Examining Unusual Patterns of Cancer and Environmental Concerns: The Importance of Community Input and Engagement.

CONTEXT: In fiscal year 2019, the Department of Health and Human Services (DHHS) received an appropriation from Congress specifically to update guidelines for investigating community cancer concerns. This resulted in the DHHS directing the Centers for Disease Control and Prevention (CDC) to fulfill this responsibility. PROGRAM: The CDC and the Agency for Toxic Substances and Disease Registry (ATSDR) provide guidance to state, tribal, local, and territorial (STLT) health departments and play important roles in supporting STLT programs in addressing community cancer concerns. IMPLEMENTATION: The updated guidelines offer enhancements addressing limitations and challenges regarding the process for investigating cancer clusters as expressed by STLT programs responsible for responding to inquiries and by communities impacted by unusual patterns of cancer. Additionally, the updated guidelines offer new tools and approaches associated with scientific advancements. Issues associated with improving communications and community engagement were a priority. Details in the updated guidelines provide suggestions for building and maintaining trust; provide resources via additional tools, templates, and methodology to facilitate sharing of information; provide suggestions for identifying agency and community points of contacts; and provide suggestions for establishing a community advisory committee. CONCLUSION: Enhancements to the previous guidelines were included to address advancements in statistical approaches and methods for understanding exposure pathways and also to respond to limitations described in the previous guidelines. Furthermore, these enhancements ensure communities have a voice in the process and offer methods to enhance transparency throughout the investigative process. Ultimately, the 2022 Guidelines are designed to ensure that community engagement, community input, and communication remains paramount to the process of assessing unusual patterns of cancer and environmental concerns.

Mental Health Conditions and Severe COVID-19 Outcomes after Hospitalization, United States.

Among 664,956 hospitalized COVID-19 patients during March 2020-July 2021 in the United States, select mental health conditions (i.e., anxiety, depression, bipolar, schizophrenia) were associated with increased risk for same-hospital readmission and longer length of stay. Anxiety was also associated with increased risk for intensive care unit admission, invasive mechanical ventilation, and death.

Gene-environment interactions increase the risk of pediatric-onset multiple sclerosis associated with ozone pollution.

BACKGROUND: We previously reported a relationship between air pollutants and increased risk of pediatric-onset multiple sclerosis (POMS). Ozone is an air pollutant that may play a role in multiple sclerosis (MS) pathoetiology. CD86 is the only non-HLA gene associated with POMS for which expression on antigen-presenting cells (APCs) is changed in response to ozone exposure. OBJECTIVES: To examine the association between county-level ozone and POMS, and the interactions between ozone pollution, CD86, and HLA-DRB1*15, the strongest genetic variant associated with POMS. METHODS: Cases and controls were enrolled in the Environmental and Genetic Risk Factors for Pediatric MS study of the US Network of Pediatric MS Centers. County-level-modeled ozone data were acquired from the CDC's Environmental Tracking Network. Participants were assigned ozone values based on county of residence. Values were categorized into tertiles based on healthy controls. The association between ozone tertiles and having MS was assessed by logistic regression. Interactions between tertiles of ozone level and the GG genotype of the rs928264 (G/A) single nucleotide polymorphism (SNP) within CD86, and the presence of DRB1*15:01 (DRB1*15) on odds of POMS were evaluated. Models were adjusted for age, sex, genetic ancestry, and mother's education. Additive interaction was estimated using relative excess risk due to interaction (RERI) and attributable proportions (APs) of disease were calculated. RESULTS: A total of 334 POMS cases and 565 controls contributed to the analyses. County-level ozone was associated with increased odds of POMS (odds ratio 2.47, 95% confidence interval (CI): 1.69-3.59 and 1.95, 95% CI: 1.32-2.88 for the upper two tertiles, respectively, compared with the lowest tertile). There was a significant additive interaction between high ozone tertiles and presence of DRB1*15, with a RERI of 2.21 (95% CI: 0.83-3.59) and an AP of 0.56 (95% CI: 0.33-0.79). Additive interaction between high ozone tertiles and the CD86 GG genotype was present, with a RERI of 1.60 (95% CI: 0.14-3.06) and an AP of 0.37 (95% CI: 0.001-0.75) compared to the lowest ozone tertile. AP results indicated that approximately half of the POMS risk in subjects can be attributed to the possible interaction between higher county-level ozone carrying either DRB1*15 or the CD86 GG genotype. CONCLUSIONS: In addition to the association between high county-level ozone and POMS, we report evidence for additive interactions between higher county-level ozone and DRB1*15 and the CD86 GG genotype. Identifying gene-environment interactions may provide mechanistic insight of biological processes at play in MS susceptibility. Our work suggests a possible role of APCs for county-level ozone-induced POMS risk.

Adverse Pregnancy Outcomes, Maternal Complications, and Severe Illness Among US Delivery Hospitalizations With and Without a Coronavirus Disease 2019 (COVID-19) Diagnosis.

BACKGROUND: Evidence on risk for adverse outcomes from coronavirus disease 2019 (COVID-19) among pregnant women is still emerging. We examined the association between COVID-19 at delivery and adverse pregnancy outcomes, maternal complications, and severe illness, and whether these associations differ by race/ethnicity, and describe discharge status by COVID-19 diagnosis and maternal complications. METHODS: Data from 703 hospitals in the Premier Healthcare Database during March-September 2020 were included. Adjusted risk ratios (aRRs) overall and stratified by race/ethnicity were estimated using Poisson regression with robust standard errors. Proportion not discharged home was calculated by maternal complications, stratified by COVID-19 diagnosis. RESULTS: Among 489 471 delivery hospitalizations, 6550 (1.3%) had a COVID-19 diagnosis. In adjusted models, COVID-19 was associated with increased risk for acute respiratory distress syndrome (aRR, 34.4), death (aRR, 17.0), sepsis (aRR, 13.6), mechanical ventilation (aRR, 12.7), shock (aRR, 5.1), intensive care unit admission (aRR, 3.6), acute renal failure (aRR, 3.5), thromboembolic disease (aRR, 2.7), adverse cardiac event/outcome (aRR, 2.2), and preterm labor with preterm delivery (aRR, 1.2). Risk for any maternal complications or for any severe illness did not significantly differ by race/ethnicity. Discharge status did not differ by COVID-19; however, among women with concurrent maternal complications, a greater proportion of those with (vs without) COVID-19 were not discharged home. CONCLUSIONS: These findings emphasize the importance of implementing recommended prevention strategies to reduce risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and further inform counseling and clinical care for pregnant women during the COVID-19 pandemic.

Late Conditions Diagnosed 1-4 Months Following an Initial Coronavirus Disease 2019 (COVID-19) Encounter: A Matched-Cohort Study Using Inpatient and Outpatient Administrative Data-United States, 1 March-30 June 2020.

BACKGROUND: Late sequelae of COVID-19 have been reported; however, few studies have investigated the time course or incidence of late new COVID-19-related health conditions (post-COVID conditions) after COVID-19 diagnosis. Studies distinguishing post-COVID conditions from late conditions caused by other etiologies are lacking. Using data from a large administrative all-payer database, we assessed type, association, and timing of post-COVID conditions following COVID-19 diagnosis. METHODS: Using the Premier Healthcare Database Special COVID-19 Release (release date, 20 October 2020) data, during March-June 2020, 27 589 inpatients and 46 857 outpatients diagnosed with COVID-19 (case-patients) were 1:1 matched with patients without COVID-19 through the 4-month follow-up period (control-patients) by using propensity score matching. In this matched-cohort study, adjusted ORs were calculated to assess for late conditions that were more common in case-patients than control-patients. Incidence proportion was calculated for conditions that were more common in case-patients than control-patients during 31-120 days following a COVID-19 encounter. RESULTS: During 31-120 days after an initial COVID-19 inpatient hospitalization, 7.0% of adults experienced ≥1 of 5 post-COVID conditions. Among adult outpatients with COVID-19, 7.7% experienced ≥1 of 10 post-COVID conditions. During 31-60 days after an initial outpatient encounter, adults with COVID-19 were 2.8 times as likely to experience acute pulmonary embolism as outpatient control-patients and also more likely to experience a range of conditions affecting multiple body systems (eg, nonspecific chest pain, fatigue, headache, and respiratory, nervous, circulatory, and gastrointestinal symptoms) than outpatient control-patients. CONCLUSIONS: These findings add to the evidence of late health conditions possibly related to COVID-19 in adults following COVID-19 diagnosis and can inform healthcare practice and resource planning for follow-up COVID-19 care.

Evaluation of Syndromic Surveillance Data for Studying Harmful Algal Bloom-Associated Illnesses - United States, 2017-2019.

Harmful algal and cyanobacterial blooms (harmful algal blooms) are large colonies of algae or cyanobacteria that can harm humans, animals, and the environment (1-3). The number of algal blooms has been increasing in the United States, augmented by increasing water temperatures and nutrients in water from industry and agricultural run-off (4,5). The extent to which harmful algal bloom exposures cause human illness or long-term health effects is unknown. As the number of blooms increases annually, the likelihood of negative health outcomes (e.g., respiratory or gastrointestinal illness) from exposure also increases (4,5). To explore the utility of syndromic surveillance data for studying health effects from harmful algal bloom exposures, CDC queried emergency department (ED) visit data from the National Syndromic Surveillance Program (NSSP) for harmful algal bloom exposure-associated administrative discharge diagnosis codes and chief complaint text terms related to harmful algal bloom exposure (6). A total of 321 harmful algal bloom-associated ED visits were identified during January 1, 2017-December 31, 2019. An increase in harmful algal bloom-associated ED visits occurred during warmer months (June-October), consistent with seasonal fluctuations of blooms and recent publications (6,7). Although syndromic surveillance data are helpful for understanding harmful algal bloom-associated ED visits in the United States, exposures were documented infrequently with discharge diagnosis codes; 67% of harmful algal bloom-associated ED visits were identified through querying chief complaint text. Improving the documentation of harmful algal bloom exposures in medical records would further benefit future health studies.

Death Certificate-Based ICD-10 Diagnosis Codes for COVID-19 Mortality Surveillance - United States, January-December 2020.

Approximately 375,000 deaths during 2020 were attributed to COVID-19 on death certificates reported to CDC (1). Concerns have been raised that some deaths are being improperly attributed to COVID-19 (2). Analysis of International Classification of Diseases, Tenth Revision (ICD-10) diagnoses on official death certificates might provide an expedient and efficient method to demonstrate whether reported COVID-19 deaths are being overestimated. CDC assessed documentation of diagnoses co-occurring with an ICD-10 code for COVID-19 (U07.1) on U.S. death certificates from 2020 that had been reported to CDC as of February 22, 2021. Among 378,048 death certificates listing U07.1, a total of 357,133 (94.5%) had at least one other ICD-10 code; 20,915 (5.5%) had only U07.1. Overall, 97.3% of 357,133 death certificates with at least one other diagnosis (91.9% of all 378,048 death certificates) were noted to have a co-occurring diagnosis that was a plausible chain-of-event condition (e.g., pneumonia or respiratory failure), a significant contributing condition (e.g., hypertension or diabetes), or both. Overall, 70%-80% of death certificates had both a chain-of-event condition and a significant contributing condition or a chain-of-event condition only; this was noted for adults aged 18-84 years, both males and females, persons of all races and ethnicities, those who died in inpatient and outpatient or emergency department settings, and those whose manner of death was listed as natural. These findings support the accuracy of COVID-19 mortality surveillance in the United States using official death certificates. High-quality documentation of co-occurring diagnoses on the death certificate is essential for a comprehensive and authoritative public record. Continued messaging and training (3) for professionals who complete death certificates remains important as the pandemic progresses. Accurate mortality surveillance is critical for understanding the impact of variants of SARS-CoV-2, the virus that causes COVID-19, and of COVID-19 vaccination and for guiding public health action.

Association Between COVID-19 and Myocarditis Using Hospital-Based Administrative Data - United States, March 2020-January 2021.

Viral infections are a common cause of myocarditis, an inflammation of the heart muscle (myocardium) that can result in hospitalization, heart failure, and sudden death (1). Emerging data suggest an association between COVID-19 and myocarditis (2-5). CDC assessed this association using a large, U.S. hospital-based administrative database of health care encounters from >900 hospitals. Myocarditis inpatient encounters were 42.3% higher in 2020 than in 2019. During March 2020-January 2021, the period that coincided with the COVID-19 pandemic, the risk for myocarditis was 0.146% among patients diagnosed with COVID-19 during an inpatient or hospital-based outpatient encounter and 0.009% among patients who were not diagnosed with COVID-19. After adjusting for patient and hospital characteristics, patients with COVID-19 during March 2020-January 2021 had, on average, 15.7 times the risk for myocarditis compared with those without COVID-19 (95% confidence interval [CI] = 14.1-17.2); by age, risk ratios ranged from approximately 7.0 for patients aged 16-39 years to >30.0 for patients aged <16 years or ≥75 years. Overall, myocarditis was uncommon among persons with and without COVID-19; however, COVID-19 was significantly associated with an increased risk for myocarditis, with risk varying by age group. These findings underscore the importance of implementing evidence-based COVID-19 prevention strategies, including vaccination, to reduce the public health impact of COVID-19 and its associated complications.

Interocular Difference in Retinal Nerve Fiber Layer Thickness Predicts Optic Neuritis in Pediatric-Onset Multiple Sclerosis.

BACKGROUND: Optical coherence tomography (OCT) is capable of quantifying retinal damage. Defining the extent of anterior visual pathway injury is important in multiple sclerosis (MS) as a way to document evidence of prior disease, including subclinical injury, and setting a baseline for patients early in the course of disease. Retinal nerve fiber layer (RNFL) thickness is typically classified as low if values fall outside of a predefined range for a healthy population. In adults, an interocular difference (IOD) in RNFL thickness greater than 5 µm identified a history of unilateral optic neuritis (ON). Through our PERCEPTION (PEdiatric Research Collaboration ExPloring Tests in Ocular Neuroimmunology) study, we explored whether RNFL IOD informs on remote ON in a multicenter pediatric-onset MS (POMS) cohort. METHODS: POMS (defined using consensus criteria and first attack <18 years) patients were recruited from 4 academic centers. A clinical history of ON (>6 months prior to an OCT scan) was confirmed by medical record review. RNFL thickness was measured on Spectralis machines (Heidelberg, Germany). Using a cohort of healthy controls from our centers tested on the same machines, RNFL thickness <86 µm (<2 SDs below the mean) was defined as abnormal. Based on previously published findings in adults, an RNFL IOD >5 µm was defined as abnormal. The proportions of POMS participants with RNFL thinning (<86 µm) and abnormal IOD (>5 µm) were calculated. Logistic regression was used to determine whether IOD was associated with remote ON. RESULTS: A total of 157 participants with POMS (mean age 15.2 years, SD 3.2; 67 [43%] with remote ON) were enrolled. RNFL thinning occurred in 45 of 90 (50%) ON eyes and 24 of 224 (11%) non-ON eyes. An IOD >5 µm was associated with a history of remote ON (P < 0.001). An IOD >5 µm occurred in 62 participants, 40 (65%) with remote ON. Among 33 participants with remote ON but normal RNFL values (≥86 µm in both eyes), 14 (42%) were confirmed to have ON by IOD criteria (>5 µm). CONCLUSIONS: In POMS, the diagnostic yield of OCT in confirming remote ON is enhanced by considering RNFL IOD, especially for those patients with RNFL thickness for each eye in the normal range. An IOD >5 µm in patients with previous visual symptoms suggests a history of remote ON.

Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021.

INTRODUCTION: Severe COVID-19 illness in adults has been linked to underlying medical conditions. This study identified frequent underlying conditions and their attributable risk of severe COVID-19 illness. METHODS: We used data from more than 800 US hospitals in the Premier Healthcare Database Special COVID-19 Release (PHD-SR) to describe hospitalized patients aged 18 years or older with COVID-19 from March 2020 through March 2021. We used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions. RESULTS: Among 4,899,447 hospitalized adults in PHD-SR, 540,667 (11.0%) were patients with COVID-19, of whom 94.9% had at least 1 underlying medical condition. Essential hypertension (50.4%), disorders of lipid metabolism (49.4%), and obesity (33.0%) were the most common. The strongest risk factors for death were obesity (adjusted risk ratio [aRR] = 1.30; 95% CI, 1.27-1.33), anxiety and fear-related disorders (aRR = 1.28; 95% CI, 1.25-1.31), and diabetes with complication (aRR = 1.26; 95% CI, 1.24-1.28), as well as the total number of conditions, with aRRs of death ranging from 1.53 (95% CI, 1.41-1.67) for patients with 1 condition to 3.82 (95% CI, 3.45-4.23) for patients with more than 10 conditions (compared with patients with no conditions). CONCLUSION: Certain underlying conditions and the number of conditions were associated with severe COVID-19 illness. Hypertension and disorders of lipid metabolism were the most frequent, whereas obesity, diabetes with complication, and anxiety disorders were the strongest risk factors for severe COVID-19 illness. Careful evaluation and management of underlying conditions among patients with COVID-19 can help stratify risk for severe illness.