The Longitudinal Effect of Area Socioeconomic Changes on Obesity: a Longitudinal Cohort Study in the USA from 2003 to 2017 View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-09-19

AUTHORS

Yeonwoo Kim, Natalie Colabianchi

ABSTRACT

Despite several dimensions of area socioeconomic status (SES), past literature has been dominated by the use of area socioeconomic position. We examined the longitudinal effect of three area SES measures (i.e., socioeconomic position, inequality, and segregation) on obesity. Using longitudinal data from the Fragile Families & Child Wellbeing Study (N = 1493), we estimated a linear mixed model to examine the effect of three time-varying area SES measures on time-varying measures of objectively measured body mass index z-score (BMIz) from ages 5 years to 15 years. Findings showed that BMIz increased steadily over time (B = 0.02, 95% CI = 0.02, 0.03). A significant interaction between time and area socioeconomic position indicates that children in areas with higher socioeconomic position had a smaller increase in BMIz than those in low socioeconomic areas (B = − 0.02, 95% CI = − 0.02, − 0.01). A non-linear relationship of area income inequality with BMIz such that BMIz was higher as area income inequality was greater, but the effect diminishes in magnitude with a higher level of area income inequality (linear term: B = 0.07; quadratic term: B = − 0.03). Area income segregation was associated with greater BMIz (B = 0.08, 95% CI = 0.03, 0.12). No time interaction effect was found for area income inequality and segregation. Results highlight a need for community health policy efforts and evidence-based interventions to address childhood obesity issues in low-SES areas. More... »

PAGES

1-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11524-022-00681-z

DOI

http://dx.doi.org/10.1007/s11524-022-00681-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1151124191

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/36121565


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Kinesiology, University of Texas at Arlington, Arlington, TX, USA", 
          "id": "http://www.grid.ac/institutes/grid.267315.4", 
          "name": [
            "Department of Kinesiology, University of Texas at Arlington, Arlington, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Yeonwoo", 
        "id": "sg:person.015702056757.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015702056757.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Kinesiology, University of Michigan, Ann Arbor, MI, USA", 
          "id": "http://www.grid.ac/institutes/grid.214458.e", 
          "name": [
            "School of Kinesiology, University of Michigan, Ann Arbor, MI, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Colabianchi", 
        "givenName": "Natalie", 
        "id": "sg:person.01355132725.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01355132725.31"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00148-015-0579-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033338149", 
          "https://doi.org/10.1007/s00148-015-0579-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03405180", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077160718", 
          "https://doi.org/10.1007/bf03405180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11524-011-9604-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011325274", 
          "https://doi.org/10.1007/s11524-011-9604-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12889-017-4691-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091370862", 
          "https://doi.org/10.1186/s12889-017-4691-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11524-020-00427-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1125130523", 
          "https://doi.org/10.1007/s11524-020-00427-9"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-09-19", 
    "datePublishedReg": "2022-09-19", 
    "description": "Abstract\u00a0Despite several dimensions of area socioeconomic status (SES), past literature has been dominated by the use of area socioeconomic position. We examined the longitudinal effect of three area SES measures (i.e., socioeconomic position, inequality, and segregation) on obesity. Using longitudinal data from the Fragile Families & Child Wellbeing Study (N\u2009=\u20091493), we estimated a linear mixed model to examine the effect of three time-varying area SES measures on time-varying measures of objectively measured body mass index z-score (BMIz) from ages 5\u00a0years to 15\u00a0years. Findings showed that BMIz increased steadily over time (B\u2009=\u20090.02, 95% CI\u2009=\u20090.02, 0.03). A significant interaction between time and area socioeconomic position indicates that children in areas with higher socioeconomic position had a smaller increase in BMIz than those in low socioeconomic areas (B\u2009=\u2009\u2009\u2212\u20090.02, 95% CI\u2009=\u2009\u2009\u2212\u20090.02,\u2009\u2212\u20090.01). A non-linear relationship of area income inequality with BMIz such that BMIz was higher as area income inequality was greater, but the effect diminishes in magnitude with a higher level of area income inequality (linear term: B\u2009=\u20090.07; quadratic term: B\u2009=\u2009\u2009\u2212\u20090.03). Area income segregation was associated with greater BMIz (B\u2009=\u20090.08, 95% CI\u2009=\u20090.03, 0.12). No time interaction effect was found for area income inequality and segregation. Results highlight a need for community health policy efforts and evidence-based interventions to address childhood obesity issues in low-SES areas.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11524-022-00681-z", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.9292589", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.3804280", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2623491", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2525849", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1356564", 
        "issn": [
          "1099-3460", 
          "1468-2869"
        ], 
        "name": "Journal of Urban Health", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }
    ], 
    "keywords": [
      "area income inequality", 
      "income inequality", 
      "body mass index z-score", 
      "area socioeconomic position", 
      "SES measures", 
      "time-varying measures", 
      "socioeconomic position", 
      "greater body mass index z-score", 
      "income segregation", 
      "non-linear relationship", 
      "socioeconomic status", 
      "index z-score", 
      "longitudinal cohort study", 
      "policy efforts", 
      "area socioeconomic status", 
      "higher socioeconomic position", 
      "time interaction effects", 
      "evidence-based interventions", 
      "health policy efforts", 
      "low-SES areas", 
      "low socioeconomic areas", 
      "inequality", 
      "longitudinal effects", 
      "longitudinal data", 
      "cohort study", 
      "Child Wellbeing Study", 
      "z-score", 
      "socioeconomic changes", 
      "obesity issues", 
      "socioeconomic areas", 
      "Fragile Families", 
      "linear mixed models", 
      "age 5", 
      "past literature", 
      "obesity", 
      "Wellbeing Study", 
      "significant interaction", 
      "small increase", 
      "measures", 
      "mixed models", 
      "high levels", 
      "years", 
      "interaction effects", 
      "intervention", 
      "effect", 
      "children", 
      "study", 
      "status", 
      "USA", 
      "findings", 
      "literature", 
      "issues", 
      "model", 
      "levels", 
      "area", 
      "relationship", 
      "time", 
      "increase", 
      "changes", 
      "position", 
      "efforts", 
      "need", 
      "use", 
      "family", 
      "data", 
      "dimensions", 
      "Abstract", 
      "results", 
      "magnitude", 
      "segregation", 
      "interaction"
    ], 
    "name": "The Longitudinal Effect of Area Socioeconomic Changes on Obesity: a Longitudinal Cohort Study in the USA from 2003 to 2017", 
    "pagination": "1-12", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1151124191"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11524-022-00681-z"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "36121565"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11524-022-00681-z", 
      "https://app.dimensions.ai/details/publication/pub.1151124191"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:08", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_929.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11524-022-00681-z"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11524-022-00681-z'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11524-022-00681-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11524-022-00681-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11524-022-00681-z'


 

This table displays all metadata directly associated to this object as RDF triples.

164 TRIPLES      21 PREDICATES      99 URIs      86 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11524-022-00681-z schema:about anzsrc-for:11
2 anzsrc-for:1117
3 schema:author Na0ff8244bba14b73af11a50ac8836c19
4 schema:citation sg:pub.10.1007/bf03405180
5 sg:pub.10.1007/s00148-015-0579-3
6 sg:pub.10.1007/s11524-011-9604-3
7 sg:pub.10.1007/s11524-020-00427-9
8 sg:pub.10.1186/s12889-017-4691-z
9 schema:datePublished 2022-09-19
10 schema:datePublishedReg 2022-09-19
11 schema:description Abstract Despite several dimensions of area socioeconomic status (SES), past literature has been dominated by the use of area socioeconomic position. We examined the longitudinal effect of three area SES measures (i.e., socioeconomic position, inequality, and segregation) on obesity. Using longitudinal data from the Fragile Families & Child Wellbeing Study (N = 1493), we estimated a linear mixed model to examine the effect of three time-varying area SES measures on time-varying measures of objectively measured body mass index z-score (BMIz) from ages 5 years to 15 years. Findings showed that BMIz increased steadily over time (B = 0.02, 95% CI = 0.02, 0.03). A significant interaction between time and area socioeconomic position indicates that children in areas with higher socioeconomic position had a smaller increase in BMIz than those in low socioeconomic areas (B =  − 0.02, 95% CI =  − 0.02, − 0.01). A non-linear relationship of area income inequality with BMIz such that BMIz was higher as area income inequality was greater, but the effect diminishes in magnitude with a higher level of area income inequality (linear term: B = 0.07; quadratic term: B =  − 0.03). Area income segregation was associated with greater BMIz (B = 0.08, 95% CI = 0.03, 0.12). No time interaction effect was found for area income inequality and segregation. Results highlight a need for community health policy efforts and evidence-based interventions to address childhood obesity issues in low-SES areas.
12 schema:genre article
13 schema:isAccessibleForFree false
14 schema:isPartOf sg:journal.1356564
15 schema:keywords Abstract
16 Child Wellbeing Study
17 Fragile Families
18 SES measures
19 USA
20 Wellbeing Study
21 age 5
22 area
23 area income inequality
24 area socioeconomic position
25 area socioeconomic status
26 body mass index z-score
27 changes
28 children
29 cohort study
30 data
31 dimensions
32 effect
33 efforts
34 evidence-based interventions
35 family
36 findings
37 greater body mass index z-score
38 health policy efforts
39 high levels
40 higher socioeconomic position
41 income inequality
42 income segregation
43 increase
44 index z-score
45 inequality
46 interaction
47 interaction effects
48 intervention
49 issues
50 levels
51 linear mixed models
52 literature
53 longitudinal cohort study
54 longitudinal data
55 longitudinal effects
56 low socioeconomic areas
57 low-SES areas
58 magnitude
59 measures
60 mixed models
61 model
62 need
63 non-linear relationship
64 obesity
65 obesity issues
66 past literature
67 policy efforts
68 position
69 relationship
70 results
71 segregation
72 significant interaction
73 small increase
74 socioeconomic areas
75 socioeconomic changes
76 socioeconomic position
77 socioeconomic status
78 status
79 study
80 time
81 time interaction effects
82 time-varying measures
83 use
84 years
85 z-score
86 schema:name The Longitudinal Effect of Area Socioeconomic Changes on Obesity: a Longitudinal Cohort Study in the USA from 2003 to 2017
87 schema:pagination 1-12
88 schema:productId N7190504425b742b18dc1fe8abe0a7c91
89 N9cf6a01692744468b2698dcbe3a75078
90 Na71b3a474cbf43bab2f9431288804ccc
91 schema:sameAs https://app.dimensions.ai/details/publication/pub.1151124191
92 https://doi.org/10.1007/s11524-022-00681-z
93 schema:sdDatePublished 2022-11-24T21:08
94 schema:sdLicense https://scigraph.springernature.com/explorer/license/
95 schema:sdPublisher N7b6ed8c690844751ada23da62c8b138a
96 schema:url https://doi.org/10.1007/s11524-022-00681-z
97 sgo:license sg:explorer/license/
98 sgo:sdDataset articles
99 rdf:type schema:ScholarlyArticle
100 N34b84f02852a4a519e6b7ab71b6c0183 rdf:first sg:person.01355132725.31
101 rdf:rest rdf:nil
102 N7190504425b742b18dc1fe8abe0a7c91 schema:name doi
103 schema:value 10.1007/s11524-022-00681-z
104 rdf:type schema:PropertyValue
105 N7b6ed8c690844751ada23da62c8b138a schema:name Springer Nature - SN SciGraph project
106 rdf:type schema:Organization
107 N9cf6a01692744468b2698dcbe3a75078 schema:name pubmed_id
108 schema:value 36121565
109 rdf:type schema:PropertyValue
110 Na0ff8244bba14b73af11a50ac8836c19 rdf:first sg:person.015702056757.31
111 rdf:rest N34b84f02852a4a519e6b7ab71b6c0183
112 Na71b3a474cbf43bab2f9431288804ccc schema:name dimensions_id
113 schema:value pub.1151124191
114 rdf:type schema:PropertyValue
115 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
116 schema:name Medical and Health Sciences
117 rdf:type schema:DefinedTerm
118 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
119 schema:name Public Health and Health Services
120 rdf:type schema:DefinedTerm
121 sg:grant.2525849 http://pending.schema.org/fundedItem sg:pub.10.1007/s11524-022-00681-z
122 rdf:type schema:MonetaryGrant
123 sg:grant.2623491 http://pending.schema.org/fundedItem sg:pub.10.1007/s11524-022-00681-z
124 rdf:type schema:MonetaryGrant
125 sg:grant.3804280 http://pending.schema.org/fundedItem sg:pub.10.1007/s11524-022-00681-z
126 rdf:type schema:MonetaryGrant
127 sg:grant.9292589 http://pending.schema.org/fundedItem sg:pub.10.1007/s11524-022-00681-z
128 rdf:type schema:MonetaryGrant
129 sg:journal.1356564 schema:issn 1099-3460
130 1468-2869
131 schema:name Journal of Urban Health
132 schema:publisher Springer Nature
133 rdf:type schema:Periodical
134 sg:person.01355132725.31 schema:affiliation grid-institutes:grid.214458.e
135 schema:familyName Colabianchi
136 schema:givenName Natalie
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01355132725.31
138 rdf:type schema:Person
139 sg:person.015702056757.31 schema:affiliation grid-institutes:grid.267315.4
140 schema:familyName Kim
141 schema:givenName Yeonwoo
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015702056757.31
143 rdf:type schema:Person
144 sg:pub.10.1007/bf03405180 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077160718
145 https://doi.org/10.1007/bf03405180
146 rdf:type schema:CreativeWork
147 sg:pub.10.1007/s00148-015-0579-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033338149
148 https://doi.org/10.1007/s00148-015-0579-3
149 rdf:type schema:CreativeWork
150 sg:pub.10.1007/s11524-011-9604-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011325274
151 https://doi.org/10.1007/s11524-011-9604-3
152 rdf:type schema:CreativeWork
153 sg:pub.10.1007/s11524-020-00427-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125130523
154 https://doi.org/10.1007/s11524-020-00427-9
155 rdf:type schema:CreativeWork
156 sg:pub.10.1186/s12889-017-4691-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1091370862
157 https://doi.org/10.1186/s12889-017-4691-z
158 rdf:type schema:CreativeWork
159 grid-institutes:grid.214458.e schema:alternateName School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
160 schema:name School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
161 rdf:type schema:Organization
162 grid-institutes:grid.267315.4 schema:alternateName Department of Kinesiology, University of Texas at Arlington, Arlington, TX, USA
163 schema:name Department of Kinesiology, University of Texas at Arlington, Arlington, TX, USA
164 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...