https://caltcm.memberclicks.net/assets/documents/acc_aha_chf_2013_guidelines.pdf

2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines

What is a case-control study?

March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

1. About These Statistics…e70

2. Cardiovascular Diseases…e72

3. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris…e89

4. Stroke…e99

5. High Blood Pressure…e111

6. Congenital Cardiovascular Defects…e116

7. Heart Failure…e119

8. Other Cardiovascular Diseases…e122 

9. Risk Factor: Smoking/Tobacco Use…e128

10. Risk Factor: High Blood Cholesterol and Other Lipids…e132

11. Risk Factor: Physical Inactivity…e136

12. Risk Factor: Overweight and Obesity…e139

13. Risk Factor: Diabetes Mellitus…e143

14. End-Stage Renal Disease and Chronic Kidney Disease…e149

15. Metabolic Syndrome…e151

16. Nutrition…e153

17. Quality of Care…e155

18. Medical Procedures…e159

19. Economic Cost of Cardiovascular Diseases…e162

20. At-a-Glance Summary Tables…e164 

21. Glossary and Abbreviation Guide…e168
    Writing Group Disclosures…e171



Appendix I: List of Statistical Fact Sheets:

http://www.americanheart.org/presenter.jhtml?identifier=2007

We thank Drs Robert Adams, Philip Gorelick, Matt Wilson, and Philip Wolf (members of the Statistics Committee or Stroke Statistics Subcommittee); Brian Eigel; Gregg Fonarow; Kathy Jenkins; Gail Pearson; and Michael Wolz for their valuable comments and contributions. We would like to acknowledge Tim Anderson and Tom Schneider for their editorial contributions and Karen Modesitt for her administrative assistance.

# 1. About These Statistics {#article-title-2}

The American Heart Association (AHA) works with the Centers for Disease Control and Prevention’s National Center for Health Statistics (CDC/NCHS); the National Heart, Lung, and Blood Institute (NHLBI); the National Institute of Neurological Disorders and Stroke (NINDS); and other government agencies to derive the annual statistics in this Update. This chapter describes the most important sources and the types of data we use from them. For more details and an alphabetical list of abbreviations, see Chapter 21 of this document, the Glossary and Abbreviation Guide.

The surveys used are:

Heart Disease and Stroke Statistics—2007 Update A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter.

Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter. 

Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

Statement of the problem
Recent research supports the premise of studying social determinants of health in conjunction with cancer survivorship (Hastert TA, 2021). It is widely appreciated that the social context in which people live and work influences their health (Coughlin SS, 2019).The World Health Organization defined the social determinants of health as the “conditions in which people are born, grow, work, live and age, and the wider set of forces and systems shaping the conditions of daily life” (WHO, 2014).

In recent years, increasing efforts have been made in health care settings to screen for a broad array of social determinants of health including inadequate food and nutrition and inadequate housing, and to refer patients to community resources (e.g., food pantries, housing programs) (Garg A, 2013; Gottlieb LM, 2015; Bazemore AW, 2016; Page-Reeves J, 2016; Pinto AD, 2016; DeVoe JE, 2016; Billioux A, 2017; Cottrell EK, 2018; LaForge K, 2018). According to this perspective, quality health care includes the amelioration of the harmful health effects of a lack of basic needs, and health care settings are an appropriate environment for assessing and intervening on social needs (social determinants of health) (Garg A, 2013; Gottlieb LM, 2015; Bazemore AW, 2016; Page-Reeves J, 2016; Pinto AD, 2016; DeVoe JE, 2016; Billioux A, 2017; Cottrell EK, 2018).

/pdf/social-determinants-of-health-and-cancer-survivorship-fref67vrhr.pdf

Social Determinants of Health and Cancer Survivorship.

Marital status and other socioeconomic and clinical factors were examined as predictors of survival in idiopathic dilated cardiomyopathy using data from a hospital-based study in Washington, DC. Twenty-five (18.1%) of the cases (n = 138) were single, 66 (47.8%) were married, 25 (18.1%) were divorced or separated, and 22 (15.9%) were widowed. Married patients were more likely to be male, to have an annual household income grater than $15,000, and to live with another person (p ⩽ 0.01) as compared with those who were single, widowed, divorced or separated. Widowed patients were older on average and more likely to abstain from drinking alcohol. The cumulative survival among widowed patients at 12 and 24 months was 54.6 and 48.5%, respectively, as compared with 75.8 and 59.0% among single patients and 80.0 and 71.2% among married patients. The survival of divorced or separated patients was relatively good with a cumulative survival of 84.0% at both 12 and 24 months. Older age, lower ejection fraction, ventricular arrhythmias, bundle branch block, and marital status were significant predictors of survival in univariate analysis using the proportional hazards model. In multivariable analysis, age, race, ejection fraction, and marital status were statistically significant independent predictors of survival, with single patients with idiopathic dilated cardiomyopathy having a poorer survival than those who were married (adjusted RR = 2.5, 95% CI 1.1–6.2, p < 0.05). The observed association with marital status may be explained by psychosocial factors not examined in the present study such as quality of social network or psychological stress.

Marital status as a predictor of survival in idiopathic dilated cardiomyopathy: The Washington, DC dilated cardiomyopathy study

There has been increasing interest in developing practical, non-theoretical tools for analyzing ethical problems in public health, biomedicine, and other scientific disciplines so that professionals can make and justify ethical decisions in their own research or practice. Tools for ethical decisionmaking, together with case studies on ethics, are often used in graduate education programs and in continuing professional education. Students can benefit from opportunities to further develop their analytical skills, to recognize ethical issues, and to develop their moral sensitivity. One practical approach for illustrating and facilitating ethical analysis uses cases with contrary facts and circumstances, an approach which complements rather than replaces theoretical approaches to moral reasoning. Cases with contrary facts and circumstances are presented in two or more alternative ways so that the facts, circumstances, or framing of one version runs counter to that of the other version (s). Cases with contrary facts, together with practical steps for identifying and analyzing ethical issues, are likely to be useful tools for illustrating and facilitating ethics analysis and stimulating the moral imagination.

Using Cases with Contrary Facts to Illustrate and Facilitate Ethical Analysis

Over the past two decades, 12 large epidemiologic studies and 2 registries have focused on U.S. veterans of the 1990–1991 Gulf War Era. We conducted a review of these studies’ research tools to identify existing gaps and overlaps of efforts to date, and to advance development of the next generation of Gulf War Era survey tools. Overall, we found that many of the studies used similar instruments. Questions regarding exposures were more similar across studies than other domains, while neurocognitive and psychological tools were the most variable. Many studies focused on self-reported survey results, with a range of validation practices. However, physical exams, biomedical assessments, and specimen storage were not common. This review suggests that while research may be able to pool data from past surveys, future surveys need to consider how their design can yield data comparable with previous surveys. Additionally, data that incorporate recent technologies in specimen and genetic analyses would greatly enhance such survey data. When combined with existing data on deployment-related exposures and post-deployment health conditions, longitudinal follow-up of existing studies within this collaborative framework could represent an important step toward improving the health of veterans.

/pdf/an-assessment-of-survey-measures-used-across-key-4as5lohybc.pdf

Steven S. Coughlin

Papers

Social Determinants of Health and Cancer Survivorship.

Marital status as a predictor of survival in idiopathic dilated cardiomyopathy: The Washington, DC dilated cardiomyopathy study

Using Cases with Contrary Facts to Illustrate and Facilitate Ethical Analysis

An assessment of survey measures used across key epidemiologic studies of United States Gulf War I Era Veterans

Enalapril and lisinopril in the treatment of mild to moderate essential hypertension