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The association between current use of oral contraceptives (OCs) among women younger than 50 years (n  = 306 541), and hormone therapy (HT) among women aged 50 years or older (n  = 323 203), and coronavirus 2019 (COVID-19) infection and hospitalization was evaluated in this population-based cohort. Current OC/HT use was recorded monthly using prescription dispensing data. COVID-19 infections were identified from March 2020 through February 2021. COVID-19 infections and hospitalizations were identified through diagnosis codes and laboratory tests. We used weighted generalized estimating equations models to estimate multivariable adjusted odds ratios (aORs) for COVID-19 infection associated with time-varying OC/HT use. Among women with COVID-19, logistic regression models were used to evaluate OC/HT use and COVID-19 hospitalization. Over 12 months, 11 727 (3.8%) women younger than 50 years and 8661 (2.7%) women aged 50 years or older experienced COVID-19 infections. There was no evidence of an association between OC use and infection (aOR = 1.05; 95% CI, 0.97-1.12). There was a modest association between HT use and infection (aOR = 1.19; 95% CI, 1.03-1.38). Women using OCs had a 39% lower risk of hospitalization (aOR = 0.61; 95% CI, 0.38-1.00), but there was no association of HT use with hospitalization (aOR = 0.89; 95% CI, 0.51-1.53). These findings do not suggest a meaningfully greater risk of COVID-19 infection associated with OC or HT use. OC use may be associated with lower COVID-19 hospitalization risk. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to examine the association between portal use and end-of-life (EOL) outcomes in the last year of life. Methods: A retrospective cohort (n = 6,517) study at Kaiser Permanente Colorado among adults with serious illness deceased between January 1, 2016, and June 30, 2019. Portal use was categorized into engagement types: no use, nonactive, active without a provider, and active with a provider. EOL outcomes were hospitalizations in the month before death, last-year advance directive completion, and hospice use. Association between EOL outcomes and levels of portal use was assessed using χ2 statistics and generalized linear models. Results: Higher portal engagement types were associated with higher rates of hospitalizations (p = 0.0492), advance directive completion (p = 0.0226), and hospice use (p = 0.0070). Conclusion: Portal use in the last year of life was associated with increases in a poor EOL outcome, hospitalizations, and beneficial EOL outcomes, advance directives, and hospice care. [ABSTRACT FROM AUTHOR]

We measured the frequency of clinicians' assessments for access to lethal means, including firearms and medications in patients at risk of suicide from electronic medical and mental health records in outpatient and emergency settings. We included adult patients who reported suicide ideation on the PHQ-9 depression screener in behavioral health and primary care outpatient settings of a large integrated health system in the U.S. and those with suicidal behavior treated in the emergency department. Two separate natural language processing queries were developed on medical record text documentation: (1) assessment for access to firearms (8,994 patients), (2) assessment for access to medications (4,939 patients). Only 35% of patients had documentation of firearm or medication assessment in the month following treatment for suicidal behavior in the emergency setting. Among those reporting suicidal ideation in outpatient setting, 31% had documentation of firearm assessment and 23% for medication assessment. The accuracy of the estimates was very good for firearm assessment (F1 = 89%) and medication assessment in the outpatient setting (F1 = 91%) and fair for medication assessment in the emergency setting (F1 = 70%) due to more varied documentation styles. Lethal means assessment following report of suicidal ideation or behavior is low in a nonacademic health care setting. Until health systems implement more structured documentation to measure lethal means assessment, such as discrete data field, NLP methods may be used to conduct research and surveillance of this important prevention practice in real-world settings. [ABSTRACT FROM AUTHOR]

The article discusses a study on the risk of death or myocardial infarction (MI) for patients who delayed filling their initial clopidogrel prescription on hospital discharge after stent implantation. The study used clinical registry and administrative data from three integrated health care delivery systems. It concludes that patients who delay filling their initial clopidogrel prescription after hospital discharge faces increased risk of death/MI and stresses the need to improve patients care during the transition process after stent implantation.

AbstractObjectiveMethodsResultsConclusions\nHIGHLIGHTSSafety planning for suicide prevention is an important quality metric for Zero Suicide implementation. We describe the development, validation, and application of electronic health record (EHR) programs to measure uptake of safety planning practices across six integrated healthcare systems as part of a Zero Suicide evaluation study.Safety planning was documented in narrative notes and structured EHR templates using the Stanley Brown Safety Planning Intervention (SBSPI) in response to a high-risk cutoff score on the Columbia Suicide Severity Rating Scale (CSSRS). Natural Language Processing (NLP) metrics were developed and validated using chart review to characterize practices documented in narrative notes. We applied NLP to measure frequency of documentation in the narrative text and standard programming methods to examine structured SBSPI templates from 2010–2022.Chart reviews found three safety planning practices documented in narrative notes that were delivered to at least half of patients at risk: professional contacts, lethal means counseling for firearms, and lethal means counseling for medication access/storage. NLP methods were developed to identify these practices in clinical text with high levels of accuracy (Sensitivity, Specificity, & PPV ≥ 82%). Among visits with a high-risk CSSRS, 40% (Range 2–73% by health system) had an SBSPI template within 1 year of implementation.This is one of the first reports describing development of measures that leverage electronic health records to track use of suicide prevention safety plans. There are opportunities to use the methods developed here in future evaluations of safety planning.Measuring safety planning delivery in real-world systems to understand quality of suicide prevention care is challenging.Natural Language Processing (NLP) methods effectively identified some safety planning practices in electronic health records (EHR) from all notes ensuring a comprehensive measurement, but NLP will require updates/testing for local documentation practices.Structured safety planning templates in the EHR using the Stanley Brown Safety Planning Intervention improve ease and accuracy of measurement but may be less comprehensive than NLP for capturing all instances of safety planning documentation.Measuring safety planning delivery in real-world systems to understand quality of suicide prevention care is challenging.Natural Language Processing (NLP) methods effectively identified some safety planning practices in electronic health records (EHR) from all notes ensuring a comprehensive measurement, but NLP will require updates/testing for local documentation practices.Structured safety planning templates in the EHR using the Stanley Brown Safety Planning Intervention improve ease and accuracy of measurement but may be less comprehensive than NLP for capturing all instances of safety planning documentation. [ABSTRACT FROM AUTHOR]

Background: Breast cancer survivors are at increased risk for developing second primary cancers compared with the general population. Little is known about whether body mass index (BMI) increases this risk. We examined the association between BMI and second cancers among women with incident invasive breast cancer., Methods: This retrospective cohort included 6481 patients from Kaiser Permanente Colorado and Washington of whom 822 (12.7%) developed a second cancer (mean follow-up was 88.0 months). BMI at the first cancer was extracted from the medical record. Outcomes included: 1) all second cancers, 2) obesity-related second cancers, 3) any second breast cancer, and 4) estrogen receptor-positive second breast cancers. Multivariable Poisson regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for second cancers associated with BMI adjusted for site, diagnosis year, treatment, demographic, and tumor characteristics., Results: The mean age at initial breast cancer diagnosis was 61.2 (SD = 11.8) years. Most cases were overweight (33.4%) or obese (33.8%) and diagnosed at stage I (62.0%). In multivariable models, for every 5 kg/m2 increase in BMI, the risk of any second cancer diagnosis increased by 7% (RR = 1.07, 95% CI = 1.01 to 1.14); 13% (RR = 1.13, 95% CI = 1.05 to 1.21) for obesity-related cancers, 11% (RR = 1.11, 95% CI = 1.02 to 1.21) for a second breast cancer, and 15% (RR = 1.15, 95% CI = 1.04 to 1.27) for a second estrogen receptor-positive breast cancer., Conclusions: We observed a statistically significant increased risk of second cancers associated with increasing BMI. These findings have important public health implications given the prevalence of overweight and obesity in breast cancer survivors and underscore the need for effective prevention strategies., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Identifying care needs for newly enrolled or newly insured individuals is important under the Affordable Care Act. Systematically collected patient-reported information can potentially identify subgroups with specific care needs prior to service use., Methods: We conducted a retrospective cohort investigation of 6,047 individuals who completed a 10-question needs assessment upon initial enrollment in Kaiser Permanente Colorado (KPCO), a not-for-profit integrated delivery system, through the Colorado State Individual Exchange. We used responses from the Brief Health Questionnaire (BHQ), to develop a predictive model for cost for receiving care in the top 25 percent, then applied cluster analytic techniques to identify different high-cost subpopulations. Per-member, per-month cost was measured from 6 to 12 months following BHQ response., Results: BHQ responses significantly predictive of high-cost care included self-reported health status, functional limitations, medication use, presence of 0-4 chronic conditions, self-reported emergency department (ED) use during the prior year, and lack of prior insurance. Age, gender, and deductible-based insurance product were also predictive. The largest possible range of predicted probabilities of being in the top 25 percent of cost was 3.5 percent to 96.4 percent. Within the top cost quartile, examples of potentially actionable clusters of patients included those with high morbidity, prior utilization, depression risk and financial constraints; those with high morbidity, previously uninsured individuals with few financial constraints; and relatively healthy, previously insured individuals with medication needs., Conclusions: Applying sequential predictive modeling and cluster analytic techniques to patient-reported information can identify subgroups of individuals within heterogeneous populations who may benefit from specific interventions to optimize initial care delivery.