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1.
Circ Cardiovasc Qual Outcomes ; 9(2): 143-53, 2016 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-26941417

RESUMO

BACKGROUND: The American Heart Association's 2020 Strategic Goals emphasize the value of optimizing risk factor status to reduce the burden of morbidity and mortality. In this study, we aimed to quantify the overall and marginal impact of favorable cardiovascular risk factor (CRF) profile on healthcare expenditure and resource utilization in the United States among those with and without cardiovascular disease (CVD). METHODS AND RESULTS: The study population was derived from the 2012 Medical Expenditure Panel Survey (MEPS). Direct and indirect costs were calculated for all-cause healthcare resource utilization. Variables of interest included CVD diagnoses (coronary artery disease, stroke, peripheral artery disease, dysrhythmias, or heart failure), ascertained by International Classification of Diseases, Ninth Edition, Clinical Modification codes, and CRF profile (hypertension, diabetes mellitus, hypercholesterolemia, smoking, physical activity, and obesity). Two-part econometric models were used to study expenditure data. The final study sample consisted of 15 651 MEPS participants (58.5±12 years, 54% female). Overall, 5921 (37.8%) had optimal, 7002 (44.7%) had average, and 2728 (17.4%) had poor CRF profile, translating to 54.2, 64.1, and 24.9 million adults in United States, respectively. Significantly lower health expenditures were noted with favorable CRF profile across CVD status. Among study participants with established CVD, overall healthcare expenditures with optimal and average CRF profile were $5946 and $3731 less compared with those with poor CRF profile. The respective differences were $4031 and $2560 in those without CVD. CONCLUSIONS: Favorable CRF profile is associated with significantly lower medical expenditure and healthcare utilization among individuals with and without established CVD.


Assuntos
Doenças Cardiovasculares/economia , Doenças Cardiovasculares/prevenção & controle , Custos de Cuidados de Saúde , Gastos em Saúde , Recursos em Saúde/economia , Recursos em Saúde/estatística & dados numéricos , Serviços Preventivos de Saúde/economia , Serviços Preventivos de Saúde/estatística & dados numéricos , Adulto , Idoso , Doenças Cardiovasculares/diagnóstico , Doenças Cardiovasculares/etiologia , Redução de Custos , Análise Custo-Benefício , Feminino , Pesquisas sobre Atenção à Saúde , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Medição de Risco , Fatores de Risco , Estados Unidos
2.
Cardiology ; 132(4): 242-8, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26329389

RESUMO

OBJECTIVE: To assess the impact of aerobic fitness on exercise heart rate (HR) indices in an asymptomatic cohort across different body mass index (BMI) categories. METHODS: We performed a cross-sectional analysis of 506 working-class Brazilian subjects, free of known clinical cardiovascular disease(e.g. ischemic heart disease and stroke) who underwent an exercise stress test. RESULTS: There was a significant trend towards decreased HR at peak exercise, HR recovery and chronotropic index (CI) measures as BMI increased, but resting HR increased significantly across BMI categories. In multivariate analysis, the change in CI per unit change in metabolic equivalents of task was greater among the obese subjects than the normal-weight (2.7 vs. ­0.07; p interaction = 0.029)and overweight (2.7 vs. 0.7; p interaction = 0.044) subjects. A similar pattern was seen with peak HR and HR recovery, although the formal tests of interaction did not achieve statistical significance. CONCLUSION: Our findings strongly suggest that fitness is associated with a favorable HR profile and is modified by BMI. Intervention programs should place emphasis on fitness and not only on weight loss.


Assuntos
Diabetes Mellitus/epidemiologia , Teste de Esforço , Frequência Cardíaca/fisiologia , Obesidade/complicações , Fumar/epidemiologia , Adulto , Índice de Massa Corporal , Brasil , Estudos Transversais , Feminino , Humanos , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Análise Multivariada , Fatores de Risco
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