Obesity and obesity-related behaviors among rural and urban adults in the USA

Trivedi, Tushar; Liu, Jihong; Probst, Janice C.; Merchant, Anwar T.; Jhones, Sonya; Martin, Amy B.

doi:10.22605/rrh3267

Cited by 110 publications

(62 citation statements)

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“…Moreover, rural/urban disparities in obesity and obesity-related diseases are a public health issue in the United States. 17 , 18 , 41 A 2015 study found that obesity prevalence was 36% higher in rural areas than in the urban areas of the United States, and specific socioeconomic, cultural, and environmental factors underlying these disparities were not identified. 42 An earlier U.S. study also found a similar prevalence of obesity in rural areas (39.6%), nearly 20% higher than that identified in urban areas (33.4%).…”

Section: Introductionmentioning

confidence: 99%

Black/White Disparities in Obesity Widen with Increasing Rurality: Evidence from a National Survey

et al. 2022

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Background Racial health disparities in obesity and obesity-related conditions and behaviors are well documented, although a small body of research suggests that geographic factors (e.g., socioeconomic status [SES] and rural/urban status) may alter the magnitude of these disparities. Methods This study explored how rurality moderates black/white health disparities using a nationally representative sample from the 2012 Behavioral Risk Factor Surveillance System ( n =359,157). Respondents' county of residence was linked to the U.S. Census information to obtain the county-level Index of Relative Rurality (IRR). Weighted logistic regression was used to model obesity, diabetes, and lack of physical activity (PA) on race (black/white), IRR, and an interaction term of race and IRR, including covariates (age, sex, education, marital status, employment, and income). Results Blacks were significantly more likely to have obesity, diabetes, and a lack of PA compared with whites. Irrespective of race, rural respondents were significantly more likely to have obesity (odds ratio [OR] 1.035, confidence interval [95% CI] 1.028–1.043) and a lack of PA (OR 1.045, 95% CI 1.038–1.053) than respondents in more urban areas. For obesity and diabetes, the interaction term for black×IRR quintile was significant and positive, indicating an increase in the magnitude of the black/white disparity with increasing rurality. Discussion These findings underscore the need for policies and programs aimed to reduce racial disparities in obesity and related conditions to consider the geographic context in which these outcomes occur.

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Section: Introductionmentioning

confidence: 99%

Black/White Disparities in Obesity Widen with Increasing Rurality: Evidence from a National Survey

et al. 2022

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“…However, the bulk of the literature was based on the stable classification of urbanization [ 16 , 17 , 18 ] and was, thus, unable to obtain the association between dynamic changes in urban features and obesity. Our study extended the previous findings on the association of urbanization and obesity in the following aspects.…”

Section: Introductionmentioning

confidence: 99%

Thirty-Year Urbanization Trajectories and Obesity in Modernizing China

Wang

et al. 2022

IJERPH

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The effects of long-term urbanization changes in obesity are unclear. Data were obtained from the China Health and Nutrition Survey (CHNS) 1989–2018. A multidimensional urbanicity index was used to define the urbanization level for communities. Group-based trajectory modeling was used to identify distinct urbanization change trajectories. Gender-stratified multilevel models were used to investigate the association between urbanization trajectories and weight/BMI, through the PROC MIXED procedure, as well as the risk of being overweight + obesity (OO)/obesity (OB), through the PROC GLIMMIX procedure. A total of three patterns of the trajectory of change in urbanization were identified in 304 communities (with 1862 measurements). A total of 25.8% of communities had a low initial urbanization level and continuous increase (termed “LU”), 22.2% of communities had a low–middle initial urbanization level and constant increase (termed “LMU”), and 52.0% of communities had a middle–high initial urbanization and significant increase before 2009, followed by a stable platform since then (termed “MHU”). During the 30 follow-up years, a total of 69490 visits, contributed by 16768 adult participants, were included in the analysis. In the period, weight and BMI were observed in an increasing trend in all urbanization trajectory groups, among both men and women. Compared with LU, men living in MHU were related to higher weight, BMI, and an increased risk of OO (OR: 1.46, 95%CI: 1.26 to 1.69). No significant associations were found between urbanization trajectories and OB risk in men. Among women, the associations between urbanization and all obesity indicators became insignificant after controlling the covariates. Obesity indicators increased along with urbanization in the past thirty years in China. However, the differences among urbanization trajectories narrowed over time. More urbanized features were only significantly associated with a higher risk of obesity indicators in Chinese men. The effects of urbanization on obesity among women were buffered.

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“… 17 , 18 The reasons behind these could be the differences in population characteristics such as sociodemographic characteristics and disease conditions. 14 , 16 , 21 , 26–29 …”

Section: Discussionmentioning

confidence: 99%

Association Between Nocturnal Sleep Duration and Obesity Indicators Among People with Type 2 Diabetes: A Cross-Sectional Study in Ningbo, China

Xu¹,

Chattopadhyay²,

Qian³

et al. 2022

DMSO

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Aim The study aimed to investigate the association between the nocturnal sleep duration and five obesity indicators, namely, visceral fat area (VFA), subcutaneous fat area (SFA), bodyweight, body mass index (BMI) and waist circumference (WC), among people with type 2 diabetes mellitus (T2DM) in Ningbo, China. Methods A cross-sectional study was conducted using the National Metabolic Management Centre (MMC) - Ningbo First Hospital data from 1st March 2018 to 28th February 2021. Adults with T2DM were included in the study. Simple and multiple (adjusted for sociodemographic and lifestyle factors and health conditions) linear regression analyses were performed to identify the associations. Results In terms of VFA, SFA, bodyweight, BMI and WC, the eligibility criteria were satisfied by 2771, 2771, 2863, 2863 and 2862 patients, respectively. In the unadjusted model, the shorter nocturnal sleep duration was associated with higher VFA, SFA, bodyweight, BMI and WC. In other words, an hour increase in the nocturnal sleep duration was associated with a decrease of 2.07 cm 2 in VFA (regression coefficient = −2.07; 95% CI = −3.25 to −0.88), 2.67 cm 2 in SFA (−2.67; −4.55 to −0.78); 0.82 kg in bodyweight (−0.82; −1.2 to −0.43), 0.2 kg/m 2 in BMI (−0.2; −0.31 to −0.09) and 0.46 cm in WC (−0.46; −0.76 to −0.16). In the adjusted models, the shorter nocturnal sleep duration was still found to be associated with higher VFA, SFA, bodyweight, BMI and WC (except SFA and WC in models where we further adjusted for health conditions). Conclusion The nocturnal sleep duration among people with T2DM in Ningbo, China is negatively associated with visceral and general obesity indicators (VFA, bodyweight and BMI). Thus, there is a need for appropriate interventions to address the issue of sleep deprivation.

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Obesity and obesity-related behaviors among rural and urban adults in the USA

Cited by 110 publications

References 0 publications

Black/White Disparities in Obesity Widen with Increasing Rurality: Evidence from a National Survey

Black/White Disparities in Obesity Widen with Increasing Rurality: Evidence from a National Survey

Thirty-Year Urbanization Trajectories and Obesity in Modernizing China

Association Between Nocturnal Sleep Duration and Obesity Indicators Among People with Type 2 Diabetes: A Cross-Sectional Study in Ningbo, China

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