Ambient air pollution and COVID-19 incidence during four 2020-2021 case surges.

BACKGROUND: Air pollution exposure may make people more vulnerable to COVID-19 infection. However, previous studies in this area mostly focused on infection before May 2020 and long-term exposure. OBJECTIVE: To assess both long-term and short-term exposure to air pollution and COVID-19 incidence across four case surges from 03/1/2020 to 02/28/2021. METHODS: The cohort included 4.6 million members from a large integrated health care system in southern California with comprehensive electronic medical records (EMR). COVID-19 cases were identified from EMR. Incidence of COVID-19 was computed at the census tract-level among members. Prior 1-month and 1-year averaged air pollutant levels (PM2.5, NO2, and O3) at the census tract-level were estimated based on hourly and daily air quality data. Data analyses were conducted by each wave: 3/1/2020-5/31/2020, 6/1/202-9/30/2020, 10/1/2020-12/31/2020, and 1/1/2021-2/28/2021 and pooled across waves using meta-analysis. Generalized linear mixed effects models with Poisson distribution and spatial autocorrelation were used with adjustment for meteorological factors and census tract-level social and health characteristics. Results were expressed as relative risk (RR) per 1 standard deviation. RESULTS: The cohort included 446,440 COVID-19 cases covering 4609 census tracts. The pooled RRs (95% CI) of COVID-19 incidence associated with 1-year exposures to PM2.5, NO2, and O3 were 1.11 (1.04, 1.18) per 2.3 µg/m3,1.09 (1.02, 1.17) per 3.2 ppb, and 1.06 (1.00, 1.12) per 5.5 ppb respectively. The corresponding RRs (95% CI) associated with prior 1-month exposures were 1.11 (1.03, 1.20) per 5.2 µg/m3 for PM2.5, 1.09 (1.01, 1.17) per 6.0 ppb for NO2 and 0.96 (0.85, 1.08) per 12.0 ppb for O3. CONCLUSION: Long-term PM2.5 and NO2 exposures were associated with increased risk of COVID-19 incidence across all case surges before February 2021. Short-term PM2.5 and NO2 exposures were also associated. Our findings suggest that air pollution may play a role in increasing the risk of COVID-19 infection.

Healthcare Utilization Among Patients Diagnosed with COVID-19 in a Large Integrated Health System.

BACKGROUND: The demands for healthcare resources following a COVID-19 diagnosis are substantial, but not currently quantified. OBJECTIVE: To describe trends in healthcare utilization within 180 days for patients diagnosed with COVID-19 and identify patient factors associated with increased healthcare use. DESIGN: Observational cohort study. PATIENTS: A total of 64,011 patients with a test-confirmed COVID-19 diagnosis from March to September 2020 in a large integrated healthcare system in Southern California. MAIN MEASURES: Overall healthcare utilization during the 180 days following COVID-19 diagnosis, as well as encounter types and reasons for visits during the first 30 days. Poisson regression was used to identify patient factors associated with higher utilization. Analyses were performed separately for patients who were and were not hospitalized for COVID-19. KEY RESULTS: Healthcare utilization was about twice as high for hospitalized patients compared to non-hospitalized patients in all time periods. The average number of visits was highest in the first 30 days (hospitalized: 12.3 visits/30 person-days; non-hospitalized: 6.6) and gradually decreased over time. In the first 30 days, the majority of healthcare visits were telehealth encounters (hospitalized: 9.0 visits; non-hospitalized: 5.6 visits), and the most prevalent reasons for visits were COVID-related diagnoses, COVID-related symptoms, and respiratory-related conditions. For hospitalized patients, older age (≥65: RR 1.27, 95% CI 1.15-1.41), female gender (RR 1.07, 95% CI 1.05-1.09), and higher BMI (≥40: RR 1.07, 95% CI 1.03-1.10) were associated with higher total utilization. For non-hospitalized patients, older age, female gender, higher BMI, non-white race/ethnicity, former smoking, and greater number of pre-existing comorbidities were all associated with increased utilization. CONCLUSIONS: Patients with COVID-19 seek healthcare frequently within 30 days of diagnosis, placing high demands on health systems. Identifying ways to support patients diagnosed with COVID-19 while adequately providing the usual recommended care to our communities will be important as we recover from the pandemic.

Asthma Disease Status, COPD, and COVID-19 Severity in a Large Multiethnic Population.

BACKGROUND: Current studies of asthma history on coronavirus disease 2019 (COVID-19) outcomes are limited and lack consideration of disease status. OBJECTIVE: To conduct a population-based study to assess asthma disease status and chronic obstructive pulmonary disease (COPD) in relation to COVID-19 severity. METHODS: Patients diagnosed with COVID-19 (n = 61,338) in a large, diverse integrated health care system were identified. Asthma/COPD history, medication use, and covariates were extracted from electronic medical records. Asthma patients were categorized into those with and without clinical visits for asthma 12 or fewer months prior to COVID-19 diagnosis and labeled as active and inactive asthma, respectively. Primary outcomes included COVID-19-related hospitalizations, intensive respiratory support (IRS), and intensive care unit admissions within 30 days, and mortality within 60 days after COVID-19 diagnosis. Logistic and Cox regression were used to relate COVID-19 outcomes to asthma/COPD history. RESULTS: The cohort was 53.9% female and 66% Hispanic and had a mean age of 43.9 years. Patients with active asthma had increased odds of hospitalization, IRS, and intensive care unit admission (odds ratio 1.47-1.66; P < .05) compared with patients without asthma or COPD. No increased risks were observed for patients with inactive asthma. Chronic obstructive pulmonary disease was associated with increased risks of hospitalization, IRS, and mortality (odds ratio and hazard ratio 1.27-1.67; P < .05). Among active asthma patients, those using asthma medications had greater than 25% lower odds for COVID-19 outcomes than those without medication. CONCLUSIONS: Patients with asthma who required clinical care 12 or fewer months prior to COVID-19 or individuals with COPD history are at increased risk for severe COVID-19 outcomes. Proper medication treatment for asthma may lower this risk.

Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules. RESEARCH QUESTION: Is it possible to identify pulmonary nodules and associated characteristics using an automated method? STUDY DESIGN AND METHODS: We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics. RESULTS: In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%). INTERPRETATION: The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.

Does Providing Positive Airway Pressure for Sleep Apnea Change Health Care Utilization?

BACKGROUND: Sleep apnea is underdiagnosed and thus undertreated. If therapy for sleep apnea results in reduced health care utilization in an entire treated population, then decision-makers will have key information for allocating limited health care resources. OBJECTIVES: To determine whether positive airway pressure (PAP) for sleep apnea was associated with reduced health care utilization in an entire treated population. RESEARCH DESIGN: This was a retrospective cohort; propensity score-matched cases and noncases; pre-post analyses of individual subject utilization. SUBJECTS: Electronic health records were used to identify adult subjects diagnosed with sleep apnea and dispensed PAP therapy (cases) and those without either diagnosed sleep apnea or dispensed PAP therapy (noncases). MEASURES: Acute care hospital days and dispensed medication days supply were compared in cases and noncases. Negative binomial regression was used to model utilization for up to 5 years before and 7 years after PAP dispensation (cases) or a random date (noncases). The association of PAP with changing annual utilization was estimated. RESULTS: There were 13,271 cases and 13,271 matched noncases from 2008 to 2012 for analyses. Trends in the annual rate of acute care utilization were no different between cases and noncases (rate ratio, 0.98, P=0.543). Trends in the annual rate of medication utilization were no different between cases and noncases (rate ratio, 1.008, P=0.112). CONCLUSIONS: PAP dispensation for sleep apnea did not appear to reduce the rate of acute care and medication utilization over several years of follow-up in a large integrated health care system.

Impact of Integrated Health Care Delivery on Racial and Ethnic Disparities in Pancreatic Cancer.

OBJECTIVES: The objective of this study was to evaluate whether disparities in pancreatic cancer diagnosis, treatment, and survival are reduced in an integrated health system. METHODS: We conducted a retrospective study (2006-2014) among patients with pancreatic cancer from Kaiser Permanente Southern California. Racial ethnic groups included non-Hispanic whites (NHW), non-Hispanic blacks (NHB), Hispanics, and Asians. We used multivariable and Cox regression analyses to evaluate disparities in diagnosis and treatment utilization (oncology care, surgery, time to surgery, chemotherapy) and overall survival, respectively. RESULTS: Among 2103 patients, 54% were diagnosed with stage IV disease, 80% received oncology consultation, 20% received surgery with mean time to surgery 27 days (standard deviation, 36.8), 50.4% received chemotherapy. Mean overall survival was 8.6 months (standard deviation, 11.5). There were no differences in odds of stage IV diagnosis, oncology consultation, surgery, or time to surgery by racial ethnic group. Asians were more likely to receive chemotherapy (odds ratio, 1.59; 95% confidence interval [CI], 1.09-2.32) compared to NHW. NHB (hazard ratio, 0.78; 95% CI, 0.67-0.91) and Asians (hazard ratio, 0.81; 95% CI, 0.66-1.00) had improved survival compared to NHW. CONCLUSIONS: Minorities were not disadvantaged in pancreatic cancer care. Improved health care coordination may improve current disparities.

Influence of Statins and Cholesterol on Mortality Among Patients With Pancreatic Cancer.

Background: Recent studies have suggested associations between statins and enhanced survival among patients with pancreatic ductal adenocarcinoma (PDAC). However, the relationship between statins, cholesterol, and survival remains unclear. Methods: We conducted a retrospective cohort study on 2142 PDAC patients in a regional integrated healthcare system from 2006 to 2014. Electronic pharmacy records were used to abstract information on the type, length, and dosage of statin exposures starting in the year prior to diagnosis. The cumulative and individual effects of simvastatin, lovastatin, atorvastatin, pravastatin, and rosuvastatin on mortality were assessed using Cox proportional hazards regression. Statins were evaluated as any use (pre- and postdiagnosis as a time-dependent variable) and baseline use (prediagnosis only). We also evaluated whether low-density lipoprotein (LDL) cholesterol, measured at various time windows prior to diagnosis, had an independent influence on survival. Additional analyses were performed to examine whether cholesterol mediated the relationship between statins and mortality. All models included age, race, stage, surgery, gemcitabine-based chemotherapy, and the Charlson comorbidity index as covariates. Results: Any (hazard ratio [HR] = 0.87, 95% confidence interval [CI] = 0.79 to 0.97) and baseline (HR = 0.88, 95% CI = 0.79 to 0.98) statin use were both associated with a decreased risk in mortality. When assessing individual statins, we found reduced mortality among simvastatin (HR = 0.87, 95% CI = 0.77 to 0.98) and atorvastatin (HR = 0.58, 95% CI = 0.46 to 0.72) users. Cholesterol was not associated with mortality and did not mediate any relationships between statins and survival. Conclusions: Statin use rather than cholesterol level was associated with lower mortality risk in patients with pancreatic cancer. Statins appear to improve survival through a lipid-independent mechanism.