COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration - United States, May 11, 2023.

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).

Mapping Geographic Variation in Infant Mortality and Related Black-White Disparities in the US.

BACKGROUND: In the US, black infants remain more than twice as likely as white infants to die in the first year of life. Previous studies of geographic variation in infant mortality disparities have been limited to large metropolitan areas where stable estimates of infant mortality rates by race can be determined, leaving much of the US unexplored. METHODS: The objective of this analysis was to describe geographic variation in county-level racial disparities in infant mortality rates across the 48 contiguous US states and District of Columbia using national linked birth and infant death period files (2004-2011). We implemented Bayesian shared component models in OpenBUGS, borrowing strength across both spatial units and racial groups. We mapped posterior estimates of mortality rates for black and white infants as well as relative and absolute disparities. RESULTS: Black infants had higher infant mortality rates than white infants in all counties, but there was geographic variation in the magnitude of both relative and absolute disparities. The mean difference between black and white rates was 5.9 per 1,000 (median: 5.8, interquartile range: 5.2 to 6.6 per 1,000), while those for black infants were 2.2 times higher than for white infants (median: 2.1, interquartile range: 1.9-2.3). One quarter of the county-level variation in rates for black infants was shared with white infants. CONCLUSIONS: Examining county-level variation in infant mortality rates among black and white infants and related racial disparities may inform efforts to redress inequities and reduce the burden of infant mortality in the US.

Tracking COVID-19 in the United States With Surveillance of Aggregate Cases and Deaths.

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.

Improving efficiency of COVID-19 aggregate case and death surveillance data transmission for jurisdictions: current and future role of application programming interfaces (APIs).

During the coronavirus disease-2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) supplemented traditional COVID-19 case and death reporting with COVID-19 aggregate case and death surveillance (ACS) to track daily cumulative numbers. Later, as public health jurisdictions (PHJs) revised the historical COVID-19 case and death data due to data reconciliation and updates, CDC devised a manual process to update these records in the ACS dataset for improving the accuracy of COVID-19 case and death data. Automatic data transfer via an application programming interface (API), an intermediary that enables software applications to communicate, reduces the time and effort in transferring data from PHJs to CDC. However, APIs must meet specific content requirements for use by CDC. As of March 2022, CDC has integrated APIs from 3 jurisdictions for COVID-19 ACS. Expanded use of APIs may provide efficiencies for COVID-19 and other emergency response planning efforts as evidenced by this proof-of-concept. In this article, we share the utility of APIs in COVID-19 ACS.

County-Level Trends in Suicide Rates in the U.S., 2005-2015.

INTRODUCTION: Understanding the geographic patterns of suicide can help inform targeted prevention efforts. Although state-level variation in age-adjusted suicide rates has been well documented, trends at the county-level have been largely unexplored. This study uses small area estimation to produce stable county-level estimates of suicide rates to examine geographic, temporal, and urban-rural patterns in suicide from 2005 to 2015. METHODS: Using National Vital Statistics Underlying Cause of Death Files (2005-2015), hierarchical Bayesian models were used to estimate suicide rates for 3,140 counties. Model-based suicide rate estimates were mapped to explore geographic and temporal patterns and examine urban-rural differences. Analyses were conducted in 2016-2017. RESULTS: Posterior predicted mean county-level suicide rates increased by >10% from 2005 to 2015 for 99% of counties in the U.S., with 87% of counties showing increases of >20%. Counties with the highest model-based suicide rates were consistently located across the western and northwestern U.S., with the exception of southern California and parts of Washington. Compared with more urban counties, more rural counties had the highest estimated suicide rates from 2005 to 2015, and also the largest increases over time. CONCLUSIONS: Mapping county-level suicide rates provides greater granularity in describing geographic patterns of suicide and contributes to a better understanding of changes in suicide rates over time. Findings may inform more targeted prevention efforts as well as future research on community-level risk and protective factors related to suicide mortality.

Spatiotemporal trends in teen birth rates in the USA, 2003-2012.

The objective of this analysis was to explore temporal and spatial variation in teen birth rates TBRs across counties in the USA, from 2003 to 2012, by using hierarchical Bayesian models. Prior examination of spatiotemporal variation in TBRs has been limited by the reliance on large-scale geographies such as states, because of the potential instability in TBRs at smaller geographical scales such as counties. We implemented hierarchical Bayesian models with space-time interaction terms and spatially structured and unstructured random effects to produce smoothed county level TBR estimates, allowing for examination of spatiotemporal patterns and trends in TBRs at a smaller geographic scale across the USA. The results may help to highlight US counties where TBRs are higher or lower and to inform efforts to reduce birth rates to adolescents in the USA further.

Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003-2012.

Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003-2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident.

A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to , JOS.

Multiple imputation is a popular approach to handling missing data. Although it was originally motivated by survey nonresponse problems, it has been readily applied to other data settings. However, its general behavior still remains unclear when applied to survey data with complex sample designs, including clustering. Recently, Lewis et al. (2014) compared single- and multiple-imputation analyses for certain incomplete variables in the 2008 National Ambulatory Medicare Care Survey, which has a nationally representative, multistage, and clustered sampling design. Their study results suggested that the increase of the variance estimate due to multiple imputation compared with single imputation largely disappears for estimates with large design effects. We complement their empirical research by providing some theoretical reasoning. We consider data sampled from an equally weighted, single-stage cluster design and characterize the process using a balanced, one-way normal random-effects model. Assuming that the missingness is completely at random, we derive analytic expressions for the within- and between-multiple-imputation variance estimators for the mean estimator, and thus conveniently reveal the impact of design effects on these variance estimators. We propose approximations for the fraction of missing information in clustered samples, extending previous results for simple random samples. We discuss some generalizations of this research and its practical implications for data release by statistical agencies.

Hot spots in mortality from drug poisoning in the United States, 2007-2009.

Over the past several years, the death rate associated with drug poisoning has increased by over 300% in the U.S. Drug poisoning mortality varies widely by state, but geographic variation at the substate level has largely not been explored. National mortality data (2007-2009) and small area estimation methods were used to predict age-adjusted death rates due to drug poisoning at the county level, which were then mapped in order to explore: whether drug poisoning mortality clusters by county, and where hot and cold spots occur (i.e., groups of counties that evidence extremely high or low age-adjusted death rates due to drug poisoning). Results highlight several regions of the U.S. where the burden of drug poisoning mortality is especially high. Findings may help inform efforts to address the growing problem of drug poisoning mortality by indicating where the epidemic is concentrated geographically.

Out-of-Sample Fusion in Risk Prediction.

The probability that mortality from certain causes exceeds high thresholds is addressed. An out-of-sample fusion method is presented where an original real data sample is fused or combined with independent computer-generated samples in the estimation of exceedance probabilities assuming a density ratio model. Since the size of the combined sample of real and artificial data is larger than that of the real sample, the fused sample produces short confidence intervals relative to traditional methods. Numerical results show that the method maintains good coverage even for some misspecified cases.

Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999-2009.

BACKGROUND: Drug poisoning mortality has increased substantially in the U.S. over the past 3 decades. Previous studies have described state-level variation and urban-rural differences in drug-poisoning deaths, but variation at the county level has largely not been explored in part because crude county-level death rates are often highly unstable. PURPOSE: The goal of the study was to use small-area estimation techniques to produce stable county-level estimates of age-adjusted death rates (AADR) associated with drug poisoning for the U.S., 1999-2009, in order to examine geographic and temporal variation. METHODS: Population-based observational study using data on 304,087 drug-poisoning deaths in the U.S. from the 1999-2009 National Vital Statistics Multiple Cause of Death Files (analyzed in 2012). Because of the zero-inflated and right-skewed distribution of drug-poisoning death rates, a two-stage modeling procedure was used in which the first stage modeled the probability of observing a death for a given county and year, and the second stage modeled the log-transformed drug-poisoning death rate given that a death occurred. Empirical Bayes estimates of county-level drug-poisoning death rates were mapped to explore temporal and geographic variation. RESULTS: Only 3% of counties had drug-poisoning AADRs greater than ten per 100,000 per year in 1999-2000, compared to 54% in 2008-2009. Drug-poisoning AADRs grew by 394% in rural areas compared to 279% for large central metropolitan counties, but the highest drug-poisoning AADRs were observed in central metropolitan areas from 1999 to 2009. CONCLUSIONS: There was substantial geographic variation in drug-poisoning mortality across the U.S.