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. Published online ahead of print July 25, 2024:e1-e10. https://doi.org/10.2105/AJPH.2024.307743).

PROBLEM/CONDITION: A 2019 report quantified the higher percentage of potentially excess (preventable) deaths in U.S. nonmetropolitan areas compared with metropolitan areas during 2010-2017. In that report, CDC compared national, regional, and state estimates of preventable premature deaths from the five leading causes of death in nonmetropolitan and metropolitan counties during 2010-2017. This report provides estimates of preventable premature deaths for additional years (2010-2022). PERIOD COVERED: 2010-2022. DESCRIPTION OF SYSTEM: Mortality data for U.S. residents from the National Vital Statistics System were used to calculate preventable premature deaths from the five leading causes of death among persons aged <80 years. CDC's National Center for Health Statistics urban-rural classification scheme for counties was used to categorize the deaths according to the urban-rural county classification level of the decedent's county of residence (1: large central metropolitan [most urban], 2: large fringe metropolitan, 3: medium metropolitan, 4: small metropolitan, 5: micropolitan, and 6: noncore [most rural]). Preventable premature deaths were defined as deaths among persons aged <80 years that exceeded the number expected if the death rates for each cause in all states were equivalent to those in the benchmark states (i.e., the three states with the lowest rates). Preventable premature deaths were calculated separately for the six urban-rural county categories nationally, the 10 U.S. Department of Health and Human Services public health regions, and the 50 states and the District of Columbia. RESULTS: During 2010-2022, the percentage of preventable premature deaths among persons aged <80 years in the United States increased for unintentional injury (e.g., unintentional poisoning including drug overdose, unintentional motor vehicle traffic crash, unintentional drowning, and unintentional fall) and stroke, decreased for cancer and chronic lower respiratory disease (CLRD), and remained stable for heart disease. The percentages of preventable premature deaths from the five leading causes of death were higher in rural counties in all years during 2010-2022. When assessed by the six urban-rural county classifications, percentages of preventable premature deaths in the most rural counties (noncore) were consistently higher than in the most urban counties (large central metropolitan and fringe metropolitan) for the five leading causes of death during the study period.During 2010-2022, preventable premature deaths from heart disease increased most in noncore (+9.5%) and micropolitan counties (+9.1%) and decreased most in large central metropolitan counties (-10.2%). Preventable premature deaths from cancer decreased in all county categories, with the largest decreases in large central metropolitan and large fringe metropolitan counties (-100.0%; benchmark achieved in both county categories in 2019). In all county categories, preventable premature deaths from unintentional injury increased, with the largest increases occurring in large central metropolitan (+147.5%) and large fringe metropolitan (+97.5%) counties. Preventable premature deaths from CLRD decreased most in large central metropolitan counties where the benchmark was achieved in 2019 and increased slightly in noncore counties (+0.8%). In all county categories, preventable premature deaths from stroke decreased from 2010 to 2013, remained constant from 2013 to 2019, and then increased in 2020 at the start of the COVID-19 pandemic. Percentages of preventable premature deaths varied across states by urban-rural county classification during 2010-2022. INTERPRETATION: During 2010-2022, nonmetropolitan counties had higher percentages of preventable premature deaths from the five leading causes of death than did metropolitan counties nationwide, across public health regions, and in most states. The gap between the most rural and most urban counties for preventable premature deaths increased during 2010-2022 for four causes of death (cancer, heart disease, CLRD, and stroke) and decreased for unintentional injury. Urban and suburban counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) experienced increases in preventable premature deaths from unintentional injury during 2010-2022, leading to a narrower gap between the already high (approximately 69% in 2022) percentage of preventable premature deaths in noncore and micropolitan counties. Sharp increases in preventable premature deaths from unintentional injury, heart disease, and stroke were observed in 2020, whereas preventable premature deaths from CLRD and cancer continued to decline. CLRD deaths decreased during 2017-2020 but increased in 2022. An increase in the percentage of preventable premature deaths for multiple leading causes of death was observed in 2020 and was likely associated with COVID-19-related conditions that contributed to increased mortality from heart disease and stroke. PUBLIC HEALTH ACTION: Routine tracking of preventable premature deaths based on urban-rural county classification might enable public health departments to identify and monitor geographic disparities in health outcomes. These disparities might be related to different levels of access to health care, social determinants of health, and other risk factors. Identifying areas with a high prevalence of potentially preventable mortality might be informative for interventions.

PURPOSE: To examine changes in death rates by demographic group and by the leading causes of death in U.S. persons 1 to 24 years of age during the COVID-19 pandemic. METHODS: A retrospective cross-sectional study using mortality data from the National Vital Statistics System from April 2017 to March 2023. Pre-pandemic death rates were compared with death rates during the pandemic overall, by race/ethnicity, age, sex, and cause group. RESULTS: Age-adjusted death rates in young persons 1-24 years of age increased by 14.3% during the pandemic. Injury-related causes accounted for 78.2% of the increase, driven mainly by increases in homicides and unintentional injuries related to drug overdose, firearms, and motor-vehicle traffic crashes. Non-Hispanic Black and Hispanic teens and young adults experienced the largest increases in deaths overall and across the leading causes of death. CONCLUSIONS: During the COVID-19 pandemic, injury-related causes accounted for the majority of the increases in deaths in children and young adults, driven mainly by firearms, drug overdoses, and motor vehicle traffic crashes. Findings highlight the importance of understanding the drivers of these marked increases in injury-related mortality and the need for injury prevention efforts among children even in the context of an infectious disease pandemic.

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.

INTRODUCTION: Housing insecurity is associated with poor health outcomes. Characterization of chronic disease outcomes among adults with and without housing assistance would enable housing programs to better understand their population's health care needs. METHODS: We used National Health and Nutrition Examination Survey (NHANES) data from 2005 through 2018 linked to US Department of Housing and Urban Development (HUD) administrative records to estimate the prevalence of obesity, diabetes, and hypertension and to assess the independent associations between housing assistance and chronic conditions among adults receiving HUD assistance and HUD-assistance-eligible adults not receiving HUD assistance at the time of their NHANES examination. We estimated propensity scores to adjust for potential confounders among linkage-eligible adults who had an income-to-poverty ratio less than 2 and were not receiving HUD assistance. Sensitivity analysis used 2013-2018 NHANES cycles to account for disability status. RESULTS: Adults not receiving HUD assistance had a significantly lower adjusted prevalence of obesity (42.1%; 95% CI, 40.4%-43.8%) compared with adults receiving HUD assistance (47.5%; 95% CI, 44.8%-50.3%), but we found no differences for diabetes and hypertension. We found significant associations between housing assistance and obesity (adjusted odds ratio = 1.29; 95% CI, 1.12-1.47), but these were not significant in the sensitivity analysis with and without controlling for disability status. We found no significant associations between housing assistance and diabetes or hypertension. CONCLUSION: Based on data from a cross-sectional survey, we observed a higher prevalence of obesity among adults with HUD assistance compared with HUD-assistance-eligible adults without HUD assistance. Results from this study can help inform research on understanding the prevalence of chronic disease among adults with HUD assistance.

A recent article1 described increases in MMRs from 1999 to 2019, racial and ethnic disparities, and differences by US state. The use of bayesian models to provide more precise estimates of MMRs for small racial and ethnic subgroups is a strength, but not a panacea for the problem of small numbers. The utility of state-level estimates by subgroup is questionable when driven mainly by the priors and associated with wide uncertainty intervals, a concern for subgroups with fewer than 20 events each year. Additionally, failure to account for differential adoption of the pregnancy checkbox by US states over time biased the trend estimates and resulting conclusions of this study.1 | | The study authors acknowledged in the Discussion section that “it is possible that some of the increases in maternal mortality over time are due to an increasing number of states incorporating the pregnancy checkbox….”1 However, it is insufficient to simply note the effect of the pregnancy checkbox as a limitation, given numerous prior studies describing trends in maternal mortality in the context of changes in ascertainment. These studies have quantified the effect of the incremental adoption of the pregnancy checkbox on the standard certificate of death (which occurred from 2003 to 2017), showing that observed increases in MMRs from the early 2000s to 2017 are entirely or nearly entirely an artifact of changes in measurement over time, with no significant trends in MMRs once the checkbox was accounted for.2-4 It is highly likely that the increases reported in this study1 were an artifact of changes in ascertainment. | | Estimated racial and ethnic disparities in MMR trends are also subject to bias due to the incremental adoption of the checkbox by states over time, given the geographic concentration of specific subpopulations such as non-Hispanic American Indian or Alaska Native persons. The effect of checkbox implementation varies by age, race and ethnicity, state, and cause of death, with larger effects seen among people aged 35 years or older, among non-Hispanic Black individuals, and for nonspecific maternal causes of death.2-4 Consequently, it is important to explicitly account for the effect of the pregnancy checkbox and other changes in measurement5 over time when estimating trends and racial and ethnic disparities in MMRs as well as state-level patterns. Without accurate and comparable measurement of MMR trends and disparities, conclusions cannot be drawn about the effect of prevention efforts.

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.

Both the USA and Europe experienced substantial excess mortality in 2020 and 2021 related to the COVID-19 pandemic. Methods used to estimate excess mortality vary, making comparisons difficult. This retrospective observational study included data on deaths from all causes occurring in the USA and 25 European countries or subnational areas participating in the network for European monitoring of excess mortality for public health action (EuroMOMO). We applied the EuroMOMO algorithm to estimate excess all-cause mortality in the USA and Europe during the first two years of the COVID-19 pandemic, 2020-2021, and compared excess mortality by age group and time periods reflecting three primary waves. During 2020-2021, the USA experienced 154.5 (95% Uncertainty Interval [UI]: 154.2-154.9) cumulative age-standardized excess all-cause deaths per 100,000 person years, compared with 110.4 (95% UI: 109.9-111.0) for the European countries. Excess all-cause mortality in the USA was higher than in Europe for nearly all age groups, with an additional 44.1 excess deaths per 100,000 person years overall from 2020-2021. If the USA had experienced an excess mortality rate similar to Europe, there would have been approximately 391 thousand (36%) fewer excess deaths in the USA.

BACKGROUND: 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 from 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 fungal deaths by fungal pathogen, COVID-19 association, demographic characteristics, and year. RESULTS: Numbers and age-adjusted rates of fungal deaths increased from 2019 (n = 4,833, rate 1.2, 95% confidence interval [CI]    1.2-1.3) to 2021 (n = 7,199, rate: 1.8, 95% CI = 1.8-1.8); of 13,121 fungal deaths during 2020-2021, 2,868 (21.9%) were COVID-19-associated. Compared with non-COVID-19-associated fungal deaths (n = 10,253), COVID-19-associated fungal deaths more frequently involved Candida (n = 776 [27.1%] versus n = 2,432 [23.7%]) and Aspergillus (n = 668 [23.3%] versus n = 1,486 [14.5%]) and less frequently involved other specific fungal pathogens. Fungal death rates were generally highest in non-White and non-Asian populations. Death rates from Aspergillus infections were approximately two times higher in the Pacific US census division compared with most other divisions. CONCLUSIONS: Fungal deaths increased during 2020-2021 compared with previous years, primarily driven by COVID-19-associated fungal deaths, particularly those involving Aspergillus and Candida. Our findings may inform efforts to prevent, identify, and treat severe fungal infections in COVID-19 patients, especially in certain racial/ethnic groups and geographic areas.

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.

Rural-urban differences in maternal mortality ratios (MMR) in the United States have been difficult to measure in recent years due to the incremental adoption of a pregnancy status checkbox on death certificates. Using 1999-2017 mortality and birth data, we examined the impact of the pregnancy checkbox on MMRs by rural-urban residence (large urban, medium/small urban, rural), using log-binomial regression models to predict trends as 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% CI: 6.3, 8.8) in large urban areas (76% increase), 11.6 (95% CI: 9.6, 13.6) in medium/small urban areas (113% increase), and 16.6 (95% CI: 12.9, 20.3) in rural areas (107% increase), compared with MMRs prior to the checkbox. Assuming all states had the checkbox as of 1999, demographic-adjusted predicted MMRs increased in rural, declined in large urban, 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 likely subject to less misclassification. Accurate ascertainment of maternal deaths, particularly in rural areas, is important for reducing disparities in maternal mortality.

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).

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.

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected racial and ethnic minority groups [1–5]. COVID-19 infection and mortality rates are higher among Hispanic/Latino, non-Hispanic Black, and non-Hispanic American Indian or Alaska Native (AI/AN) populations than non-Hispanic White populations [5]. Although most U.S. COVID-19 deaths have occurred among adults aged ≥65 years, younger persons represent a larger percentage of COVID-19 deaths in Hispanic/Latino, non-Hispanic Black, and non-Hispanic AI/AN populations [1]. These racial/ethnic groups also have younger age distributions across the population generally [3], and face increased risk of COVID-19 infection and related morbidity and mortality as a result of many different factors such as the degree of occupational exposure, housing or residential risk factors, the prevalence of preexisting health conditions, reduced access to care, and structural racism [1], [2], [3], [4].

Estimates of excess deaths, defined as the number of persons who have died from all causes, above the expected number of deaths for a given place and time, can provide a comprehensive account of mortality likely related to the COVID-19 pandemic, including deaths that are both directly and indirectly associated with COVID-19. Since April 2020, CDC’s National Center for Health Statistics (NCHS) has published weekly data on excess deaths associated with the COVID-19 pandemic (1). A previous report identified nearly 300,000 excess deaths during January 26–October 3, 2020, with two thirds directly associated with COVID-19 (2). Using more recent data from the National Vital Statistics System (NVSS), CDC estimated that 545,600–660,200 excess deaths occurred in the United States during January 26, 2020–February 27, 2021.

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.

As of October 15, 216,025 deaths from coronavirus disease 2019 (COVID-19) have been reported in the United States*; however, this number might underestimate the total impact of the pandemic on mortality. Measures of excess deaths have been used to estimate the impact of public health pandemics or disasters, particularly when there are questions about underascertainment of deaths directly attributable to a given event or cause (1-6).(†) Excess deaths are defined as the number of persons who have died from all causes, in excess of the expected number of deaths for a given place and time. This report describes trends and demographic patterns in excess deaths during January 26-October 3, 2020. Expected numbers of deaths were estimated using overdispersed Poisson regression models with spline terms to account for seasonal patterns, using provisional mortality data from CDC's National Vital Statistics System (NVSS) (7). Weekly numbers of deaths by age group and race/ethnicity were assessed to examine the difference between the weekly number of deaths occurring in 2020 and the average number occurring in the same week during 2015-2019 and the percentage change in 2020. Overall, an estimated 299,028 excess deaths have occurred in the United States from late January through October 3, 2020, with two thirds of these attributed to COVID-19. The largest percentage increases were seen among adults aged 25-44 years and among Hispanic or Latino (Hispanic) persons. These results provide information about the degree to which COVID-19 deaths might be underascertained and inform efforts to prevent mortality directly or indirectly associated with the COVID-19 pandemic, such as efforts to minimize disruptions to health care.

During February 12-October 15, 2020, the coronavirus disease 2019 (COVID-19) pandemic resulted in approximately 7,900,000 aggregated reported cases and approximately 216,000 deaths in the United States.* Among COVID-19-associated deaths reported to national case surveillance during February 12-May 18, persons aged ≥65 years and members of racial and ethnic minority groups were disproportionately represented (1). This report describes demographic and geographic trends in COVID-19-associated deaths reported to the National Vital Statistics System(†) (NVSS) during May 1-August 31, 2020, by 50 states and the District of Columbia. During this period, 114,411 COVID-19-associated deaths were reported. Overall, 78.2% of decedents were aged ≥65 years, and 53.3% were male; 51.3% were non-Hispanic White (White), 24.2% were Hispanic or Latino (Hispanic), and 18.7% were non-Hispanic Black (Black). The number of COVID-19-associated deaths decreased from 37,940 in May to 17,718 in June; subsequently, counts increased to 30,401 in July and declined to 28,352 in August. From May to August, the percentage distribution of COVID-19-associated deaths by U.S. Census region increased from 23.4% to 62.7% in the South and from 10.6% to 21.4% in the West. Over the same period, the percentage distribution of decedents who were Hispanic increased from 16.3% to 26.4%. COVID-19 remains a major public health threat regardless of age or race and ethnicity. Deaths continued to occur disproportionately among older persons and certain racial and ethnic minorities, particularly among Hispanic persons. These results can inform public health messaging and mitigation efforts focused on prevention and early detection of infection among disproportionately affected groups.

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.

For the period 2007-17 rural death rates were higher than urban rates for the seven major causes of death analyzed, and disparities widened for five of the seven. In 2017 urban areas had met national targets for three of the seven causes, while rural areas had met none of the targets.

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.

PROBLEM/CONDITION: A 2017 report quantified the higher percentage of potentially excess (or preventable) deaths in nonmetropolitan areas (often referred to as rural areas) compared with metropolitan areas. In that report, CDC compared national, regional, and state estimates of potentially excess deaths among the five leading causes of death in nonmetropolitan and metropolitan counties for 2010 and 2014. This report enhances the geographic detail by using the six levels of the 2013 National Center for Health Statistics (NCHS) urban-rural classification scheme for counties and extending estimates of potentially excess deaths by annual percent change (APC) and for additional years (2010-2017). Trends were tested both with linear and quadratic terms. PERIOD COVERED: 2010-2017. DESCRIPTION OF SYSTEM: Mortality data for U.S. residents from the National Vital Statistics System were used to calculate potentially excess deaths from the five leading causes of death among persons aged <80 years. CDC's NCHS urban-rural classification scheme for counties was used to categorize the deaths according to the urban-rural county classification level of the decedent's county of residence (1: large central metropolitan [most urban], 2: large fringe metropolitan, 3: medium metropolitan, 4: small metropolitan, 5: micropolitan, and 6: noncore [most rural]). Potentially excess deaths were defined as deaths among persons aged <80 years that exceeded the number expected if the death rates for each cause in all states were equivalent to those in the benchmark states (i.e., the three states with the lowest rates). Potentially excess deaths were calculated separately for the six urban-rural county categories nationally, the 10 U.S. Department of Health and Human Services public health regions, and the 50 states and District of Columbia. RESULTS: The number of potentially excess deaths among persons aged <80 years in the United States increased during 2010-2017 for unintentional injuries (APC: 11.2%), decreased for cancer (APC: -9.1%), and remained stable for heart disease (APC: 1.1%), chronic lower respiratory disease (CLRD) (APC: 1.7%), and stroke (APC: 0.3). Across the United States, percentages of potentially excess deaths from the five leading causes were higher in nonmetropolitan counties in all years during 2010-2017. When assessed by the six urban-rural county classifications, percentages of potentially excess deaths in the most rural counties (noncore) were consistently higher than in the most urban counties (large central metropolitan) for the study period. Potentially excess deaths from heart disease increased most in micropolitan counties (APC: 2.5%) and decreased most in large fringe metropolitan counties (APC: -1.1%). Potentially excess deaths from cancer decreased in all county categories, with the largest decreases in large central metropolitan (APC: -16.1%) and large fringe metropolitan (APC: -15.1%) counties. In all county categories, potentially excess deaths from the five leading causes increased, with the largest increases occurring in large central metropolitan (APC: 18.3%), large fringe metropolitan (APC: 17.1%), and medium metropolitan (APC: 11.1%) counties. Potentially excess deaths from CLRD decreased most in large central metropolitan counties (APC: -5.6%) and increased most in micropolitan (APC: 3.7%) and noncore (APC: 3.6%) counties. In all county categories, potentially excess deaths from stroke exhibited a quadratic trend (i.e., decreased then increased), except in micropolitan counties, where no change occurred. Percentages of potentially excess deaths also differed among and within public health regions and across states by urban-rural county classification during 2010-2017. INTERPRETATION: Nonmetropolitan counties had higher percentages of potentially excess deaths from the five leading causes than metropolitan counties during 2010-2017 nationwide, across public health regions, and in the majority of states. The gap between the most rural and most urban counties for potentially excess deaths increased during 2010-2017 for three causes of death (cancer, heart disease, and CLRD), decreased for unintentional injury, and remained relatively stable for stroke. Urban and suburban counties (large central metropolitan and large fringe metropolitan, medium metropolitan, and small metropolitan) experienced increases in potentially excess deaths from unintentional injury during 2010-2017, leading to a narrower gap between the already high (approximately 55%) percentage of excess deaths in noncore and micropolitan counties. PUBLIC HEALTH ACTION: Routine tracking of potentially excess deaths by urban-rural county classification might help public health departments and decision-makers identify and monitor public health problems and focus interventions to reduce potentially excess deaths in these areas.

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.

PURPOSE: To examine race/ethnicity-specific associations between area-level poverty and cardiometabolic dysfunction among U.S. adolescents. METHODS: Data were from 10,415 adolescents aged 12-19 in the National Health and Nutrition Examination Survey (1999-2012), linked with census tract data on area-level poverty (the percent population living in poverty, grouped into race/ethnicity-specific quartiles). Cardiometabolic dysfunction was parameterized by summing z-scores of six cardiometabolic biomarkers, grouped into quintiles. Hierarchical ordinal models estimated overall and race/ethnicity specific associations. Posthoc analysis explored associations between area-level poverty and family poverty-to-income ratio. RESULTS: Overall, compared to adolescents residing in areas with the lowest area-level poverty (i.e., first quartile), residents in third (OR 1.32, 95% CI 1.13, 1.53) and fourth (OR 1.27, 95% CI 1.08, 1.50) quartiles of area-level poverty experienced elevated odds of cardiometabolic dysfunction. Area-level poverty predicted cardiometabolic dysfunction between non-Hispanic white and Mexican American adolescents, but not between non-Hispanic black adolescents. CONCLUSIONS: We found race/ethnicity-specific associations between area-level poverty and cardiometabolic dysfunction among U.S. adolescents, highlighting the moderating effect of race-ethnicity. Among non-Hispanic black adolescents, neither higher area-level nor family-level socioeconomic status is associated with cardiometabolic health, in contrast to non-Hispanic white adolescents. Similar associations among non-Hispanic white and Mexican American groups aligns with evidence of the Hispanic Paradox. Future studies of effect of area-level determinants of cardiometabolic dysfunction may consider race/ethnicity-specific associations.

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.

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.

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.

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 county-specific 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 R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.

Hanley et al1 present a compelling analysis of interpregnancy intervals and adverse pregnancy outcomes using a case-crossover design to analyze data from Canadian women with at least three deliveries between 2000 and 2015. Consistent with a previous analysis,2 the authors found that adverse associations between short interpregnancy interval and neonatal outcomes commonly reported in studies using unmatched analyses were erased (or even reversed) after conducting a case-crossover analysis. Although we agree with Hanley et al that time-invariant confounding is most likely reduced when using a within-woman study design such as case-crossover analysis, it may be at the expense of generalizability. | | As the study authors point out, unmatched designs use information from all women, whereas case-crossover analyses use information only from women with discordant exposures and outcomes.1,3 According to the article’s appendix tables, of the 38,178 women with at least three deliveries included in the unmatched analysis, 14% had discordant preterm birth outcomes (n = 5,195). Because 27% of women overall had discordant interpregnancy intervals (one in the reference group, one not in the reference group), approximately 3.8% (n = 1,457) of the total cohort presumably provided data for the case-crossover analysis on preterm birth. Although the authors note that their results may not generalize to other settings, further work on the generalizability of case-crossover analyses, which by design exclude individuals who have not experienced the outcome under study, to the broader target population would be welcome.4

BACKGROUND: Obesity is a risk factor for asthma. However, it is unclear if increased obesity prevalence contributed to rising childhood asthma prevalence. OBJECTIVE: To assess if population-level changes in weight status impacted asthma prevalence over time. METHODS: Using nationally representative 1988-2014 National Health and Nutrition Examination Survey data for 40 644 children aged 2-19 years, we analyzed asthma trends by weight status (body mass index age-specific percentiles determined using measured weight and height). Logistic regression and population attributable fraction were used to assess the association between obesity and asthma prevalence. RESULTS: Although obesity was a risk factor for asthma throughout the period, asthma prevalence increased only among children with normal weight; there was no interaction between weight status and time. The population attributable fraction for overweight/obesity rose from 8.5% in 1988-1994 to 11.9% in 2011-2014, but this increase was not significant (P = 0.44). CONCLUSIONS: Together, these data do not support a contribution of obesity trends to asthma prevalence trends.