To what extent do biomarkers account for the large social disparities in health in Moscow?
The Russian population continues to face political and economic challenges, has experienced poor general health and high mortality for decades, and has exhibited widening health disparities. The physiological factors underlying links between health and socioeconomic position in the Russian populatio...
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description | The Russian population continues to face political and economic challenges, has experienced poor general health and high mortality for decades, and has exhibited widening health disparities. The physiological factors underlying links between health and socioeconomic position in the Russian population are therefore an important topic to investigate. We used data from a population-based survey of Moscow residents aged 55 and older (n = 1495), fielded between December 2006 and June 2009, to address two questions. First, are social disparities evident across different clusters of biomarkers? Second, does biological risk mediate the link between socioeconomic status and health?
Health outcomes included subscales for general health, physical function, and bodily pain. Socioeconomic status was represented by education and an index of material resources. Biological risk was measured by 20 biomarkers including cardiovascular, inflammatory, and neuroendocrine markers as well as heart rate parameters from 24-h ECG monitoring.
For both sexes, the age-adjusted educational disparity in standard cardiovascular risk factors was substantial (men: standardized β = −0.16, 95% CI = −0.23 to −0.09; women: β = −0.25, CI = −0.32 to −0.18). Education differences in inflammation were also evident in both men (β = −0.17, CI = −0.25 to −0.09) and women (β = −0.09, CI = −0.17 to −0.01). Heart rate parameters differed by education only in men (β = −0.10, CI = −0.18 to −0.02). The associations between material resources and biological risk scores were generally weaker than those for education. Social disparities in neuroendocrine markers were negligible for men and women.
In terms of mediating effects, biological risk accounted for more of the education gap in general health and physical function (19–36%) than in bodily pain (12–18%). Inclusion of inflammatory markers and heart rate parameters—which were important predictors of health outcomes—may explain how we accounted for more of the social disparities than previous studies.
► Older Muscovites of both sexes exhibited substantial educational disparities in standard cardiovascular risk factors. ► Both sexes had an educational gradient with inflammatory markers; men had an educational gradient with heart rate parameters. ► Overall, biomarkers accounted for 19–36% of the education gap in general health and physical function. ► This share is larger than that found in prior studies, perhaps owing to the inclusion of inflammatory and hear |
doi_str_mv | 10.1016/j.socscimed.2012.11.022 |
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Health outcomes included subscales for general health, physical function, and bodily pain. Socioeconomic status was represented by education and an index of material resources. Biological risk was measured by 20 biomarkers including cardiovascular, inflammatory, and neuroendocrine markers as well as heart rate parameters from 24-h ECG monitoring.
For both sexes, the age-adjusted educational disparity in standard cardiovascular risk factors was substantial (men: standardized β = −0.16, 95% CI = −0.23 to −0.09; women: β = −0.25, CI = −0.32 to −0.18). Education differences in inflammation were also evident in both men (β = −0.17, CI = −0.25 to −0.09) and women (β = −0.09, CI = −0.17 to −0.01). Heart rate parameters differed by education only in men (β = −0.10, CI = −0.18 to −0.02). The associations between material resources and biological risk scores were generally weaker than those for education. Social disparities in neuroendocrine markers were negligible for men and women.
In terms of mediating effects, biological risk accounted for more of the education gap in general health and physical function (19–36%) than in bodily pain (12–18%). Inclusion of inflammatory markers and heart rate parameters—which were important predictors of health outcomes—may explain how we accounted for more of the social disparities than previous studies.
► Older Muscovites of both sexes exhibited substantial educational disparities in standard cardiovascular risk factors. ► Both sexes had an educational gradient with inflammatory markers; men had an educational gradient with heart rate parameters. ► Overall, biomarkers accounted for 19–36% of the education gap in general health and physical function. ► This share is larger than that found in prior studies, perhaps owing to the inclusion of inflammatory and heart parameters. ► These two sets of markers appeared to be important predictors of health outcomes.</description><identifier>ISSN: 0277-9536</identifier><identifier>EISSN: 1873-5347</identifier><identifier>DOI: 10.1016/j.socscimed.2012.11.022</identifier><identifier>PMID: 23228966</identifier><identifier>CODEN: SSMDEP</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Aged ; Aged, 80 and over ; Biological markers ; Biomarkers ; Cardiovascular Diseases ; Cluster analysis ; Education ; Female ; Health ; Health education ; Health risk assessment ; Health Status Disparities ; Health Surveys ; Heart Rate ; Humans ; Inflammation ; Male ; Middle Aged ; Mortality ; Moscow ; Neurosecretory Systems ; Risk Factors ; Russia ; Russian Federation ; Social Class ; Social sciences ; Socioeconomic factors ; Socioeconomic status</subject><ispartof>Social science & medicine (1982), 2013-01, Vol.77, p.164-172</ispartof><rights>2012 Elsevier Ltd</rights><rights>Copyright © 2012 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Pergamon Press Inc. Jan 2013</rights><rights>2012 Elsevier Ltd. All rights reserved. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c536t-9231640e0be440b143d8772f6ff04db03370772f8f58ee3e476d100b8fdcff93</citedby><cites>FETCH-LOGICAL-c536t-9231640e0be440b143d8772f6ff04db03370772f8f58ee3e476d100b8fdcff93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S027795361200771X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3536,27903,27904,33753,65309</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23228966$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Glei, Dana A.</creatorcontrib><creatorcontrib>Goldman, Noreen</creatorcontrib><creatorcontrib>Shkolnikov, Vladimir M.</creatorcontrib><creatorcontrib>Jdanov, Dmitri</creatorcontrib><creatorcontrib>Shalnova, Svetlana</creatorcontrib><creatorcontrib>Shkolnikova, Maria</creatorcontrib><creatorcontrib>Weinstein, Maxine</creatorcontrib><title>To what extent do biomarkers account for the large social disparities in health in Moscow?</title><title>Social science & medicine (1982)</title><addtitle>Soc Sci Med</addtitle><description>The Russian population continues to face political and economic challenges, has experienced poor general health and high mortality for decades, and has exhibited widening health disparities. The physiological factors underlying links between health and socioeconomic position in the Russian population are therefore an important topic to investigate. We used data from a population-based survey of Moscow residents aged 55 and older (n = 1495), fielded between December 2006 and June 2009, to address two questions. First, are social disparities evident across different clusters of biomarkers? Second, does biological risk mediate the link between socioeconomic status and health?
Health outcomes included subscales for general health, physical function, and bodily pain. Socioeconomic status was represented by education and an index of material resources. Biological risk was measured by 20 biomarkers including cardiovascular, inflammatory, and neuroendocrine markers as well as heart rate parameters from 24-h ECG monitoring.
For both sexes, the age-adjusted educational disparity in standard cardiovascular risk factors was substantial (men: standardized β = −0.16, 95% CI = −0.23 to −0.09; women: β = −0.25, CI = −0.32 to −0.18). Education differences in inflammation were also evident in both men (β = −0.17, CI = −0.25 to −0.09) and women (β = −0.09, CI = −0.17 to −0.01). Heart rate parameters differed by education only in men (β = −0.10, CI = −0.18 to −0.02). The associations between material resources and biological risk scores were generally weaker than those for education. Social disparities in neuroendocrine markers were negligible for men and women.
In terms of mediating effects, biological risk accounted for more of the education gap in general health and physical function (19–36%) than in bodily pain (12–18%). Inclusion of inflammatory markers and heart rate parameters—which were important predictors of health outcomes—may explain how we accounted for more of the social disparities than previous studies.
► Older Muscovites of both sexes exhibited substantial educational disparities in standard cardiovascular risk factors. ► Both sexes had an educational gradient with inflammatory markers; men had an educational gradient with heart rate parameters. ► Overall, biomarkers accounted for 19–36% of the education gap in general health and physical function. ► This share is larger than that found in prior studies, perhaps owing to the inclusion of inflammatory and heart parameters. ► These two sets of markers appeared to be important predictors of health outcomes.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Cardiovascular Diseases</subject><subject>Cluster analysis</subject><subject>Education</subject><subject>Female</subject><subject>Health</subject><subject>Health education</subject><subject>Health risk assessment</subject><subject>Health Status Disparities</subject><subject>Health Surveys</subject><subject>Heart Rate</subject><subject>Humans</subject><subject>Inflammation</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Moscow</subject><subject>Neurosecretory Systems</subject><subject>Risk Factors</subject><subject>Russia</subject><subject>Russian Federation</subject><subject>Social Class</subject><subject>Social sciences</subject><subject>Socioeconomic factors</subject><subject>Socioeconomic status</subject><issn>0277-9536</issn><issn>1873-5347</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BHHNA</sourceid><recordid>eNqNkctu1DAUhi1ERYfCK4AlNt0k9S22swFVFTepiM2s2FiOc9x4yMSDnbTw9ng0ZUS7AXnh23f-c_kRek1JTQmVF5s6R5dd2EJfM0JZTWlNGHuCVlQrXjVcqKdoRZhSVdtweYqe57whhFCi-TN0yjhjupVyhb6tI74b7Izh5wzTjPuIuxC3Nn2HlLF1Li7l1ceE5wHwaNMN4JI62BH3Ie9sCnOAjMOEB7DjPOxPX2J28e7dC3Ti7Zjh5f1-htYf3q-vPlXXXz9-vrq8rlypbK5axqkUBEgHQpCOCt5rpZiX3hPRd4RzRfZ37RsNwEEo2VNCOu17533Lz9Dbg-xu6co4XOki2dHsUihd_DLRBvPwZwqDuYm3hjdCsFYXgfN7gRR_LJBnsw3ZwTjaCeKSDWW6LMmU-A9U8UZJSVRB3zxCN3FJUxlEoaQQVGvJCqUOlEsx5wT-WDclZu-02Zij02bvtKHUFKdL5Ku_2z7G_bG2AJcHAMrsbwMkU1RgctCHBG42fQz_TPIb9RC-cA</recordid><startdate>20130101</startdate><enddate>20130101</enddate><creator>Glei, Dana A.</creator><creator>Goldman, Noreen</creator><creator>Shkolnikov, Vladimir M.</creator><creator>Jdanov, Dmitri</creator><creator>Shalnova, Svetlana</creator><creator>Shkolnikova, Maria</creator><creator>Weinstein, Maxine</creator><general>Elsevier Ltd</general><general>Pergamon Press Inc</general><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>7U3</scope><scope>7U4</scope><scope>8BJ</scope><scope>BHHNA</scope><scope>DWI</scope><scope>FQK</scope><scope>JBE</scope><scope>K9.</scope><scope>WZK</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20130101</creationdate><title>To what extent do biomarkers account for the large social disparities in health in Moscow?</title><author>Glei, Dana A. ; Goldman, Noreen ; Shkolnikov, Vladimir M. ; Jdanov, Dmitri ; Shalnova, Svetlana ; Shkolnikova, Maria ; Weinstein, Maxine</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c536t-9231640e0be440b143d8772f6ff04db03370772f8f58ee3e476d100b8fdcff93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Cardiovascular Diseases</topic><topic>Cluster analysis</topic><topic>Education</topic><topic>Female</topic><topic>Health</topic><topic>Health education</topic><topic>Health risk assessment</topic><topic>Health Status Disparities</topic><topic>Health Surveys</topic><topic>Heart Rate</topic><topic>Humans</topic><topic>Inflammation</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Mortality</topic><topic>Moscow</topic><topic>Neurosecretory Systems</topic><topic>Risk Factors</topic><topic>Russia</topic><topic>Russian Federation</topic><topic>Social Class</topic><topic>Social sciences</topic><topic>Socioeconomic factors</topic><topic>Socioeconomic status</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Glei, Dana A.</creatorcontrib><creatorcontrib>Goldman, Noreen</creatorcontrib><creatorcontrib>Shkolnikov, Vladimir M.</creatorcontrib><creatorcontrib>Jdanov, Dmitri</creatorcontrib><creatorcontrib>Shalnova, Svetlana</creatorcontrib><creatorcontrib>Shkolnikova, Maria</creatorcontrib><creatorcontrib>Weinstein, Maxine</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Social Services Abstracts</collection><collection>Sociological Abstracts (pre-2017)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Sociological Abstracts</collection><collection>Sociological Abstracts</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Sociological Abstracts (Ovid)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Social science & medicine (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Glei, Dana A.</au><au>Goldman, Noreen</au><au>Shkolnikov, Vladimir M.</au><au>Jdanov, Dmitri</au><au>Shalnova, Svetlana</au><au>Shkolnikova, Maria</au><au>Weinstein, Maxine</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>To what extent do biomarkers account for the large social disparities in health in Moscow?</atitle><jtitle>Social science & medicine (1982)</jtitle><addtitle>Soc Sci Med</addtitle><date>2013-01-01</date><risdate>2013</risdate><volume>77</volume><spage>164</spage><epage>172</epage><pages>164-172</pages><issn>0277-9536</issn><eissn>1873-5347</eissn><coden>SSMDEP</coden><abstract>The Russian population continues to face political and economic challenges, has experienced poor general health and high mortality for decades, and has exhibited widening health disparities. The physiological factors underlying links between health and socioeconomic position in the Russian population are therefore an important topic to investigate. We used data from a population-based survey of Moscow residents aged 55 and older (n = 1495), fielded between December 2006 and June 2009, to address two questions. First, are social disparities evident across different clusters of biomarkers? Second, does biological risk mediate the link between socioeconomic status and health?
Health outcomes included subscales for general health, physical function, and bodily pain. Socioeconomic status was represented by education and an index of material resources. Biological risk was measured by 20 biomarkers including cardiovascular, inflammatory, and neuroendocrine markers as well as heart rate parameters from 24-h ECG monitoring.
For both sexes, the age-adjusted educational disparity in standard cardiovascular risk factors was substantial (men: standardized β = −0.16, 95% CI = −0.23 to −0.09; women: β = −0.25, CI = −0.32 to −0.18). Education differences in inflammation were also evident in both men (β = −0.17, CI = −0.25 to −0.09) and women (β = −0.09, CI = −0.17 to −0.01). Heart rate parameters differed by education only in men (β = −0.10, CI = −0.18 to −0.02). The associations between material resources and biological risk scores were generally weaker than those for education. Social disparities in neuroendocrine markers were negligible for men and women.
In terms of mediating effects, biological risk accounted for more of the education gap in general health and physical function (19–36%) than in bodily pain (12–18%). Inclusion of inflammatory markers and heart rate parameters—which were important predictors of health outcomes—may explain how we accounted for more of the social disparities than previous studies.
► Older Muscovites of both sexes exhibited substantial educational disparities in standard cardiovascular risk factors. ► Both sexes had an educational gradient with inflammatory markers; men had an educational gradient with heart rate parameters. ► Overall, biomarkers accounted for 19–36% of the education gap in general health and physical function. ► This share is larger than that found in prior studies, perhaps owing to the inclusion of inflammatory and heart parameters. ► These two sets of markers appeared to be important predictors of health outcomes.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>23228966</pmid><doi>10.1016/j.socscimed.2012.11.022</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Aged Aged, 80 and over Biological markers Biomarkers Cardiovascular Diseases Cluster analysis Education Female Health Health education Health risk assessment Health Status Disparities Health Surveys Heart Rate Humans Inflammation Male Middle Aged Mortality Moscow Neurosecretory Systems Risk Factors Russia Russian Federation Social Class Social sciences Socioeconomic factors Socioeconomic status |
title | To what extent do biomarkers account for the large social disparities in health in Moscow? |
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