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Project Information

MODELING SOCIAL BEHAVIOR FOR HEALTHCARE UTILIZATION IN DEPRESSION

Agency:
NIMH

HHS/NIH/National Institute of Mental Health (NIMH)

Project Number:
7R01MH105384-02
Contact PI / Project Leader:
PATHAK, JYOTISHMAN
Awardee Organization:
WEILL MEDICAL COLLEGE OF CONRELL UNIVERSITY

Description

Abstract Text:
 DESCRIPTION (provided by applicant): Depression is highly prevalent, both in the US and worldwide. Among US adults, the estimated 12-month and lifetime prevalence rates are 8.3% and 19.2%, respectively. The World Health Organization considers major depressive disorder (MDD) as the third-highest cause of disease burden worldwide, and the highest cause of disease burden in the developed world. However, despite its prevalence and burden, depression remains significantly under-recognized and under-treated in all practice settings, including managed care where less than one third of adults with depression obtain appropriate professional treatment. Denial of illness and stigma are two primary barriers to proper identification and treatment of depression. Many individuals with depression are ashamed to seek out a mental health professional and consider depression a sign of personal weakness. In particular, "self-stigma" has been associated to affect adherence to psychiatric services, hope and quality of life negatively, and also poses as a barrier for social integration. Further, since self-stigma can exist without actual stigma from the public, and is more hidden and inside, it seems to be the worst form of stigma against people with depression and can directly affect the patients' over all well-being. Studies suggest that early recognition and treatment of depressive behavior and symptoms can improve social function, increase productivity, and decrease absenteeism in the workplace. However, recognition of depression, particularly in early stages, is still challenging. To address this problem, in this proposal we plan to develop effective methods for detection of depressive behavior, not only at an individual-level, but also at a community-level. The latter is highly pertinent because depression is significantly influenced by variations in social determinants and socio- ecological factors. In particular, we will leverage robust and longitudinal electronic health record (EHR) systems at Mayo Clinic and private insurance (UnitedHealthCare/Optum Labs) reimbursement and claims data along with online social media data from Twitter and PatientsLikeMe as well as geo-coded neighborhood and environmental data to develop a "big data" platform for identifying combinations of online socio-behavioral factors and neighborhood environmental conditions to enable innovative ways for detection of depressive behavior within communities and identify patterns and changes in health care utilization for depression across different communities and geographies within U.S.
Project Terms:
Absenteeism; Active Learning; Address; Adherence; Adult; Affect; Algorithms; American; Asses; base; Behavior; Behavioral; behavioral health; behavioral outcome; beneficiary; Big Data; Bioethics; biomedical informatics; burden of illness; Censuses; Characteristics; Clinic; Code; Communities; computer science; Computer Simulation; Data; data integration; data mining; Data Set; Data Sources; depressive behavior; depressive symptoms; design; Detection; Diagnosis; disability; Early identification; Early treatment; Electronic Health Record; Epidemiology; Focus Groups; Foundations; Geography; Goals; Health; health care service utilization; Health Professional; Health Services Research; Healthcare; Healthcare Systems; improved; Individual; Information Resources Management; Information Retrieval; information seeking behavior; innovation; Insurance; Internet; Intervention Studies; Lead; Link; Longevity; Major Depressive Disorder; Managed Care; Mental Depression; Mental disorders; Mental Health; mental health center; Mental Health Services; Methods; Mining; Modeling; Motivation; Neighborhoods; Outcome; Patients; Pattern; Personal Satisfaction; Population; prescription procedure; Prevalence; Productivity; Provider; Psychiatrist; Public Health; public health relevance; Quality of life; Research; Risk; Services; Signal Transduction; social; Social Behavior; Social Functioning; social integration; Social Network; social stigma; Source; Staging; Surveys; System; Techniques; Technology; Testing; Validation; Variant; web-based social networking; Wood material; Workplace; World Health Organization

Details

Contact PI / Project Leader Information:
Name:  PATHAK, JYOTISHMAN
Other PI Information:
SHETH, AMIT P
Awardee Organization:
Name:  WEILL MEDICAL COLLEGE OF CONRELL UNIVERSITY
City:  NEW YORK    
Country:  UNITED STATES
Congressional District:
State Code:  NY
District:  12
Other Information:
Fiscal Year: 2015
Award Notice Date: 05-May-2016
DUNS Number: 060217502
Project Start Date: 01-Mar-2016
Budget Start Date: 01-Mar-2016
CFDA Code: 242
Project End Date: 30-Jun-2019
Budget End Date: 30-Jun-2016
Agency: ?

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

HHS/NIH/National Institute of Mental Health (NIMH)
Project Funding Information for 2015:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

FY Total Cost
2015 NIMH

HHS/NIH/National Institute of Mental Health (NIMH)

$505,602

Results

i

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