County-Level Variation in Hepatitis C Virus Mortality and Trends in the United States, 2005-2017.

BACKGROUND AND AIMS: Since 2013, the national hepatitis C virus (HCV) death rate has steadily declined, but this decline has not been quantified or described on a local level. APPROACH AND RESULTS: We estimated county-level HCV death rates and assessed trends in HCV mortality from 2005 to 2013 and from 2013 to 2017. We used mortality data from the National Vital Statistics System and used a Bayesian multivariate space-time conditional autoregressive model to estimate age-standardized HCV death rates from 2005 through 2017 for 3,115 U.S. counties. Additionally, we estimated county-level, age-standardized rates for persons <40 and 40+ years of age. We used log-linear regression models to estimate the average annual percent change in HCV mortality during periods of interest and compared county-level trends with national trends. Nationally, the age-adjusted HCV death rate peaked in 2013 at 5.20 HCV deaths per 100,000 persons (95% credible interval [CI], 5.12, 5.26) before decreasing to 4.34 per 100,000 persons (95% CI, 4.28, 4.41) in 2017 (average annual percent change = -4.69; 95% CI, -5.01, -4.33). County-level rates revealed heterogeneity in HCV mortality (2017 median rate = 3.6; interdecile range, 2.19, 6.77), with the highest rates being concentrated in the West, Southwest, Appalachia, and northern Florida. Between 2013 and 2017, HCV mortality decreased in 80.0% (n = 2,274) of all U.S. counties with a reliable trend estimate, with 25.8% (n = 803) of all counties experiencing a decrease larger than the national decline. CONCLUSIONS: Although many counties have experienced a shift in HCV mortality trends since 2013, the magnitude and composition of that shift have varied by place. These data provide a better understanding of geographic differences in HCV mortality and can be used by local jurisdictions to evaluate HCV mortality in their areas relative to surrounding areas and the nation.

A Dynamic Visualization Tool of Local Trends in Heart Disease and Stroke Mortality in the United States.

Efforts in the US to prevent and treat cardiovascular disease (CVD) contributed to large decreases in death rates for decades; however, in the last decade, progress has stalled, and in many counties, CVD death rates have increased. Because of these increases, there is heightened urgency to disseminate high-quality data on the temporal trends in CVD mortality. The Local Trends in Heart Disease and Stroke Mortality Dashboard is an online, interactive visualization of US county-level death rates and trends for several CVD outcomes across stratifications of age, race and ethnicity, and sex. This powerful visualization tool generates national maps of death rates and trends, state maps of death rates and trends, county-level line plots of annual death rates, and bar charts of percentage changes. County-level death rates and trends were estimated by applying a Bayesian spatiotemporal model to data obtained from the National Vital Statistics System of the National Center for Health Statistics and US Census bridged-race intercensal estimates for the years 1999 through 2019. The Local Trends in Heart Disease and Stroke Mortality Dashboard makes it easy for public health practitioners, health care providers, and community leaders to monitor county-level spatiotemporal trends in CVD mortality by age group, race and ethnicity, and sex and provides key information for identifying and addressing local health inequities in CVD mortality trends.

Unequal Local Progress Towards Healthy People 2020 Objectives for Stroke and Coronary Heart Disease Mortality.

BACKGROUND AND PURPOSE: Healthy People establishes objectives to monitor the nation's health. Healthy People 2020 included objectives to reduce national stroke and coronary heart disease (CHD) mortality by 20% (to 34.8 and 103.4 deaths per 100 000, respectively). Documenting the proportion and geographic distribution of counties meeting neither the Healthy People 2020 target nor an equivalent proportional reduction can help identify high-priority geographic areas for future intervention. METHODS: County-level mortality data for stroke (International Classification of Diseases, Tenth Revision codes I60-I69) and CHD (I20-I25) and bridged-race population estimates were used. Bayesian spatiotemporal models estimated age-standardized county-level death rates in 2007 and 2017 which were used to calculate and map the proportion and 95% credible interval of counties achieving neither the national Healthy People 2020 target nor a 20% reduction in mortality. RESULTS: In 2017, 45.8% of counties (credible interval, 42.9-48.3) met neither metric for stroke mortality. These counties had a median stroke death rate of 42.2 deaths per 100 000 in 2017, representing a median 12.8% decline. For CHD mortality, 26.1% (credible interval, 25.0-27.8) of counties met neither metric. These counties had a median CHD death rate of 127.1 deaths per 100 000 in 2017, representing a 10.2% decline. For both outcomes, counties achieving neither metric were not limited to counties with traditionally high stroke and CHD death rates. CONCLUSIONS: Recent declines in stroke and CHD mortality have not been equal across US counties. Focusing solely on high mortality counties may miss opportunities in the prevention and treatment of cardiovascular disease and in learning more about factors leading to successful reductions in mortality.

Stagnating National Declines in Stroke Mortality Mask Widespread County-Level Increases, 2010-2016.

Background and Purpose- Recent national and state-level trends show a stalling or reversal of previously declining stroke death rates. These national trends may mask local geographic variation and changes in stroke mortality. We assessed county-level trends in stroke mortality among adults aged 35 to 64 and ≥65 years. Methods- We used data from National Vital Statistics Systems and a Bayesian multivariate space-time conditional autoregressive model to estimate age-standardized annual stroke death rates for 2010 through 2016 among middle-aged adults (35-64 years) and older adults (≥65 years) in US counties. We used log-linear regression models to estimate average annual and total percent change in stroke mortality during the period. Results- Nationally, the annual percent change in stroke mortality from 2010 to 2016 was -0.7% (95% CI, -4.2% to 3.0%) among middle-aged adults and -3.5% (95% CI, -10.7% to 4.3%) among older adults, resulting in 2016 rates of 15.0 per 100 000 and 259.8 per 100 000, respectively. Increasing county-level stroke mortality was more prevalent among middle-aged adults (56.6% of counties) compared with among older adults (26.1% of counties). About half (48.3%) of middle-aged adults, representing 60.2 million individuals, lived in counties in which stroke mortality increased. Conclusions- County-level increases in stroke mortality clarify previously reported national and state-level trends, particularly among middle-aged adults. Roughly 3×as many counties experienced increases in stroke death rates for middle-aged adults compared with older adults. This highlights a need to address stroke prevention and treatment for middle-aged adults while continuing efforts to reduce stroke mortality among the more highly burdened older adults. Efforts to reverse these troubling local trends will likely require joint public health and clinical efforts to develop innovative and integrated approaches for stroke prevention and care, with a focus on community-level characteristics that support stroke-free living for all.

The Rate Stabilizing Tool: Generating Stable Local-Level Measures of Chronic Disease.

Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. In response, we developed the Rate Stabilizing Tool (RST), an ArcGIS-based tool that enables users to input their own record-level data to generate more reliable age-standardized measures of chronic disease (eg, prevalence rates, mortality rates) or other population health outcomes at the county or census tract levels. The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units. The RST also provides indicators of the reliability of point estimates. In addition to reviewing the RST's statistical techniques, we present results from a simulation study that illustrates the key benefit of smoothing. We demonstrate the dramatic reduction in root mean-squared error (rMSE), indicating a better compromise between accuracy and stability for both smoothing approaches relative to the unsmoothed estimates. Finally, we provide an example of the RST's use. This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test.

Changes in the Geographic Patterns of Heart Disease Mortality in the United States: 1973 to 2010.

BACKGROUND: Although many studies have documented the dramatic declines in heart disease mortality in the United States at the national level, little attention has been given to the temporal changes in the geographic patterns of heart disease mortality. METHODS AND RESULTS: Age-adjusted and spatially smoothed county-level heart disease death rates were calculated for 2-year intervals from 1973 to 1974 to 2009 to 2010 for those aged ≥35 years. Heart disease deaths were defined according to the International Classification of Diseases codes for diseases of the heart in the eighth, ninth, and tenth revisions of the International Classification of Diseases. A fully Bayesian spatiotemporal model was used to produce precise rate estimates, even in counties with small populations. A substantial shift in the concentration of high-rate counties from the Northeast to the Deep South was observed, along with a concentration of slow-decline counties in the South and a nearly 2-fold increase in the geographic inequality among counties. CONCLUSIONS: The dramatic change in the geographic patterns of heart disease mortality during 40 years highlights the importance of small-area surveillance to reveal patterns that are hidden at the national level, gives communities the historical context for understanding their current burden of heart disease, and provides important clues for understanding the determinants of the geographic disparities in heart disease mortality.

Completeness and Reliability of Location Data Collected on the Web: Assessing the Quality of Self-Reported Locations in an Internet Sample of Men Who Have Sex With Men.

BACKGROUND: Place is critical to our understanding of human immunodeficiency virus (HIV) infections among men who have sex with men (MSM) in the United States. However, within the scientific literature, place is almost always represented by residential location, suggesting a fundamental assumption of equivalency between neighborhood of residence, place of risk, and place of prevention. However, the locations of behaviors among MSM show significant spatial variation, and theory has posited the importance of nonresidential contextual exposures. This focus on residential locations has been at least partially necessitated by the difficulties in collecting detailed geolocated data required to explore nonresidential locations. OBJECTIVE: Using a Web-based map tool to collect locations, which may be relevant to the daily lives and health behaviors of MSM, this study examines the completeness and reliability of the collected data. METHODS: MSM were recruited on the Web and completed a Web-based survey. Within this survey, men used a map tool embedded within a question to indicate their homes and multiple nonresidential locations, including those representing work, sex, socialization, physician, and others. We assessed data quality by examining data completeness and reliability. We used logistic regression to identify demographic, contextual, and location-specific predictors of answering all eligible map questions and answering specific map questions. We assessed data reliability by comparing selected locations with other participant-reported data. RESULTS: Of 247 men completing the survey, 167 (67.6%) answered the entire set of eligible map questions. Most participants (>80%) answered specific map questions, with sex locations being the least reported (80.6%). Participants with no college education were less likely than those with a college education to answer all map questions (prevalence ratio, 0.4; 95% CI, 0.2-0.8). Participants who reported sex at their partner's home were less likely to indicate the location of that sex (prevalence ratio, 0.8; 95% CI, 0.7-1.0). Overall, 83% of participants placed their home's location within the boundaries of their reported residential ZIP code. Of locations having a specific text description, the median distance between the participant-selected location and the location determined using the specific text description was 0.29 miles (25th and 75th percentiles, 0.06-0.88). CONCLUSIONS: Using this Web-based map tool, this Web-based sample of MSM was generally willing and able to provide accurate data regarding both home and nonresidential locations. This tool provides a mechanism to collect data that can be used in more nuanced studies of place and sexual risk and preventive behaviors of MSM.

An application of propensity score weighting to quantify the causal effect of rectal sexually transmitted infections on incident HIV among men who have sex with men.

BACKGROUND: Exploring causal associations in HIV research requires careful consideration of numerous epidemiologic limitations. First, a primary cause of HIV, unprotected anal intercourse (UAI), is time-varying and, if it is also associated with an exposure of interest, may be on a confounding path. Second, HIV is a rare outcome, even in high-risk populations. Finally, for most causal, non-preventive exposures, a randomized trial is impossible. In order to address these limitations and provide a practical illustration of efficient statistical control via propensity-score weighting, we examine the causal association between rectal STI and HIV acquisition in the InvolveMENt study, a cohort of Atlanta-area men who have sex with men (MSM). We hypothesized that, after controlling for potentially confounding behavioral and demographic factors, the significant STI-HIV association would attenuate, but yield an estimate of the causal effect. METHODS: The exposure of interest was incident rectal gonorrhea or chlamydia infection; the outcome was incident HIV infection. To adjust for behavioral confounding, while accounting for limited HIV infections, we used an inverse probability of treatment weighted (IPTW) Cox proportional hazards (PH) model for incident HIV. Weights were derived from propensity score modeling of the probability of incident rectal STI as a function of potential confounders, including UAI in the interval of rectal STI acquisition/censoring. RESULTS: Of 556 HIV-negative MSM at baseline, 552 (99%) men were included in this analysis. 79 men were diagnosed with an incident rectal STI and 26 with HIV. 6 HIV-infected men were previously diagnosed with a rectal STI. In unadjusted analysis, incident rectal STI was significantly associated with subsequent incident HIV (HR (95%CI): 3.6 (1.4-9.2)). In the final weighted and adjusted model, the association was attenuated and more precise (HR (95% CI): 2.7 (1.2-6.4)). CONCLUSIONS: We found that, controlling for time-varying risk behaviors and time-invariant demographic factors, diagnosis with HIV was significantly associated with prior diagnosis of rectal CT or GC. Our analysis lends support to the causal effect of incident rectal STI on HIV diagnosis and provides a framework for similar analyses of HIV incidence.

Connecting race and place: a county-level analysis of White, Black, and Hispanic HIV prevalence, poverty, and level of urbanization.

OBJECTIVES: We evaluated the role of poverty in racial/ethnic disparities in HIV prevalence across levels of urbanization. METHODS: Using national HIV surveillance data from the year 2009, we constructed negative binomial models, stratified by urbanization, with an outcome of race-specific, county-level HIV prevalence rates and covariates of race/ethnicity, poverty, and other publicly available data. We estimated model-based Black-White and Hispanic-White prevalence rate ratios (PRRs) across levels of urbanization and poverty. RESULTS: We observed racial/ethnic disparities for all strata of urbanization across 1111 included counties. Poverty was associated with HIV prevalence only in major metropolitan counties. At the same level of urbanization, Black-White and Hispanic-White PRRs were not statistically different from 1.0 at high poverty rates (Black-White PRR = 1.0, 95% confidence interval [CI] = 0.4, 2.9; Hispanic-White PRR = 0.4, 95% CI = 0.1, 1.6). In nonurban counties, racial/ethnic disparities remained after we controlled for poverty. CONCLUSIONS: The association between HIV prevalence and poverty varies by level of urbanization. HIV prevention interventions should be tailored to this understanding. Reducing racial/ethnic disparities will require multifactorial interventions linking social factors with sexual networks and individual risks.

Hypertension in Pregnancy: Current Challenges and Future Opportunities for Surveillance and Research.

Hypertension in pregnancy (HP) includes eclampsia/preeclampsia, chronic hypertension, superimposed preeclampsia, and gestational hypertension. In the United States, HP prevalence doubled over the last three decades, based on birth certificate data. In 2019, the estimated percent of births with a history of HP varied from 10.1% to 15.9% for birth certificate data and hospital discharge records, respectively. The use of electronic medical records may result in identifying an additional third to half of undiagnosed cases of HP. Individuals with gestational hypertension or preeclampsia are at 3.5 times higher risk of progressing to chronic hypertension and from 1.7 to 2.8 times higher risk of developing cardiovascular disease (CVD) after childbirth compared with individuals without these conditions. Interventions to identify and address CVD risk factors among individuals with HP are most effective if started during the first 6 weeks postpartum and implemented during the first year after childbirth. Providing access to affordable health care during the first 12 months after delivery may ensure healthy longevity for individuals with HP. Average attendance rates for postpartum visits in the United States are 72.1%, but the rates vary significantly (from 24.9% to 96.5%). Moreover, even among individuals with CVD risk factors who attend postpartum visits, approximately 40% do not receive counseling on a healthy lifestyle. In the United States, as of the end of September 2023, 38 states and the District of Columbia have extended Medicaid coverage eligibility, eight states plan to implement it, and two states proposed a limited coverage extension from 2 to 12 months after childbirth. Currently, data gaps exist in national health surveillance and health systems to identify and monitor HP. Using multiple data sources, incorporating electronic medical record data algorithms, and standardizing data definitions can improve surveillance, provide opportunities to better track progress, and may help in developing targeted policy recommendations.

Trends in Cardiovascular Disease Mortality Rates and Excess Deaths, 2010-2022.

INTRODUCTION: Cardiovascular disease (CVD) mortality increased during the initial years of the COVID-19 pandemic, but whether these trends endured in 2022 is unknown. This analysis describes temporal trends in CVD death rates from 2010 to 2022 and estimates excess CVD deaths from 2020 to 2022. METHODS: Using national mortality data from the National Vital Statistics System, deaths among adults aged ≥35 years were classified by underlying cause of death International Classification of Diseases 10th Revision codes for CVD (I00-I99), heart disease (I00-I09, I11, I13, I20-I51), and stroke (I60-I69). Analyses in Joinpoint software identified trends in CVD age-adjusted mortality rates (AAMR) per 100,000 and estimated the number of excess CVD deaths from 2020 to 2022. RESULTS: During 2010-2022, 10,951,403 CVD deaths occurred (75.6% heart disease, 16.9% stroke). The national CVD AAMR declined by 8.9% from 2010 to 2019 (456.6-416.0 per 100,000) and then increased by 9.3% from 2019 to 2022 to 454.5 per 100,000, which approximated the 2010 rate (456.7 per 100,000). From 2020 to 2022, 228,524 excess CVD deaths occurred, which was 9% more CVD deaths than expected based on trends from 2010 to 2019. Results varied by CVD subtype and population subgroup. CONCLUSIONS: Despite stabilization of the public health emergency, declines in CVD mortality rates reversed in 2020 and remained high in 2022, representing almost a decade of lost progress and over 228,000 excess CVD deaths. Findings underscore the importance of prioritizing prevention and management of CVD to improve outcomes.

Timing of outpatient postpartum care utilization among women with chronic hypertension and hypertensive disorders of pregnancy.

BACKGROUND: The postpartum period represents an opportunity to assess the cardiovascular health of women who experience chronic hypertension or hypertensive disorders of pregnancy. OBJECTIVE: This study aimed to determine whether women with chronic hypertension or hypertensive disorders of pregnancy access outpatient postpartum care more quickly compared to women with no hypertension. STUDY DESIGN: We used data from the Merative MarketScan Commercial Claims and Encounters Database. We included 275,937 commercially insured women aged 12 to 55 years who had a live birth or stillbirth delivery hospitalization between 2017 and 2018 and continuous insurance enrollment from 3 months before the estimated start of pregnancy to 6 months after delivery discharge. Using the International Classification of Diseases Tenth Revision Clinical Modification codes, we identified hypertensive disorders of pregnancy from inpatient or outpatient claims from 20 weeks gestation through delivery hospitalization and identified chronic hypertension from inpatient or outpatient claims from the beginning of the continuous enrollment period through delivery hospitalization. Distributions of time-to-event survival curves (time-to-first outpatient postpartum visit with a women's health provider, primary care provider, or cardiology provider) were compared between the hypertension types using Kaplan-Meier estimators and log rank tests. We used Cox proportional hazards models to estimate adjusted hazard ratios and 95% confidence intervals. Time points of interest (3, 6, and 12 weeks) were evaluated per clinical postpartum care guidelines. RESULTS: Among commercially insured women, the prevalences of hypertensive disorders of pregnancy, chronic hypertension, and no documented hypertension were 11.7%, 3.4%, and 84.8%, respectively. The proportions of women with a visit within 3 weeks of delivery discharge were 28.5%, 26.4%, and 16.0% for hypertensive disorders of pregnancy, chronic, and no documented hypertension, respectively; by 12 weeks, the proportions increased to 62.4%, 64.5%, and 54.2%, respectively. Kaplan-Meier analyses indicated significant differences in utilization by hypertension type and interaction between hypertension type, and time before and after 6 weeks. In adjusted Cox proportional hazards models, the utilization rate before 6 weeks among women with hypertensive disorders of pregnancy was 1.42 times the rate for women with no documented hypertension (adjusted hazard ratio, 1.42; 95% confidence interval, 1.39-1.45). Women with chronic hypertension also had higher utilization rates compared to women with no documented hypertension before 6 weeks (adjusted hazard ratio, 1.28; 95% confidence interval, 1.24-1.33). Only chronic hypertension was significantly associated with utilization compared to the no documented hypertension group after 6 weeks (adjusted hazard ratio, 1.09; 95% confidence interval, 1.03-1.14). CONCLUSION: In the 6 weeks following delivery discharge, women with hypertensive disorders of pregnancy and chronic hypertension attended outpatient postpartum care visits sooner than women with no documented hypertension. However, after 6 weeks this difference extended only to women with chronic hypertension. Overall, postpartum care utilization remained around 50% to 60% by 12 weeks in all groups. Addressing barriers to postpartum care attendance can ensure timely care for women at high risk for cardiovascular disease.

US County-Level Variation in Preterm Birth Rates, 2007-2019.

Importance: Preterm birth is a leading cause of preventable neonatal morbidity and mortality. Preterm birth rates at the national level may mask important geographic variation in rates and trends at the county level. Objective: To estimate age-standardized preterm birth rates by US county from 2007 to 2019. Design, Setting, and Participants: This serial cross-sectional study used data from the National Center for Health Statistics composed of all live births in the US between 2007 and 2019. Data analyses were performed between March 22, 2022, and September 29, 2022. Main Outcomes and Measures: Age-standardized preterm birth (<37 weeks' gestation) and secondarily early preterm birth (<34 weeks' gestation) rates by county and year calculated with a validated small area estimation model (hierarchical bayesian spatiotemporal model) and percent change in preterm birth rates using log-linear regression models. Results: Between 2007 and 2019, there were 51 044 482 live births in 2383 counties. In 2007, the national age-standardized preterm birth rate was 12.6 (95% CI, 12.6-12.7) per 100 live births. Preterm birth rates varied significantly among counties, with an absolute difference between the 90th and 10th percentile counties of 6.4 (95% CI, 6.2-6.7). The gap between the highest and lowest counties for preterm births was 20.7 per 100 live births in 2007. Several counties in the Southeast consistently had the highest preterm birth rates compared with counties in California and New England, which had the lowest preterm birth rates. Although there was no statistically significant change in preterm birth rates between 2007 and 2019 at the national level (percent change, -5.0%; 95% CI, -10.7% to 0.9%), increases occurred in 15.4% (95% CI, 14.1%-16.9%) of counties. The absolute and relative geographic inequalities were similar across all maternal age groups. Higher quartile of the Social Vulnerability Index was associated with higher preterm birth rates (quartile 4 vs quartile 1 risk ratio, 1.34; 95% CI, 1.31-1.36), which persisted across the study period. Similar patterns were observed for early preterm birth rates. Conclusions and Relevance: In this serial cross-sectional study of county-level preterm and early preterm birth rates, substantial geographic disparities were observed, which were associated with place-based social disadvantage. Stability in aggregated rates of preterm birth at the national level masked increases in nearly 1 in 6 counties between 2007 and 2019.

The where of when: Geographic variation in the timing of recent increases in US county-level heart disease death rates.

PURPOSE: Within the context of local increases in US heart disease death rates, we estimated when increasing heart disease death rates began by county among adults aged 35-64 years and characterized geographic variation. METHODS: We applied Bayesian spatiotemporal models to vital statistics data to estimate the timing (i.e., the year) of increasing county-level heart disease death rates during 1999-2019 among adults aged 35-64 years. To examine geographic variation, we stratified results by US Census region and urban-rural classification. RESULTS: The onset of increasing heart disease death rates among adults aged 35-64 years spanned the two-decade study period from 1999 to 2019. Overall, 43.5% (95% CI: 41.3, 45.6) of counties began increasing before 2011, with early increases more prevalent outside of the most urban counties and outside of the Northeast. Roughly one-in-five (18.4% [95% CI: 15.6, 20.7]) counties continued to decline throughout the study period. CONCLUSIONS: This variation suggests that factors associated with these geographic classifications may be critical in establishing the timing of changing trends in heart disease death rates. These results reinforce the importance of spatiotemporal surveillance in the early identification of adverse trends and in informing opportunities for tailored policies and programs.

County-Level Trends in Hypertension-Related Cardiovascular Disease Mortality-United States, 2000 to 2019.

Background Amid stagnating declines in national cardiovascular disease (CVD) mortality, documenting trends in county-level hypertension-related CVD death rates can help activate local efforts prioritizing hypertension prevention, detection, and control. Methods and Results Using death certificate data from the National Vital Statistics System, Bayesian spatiotemporal models were used to estimate county-level hypertension-related CVD death rates and corresponding trends during 2000 to 2010 and 2010 to 2019 for adults aged ≥35 years overall and by age group, race or ethnicity, and sex. Among adults aged 35 to 64 years, county-level hypertension-related CVD death rates increased from a median of 23.2 per 100 000 in 2000 to 43.4 per 100 000 in 2019. Among adults aged ≥65 years, county-level hypertension-related CVD death rates increased from a median of 362.1 per 100 000 in 2000 to 430.1 per 100 000 in 2019. Increases were larger and more prevalent among adults aged 35 to 64 years than those aged ≥65 years. More than 75% of counties experienced increasing hypertension-related CVD death rates among patients aged 35 to 64 years during 2000 to 2010 and 2010 to 2019 (76.2% [95% credible interval, 74.7-78.4] and 86.2% [95% credible interval, 84.6-87.6], respectively), compared with 48.2% (95% credible interval, 47.0-49.7) during 2000 to 2010 and 66.1% (95% credible interval, 64.9-67.1) for patients aged ≥65 years. The highest rates for both age groups were among men and Black populations. All racial and ethnic categories in both age groups experienced widespread county-level increases. Conclusions Large, widespread county-level increases in hypertension-related CVD mortality sound an alarm for intensified clinical and public health actions to improve hypertension prevention, detection, and control and prevent subsequent CVD deaths in counties across the nation.

Changing Spatiotemporal Trends in County-Level Heart Failure Death Rates in the United States, 1999 to 2018.

Background Amid recently rising heart failure (HF) death rates in the United States, we describe county-level trends in HF mortality from 1999 to 2018 by racial/ethnic group and sex for ages 35 to 64 years and 65 years and older. Methods and Results Applying a hierarchical Bayesian model to National Vital Statistics data representing all US deaths, ages 35 years and older, we estimated annual age-standardized county-level HF death rates and percent change by age group, racial/ethnic group, and sex from 1999 through 2018. During 1999 to 2011, ~30% of counties experienced increasing HF death rates among adults ages 35 to 64 years. However, during 2011 to 2018, 86.9% (95% CI, 85.2-88.2) of counties experienced increasing mortality. Likewise, for ages 65 years and older, during 1999 to 2005 and 2005 to 2011, 27.8% (95% CI, 25.8-29.8) and 12.6% (95% CI, 11.2-13.9) of counties, respectively, experienced increasing mortality. However, during 2011 to 2018, most counties (67.4% [95% CI, 65.4-69.5]) experienced increasing mortality. These temporal patterns by age group held across racial/ethnic group and sex. Conclusions These results provide local context to previously documented recent national increases in HF death rates. Although county-level declines were most common before 2011, some counties and demographic groups experienced increasing HF death rates during this period of national declines. However, recent county-level increases were pervasive, occurring across counties, racial/ethnic group, and sex, particularly among ages 35 to 64 years. These spatiotemporal patterns highlight the need to identify and address underlying clinical risk factors and social determinants of health contributing to these increasing trends.