Association of Social Support With Functional Outcomes in Older Adults Who Live Alone.

Importance: Older adults who live alone are at risk for poor health outcomes. Whether social support mitigates the risk of living alone, particularly when facing a sudden change in health, has not been adequately reported.

Objective: To assess if identifiable support buffers the vulnerability of a health shock while living alone.

Lifecourse Educational Trajectories and Hypertension in Midlife: An Application of Sequence Analysis.

Background: Higher educational attainment predicts lower hypertension. Yet, associations between nontraditional educational trajectories (eg, interrupted degree programs) and hypertension are less well understood, particularly among structurally marginalized groups who are more likely to experience these non-traditional trajectories.

Methods: In National Longitudinal Survey of Youth 1979 cohort data (N = 6 317), we used sequence and cluster analyses to identify groups of similar educational sequences-characterized by timing and type of terminal credential-that participants followed from age 14-48 years.

Understanding the benefits of different types and timing of education for mental health: A sequence analysis approach.

Objectives: Individuals increasingly experience delays or interruptions in schooling; we evaluate the association between these non-traditional education trajectories and mental health.

Methods: Using year-by-year education data for 7,501 National Longitudinal Survey of Youth 1979 participants, ages 14-48 (262,535 person-years of education data), we applied sequence analysis and a clustering algorithm to identify educational trajectory groups, incorporating both type and timing to credential. Linear regression models, adjusted for early-life confounders, evaluated relationships between educational trajectories and mental health component scores (MCS) from the 12-item short form instrument at age 50.

The Impact of Different Types of Violence on Ebola Virus Transmission During the 2018-2020 Outbreak in the Democratic Republic of the Congo.

Background: Our understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited.

Methods: We used time-series data of case counts to compare individuals in Ebola-affected health zones in DRC, April 2018-August 2019. Exposure was number of violent events per health zone, categorized into Ebola-targeted or Ebola-untargeted, and into civilian-induced, (para)military/political, or protests.

Estimating the impact of violent events on transmission in Ebola virus disease outbreak, Democratic Republic of the Congo, 2018-2019.

Introduction: As of April 2019, the current Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) is occurring in a longstanding conflict zone and has become the second largest EVD outbreak in history. It is suspected that after violent events occur, EVD transmission will increase; however, empirical studies to understand the impact of violence on transmission are lacking. Here, we use spatial and temporal trends of EVD case counts to compare transmission rates between health zones that have versus have not experienced recent violent events during the outbreak.

Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment).