the analysis of prevention and intervention studies, it is often important to investigate whether treatment effects vary among subgroups of patients defined by individual characteristics. These “subgroup analyses” can provide information about how best to use a new prevention or intervention program. However, subgroup analyses can be misleading if they test data-driven hypotheses, employ inappropriate statistical methods, or fail to account for multiple testing. These problems have led to a general suspicion of findings from subgroup analyses. This article discusses sound methods for conducting subgroup analyses to detect moderators. Multiple authors have argued that, to assess whether a treatment effect varies across subgroups defined by patient characteristics, analyses should be based on tests for interaction rather than treatment comparisons within the subgroups. We discuss the concept of heterogeneity and its dependence on the metric used to describe treatment effects. We discuss issues of multiple comparisons related to subgroup analyses and the importance of considering multiplicity in the interpretation of results. We also discuss the types of questions that would lead to subgroup analyses and how different scientific goals may affect the study at the design stage. Finally, we discuss subgroup analyses based on post-baseline factors and the complexity associated with this type of subgroup analysis.

Objectives: Prior studies have reported that hospital-onset sepsis is associated with higher mortality rates than community-onset sepsis. Most studies, however, have used inconsistent case-finding methods and applied limited risk-adjustment for potential confounders. We used consistent sepsis criteria and detailed electronic clinical data to elucidate the epidemiology and mortality associated with hospital-onset sepsis. Design: Retrospective cohort study. Setting: 136 U.S. hospitals in the Cerner HealthFacts dataset. Patients: Adults hospitalized in 2009–2015. Interventions: None. Measurements and Main Results: We identified sepsis using Centers for Disease Control and Prevention Adult Sepsis Event criteria and estimated the risk of in-hospital death for hospital-onset sepsis versus community-onset sepsis using logistic regression models. In patients admitted without community-onset sepsis, we estimated risk of death associated with hospital-onset sepsis using Cox regression models with sepsis as a time-varying covariate. Models were adjusted for baseline characteristics and severity of illness. Among 2.2 million hospitalizations, there were 95,154 sepsis cases: 83,620 (87.9%) community-onset sepsis and 11,534 (12.1%) hospital-onset sepsis (0.5% of hospitalized cohort). Compared to community-onset sepsis, hospital-onset sepsis patients were younger (median 66 vs 68 yr) but had more comorbidities (median Elixhauser score 14 vs 11), higher Sequential Organ Failure Assessment scores (median 4 vs 3), higher ICU admission rates (61% vs 44%), longer hospital length of stay (median 19 vs 8 d), and higher in-hospital mortality (33% vs 17%) (p < 0.001 for all comparisons). On multivariate analysis, hospital-onset sepsis was associated with higher mortality versus community-onset sepsis (odds ratio, 2.1; 95% CI, 2.0–2.2) and patients admitted without sepsis (hazard ratio, 3.0; 95% CI, 2.9–3.2). Conclusions: Hospital-onset sepsis complicated one in 200 hospitalizations and accounted for one in eight sepsis cases, with one in three patients dying in-hospital. Hospital-onset sepsis preferentially afflicted ill patients but even after risk-adjustment, they were twice as likely to die as community-onset sepsis patients; in patients admitted without sepsis, hospital-onset sepsis tripled the risk of death. Hospital-onset sepsis is an important target for surveillance, prevention, and quality improvement initiatives.

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