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Linking Primary and Secondary Care after Psychiatric Hospitalization: Comparison between Transitional Case Management Setting and Routine Care for Common Mental Disorders.

ABSTRACT: OBJECTIVES:To improve engagement with care and prevent psychiatric readmission, a transitional case management intervention has been established to link with primary and secondary care. The intervention begins during hospitalization and ends 1?month after discharge. The goal of this study was to assess the effectiveness of this short intervention in terms of the level of engagement with outpatient care and the rate of readmissions during 1?year after discharge. METHODS:Individuals hospitalized with common mental disorders were randomly assigned to be discharged to routine follow-up by private psychiatrists or general practitioners with (n?=?51) or without (n?=?51) the addition of a transitional case management intervention. Main outcome measures were number of contacts with outpatient care and rate of readmission during 12?months after discharge. RESULTS:Transitional case management patients reported more contacts with care service in the period between 1 and 3?months after discharge (p?=?0.004). Later after discharge (3-12?months), no significant differences of number of contacts remained. The transitional case management intervention had no statistically significant beneficial impact on the rate of readmission (hazard ratio?=?0.585, p?=?0.114). CONCLUSION:The focus on follow-up after discharge during hospitalization leads to an increased short-term rate of engagement with ambulatory care despite no differences between the two groups after 3?months of follow-up. This short transitional intervention did, however, not significantly reduce the rate of readmissions during the first year following discharge. TRIAL REGISTRATION NUMBER:ClinicalTrials.gov Identifier NCT02258737.

SUBMITTER: Bonsack C

PROVIDER: S-EPMC4889580 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Linking Primary and Secondary Care after Psychiatric Hospitalization: Comparison between Transitional Case Management Setting and Routine Care for Common Mental Disorders.

Bonsack Charles C Golay Philippe P Gibellini Manetti Silvia S Gebel Sophia S Ferrari Pascale P Besse Christine C Favrod Jérome J Morandi Stéphane S

Frontiers in psychiatry 20160602

<h4>Objectives</h4>To improve engagement with care and prevent psychiatric readmission, a transitional case management intervention has been established to link with primary and secondary care. The intervention begins during hospitalization and ends 1 month after discharge. The goal of this study was to assess the effectiveness of this short intervention in terms of the level of engagement with outpatient care and the rate of readmissions during 1 year after discharge.<h4>Methods</h4>Individuals ...[more]

PMID: 27313547

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Project description:Background/objectivesCare-partner support affects outcomes among assisted living (AL) residents. Yet, little is known about care-partner support and its effects on hospitalization during post-acute care transitions. This study examined the variation in care-partner support and its impact on hospitalizations among AL residents receiving Medicare home health (HH) services.DesignAnalysis of national data from the Outcome and Assessment Information Set, Medicare claims, Area Health Resources File, and the Social Deprivation Index File.SettingAL facilities and Medicare HH agencies in the United States.Participants741,926 Medicare HH admissions of AL residents in 2017.MeasurementsCare-partner support during the HH admission was measured based on the type and frequency of assistance from AL staff in seven domains (i.e., activities of daily living (ADL), instrumental ADLs, medication administration, treatment, medical equipment, home safety, and transportation). Care-partner support in each domain was measured as "assistance not needed" (reference group), "Care-partner currently provides assistance," "care-partner need additional training/support to provide assistance" (i.e., inadequate care-partner support), and "care-partner unavailable/unlikely to provide assistance" (i.e., unavailable care-partner support). Outcome was time-to-hospitalization during the HH admission.ResultsAmong the 741,926 Medicare HH admissions of AL residents, inadequate care-partner support was identified for all seven domains that ranged from 13.1% (for transportation) to 49.8% (for treatment), and care-partner support was unavailable from 0.9% (for transportation) to 11.0% (for treatment). In Cox proportional hazard models adjusted for patient covariates and geography, compared with "assistance not needed", having inadequate and unavailable care-partner support was related to increased risk of hospitalization by 8.9% (treatment (hazard ratio (HR) =1.089, P < .001)) to 41.3% (medication administration (HR =1.413, P < .001)).ConclusionFor AL residents receiving HH services, having less care-partner support was related to increased risk of hospitalization, particularly regarding medication administration, medical equipment, and transportation/advocacy.

Project description:BackgroundAccurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization.MethodsData from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients' socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking.ResultsAll models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use.ConclusionsGradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.

Project description:ImportancePsychiatric disorders are common among autistic children and adults. Little is known about sex differences in psychiatric disorders and hospitalization in early adulthood.ObjectiveTo examine sex differences in psychiatric diagnoses and hospitalizations in autistic compared with nonautistic young adults.Design, setting, and participantsThis population-based cohort study assessed all individuals born in Sweden between 1985 and 1997. A total of 1 335 753 individuals, including 20 841 autistic individuals (7129 [34.2%] female individuals), were followed up from age 16 through 24 years between 2001 and 2013. Analysis took place between June 2021 and August 2022.ExposuresAutism was defined as having received at least 1 clinical diagnosis of autism based on the International Classification of Diseases.Main outcomes and measuresThe cumulative incidence of 11 psychiatric diagnoses up until age 25 years was estimated, and birth year-standardized risk difference was used to compare autistic female and male individuals directly. Sex-specific birth year-adjusted hazard ratios (HRs) with 95% CIs were calculated using Cox regression. Analyses were repeated for inpatient diagnoses to assess psychiatric hospitalization.ResultsOf 1 335 753 individuals included in this study, 650 314 (48.7%) were assigned female at birth. Autism was clinically diagnosed in 20 841 individuals (1.6%; 7129 [34.2%] female) with a mean (SD) age of 16.1 (5.1) years (17.0 [4.8] years in female individuals and 15.7 [5.2] years in male individuals) for the first recorded autism diagnosis. For most disorders, autistic female individuals were at higher risk for psychiatric diagnoses and hospitalizations. By age 25 years, 77 of 100 autistic female individuals and 62 of 100 autistic male individuals received at least 1 psychiatric diagnosis. Statistically significant standardized risk differences were observed between autistic female and male individuals for any psychiatric disorder (-0.18; 95% CI, -0.26 to -0.10) and specifically for anxiety, depressive, and sleep disorders. Risk differences were larger among autistic than nonautistic individuals. Compared with nonautistic same-sex individuals, autistic female individuals (HR range [95% CI], 3.17 [2.50-4.04.]-20.78 [18.48-23.37]) and male individuals (HR range [95% CI], 2.98 [2.75-3.23]-18.52 [17.07-20.08]) were both at increased risk for all psychiatric diagnoses. Any psychiatric hospitalization was statistically significantly more common in autistic female individuals (32 of 100) compared with autistic male individuals (19 of 100). However, both autistic female and male individuals had a higher relative risk for psychiatric hospitalization compared with nonautistic female and male individuals for all disorders (female individuals: HR range [95% CI], 5.55 [4.63-6.66]-26.30 [21.50-32.16]; male individuals: HR range [95% CI], 3.79 [3.22-4.45]-29.36 [24.04-35.87]).Conclusions and relevanceThese findings highlight the need for profound mental health services among autistic young adults. Autistic female individuals, who experience more psychiatric difficulties at different levels of care, require increased clinical surveillance and support.

Project description:ImportancePrompted by null findings from several care transition trials and practice changes for heart failure in recent years, leaders from a large integrated health care system aimed to reassess the outcomes of its 10-year multicomponent transitional care program for heart failure (HF-TCP).ObjectiveTo examine the association of the individual HF-TCP components and their bundle with the primary outcome of all-cause 30-day inpatient or observation stay readmissions.Design, setting, and participantsThis retrospective cohort study included patients enrolled in the HF-TCP during an inpatient encounter for heart failure at 13 Kaiser Permanente Southern California hospitals from January 1, 2013, to October 31, 2018, who were followed up from discharge until 30 days, readmission, or death. Data were analyzed from May 7, 2019, to May 1, 2020, with additional review from September 2 to October 1, 2020.ExposuresPatients received 1 home health visit or telecare (telephone) visit from a registered nurse within 2 days of hospital discharge, a heart failure care manager call within 7 days, and a clinic visit with a physician or a nurse practitioner within 7 days.Main outcomes and measuresMultivariable proportional hazards regression models were used to estimate the probability of 30-day readmission for those who received the individual or bundled HF-TCP components compared with those who did not.ResultsA total of 26 128 patients were included; 57.0% were male, and the mean (SD) age was 73 (13) years. The 30-day readmission rate was 18.1%. Both exposure to a home health visit within 2 days of discharge (hazard ratio [HR], 1.03; 95% CI, 0.96-1.10) and a 7-day heart failure case manager call (HR, 1.08; 95% CI, 0.99-1.18) compared with no visit or call were not associated with a lower rate of readmission. Completion of a 7-day clinic visit was associated with a lower readmission rate (HR, 0.88; 95% CI, 0.81-0.94) compared with no clinic visit. There were no synergistic effects of all 3 components compared with clinic visit alone (HR, 1.05; 95% CI, 0.87-1.28).Conclusions and relevanceThis study found that HF-TCP as a whole was not associated with a reduction in 30-day readmission rates, although a follow-up clinic visit within 7 days of discharge may be helpful. These findings highlight the importance of continuous quality improvement and refinement of existing clinical programs.

Project description:BackgroundThe EMPA-REG OUTCOME trial (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 diabetes Mellitus Patients) showed that empagliflozin, a sodium-glucose cotransporter-2 inhibitor, reduces the risk of hospitalization for heart failure (HHF) by 35%, on top of standard of care in patients with type 2 diabetes mellitus (T2D) and established cardiovascular disease. The EMPRISE (Empagliflozin Comparative Effectiveness and Safety) study aims to assess empagliflozin's effectiveness, safety, and healthcare utilization in routine care from August 2014 through September 2019. In this first interim analysis, we investigated the risk of HHF among T2D patients initiating empagliflozin versus sitagliptin, a dipeptidyl peptidase-4 inhibitor.MethodsWithin 2 commercial and 1 federal (Medicare) claims data sources in the United States, we identified a 1:1 propensity score-matched cohort of T2D patients ≥18 years old initiating empagliflozin or sitagliptin from August 2014 through September 2016. The HHF outcome was defined as a HF discharge diagnosis in the primary position (HHF-specific); a broader definition was based on a HF discharge diagnosis in any position (HHF-broad). Hazard ratios (HRs) and 95% CIs were estimated controlling for over 140 baseline characteristics in each data source and pooled by fixed-effects meta-analysis.ResultsAfter propensity-score matching, we identified 16,443 patient pairs who initiated empagliflozin or sitagliptin. Average age was approximately 59 years, almost 54% of the participants were males, and approximately 25% had records of existing cardiovascular disease. Compared with sitagliptin, the initiation of empagliflozin decreased the risk of HHF-specific by 50% (HR, 0.50; 95% CI, 0.28-0.91), and the risk of HHF-broad by 49% (HR, 0.51;95% CI, 0.39-0.68), over a mean follow-up of 5.3 months. The results were consistent in patients with and without baseline cardiovascular disease, and for empagliflozin at both the 10- and 25-mg daily doses; analyses comparing empagliflozin versus the dipeptidyl peptidase-4 inhibitor class, and comparing sodium-glucose cotransporter-2 inhibitor versus dipeptidyl peptidase-4 inhibitor classes also produced consistent findings.ConclusionsThe first interim analysis from EMPRISE showed that compared with sitagliptin, the initiation of empagliflozin was associated with a decreased risk of HHF among patients with T2D as treated in routine care, with and without a history of cardiovascular disease.Clinical trial registrationURL: https://www.clinicaltrials.gov . Unique identifier: NCT03363464.

Dataset Information

Linking Primary and Secondary Care after Psychiatric Hospitalization: Comparison between Transitional Case Management Setting and Routine Care for Common Mental Disorders.

Publications

Linking Primary and Secondary Care after Psychiatric Hospitalization: Comparison between Transitional Case Management Setting and Routine Care for Common Mental Disorders.

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