PLACES: Local Data for Better Health
Local-level data on the health of populations are important to inform and drive effective and efficient actions to improve health, but such data are often expensive to collect and thus rare. Population Level Analysis and Community EStimates (PLACES) (www.cdc.gov/places/), a collaboration between the...
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Veröffentlicht in: | Preventing chronic disease 2022-06, Vol.19, p.E31-E31, Article 210459 |
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creator | Greenlund, Kurt J. Lu, Hua Wang, Yan Matthews, Kevin A. LeClercq, Jennifer M. Lee, Benjamin Carlson, Susan A. |
description | Local-level data on the health of populations are important to inform and drive effective and efficient actions to improve health, but such data are often expensive to collect and thus rare. Population Level Analysis and Community EStimates (PLACES) (www.cdc.gov/places/), a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation, provides model-based estimates for 29 measures among all counties and most incorporated and census-designated places, census tracts, and ZIP Code tabulation areas across the US. PLACES allows local health departments and others to better understand the burden and geographic distribution of chronic disease–related outcomes in their areas regardless of population size and urban–rural status and assists them in planning public health interventions. Online resources allow users to visually explore health estimates geographically, compare estimates, and download data for further use and exploration. By understanding the PLACES overall approach and using the easy-to-use PLACES applications, practitioners, policy makers, and others can enhance their efforts to improve public health, including informing prevention activities, programs, and policies; identifying priority health risk behaviors for action; prioritizing investments to areas with the biggest gaps or inequities; and establishing key health objectives to achieve community health and health equity. |
doi_str_mv | 10.5888/pcd19.210459 |
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Population Level Analysis and Community EStimates (PLACES) (www.cdc.gov/places/), a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation, provides model-based estimates for 29 measures among all counties and most incorporated and census-designated places, census tracts, and ZIP Code tabulation areas across the US. PLACES allows local health departments and others to better understand the burden and geographic distribution of chronic disease–related outcomes in their areas regardless of population size and urban–rural status and assists them in planning public health interventions. Online resources allow users to visually explore health estimates geographically, compare estimates, and download data for further use and exploration. By understanding the PLACES overall approach and using the easy-to-use PLACES applications, practitioners, policy makers, and others can enhance their efforts to improve public health, including informing prevention activities, programs, and policies; identifying priority health risk behaviors for action; prioritizing investments to areas with the biggest gaps or inequities; and establishing key health objectives to achieve community health and health equity.</description><identifier>ISSN: 1545-1151</identifier><identifier>EISSN: 1545-1151</identifier><identifier>DOI: 10.5888/pcd19.210459</identifier><identifier>PMID: 35709356</identifier><language>eng</language><publisher>Atlanta: Centers for Disease Control and Prevention</publisher><subject>Adults ; Chronic illnesses ; COVID-19 ; Disease control ; Disease prevention ; Economic indicators ; Ethnicity ; Health risk assessment ; Health risks ; Intervention ; Monte Carlo simulation ; Population ; Tools for Public Health Practice</subject><ispartof>Preventing chronic disease, 2022-06, Vol.19, p.E31-E31, Article 210459</ispartof><rights>Published 2022. This article is a U.S. Government work and is in the public domain in the USA.</rights><rights>2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-6bfe65715d228ad924729e7d08d35ce0a0d6f3708121738820dc0a16fd9d8a0f3</citedby><cites>FETCH-LOGICAL-c389t-6bfe65715d228ad924729e7d08d35ce0a0d6f3708121738820dc0a16fd9d8a0f3</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/PMC9258452/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258452/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids></links><search><creatorcontrib>Greenlund, Kurt J.</creatorcontrib><creatorcontrib>Lu, Hua</creatorcontrib><creatorcontrib>Wang, Yan</creatorcontrib><creatorcontrib>Matthews, Kevin A.</creatorcontrib><creatorcontrib>LeClercq, Jennifer M.</creatorcontrib><creatorcontrib>Lee, Benjamin</creatorcontrib><creatorcontrib>Carlson, Susan A.</creatorcontrib><title>PLACES: Local Data for Better Health</title><title>Preventing chronic disease</title><description>Local-level data on the health of populations are important to inform and drive effective and efficient actions to improve health, but such data are often expensive to collect and thus rare. 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By understanding the PLACES overall approach and using the easy-to-use PLACES applications, practitioners, policy makers, and others can enhance their efforts to improve public health, including informing prevention activities, programs, and policies; identifying priority health risk behaviors for action; prioritizing investments to areas with the biggest gaps or inequities; and establishing key health objectives to achieve community health and health equity.</description><subject>Adults</subject><subject>Chronic illnesses</subject><subject>COVID-19</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Economic indicators</subject><subject>Ethnicity</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Intervention</subject><subject>Monte Carlo simulation</subject><subject>Population</subject><subject>Tools for Public Health Practice</subject><issn>1545-1151</issn><issn>1545-1151</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkE1LAzEQhoMotlZv_oAFPXhw6yTZfHkQaq1WWFBQzyFNsrZlu1uTXcF_b78Q9TQvzMPLzIPQKYY-k1JeLa3Dqk8wZEztoS5mGUsxZnj_V-6goxjnAESA4IeoQ5kARRnvovPnfDAcvVwneW1NmdyZxiRFHZJb3zQ-JGNvymZ6jA4KU0Z_sps99HY_eh2O0_zp4XE4yFNLpWpSPik8ZwIzR4g0TpFMEOWFA-kosx4MOF5QARITLKiUBJwFg3nhlJMGCtpDN9veZTtZeGd91QRT6mWYLUz40rWZ6b-bajbV7_WnVoTJjJFVwcWuINQfrY-NXsyi9WVpKl-3URMuZEY4ZGv07B86r9tQrd7bUIRLCdmKutxSNtQxBl_8HINBr_XrjX691U-_AYyxc3Q</recordid><startdate>20220616</startdate><enddate>20220616</enddate><creator>Greenlund, Kurt J.</creator><creator>Lu, Hua</creator><creator>Wang, Yan</creator><creator>Matthews, Kevin A.</creator><creator>LeClercq, Jennifer M.</creator><creator>Lee, Benjamin</creator><creator>Carlson, Susan A.</creator><general>Centers for Disease Control and Prevention</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>M0R</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220616</creationdate><title>PLACES: Local Data for Better Health</title><author>Greenlund, Kurt J. ; Lu, Hua ; Wang, Yan ; Matthews, Kevin A. ; LeClercq, Jennifer M. ; Lee, Benjamin ; Carlson, Susan A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-6bfe65715d228ad924729e7d08d35ce0a0d6f3708121738820dc0a16fd9d8a0f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adults</topic><topic>Chronic illnesses</topic><topic>COVID-19</topic><topic>Disease control</topic><topic>Disease prevention</topic><topic>Economic indicators</topic><topic>Ethnicity</topic><topic>Health risk assessment</topic><topic>Health risks</topic><topic>Intervention</topic><topic>Monte Carlo simulation</topic><topic>Population</topic><topic>Tools for Public Health Practice</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Greenlund, Kurt J.</creatorcontrib><creatorcontrib>Lu, Hua</creatorcontrib><creatorcontrib>Wang, Yan</creatorcontrib><creatorcontrib>Matthews, Kevin A.</creatorcontrib><creatorcontrib>LeClercq, Jennifer M.</creatorcontrib><creatorcontrib>Lee, Benjamin</creatorcontrib><creatorcontrib>Carlson, Susan A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Preventing chronic disease</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Greenlund, Kurt J.</au><au>Lu, Hua</au><au>Wang, Yan</au><au>Matthews, Kevin A.</au><au>LeClercq, Jennifer M.</au><au>Lee, Benjamin</au><au>Carlson, Susan A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PLACES: Local Data for Better Health</atitle><jtitle>Preventing chronic disease</jtitle><date>2022-06-16</date><risdate>2022</risdate><volume>19</volume><spage>E31</spage><epage>E31</epage><pages>E31-E31</pages><artnum>210459</artnum><issn>1545-1151</issn><eissn>1545-1151</eissn><abstract>Local-level data on the health of populations are important to inform and drive effective and efficient actions to improve health, but such data are often expensive to collect and thus rare. Population Level Analysis and Community EStimates (PLACES) (www.cdc.gov/places/), a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation, provides model-based estimates for 29 measures among all counties and most incorporated and census-designated places, census tracts, and ZIP Code tabulation areas across the US. PLACES allows local health departments and others to better understand the burden and geographic distribution of chronic disease–related outcomes in their areas regardless of population size and urban–rural status and assists them in planning public health interventions. Online resources allow users to visually explore health estimates geographically, compare estimates, and download data for further use and exploration. By understanding the PLACES overall approach and using the easy-to-use PLACES applications, practitioners, policy makers, and others can enhance their efforts to improve public health, including informing prevention activities, programs, and policies; identifying priority health risk behaviors for action; prioritizing investments to areas with the biggest gaps or inequities; and establishing key health objectives to achieve community health and health equity.</abstract><cop>Atlanta</cop><pub>Centers for Disease Control and Prevention</pub><pmid>35709356</pmid><doi>10.5888/pcd19.210459</doi><oa>free_for_read</oa></addata></record> |
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subjects | Adults Chronic illnesses COVID-19 Disease control Disease prevention Economic indicators Ethnicity Health risk assessment Health risks Intervention Monte Carlo simulation Population Tools for Public Health Practice |
title | PLACES: Local Data for Better Health |
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