Trends in firearm injury in a southern California health care system from 2010 to 2020.

BACKGROUND: Firearm injury is a significant public health concern in the United States. METHODS: Data on fatal and nonfatal firearm injuries were obtained from a cohort of N = 7,473,650 members of Kaiser Permanente Southern California, a large integrated healthcare system between 2010 and 2020. Age-adjusted rates of combined fatal and nonfatal firearm injury per 100,000 members were calculated by year, with the 2010 US census as the reference population. Trends were evaluated using Poisson or negative binomial regression. RESULTS: There was an increasing trend in overall firearm injuries between 2010 and 2020 among adults in this large integrated healthcare system (p < .0001), primarily driven by non-self-inflicted firearm injuries (p < .0001). Self-inflicted injuries decreased during this time (p = .01). Injuries among youth showed no significant change. CONCLUSION: There was an increasing trend in firearm injuries between 2010 and 2020 among adults in this large integrated healthcare system, primarily driven by non-self-inflicted firearm injuries; however, self-inflicted injuries decreased during this time. Injuries among youth showed no significant change.

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.

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.

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.

Factors Associated With Firearm Injury Among Pediatric Members of a Large Integrated Healthcare System.

BACKGROUND AND OBJECTIVES: Few studies have tested multiple socio-ecological risk factors assocated with firearm injury among pediatric populations and distinguished self-inflicted from non-self-inflicted injury. To address this gap, the current study examined demographic, individual psychosocial, and neighborhood variables as risk factors for firearm injury among a large cohort of children and adolescents. METHODS: Retrospective cohort study. Data were obtained from the electronic health records of a large integrated healthcare system. The cohort included children <18 years with at least one clinical encounter between January 1, 2010 and December 31, 2018. Poisson regression was used to examine demographic (age, gender, race and ethnicity, Medicaid status), psychosocial (depression, substance use disorder, medical comorbidities), and neighborhood education variables as potential risk factors for non-self-inflicted and self-inflicted firearm injuries. RESULTS: For non-self-inflicted injury, the highest relative risk was found for children age 12-17 years old compared to 0-5 year olds (RR = 37.57); other risk factors included male gender, Black and Hispanic race and ethnicity (compared to White race), being a Medicaid recipient, lower neighborhood education, and substance use disorder diagnosis. For self-inflicted injury, only age 12-17 years old and male gender were associated with increased risk. CONCLUSIONS: These results reinforce the established higher risk for firearm injury among adolescent males, highlight differences between self-inflicted and non-self-inflicted injuries, and the need to consider demographic, psychosocial, and neighborhood variables as risk factors to inform interventions aimed to reduce firearm injuries among children and adolescents.

Cross-sectional Comparison of Disparities by Race Using White vs Hispanic as Reference Among Children and Youths With Developmental Disabilities Referred for Speech Therapy.

Importance: Health care research on racial disparities among children and youths has historically used the White race as a reference category with which other racial and ethnic groups are compared, which may inadvertently set up Whiteness as a standard for health. Objective: To compare 2 interpretations of an analysis of racial disparities in speech therapy receipt among children and youths with developmental disabilities: a traditional, White-referenced analysis and a Hispanic majority-referenced analysis. Design, Setting, and Participants: This cross-sectional study used multiple logistic regression to analyze speech therapy referrals for children, adolescents, and transition age youths in an integrated health care system in Southern California from 2017 to 2020. Eligible participants were children and youths up to age 26 years with 1 or more diagnosed intellectual or developmental disability (eg, autism spectrum disorder, speech or language delay, developmental delay, Down syndrome, and others). Exposures: Child or youth race and ethnicity as reported by parents or caregivers (Asian, Black and African American, Hispanic and Latinx, American Indian or Alaskan Native, Native Hawaiian or Pacific Islander, White, multiple, and other). Main Outcomes and Measures: Receipt of speech therapy within 1 year of referral. Results: A total 66 402 referrals were included; 65 833 referrals (99.1%) were for children under age 17 years, 47 323 (71.3%) were for boys, and 39 959 (60.2%) were commercially insured. A majority of participants were identified as Hispanic (36 705 [55.3%]); 6167 (9.3%) were identified as Asian, 4810 (7.2%) as Black, and 14 951 (22.5%) as White. In the traditional racial disparities model where the reference category was White, referrals of children and youths who identified as Hispanic, Black, Pacific Islander, and other had lower odds of actual receipt of speech therapy compared with referrals for White children and youths (Hispanic: OR, 0.79; 95% CI, 0.75-0.83; Black: OR, 0.72; 95% CI, 0.66-0.78; Pacific Islander: OR, 0.74; 95% CI, 0.57-0.98). When using the majority race group (Hispanic) as the reference category, referrals for children and youths who identified as White (OR, 1.26; 95% CI, 1.20-1.30), Asian (OR, 1.21; 95% CI, 1.12-1.30), and multiracial (OR, 1.35; 95% CI, 1.08-1.71) had higher odds of resulting in actual service receipt in comparison with referrals for Hispanic children and youths. Conclusions and Relevance: The cross-sectional study demonstrates the value of decentering Whiteness in interpreting racial disparities research and considering racial differences against multiple referents. Racial disparities researchers should consider investigating multiple between-group differences instead of exclusively using White as the default reference category.

Social Risk Factors and Desire for Assistance Among Patients Receiving Subsidized Health Care Insurance in a US-Based Integrated Delivery System.

PURPOSE: Because social conditions such as food insecurity and housing instability shape health outcomes, health systems are increasingly screening for and addressing patients' social risks. This study documented the prevalence of social risks and examined the desire for assistance in addressing those risks in a US-based integrated delivery system. METHODS: A survey was administered to Kaiser Permanente members on subsidized exchange health insurance plans (2018-2019). The survey included questions about 4 domains of social risks, desire for help, and attitudes. We conducted a descriptive analysis and estimated multivariate modified Poisson regression models. RESULTS: Of 438 participants, 212 (48%) reported at least 1 social risk factor. Housing instability was the most common (70%) factor reported. Members with social risks reported more discomfort being screened for social risks (14.2% vs 5.4%; P = .002) than those without risks, although 90% of participants believed that health systems should assist in addressing social risks. Among those with 1-2 social risks, however, only 27% desired assistance. Non-Hispanic Black participants who reported a social risk were more than twice as likely to desire assistance compared with non-Hispanic White participants (adjusted relative risk [RR] 2.2; 95% CI, 1.3-3.8). CONCLUSIONS: Athough most survey participants believed health systems have a role in addressing social risks, a minority of those reporting a risk wanted assistance and reported more discomfort being screened for risk factors than those without risks. Health systems should work to increase the comfort of patients in reporting risks, explore how to successfully assist them when desired, and offer resources to address these risks outside the health care sector.VISUAL ABSTRACT.

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.

Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis.

BACKGROUND: Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. OBJECTIVE: This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). METHODS: The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. RESULTS: Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). CONCLUSIONS: Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.

Obesity and Mortality Among Patients Diagnosed With COVID-19: Results From an Integrated Health Care Organization.

BACKGROUND: Obesity, race/ethnicity, and other correlated characteristics have emerged as high-profile risk factors for adverse coronavirus disease 2019 (COVID-19)-associated outcomes, yet studies have not adequately disentangled their effects. OBJECTIVE: To determine the adjusted effect of body mass index (BMI), associated comorbidities, time, neighborhood-level sociodemographic factors, and other factors on risk for death due to COVID-19. DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Southern California, a large integrated health care organization. PATIENTS: Kaiser Permanente Southern California members diagnosed with COVID-19 from 13 February to 2 May 2020. MEASUREMENTS: Multivariable Poisson regression estimated the adjusted effect of BMI and other factors on risk for death at 21 days; models were also stratified by age and sex. RESULTS: Among 6916 patients with COVID-19, there was a J-shaped association between BMI and risk for death, even after adjustment for obesity-related comorbidities. Compared with patients with a BMI of 18.5 to 24 kg/m2, those with BMIs of 40 to 44 kg/m2 and greater than 45 kg/m2 had relative risks of 2.68 (95% CI, 1.43 to 5.04) and 4.18 (CI, 2.12 to 8.26), respectively. This risk was most striking among those aged 60 years or younger and men. Increased risk for death associated with Black or Latino race/ethnicity or other sociodemographic characteristics was not detected. LIMITATION: Deaths occurring outside a health care setting and not captured in membership files may have been missed. CONCLUSION: Obesity plays a profound role in risk for death from COVID-19, particularly in male patients and younger populations. Our capitated system with more equalized health care access may explain the absence of effect of racial/ethnic and socioeconomic disparities on death. Our data highlight the leading role of severe obesity over correlated risk factors, providing a target for early intervention. PRIMARY FUNDING SOURCE: Roche-Genentech.

The Application of Community-Based Information from the American Community Survey in a Large Integrated Health Care Organization.

BACKGROUND: The American Community Survey (ACS) is the largest household survey conducted by the US Census Bureau. We sought to describe the community-level characteristics derived from the ACS among enrollees of Kaiser Permanente Southern California (KPSC), evaluate the associations between ACS estimates and selective individual-level health outcomes, and explore how using different scales of the census geography and the linearity assumption affect the associations. METHODS: We examined the associations between track-level and block group-level ACS 5-year estimates and 4 individual-level Healthcare Effectiveness Data and Information Set (HEDIS) outcome measures (comprehensive diabetes care, postpartum care, antidepressant medication management, and childhood immunization status) using multilevel generalized linear models. Odds ratios and their 95% confidence intervals were estimated for every 10% increase in ACS measures. RESULTS: 6,357,841 addresses were successfully geocoded to at least the tract level. The community-level demographic, socioeconomic, residential, and other ACS measures varied among KPSC health plan enrollees. A majority of these ACS measures were associated with the selected HEDIS health outcomes. The directions of the effects were consistent across health outcomes; however, the magnitudes of the effect sizes varied. Within each HEDIS health outcome, the relative size of the effects appeared to remain similar. Differences between the census tract- and block group-level estimates were minor, especially for measures related to race/ethnicity, education, income, and occupation. CONCLUSION: These findings support the use of many ACS measures at neighborhood levels to predict health outcomes. The geographic units might have little effect on the results. The linearity assumption should be made with caution.