Air pollution and the sequelae of COVID-19 patients: A multistate analysis.

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. DESIGN AND OUTCOMES: We used a cohort design that followed patients admitted to hospital in the Kaiser Permanente Southern California (KPSC) Health System, which has 4.7 million members with characteristics similar to the general population. Enrollment began on 06/01/2020 and ran until 01/30/2021 for all patients admitted to hospital while ill with COVID-19. All possible states of sequelae were considered, including deterioration to intensive care, to death, discharge to recovery, or discharge to death. Transition risks were estimated with a multistate model. We assessed exposure using chemical transport model that predicted ambient concentrations of nitrogen dioxide, ozone, and fine particulate matter (PM2.5) at a 1 km scale. RESULTS: Each increase in PM2.5 concentration equivalent to the interquartile range was associated with increased risk of deterioration to intensive care (HR of 1.16; 95% CI: 1.12-1.20) and deterioration to death (HR of 1.11; 95% CI: 1.04-1.17). Results for ozone were consistent with PM2.5 effects, but ozone also affected the transition from recovery to death: HR of 1.24 (95% CI: 1.01-1.51). NO2 had weaker effects but displayed some elevated risks. CONCLUSIONS: PM2.5 and ozone were significantly associated with transitions to more severe states while in hospital and to death after discharge from hospital. Reducing air pollution could therefore lead to improved prognosis for COVID-19 patients and a sustainable means of reducing the health impacts of coronaviruses now and in the future.

Using natural language processing to identify the status of homelessness and housing instability among serious illness patients from clinical notes in an integrated healthcare system.

Background: Efficiently identifying the social risks of patients with serious illnesses (SIs) is the critical first step in providing patient-centered and value-driven care for this medically vulnerable population. Objective: To apply and further hone an existing natural language process (NLP) algorithm that identifies patients who are homeless/at risk of homeless to a SI population. Methods: Patients diagnosed with SI between 2019 and 2020 were identified using an adapted list of diagnosis codes from the Center for Advance Palliative Care from the Kaiser Permanente Southern California electronic health record. Clinical notes associated with medical encounters within 6 months before and after the diagnosis date were processed by a previously developed NLP algorithm to identify patients who were homeless/at risk of homelessness. To improve the generalizability to the SI population, the algorithm was refined by multiple iterations of chart review and adjudication. The updated algorithm was then applied to the SI population. Results: Among 206 993 patients with a SI diagnosis, 1737 (0.84%) were identified as homeless/at risk of homelessness. These patients were more likely to be male (51.1%), age among 45-64 years (44.7%), and have one or more emergency visit (65.8%) within a year of their diagnosis date. Validation of the updated algorithm yielded a sensitivity of 100.0% and a positive predictive value of 93.8%. Conclusions: The improved NLP algorithm effectively identified patients with SI who were homeless/at risk of homelessness and can be used to target interventions for this vulnerable group.

Association of neighborhood gentrification and residential moves with hypertension and diabetes control in Los Angeles County, 2014-2019: A retrospective cohort study.

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. Secondary analyses considered the role of residential moves (possible displacement), race and ethnicity, and age. RESULTS: Gentrification exposure was associated with improved odds of hypertension control (aOR: 1.08; 95% CI: 1.00, 1.17), especially among non-Hispanic Whites and adults >65 years. Gentrification was not associated with diabetes control overall, but control improved in the Hispanic subgroup. Disease control was similar regardless of residential moves in the overall sample, but disparate associations emerged in models stratified by race and ethnicity. CONCLUSIONS: Residents of newly gentrifying neighborhoods may experience modestly improved odds of hypertension and/or diabetes control, but associations may differ across population subgroups. POLICY IMPLICATIONS: Gentrification may support-or at least not harm-cardiometabolic health for some residents. City leaders and health systems could partner with impacted communities to ensure that neighborhood development meets the goals and health needs of all residents and does not exacerbate health disparities.

Assessment of Disruptive Life Events for Individuals Diagnosed With Schizophrenia or Bipolar I Disorder Using Data From a Consumer Credit Reporting Agency.

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. Cohorts of patients diagnosed from 2007 to 2019 with BPI or schizophrenia were matched 1:1 by age at analysis, age at diagnosis (if applicable), sex, race and ethnicity, and Medicaid status to (1) an active comparison group with diagnoses of major depressive disorder (MDD) and (2) a general health (GH) cohort without diagnoses of BPI, schizophrenia, or MDD. Patients with diagnoses of BPI or schizophrenia and their respective comparison cohorts were matched to public records data aggregated by a consumer credit reporting agency (98% match rate). Analysis took place between November 2020 and December 2022. Main Outcomes and Measures: The differences in the occurrence of disruptive life events among patients with BPI or schizophrenia and their comparison groups. Results: Of 46 167 patients, 30 008 (65%) had BPI (mean [SD] age, 42.6 [14.2] years) and 16 159 (35%) had schizophrenia (mean [SD], 41.4 [15.1] years). The majoriy of patients were White (30 167 [65%]). In addition, 18 500 patients with BPI (62%) and 6552 patients with schizophrenia (41%) were female. Patients with BPI were more likely to change addresses than patients in either comparison cohort (with the incidence ratio being as high as 1.25 [95% CI, 1.23-1.28]) when compared with GH cohort. Patients with BPI were also more likely to experience any of the financial disruptive life events with odds ratio ranging from 1.15 [95% CI, 1.07-1.24] to 1.50 [95% CI, 1.42-1.58]). The largest differences in disruptive life events were seen in arrests of patients with either BPI or schizophrenia compared with GH peers (3.27 [95% CI, 2.84-3.78] and 3.04 [95% CI, 2.57-3.59], respectively). Patients with schizophrenia had fewer address changes and were less likely to experience a financial event than their matched comparison cohorts. Conclusions and Relevance: This study demonstrated that data aggregated by a consumer credit reporting agency can support population-level studies on disruptive life events among patients with BPI or schizophrenia.

Beyond clinical outcomes: Case control study of the role of race in disruptive life events for people with serious mental illness.

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. We hypothesized that Black patients with SCZ or BPI would be the most likely to experience a disruptive life event when compared to Black patients without SMI, and all White or Asian patients regardless of mental illness. RESULTS: Black patients with SCZ had the least likelihood (37% lower) and Asian patients with BPI had the greatest likelihood (2.25 times higher) of experiencing a financial disruptive life event among all patients in the study. There was no interaction of race with either SCZ or BPI for experiencing an arrest. The findings did not support our hypotheses for patients with SCZ and partially supported them for patients with BPI. CONCLUSIONS: Clinical initiatives to assess social determinants of health should consider a focus on Asian patients with BPI.

Satisfaction With Telehealth Among Patients With Irritable Bowel Syndrome During COVID-19: Cross-Sectional Survey Findings.

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. Eligible patients were emailed a survey assessing telehealth satisfaction overall and by patient-related factors, IBS characteristics, health and technologic literacy, utilization, and coronavirus disease 2019 perceptions. We identified perceived telehealth benefits and challenges. Multivariable logistic regression identified predictors of telehealth dissatisfaction. RESULTS: Of 44,789 surveys sent, 5,832 (13.0%) patients responded and 1,632 (3.6%) had Rome IV IBS. Among 1,314 (22.5%) patients with IBS and prior telehealth use (mean age 52.6 years [17.4]; 84.9% female; and 59.4% non-Hispanic White, 29.0% Hispanic, and 5.6% non-Hispanic Black), 898 (68.3%) were satisfied, 130 (9.9%) were dissatisfied, and 286 (21.8%) felt neutral. In addition, 78.6% would use telehealth again. Independent predictors of telehealth dissatisfaction include social media use of once a week or less (adjusted odds ratio [OR] = 2.1; 1.3-3.5), duration of IBS for <1 year (adjusted OR = 8.2; 1.9-35.8), and willingness to travel 60 plus minutes for face-to-face visits (adjusted OR = 2.6; 1.4-3.7). Patients' main concern with telehealth was a lack of physical examination. DISCUSSION: Most of the patients with IBS are satisfied with telehealth. Shorter duration of IBS diagnosis, comfort with technology, and increased willingness to travel were associated with telehealth dissatisfaction. These predictors may help identify a target population for a focused IBS-telehealth program.

Development and assessment of a natural language processing model to identify residential instability in electronic health records' unstructured data: a comparison of 3 integrated healthcare delivery systems.

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. The approach to the development of the gold standard for assessment of validity differed across sites. Using the EntityRuler module of spaCy 2.3 Python toolkit, we created a rule-based NLP system made up of expert-developed patterns indicating residential instability at the lead site and enriched the NLP system using insight gained from its application at the other 2 sites. We adapted the algorithm at each site then validated the algorithm using a split-sample approach. We assessed the performance of the algorithm by measures of positive predictive value (precision), sensitivity (recall), and specificity. RESULTS: The NLP algorithm performed with moderate precision (0.45, 0.73, and 1.0) at 3 sites. The sensitivity and specificity of the NLP algorithm varied across 3 sites (sensitivity: 0.68, 0.85, and 0.96; specificity: 0.69, 0.89, and 1.0). DISCUSSION: The performance of this NLP algorithm to identify residential instability in 3 different healthcare systems suggests the algorithm is generally valid and applicable in other healthcare systems with similar EHRs. CONCLUSION: The NLP approach developed in this project is adaptable and can be modified to extract types of social needs other than residential instability from EHRs across different healthcare systems.

Lessons learned from the use of COVID-19 convalescent plasma at Kaiser Permanente.

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. Methods: Multivariable analysis was controlled for demographic characteristics, level of oxygen delivery, intensive care unit stay, selected laboratory findings, and other concurrent treatment-related variables. Tubing segments from 151 CCP units transfused between October 2020 and April 2021 were retrospectively tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike protein receptor-binding domain IgG. Multivariable analysis showed that CCP transfusion did not affect mortality rates at 30 days and 5 months (odds ratio, 1.04, 95% CI, 0.87-1.25, and hazard ratio, 1.05, 95% CI, 0.93-1.19). Conclusions: If convalescent plasma is offered as a therapeutic in a future viral pandemic, we recommend (1) transfusing only those patients who are negative for neutralizing antibodies, (2) transfusing very early during the disease course, (3) only using convalescent plasma with known levels of neutralizing antibodies, or (4) alternatively providing fractionated hyperimmune globulin.