Using aggregate geographic data to proxy individual socioeconomic status: does size matter?
This study assessed whether aggregate-level measures of socioeconomic status (SES) are less biased as proxies for individual-level measures if the unit of geographic aggregation is small in size and population. National Health Interview Survey and census data were used to replicate analyses that ide...
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Veröffentlicht in: | American journal of public health (1971) 2001-04, Vol.91 (4), p.632-636 |
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creator | Soobader, M LeClere, FB Hadden, W Maury, B |
description | This study assessed whether aggregate-level measures of socioeconomic status (SES) are less biased as proxies for individual-level measures if the unit of geographic aggregation is small in size and population.
National Health Interview Survey and census data were used to replicate analyses that identified the degree to which aggregate proxies of individual SES bias interpretations of the effects of SES on health.
Ordinary least squares regressions on self-perceived health showed that the coefficients for income and education measured at the tract and block group levels were larger than those at the individual level but smaller than those estimated by Geronimus et al. at the zip code level.
Researchers should be cautious about use of proxy measurement of individual SES even if proxies are calculated from small geographic units. |
doi_str_mv | 10.2105/AJPH.91.4.632 |
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National Health Interview Survey and census data were used to replicate analyses that identified the degree to which aggregate proxies of individual SES bias interpretations of the effects of SES on health.
Ordinary least squares regressions on self-perceived health showed that the coefficients for income and education measured at the tract and block group levels were larger than those at the individual level but smaller than those estimated by Geronimus et al. at the zip code level.
Researchers should be cautious about use of proxy measurement of individual SES even if proxies are calculated from small geographic units.</description><identifier>ISSN: 0090-0036</identifier><identifier>EISSN: 1541-0048</identifier><identifier>DOI: 10.2105/AJPH.91.4.632</identifier><identifier>PMID: 11291379</identifier><identifier>CODEN: AJPEAG</identifier><language>eng</language><publisher>Washington, DC: Am Public Health Assoc</publisher><subject>Aggregate level data ; Aggregates ; Bias ; Biological and medical sciences ; Census ; Censuses ; Data Collection ; Demographics ; Education ; Educational attainment ; Epidemiology ; Estimates ; General aspects ; Geography ; Health ; Health Status Indicators ; Hispanic people ; Humans ; Medical sciences ; Methodology ; Postal codes ; Proxies ; Public health ; Public health. Hygiene ; Public health. Hygiene-occupational medicine ; Research Design ; Selection Bias ; Social Class ; Socioeconomic factors ; Socioeconomic status ; United States ; USA ; Variables</subject><ispartof>American journal of public health (1971), 2001-04, Vol.91 (4), p.632-636</ispartof><rights>2001 INIST-CNRS</rights><rights>Copyright American Public Health Association Apr 2001</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c500t-fac4147d3a24d415e0e0a35db0a8154b2a04499fd32e1db50c5ece6b3f1231923</citedby><cites>FETCH-LOGICAL-c500t-fac4147d3a24d415e0e0a35db0a8154b2a04499fd32e1db50c5ece6b3f1231923</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1446644/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1446644/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27865,27923,27924,30999,53790,53792</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=972938$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11291379$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Soobader, M</creatorcontrib><creatorcontrib>LeClere, FB</creatorcontrib><creatorcontrib>Hadden, W</creatorcontrib><creatorcontrib>Maury, B</creatorcontrib><title>Using aggregate geographic data to proxy individual socioeconomic status: does size matter?</title><title>American journal of public health (1971)</title><addtitle>Am J Public Health</addtitle><description>This study assessed whether aggregate-level measures of socioeconomic status (SES) are less biased as proxies for individual-level measures if the unit of geographic aggregation is small in size and population.
National Health Interview Survey and census data were used to replicate analyses that identified the degree to which aggregate proxies of individual SES bias interpretations of the effects of SES on health.
Ordinary least squares regressions on self-perceived health showed that the coefficients for income and education measured at the tract and block group levels were larger than those at the individual level but smaller than those estimated by Geronimus et al. at the zip code level.
Researchers should be cautious about use of proxy measurement of individual SES even if proxies are calculated from small geographic units.</description><subject>Aggregate level data</subject><subject>Aggregates</subject><subject>Bias</subject><subject>Biological and medical sciences</subject><subject>Census</subject><subject>Censuses</subject><subject>Data Collection</subject><subject>Demographics</subject><subject>Education</subject><subject>Educational attainment</subject><subject>Epidemiology</subject><subject>Estimates</subject><subject>General aspects</subject><subject>Geography</subject><subject>Health</subject><subject>Health Status Indicators</subject><subject>Hispanic people</subject><subject>Humans</subject><subject>Medical sciences</subject><subject>Methodology</subject><subject>Postal codes</subject><subject>Proxies</subject><subject>Public health</subject><subject>Public health. Hygiene</subject><subject>Public health. Hygiene-occupational medicine</subject><subject>Research Design</subject><subject>Selection Bias</subject><subject>Social Class</subject><subject>Socioeconomic factors</subject><subject>Socioeconomic status</subject><subject>United States</subject><subject>USA</subject><subject>Variables</subject><issn>0090-0036</issn><issn>1541-0048</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>7TQ</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><sourceid>7QJ</sourceid><recordid>eNqF0bFv1DAUBvAIgehRGFlRBBJbjvdiOzkzgKqKUlAlGOjEYL04TuJTEh-2Uyh_fV31dAUWJlvyT5_9_GXZc4R1iSDenHz-er6WuObripUPshUKjgUA3zzMVgAS0p5VR9mTELYAiFLg4-wIsZTIarnKvl8GO_c59b03PUWT98b1nnaD1XlLkfLo8p13v65zO7f2yrYLjXlw2jqj3eymxEKkuIS3eetMyIP9bfKJYjT-_dPsUUdjMM_263F2efbh2-l5cfHl46fTk4tCC4BYdKQ58rplVPKWozBggJhoG6BNmqYpCTiXsmtZabBtBGhhtKka1mHJUJbsOHt3l7tbmsm02szR06h23k7kr5Ujq_4-me2genelkPOq4jwFvN4HePdjMSGqyQZtxpFm45ag6hrYBqH-LxR1nRIFJvjyH7h1i5_TL6gSBQKXUCVU3CHtXQjedIcnI6jbctVtuUqi4iqVm_yLP-e81_s2E3i1BxQ0jZ2nWdtwcLIuJdvcDzHYfvhpvVFhonFMoahouxsO990ARhK6hg</recordid><startdate>20010401</startdate><enddate>20010401</enddate><creator>Soobader, M</creator><creator>LeClere, FB</creator><creator>Hadden, W</creator><creator>Maury, B</creator><general>Am Public Health Assoc</general><general>American Public Health Association</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7RV</scope><scope>7TQ</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88A</scope><scope>88C</scope><scope>88E</scope><scope>88G</scope><scope>88I</scope><scope>88J</scope><scope>8AF</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DHY</scope><scope>DON</scope><scope>DPSOV</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>KC-</scope><scope>L.-</scope><scope>L.0</scope><scope>LK8</scope><scope>M0C</scope><scope>M0R</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2L</scope><scope>M2M</scope><scope>M2O</scope><scope>M2P</scope><scope>M2R</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>7QJ</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20010401</creationdate><title>Using aggregate geographic data to proxy individual socioeconomic status: does size matter?</title><author>Soobader, M ; LeClere, FB ; Hadden, W ; Maury, B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c500t-fac4147d3a24d415e0e0a35db0a8154b2a04499fd32e1db50c5ece6b3f1231923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Aggregate level data</topic><topic>Aggregates</topic><topic>Bias</topic><topic>Biological and medical sciences</topic><topic>Census</topic><topic>Censuses</topic><topic>Data Collection</topic><topic>Demographics</topic><topic>Education</topic><topic>Educational attainment</topic><topic>Epidemiology</topic><topic>Estimates</topic><topic>General aspects</topic><topic>Geography</topic><topic>Health</topic><topic>Health Status Indicators</topic><topic>Hispanic people</topic><topic>Humans</topic><topic>Medical sciences</topic><topic>Methodology</topic><topic>Postal codes</topic><topic>Proxies</topic><topic>Public health</topic><topic>Public health. 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Health</addtitle><date>2001-04-01</date><risdate>2001</risdate><volume>91</volume><issue>4</issue><spage>632</spage><epage>636</epage><pages>632-636</pages><issn>0090-0036</issn><eissn>1541-0048</eissn><coden>AJPEAG</coden><abstract>This study assessed whether aggregate-level measures of socioeconomic status (SES) are less biased as proxies for individual-level measures if the unit of geographic aggregation is small in size and population.
National Health Interview Survey and census data were used to replicate analyses that identified the degree to which aggregate proxies of individual SES bias interpretations of the effects of SES on health.
Ordinary least squares regressions on self-perceived health showed that the coefficients for income and education measured at the tract and block group levels were larger than those at the individual level but smaller than those estimated by Geronimus et al. at the zip code level.
Researchers should be cautious about use of proxy measurement of individual SES even if proxies are calculated from small geographic units.</abstract><cop>Washington, DC</cop><pub>Am Public Health Assoc</pub><pmid>11291379</pmid><doi>10.2105/AJPH.91.4.632</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; PAIS Index; Business Source Complete; Applied Social Sciences Index & Abstracts (ASSIA); EBSCOhost Education Source; PubMed Central; Alma/SFX Local Collection |
subjects | Aggregate level data Aggregates Bias Biological and medical sciences Census Censuses Data Collection Demographics Education Educational attainment Epidemiology Estimates General aspects Geography Health Health Status Indicators Hispanic people Humans Medical sciences Methodology Postal codes Proxies Public health Public health. Hygiene Public health. Hygiene-occupational medicine Research Design Selection Bias Social Class Socioeconomic factors Socioeconomic status United States USA Variables |
title | Using aggregate geographic data to proxy individual socioeconomic status: does size matter? |
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