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Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response—line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System—to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. 2024;114(10):1071–1080. https://doi.org/10.2105/AJPH.2024.307743) [ABSTRACT FROM AUTHOR]

IntroductionPrior studies have found considerable racial and ethnic disparities in secondhand smoke (SHS) exposure. Although a number of individual-level determinants of this disparity have been identified, contextual determinants of racial and ethnic disparities in SHS exposure remain unexamined. The objective of this study was to examine disparities in serum cotinine in relation to area-level income inequality among 14 649 children from the National Health and Nutrition Examination Survey.MethodsWe fit log-normal regression models to examine disparities in serum cotinine in relation to Metropolitan Statistical Areas level income inequality among 14 649 nonsmoking children aged 3-15 from the National Health and Nutrition Examination Survey (1999-2012).ResultNon-Hispanic black children had significantly lower serum cotinine than non-Hispanic white children (-0.26; 95% CI: -0.38, -0.15) in low income inequality areas, but this difference was attenuated in areas with high income inequality (0.01; 95% CI: -0.16, 0.18). Serum cotinine declined for non-Hispanic white and Mexican American children with increasing income inequality. Serum cotinine did not change as a function of the level of income inequality among non-Hispanic black children.ConclusionsWe have found evidence of differential associations between SHS exposure and income inequality by race and ethnicity. Further examination of environments which engender SHS exposure among children across various racial/ethnic subgroups can foster a better understanding of how area-level income inequality relates to health outcomes such as levels of SHS exposure and how those associations differ by race/ethnicity.ImplicationsIn the United States, the association between children's risk of SHS exposure and income inequality is modified by race/ethnicity in a manner that is inconsistent with theories of income inequality. In overall analysis this association appears to be as predicted by theory. However, race-specific analyses reveal that higher levels of income inequality are associated with lower levels of SHS exposure among white children, while levels of SHS exposure among non-Hispanic black children are largely invariant to area-level income inequality. Future examination of the link between income inequality and smoking-related health outcomes should consider differential associations across racial and ethnic subpopulations.

Purpose: To estimate percent excess deaths during the COVID‐19 pandemic by rural‐urban residence in the United States and to describe rural‐urban disparities by age, sex, and race/ethnicity. Methods: Using US mortality data, we used overdispersed Poisson regression models to estimate monthly expected death counts by rurality of residence, age group, sex, and race/ethnicity, and compared expected death counts with observed deaths. We then summarized excess deaths over 6 6‐month time periods. Findings: There were 16.9% (95% confidence interval [CI]: 16.8, 17.0) more deaths than expected between March 2020 and February 2023. The percent excess varied by rurality (large central metro: 18.2% [18.1, 18.4], large fringe metro: 15.6% [15.5, 15.8], medium metro: 18.1% [18.0, 18.3], small metro: 15.5% [15.3, 15.7], micropolitan rural: 16.3% [16.1, 16.5], and noncore rural: 15.8% [15.6, 16.1]). The percent excess deaths were 20.2% (20.1, 20.3) for males and 13.6% (13.5, 13.7) for females, and highest for Hispanic persons (49% [49.0, 49.6]), followed by non‐Hispanic Black persons (28% [27.5, 27.9]) and non‐Hispanic White persons (12% [11.6, 11.8]). The 6‐month time periods with the highest percent excess deaths for large central metro areas were March 2020‐August 2020 and September 2020‐February 2021; for all other areas, these time periods were September 2020‐February 2021 and September 2021‐February 2022. Conclusion: Percent excess deaths varied by rurality, age group, sex, race/ethnicity, and time period. Monitoring excess deaths by rurality may be useful in assessing the impact of the pandemic over time, as rural‐urban patterns appear to differ. [ABSTRACT FROM AUTHOR]

Background: Trends in the prevalence of hepatitis C virus (HCV) infection among women delivering live births may differ in rural vs. urban areas of the United States, but estimation of trends based on observed counts may lead to unstable estimates in rural counties due to small numbers. Objectives: The objective of the study was to use small area estimation methods to provide updated county‐level prevalence estimates and, for the first time, trends in maternal HCV infection among live births by county‐level rurality. Methods: Cross‐sectional natality data from 2016 to 2020 were used to estimate maternal hepatitis C prevalence using hierarchical Bayesian models with spatiotemporal random effects to produce annual county‐level estimates of maternal HCV infection and trends over time. Models included a 6‐level rural–urban county classification, year, maternal characteristics and county‐specific covariates. Data were analysed in 2022. Results: There were 90,764/18,905,314 live births (4.8 per 1000) with HCV infection reported on the birth certificate. Hepatitis C prevalence was higher among rural counties as compared to urban counties. Rural counties had the largest annual increases in maternal hepatitis C prevalence (per 1000 births) from 2016 to 2020 (micropolitan: 0.39; noncore: 0.40), with smaller increases among less densely populated urban counties (medium metro: 0.28; small metro: 0.28) and urban counties (large central metro:0.11; large fringe metro: 0.14). Conclusions: The prevalence of maternal HCV infection was the highest in rural counties, and rural counties saw the greatest average prevalence increase during 2016–2020. County‐level data can help in monitoring rural–urban trends in maternal HCV infection to reduce geographic disparities. [ABSTRACT FROM AUTHOR]

Background Coronavirus disease 2019 (COVID-19)–associated fungal infections cause severe illness, but comprehensive data on disease burden are lacking. We analyzed US National Vital Statistics System (NVSS) data to characterize disease burden, temporal trends, and demographic characteristics of persons dying of fungal infections during the COVID-19 pandemic. Methods Using NVSS's January 2018–December 2021 Multiple Cause of Death Database, we examined numbers and age-adjusted rates (per 100 000 population) of deaths due to fungal infection by fungal pathogen, COVID-19 association, demographic characteristics, and year. Results Numbers and age-adjusted rates of deaths due to fungal infection increased from 2019 (n = 4833; rate, 1.2 [95% confidence interval, 1.2–1.3]) to 2021 (n = 7199; rate, 1.8 [1.8–1.8] per 100 000); of 13 121 such deaths during 2020–2021, 2868 (21.9%) were COVID-19 associated. Compared with non–COVID-19–associated deaths (n = 10 253), COVID-19–associated deaths more frequently involved Candida (n = 776 [27.1%] vs n = 2432 [23.7%], respectively) and Aspergillus (n = 668 [23.3%] vs n = 1486 [14.5%]) and less frequently involved other specific fungal pathogens. Rates of death due to fungal infection were generally highest in nonwhite and non-Asian populations. Death rates from Aspergillus infections were approximately 2 times higher in the Pacific US census division compared with most other divisions. Conclusions Deaths from fungal infection increased during 2020–2021 compared with previous years, primarily driven by COVID-19–associated deaths, particularly those involving Aspergillus and Candida. Our findings may inform efforts to prevent, identify, and treat severe fungal infections in patients with COVID-19, especially in certain racial/ethnic groups and geographic areas. [ABSTRACT FROM AUTHOR]

It has been difficult to measure rural-urban differences in maternal mortality ratios (MMRs) in the United States in recent years because of the incremental adoption of a pregnancy status checkbox on the standard US death certificate. Using 1999–2017 mortality and birth data, we examined the impact of the pregnancy checkbox on MMRs according to urbanicity of residence (large urban area, medium/small urban area, or rural area), using log-binomial regression models to predict trends that would have been observed if all states had adopted the checkbox as of 1999. Implementation of the checkbox resulted in an average estimated increase of 7.5 maternal deaths per 100,000 live births (95% confidence interval (CI): 6.3, 8.8) in large urban areas (a 76% increase), 11.6 (95% CI: 9.6, 13.6) in medium/small urban areas (a 113% increase), and 16.6 (95% CI: 12.9, 20.3) in rural areas (a 107% increase), compared with MMRs prior to the checkbox. Assuming that all states had the checkbox as of 1999, demographic-factor–adjusted predicted MMRs increased in rural areas, declined in large urban areas, and did not change in medium/small urban areas. However, trends and urban-rural differences were substantially attenuated when analyses were limited to direct/specific causes of maternal death, which are probably subject to less misclassification. Accurate ascertainment of maternal deaths, particularly in rural areas, is important for reducing disparities in maternal mortality. [ABSTRACT FROM AUTHOR]

Background: In the United States, infection with SARS‐CoV‐2 caused 380,000 reported deaths from March to December 2020. Methods: We adapted the Moving Epidemic Method to all‐cause mortality data from the United States to assess the severity of the COVID‐19 pandemic across age groups and all 50 states. By comparing all‐cause mortality during the pandemic with intensity thresholds derived from recent, historical all‐cause mortality, we categorized each week from March to December 2020 as either low severity, moderate severity, high severity, or very high severity. Results: Nationally for all ages combined, all‐cause mortality was in the very high severity category for 9 weeks. Among people 18 to 49 years of age, there were 29 weeks of consecutive very high severity mortality. Forty‐seven states, the District of Columbia, and New York City each experienced at least 1 week of very high severity mortality for all ages combined. Conclusions: These periods of very high severity of mortality during March through December 2020 are likely directly or indirectly attributable to the COVID‐19 pandemic. This method for standardized comparison of severity over time across different geographies and demographic groups provides valuable information to understand the impact of the COVID‐19 pandemic and to identify specific locations or subgroups for deeper investigations into differences in severity. [ABSTRACT FROM AUTHOR]

The National Center for Health Statistics' (NCHS's) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133–2140. https://doi.org/10.2105/AJPH.2021.306519) [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged <25 years (-2.9 to 14.1). Among persons aged <65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of >1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates. [ABSTRACT FROM AUTHOR]

Introduction: The prevalence of hepatitis C virus infection among women delivering live births in the U.S. may be higher in rural areas where county-level estimates may be unreliable. The aim of this study is to model county-level maternal hepatitis C virus infection among deliveries in the U.S.Methods: In 2020, U.S. natality files (2010-2018) with county-level maternal residence information were used from states that had adopted the 2003 revised U.S. birth certificate, which included a field for hepatitis C virus infection present during pregnancy. Hierarchical Bayesian spatial models with spatiotemporal random effects were applied to produce stable annual county-level estimates of maternal hepatitis C virus infection for years when all states had adopted the revised birth certificate (2016-2018). Models included a 6-Level Urban-Rural County Classification Scheme along with the birth year and county-specific covariates to improve posterior predictions.Results: Among approximately 32 million live births, the overall prevalence of maternal hepatitis C virus infection was 3.5 per 1,000 births (increased from 2.0 in 2010 to 5.0 in 2018). During 2016-2018, posterior predicted median county-level maternal hepatitis C virus infection rates showed that nonurban counties had 3.5-3.8 times higher rates of hepatitis C virus than large central metropolitan counties. The counties in the top 10th percentile for maternal hepatitis C virus rates in 2018 were generally located in Appalachia, in Northern New England, along the northern border in the Upper Midwest, and in New Mexico.Conclusions: Further implementation of community-level interventions that are effective in reducing maternal hepatitis C virus infection and its subsequent morbidity may help to reduce geographic and rural disparities. [ABSTRACT FROM AUTHOR]

Introduction: Infant mortality rates are higher in nonmetropolitan areas versus large metropolitan areas. Variation by race/ethnicity and cause of death has not been assessed. Urban-rural infant mortality rate differences were quantified by race/ethnicity and cause of death.Methods: National Vital Statistics System linked birth/infant death data (2014-2016) were analyzed in 2019 by 3 urban-rural county classifications: large metropolitan, medium/small metropolitan, and nonmetropolitan. Excess infant mortality rates (rate differences) by urban-rural classification were calculated relative to large metropolitan areas overall and for each racial/ethnic group. The number of excess deaths, population attributable fraction, and proportion of excess deaths attributable to underlying causes of death was calculated.Results: Nonmetropolitan areas had the highest excess infant mortality rate overall. Excess infant mortality rates were substantially lower for Hispanic infants than other races/ethnicities. Overall, 7.4% of infant deaths would be prevented if all areas had the infant mortality rate of large metropolitan areas. With more than half of births occurring outside of large metropolitan areas, the population attributable fraction was highest for American Indian/Alaska Natives (20.3%) and whites, non-Hispanic (14.3%). Excess infant mortality rates in both nonmetropolitan and medium/small metropolitan areas were primarily attributable to sudden unexpected infant deaths (42.3% and 31.9%) and congenital anomalies (30.1% and 26.8%). This pattern was consistent for all racial/ethnic groups except black, non-Hispanic infants, for whom preterm-related and sudden unexpected infant deaths accounted for the largest share of excess infant mortality rates.Conclusions: Infant mortality increases with rurality, and excess infant mortality rates are predominantly attributable to sudden unexpected infant deaths and congenital anomalies, with differences by race/ethnicity regarding magnitude and cause of death. [ABSTRACT FROM AUTHOR]

Background: Walking to school is an important source of physical activity among children. There is a paucity of research exploring environmental determinants of walking to school among children in urban areas. Methods: A cross-sectional secondary analysis of baseline data (2007) from 365 children in the "Multiple Opportunities to Reach Excellence" (MORE) Study (8 to 13 years; Mean 9.60 years, SD 1.04). Children and caregivers were asked about walking to school and perceived safety. Objective measures of the environment were obtained using a validated environmental neighborhood assessment. Results: Over half (55.83%) of children reported walking to school most of the time. High levels of neighborhood incivilities were associated with lower levels of perceived safety (OR: 0.39, 95% CI: 0.21 to 0.72). Living on a block above the median in incivilities was associated with a 353% increase in odds of walking to school (OR: 3.53; 95% CI: 1.68 to 7.39). Conclusions: Children residing in neighborhoods high in incivilities are more likely to walk to school, in spite of lower levels of perceived safety. As a high proportion of children residing in disadvantaged neighborhoods walk to school, efforts should be directed at minimizing exposure to neighborhood hazards by ensuring safe routes to and from school. [ABSTRACT FROM AUTHOR]

Background: Several studies have examined the association between IPI and birth outcomes, but few have explored the association between interpregnancy interval (IPI) and postnatal outcomes.Objective: We examined the association between IPI and injury-related infant mortality, a leading cause of postneonatal mortality.Methods: We used 2011-2015 US period-linked birth-infant death vital statistics data to generate a multiyear birth cohort of non-first-born singleton births (N = 9 782 029). IPI was defined as the number of months between a live birth and the start of the pregnancy leading to the next live birth. Causes of death in the first year of life were identified using ICD-10 codes. Hazard ratios (HR) for IPI categories were estimated using Cox proportional hazards models adjusted for birth order, county poverty level, and maternal characteristics (marital status, race/ethnicity, education, age at previous birth).Results: After adjustment, overall infant mortality (48.1 per 10 000 births) was higher for short and long IPIs compared with IPI 18-23 months (reference): <6, aHR 1.61, 95% CI 1.54, 1.68; 6-11, aHR 1.22, 95% CI 1.17, 1.26; and 60+ months, aHR 1.12, 95% CI 1.08, 1.16. In comparison, the risk of injury-related infant mortality (4.4 per 10 000 births) decreased with longer IPIs: <6, aHR 1.77, 95% CI 1.55, 2.01; 6-11, aHR 1.41, 95% CI 1.25, 1.59; 12-17, aHR 1.25, 95% CI 1.10, 1.41; 24-59, aHR 0.78, 95% CI 0.69, 0.87; and 60+ months, aHR 0.55, 95% CI 0.48, 0.62.Conclusion: Unlike overall infant mortality, injury-related infant mortality decreased with IPI length. While injury-related deaths are rare, these patterns suggest that the timing between births may be a marker of risk for fatal infant injuries. The first year postpartum may be an ideal time for the delivery of evidence-based injury prevention programmes as well as family planning services. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization (WHO) recommends that women wait at least 24 months after a livebirth before attempting a subsequent pregnancy to reduce the risk of adverse maternal, perinatal, and infant health outcomes. However, the applicability of the WHO recommendations for women in the United States is unclear, as breast feeding, nutrition, maternal age at first birth, and total fertility rate differs substantially between the United States and the low- and middle-resource countries upon which most of the evidence is based.Methods: To inform guideline development for birth spacing specific to women in the United States, the Office of Population Affairs (OPA) convened an expert work group meeting in Washington, DC, on 14-15 September 2017 among reproductive, perinatal, paediatric, social, and public health epidemiologists; obstetrician-gynaecologists; biostatisticians; and experts in evidence synthesis related to women's health.Results: Presentations and discussion topics included the methodological quality of existing studies, evaluation of the evidence for causal effects of short interpregnancy intervals on adverse perinatal and maternal health outcomes, good practices for future research, and identification of research gaps and priorities for future work.Conclusions: This report provides an overview of the presentations, discussions, and conclusions from the expert work group meeting. [ABSTRACT FROM AUTHOR]

Background: Meta-analyses of observational studies have shown that women with a shorter interpregnancy interval (the time from delivery to start of a subsequent pregnancy) are more likely to experience adverse pregnancy outcomes, such as preterm delivery or small for gestational age birth, than women who space their births further apart. However, the studies used to inform these estimates have methodological shortcomings.Methods: In this commentary, we summarise the discussions of an expert workgroup describing good practices for the design, analysis, and interpretation of observational studies of interpregnancy interval and adverse perinatal health outcomes.Results: We argue that inferences drawn from research in this field will be improved by careful attention to elements such as: (a) refining the research question to clarify whether the goal is to estimate a causal effect vs describe patterns of association; (b) using directed acyclic graphs to represent potential causal networks and guide the analytic plan of studies seeking to estimate causal effects; (c) assessing how miscarriages and pregnancy terminations may have influenced interpregnancy interval classifications; (d) specifying how key factors such as previous pregnancy loss, pregnancy intention, and maternal socio-economic position will be considered; and (e) examining if the association between interpregnancy interval and perinatal outcome differs by factors such as maternal age.Conclusion: This commentary outlines the discussions of this recent expert workgroup, and describes several suggested principles for study design and analysis that could mitigate many potential sources of bias. [ABSTRACT FROM AUTHOR]

This study used data from the National Vital Statistics System Mortality to evaluate trends in US drug overdose deaths involving opioids certified as unintentional, suicide, or undetermined intent. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005- 2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and countyspecific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software RINLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation. Running head: Mapping Geographic Variation in Mortality Rates. [ABSTRACT FROM AUTHOR]