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Homelessness prevention and rapid rehousing (RRH) programs are increasingly important components of the homeless assistance system in the United States. Yet, there are key gaps in knowledge about the dynamics of the utilization of these programs, with scant attention paid to examining the duration of homelessness prevention and RRH service episodes or to patterns of repeated use of these programs over time. To address these gaps, we use data from the U.S. Department of Veterans Affairs' (VA) Supportive Services for Veteran Families (SSVF) program-the largest program in the country providing homelessness prevention and RRH services-to assess the relationship between individual and program-level factors and exits to stable housing, length of service episodes, and patterns of repeated service use over time. We analyze data for a primary cohort of 570,798 of Veterans who received SSVF services during Fiscal Years (FY) 2012-2021, and for separate cohorts of Veterans who received SSVF prevention and RRH services, respectively, during FY 2016-2021. We find that participants' income, indicators of their health status, their use of other VA homeless programs, and rurality are consistent predictors of our outcomes. These findings have implications for how to allocate homelessness prevention and RRH resources in the most efficient manner to help households maintain or obtain stable housing.

Housing stability is an important determinant of health. The US Department of Veterans Affairs (VA) administers several programs to assist Veterans experiencing unstable housing. Measuring long-term housing stability of Veterans who receive assistance from VA is difficult due to a lack of standardized structured documentation in the Electronic Health Record (EHR). However, the text of clinical notes often contains detailed information about Veterans’ housing situations that may be extracted using natural language processing (NLP). We present a novel NLP-based measurement of Veteran housing stability: Relative Housing Stability in Electronic Documentation (ReHouSED). We first develop and evaluate a system for classifying documents containing information about Veterans’ housing situations. Next, we aggregate information from multiple documents to derive a patient-level measurement of housing stability. Finally, we demonstrate this method’s ability to differentiate between Veterans who are stably and unstably housed. Thus, ReHouSED provides an important methodological framework for the study of long-term housing stability among Veterans receiving housing assistance.

Purpose Posttraumatic stress disorder (PTSD), depressive disorders (DD), and substance use disorders (SUD) are prevalent among veterans. A positive military sexual trauma (MST) screen is associated with higher likelihood of each of these disorders. The current study examined the associations between MST, gender, and co-occurring PTSD, DD, and SUD among veterans receiving services at the Department of Veterans Affairs to inform assessment and treatment. We were specifically interested in the interactions between MST and gender on co-occurring disorders. Methods The sample included 494,822 Department of Veterans Affairs service–seeking veterans (12.5% women) deployed to Iraq and Afghanistan who recently separated from the military and were screened for MST between 2004 and 2013. Main Findings Veterans with positive MST screens had higher odds than those with negative screens of individual and co-occurring PTSD, DD, and SUD. The association between positive MST screens and diagnostic outcomes, including PTSD, was stronger for women than for men, and the association between positive MST screens and some diagnostic outcomes, including DD, was stronger for men than for women. Conclusions These results highlight the importance of assessing for and recognizing the potential MST and gender interactions in the clinical context among veterans with co-occurring PTSD, DD, and/or SUD.

The Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor method requires removal of 18 types of protected health information (PHI) from clinical documents to be considered “de-identified” prior to use for research purposes. Human review of PHI elements from a large corpus of clinical documents can be tedious and error-prone. Indeed, multiple annotators may be required to consistently redact information that represents each PHI class. Automated de-identification has the potential to improve annotation quality and reduce annotation time. For instance, using machine-assisted annotation by combining de-identification system outputs used as pre-annotations and an interactive annotation interface to provide annotators with PHI annotations for “curation” rather than manual annotation from “scratch” on raw clinical documents. In order to assess whether machine-assisted annotation improves the reliability and accuracy of the reference standard quality and reduces annotation effort, we conducted an annotation experiment. In this annotation study, we assessed the generalizability of the VA Consortium for Healthcare Informatics Research (CHIR) annotation schema and guidelines applied to a corpus of publicly available clinical documents called MTSamples. Specifically, our goals were to (1) characterize a heterogeneous corpus of clinical documents manually annotated for risk-ranked PHI and other annotation types (clinical eponyms and person relations), (2) evaluate how well annotators apply the CHIR schema to the heterogeneous corpus, (3) compare whether machine-assisted annotation (experiment) improves annotation quality and reduces annotation time compared to manual annotation (control), and (4) assess the change in quality of reference standard coverage with each added annotator’s annotations.

The TDMM was successfully used to analyze the capacitance, the electric field, and the ESD current of an ESD suppressor. The obtained capacitance was also validated by measurement data. It is found that the maximum electric fields in the air gap between two discharge electrodes for the air gap width of 5–50 μm are much higher than the threshold electric field for air breakdown. A maximum ESD current of 4.7 mA was obtained during an ESD event, which may be sufficient to cause a malfunction in a high sensitivity integrated circuit.