WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

ContextAbout 5% of the US population is morbidly obese. This disease remains
largely refractory to diet and drug therapy, but generally responds well to
bariatric surgery.ObjectiveTo determine the impact of bariatric surgery on weight loss, operative
mortality outcome, and 4 obesity comorbidities (diabetes, hyperlipidemia,
hypertension, and obstructive sleep apnea).Data Sources and Study SelectionElectronic literature search of MEDLINE, Current Contents, and the Cochrane
Library databases plus manual reference checks of all articles on bariatric
surgery published in the English language between 1990 and 2003. Two levels
of screening were used on 2738 citations.Data ExtractionA total of 136 fully extracted studies, which included 91 overlapping
patient populations (kin studies), were included for a total of 22 094
patients. Nineteen percent of the patients were men and 72.6% were women,
with a mean age of 39 years (range, 16-64 years). Sex was not reported for
1537 patients (8%). The baseline mean body mass index for 16 944 patients
was 46.9 (range, 32.3-68.8).Data SynthesisA random effects model was used in the meta-analysis. The mean (95%
confidence interval) percentage of excess weight loss was 61.2% (58.1%-64.4%)
for all patients; 47.5% (40.7%-54.2%) for patients who underwent gastric banding;
61.6% (56.7%-66.5%), gastric bypass; 68.2% (61.5%-74.8%), gastroplasty; and
70.1% (66.3%-73.9%), biliopancreatic diversion or duodenal switch. Operative
mortality (≤30 days) in the extracted studies was 0.1% for the purely restrictive
procedures, 0.5% for gastric bypass, and 1.1% for biliopancreatic diversion
or duodenal switch. Diabetes was completely resolved in 76.8% of patients
and resolved or improved in 86.0%. Hyperlipidemia improved in 70% or more
of patients. Hypertension was resolved in 61.7% of patients and resolved or
improved in 78.5%. Obstructive sleep apnea was resolved in 85.7% of patients
and was resolved or improved in 83.6% of patients.ConclusionsEffective weight loss was achieved in morbidly obese patients after
undergoing bariatric surgery. A substantial majority of patients with diabetes,
hyperlipidemia, hypertension, and obstructive sleep apnea experienced complete
resolution or improvement.

https://jamanetwork.com/journals/jama/articlepdf/199587/JRV40082.pdf

Bariatric surgery: a systematic review and meta-analysis.

Author(s): Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association

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

Author(s): Go, Alan S; Mozaffarian, Dariush; Roger, Veronique L; Benjamin, Emelia J; Berry, Jarett D; Borden, William B; Bravata, Dawn M; Dai, Shifan; Ford, Earl S; Fox, Caroline S; Franco, Sheila; Fullerton, Heather J; Gillespie, Cathleen; Hailpern, Susan M; Heit, John A; Howard, Virginia J; Huffman, Mark D; Kissela, Brett M; Kittner, Steven J; Lackland, Daniel T; Lichtman, Judith H; Lisabeth, Lynda D; Magid, David; Marcus, Gregory M; Marelli, Ariane; Matchar, David B; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Mussolino, Michael E; Nichol, Graham; Paynter, Nina P; Schreiner, Pamela J; Sorlie, Paul D; Stein, Joel; Turan, Tanya N; Virani, Salim S; Wong, Nathan D; Woo, Daniel; Turner, Melanie B; American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2013 Update A Report From the American Heart Association

Background The effect of age on optimal body weight is controversial, and few studies have had adequate numbers of subjects to analyze mortality as a function of body-mass index across age groups. Methods We studied mortality over 12 years among white men and women who participated in the American Cancer Society's Cancer Prevention Study I (from 1960 through 1972). The 62,116 men and 262,019 women included in this analysis had never smoked cigarettes, had no history of heart disease, stroke, or cancer (other than skin cancer) at base line in 1959–1960, and had no history of recent unintentional weight loss. The date and cause of death for subjects who died were determined from death certificates. The associations between body-mass index (defined as the weight in kilograms divided by the square of the height in meters) and mortality were examined for six age groups in analyses in which we adjusted for age, educational level, physical activity, and alcohol consumption. Results Greater body-mass index was as...

/pdf/the-effect-of-age-on-the-association-between-body-mass-index-4nudvcngnd.pdf

The Effect of Age on the Association between Body-Mass Index and Mortality

Objective : To examine the relation between health outcomes and the equality with which income is distributed in the United States. Design : The degree of income inequality, defined as the percentage of total household income received by the less well off 50% of households, and changes in income inequality were calculated for the 50 states in 1980 and 1990. These measures were then examined in relation to all cause mortality adjusted for age for each state, age specific deaths, changes in mortalities, and other health outcomes and potential pathways for 1980, 1990, and 1989-91. Main outcome measure : Age adjusted mortality from all causes. Results : There was a significant correlation (r=0.62, P Conclusions : Variations between states in the inequality of the distribution of income are significantly associated with variations between states in a large number of health outcomes and social indicators and with mortality trends. These differences parallel relative investments in human and social capital. Economic policies that influence income and wealth inequality may have an important impact on the health of countries. Key messages There was a significant correlation (r=0.62) between the proportion of total household income received by the less well off 50% of households and variation between states in death rates for the United States Income inequality was also significantly related to changes in mortality with smaller declines between 1980-90 in those states with greater income inequality Income inequality was associated with a large number of other health outcomes and with measures related to investments in human and social capital Economic policies that increase income inequality may also have a deleterious effect on population health

/pdf/inequality-in-income-and-mortality-in-the-united-states-psq6bkx58b.pdf

Inequality in income and mortality in the United States: analysis of mortality and potential pathways.

Objectives. We aimed to describe socioeconomic disparities in the United States across multiple health indicators and socioeconomic groups.Methods. Using recent national data on 5 child (infant mortality, health status, activity limitation, healthy eating, sedentary adolescents) and 6 adult (life expectancy, health status, activity limitation, heart disease, diabetes, obesity) health indicators, we examined indicator rates across multiple income or education categories, overall and within racial/ethnic groups.Results. Those with the lowest income and who were least educated were consistently least healthy, but for most indicators, even groups with intermediate income and education levels were less healthy than the wealthiest and most educated. Gradient patterns were seen often among non-Hispanic Blacks and Whites but less consistently among Hispanics.Conclusions. Health in the United States is often, though not invariably, patterned strongly along both socioeconomic and racial/ethnic lines, suggesting lin...

https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.2009.166082

Socioeconomic Disparities in Health in the United States: What the Patterns Tell Us

We examined differences in health measures among rural, suburban, and urban residents and factors that contribute to these differences. Whereas differences between rural and urban residents were observed for some health measures, a consistent rural-to-urban gradient was not always found. Often, the most rural and the most urban areas were found to be disadvantaged compared with suburban areas. If health disparities are to be successfully addressed, the relationship between place of residence and health must be understood.

https://ajph.aphapublications.org/doi/pdfplus/10.2105/AJPH.94.10.1682

The Importance of Place of Residence: Examining Health in Rural and Nonrural Areas

OBJECTIVES: This study examined associations between income inequality and mortality in 282 US metropolitan areas. METHODS: Income inequality measures were calculated from the 1990 US Census. Mortality was calculated from National Center for Health Statistics data and modeled with weighted linear regressions of the log age-adjusted rate. RESULTS: Excess mortality between metropolitan areas with high and low income inequality ranged from 64.7 to 95.8 deaths per 100,000 depending on the inequality measure. In age-specific analyses, income inequality was most evident for infant mortality and for mortality between ages 15 and 64. CONCLUSIONS: Higher income inequality is associated with increased mortality at all per capita income levels. Areas with high income inequality and low average income had excess mortality of 139.8 deaths per 100,000 compared with areas with low inequality and high income. The magnitude of this mortality difference is comparable to the combined loss of life from lung cancer, diabetes,...

Elsie R. Pamuk

Papers

The Effect of Age on the Association between Body-Mass Index and Mortality

Inequality in income and mortality in the United States: analysis of mortality and potential pathways.

Socioeconomic Disparities in Health in the United States: What the Patterns Tell Us

The Importance of Place of Residence: Examining Health in Rural and Nonrural Areas

Income inequality and mortality in metropolitan areas of the United States.