Search

BACKGROUND: There is mixed evidence regarding the direction of a potential association between post-traumatic stress disorder (PTSD) and suicide mortality. AIMS: This is the first population-based study to account for both PTSD diagnosis and PTSD symptom severity simultaneously in the examination of suicide mortality. METHOD: Retrospective study that included all US Department of Veterans Affairs (VA) patients with a PTSD diagnosis and at least one symptom severity assessment using the PTSD Checklist (PCL) between 1 October 1999 and 31 December 2018 (n = 754 197). We performed multivariable proportional hazards regression models using exposure groups defined by level of PTSD symptom severity to estimate suicide mortality rates. For patients with multiple PCL scores, we performed additional models using exposure groups defined by level of change in PTSD symptom severity. We assessed suicide mortality using the VA/Department of Defense Mortality Data Repository. RESULTS: Any level of PTSD symptoms above the minimum threshold for symptomatic remission (i.e. PCL score >18) was associated with double the suicide mortality rate at 1 month after assessment. This relationship decreased over time but patients with moderate to high symptoms continued to have elevated suicide rates. Worsening PTSD symptoms were associated with a 25% higher long-term suicide mortality rate. Among patients with improved PTSD symptoms, those with symptomatic remission had a substantial and sustained reduction in the suicide rate compared with those without symptomatic remission (HR = 0.56; 95% CI 0.37-0.88). CONCLUSIONS: Ameliorating PTSD can reduce risk of suicide mortality, but patients must achieve symptomatic remission to attain this benefit.

A longitudinal study of low income North Carolina mothers (N=842) found that variables significantly associated with a substantiated maltreatment report in the second or third year of life were first year maltreatment reports and participation in Medicaid. Also, three interactions between a stressful life indicator variable and a social support indicator variable were predictors of substantiated maltreatment reports. (DB)

Rationale aims and objectives: As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These tools may not be available, feasible, or acceptable in all clinical scenarios. Alternative methods of assessment, including natural language processing (NLP) of clinical notes, may improve the completeness of quality measurement in real‐world practice. Our objective was to measure the quality of care for a set of evidence‐based practices using structured EMR data alone, and then supplement those measures with additional data derived from NLP. Method: As a case example, we studied the quality of care for posttraumatic stress disorder (PTSD) in the United States Department of Veterans Affairs (VA) over a 20‐year period. We measured two aspects of PTSD care, including delivery of evidence‐based psychotherapy (EBP) and associated use of measurement‐based care (MBC), using structured EMR data. We then recalculated these measures using additional data derived from NLP of clinical note text. Results: There were 2 098 389 VA patients with a diagnosis of PTSD between 2000 and 2019, 72% (n = 1 515 345) of whom had not previously received EBP for PTSD and were treated after a 2015 mandate to document EBP using templates that generate structured EMR data. Using structured EMR data, we determined that 3.2% (n = 48 004) of those patients met our EBP for PTSD quality standard between 2015 and 2019, and 48.1% (n = 23 088) received associated MBC. With the addition of NLP‐derived data, estimates increased to 4.1% (n = 62 789) and 58.0% (n = 36 435), respectively. Conclusion: Healthcare quality data can be significantly improved by supplementing structured EMR data with NLP‐derived data. By using NLP, health systems may be able to fill the gaps in documentation when structured tools are not yet available or there are barriers to using them in clinical practice. [ABSTRACT FROM AUTHOR]

This article examines mental health outcomes of children who have witnessed violence in their social environment and/or have been physically abused. Participants (n = 167) come from a longitudinal study on child maltreatment. Out-comes—including depression, anger, and anxiety—are measured by the Child Behavior Checklist and the Trauma Symptom Checklist for Children. The authors used adjusted multivariate analyses to test the statistical significance of associations. The majority of children were female (57%) and non-White (64%). One third had been physically victimized; 46% had witnessed moderate-high levels of violence. Results confirm that children are negatively affected by victimization and violence they witness in their homes and neighborhoods. Victimization was a significant predictor of child aggression and depression; witnessed violence was found to be a significant predictor of aggression, depression, anger, and anxiety. Implications will be discussed. [ABSTRACT FROM AUTHOR]

Objectives: A recent study found that lung cancer may be associated with exposures encountered in the electrical utility industry. To further evaluate this possibility, data were collected and analysed from five large electrical utility companies in the United States.Methods: A cohort of 138905 male workers employed between 1950 and 1986 was followed up for mortality to the end of 1988, with 20733 deaths identified of which 1692 were due to lung cancer. Mortality from lung cancer was examined in relation to the duration of employment in specific jobs thought to have high exposure to 60 Hz magnetic fields and to an index of cumulative exposure to magnetic fields based on personal measurements. Exposure to pulsed electromagnetic fields (PEMFs) as estimated from another study was also considered. Poisson regression generated rate ratios for categories of exposure based on comparisons within the cohort adjusted for age, calendar year, race, socioeconomic status, work status, and estimated exposure to asbestos.Results: Mortality rose modestly with duration of work as an electrician or power plant operator reaching rate ratios of 1.4 with > or = 20 years in those jobs but not with duration of work as a lineman or a combination of jobs thought to have high exposures to 60 Hz magnetic fields or PEMFs. Cumulative indices of exposure to 60 Hz magnetic fields and PEMFs were both associated with rate ratios of 1.2-1.3 in the highest intervals.Conclusions: These data suggest that lung cancer is not strongly associated with duration of employment in specific jobs associated with high potential exposure to 60 Hz magnetic fields or to PEMFs. Small associations of lung cancer with indices of both 60 Hz magnetic fields and PEMFs leave open the possibility that larger associations have been diluted through exposure misclassification. Refined exposure assessment, especially to PEMFs, would be required to evaluate that possibility. [ABSTRACT FROM AUTHOR]

Background: Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide., Methods: Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups., Results: High-risk ( n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- ( n = 2149) and low-risk ( n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients., Conclusions: Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.

Background: Suicide risk prediction models frequently rely on structured electronic health record (EHR) data, including patient demographics and health care usage variables. Unstructured EHR data, such as clinical notes, may improve predictive accuracy by allowing access to detailed information that does not exist in structured data fields. To assess comparative benefits of including unstructured data, we developed a large case-control dataset matched on a state-of-the-art structured EHR suicide risk algorithm, utilized natural language processing (NLP) to derive a clinical note predictive model, and evaluated to what extent this model provided predictive accuracy over and above existing predictive thresholds., Methods: We developed a matched case-control sample of Veterans Health Administration (VHA) patients in 2017 and 2018. Each case (all patients that died by suicide in that interval, n = 4,584) was matched with 5 controls (patients who remained alive during treatment year) who shared the same suicide risk percentile. All sample EHR notes were selected and abstracted using NLP methods. We applied machine-learning classification algorithms to NLP output to develop predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy overall and for high-risk patients., Results: The best performing NLP-derived models provided 19% overall additional predictive accuracy (AUC = 0.69; 95% CI, 0.67, 0.72) and 6-fold additional risk concentration for patients at the highest risk tier (top 0.1%), relative to the structured EHR model., Conclusions: The NLP-supplemented predictive models provided considerable benefit when compared to conventional structured EHR models. Results support future structured and unstructured EHR risk model integrations., (© Copyright 2023 Physicians Postgraduate Press, Inc.)