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.

Potential acute and chronic human health effects associated with exposure to cyanobacteria and cyanotoxins, including respiratory symptoms, are an understudied public health concern. We examined the relationship between estimated cyanobacteria biomass and the frequency of respiratory-related hospital visits for residents living near Green Bay, Lake Michigan, Wisconsin during 2017-2019. Remote sensing data from the Cyanobacteria Assessment Network was used to approximate cyanobacteria exposure through creation of a metric for cyanobacteria chlorophyll-a (Chl(BS)). We obtained counts of hospital visits for asthma, wheezing, and allergic rhinitis from the Wisconsin Hospital Association for ZIP codes within a 3-mile radius of Green Bay. We analyzed weekly counts of hospital visits versus cyanobacteria, which was modelled as a continuous measure (Chl(BS)) or categorized according to World Health Organization's (WHO) alert levels using Poisson generalized linear models. Our data included 2743 individual hospital visits and 114 weeks of satellite derived cyanobacteria biomass indicator data. Peak values of Chl(BS) were observed between the months of June and October. Using the WHO alert levels, 60% of weeks were categorized as no risk, 19% as Vigilance Level, 15% as Alert Level 1, and 6% as Alert Level 2. In Poisson regression models adjusted for temperature, dewpoint, season, and year, there was no association between Chl(BS) and hospital visits (rate ratio [RR] [95% Confidence Interval (CI)] = 0.98 [0.77, 1.24]). There was also no consistent association between WHO alert level and hospital visits when adjusting for covariates (Vigilance Level: RR [95% CI] 0.88 [0.74, 1.05], Alert Level 1: 0.82 [0.67, 0.99], Alert Level 2: 0.98 [0.77, 1.24], compared to the reference no risk category). Our methodology and model provide a template for future studies that assess the association between cyanobacterial blooms and respiratory health.

The National Poison Data System (NPDS) comprises self-reported information from people who call US poison center hotlines. NPDS data have proven to be important in identifying emerging public health threats. We used NPDS to examine records of people who had self-reported exposure to harmful algal blooms (HABs). Participating poison centers then contacted people who had called their centers from May through October 2019 about their HAB exposure to ask about exposure route, symptoms, health care follow-up, and awareness of possible risks of exposure. Of 55 callers who agreed to participate, 47 (85%) reported exposure to HABs while swimming or bathing in HAB-contaminated water. Nine callers reported health symptoms from being near waters contaminated with HABs, suggesting potential exposure via aerosolized toxins. Symptoms varied by the reported routes of exposure; the most commonly reported symptoms were gastrointestinal and respiratory. More public and health care provider education and outreach are needed to improve the understanding of HAB-related risks, to address ways to prevent HAB-related illnesses, and to describe appropriate support when exposures occur.

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.

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.

OBJECTIVES: The aim of this study was to evaluate the association between intellectual and developmental disabilities (IDDs) and severe COVID-19 outcomes, 30-day readmission, and/or increased length of stay (LOS) using a large electronic administrative database. METHODS: Patients hospitalized with COVID-19 were identified between March 2020 and June 2021 from more than 900 hospitals in the United States. IDDs included intellectual disability, cerebral palsy, Down syndrome, autism spectrum disorder (ASD), and other intellectual disabilities. Outcomes included intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), 30-day readmission, mortality, and LOS. RESULTS: Among 643,765 patients with COVID-19, multivariate models showed that patients with any IDD were at a significantly greater risk of at least 1 severe outcome, 30-day readmission, or longer LOS than patients without any IDD. Compared with those without any IDD, patients with Down syndrome had the greatest odds of ICU admission (odds ratio [OR] and 95% confidence interval [CI]: 1.96 [1.73-2.21]), IMV (OR: 2.37 [2.07-2.70]), and mortality (OR: 2.33 [2.00-2.73]). Patients with ASD and those with Down syndrome both had over a 40% longer mean LOS. Patients with intellectual disabilities had a 23% (12-35%) increased odds of 30-day readmission. CONCLUSIONS: Results suggest that patients hospitalized with COVID-19 with IDD have a significantly increased risk of severe outcomes, 30-day readmission, and longer LOS.

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.

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.

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.

Harmful algal blooms (HABs) are the rapid growth of algae that can produce toxic or harmful effects in people and animals. Potential health effects include respiratory illness, gastrointestinal illness, skin and eye irritation, and sometimes more severe toxic effects such as liver damage. Defining HAB exposure and related illness is challenging for many reasons, including characterizing the exposure. Large electronic health record databases present an opportunity to study health encounters specifically related to HAB exposure through querying medical diagnostic codes. We queried the MarketScan Research Databases between January 2009 and April 2019 for use of the International Classification of Diseases (ICD) codes for HAB exposure. We found a total of 558 records that used either the ICD-9 or ICD-10 code for HAB exposure. Respiratory illness was most commonly reported along with the HAB exposure code. Use of HAB exposure codes showed seasonal fluctuations during 2012–2019. We found that although the HAB-related ICD-9 and ICD-10 codes were used infrequently, they were most often recorded during bloom seasons in warmer months. This analysis is the first that utilizes a large-scale national database of de-identified health records to understand the use of medical diagnostic codes related to algae exposure. © 2021, National Environmental Health Association. All rights reserved.

BACKGROUND: Evidence on risk for adverse outcomes from 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, whether these associations differ by race/ethnicity; and described 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 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, 6,550 (1.3%) had a COVID-19 diagnosis. In adjusted models, COVID-19 was associated with increased risk for: acute respiratory distress syndrome (adjusted risk ratio [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 (versus without) COVID-19 were not discharged home. CONCLUSIONS: These findings emphasize the importance of implementing recommended mitigation strategies to reduce risk for SARS-CoV-2 infection and further inform counseling and clinical care for pregnant women during the COVID-19 pandemic.

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 the type, association, and timing of post-COVID conditions following COVID-19 diagnosis. METHODS: Using the Premier Healthcare Database Special COVID-19 Release (PHD-SR) (release date, October 20, 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 odds ratios were calculated to assess for late conditions that were more common in case-patients compared with 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 at least one of five post-COVID conditions. Among adult outpatients with COVID-19, 7.7% experienced at least one of ten 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 were also more likely to experience a range of conditions affecting multiple body systems (e.g. nonspecific chest pain, fatigue, headache, and respiratory, nervous, circulatory, and gastrointestinal system symptoms) than outpatient control-patients. Children with COVID-19 were not more likely to experience late conditions than children without COVID-19. 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 health care practice and resource planning for follow-up COVID-19 care.

This cohort study uses data from the Premier Healthcare Database Special COVID-19 Release to assess the association of demographic and clinical characteristics with severe COVID-19 illness among hospitalized US pediatric patients with COVID-19.

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.

In the context of stalling progress against malaria, resistance of mosquitoes to insecticides, and residual transmission, mass drug administration (MDA) of ivermectin, an endectocide used for neglected tropical diseases (NTDs), has emerged as a promising complementary vector control method. Ivermectin reduces the life span of Anopheles mosquitoes that feed on treated humans and/or livestock, potentially decreasing malaria parasite transmission when administered at the community level. Following the publication by WHO of the preferred product characteristics for endectocides as vector control tools, this roadmap provides a comprehensive view of processes needed to make ivermectin available as a vector control tool by 2024 with a completely novel mechanism of action. The roadmap covers various aspects, which include 1) the definition of optimal dosage/regimens for ivermectin MDA in both humans and livestock, 2) the risk of resistance to the drug and environmental impact, 3) ethical issues, 4) political and community engagement, 5) translation of evidence into policy, and 6) operational aspects of large-scale deployment of the drug, all in the context of a drug given as a prevention tool acting at the community level. The roadmap reflects the insights of a multidisciplinary group of global health experts who worked together to elucidate the path to inclusion of ivermectin in the toolbox against malaria, to address residual transmission, counteract insecticide resistance, and contribute to the end of this deadly disease.

Coronavirus disease 2019 (COVID-19) is a complex clinical illness with potential complications that might require ongoing clinical care (1-3). Few studies have investigated discharge patterns and hospital readmissions among large groups of patients after an initial COVID-19 hospitalization (4-7). Using electronic health record and administrative data from the Premier Healthcare Database,* CDC assessed patterns of hospital discharge, readmission, and demographic and clinical characteristics associated with hospital readmission after a patient's initial COVID-19 hospitalization (index hospitalization). Among 126,137 unique patients with an index COVID-19 admission during March-July 2020, 15% died during the index hospitalization. Among the 106,543 (85%) surviving patients, 9% (9,504) were readmitted to the same hospital within 2 months of discharge through August 2020. More than a single readmission occurred among 1.6% of patients discharged after the index hospitalization. Readmissions occurred more often among patients discharged to a skilled nursing facility (SNF) (15%) or those needing home health care (12%) than among patients discharged to home or self-care (7%). The odds of hospital readmission increased with age among persons aged ≥65 years, presence of certain chronic conditions, hospitalization within the 3 months preceding the index hospitalization, and if discharge from the index hospitalization was to a SNF or to home with health care assistance. These results support recent analyses that found chronic conditions to be significantly associated with hospital readmission (6,7) and could be explained by the complications of underlying conditions in the presence of COVID-19 (8), COVID-19 sequelae (3), or indirect effects of the COVID-19 pandemic (9). Understanding the frequency of, and risk factors for, readmission can inform clinical practice, discharge disposition decisions, and public health priorities such as health care planning to ensure availability of resources needed for acute and follow-up care of COVID-19 patients. With the recent increases in cases nationwide, hospital planning can account for these increasing numbers along with the potential for at least 9% of patients to be readmitted, requiring additional beds and resources.

Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simple method of inverse distance weight modeling offers an alternative. Using stream gauges as inputs, this study interpolated stream elevation via inverse distance weight modeling under 15 different model input parameter scenarios for Harris County, Texas, USA, from August 25th to September 15th, 2017 (before, during, and after Hurricane Harvey inundated the county). A digital elevation model was used to identify areas where modeled stream elevation exceeded ground elevation, indicating flooding. Imagery and observed high water marks were used to validate the models’ outputs. There was a high degree of agreement (between 79 and 88%) between imagery and model outputs of parameterizations visually validated. Quantitative validations based on high water marks were also positive, with a Nash–Sutcliffe efficiency of in excess of.6 for all parameterizations relative to a Nash–Sutcliffe efficiency of the benchmark of 0.56. Inverse distance weight modeling offers a simple, accurate method for first-order estimations of riverine flooding in near real-time using readily available data, and outputs are robust to some alterations to input parameters.

Until recent decades, the focus of disaster management remained largely on attributes of the physical world, primarily risk assessments of the threat of natural and anthropogenic hazards to the built environment. The concept of social vulnerability within a disaster management context received increasing attention when researchers recognized that a more complete assessment of risk must also include the socioeconomic and demographic factors that affect community resilience (Flanagan, Gregory, Hallisey, Heitgerd, & Lewis, 2011; Juntunen, 2005).

Synthetic cannabinoids, also known as K2 and spice, are heterogeneous psychoactive compounds identified as substances of abuse (1,2). On March 22, 2018, the Illinois Department of Public Health was notified by the Illinois Poison Center of four patients seen in emergency departments (EDs) during the preceding 2 weeks with unexplained bleeding and high international normalized ratios (INRs; range from 5 to >20 [normal <1.1]), indicating a clotting disorder, and reported synthetic cannabinoid use during the previous 3 days. None reported taking prescription anticoagulants or exposure to anticoagulant rodenticides. An investigation by the Illinois Department of Public Health, the Illinois Poison Center, CDC, local health departments, and law enforcement agencies was initiated to identify additional cases, ascertain epidemiologic links among patients, and implement control measures.

A thorough search of the existing literature has revealed that there are currently no published recommendations or guidelines for the interpretation of US regulations on the use of human participants in vector biology research (VBR). An informal survey of vector biologists has indicated that issues related to human participation in vector research have been largely debated by academic, national, and local Institutional Review Boards (IRBs) in the countries where the research is being conducted, and that interpretations and subsequent requirements made by these IRBs have varied widely. This document is intended to provide investigators and corresponding scientific and ethical review committee members an introduction to VBR methods involving human participation and the legal and ethical framework in which such studies are conducted with a focus on US Federal Regulations. It is also intended to provide a common perspective for guiding researchers, IRB members, and other interested parties (i.e., public health officials conducting routine entomological surveillance) in the interpretation of human subjects regulations pertaining to VBR.