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Social connectedness, depression symptoms, and health service utilization: a longitudinal study of Veterans Health Administration patients.

ABSTRACT: PURPOSE:Our study explored whether aspects of veterans' social connectedness (social support, interpersonal conflict, loneliness, social norms, number of confidants) are associated with change in their depression symptoms and health services utilization over 1 year. METHODS:We conducted a prospective, longitudinal study of 262 military veterans who obtained primary care and other services at a Veterans Health Administration (VHA) facility and screened positive for depression. Participants completed surveys at baseline and 12-month follow-up. We measured social connectedness variables using the NIH Toolbox Adult Social Relationship Scales. We used the Patient Health Questionnaire to assess depression symptoms and suicidal ideation and administrative medical record data for health services utilization. We calculated change scores to model outcomes over time using multivariable regressions. RESULTS:We found that higher levels of baseline loneliness were associated with decreased depression severity over 1 year (B?=?-?1.55, 95% CI [-?2.53, -?.56], p?

SUBMITTER: Chen JI

PROVIDER: S-EPMC7304483 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Social connectedness, depression symptoms, and health service utilization: a longitudinal study of Veterans Health Administration patients.

Chen Jason I JI Hooker Elizabeth R ER Niederhausen Meike M E Marsh Heather H Saha Somnath S Dobscha Steven K SK Teo Alan R AR

Social psychiatry and psychiatric epidemiology 20191106 5

<h4>Purpose</h4>Our study explored whether aspects of veterans' social connectedness (social support, interpersonal conflict, loneliness, social norms, number of confidants) are associated with change in their depression symptoms and health services utilization over 1 year.<h4>Methods</h4>We conducted a prospective, longitudinal study of 262 military veterans who obtained primary care and other services at a Veterans Health Administration (VHA) facility and screened positive for depression. Part ...[more]

PMID: 31691842

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Project description:BackgroundChronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care.MethodsWe included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features.ResultsThe selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each one-versus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification.ConclusionsUsing supervised machine learning to predict chiropractic service utilization remains challenging, with only a small shift in predictive probability over naïve classification and limited clinical utility. Future work should examine mechanisms to improve model performance.

Project description:BackgroundPsychiatric comorbidities may complicate depression treatment by being associated with increased role impairments. However, depression symptom severity might account for these associations. Understanding the independent associations of depression severity and comorbidity with impairments could help in treatment planning. This is especially true for depressed Veterans, who have high psychiatric comorbidity rates.Methods2,610 Veterans beginning major depression treatment at the Veterans Health Administration (VHA) were administered a baseline self-report survey that screened for diverse psychiatric comorbidities and assessed depression severity and role impairments. Logistic and generalized linear regression models estimated univariable and multivariable associations of depression severity and comorbidities with impairments. Population attributable risk proportions (PARPs) estimated the relative importance of depression severity and comorbidities in accounting for role impairments.ResultsNearly all patients (97.8%) screened positive for at least one comorbidity and half (49.8%) for 4+ comorbidities. The most common positive screens were for generalized anxiety disorder (80.2%), posttraumatic stress disorder (77.9%), and panic/phobia (77.4%). Depression severity and comorbidities were significantly and additively associated with impairments in multivariable models. Associations were attenuated much less for depression severity than for comorbidities in multivariable versus univariable models. PARPs indicated that 15-60% of role impairments were attributable to depression severity and 5-32% to comorbidities.LimitationsThe screening scales could have over-estimated comorbidity prevalence. The cross-sectional observational design cannot determine either temporal or causal priorities.ConclusionsAlthough positive screens for psychiatric comorbidity are pervasive among depressed VHA patients, depression severity accounts for most of the associations of these comorbidities with role impairments.

Project description:BackgroundPhysician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes.ObjectiveTo examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment.DesignPatient self-report surveys at treatment initiation linked to medical records.SettingVeterans Health Administration (VA) clinics nationally, 2018-2020.ParticipantsA total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables.Key resultsMore patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence.ConclusionsMismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Project description:ImportanceHurricanes and flooding can interrupt health care utilization. Understanding the magnitude and duration of interruptions, as well as how they vary according to hazard exposure, race, and income, are important for identifying populations in need of greater retention in care.ObjectiveTo determine how the differential exposure to Hurricane Harvey in August 2017 is associated with changes in utilization of Veterans Health Administration health care.Design, setting, and participantsThis is a retrospective cohort analysis of primary care practitioner (PCP) visits, emergency department visits, and inpatient admissions in the Veterans Health Administration among Texas veterans residing in counties impacted by Hurricane Harvey from 2016 to 2018. Data analysis was performed from September 2020 to May 2021.ExposuresResidential flooding after Hurricane Harvey.Main outcomes and measuresInterrupted time series analysis measured changes in health care utilization over time, stratified by residential flood exposure, race, and income.ResultsOf the 99 858 patients in the cohort, 89 931 (90.06%) were male, and their median (range) age was 58 (21 to 102) years. Compared with veterans in nonflooded areas, veterans living in flooded areas were more likely to be Black (24 715 veterans [33.80%] vs 4237 veterans [15.85%]) and low-income (14 895 veterans [20.37%] vs 4853 veterans [18.15%]). Rates of PCP visits decreased by 49.78% (95% CI, -64.52% to -35.15%) for veterans in flooded areas and by 45.89% (95% CI, -61.93% to -29.91%) for veterans in nonflooded areas and did not rebound until more than 8 weeks after the hurricane. Rates of PCP visits in flooded areas remained lower than expected for 11 weeks among White veterans (-6.99%; 95% CI, -14.36% to 0.81%) and for 13 weeks among racial minority veterans (-7.22%; 95% CI, -14.11% to 0.30%). Low-income veterans, regardless of flood status, experienced greater suppression of PCP visits in the 8 weeks following the hurricane (-13.72%; 95% CI, -20.51% to -6.68%) compared with their wealthier counterparts (-9.63%; 95% CI, -16.74% to -2.26%).Conclusions and relevanceThese findings suggest that flood disasters such as Hurricane Harvey may be associated with declines in health care utilization that differ according to flood status, race, and income strata. Patients most exposed to the disaster also had the greatest delay or nonreceipt of care.

Dataset Information

Social connectedness, depression symptoms, and health service utilization: a longitudinal study of Veterans Health Administration patients.

Publications

Social connectedness, depression symptoms, and health service utilization: a longitudinal study of Veterans Health Administration patients.

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