Psychiatric Medication Changes Associated With Increased Rate of Medical Readmissions in Patients With Serious Mental Illness

Factors associated with unplanned readmissions for patients with mental and behavioural disorders in China: a quantitative analysis

BACKGROUND

Unplanned readmissions among patients with mental and behavioural disorders (MBDs) disrupt inpatient recovery and impose financial burdens on families and healthcare systems.

OBJECTIVES

To estimate the 31-day unplanned inpatient readmission rates for MBDs in China and identify determinant profiles from the perspective of individual, hospital, and contextual levels.

METHODS

Data from patients with MBDs were collected from the medical records of 99 public hospitals across 10 cities. A total of 49,352 inpatient admissions were analysed based on the proposed conceptual model using multilevel logistic regressions.

RESULTS

The 31-day unplanned readmission rate (excluding 0-1-day returns) was 8.6% (95% CI: 8.4-8.9%). Determinant profiles differed across the overall group and subgroups. The number of general practitioners within cities was associated with reduced risk of unplanned readmissions. Hospital factors such as facility type and size, human resources, and revenue size were associated with unplanned readmissions only in specific subgroups. Additionally, individual-level factors, including demographic information (e.g. gender, age, marital status, and occupational status), disease-related factors (e.g. primary diagnostic group, condition at admission, and other diagnoses), and clinical characteristics (e.g. length of stay and medical costs), were associated with unplanned readmissions across all subgroups.

CONCLUSION

The study emphasises collaborative efforts from health systems, hospitals, and patients to reduce unplanned readmissions for MDBs. Health systems should focus on improving access to care, enhancing quality, and ensuring continuity while providing incentives for hospitals. Additionally, hospitals should prioritise the identification and effective management of their high-risk patients.

Pre-Implementation Strategies to Support Adaptation of Thrive: A Care Transitions Model for Economically Disadvantaged Patients with Serious Mental Illness

BACKGROUND

Economically disadvantaged patients diagnosed with serious mental illness (SMI) experience post-hospitalizations disparities due to fragmented care transitions.

PURPOSE

To describe the pre-implementation strategies used to adapt and implement a nurse-led transitional care intervention (Thrive) to meet the needs of economically disadvantaged patients diagnosed with an SMI.

METHODS

Two pre-implementation strategies, Evidence Based Quality Improvement (EBQI) meetings and Formative Evaluation (FE) research, were used to adapt intervention components. FE data included semi-structured interviews analyzed using Rapid Qualitative Analysis.

FINDINGS

Adaptations were made to core components of Thrive and strategies to support implementation were identified.

CONCLUSIONS

Participatory strategies help to adapt interventions that are person-centered and tailored to the organizational context.

TRIAL

NCT06203509.

Clinical, sociodemographic and environmental predicting factors for relapse in bipolar disorder: A systematic review

INTRODUCTION

Bipolar disorder (BD) is a chronic and recurrent illness characterized by manic, mixed or depressive episodes, alternated with periods of euthymia. Several prognostic factors are associated with higher rates of relapse, which is crucial for the identification of high-risk individuals. This study aimed at systematically reviewing the existing literature regarding the impact of sociodemographic, clinical and environmental factors, in clinical relapses, recurrences and hospitalizations due to mood episodes in BD.

METHODS

A systematic search of electronic databases (PubMed, Cochrane library and Web of Science) was conducted to integrate current evidence about the impact of specific risk factors in these outcomes.

RESULTS

Fifty-eight articles met the inclusion criteria. Studies were grouped by the type of factors assessed. Family and personal psychiatric history, more severe previous episodes, earlier age of onset, and history of rapid cycling are associated with clinical relapses, along with lower global functioning and cognitive impairments. Unemployment, low educational status, poorer social adjustment and life events are also associated with higher frequency of episodes, and cannabis with a higher likelihood for rehospitalization.

LIMITATIONS

Small sample sizes, absence of randomized clinical trials, diverse follow-up periods, lack of control for some confounding factors, heterogeneous study designs and diverse clinical outcomes.

CONCLUSIONS

Although current evidence remains controversial, several factors have been associated with an impaired prognosis, which might allow clinicians to identify patients at higher risk for adverse clinical outcomes and find modifiable factors. Further research is needed to elucidate the impact of each risk factor in the mentioned outcomes.

Risk factors associated with 30-day unplanned hospital readmission for patients with mental illness

Unplanned hospital readmission rate is up to 43% in mental health settings, which is higher than in general health settings. Unplanned readmissions delay the recovery of patients with mental illness and add financial burden on families and healthcare services. There have been efforts to reduce readmissions with a particular interest in identifying patients at higher readmission risk after index admission; however, the results have been inconsistent. This systematic review synthesized risk factors associated with 30-day unplanned hospital readmissions for patients with mental illness. Eleven electronic databases were searched from 2010 to 30 September 2021 using key terms of 'mental illness', 'readmission' and 'risk factors'. Sixteen studies met the selection criteria for this review. Data were synthesized using content analysis and presented in narrative and tabular form because the extracted risk factors could not be pooled statistically due to methodological heterogeneity of the included studies. Consistently cited readmission predictors were patients with lower educational background, unemployment, previous mental illness hospital admission and more than 7 days of the index hospitalization. Results revealed the complexity of identifying unplanned hospital readmission predictors for people with mental illness. Policymakers need to specify the expected standards that written discharge summary must reach general practitioners concurrently at discharge. Hospital clinicians should ensure that discharge summary summaries are distributed to general practitioners for effective ongoing patient care and management. Having an advanced mental health nurse for patients during their transition period needs to be explored to understand how this role could ensure referrals to the general practitioner are eventuated.

Rural-urban prescribing patterns by primary care and behavioral health providers in older adults with serious mental illness

BACKGROUND

Older adults with serious mental illness (SMI) often have multiple comorbidities and complex medication schedules. Shortages of behavioral health specialists (BHSs), especially in rural areas, frequently make primary care providers (PCPs) the only clinician managing this complex population. The aim of this study was to describe rural/urban psychiatric medication prescribing in older adults with SMI by PCPs and BHSs, and by clinician type.

METHODS

This retrospective descriptive analysis used 2018 Medicare data to identify individuals with a bipolar, major depression, schizophrenia, or psychosis diagnosis and examined medication claims for antianxiety, antidepressants, antipsychotics, hypnotics, and anticonvulsants. Descriptive statistics summarized percentage of medications provided by PCPs and BHSs stratified by rural and urban areas and by drug class. Additional analyses compared psychiatric prescribing patterns by physicians, advanced practice registered nurses (APRNs), and physician assistants (PAs).

RESULTS

In urban areas, PCPs prescribed at least 50% of each psychiatric medication class, except antipsychotics, which was 45.2%. BHSs prescribed 40.7% of antipsychotics and less than 25% of all other classes. In rural areas, percentages of psychiatric medications from PCPs were over 70% for each medication class, except antipsychotics, which was 60.1%. Primary care physicians provided most psychiatric medications, between 36%-57% in urban areas and 47%-65% in rural areas. Primary care APRNs provided up to 13% of prescriptions in rural areas, which was more than the amount prescribed by BHS physicians, expect for antipsychotics. Psychiatric mental health APRNs provided up to 7.5% of antipsychotics in rural areas, but their prescribing contribution among other classes ranged between 1.1%-3.6%. PAs provided 2.5%-3.4% of medications in urban areas and this increased to 3.9%-5.1% in rural areas.

CONCLUSIONS

Results highlight the extensive roles of PCPs, including APRNs, in managing psychiatric medications for older adults with SMI.