Applying Computational Phenotypes To Assess Mental Health Disorders Among Transgender Patients in the United States
Applying Computational Phenotypes To Assess Mental Health Disorders Among Transgender Patients in the United States
批准号:
10604723
负责人:
Theo Beltran
金额:
$3.97万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2025-07-31
关键词:
AddressAdherenceAdoptedAlgorithmsAmericanAnxietyAttention Deficit DisorderCaringChronic DiseaseCodeCommunicationComputerized Medical RecordDataData SourcesDatabasesDecision MakingDepressive disorderDiagnosisDiagnosticDiscriminationDisparity populationDrug PrescriptionsEffectivenessEpidemiologyEvaluationFacultyFutureGender IdentityGeneral PopulationGoalsGuidelinesHIVHealth PolicyHealth trendsHealthcareHigh PrevalenceIndividualInpatientsInterventionKnowledgeManaged CareMeasuresMedicalMental DepressionMental HealthMental Health ServicesMental disordersMethodsModelingMonitorOutcomeOutpatientsPatientsPatternPerformancePersonsPharmaceutical PreparationsPharmacoepidemiologyPharmacologic SubstancePhasePhenotypePoliciesPopulationPrevalencePrivatizationProbabilityProceduresPsychiatric DiagnosisPsychiatric therapeutic procedurePsychologistPsychotherapyPublic HealthRecommendationRecordsReportingResearchResearch PersonnelRiskRoleSample SizeSelf-Injurious BehaviorServicesSuicide attemptTimeTime trendTrainingTranslatingUnited StatesVictimizationViolenceVisitVulnerable PopulationsWeightWorkantiretroviral therapybilling datacareercisgenderclinical epidemiologyclinically actionableeHealthelectronic medical record systemepidemiology studyexperiencegender affirming carehealth assessmenthealth care service utilizationhealth disparityhealth equityhealth inequalitieshigh riskimprovedinsurance claimslarge datasetslongitudinal analysismedication compliancephenotyping algorithmsex assigned at birthskillssocialsocial groupstudy populationtransgendertransgender womentreatment durationtrend
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PROJECT SUMMARY
Transgender (TG) individuals have high prevalence of depression (64%) and are nearly nine times as likely to
attempt suicide compared to the general population. Studies have not consistently collected data related to TG
identity nor have they used the recommended two-step method of asking for assigned sex at birth and current
gender identity. This population is largely overlooked in epidemiologic studies due to small sample size. With
inconsistent and inaccurate ascertainment of TG patients in real-world data sources, TG people are missed
and their health trends over time are understudied. Researchers are unable to identify and meaningfully
address mental health inequities for this population, which can further exacerbate and perpetuate psychiatric
conditions. It remains unclear what the health care utilization trends are for TG patients with psychiatric
disorders and their adherence and persistence to psychiatric medications over time. Understanding this
population’s mental health using large datasets is imperative in order to optimize the care management of
psychiatric conditions experienced by TG people.
The objectives of this proposal are to: (1) apply and evaluate the performance of existing computational
phenotypes (CPs) to identify TG patients with depressive disorders (DD), anxiety, and attention deficit
disorders (ADD), (2) assess adherence and prevalence of psychiatric medications among TG patients
diagnosed with psychiatric disorders compared to cisgender patients with these diagnoses, and (3)
examine the risk of non-fatal self-harm among TG patients with psychiatric disorders receiving
psychotherapy compared to TG patients not receiving psychotherapy. For all aims, IBM MarketScan from
years 2008 to 2020 will be used, which is a large, longitudinal medical claims database that includes
inpatient and outpatient visits and prescription medication use. The completion of the proposed aims will
provide real-world evidence on mental health care for TG individuals in the United States.
The training plan outlined in this proposal will equip the applicant with critical knowledge and necessary
skills in social and pharmacoepidemiology and transgender mental health. This plan will prepare him to
successfully complete the proposed aims and to progress into a role as an independent, interdisciplinary
researcher studying the intersection of pharmacoepidemiology and transgender health equity in the US.
The applicant is extremely well supported by an interdisciplinary group of social, psychiatric, and
pharmacoepidemiology faculty, health disparities researchers, and pharmaceutical researchers with the
requisite expertise to support his doctoral research and prepare him for the next phase of his career.
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