Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study
Objective:Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods:Data were aggr...
Gespeichert in:
| Veröffentlicht in: | Psychiatric services (Washington, D.C.) D.C.), 2018-08, Vol.69 (8), p.927-934 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 934 |
|---|---|
| container_issue | 8 |
| container_start_page | 927 |
| container_title | Psychiatric services (Washington, D.C.) |
| container_volume | 69 |
| creator | Chekroud, Adam M Foster, David Zheutlin, Amanda B Gerhard, Danielle M Roy, Brita Koutsouleris, Nikolaos Chandra, Abhishek Esposti, Michelle Degli Subramanyan, Girish Gueorguieva, Ralitza Paulus, Martin Krystal, John H |
| description | Objective:Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods:Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results:A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p |
| doi_str_mv | 10.1176/appi.ps.201800094 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7232987</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2097655397</sourcerecordid><originalsourceid>FETCH-LOGICAL-a466t-9e6b648604075ec7dda93b4f729a28a91161a1b00d5f84da52708466c3603623</originalsourceid><addsrcrecordid>eNp1kV-L1DAUxYso7h_9AL5IwBcftvUmbZPGB2EdXXdh0YUZn8OdNl2ztE02SYX99qbO7KCCEEjI_Z2TnHuz7BWFglLB36FzpnChYEAbAJDVk-yY1rXIpQB4ms4g6pyJEo6ykxDuEkIF5c-zIyYlZyWI4yzeeN2ZNprplnxE7432gURLNl5jHPUUSW89-aSd1yEYOxEzESTfi3VBvmJMFziQNY5u0O_JOVl5G0K-1u2uckZuvLV9ntbKTq12kazj3D28yJ71OAT9cr-fZpuLz5vVZX797cvV6vw6x4rzmEvNt7xqOFQph25F16Est1UvmETWoKSUU6RbgK7um6rDmglokrItOZQp32n2YWfr5u2ouzal8Tgo582I_kFZNOrvymR-qFv7UwlWMtmIZPB2b-Dt_axDVKMJrR4GnLSdg2LAS0GppJDQN_-gd3b2qQcLJQWv61IuhnRHtUujvO4Pn6GglpGqZaTKLaL9SJPm9Z8pDorHGSag2AG_tYdn_-_4C0cSrGM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2097655397</pqid></control><display><type>article</type><title>Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study</title><source>MEDLINE</source><source>American Psychiatric Publishing Journals (1997-Present)</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Chekroud, Adam M ; Foster, David ; Zheutlin, Amanda B ; Gerhard, Danielle M ; Roy, Brita ; Koutsouleris, Nikolaos ; Chandra, Abhishek ; Esposti, Michelle Degli ; Subramanyan, Girish ; Gueorguieva, Ralitza ; Paulus, Martin ; Krystal, John H</creator><creatorcontrib>Chekroud, Adam M ; Foster, David ; Zheutlin, Amanda B ; Gerhard, Danielle M ; Roy, Brita ; Koutsouleris, Nikolaos ; Chandra, Abhishek ; Esposti, Michelle Degli ; Subramanyan, Girish ; Gueorguieva, Ralitza ; Paulus, Martin ; Krystal, John H</creatorcontrib><description>Objective:Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods:Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results:A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all).Conclusions:Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.</description><identifier>ISSN: 1075-2730</identifier><identifier>EISSN: 1557-9700</identifier><identifier>DOI: 10.1176/appi.ps.201800094</identifier><identifier>PMID: 29962307</identifier><language>eng</language><publisher>United States: American Psychiatric Association</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Cross-Sectional Studies ; Depressive Disorder - diagnosis ; Depressive Disorder - therapy ; Female ; Health Services Accessibility - statistics & numerical data ; Humans ; Logistic Models ; Male ; Medical diagnosis ; Medical treatment ; Mental depression ; Mental health care ; Middle Aged ; Patient Acceptance of Health Care - psychology ; Primary Health Care ; Proof of Concept Study ; Psychotherapy ; Sampling Studies ; Self-Assessment ; Surveys and Questionnaires ; Treatment Refusal - psychology ; United States ; Young Adult</subject><ispartof>Psychiatric services (Washington, D.C.), 2018-08, Vol.69 (8), p.927-934</ispartof><rights>Copyright © 2018 by the American Psychiatric Association 2018</rights><rights>Copyright American Psychiatric Publishing, Inc. Aug 1, 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a466t-9e6b648604075ec7dda93b4f729a28a91161a1b00d5f84da52708466c3603623</citedby><cites>FETCH-LOGICAL-a466t-9e6b648604075ec7dda93b4f729a28a91161a1b00d5f84da52708466c3603623</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://psychiatryonline.org/doi/epdf/10.1176/appi.ps.201800094$$EPDF$$P50$$Gappi$$H</linktopdf><linktohtml>$$Uhttps://psychiatryonline.org/doi/full/10.1176/appi.ps.201800094$$EHTML$$P50$$Gappi$$H</linktohtml><link.rule.ids>230,314,776,780,881,2842,21607,21608,21609,27903,27904,77540,77545</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29962307$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chekroud, Adam M</creatorcontrib><creatorcontrib>Foster, David</creatorcontrib><creatorcontrib>Zheutlin, Amanda B</creatorcontrib><creatorcontrib>Gerhard, Danielle M</creatorcontrib><creatorcontrib>Roy, Brita</creatorcontrib><creatorcontrib>Koutsouleris, Nikolaos</creatorcontrib><creatorcontrib>Chandra, Abhishek</creatorcontrib><creatorcontrib>Esposti, Michelle Degli</creatorcontrib><creatorcontrib>Subramanyan, Girish</creatorcontrib><creatorcontrib>Gueorguieva, Ralitza</creatorcontrib><creatorcontrib>Paulus, Martin</creatorcontrib><creatorcontrib>Krystal, John H</creatorcontrib><title>Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study</title><title>Psychiatric services (Washington, D.C.)</title><addtitle>Psychiatr Serv</addtitle><description>Objective:Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods:Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results:A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all).Conclusions:Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Cross-Sectional Studies</subject><subject>Depressive Disorder - diagnosis</subject><subject>Depressive Disorder - therapy</subject><subject>Female</subject><subject>Health Services Accessibility - statistics & numerical data</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical treatment</subject><subject>Mental depression</subject><subject>Mental health care</subject><subject>Middle Aged</subject><subject>Patient Acceptance of Health Care - psychology</subject><subject>Primary Health Care</subject><subject>Proof of Concept Study</subject><subject>Psychotherapy</subject><subject>Sampling Studies</subject><subject>Self-Assessment</subject><subject>Surveys and Questionnaires</subject><subject>Treatment Refusal - psychology</subject><subject>United States</subject><subject>Young Adult</subject><issn>1075-2730</issn><issn>1557-9700</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kV-L1DAUxYso7h_9AL5IwBcftvUmbZPGB2EdXXdh0YUZn8OdNl2ztE02SYX99qbO7KCCEEjI_Z2TnHuz7BWFglLB36FzpnChYEAbAJDVk-yY1rXIpQB4ms4g6pyJEo6ykxDuEkIF5c-zIyYlZyWI4yzeeN2ZNprplnxE7432gURLNl5jHPUUSW89-aSd1yEYOxEzESTfi3VBvmJMFziQNY5u0O_JOVl5G0K-1u2uckZuvLV9ntbKTq12kazj3D28yJ71OAT9cr-fZpuLz5vVZX797cvV6vw6x4rzmEvNt7xqOFQph25F16Est1UvmETWoKSUU6RbgK7um6rDmglokrItOZQp32n2YWfr5u2ouzal8Tgo582I_kFZNOrvymR-qFv7UwlWMtmIZPB2b-Dt_axDVKMJrR4GnLSdg2LAS0GppJDQN_-gd3b2qQcLJQWv61IuhnRHtUujvO4Pn6GglpGqZaTKLaL9SJPm9Z8pDorHGSag2AG_tYdn_-_4C0cSrGM</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Chekroud, Adam M</creator><creator>Foster, David</creator><creator>Zheutlin, Amanda B</creator><creator>Gerhard, Danielle M</creator><creator>Roy, Brita</creator><creator>Koutsouleris, Nikolaos</creator><creator>Chandra, Abhishek</creator><creator>Esposti, Michelle Degli</creator><creator>Subramanyan, Girish</creator><creator>Gueorguieva, Ralitza</creator><creator>Paulus, Martin</creator><creator>Krystal, John H</creator><general>American Psychiatric Association</general><general>American Psychiatric Publishing, 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>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20180801</creationdate><title>Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study</title><author>Chekroud, Adam M ; Foster, David ; Zheutlin, Amanda B ; Gerhard, Danielle M ; Roy, Brita ; Koutsouleris, Nikolaos ; Chandra, Abhishek ; Esposti, Michelle Degli ; Subramanyan, Girish ; Gueorguieva, Ralitza ; Paulus, Martin ; Krystal, John H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a466t-9e6b648604075ec7dda93b4f729a28a91161a1b00d5f84da52708466c3603623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Cross-Sectional Studies</topic><topic>Depressive Disorder - diagnosis</topic><topic>Depressive Disorder - therapy</topic><topic>Female</topic><topic>Health Services Accessibility - statistics & numerical data</topic><topic>Humans</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical treatment</topic><topic>Mental depression</topic><topic>Mental health care</topic><topic>Middle Aged</topic><topic>Patient Acceptance of Health Care - psychology</topic><topic>Primary Health Care</topic><topic>Proof of Concept Study</topic><topic>Psychotherapy</topic><topic>Sampling Studies</topic><topic>Self-Assessment</topic><topic>Surveys and Questionnaires</topic><topic>Treatment Refusal - psychology</topic><topic>United States</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chekroud, Adam M</creatorcontrib><creatorcontrib>Foster, David</creatorcontrib><creatorcontrib>Zheutlin, Amanda B</creatorcontrib><creatorcontrib>Gerhard, Danielle M</creatorcontrib><creatorcontrib>Roy, Brita</creatorcontrib><creatorcontrib>Koutsouleris, Nikolaos</creatorcontrib><creatorcontrib>Chandra, Abhishek</creatorcontrib><creatorcontrib>Esposti, Michelle Degli</creatorcontrib><creatorcontrib>Subramanyan, Girish</creatorcontrib><creatorcontrib>Gueorguieva, Ralitza</creatorcontrib><creatorcontrib>Paulus, Martin</creatorcontrib><creatorcontrib>Krystal, John H</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Psychiatric services (Washington, D.C.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chekroud, Adam M</au><au>Foster, David</au><au>Zheutlin, Amanda B</au><au>Gerhard, Danielle M</au><au>Roy, Brita</au><au>Koutsouleris, Nikolaos</au><au>Chandra, Abhishek</au><au>Esposti, Michelle Degli</au><au>Subramanyan, Girish</au><au>Gueorguieva, Ralitza</au><au>Paulus, Martin</au><au>Krystal, John H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study</atitle><jtitle>Psychiatric services (Washington, D.C.)</jtitle><addtitle>Psychiatr Serv</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>69</volume><issue>8</issue><spage>927</spage><epage>934</epage><pages>927-934</pages><issn>1075-2730</issn><eissn>1557-9700</eissn><abstract>Objective:Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods:Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results:A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all).Conclusions:Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.</abstract><cop>United States</cop><pub>American Psychiatric Association</pub><pmid>29962307</pmid><doi>10.1176/appi.ps.201800094</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1075-2730 |
| ispartof | Psychiatric services (Washington, D.C.), 2018-08, Vol.69 (8), p.927-934 |
| issn | 1075-2730 1557-9700 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7232987 |
| source | MEDLINE; American Psychiatric Publishing Journals (1997-Present); Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Adult Aged Aged, 80 and over Cross-Sectional Studies Depressive Disorder - diagnosis Depressive Disorder - therapy Female Health Services Accessibility - statistics & numerical data Humans Logistic Models Male Medical diagnosis Medical treatment Mental depression Mental health care Middle Aged Patient Acceptance of Health Care - psychology Primary Health Care Proof of Concept Study Psychotherapy Sampling Studies Self-Assessment Surveys and Questionnaires Treatment Refusal - psychology United States Young Adult |
| title | Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T05%3A48%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20Barriers%20to%20Treatment%20for%20Depression%20in%20a%20U.S.%20National%20Sample:%20A%20Cross-Sectional,%20Proof-of-Concept%20Study&rft.jtitle=Psychiatric%20services%20(Washington,%20D.C.)&rft.au=Chekroud,%20Adam%20M&rft.date=2018-08-01&rft.volume=69&rft.issue=8&rft.spage=927&rft.epage=934&rft.pages=927-934&rft.issn=1075-2730&rft.eissn=1557-9700&rft_id=info:doi/10.1176/appi.ps.201800094&rft_dat=%3Cproquest_pubme%3E2097655397%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2097655397&rft_id=info:pmid/29962307&rfr_iscdi=true |