A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.
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ABSTRACT:
SUBMITTER: Guemes A
PROVIDER: S-EPMC7907397 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing coronavirus disease 2019 (COVID-19) pandemic globally. To augment traditional lab and hospital-based surveillance, Bangladesh established a participatory surveillance system for the public to self-report symptoms consistent with COVID-19 through multiple channels. Here, we report on the use of this system, which received over 3 million responses within two months, for tracking the COVID-19 outbreak in Bangladesh. Although we observe considerable noise in the data and initial volatility in the use of the different reporting mechanisms, the self-reported syndromic data exhibits a strong association with lab-confirmed cases at a local scale. Moreover, the syndromic data also suggests an earlier spread of the outbreak across Bangladesh than is evident from the confirmed case counts, consistent with predicted spread of the outbreak based on population mobility data. Our results highlight the usefulness of participatory syndromic surveillance for mapping disease burden generally, and particularly during the initial phases of an emerging outbreak.
| S-EPMC8054699 | biostudies-literature
Project description:Suicide is a growing public health problem in the United States, claiming approximately 47,000 lives in 2017 (1). However, deaths from suicide represent only a small part of a larger problem because each year millions of persons experience suicidal ideation and engage in suicidal and nonsuicidal self-directed violence, both risk factors for suicide (2). Emergency departments (EDs) are an important setting for monitoring these events in near real time (3-5). From 2001 to 2016, ED visit rates for nonfatal self-harm increased 42% among persons aged ?10 years (1). Using data from CDC's National Syndromic Surveillance Program (NSSP), ED visits for suicidal ideation, self-directed violence, or both among persons aged ?10 years during January 2017-December 2018 were examined by sex, age group, and U.S. region. During the 24-month period, the rate of ED visits for suicidal ideation, self-directed violence, or both increased 25.5% overall, with an average increase of 1.2% per month. Suicide prevention requires comprehensive and multisectoral approaches to addressing risk at personal, relationship, community, and societal levels. ED syndromic surveillance data can provide timely trend information and can support more targeted and prompt public health investigation and response. CDC's Preventing Suicide: A Technical Package of Policy, Programs, and Practices includes tailored suicide prevention strategies for health care settings (6).
| S-EPMC7004405 | biostudies-literature
Project description:BackgroundSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel pathogen causing the current worldwide coronavirus disease 2019 (COVID-19) pandemic. Due to insufficient diagnostic testing in the United States, there is a need for clinical decision-making algorithms to guide testing prioritization.MethodsWe recruited participants nationwide for a randomized clinical trial. We categorized participants into 3 groups: (1) those with confirmed SARS-CoV-2 infection, (2) those with probable SARS-CoV-2 infection (pending test or not tested but with a confirmed COVID-19 contact), and (3) those with possible SARS-CoV-2 infection (pending test or not tested and with a contact for whom testing was pending or not performed). We compared the frequency of self-reported symptoms in each group and categorized those reporting symptoms in early infection (0-2 days), midinfection (3-5 days), and late infection (>5 days).ResultsAmong 1252 symptomatic persons screened, 316 had confirmed, 393 had probable, and 543 had possible SARS-CoV-2 infection. In early infection, those with confirmed and probable SARS-CoV-2 infection shared similar symptom profiles, with fever most likely in confirmed cases (P = .002). Confirmed cases did not show any statistically significant differences compared with unconfirmed cases in symptom frequency at any time point. The most commonly reported symptoms in those with confirmed infection were cough (82%), fever (67%), fatigue (62%), and headache (60%), with only 52% reporting both fever and cough.ConclusionsSymptomatic persons with probable SARS-CoV-2 infection present similarly to those with confirmed SARS-CoV-2 infection. There was no pattern of symptom frequency over time.
| S-EPMC7337847 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China in December 2019, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a pandemic with an estimated death rate between 1% and 5%; and an estimated R0 between 2.2 and 6.7 according to various sources. As of March 28th, 2020, there were over 649,000 confirmed cases and 30,249 total deaths, globally. In the United States, there were over 115,500 cases and 1891 deaths and this number is likely to increase rapidly. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the outbreaks continue to grow. Using daily case data at the county level provided by Johns Hopkins University, we conducted a prospective spatial-temporal analysis with SaTScan. We detect statistically significant space-time clusters of COVID-19 at the county level in the U.S. between January 22nd-March 9th, 2020, and January 22nd-March 27th, 2020. The space-time prospective scan statistic detected "active" and emerging clusters that are present at the end of our study periods - notably, 18 more clusters were detected when adding the updated case data. These timely results can inform public health officials and decision makers about where to improve the allocation of resources, testing sites; also, where to implement stricter quarantines and travel bans. As more data becomes available, the statistic can be rerun to support timely surveillance of COVID-19, demonstrated here. Our research is the first geographic study that utilizes space-time statistics to monitor COVID-19 in the U.S.
| S-EPMC7139246 | biostudies-literature
Project description:Pulmonary hypertension (PH) is an uncommon but progressive condition, and much of what we know about it comes from specialized disease registries. With expanding research into the diagnosis and treatment of PH, it is important to provide updated surveillance on the impact of this disease on hospitalizations and mortality. This study, which builds on previous PH surveillance of mortality and hospitalization, analyzed mortality data from the National Vital Statistics System and data from the National Hospital Discharge Survey between 2001 and 2010. PH deaths were identified using International Classification of Diseases, Tenth Revision codes I27.0, I27.2, I27.8, or I27.9 as any contributing cause of death on the death certificate. Hospital discharges associated with PH were identified using International Classification of Diseases, Ninth Revision, Clinical Modification codes 416.0, 416.8, or 416.9 as one of up to seven listed medical diagnoses. The decline in death rates associated with PH among men from 1980 to 2005 has reversed and now shows a significant increasing trend. Similarly, the death rates for women with PH have continued to increase significantly during the past decade. PH-associated mortality rates for those aged 85 years and older have accelerated compared with rates for younger age groups. There have been significant declines in PH-associated mortality rates for those with pulmonary embolism and emphysema. Rates of hospitalization for PH have increased significantly for both men and women during the past decade; for those aged 85 years and older, hospitalization rates have nearly doubled. Continued surveillance helps us understand and address the evolving trends in hospitalization and mortality associated with PH and PH-associated conditions, especially regarding sex, age, and race/ethnicity disparities.
| S-EPMC4122278 | biostudies-other
Project description:This report updates surveillance results for COPD in the United States. For 1999 to 2011, data from national data systems for adults aged?25 years were analyzed. In 2011, 6.5% of adults (approximately 13.7 million) reported having been diagnosed with COPD. From 1999 to 2011, the overall age-adjusted prevalence of having been diagnosed with COPD declined (P=.019). In 2010, there were 10.3 million (494.8 per 10,000) physician office visits, 1.5 million (72.0 per 10,000) ED visits, and 699,000 (32.2 per 10,000) hospital discharges for COPD. From 1999 to 2010, no significant overall trends were noted for physician office visits and ED visits; however, the age-adjusted hospital discharge rate for COPD declined significantly (P=.001). In 2010 there were 312,654 (11.2 per 1,000) Medicare hospital discharge claims submitted for COPD. Medicare claims (1999-2010) declined overall (P=.045), among men (P=.022) and among enrollees aged 65 to 74 years (P=.033). There were 133,575 deaths (63.1 per 100,000) from COPD in 2010. The overall age-adjusted death rate for COPD did not change during 1999 to 2010 (P=.163). Death rates (1999-2010) increased among adults aged 45 to 54 years (P<.001) and among American Indian/Alaska Natives (P=.008) but declined among those aged 55 to 64 years (P=.002) and 65 to 74 years (P<.001), Hispanics (P=.038), Asian/Pacific Islanders (P<.001), and men (P=.001). Geographic clustering of prevalence, Medicare hospitalizations, and deaths were observed. Declines in the age-adjusted prevalence, death rate in men, and hospitalizations for COPD since 1999 suggest progress in the prevention of COPD in the United States.
| S-EPMC3707177 | biostudies-literature
Project description: Not available
| S-EPMC8007286 | biostudies-literature
Project description:In the United States, sepsis is a major public health problem accounting for over 200,000 annual deaths. The aims of this study were to identify U.S. counties with high sepsis mortality and to assess the community characteristics associated with increased sepsis mortality.We performed a descriptive analysis of 2003 through 2012 Compressed Mortality File data. We defined sepsis deaths as deaths associated with an infection, classified according to the International Classification of Diseases, 10th Version.Three thousand one hundred and eight counties in the contiguous U.S. counties, excluding Hawaii and Alaska.Using geospatial autocorrelation methods, we defined county-level sepsis mortality as strongly clustered, moderately clustered, and nonclustered. We approximated the mean crude, age-adjusted, and community-adjusted sepsis mortality rates nationally and for clustering groups. We contrasted demographic and community characteristics between clustering groups. We performed logistic regression for the association between strongly clustered counties and community characteristics. Among 3,108 U.S. counties, the age-adjusted sepsis mortality rate was 59.6 deaths per 100,000 persons (95% CI, 58.9-60.4). Sepsis mortality was higher in the Southern U.S. and clustered in three major regions: Mississippi Valley, Middle Georgia, and Central Appalachia. Among 161 (5.2%) strongly clustered counties, age-adjusted sepsis mortality was 93.1 deaths per 100,000 persons (95% CI, 90.5-95.7). Strongly clustered sepsis counties were more likely to be located in the south (92.6%; p < 0.001), exhibit lower education, higher impoverished population, without medical insurance, higher medically uninsured rates, and had higher unemployment rates (p < 0.001).Sepsis mortality is higher in the Southern United States, with three regional clusters: "Mississippi Valley," "Middle Georgia," and "Central Appalachia": Regions of high sepsis mortality are characterized by lower education, income, employment, and insurance coverage.
| S-EPMC4911271 | biostudies-literature
Project description:Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near-real-time situational awareness and timely data.
| S-EPMC10040484 | biostudies-literature
Project description:Estimates of influenza disease burden are broadly useful for public health, helping national and local authorities monitor epidemiologic trends, plan and allocate resources, and promote influenza vaccination. Historically, estimates of the burden of seasonal influenza in the United States, focused mainly on influenza-related mortality and hospitalization, were generated every few years. Since the 2010-2011 influenza season, annual US influenza burden estimates have been generated and expanded to include estimates of influenza-related outpatient medical visits and symptomatic illness in the community.We used routinely collected surveillance data, outbreak field investigations, and proportions of people seeking health care from survey results to estimate the number of illnesses, medical visits, hospitalizations, and deaths due to influenza during six influenza seasons (2010-2011 through 2015-2016).We estimate that the number of influenza-related illnesses that have occurred during influenza season has ranged from 9.2 million to 35.6 million, including 140 000 to 710 000 influenza-related hospitalizations.These annual efforts have strengthened public health communications products and supported timely assessment of the impact of vaccination through estimates of illness and hospitalizations averted. Additionally, annual estimates of influenza burden have highlighted areas where disease surveillance needs improvement to better support public health decision making for seasonal influenza epidemics as well as future pandemics.
| S-EPMC5818346 | biostudies-other