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.

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.

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.

Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis.

BACKGROUND: Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown. OBJECTIVE: The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits. METHODS: Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018. RESULTS: Our method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months. CONCLUSIONS: For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.5039.

Neighborhood factors associated with late effects among survivors of adolescent and young adult cancer.

PURPOSE: The purpose of this study is to evaluate the associations between neighborhood income, education, and neighborhood racial composition (measured as a low percentage of white residents) and risk of developing cardiovascular diseases (CVD), diabetes (DM), and severe depression among survivors of AYA cancer and matched non-cancer peers. METHODS: Two-year survivors of AYA cancers diagnosed at age 15-39 yrs at Kaiser Permanente Southern California (diagnosed 2000-2012) and individually matched (1:13) non-cancer subjects were included. The development of CVD, DM, and severe depression was ascertained via electronic health records. Neighborhood characteristics were obtained from census-based geocoded data. Cox regression evaluated associations between neighborhood characteristics and the health outcomes of interest among both the cancer survivors and the non-cancer comparison cohort and effect modification by cancer survivor status on these relationships. RESULTS: Among cancer survivors (n = 6774), living in mostly non-white neighborhoods, was associated with risk of CVD (hazard ratio (HR) = 1.54 (95% CI 1.00-2.36)), while lower education level (HR = 1.41 (95% CI 1.02-1.94)) was associated with risk of severe depression. None of the neighborhood characteristics were associated with risk of DM. Effect modification was found for neighborhood education and risk of DM and severe depression. CONCLUSION: When jointly considered, cancer survivors who resided in the most disadvantaged neighborhoods were at the highest risk of developing these health outcomes compared to other subgroups. IMPLICATIONS FOR CANCER SURVIVORS: Our findings may inform screening strategy and addressing social determinants of health among AYA cancer survivors.

Rationale and design of the KP ENRICH trial: A food is medicine intervention in low-income high-risk adults with diabetes within Kaiser Permanente.

BACKGROUND: Food insecurity is associated with poor glycemic control and increased risk for diabetes-related complications. The clinical benefit of addressing these challenges through a medically supportive grocery prescription (GRx) program in patients with type 2 diabetes mellitus (T2D) remains unclear. We report the aims and design of a randomized clinical trial to evaluate the effectiveness of a 6-month GRx intervention on hemoglobin A1c (HbA1c) levels among low-income adults with T2D. METHODS: The Kaiser Permanente Evaluating Nutritional Interventions in Food-Insecure High-Risk Adults (KP ENRICH) Study is a pragmatic randomized trial enrolling 1100 participants within Kaiser Permanente Northern California and Southern California, two integrated health care delivery systems serving >9 million members. Medicaid-insured adults with T2D and baseline HbA1c ≥7.5% will be randomized at a 1:1 ratio to either GRx, delivered as $100 per month for select items from among a curated list of healthful food groups in an online grocery ordering and home-delivery platform along with biweekly digital nutrition educational materials, or control, consisting of free membership and deliveries from the online grocery platform but without curated food groups or purchasing dollars. The primary outcome is 6-month change in HbA1c. Secondary outcomes include 12-month change in HbA1c, and 6- and 12-month change in medical resource utilization, food security, nutrition security, dietary habits, diabetes-related quality of life, and dietary self-efficacy. CONCLUSIONS: The results of this large randomized clinical trial of GRx will help inform future policy and health system-based initiatives to improve food and nutrition security, disease management, and health equity among patients with T2D.

Associations of Physical Inactivity and COVID-19 Outcomes Among Subgroups.

INTRODUCTION: Physical activity before COVID-19 infection is associated with less severe outcomes. The study determined whether a dose‒response association was observed and whether the associations were consistent across demographic subgroups and chronic conditions. METHODS: A retrospective cohort study of Kaiser Permanente Southern California adult patients who had a positive COVID-19 diagnosis between January 1, 2020 and May 31, 2021 was created. The exposure was the median of at least 3 physical activity self-reports before diagnosis. Patients were categorized as follows: always inactive, all assessments at 10 minutes/week or less; mostly inactive, median of 0-60 minutes per week; some activity, median of 60-150 minutes per week; consistently active, median>150 minutes per week; and always active, all assessments>150 minutes per week. Outcomes were hospitalization, deterioration event, or death 90 days after a COVID-19 diagnosis. Data were analyzed in 2022. RESULTS: Of 194,191 adults with COVID-19 infection, 6.3% were hospitalized, 3.1% experienced a deterioration event, and 2.8% died within 90 days. Dose‒response effects were strong; for example, patients in the some activity category had higher odds of hospitalization (OR=1.43; 95% CI=1.26, 1.63), deterioration (OR=1.83; 95% CI=1.49, 2.25), and death (OR=1.92; 95% CI=1.48, 2.49) than those in the always active category. Results were generally consistent across sex, race and ethnicity, age, and BMI categories and for patients with cardiovascular disease or hypertension. CONCLUSIONS: There were protective associations of physical activity for adverse COVID-19 outcomes across demographic and clinical characteristics. Public health leaders should add physical activity to pandemic control strategies.

Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California.

BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

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.

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.

Characteristics of adults with overweight and obesity who have not attempted weight loss in the prior 12 months.

OBJECTIVE: To determine weight-related characteristics of adults with overweight or obesity who had not attempted weight loss in the prior 12 months. METHODS: Adults from the obesity cohort of the PORTAL clinical data research network were randomly selected to complete a survey in 2015. It included topics of experiences with health care providers, weight history, and health-promoting behaviors. RESULTS: A total of 2811 adults completed the survey, of whom 24% reported not attempting weight loss in the previous 12 months. Controlling for sociodemographic characteristics and body mass index, respondents who reported that their health care provider never talked about weight were more likely to not attempt weight loss in the previous 12 months compared with respondents who reported providers frequently brought up weight (OR: 0.45; 95% CI: 0.24, 0.66). Respondents who perceived themselves as normal weight or underweight were 4.22 times more likely (95% CI: 3.53, 5.05) to not attempt weight loss compared with respondents who perceived themselves as overweight. Respondents who reported that they never tried to lose at least 10 lbs, ate 0-2 servings of fruits and vegetables daily, or engaged in 0-29 min of physical activity weekly were less likely to attempt weight loss. CONCLUSIONS: Receiving advice on weight loss from health care providers to improve awareness of overweight and obesity status may be important factors for a person's weight management. Provider discussions about weight may be a strategy to motivate weight loss attempts.

Racial and Ethnic Differences in Myopia Progression in a Large, Diverse Cohort of Pediatric Patients.

Purpose: The purpose of this study was to characterize the differences in myopic progression in children by race/ethnicity and age. Methods: Patients enrolled in Kaiser Permanente Southern California between 2011 and 2016 and between the ages of 4 and 11 years old with a documented refraction between -6 and -1 diopters (Ds) were included in this retrospective cohort study. Patients with a history of amblyopia, strabismus, retinopathy of prematurity, or prior ocular surgery were excluded from analyses. Patients' race/ethnicity and language information were used to create the following groups for analysis: white, Black, Hispanic, South Asian, East/Southeast Asian, Other Asian, and other/unknown. A growth curve analysis using linear mixed-effects modeling was used to trace longitudinal progression of spherical equivalents over time, modeled by race/ethnicity. Analyses adjusted for potential confounders, including body mass index (BMI), screen time, and physical activity. Results: There were 11,595 patients who met the inclusion criteria. Patients were 53% girls, 55% Latino, 15% white, 9% black, 9% East/Southeast Asian, and 2% South Asian. Mean age (standard deviation [SD]) at the time of initial refraction was 8.9 years (1.6 years). Patients had an average (SD) of 3.4 (1.5) refractions, including the baseline measurement, during the study period. A three-way interaction model that assessed the effects of age at baseline, time since baseline, and race/ethnicity found that children of East/Southeast Asian descent showed significantly faster myopia progression across time (P < 0.001). East/Southeast Asian patients who presented with myopia between 6 to < 8 years progressed similarly to white patients in the same age group and significantly faster compared with white patients in other age groups. Conclusions: Myopia progression differed significantly between East/Southeast Asian and white patients depending on the patients' age.

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.

Associations of overweight/obesity and socioeconomic status with hypertension prevalence across racial and ethnic groups.

Racial/ethnic disparities in the prevalence of diagnosed hypertension are persistent but may be partially explained by racial/ethnic differences in weight category and neighborhood socioeconomic status. The authors compared hypertension prevalence rates among 4 060 585 adults with overweight or obesity across 10 healthcare systems by weight category and neighborhood education level in geographically and racially diverse individuals. Data were obtained from electronic health records. Hypertension was defined as at least two outpatient visits or one inpatient hospitalization with a coded diagnosis. Logistic regression, adjusted for age, sex, and site, with two-way interactions between race/ethnicity and weight category or neighborhood education, was used to examine the association between hypertension and race/ethnicity, with whites as the reference. Results documented that odds ratios for hypertension prevalence were greater for blacks, American Indians/Alaskan Natives, Asians, and Native Hawaiians/other Pacific Islanders compared with whites and lower for Hispanics in similar weight categories and neighborhood education levels. Although two-way interactions were statistically significant, the magnitude of the odds of hypertension compared with whites did not substantially vary across weight or neighborhood education. Hypertension odds were almost double relative to whites for blacks and Native Hawaiians/other Pacific Islanders across most weight categories and all neighborhood education levels. Odds of hypertension were about 50% greater for Asians relative to whites across weight categories. Results suggest that other factors might be associated with racial/ethnic disparities in hypertension. More research is needed to understand the many factors that may contribute to variation in diagnosed hypertension across racial/ethnic groups with overweight or obesity.