Publications by authors named "Ariadna Padilla"

Objective: To understand how race and serious mental illness (SMI) interact for disruptive life events defined as financial (bankruptcy and judgement filings), and non-financial (arrests).

Methods: Patients were adults with schizophrenia (SCZ; N = 16,159) or bipolar I disorder (BPI; N = 30,008) matched 1:1 to patients without SMI (non-SMI) from health systems in Michigan and Southern California during 1/1/2007 through 12/31/2018. The main exposure was self-reported race, and the outcome was disruptive life events aggregated by Transunion.

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Article Synopsis
  • Efficient identification of social risks in seriously ill patients is vital for delivering patient-centered care, particularly for those who may be homeless or at risk of homelessness.* -
  • A natural language processing (NLP) algorithm was refined and applied to electronic health records, successfully identifying 1,737 out of 206,993 patients with serious illnesses as homeless or at risk.* -
  • The updated algorithm demonstrated high accuracy, with a sensitivity of 100% and a positive predictive value of 93.8%, indicating its potential for targeting interventions for these vulnerable patients.*
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Objective: To examine whether gentrification exposure is associated with future hypertension and diabetes control.

Methods: Linking records from an integrated health care system to census-tract characteristics, we identified adults with hypertension and/or diabetes residing in stably low-SES census tracts in 2014 (n = 69,524). We tested associations of census tract gentrification occurring between 2015 and 2019 with participants' disease control in 2019.

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Importance: Recent evidence links air pollution to the severity COVID-19 symptoms and to death from the disease. To date, however, few studies have assessed whether air pollution affects the sequelae to more severe states or recovery from COVID-19 in a cohort with individual data.

Objective: To assess how air pollution affects the transition to more severe COVID-19 states or to recovery from COVID-19 infection in a cohort with detailed patient information.

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Importance: There is a dearth of population-level data on major disruptive life events (defined here as arrests by a legal authority, address changes, bankruptcy, lien, and judgment filings) for patients with bipolar I disorder (BPI) or schizophrenia, which has limited studies on mental health and treatment outcomes.

Objective: To conduct a population-level study on disruptive life events by using publicly available data on disruptive life events, aggregated by a consumer credit reporting agency in conjunction with electronic health record (EHR) data.

Design, Setting, And Participants: This study used EHR data from 2 large, integrated health care systems, Kaiser Permanente Southern California and Henry Ford Health.

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Background: In April 2020, the Mayo Clinic helped establish the US Food and Drug Administration Expanded Access Protocol for COVID-19 (coronavirus disease 2019) convalescent plasma (CCP). The effectiveness of CCP in the published literature is contradictory because some retrospective studies showed benefit in reducing mortality and severe illness, whereas prospective randomized controlled trials demonstrated no benefit of CCP.

Objectives: To discuss (1) the implementation of CCP across Kaiser Permanente Southern California between April 2020 and April 2021, (2) retrospective multivariable analysis of 2,831 patients with COVID-19 who were transfused with CCP compared with 18,475 patients with COVID-19 who did not receive CCP, (3) how to reconcile contradictory published data regarding the efficacy of CCP, and (4) guidance regarding the future use of convalescent plasma in a large community hospital setting.

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Introduction: Coronavirus disease 2019 rapidly shifted health care toward telehealth. We assessed satisfaction with and preferences for telehealth among patients with irritable bowel syndrome (IBS).

Methods: We conducted a cross-sectional survey in an integrated healthcare system in Southern California with members aged 18-90 years with an International Classification of Diseases 9 and 10 codes for IBS from office-based encounters between June 1, 2018, and June 1, 2020.

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Objective: To evaluate whether a natural language processing (NLP) algorithm could be adapted to extract, with acceptable validity, markers of residential instability (ie, homelessness and housing insecurity) from electronic health records (EHRs) of 3 healthcare systems.

Materials And Methods: We included patients 18 years and older who received care at 1 of 3 healthcare systems from 2016 through 2020 and had at least 1 free-text note in the EHR during this period. We conducted the study independently; the NLP algorithm logic and method of validity assessment were identical across sites.

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