Regional Variation in the Association of Poverty and Heart Failure Mortality in the 3135 Counties of the United States

Latent class analysis of chronic disease co-occurrence, clustering and their determinants in India using Study on global AGEing and adult health (SAGE) India Wave-2

Background

Understanding chronic disease prevalence, patterns, and co-occurrence is pivotal for effective health care planning and disease prevention strategies. In this paper, we aimed to identify the clustering of major non-communicable diseases among Indian adults aged ≥50 years based on their self-reported diagnosed non-communicable disease status and to find the risk factors that heighten the risk of developing the identified disease clusters.

Methods

We utilised data from the nationally representative survey Study on Global AGEing and Adult Health (SAGE Wave-2). The eligible sample size was 6298 adults aged ≥50 years. We conducted the latent class analysis to uncover latent subgroups of multimorbidity and the multinomial logistic regression to identify the factors linked to observed latent class membership.

Results

The latent class analysis grouped our sample of men and women >49 years old into three groups - mild multimorbidity risk (41%), moderate multimorbidity risk (30%), and severe multimorbidity risk (29%). In the mild multimorbidity risk group, the most prevalent diseases were asthma and arthritis, and the major prevalent disease in the moderate multimorbidity risk group was low near/distance vision, followed by depression, asthma, and lung disease. Angina, diabetes, hypertension, and stroke were the major diseases in the severe multimorbidity risk category. Individuals with higher ages had an 18% and 15% higher risk of having moderate multimorbidity and severe multimorbidity compared to those in the mild multimorbidity category. Females were more likely to have a moderate risk (3.36 times) and 2.82 times more likely to have severe multimorbidity risk.

Conclusions

The clustering of diseases highlights the importance of integrated disease management in primary care settings and improving the health care system to accommodate the individual's needs. Implementing preventive measures and tailored interventions, strengthening the health and wellness centres, and delivering comprehensive primary health care services for secondary and tertiary level hospitalisation may cater to the needs of multimorbid patients.

The Impact of Social Determinants of Health on Transition From Pediatric to Adult Cardiology Care

Social determinants of health (SDoH) are the economic, social, environmental, and psychosocial factors that influence health. Adolescents and young adults with congenital heart disease (CHD) require lifelong cardiology follow-up and therefore coordinated transition from pediatric to adult healthcare systems. However, gaps in care are common during transition, and they are driven in part by pervasive disparities in SDoH, including race, ethnicity, socioeconomic status, access to insurance, and remote location of residence. These disparities often coexist and compound the challenges faced by patients and families. For example, Black and Indigenous individuals are more likely to be subject to systemic racism and implicit bias within healthcare and other settings, to be unemployed and poor, to have limited access to insurance, and to have a lower likelihood of transfer of care to adult CHD specialists. SDoH also are associated with acquired cardiovascular disease, a comorbidity that adults with CHD face. This review summarizes existing evidence regarding the impact of SDoH on the transition to adult care and proposes strategies at the individual, institutional, and population and/or system levels. to reduce inequities faced by transition-age youth. These strategies include routinely screening for SDoH in clinical settings with referral to appropriate services, providing formal transition education for all transition-age youth, including training on navigating complex medical systems, creating satellite cardiology clinics to facilitate access to care for those who live remote from tertiary centres, advocating for lifelong insurance coverage where applicable, mandating cultural-sensitivity training for providers, and increasing the diversity of healthcare providers in pediatric and adult CHD care.

Impact of Socioeconomic Status on Heart Failure

Heart failure has emerged as a substantial health burden in the United States in the last few decades. This study examined the hypothesis that socioeconomic factors such as education level, social position, employment status, and poverty have a strong confounding influence on the risk for heart failure. To access relevant data, 12 published studies were retrieved from MEDLINE, Google Scholar, and Web of Science. A cross-sectional analysis of the identified studies confirmed that the four socioeconomic factors predisposed individuals to an elevated risk of heart failure-related complications. Despite their interdependencies, educational level, employment status, social position, and poverty independently confounded cardiovascular risk among individuals. Notably, individuals from households with low education were at a higher risk of these diseases. At the same time, households without employed family members were less likely to report cases of heart failure than those with low socioeconomic status. Additionally, individuals from disadvantaged backgrounds faced a greater risk for heart failure complications. The findings from this study found a strong association between socioeconomic status and heart failure risks.

Disparities in Postdischarge Ambulatory Care Follow-Up Among Medicaid Beneficiaries With Diabetes, Hospitalized for Heart Failure

Background Ambulatory follow-up for all patients with heart failure (HF) is recommended within 7 to 14 days after hospital discharge to improve HF outcomes. We examined postdischarge ambulatory follow-up of patients with comorbid diabetes and HF from a low-income population in primary and specialty care. Methods and Results Adults with diabetes and first hospitalizations for HF, covered by Alabama Medicaid in 2010 to 2019, were included and the claims analyzed for ambulatory care use (any, primary care, cardiology, or endocrinology) within 60 days after discharge using restricted mean survival time regression and negative binomial regression. Among 9859 Medicaid-covered adults with diabetes and first hospitalization for HF (mean age, 53.7 years; SD, 9.2 years; 47.3% Black; 41.8% non-Hispanic White; 10.9% Hispanic/Other [Other included non-White Hispanic, American Indian, Pacific Islander and Asian adults]; 65.4% women, 34.6% men), 26.7% had an ambulatory visit within 0 to 7 days, 15.2% within 8 to 14 days, 31.3% within 15 to 60 days, and 26.8% had no visit; 71% saw a primary care physician and 12% a cardiology physician. Black and Hispanic/Other adults were less likely to have any postdischarge ambulatory visit (P<0.0001) or the visit was delayed (by 1.8 days, P=0.0006 and by 2.8 days, P=0.0016, respectively) and were less likely to see a primary care physician than non-Hispanic White adults (adjusted incidence rate ratio, 0.96 [95% CI, 0.91-1.00] and 0.91 [95% CI, 0.89-0.98]; respectively). Conclusions More than half of Medicaid-covered adults with diabetes and HF in Alabama did not receive guideline-concordant postdischarge care. Black and Hispanic/Other adults were less likely to receive recommended postdischarge care for comorbid diabetes and HF.

Rural and socioeconomic differences in the effectiveness of the HEART Pathway accelerated diagnostic protocol

BACKGROUND

The HEART Pathway is a validated accelerated diagnostic protocol (ADP) for patients with possible acute coronary syndrome (ACS). This study aimed to compare the safety and effectiveness of the HEART Pathway based on patient rurality (rural vs. urban) or socioeconomic status (SES).

METHODS

We performed a preplanned subgroup analysis of the HEART Pathway Implementation Study. The primary outcomes were death or myocardial infarction (MI) and hospitalization at 30 days. Proportions were compared by SES and rurality with Fisher's exact tests. Logistic regression evaluated for interactions of ADP implementation with SES or rurality and changes in outcomes within subgroups.

RESULTS

Among 7245 patients with rurality and SES data, 39.9% (2887/7245) were rural and 22.2% were low SES (1607/7245). The HEART Pathway identified patients as low risk in 32.2% (818/2540) of urban versus 28.1% (425/1512) of rural patients (p = 0.007) and 34.0% (311/915) of low SES versus 29.7% (932/3137) high SES patients (p = 0.02). Among low-risk patients, 30-day death or MI occurred in 0.6% (5/818) of urban versus 0.2% (1/425) rural (p = 0.67) and 0.6% (2/311) with low SES versus 0.4% (4/932) high SES (p = 0.64). Following implementation, 30-day hospitalization was reduced by 7.7% in urban patients (adjusted odds ratio [aOR] 0.76, 95% confidence interval [CI] 0.66-0.87), 10.6% in low SES patients (aOR 0.68, 95% CI 0.54-0.86), and 4.5% in high SES patients (aOR 0.83, 95% CI 0.73-0.94). However, rural patients had a nonsignificant 3.3% reduction in hospitalizations.

CONCLUSIONS

HEART Pathway implementation decreased 30-day hospitalizations regardless of SES and for urban patients but not rural patients. The 30-day death or MI rate was similar among low-risk patients.

Particulate matter 2.5, metropolitan status, and heart failure outcomes in US counties: A nationwide ecologic analysis

The relationship between particulate matter with a diameter of 2.5 micrometers or less (PM2.5) and heart failure (HF) hospitalizations and mortality in the US is unclear. Prior studies are limited to studying the effects of daily PM2.5 exposure on HF hospitalizations in specific geographic regions. Because PM2.5 can vary by geography, this study examines the effects of annual ambient PM2.5 exposure on HF hospitalizations and mortality at a county-level across the US. A cross-sectional analysis of county-level ambient PM2.5 concentration, HF hospitalizations, and HF mortality across 3135 US counties nationwide was performed, adjusting for county-level demographics, socioeconomic factors, comorbidities, and healthcare-associated behaviors. There was a moderate correlation between county PM2.5 and HF hospitalization among Medicare beneficiaries (r = 0.41) and a weak correlation between county PM2.5 and HF mortality (r = 0.08) (p-values < 0.01). After adjustment for various county level covariates, every 1 ug/m3 increase in annual PM2.5 concentration was associated with an increase of 0.51 HF Hospitalizations/1,000 Medicare Beneficiaries and 0.74 HF deaths/100,000 residents (p-values < 0.05). In addition, the relationship between PM2.5 and HF hospitalizations was similar when factoring in metropolitan status of the counties. In conclusion, increased ambient PM2.5 concentration level was associated with increased incidence of HF hospitalizations and mortality at the county level across the US. This calls for future studies exploring policies that reduce ambient particulate matter pollution and their downstream effects on potentially improving HF outcomes.

Association between median household income, state Medicaid expansion status, and COVID-19 outcomes across US counties

Objective

To study the relationship between county-level COVID-19 outcomes (incidence and mortality) and county-level median household income and status of Medicaid expansion of US counties.

Methods

Retrospective analysis of 3142 US counties was conducted to study the relationship between County-level median-household-income and COVID-19 incidence and mortality per 100,000 people in US counties, January-20th-2021 through December-6^th-2021. County median-household-income was log-transformed and stratified by quartiles. Multilevel-mixed-effects-generalized-linear-modeling adjusted for county socio-demographic and comorbidities and tested for Medicaid-expansion-times-income-quartile interaction on COVID-19 outcomes.

Results

There was no significant difference in COVID-19 incidence-rate across counties by income quartiles or by Medicaid expansion status. Conversely, for non-Medicaid-expansion states, counties in the lowest income quartile had a 41% increase in COVID-19 mortality-rate compared to counties in the highest income quartile. Mortality-rate was not related to income in counties from Medicaid-expansion states.

Conclusions

Median-household-income was not related to COVID-19 incidence-rate but negatively related to COVID-19 mortality-rate in US counties of states without Medicaid-expansion.

Establishing Care Post Discharge Following a Heart Failure Hospitalization in an Uninsured Heart Failure Population

Multidisciplinary interprofessional outpatient care improves mortality for patients with heart failure (HF) but is underutilized. We sought to identify factors associated with not establishing outpatient care among uninsured individuals with HF. We included uninsured individuals referred to an interprofessional clinic after a hospitalization with HF from 2016 to 2019. The primary outcome was establishing care, defined as presenting to clinic within 7 days of discharge from the hospital. We constructed multivariable adjusted logistic regression models to identify predictors of establishing care. A total of 698 uninsured individuals were referred, of whom 583 (84%) established care. Mean age was 49.5 ± 11 years, 15% were rural-dwelling, 59% were black, and 31% were female. Black participants who were rural-dwelling (adusted odds ratio [aOR] 0.07, 95% confidence interval [CI] 0.03 to 0.17) or reported alcohol use (aOR 0.32, 95% CI 0.16 to 0.64) had lower odds of establishing care. White participants who were rural-dwelling (aOR 2.63, 95% CI 1.17 to 5.90) had higher odds of establishing care. Uninsured black individuals with HF who live in rural communities or who are active alcohol users represent a group that is at high risk of not establishing outpatient follow-up after a hospitalization with HF. Efforts to reduce this disparity are warranted to improve health outcomes in this population.

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 Social Vulnerability Is Positively Associated with Cardiometabolic Disease in Colorado

Cardiometabolic diseases are a group of interrelated diseases that pose greater burden among socially vulnerable communities. The social vulnerability index (SVI) identifies communities vulnerable to emergencies and may also help determine communities at risk of adverse chronic health outcomes. However, no studies have examined the relationship between the SVI and cardiometabolic health outcomes in Colorado or focused on rural settings. The aim of this ecological study was to determine whether the county-level SVI is associated with county-level cardiometabolic health indicators with a particular focus on rurality and racial/ethnic diversity. We obtained 2014 SVI scores from the Centers for Disease Control and Prevention (scored 0–1; higher = more vulnerable) and 2013–2015 cardiometabolic health estimates from the Colorado Department of Public Health and Environment. The distribution of social determinants of health was spatially evaluated. Bivariate relationships between the SVI and cardiometabolic indicators were estimated using simple linear regression models. The highest SVI scores were observed in rural areas, including the San Luis Valley (mean: 0.78, median: 0.91), Southeast (mean: 0.72, median: 0.73), and Northeast (mean: 0.66, median: 0.76) regions. Across Colorado, the SVI accounted for 41% of the variability in overweight and obesity prevalence (p < 0.001), 17% of the variability in diabetes prevalence (p = 0.001), and 58% of the age-adjusted myocardial infarction hospitalization rate (p < 0.001). SVI values may be useful in determining a community’s burden of cardiometabolic diseases.

Impact of COVID-19 on Heart Failure Self-Care: A Qualitative Study

BACKGROUND

Difficulties in coping with and self-managing heart failure (HF) are well known. The COVID-19 pandemic may further complicate self-care practices associated with HF.

OBJECTIVE

The aim of this study was to understand COVID-19's impact on HF self-care, as well as related coping adaptations that may blunt the impact of COVID-19 on HF health outcomes.

METHODS

A qualitative study using phone interviews, guided by the framework of vulnerability analysis for sustainability, was used to explore HF self-care among older adults in central Texas during the late spring of 2020. Qualitative data were analyzed using directed content analysis.

RESULTS

Seventeen older adults with HF participated (mean [SD] age, 68 [9.1] years; 62% female, 68% White, 40% below poverty line, 35% from rural areas). Overall, the COVID-19 pandemic had an adverse impact on the HF self-care behavior of physical activity. Themes of social isolation, financial concerns, and disruptions in access to medications and food indicated exposure, and rural residence and source of income increased sensitivity, whereas adaptations by healthcare system, health-promoting activities, socializing via technology, and spiritual connections increased resilience to the COVID-19 pandemic.

CONCLUSIONS

The study's findings have implications for identifying vulnerabilities in sustaining HF self-care by older adults and empowering older adults with coping strategies to improve overall satisfaction with care and quality of life.

Longitudinal Trajectories and Factors Associated With US County-Level Cardiovascular Mortality, 1980 to 2014

IMPORTANCE

Cardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized.

OBJECTIVE

To identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories.

DESIGN, SETTING, AND PARTICIPANTS

This longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021.

EXPOSURES

County-level characteristics, collected from 5 county-level data sets.

MAIN OUTCOMES AND MEASURES

Cardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership.

RESULTS

Among 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory.

CONCLUSIONS AND RELEVANCE

Cardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.

Census tract socioeconomic indicators and COVID-19-associated hospitalization rates-COVID-NET surveillance areas in 14 states, March 1-April 30, 2020

OBJECTIVES

Some studies suggested more COVID-19-associated hospitalizations among racial and ethnic minorities. To inform public health practice, the COVID-19-associated Hospitalization Surveillance Network (COVID-NET) quantified associations between race/ethnicity, census tract socioeconomic indicators, and COVID-19-associated hospitalization rates.

METHODS

Using data from COVID-NET population-based surveillance reported during March 1-April 30, 2020 along with socioeconomic and denominator data from the US Census Bureau, we calculated COVID-19-associated hospitalization rates by racial/ethnic and census tract-level socioeconomic strata.

RESULTS

Among 16,000 COVID-19-associated hospitalizations, 34.8% occurred among non-Hispanic White (White) persons, 36.3% among non-Hispanic Black (Black) persons, and 18.2% among Hispanic or Latino (Hispanic) persons. Age-adjusted COVID-19-associated hospitalization rate were 151.6 (95% Confidence Interval (CI): 147.1-156.1) in census tracts with >15.2%-83.2% of persons living below the federal poverty level (high-poverty census tracts) and 75.5 (95% CI: 72.9-78.1) in census tracts with 0%-4.9% of persons living below the federal poverty level (low-poverty census tracts). Among White, Black, and Hispanic persons living in high-poverty census tracts, age-adjusted hospitalization rates were 120.3 (95% CI: 112.3-128.2), 252.2 (95% CI: 241.4-263.0), and 341.1 (95% CI: 317.3-365.0), respectively, compared with 58.2 (95% CI: 55.4-61.1), 304.0 (95%: 282.4-325.6), and 540.3 (95% CI: 477.0-603.6), respectively, in low-poverty census tracts.

CONCLUSIONS

Overall, COVID-19-associated hospitalization rates were highest in high-poverty census tracts, but rates among Black and Hispanic persons were high regardless of poverty level. Public health practitioners must ensure mitigation measures and vaccination campaigns address needs of racial/ethnic minority groups and people living in high-poverty census tracts.

The impact of socioeconomic status in patients with left ventricular assist devices (LVADs)

BACKGROUND

Socioeconomic status (SES) can be a powerful predictor of adverse outcomes among heart failure patients but its impact on survival and readmission following left ventricular assist device (LVAD) implantation surgery is poorly understood. We investigated if the LVAD recipients from more deprived neighborhoods experienced higher mortality and readmission rate after device implantation as compared to those from less deprived areas.

METHODS

This is a single center, retrospective analysis evaluating adults who received Heartmate III and Heartware HVAD implants between 2009 and 2018. SES indicators were area of deprivation index (ADI), race and income. Our cohort was grouped by ADI quartiles from least deprived (Q1), Q2, Q3 to the most deprived (Q4). Outcomes included overall mortality and readmission following surgery.

RESULTS

A total of 191 patients were included in the study. Demographics by SES indicators demonstrated that least deprived (Q1) patients were older than the most deprived (65 vs. 57, p < .01), African-American patients originated from more deprived neighborhoods than Caucasians (ADI 87 vs. 62, p < .001), and high-income patients had higher preoperative BUN and creatinine. Outcome differences included a decreased risk of death in most deprived patients (Q4) compared to the least deprived (Q1), however after adjusting for age, LVAD indication, and INTERMACS profile this was no longer significant. No differences in survival or readmission by race or income was observed CONCLUSION: SES does not independently impact survival and readmission after Heartware HVAD and Heartmate III LVAD implantation. More studies are needed to evaluate if other SES factors affect these outcomes.

Non-employment and low educational level as risk factors for inequitable treatment and mortality in heart failure: a population-based cohort study of register data

Background

The risk of heart failure is disproportionately high among the socioeconomically disadvantaged. Furthermore, socioeconomically deprived patients are at risk of inequitable access to heart failure treatment and poor outcomes. Non-employment as a risk factor in this respect has not previously been studied at the level of the individual. The aim of this register-based cohort study was to analyse equity in access to renin-angiotensin system blockers and mortality, by employment status and educational level.

Methods

The study population consisted of Swedish patients aged 20–64 years hospitalised for heart failure in July 2006–December 2010, without a heart failure hospitalisation within one year or more before index hospitalisation and without renin-angiotensin system blocker dispensation in the 6 months preceding index hospitalisation. Non-access to renin-angiotensin system blockers, measured as drug dispensations, was investigated by employment status and educational level through logistic regression. Cox regression models were used to obtain hazard ratios for all-cause death by educational level and employment status. Interaction analysis was used to test whether associations between access to treatment and mortality differed by employment status.

Results

Among the 3874 patients, 1239 (32%) were women. The median age was 57 years. Fifty-three percent were employed. The non-employed patients had more comorbidity and lower access (68%) to renin-angiotensin system blockers compared with the employed (82%). The adjusted odds ratio for non-access to renin-angiotensin system blockers among the non-employed was 1.76. Non-employment was associated with an adjusted hazard ratio of 1.76 for death. Low educational level was associated with a higher death risk. Mortality was highest among the non-employed without access to renin-angiotensin system blockers and the association between access to renin-angiotensin system blockers and survival was slightly weaker in this group.

Conclusions

Non-employment and low educational level were associated with elevated mortality in heart failure. Non-employment was a risk factor for lower access to evidence-based treatment, and among the non-employed access to treatment was associated with a slightly smaller risk reduction than among the employed. The results underscore that clinicians need to be aware of the importance of socioeconomic factors in heart failure care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10919-1.

Geographic Variation in Trends and Disparities in Heart Failure Mortality in the United States, 1999 to 2017

Background Cardiovascular disease mortality related to heart failure (HF) is rising in the United States. It is unknown whether trends in HF mortality are consistent across geographic areas and are associated with state-level variation in cardiovascular health (CVH). The goal of the present study was to assess regional and state-level trends in cardiovascular disease mortality related to HF and their association with variation in state-level CVH. Methods and Results Age-adjusted mortality rates (AAMR) per 100 000 attributable to HF were ascertained using the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research from 1999 to 2017. CVH at the state-level was quantified using the Behavioral Risk Factor Surveillance System. Linear regression was used to assess temporal trends in HF AAMR were examined by census region and state and to examine the association between state-level CVH and HF AAMR. AAMR attributable to HF declined from 1999 to 2011 and increased between 2011 and 2017 across all census regions. Annual increases after 2011 were greatest in the Midwest (β=1.14 [95% CI, 0.75, 1.53]) and South (β=0.96 [0.66, 1.26]). States in the South and Midwest consistently had the highest HF AAMR in all time periods, with Mississippi having the highest AAMR (109.6 [104.5, 114.6] in 2017). Within race‒sex groups, consistent geographic patterns were observed. The variability in HF AAMR was associated with state-level CVH (P<0.001). Conclusions Wide geographic variation exists in HF mortality, with the highest rates and greatest recent increases observed in the South and Midwest. Higher levels of poor CVH in these states suggest the potential for interventions to promote CVH and reduce the burden of HF.

Trends in heart failure-related cardiovascular mortality in rural versus urban United States counties, 2011-2018: A cross-sectional study

BACKGROUND

Adults in rural counties in the United States (US) experience higher rates broadly of cardiovascular disease (CVD) compared with adults in urban counties. Mortality rates specifically due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality have not been produced. The objectives of this study were 1) to quantify nationwide trends by rural-urban designation and 2) examine county-level factors associated with rural-urban differences in HF-related mortality rates.

METHODS AND FINDINGS

We queried CDC WONDER to identify HF deaths between 2011-2018 defined as CVD (I00-78) as the underlying cause of death and HF (I50) as a contributing cause of death. First, we calculated national age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35-64 and 65-84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011-2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for demographic and socioeconomic characteristics, risk factor prevalence, and physician density. Between 2011-2018, 162,314 and 580,305 HF-related deaths occurred in rural and urban counties, respectively. AAMRs were consistently higher for residents in rural compared with urban counties (73.2 [95% CI: 72.2-74.2] vs. 57.2 [56.8-57.6] in 2018, respectively). The highest AAMR was observed in rural Black men (131.1 [123.3-138.9] in 2018) with greatest increases in HF-related mortality in those 35-64 years (+6.1%/year). The rural-urban IRR persisted among both younger (1.10 [1.04-1.16]) and older adults (1.04 [1.02-1.07]) after adjustment for county-level factors. Main limitations included lack of individual-level data and county dropout due to low event rates (<20).

CONCLUSIONS

Differences in county-level factors may account for a significant amount of the observed variation in HF-related mortality between rural and urban counties. Efforts to reduce the rural-urban disparity in HF-related mortality rates will likely require diverse public health and clinical interventions targeting the underlying causes of this disparity.

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.

Reduction in heart failure hospitalization rate during coronavirus disease 19 pandemic outbreak

AIMS

The recent coronavirus disease 19 (COVID-19) pandemic outbreak forced the adoption of restraint measures, which modified the hospital admission patterns for several diseases. The aim of the study is to investigate the rate of hospital admissions for heart failure (HF) during the early days of the COVID-19 outbreak in Italy, compared with a corresponding period during the previous year and an earlier period during the same year.

METHODS AND RESULTS

We performed a retrospective analysis on HF admissions number at eight hospitals in Italy throughout the study period (21 February to 31 March 2020), compared with an inter-year period (21 February to 31 March 2019) and an intra-year period (1 January to 20 February 2020). The primary outcome was the overall rate of hospital admissions for HF. A total of 505 HF patients were included in this survey: 112 during the case period, 201 during intra-year period, and 192 during inter-year period. The mean admission rate during the case period was 2.80 admissions per day, significantly lower compared with intra-year period (3.94 admissions per day; incidence rate ratio, 0.71; 95% confidence interval [CI], 0.56-0.89; P = 0.0037), or with inter-year (4.92 admissions per day; incidence rate ratio, 0.57; 95% confidence interval, 0.45-0.72; P < 0.001). Patients admitted during study period were less frequently admitted in New York Heart Association (NYHA) Class II compared with inter-year period (P = 0.019). At covariance analysis NYHA class was significantly lower in patients admitted during inter-year control period, compared with patients admitted during case period (P = 0.014).

CONCLUSIONS

Admissions for HF were significantly reduced during the lockdown due to the COVID-19 pandemic in Italy.

Contemporary and Future Concepts on Hypertension in African Americans: COVID-19 and Beyond

Background

Cardiovascular disease related mortality is the leading cause of death in the United States, with hypertension being the most prevalent and potent risk factor. For decades hypertension has disproportionately affected African Americans, who also have a higher burden of associated comorbidities including diabetes and heart failure.

Methods

Current literature including guideline reports and newer studies on hypertension in African Americans in PubMed were reviewed. We also reviewed newer publications on the relationship between COVID-19 and cardiovascular disease.

Findings

While APOL1 has been theorized in the epidemiology of hypertension, the increased prevalence and associated risks are primarily due to environmental and lifestyle factors. These factors include poor diet, adverse lifestyle, and social determinants. Hypertension control can be achieved by lifestyle modifications such as low sodium diet, weight loss, and adequate physical activity. When lifestyle modifications alone do not adequately control hypertension, a common occurrence among African Americans who suffer with greater prevalence of resistant hypertension, pharmacological intervention is indicated. The efficacy of renal denervation, and the use of sodium-glucose cotransporter 2 and aminopeptidase A inhibitors, have been studied for treatment of resistant hypertension. Furthermore, the recent COVID-19 crisis has been particularly devastating among African Americans who demonstrate increased incidence and poorer health outcomes related to the disease.

Conclusion

The disparities in outcomes, which are largely attributable to a greater prevalence of comorbidities such as hypertension and obesity, in addition to adverse environmental and socioeconomic factors, highlight the necessity of specialized clinical approaches and programs for African Americans to address longstanding barriers to equitable care.

Regional Variation in the Association of Poverty and Heart Failure Mortality in the 3135 Counties of the United States

Background There is significant geographical variation in heart failure (HF) mortality across the United States. County socioeconomic factors that influence these outcomes are unknown. We studied the association between county socioeconomic factors and HF mortality and compared it with coronary heart disease (CHD) mortality. Methods and Results This is a cross-sectional analysis of socioeconomic factors and mortality in HF and CHD across 3135 US counties from 2010 to 2015. County-level poverty, education, income, unemployment, health insurance status, and cause-specific mortality rates were collected from the Centers for Disease Control and Prevention and US Census Bureau databases. Poverty had the strongest correlation with both HF and CHD mortality, disproportionately higher for HF (r=0.48) than CHD (r=0.24). HF mortality increased by 5.2 deaths/100 000 for each percentage increase in county poverty prevalence in a frequency-weighted, demographic-adjusted, multivariate regression model. The greatest attenuation in the poverty regression coefficient (66.4%) was seen after adjustment for prevalence of diabetes mellitus and obesity. Subgroup analysis by census region showed that this relationship was the strongest in the South and weakest in the Northeast (6.1 versus 1.4 deaths/100 000 per 1% increase in county poverty in a demographics-adjusted model). Conclusions County poverty is the strongest socioeconomic factor associated with HF and CHD mortality, an association that is stronger with HF than with CHD and varied by census region. Over half of the association was explained by differences in the prevalence of diabetes mellitus and obesity across the counties. Health policies targeting improvement in these risk factors may address and possibly minimize health disparities caused by socioeconomic factors.