Social determinants of health, health disparities, and adiposity

Socioeconomic Status and Obesity

Obesity is a pervasive public health problem that causes debilitating complications across the life course. One opportunity for preventing the onset of obesity is to focus on its social determinants. Socioeconomic status (SES), which includes factors such as income, educational attainment, occupational prestige, and access to resources, is a key determinant of obesity. In this scoping mini-review, we summarized review articles and meta-analyses of the SES-obesity association. From the 1980s to the present, cross-sectional studies have demonstrated a persistent socioeconomic gradient in obesity in which the association is negative in developed countries and positive in developing countries. Longitudinal studies have revealed the bidirectionality of the SES-obesity association; some studies demonstrate that socioeconomic adversity precedes the onset of obesity, while others provide evidence of reverse causality. While earlier studies relied on anthropometric assessments of weight and height to define obesity, the use of modern technologies like dual-energy x-ray absorptiometry and bioelectrical impedance have demonstrated that the socioeconomic gradient in obesity is robust across multiple indicators of body composition, including direct measures of lean and fat mass. More recently, examination of mediators and moderators of the SES-obesity association have highlighted causal pathways and potential intervention targets, with a focus on health behaviors, environmental conditions, psychological factors, and biological processes. We describe current gaps in knowledge and propose opportunities for future innovation to reduce the burden of obesity and related socioeconomic disparities.

Social Risk Profile and Cardiovascular-Kidney-Metabolic Syndrome in US Adults

BACKGROUND

Poor cardiovascular-kidney-metabolic (CKM) health is associated with premature mortality and excess morbidity in the United States. Adverse social conditions have a prominent impact on cardiometabolic diseases during the life course. We aim to examine the association between social risk profile (SRP) and CKM multimorbidity among US adults.

METHODS AND RESULTS

We used data from the National Health and Nutrition Examination Survey from 1999 to 2018. The definition of CKM syndrome is the coexistence of subclinical or clinical cardiovascular disease, chronic kidney disease, and metabolic disorders. We classified participants by 4 CKM stages according to the different clinical severity of different forms of CKM syndrome. We calculated the summed number of positive SRP measures, including employed, high-income level, food secure, high education attainment, private insurance, owning a house, and married, as SRP scores and classified them into 4 levels by quartiles: low (0-2), lower-middle (3-4), upper-middle (5-6), and high (7-8). A total of 18 373 US adults, aged 20 to 79 years, were included in our analyses. There were 2567 (9.4%) participants with low SRP score level. Most individual SRP measures and a combined SRP score were associated with CKM stages. Compared with high SRP score level, low SRP level was associated with higher odds of having CKM stage 1 (odds ratio [OR], 1.34 [95% CI, 1.06-1.70]), CKM stage 2 (OR, 2.03 [95% CI, 1.59-2.58]), CKM stage 3 (OR, 5.28 [95% CI, 3.29-8.47]), and CKM stage 4 (OR, 5.97 [95% CI, 4.20-8.49]).

CONCLUSIONS

Cumulative social disadvantage, denoted by higher SRP burden, was associated with higher odds of CKM multimorbidity, independent of demographic and lifestyle factors.

Weight Misperception is Prevalent Among Turkish Adults and Associated with Higher Age, Body Mass Index and Lower Education Status

Background

Weight misperception (WM) is common among adults, and it is associated with adverse health outcomes. Research has shown that various factors are associated with weight misperception. Turkish adult population data for weight misperception and related factors do not exist.

Methodology

We conducted a face-to-face cross-sectional descriptive survey in the general internal medicine outpatient clinics of two academic centers. Perception was analyzed both verbally and visually. Misperception was defined for both verbal and visual scales as being thinner than reality misperceptions (TTRM), fatter than reality misperceptions (FTRM), or either of them (ETFTRM). Demographics, anthropometrics, and social determinants of health were analyzed in different misperception groups.

Results

250 patients participated in the study. The median (interquartile range) age was 55 (14), and the BMI was 28.2 (6.9) for females and 26.9 (4.4) for males. 81.2% had ETFTRM, 45.2% had TTRM, and 22.4% had FTRM. Age and BMI were higher in the ETFTRM and TTRM groups, while education level was lower in both. Multivariate logistic regression showed that higher age, higher BMI, and lower education levels were associated with higher TTRM.

Discussion

WM is common among the Turkish adult population. Similar to the previous studies, aging, high BMI, and low education levels are associated with weight misperception. However, in contrast to previous studies, gender, marital status, and employment were not associated with weight misperception in our cohort.

Stigma, Bias, and the Shortfalls of Body Mass Index: A Reflection on the State of Weight Management

Obesity is a complex, multifactorial chronic disease. With the development of novel endoscopic techniques and devices for the treatment of obesity, combined with expanding indications for medications, gastroenterologists are more involved in weight management than ever before. Despite the modern definition of obesity as a disease, weight bias and stigma are pervasive in the medical community and beyond. These sentiments contribute to worse outcomes for patients. Furthermore, body mass index (BMI), which is the primary metric to define obesity, does not always approximate visceral adiposity in all populations. A weight-centric model of health, which relies on BMI, misclassifies individuals who may be metabolically healthy at elevated weights. This review will summarize the history of BMI, highlight the problems that arise with a weight-centric model of health, and propose alternative weight-inclusive frameworks for assessment and intervention.

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.

Tackling obesity while preventing obesity stigma

Obesity is a significant public health problem. Prevalence is rising in children and young people, with lifelong health impacts and implications for paediatric clinical practice. Obesity stigma is increasingly acknowledged as a problem within health services. Health professionals can inadvertently contribute to this stigma, which is harmful and in itself can promote weight gain. A complex web of factors contributes to obesity, and a simplistic approach exclusively focused on personal responsibility, diet and exercise is unhelpful. A more nuanced, sensitive and informed approach is needed, with careful use of language and non-judgemental partnership working.

Risk Stratification and Treatment of Obesity for Primary and Secondary Prevention of Cardiovascular Disease

PURPOSE OF REVIEW

In this review, we discuss contemporary and emerging approaches for risk stratification and management of excess adiposity for the primary and secondary prevention of cardiovascular disease.

RECENT FINDINGS

Obesity is simultaneously a pandemic-scale disease and major risk factor for the incidence and progression of a wide range of cardiometabolic conditions, but risk stratification and treatment remain clinically challenging. However, sex-, race-, and ethnicity-sensitive anthropometric measures, body composition-focused imaging, and health burden-centric staging systems have emerged as important facilitators of holistic risk prediction. Further, expanding therapeutic approaches, including comprehensive lifestyle programs, anti-obesity pharmacotherapies, device/endoscopy-based interventions, metabolic surgery, and novel healthcare delivery resources offer new empowerment for cardiovascular risk reduction in individuals with obesity. Personalized risk stratification and weight management are central to reducing the lifetime prevalence and impact of cardiovascular disease. Further evidence informing long-term safety, efficacy, and cost-effectiveness of novel approaches targeting obesity are critically needed.

Impact of the Neighborhood Food Environment on Dietary Intake and Obesity: a Review of the Recent Literature

PURPOSE OF REVIEW

The built environment impacts obesogenic behaviors and in turn body weight outcomes. This review summarizes recent research demonstrating environmental impacts on dietary intake and obesity with a specific focus on the neighborhood food environment.

RECENT FINDINGS

In the previous five years, an abundance of reviews and research studies have been undertaken to elucidate how the neighborhood food environment impacts diet and obesity. This includes studies using primary data collection and secondary data analyses in various populations across the globe. Taken together, current research presents mixed evidence on the impact of the neighborhood food environment on both dietary intake and obesity. While there is some evidence that certain features of the neighborhood food environment influence health behaviors and outcomes in particular populations, it is imperative to acknowledge the complexity of how neighborhood features interact and constantly evolve when considering place-based influences on health behaviors and outcomes.

Association between racial residential segregation and Black-White disparities in cardiovascular disease mortality

BACKGROUND

Racial residential segregation is associated with racial health inequities, but it is unclear if segregation may exacerbate Black-White disparities in cardiovascular disease (CVD) mortality. This study aimed to assess associations between Black-White residential segregation, CVD mortality rates among non-Hispanic (NH) Black and NH White populations, and Black-White disparities in CVD mortality.

METHODS

This cross-sectional study analyzed Black-White residential segregation, as measured by county-level interaction index, of US counties, county-level CVD mortality among NH White and NH black adults aged 25 years and older, and county-level Black-White disparities in CVD mortality in years 2014 to 2017. Age-adjusted, county-level NH Black CVD mortality rates and NH White cardiovascular disease mortality rates, as well as group-level relative risk ratios for Black-White cardiovascular disease mortality, were calculated. Sequential generalized linear models adjusted for county-level socioeconomic and neighborhood factors were used to estimate associations between residential segregation and cardiovascular mortality rates among NH Black and NH White populations. Relative risk ratio tests were used to compare Black-White disparities in the most segregated counties to disparities in the least segregated counties.

RESULTS

We included 1,286 counties with ≥5% Black populations in the main analysis. Among adults aged ≥25 years, there were 2,611,560 and 408,429 CVD deaths among NH White and NH Black individuals, respectively. In the unadjusted model, counties in the highest tertile of segregation had 9% higher (95% CI, 1%-20% higher, P = .04) rates of NH Black CVD mortality than counties in the lowest tertile of segregation. In the multivariable adjusted model, the most segregated counties had 15% higher (95% CI, 0.5% to 38% higher, P = .04) rates of NH Black CVD mortality than the least segregated counties. In the most segregated counties, NH Black individuals were 33% more likely to die of CVD than NH White individuals (RR 1.33, 95% CI 1.32 to 1.33, P < .001).

CONCLUSIONS

Counties with increased Black-White residential segregation have higher rates of NH Black CVD mortality and larger Black-White disparities in CVD mortality. Identifying the causal mechanisms through which racial residential segregation widens disparities in CVD mortality requires further study.