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Developing Dashboards to Address Children's Health Disparities in Ohio
Abstract Objectives Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team h...
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Published in: | Applied clinical informatics 2022-01, Vol.13 (1), p.100-112 |
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creator | Jonnalagadda, Pallavi Swoboda, Christine Singh, Priti Gureddygari, Harish Scarborough, Seth Dunn, Ian Doogan, Nathan J. Fareed, Naleef |
description | Abstract
Objectives
Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI.
Methods
The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards—main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels.
Results
The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population.
Conclusion
The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels. |
doi_str_mv | 10.1055/s-0041-1741482 |
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Objectives
Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI.
Methods
The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards—main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels.
Results
The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population.
Conclusion
The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels.</description><identifier>ISSN: 1869-0327</identifier><identifier>EISSN: 1869-0327</identifier><identifier>DOI: 10.1055/s-0041-1741482</identifier><identifier>PMID: 35081656</identifier><language>eng</language><publisher>Rüdigerstraße 14, 70469 Stuttgart, Germany: Georg Thieme Verlag KG</publisher><subject>Child ; Child Health ; Data Visualization ; Health Status Disparities ; Humans ; Infant ; Ohio - epidemiology ; Poverty Areas ; Residence Characteristics - statistics & numerical data ; Social Determinants of Health ; Special Section on Disparities, Biases, and Disinformation in Healthcare ; Surveys and Questionnaires</subject><ispartof>Applied clinical informatics, 2022-01, Vol.13 (1), p.100-112</ispartof><rights>Thieme. All rights reserved.</rights><rights>Thieme. All rights reserved. 2022 Georg Thieme Verlag KG</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c428t-4ed0f3f8b253bb9f76495fbbf357fb9dcd07d316bba1f5ad464e25f43663791d3</citedby><cites>FETCH-LOGICAL-c428t-4ed0f3f8b253bb9f76495fbbf357fb9dcd07d316bba1f5ad464e25f43663791d3</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/PMC8791762/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791762/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35081656$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jonnalagadda, Pallavi</creatorcontrib><creatorcontrib>Swoboda, Christine</creatorcontrib><creatorcontrib>Singh, Priti</creatorcontrib><creatorcontrib>Gureddygari, Harish</creatorcontrib><creatorcontrib>Scarborough, Seth</creatorcontrib><creatorcontrib>Dunn, Ian</creatorcontrib><creatorcontrib>Doogan, Nathan J.</creatorcontrib><creatorcontrib>Fareed, Naleef</creatorcontrib><title>Developing Dashboards to Address Children's Health Disparities in Ohio</title><title>Applied clinical informatics</title><addtitle>Appl Clin Inform</addtitle><description>Abstract
Objectives
Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI.
Methods
The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards—main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels.
Results
The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population.
Conclusion
The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels.</description><subject>Child</subject><subject>Child Health</subject><subject>Data Visualization</subject><subject>Health Status Disparities</subject><subject>Humans</subject><subject>Infant</subject><subject>Ohio - epidemiology</subject><subject>Poverty Areas</subject><subject>Residence Characteristics - statistics & numerical data</subject><subject>Social Determinants of Health</subject><subject>Special Section on Disparities, Biases, and Disinformation in Healthcare</subject><subject>Surveys and Questionnaires</subject><issn>1869-0327</issn><issn>1869-0327</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kE1LAzEQhoMoKrVXj7I3vazme3cvQmmtFQq96Dkkm8SNbDc1sxX8925pFT04lxnIM--EB6FLgm8JFuIOcow5yUnBCS_pETonpaxyzGhx_Gs-Q2OANzyUkKQsi1N0xgQuiRTyHM1n7sO1cRO612ymoTFRJwtZH7OJtckBZNMmtMPUXUO2cLrtm2wWYKNT6IODLHTZqgnxAp143YIbH_oIvcwfnqeLfLl6fJpOlnnNadnn3FnsmS8NFcyYyheSV8Ib45kovKlsbXFhGZHGaOKFtlxyR4XnTEpWVMSyEbrf5262Zu1s7bo-6VZtUljr9KmiDurvSxca9Ro_VDmsF5IOATeHgBTftw56tQ5Qu7bVnYtbUHRgGOWU7tDbPVqnCJCc_zlDsNr5V6B2_tXB_7Bw9ftzP_i37QHI90DfBLd26i1uUzfo-i_wCxJVjwc</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Jonnalagadda, Pallavi</creator><creator>Swoboda, Christine</creator><creator>Singh, Priti</creator><creator>Gureddygari, Harish</creator><creator>Scarborough, Seth</creator><creator>Dunn, Ian</creator><creator>Doogan, Nathan J.</creator><creator>Fareed, Naleef</creator><general>Georg Thieme Verlag KG</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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220101</creationdate><title>Developing Dashboards to Address Children's Health Disparities in Ohio</title><author>Jonnalagadda, Pallavi ; Swoboda, Christine ; Singh, Priti ; Gureddygari, Harish ; Scarborough, Seth ; Dunn, Ian ; Doogan, Nathan J. ; Fareed, Naleef</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-4ed0f3f8b253bb9f76495fbbf357fb9dcd07d316bba1f5ad464e25f43663791d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Child</topic><topic>Child Health</topic><topic>Data Visualization</topic><topic>Health Status Disparities</topic><topic>Humans</topic><topic>Infant</topic><topic>Ohio - epidemiology</topic><topic>Poverty Areas</topic><topic>Residence Characteristics - statistics & numerical data</topic><topic>Social Determinants of Health</topic><topic>Special Section on Disparities, Biases, and Disinformation in Healthcare</topic><topic>Surveys and Questionnaires</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jonnalagadda, Pallavi</creatorcontrib><creatorcontrib>Swoboda, Christine</creatorcontrib><creatorcontrib>Singh, Priti</creatorcontrib><creatorcontrib>Gureddygari, Harish</creatorcontrib><creatorcontrib>Scarborough, Seth</creatorcontrib><creatorcontrib>Dunn, Ian</creatorcontrib><creatorcontrib>Doogan, Nathan J.</creatorcontrib><creatorcontrib>Fareed, Naleef</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Applied clinical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jonnalagadda, Pallavi</au><au>Swoboda, Christine</au><au>Singh, Priti</au><au>Gureddygari, Harish</au><au>Scarborough, Seth</au><au>Dunn, Ian</au><au>Doogan, Nathan J.</au><au>Fareed, Naleef</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Developing Dashboards to Address Children's Health Disparities in Ohio</atitle><jtitle>Applied clinical informatics</jtitle><addtitle>Appl Clin Inform</addtitle><date>2022-01-01</date><risdate>2022</risdate><volume>13</volume><issue>1</issue><spage>100</spage><epage>112</epage><pages>100-112</pages><issn>1869-0327</issn><eissn>1869-0327</eissn><abstract>Abstract
Objectives
Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI.
Methods
The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards—main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels.
Results
The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population.
Conclusion
The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels.</abstract><cop>Rüdigerstraße 14, 70469 Stuttgart, Germany</cop><pub>Georg Thieme Verlag KG</pub><pmid>35081656</pmid><doi>10.1055/s-0041-1741482</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Child Child Health Data Visualization Health Status Disparities Humans Infant Ohio - epidemiology Poverty Areas Residence Characteristics - statistics & numerical data Social Determinants of Health Special Section on Disparities, Biases, and Disinformation in Healthcare Surveys and Questionnaires |
title | Developing Dashboards to Address Children's Health Disparities in Ohio |
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