Bayesian Model Projecting Cardiovascular Disease Related Mortality Trends in the United States.

BACKGROUND: As future cardiovascular disease mortality trends have public health implications, we aimed to project ischemic heart disease (IHD), cerebrovascular disease (CeVD), and heart failure (HF) mortality rates for adults (40-79 years). METHODS AND RESULTS: In this population-level study, we linked the yearly mortality rates (per 100 000 US residents) (2000-2019) with IHD, CeVD, or HF as the primary cause of death from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research with the midyear US population estimates (2000-2035) for adults (40-79 years). We calculated the observed age-standardized mortality rates (2000-2019) (per 100 000 residents) (aSMR) and fitted Bayesian age-period-cohort models to project aSMR for IHD, CeVD, and HF up to 2035 in the United States. Between 2019 (last year of observed data) and 2035 (last year of projected results), the US population (40-79 years) will increase by 16% and age. The IHD aSMR will reduce from 111.9 (in 2019) to 81.8 (66.7-96.9) in 2035, an effect observed for all age groups. The CeVD aSMR will remain comparable between 2019 (37.4) and 2035 (38.6 [30.7-46.5]). The HF aSMR will increase from 16.5 in 2019 to reach 30.9 (13-48.8) in 2035; such increases were observed in all age groups. CONCLUSIONS: In the United States, between 2020 and 2035, the aSMR for IHD is expected to decrease, for CeVD will remain stable, and for HF will increase substantially. These data can inform resource allocation for future public health initiatives.

The association between county-level premature cardiovascular mortality related to cardio-kidney-metabolic disease and the social determinants of health in the US.

Cardio-kidney-metabolic (CKM) syndrome is defined by the American Heart Association as the intersection between metabolic, renal and cardiovascular disease. Understanding the contemporary estimates of CKM related mortality and recent trends in the US is essential for developing targeted public interventions. We collected state-level and county-level CKM-associated age-adjusted premature cardiovascular mortality (aaCVM) (2010-2019) rates from the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER). We linked the county-level aaCVM with a multi-component social deprivation metric: the Social Deprivation Index (SDI: range 0-100) and grouped them as follows: I: 0-25, II: 26-50, III: 51-75, and IV: 76-100. We conducted pair-wise comparison of aaCVM between SDI groups with the multiplicity adjusted Wilcoxon test; we compared aaCVM in men versus women, metropolitan versus nonmetropolitan counties, and non-hispanic white versus non-hispanic black residents. In 3101 analyzed counties in the US, the median CKM associated aaCVM was 61 [interquartile range (IQR): 45, 82]/100 000. Mississippi (99/100 000) and Minnesota (33/100 000) had the highest and lowest values respectively. CKM associated aaMR increased across SDI groups [I - 45 (IQR: 36, 55)/100 000, II- 61 (IQR: 49, 77)/100 000, III- 77 (IQR: 61, 94)/100 000, IV- 89 (IQR: 70, 110)/100 000; all pair-wise p-values < 0.001]. Men had higher rates [85 (64, 91)/100 000] than women [41 (28, 58)/100 000](p-value < 0.001), metropolitan counties [54 (40, 72)/100 000] had lower rates than non-metropolitan counties [66 (49, 90)/100 000](p-value < 0.001), and non-Hispanic Black [110 (86, 137)/100 000] had higher aaMR than non-Hispanic White residents [59 (44, 78)/100 000](p-value < 0.001). In the US, CKM mortality remains high and disproportionately occurs in more socially deprived counties and non-metropolitan counties. Our inability to reduce CKM mortality rates over the study period highlights the need for targeted policy interventions to curb the ongoing high burden.

Adding traditional and emerging biomarkers for risk assessment in secondary prevention: A prospective cohort study of 20,656 patients with cardiovascular disease.

BACKGROUND: This study aims to explore whether conventional and emerging biomarkers could improve risk discrimination and calibration in secondary prevention of recurrent atherosclerotic cardiovascular disease (ASCVD), based on a model using predictors from SMART2. METHODS: In a cohort of 20,658 UK Biobank participants with medical history of ASCVD, we analysed any improvement in C indices and net reclassification index (NRI) for future ASCVD events, following addition of LP-a, ApoB, cystatin C, HbA1c, GGT, AST, ALT, and ALP, to a model with predictors used in SMART2 for the outcome of recurrent major cardiovascular event. We also examined any improvement in C indices and NRIs replacing creatinine based estimated glomerular filtration rate (eGFR) with cystatin C based estimates. Calibration plots between different models were also compared. RESULTS: Compared with the baseline model (C index=0.663), modest increment in C indices were observed when adding HbA1c (ΔC=0.0064, p<0.001), cystatin C (ΔC=0.0037, p<0.001), GGT (ΔC=0.0023, p<0.001), AST (ΔC= 0.0007, p<0.005) or ALP (ΔC=0.0010, p<0.001) or replacing eGFRCr with eGFRCysC (ΔC=0.0036, p<0.001) or eGFRCr-CysC (ΔC=0.00336, p<0.001). Similarly, the strongest improvements in NRI were observed with the addition of HbA1c (NRI=0.014), or cystatin C (NRI= 0.006) or replacing eGFRCr with eGFRCr-CysC (NRI=0.001) or eGFRCysC (NRI=0.002). There was no evidence that adding biomarkers modify calibration. CONCLUSIONS: Adding several biomarkers, most notably cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination.

Social and Environmental Determinants of Health and Cardio-Kidney-Metabolic Syndrome-Related Mortality.

Importance: It is not well understood if and how various social and environmental determinants of health (SEDoH) are associated with mortality rates related to cardio-kidney-metabolic syndrome (CKM) across the US. Objective: To study the magnitude of the association strength of SEDoH with CKM-related mortality at the county level across the US. Design, Setting, and Participants: This cross-sectional, retrospective, population-based study used aggregate county-level data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) data portal from 2010-2019. Data analysis occurred from September 2023 to January 2024. Exposures: A total of 7 diverse SEDoH were chosen, including median annual household income, percentage of racial and ethnic minority residents per county, fine particulate air pollution (PM2.5) concentrations, high-school completion rate, primary health care access, food insecurity, and rurality rate. Main Outcomes and Measures: The primary outcome was county-level age-adjusted mortality rate (aaMR) attributable to CKM. The association of county-level CKM-related aaMR with the 7 SEDoH was analyzed using geographically weighted models and the model median coefficients for each covariate studied. Results: Data from 3101 of 3243 counties (95.6%) were analyzed. There was substantial variation in SEDoH between states and counties. The overall pooled median (IQR) aaMR (2010-2019) in the US was 505.5 (441.3-578.9) per 100 000 residents. Most counties in the lower half of the US had rates much higher than the pooled median (eg, Southern US median [IQR] aaMR, 537.3 [466.0-615.9] per 100 000 residents). CKM-related mortality was positively associated with the food insecurity rate (median [IQR] ß = 6.78 [2.78-11.56]) and PM2.5 concentrations (median [IQR] ß = 5.52 [-11.06 to 19.70]), while it was negatively associated with median annual household income (median [IQR] ß = -0.002 [-0.003 to -0.001]), rurality (median [IQR] ß = -0.32 [-0.67 to 0.02]), high school completion rate (median [IQR] ß = -1.89 [-4.54 to 0.10]), racial and ethnic minority rate (median [IQR] ß = -0.66 [-1.85 to 0.89]), and primary health care access rate (median [IQR] ß = -0.18 [-0.35 to 0.07]). Conclusions and Relevance: In this cross-sectional study of county-level data across the US, there were substantial geographical differences in the magnitude of the association of SEDoH with CKM-related aaMR. These findings may provide guidance for deciding local health care policy.

In which common chronic conditions can (or cannot) obesity and lifestyle factors explain higher concentrations of C-reactive protein?

AIM: Elevated C-reactive protein (CRP), a marker of inflammation, is common in many chronic conditions. We aimed to examine to what extent elevated CRP in chronic conditions could be explained by concurrent adiposity. MATERIALS AND METHODS: This cross-sectional study analysed UK Biobank data on 10 chronic conditions reported at baseline. Linear regression models explored the extent to which CRP concentrations were elevated in each condition, unadjusted; adjusted for sociodemographic confounders and lifestyle and body mass index (BMI) in a series of models; or adjusted for BMI and waist circumference together or for adiposity alone. RESULTS: After exclusion of participants with a potential acute infection at baseline, we tested the association in 292 772 UK Biobank participants. Linear regression showed that elevated CRP concentration was associated with all included conditions. After adjustment for sociodemographic confounders, lifestyle and BMI, chronic kidney disease, heart failure, liver disease, psoriasis, rheumatoid arthritis and chronic obstructive pulmonary disease were still associated with elevated CRP. In contrast, the association between prevalent diabetes, prior myocardial infarction (MI), hypertension and sleep apnoea and CRP could be mostly explained by adiposity alone. For example, the 42% higher CRP concentrations in diabetes compared to those without diabetes in the unadjusted model (lnCRP ß: 0.35; 95% confidence interval [CI]: 0.32-0.37, p < 0.001) were completely attenuated after adjustment for BMI (lnCRP ß: -0.07; 95% CI: -0.09-0.05, p < 0.001). CONCLUSIONS/INTERPRETATION: In diabetes, MI, hypertension and sleep apnoea and elevated CRP appears to be accounted for by the greater adiposity typically evident in these conditions. However, for the other conditions, systemic inflammation cannot be explained by excess adiposity alone.

Ambient Air Pollution Exposure and Adverse Outcomes Among Medicare Beneficiaries With Heart Failure.

BACKGROUND: Exposure to fine particulate matter (<2.5 um, particulate matter with an aerodynamic diameter <2.5 microns [PM2.5]) has been implicated in atherogenesis. Limited data in animal studies suggest that PM2.5 exposure leads to myocardial fibrosis and increased incidence of heart failure (HF). Whether PM2.5 is associated with adverse outcomes in patients with preexisting HF has not been widely studied. METHODS AND RESULTS: In this retrospective cohort study, Medicare patients hospitalized with first HF between 2013 and 2020 were identified from the Medicare Provider Analysis and Review Part A 100% files. Patients were linked with integrated estimates of ambient PM2.5 obtained at 1×1 km using the zip code of participants' residence. The study outcomes were all-cause death, HF, and all-cause readmissions burden. A total of 2 599 525 patients were included in this study, with 6 321 731 person-years of follow-up. Mean PM2.5 was 7.3±1.7 µg/m3. Each interquartile range of PM2.5 was associated with 0.9% increased hazard of all-cause death, 4.5% increased hazard of first HF readmission, 3.1% increased risk of HF hospitalization burden, and 5.2% increase in all-cause readmission burden, after adjusting for 11 sociodemographic and medical factors. Subgroup analyses showed that the effects were more pronounced at levels <7 µg/m3 and in patients aged <75 years, Asians, and those residing in rural areas. CONCLUSIONS: Ambient air pollution is associated with higher risk of adverse events in Medicare beneficiaries with established HF. These associations persist below the National Air Quality Standards (12 µg/m3), supporting that no threshold effect exists for health effects of air pollution exposure.

Cholesterol Lowering in Older Adults: Should We Wait for Further Evidence?

PURPOSE OF REVIEW: Current guidelines for primary and secondary prevention of cardiovascular events in adults up to age 75 years are well-established. However, recommendations for lipid-lowering therapies (LLT), particularly for primary prevention, are inconclusive after age 75. In this review, we focus on adults ≥ 75 years to assess low-density lipoprotein-cholesterol (LDL-C) as a marker for predicting atherosclerotic cardiovascular disease (ASCVD) risk, review risk assessment tools, highlight guidelines for LLT, and discuss benefits, risks, and deprescribing strategies. RECENT FINDINGS: The relationship between LDL-C and all-cause mortality and cardiovascular outcomes in older adults is complex and confounded. Current ASCVD risk estimators heavily depend on age and lack geriatric-specific variables. Emerging tools may reclassify individuals based on biologic rather than chronologic age, with coronary artery calcium scores gaining popularity. After initiating LLT for primary or secondary prevention, target LDL-C levels for older adults are lacking, and non-statin therapy thresholds remain unknown, relying on evidence from younger populations. Shared decision-making is crucial, considering therapy's time to benefit, life expectancy, adverse events, and geriatric syndromes. Deprescribing is recommended in end-of-life care but remains unclear in fit or frail older adults. After an ASCVD event, LLT is appropriate for most older adults, and deprescribing can be considered for those approaching the last months of life. Ongoing trials will guide statin prescription and deprescribing among older adults free of ASCVD. In the interim, for adults ≥ 75 years without a limited life expectancy who are free of ASCVD, an LLT approach that includes both lifestyle and medications, specifically statins, may be considered after shared decision-making.

Historical neighborhood redlining and bystander CPR disparities in out-of-hospital cardiac arrest.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is associated with low survival rates. Bystander cardiopulmonary resuscitation (CPR) is essential for improving outcomes, but its utilization remains limited, particularly among racial and ethnic minorities. Historical redlining, a practice that classified neighborhoods for mortgage risk in 1930s, may have lasting implications for social and health outcomes. This study sought to investigate the influence of redlining on the provision of bystander CPR during witnessed OHCA. METHODS: We conducted an analysis using data from the comprehensive Cardiac Arrest Registry to Enhance Survival (CARES), encompassing 736,066 non-traumatic OHCA cases across the United States. The Home Owners' Loan Corporation (HOLC) map shapefiles were utilized to categorize census tracts of arrests into four grades (A signifying "best", B "still desirable", C "declining", and D "hazardous"). Multivariable hierarchical logistic regression models were employed to predict the likelihood of CPR provision, adjusting for various factors including age, sex, race/ethnicity, arrest location, calendar year, and state of occurrence. Additionally, we accounted for the percentage of Black residents and residents below poverty levels at the census tract level. RESULTS: Among the 43,186 witnessed cases of OHCA in graded HOLC census tracts, 37.2% received bystander CPR. The rates of bystander CPR exhibited a gradual decline across HOLC grades, ranging from 41.8% in HOLC grade A to 35.8% in HOLC grade D. In fully adjusted model, we observed significantly lower odds of receiving bystander CPR in HOLC grades C (OR 0.89, 95% CI 0.81-0.98, p = 0.016) and D (OR 0.86, 95% CI 0.78-0.95, p = 0.002) compared to HOLC grade A. CONCLUSION: Redlining, a historical segregation practice, is associated with reduced contemporary rates of bystander CPR during OHCA. Targeted CPR training in redlined neighborhoods may be imperative to enhance survival outcomes.

Air Pollution and Adverse Cardiovascular Events After Coronary Artery Bypass Grafting: A 10-Year Nationwide Study.

Background: Increased particulate matter <2.5 µm (PM2.5) air pollution is associated with adverse cardiovascular outcomes. However, its impact on patients with prior coronary artery bypass grafting (CABG) is unknown. Objectives: The purpose of this study was to evaluate the association between major adverse cardiovascular events (MACE) (defined as myocardial infarction, stroke, or cardiovascular death) and air pollution after CABG. Methods: We linked 26,403 U.S. veterans who underwent CABG (2010-2019) nationally with average annual ambient PM2.5 estimates using residential address. Over a 5-year median follow-up period, we identified MACE and fit a multivariable Cox proportional hazard model to determine the risk of MACE as per PM2.5 exposure. We also estimated the absolute potential reduction in PM2.5 attributable MACE simulating a hypothetical PM2.5 lowered to the revised World Health Organization standard of 5 µg/m3. Results: The observed median PM2.5 exposure was 7.9 µg/m3 (IQR: 7.0-8.9 µg/m3; 95% of patients were exposed to PM2.5 above 5 µg/m3). Increased PM2.5 exposure was associated with a higher 10-year MACE rate (first tertile 38% vs third tertile 45%; P < 0.001). Adjusting for demographic, racial, and clinical characteristics, a 10 µg/m3 increase in PM2.5 resulted in 27% relative risk for MACE (HR: 1.27, 95% CI: 1.10-1.46; P < 0.001). Currently, 10% of total MACE is attributable to PM2.5 exposure. Reducing maximum PM2.5 to 5 µg/m3 could result in a 7% absolute reduction in 10-year MACE rates. Conclusions: In this large nationwide CABG cohort, ambient PM2.5 air pollution was strongly associated with adverse 10-year cardiovascular outcomes. Reducing levels to World Health Organization-recommended standards would result in a substantial risk reduction at the population level.

Does Timing of Coronary Artery Bypass Grafting after ST-Elevation Myocardial Infarction Impact Early- and Long-Term Outcomes?

BACKGROUND: The optimal timing of surgical revascularization after ST-elevation myocardial infarction (STEMI) is controversial, with some suggesting higher mortality rates in patients undergoing early surgery. The aim of the study is to determine the effect of the timing of surgical revascularization on 30-day mortality and long-term outcomes in these patients. METHODS: Retrospective single-center analysis of patients with STEMI undergoing coronary artery bypass grafting (CABG) between January 2008 and December 2019 at our institution. The cohort was split into three groups based on time from symptom onset until surgical revascularization (Group 1: <12 hours, Group 2: 12-72 hours, Group 3: >72 hours). Statistical analyses were performed with and without patients in cardiogenic shock. Primary outcomes were 30-day mortality and 10-year survival. RESULTS: During the study period, 437 consecutive patients underwent surgical revascularization in the setting of STEMI. The mean age was 67.0 years, 96 (22.0%) patients were female, and 281 (64.3%) patients underwent off-pump CABG. The overall 30-day mortality including patients with cardiogenic shock was 12.8%. The 30-day mortality was 16.1, 13.9, and 9.3% in Groups 1, 2, and 3 (p = 0.31), whereas 10-year survival was 48.5, 57.3, and 54.9% (log-rank: p = 0.40). After exclusion of patients in cardiogenic shock, there was no difference between the three groups in 30-day and 10-year mortality. Timing of surgery had no influence on early- and long-term survival. CONCLUSION: In patients with STEMI, early surgical revascularization achieved similar early- and long-term survival rates compared with a delayed surgical revascularization strategy. Hence, when indicated, an early CABG strategy has no disadvantages in comparison to a delayed strategy.

Geographic proximity to cardiovascular clinical trial sites: A National analysis in the United States.

INTRODUCTION: Suboptimal geographical access to cardiovascular clinical trial sites (CV-CTS) may be a cause of inadequate demographic representation in contemporary trials. Thus, we investigate access to CV-CTS in the US. METHODS: We obtained the location of CV-CTS from Clinicaltrials.gov. We calculated the distance in kilometers from each ZIP code to the nearest CV-CTS, stratifying our results based on urban/rural setting, sex and race. RESULTS: We identified a total of 10,506 studies in 4,630 US ZIP codes (10.5 %), of those only 237 (5 %) were rural. The overall median CV-CTS distance was 5.8 km (IQR: 2.7, 15.8). For urban residents, this distance was 4.5 km (IQR: 2.3, 9.2), while for rural residents, it was 24.2 km (IQR: 13.8, 42.2). RESULTS: We revealed important disparities involving geographical proximity to cardiovascular clinical trial sites. Increasing the representation of these populations in clinical trials is paramount to improving the applicability of their findings to real-world settings.

County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from all stroke subtypes between 2016 and 2020. We analyzed 54 county-level SEDH possibly associated with age-adjusted stroke mortality rates/100,000 people. Classification and Regression Tree (CART) was used to identify specific county-level clusters associated with stroke mortality. Variable importance was assessed using Random Forest analysis. A total of 501,391 decedents from 2397 counties were included. CART identified 10 clusters, with 77.5% relative increase in stroke mortality rates across the spectrum (28.5 vs 50.7 per 100,000 persons). CART identified 8 SEDH to guide the classification of the county clusters. Including, annual Median Household Income ($), live births with Low Birthweight (%), current adult Smokers (%), adults reporting Severe Housing Problems (%), adequate Access to Exercise (%), adults reporting Physical Inactivity (%), adults with diagnosed Diabetes (%), and adults reporting Excessive Drinking (%). In conclusion, SEDH exposures have a complex relationship with stroke. Machine learning approaches can help deconstruct this relationship and demonstrate associations that allow improved understanding of the socio-environmental drivers of stroke and development of targeted interventions.

Quantifying lead-attributable cardiovascular disease burden in the United States.

Lead exposure has been linked to a myriad of cardiovascular diseases. Utilizing data from the 2019 Global Burden of Disease Study, we quantified age-standardized lead exposure-related mortality and disability-adjusted life years (DALYs) in the United States between 1990 and 2019. Our analysis revealed a substantial reduction in age-standardized cardiovascular disease (CVD) mortality attributable to lead exposure by 60 % (from 7.4 to 2.9 per 100,000), along with a concurrent decrease in age-standardized CVD DALYs by 66 % (from 143.2 to 48.7 per 100,000).

Associations of diabesity with all-cause and cardiovascular disease mortality: Findings from the Mexico City Prospective Study.

AIM: To investigate the joint associations of diabetes and obesity with all-cause and cardiovascular disease (CVD) mortality in the Mexico City Prospective Study. MATERIALS AND METHODS: In total, 154 128 participants (67.2% women) were included in this prospective analysis. Diabetes was self-reported, while body mass index was used to calculate obesity. Using diabetes and obesity classifications, six groups were created: (a) normal (no diabetes and normal weight); (b) normal weight and diabetes; (c) overweight but not diabetes (overweight); (d) overweight and diabetes (prediabesity); (e) obesity but not diabetes (obesity); and (f) obesity and diabetes (diabesity). Associations between these categories and outcomes were investigated using Cox proportional hazard models adjusted for confounder factors. RESULTS: During 18.3 years of follow-up, 27 197 (17.6%) participants died (28.5% because of CV causes). In the maximally adjusted model, participants those with the highest risk {hazard ratio (HR): 2.37 [95% confidence interval (CI): 2.24-2.51]}, followed by those with diabesity [HR: 2.04 (95% CI: 1.94-2.15)]. Similar trends of associations were observed for CVD mortality. The highest CV mortality risk was observed in individuals with diabesity [HR: 1.80 (95% CI: 1.63-1.99)], followed by normal weight and diabetic individuals [HR: 1.78 (95% CI: 1.60-1.98)]. CONCLUSION: This large prospective study identified that diabetes was the main driver of all-cause and CVD mortality in all the categories studied, with diabesity being the riskiest. Given the high prevalence of both conditions in Mexico, our results reinforce the importance of initiating prevention strategies from an early age.

County-level socio-environmental factors and obesity prevalence in the United States.

AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest. RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Air pollution exposure and cardiometabolic risk.

The Global Burden of Disease assessment estimates that 20% of global type 2 diabetes cases are related to chronic exposure to particulate matter (PM) with a diameter of 2·5 µm or less (PM2·5). With 99% of the global population residing in areas where air pollution levels are above current WHO air quality guidelines, and increasing concern in regard to the common drivers of air pollution and climate change, there is a compelling need to understand the connection between air pollution and cardiometabolic disease, and pathways to address this preventable risk factor. This Review provides an up to date summary of the epidemiological evidence and mechanistic underpinnings linking air pollution with cardiometabolic risk. We also outline approaches to improve awareness, and discuss personal-level, community, governmental, and policy interventions to help mitigate the growing global public health risk of air pollution exposure.

Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data.

BACKGROUND: Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI). METHODS: We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D'Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor. RESULTS: In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801-27 in SDI groups I; 623-23 in SDI group V). CONCLUSIONS: Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models.