Social determinants of health and obesity: Findings from a national study of US adults

Javed, Zulqarnain; Maqsood, Muhammad Haisum; Mahajan, Shiwani; Taha, Mohamad; Patel, Kershaw V.; Sharma, Garima; Hagan, Kobina; Blaha, Michael J.; Blankstein, Ron; Mossialos, Elías; Virani, Salim S.; Cainzos‐Achirica, Miguel; Nasir, Khurram

doi:10.1002/oby.23336

Cited by 41 publications

(20 citation statements)

References 35 publications

(43 reference statements)

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“…Unlike multiple studies [5][6][7][8][9][10][11][12][27][28][29][30][31][32][33][34] that have examined obesity and SDOH, we provided an analysis to assess adult obesity and SDOH at the census tract level in Shelby County, Tennessee. Admittedly, some limitations should be considered with our findings.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, SDOH indicators are perceived to be among major driving forces behind systematic social inequalities [ 11 ]. As a result, certain susceptible populations are more likely to be affected by obesity-associated SDOH stressors than other groups and populations [ 12 ]. The ongoing global pandemic caused by COVID-19 has further worsened SDOH burdens, since individuals diagnosed with preexisting conditions such as obesity have been disproportionately affected by COVID-19 morbidity and mortality [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach

Brakefield¹,

Olusanya²,

Shaban-Nejad³

2022

JMIR Public Health Surveill

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Background Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of health (SDOH) disparities. Objective The objective of this study was to investigate the effects of SDOH on obesity prevalence among adults in Shelby County, Tennessee, the United States, using a geospatial machine learning approach. Methods Obesity prevalence was obtained from the publicly available 500 Cities database of Centers for Disease Control and Prevention, and SDOH indicators were extracted from the US census and the US Department of Agriculture. We examined the geographic distributions of obesity prevalence patterns, using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDOH and adult obesity. Unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDOH indicators. Results Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, the median household income, as well as the percentage of individuals who were Black, home renters, living below the poverty level, 55 years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence among disadvantaged neighborhoods. Conclusions More research is needed to examine links between geographical location, SDOH, and chronic diseases. The findings of this study, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights, informing health decision-making and interventions that mitigate risk factors of increasing obesity prevalence.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach

Brakefield¹,

Olusanya²,

Shaban-Nejad³

2022

JMIR Public Health Surveill

View full text Add to dashboard Cite

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“…Socioeconomic status is mainly defined by income, occupation, and educational level, which could reflect that these groups have less healthy lifestyles. Javed et al found a prevalence of obesity between 50 and 70% higher in this group, in which psychological stress can also play an important role [ 52 ]. Several authors have found an important relationship between stress and obesity [ 53 , 54 , 55 ]…”

Section: Discussionmentioning

confidence: 99%

Influence of Educational Level and Healthy Habits on the Prevalence of Diabesity in a Spanish Working Population

et al. 2022

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Background: Diabesity is a growing problem internationally. Taking into account the importance of physical activity and diet in its prevention and treatment, the objective of this study was to delve into the impact of healthy habits on diabesity. Methods: A descriptive, cross-sectional study was carried out in 386,924 Spanish adult workers. Obesity was determined according to eleven different formulas. Life habits were also valued; sociodemographic variables; and educational level; as well as analytical and clinical variables such as blood pressure and blood glucose levels. The association between the different variables was performed using the chi-square and the Student’s t-tests when the samples were independent. A multivariate analysis was performed using the multinomial logistic regression test by calculating the odds ratio and a 95% confidence interval. The Hosmer–Lemeshow goodness-of-fit test was also performed. Results: The overall prevalence of diabesity ranged between 0.3% (95% CI 0.3–0.4) when obesity was assessed according to the abdominal volume index and 8.3% (95% CI 8.2–8.4) when evaluated according to the CUN-BAE (Clínica Universitaria de Navarra Body Adiposity Estimator) formula. The prevalence of diabesity was also higher in workers with a non-heart-healthy diet and in those who did not exercise regularly. Conclusions: The most disadvantaged socioeconomic classes are those with the highest prevalence of diabesity. It is important to prioritise prevention in populations and communities with the most unfavourable social and environmental conditions to reduce the burden of diabesity.

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“…Each item was scored as "0" if favorable and "1" if "unfavorable," with a range of possible values 0 to 46. For all respondents eligible for the current study, we summed the scored responses (24,25) and ranked individuals by quartiles of the aggregate score. Higher SDoH index values represented greater cumulative social disadvantage.…”

Section: Independent Variablementioning

confidence: 99%

Cumulative Social Disadvantage and Health-Related Quality of Life: National Health Interview Survey 2013-2017

Hagan

Javed

Cainzos‐Achirica

et al. 2022

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Background: Evidence of the association between social determinants of health (SDoH) and health-related quality of life (HRQoL) is largely based on single SDoH measures, with limited evaluation of cumulative social disadvantage. We examined the association between cumulative social disadvantage and the Health and Activity Limitation Index (HALex). Methods: We used data of respondents from the 2013-2017 National Health Interview Survey. A cumulative SDoH index was created by aggregating 46 SDoH from 6 domains, and respondents were grouped by quartiles (SDoH-Q1 to Q4). A higher SDoH index or quartile indicates greater disadvantage. Two outcomes were assessed: HALex scores using two-part models, and a binary poor HALex (HALex score < 0.79 [20th percentile]) using logistic regression. Regression analyses adjusted for demographics and comorbidities and were performed in the overall sample, and by age, sex, and race/ethnicity categories. Results: Among 156,182 adults (mean age 46 years; 51.7% women), higher SDoH quartile groups averaged lower HALex scores and had higher proportions with poor HALex. A unit increase in SDoH index was associated with 0.01 decrease in HALex score (β = -0.01; 95% CI [-0.01, -0.01]) and 14% higher odds of poor HALex (odds ratio, OR = 1.14; 95% CI [1.14, 1.15]). Relative to SDoH-Q1, SDoH-Q4 was associated with HALex score decrease of -0.13 (95% CI [-0.13, -0.13]) and OR 8.67 (95% CI [8.08, 9.30]) for poor HALex. Hispanic persons, despite a relatively higher burden of cumulative social disadvantage, had a weaker SDoH-HALex association than their counterparts. Conclusion: Higher cumulative social disadvantage was independently associated with lower HRQoL in an incremental fashion. The more favorable HRQoL profile observed in the Hispanic could be related to a resilient concept of health derived from cultural values and acceptance, and this highlights the need for population wellness interventions to be responsive to such phenomenon.

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Social determinants of health and obesity: Findings from a national study of US adults

Cited by 41 publications

References 35 publications

Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach

Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach

Influence of Educational Level and Healthy Habits on the Prevalence of Diabesity in a Spanish Working Population

Cumulative Social Disadvantage and Health-Related Quality of Life: National Health Interview Survey 2013-2017

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