Dataset Information

Trends in Socioeconomic Disparities in Obesity Prevalence among Low-Income Children Aged 2-4 Years in Los Angeles County, 2003-2014.

ABSTRACT:

Background

Obesity prevalence among low-income preschool-aged children in the United States decreased between 2010 and 2014. However, this decreasing trend may be varied across socioeconomic subgroups. This study examined trends in obesity prevalence among low-income children from 2003 to 2014 by child's age and household and neighborhood socioeconomic status (SES).

Methods

This study used administrative data for all children, aged 2-4 years, participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) in Los Angeles County (LAC) during the years 2003-2014. Obesity was defined as having a BMI ?95th percentile of CDC's age- and sex-specific growth charts. Household income and household educational attainment were indicators of household SES. Neighborhood median household income was an indicator of neighborhood SES.

Results

Childhood obesity prevalence increased sharply from 15.7% in 2003 to 19.1% in 2005, and remained constant until 2010, when it started decreasing. During most years, the prevalence of obesity was higher among the lowest SES groups. Despite the recent decreasing trend, the 2014 estimates were still generally higher than the 2003 levels except among some low-income children living in less-poor and more-educated households.

Conclusions

The decreasing trend between 2010 and 2014 among WIC-participating children in LAC is encouraging and mirrors national trends among WIC-participating children. However, continued efforts should be made to focus obesity prevention efforts on low-income children, especially those who are the most vulnerable as they have experienced significant gains in obesity since 2003.

SUBMITTER: Nobari TZ

PROVIDER: S-EPMC6001382 | biostudies-literature | 2018 May/Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Trends in Socioeconomic Disparities in Obesity Prevalence among Low-Income Children Aged 2-4 Years in Los Angeles County, 2003-2014.

Nobari Tabashir Z TZ Whaley Shannon E SE Prelip Michael L ML Crespi Catherine M CM Wang May C MC

Childhood obesity (Print) 20180509 4

<h4>Background</h4>Obesity prevalence among low-income preschool-aged children in the United States decreased between 2010 and 2014. However, this decreasing trend may be varied across socioeconomic subgroups. This study examined trends in obesity prevalence among low-income children from 2003 to 2014 by child's age and household and neighborhood socioeconomic status (SES).<h4>Methods</h4>This study used administrative data for all children, aged 2-4 years, participating in the Special Supplemen ...[more]

PMID: 29741920

Similar Datasets

Project description:ImportanceDescribing the changes in trauma volume and injury patterns during the course of the coronavirus disease 2019 (COVID-19) pandemic could help to inform policy development and hospital resource planning.ObjectiveTo examine trends in trauma admissions throughout Los Angeles County (LAC) during the pandemic.Design, setting, and participantsIn this cohort study, all trauma admissions to the 15 verified level 1 and level 2 trauma centers in LAC from January 1 to June 7, 2020 were reviewed. All trauma admissions from the same period in 2019 were used as historical control. For overall admissions, the study period was divided into 3 intervals based on daily admission trend analysis (January 1 through February 28, March 1 through April 9, April 10 through June 7). For the blunt trauma subgroup analysis, the study period was divided into 3 similar intervals (January 1 through February 27, February 28 through April 5, April 6 through June 7).ExposuresCOVID-19 pandemic.Main outcomes and measuresTrends in trauma admission volume and injury patterns.ResultsA total of 6777 patients in 2020 and 6937 in 2019 met inclusion criteria. Of those admitted in 2020, the median (interquartile range) age was 42 (28-61) years and 5100 (75.3%) were men. Mechanisms of injury were significantly different between the 2 years, with a higher incidence of penetrating trauma and fewer blunt injuries in 2020 compared with 2019 (penetrating: 1065 [15.7%] vs 1065 [15.4%]; blunt: 5309 [78.3%] vs 5528 [79.7%]). Overall admissions by interval in 2020 were 2681, 1684, and 2412, whereas in 2019 they were 2462, 1862, and 2613, respectively. There was a significant increase in overall admissions per week during the first interval (incidence rate ratio [IRR], 1.02; 95% CI, 1.002-1.04; P = .03) followed by a decrease in the second interval (IRR, 0.92; 95% CI, 0.90-0.94; P < .001) and, finally, an increase in the third interval (IRR, 1.05; CI, 1.03-1.07; P < .001). On subgroup analysis, blunt admissions followed a similar pattern to overall admissions, while penetrating admissions increased throughout the study period.Conclusions and relevanceIn this study, trauma centers throughout LAC experienced a significant change in injury patterns and admission trends during the COVID-19 pandemic. A transient decrease in volume was followed by a quick return to baseline levels. Trauma centers should prioritize maintaining access, capacity, and functionality during pandemics and other national emergencies.

Project description:BackgroundCompared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.ObjectivesThis study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.MethodsSES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.ResultsThe accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.ConclusionStreet view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Dataset Information

Trends in Socioeconomic Disparities in Obesity Prevalence among Low-Income Children Aged 2-4 Years in Los Angeles County, 2003-2014.

Background

Methods

Results

Conclusions

Publications

Trends in Socioeconomic Disparities in Obesity Prevalence among Low-Income Children Aged 2-4 Years in Los Angeles County, 2003-2014.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets