Dataset Information

Hospital and patient factors influencing the health status among patients with schizophrenia, thirty days after hospital discharge: multi-level analysis

ABSTRACT:

Background

The time between discharge from hospital and transition to community and home is a critical period for health status among patients with a mental illness, including patients with schizophrenia. This study aimed to investigate crucial patient factors (patient-level) and hospital factors (hospital-level) affecting health status and see whether patient factor effects on health status vary with hospital factors, 30 days after hospital discharge.

Methods

This is a prospective study of 1255 patients with schizophrenia and their primary caregivers from 13 public mental hospitals across Thailand. Logistic regression and multi-level logistic regression was used to investigate the effects of patient and hospital factors simultaneously on health status, 30 days after hospital discharge.

Results

The intraclass correlation coefficient indicated that 14% of the change in health status was explained by the differences between hospital. Poor health status was identified in 14.26% of patients, 30 days after hospital discharge. The majority of participant patients were male (69.8%), single (71.87%), and the average age was 38.09 (SD?= 9.74). The finding also showed that the patient factors; being female (ORadj .53, 95%CI .31,.92), perceived moderate and high levels of positive aspect of caregiving (ORadj .24, 95%CI .14,.42 and ORadj .05, 95%CI .02,.09), perceived readiness for hospital discharge (ORadj .21, 95%CI .13,.33), partial and full adherence to treatment (ORadj .24, 95%CI .14,.42 and ORadj .31, 95%CI .20,.47) showed a reduced likelihood of developing poor health status except substance use (ORadj 1.55, 95%CI .98, 2.44). Hospital factors; discharge planning process and nurse-patient ratio (ORadj 1.64, 95%CI 1.17, 2.30 and ORadj 1.16, 95%CI 1.09, 1.22) showed an increased likelihood of developing poor health status, 30 days after hospital discharge.

Conclusions

Findings provide relevant information on how both patient and hospital factors determine health status. These results might lead to better targeting of mental health service policy and enable more precise information gathering and allocation of resources. However, future research should be more focused and continue investigating the pathways through which hospital factors influence health status post-discharge.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-020-03001-4.

SUBMITTER: Smithnaraseth A

PROVIDER: S-EPMC7737347 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ImportanceThe Centers for Medicare & Medicaid Services recently identified 30-day readmission after discharge from inpatient rehabilitation facilities as a national quality indicator. Research is needed to determine the rates and factors related to readmission in this patient population.ObjectiveTo determine 30-day readmission rates and factors related to readmission for patients receiving postacute inpatient rehabilitation.Design, setting, and patientsRetrospective cohort study of records for 736,536 Medicare fee-for-service beneficiaries (mean age, 78.0 [SD, 7.3] years) discharged from 1365 inpatient rehabilitation facilities to the community in 2006 through 2011. Sixty-three percent of patients were women, and 85.1% were non-Hispanic white.Main outcomes and measuresThirty-day readmission rates for the 6 largest diagnostic impairment categories receiving inpatient rehabilitation. These included stroke, lower extremity fracture, lower extremity joint replacement, debility, neurologic disorders, and brain dysfunction.ResultsMean rehabilitation length of stay was 12.4 (SD, 5.3) days. The overall 30-day readmission rate was 11.8% (95% CI, 11.7%-11.8%). Rates ranged from 5.8% (95% CI, 5.8%-5.9%) for patients with lower extremity joint replacement to 18.8% (95% CI, 18.8%-18.9%). for patients with debility. Rates were highest in men (13.0% [ 95% CI, 12.8%-13.1%], vs 11.0% [95% CI, 11.0%-11.1%] in women), non-Hispanic blacks (13.8% [95% CI, 13.5%-14.1%], vs 11.5% [95% CI, 11.5%-11.6%] in whites, 12.5% [95% CI, 12.1%-12.8%] in Hispanics, and 11.9% [95% CI, 11.4%-12.4%] in other races/ethnicities), beneficiaries with dual eligibility (15.1% [95% CI, 14.9%-15.4%], vs 11.1% [95% CI, 11.0%-11.2%] for no dual eligibility), and in patients with tier 1 comorbidities (25.6% [95% CI, 24.9%-26.3%], vs 18.9% [95% CI, 18.5%-19.3%] for tier 2, 15.1% [95% CI, 14.9%-15.3%] for tier 3, and 9.9% [95% CI, 9.9%-10.0%] for no tier comorbidities). Higher motor and cognitive functional status were associated with lower hospital readmission rates across the 6 impairment categories. Adjusted readmission rates by state ranged from 9.2% to 13.6%. Approximately 50% of patients rehospitalized within the 30-day period were readmitted within 11 days of discharge. Medicare Severity Diagnosis-Related Group codes for heart failure, urinary tract infection, pneumonia, septicemia, nutritional and metabolic disorders, esophagitis, gastroenteritis, and digestive disorders were common reasons for readmission.Conclusions and relevanceAmong postacute rehabilitation facilities providing services to Medicare fee-for-service beneficiaries, 30-day readmission rates ranged from 5.8% to 18.8% for selected impairment groups. Further research is needed to understand the causes of readmission.

Project description:BACKGROUND:Status epilepticus (SE) is an emergency neurological disorder that affects quality of life and is associated with high mortality risk. Three scores have been developed to predict the risk of in-hospital death, but these scores are poor discrimination of mortality after discharge. This study aimed to develop and validate a simple risk score for long-term mortality in SE patients. METHODS:This retrospective cohort study was conducted using SE patient data collected from Thailand's Universal Coverage Scheme database between the fiscal years of 2005 and 2015 and followed-up to 2016. Patients who died in hospital or within 30?days after discharge were excluded. Data were divided at random into either a derivation or validation set. A proportional hazards model for the sub-distribution of competing risks was fitted with backward stepwise method. The coefficients from the model were used to develop a point-based scoring system. The discrimination ability of the model was evaluated using a time-dependent receiver operating characteristic (ROC) curve. RESULTS:A total of 20,792 SE patients (with ages ranging from the first day of life to 99?years at first admission) were randomly separated into two groups: 13,910 in the development group and 6882 in the validation group. A sub-distribution hazard model was used to determine nine predictors to be included in the final model, which was, in turn, used to develop the scoring system: age (0-19 points), male (two points), brain tumor (12 points), stroke (three points), cancer (11 points), diabetes (three points), chronic kidney disease (five points), pneumonia (five points), and urinary tract infection (four points). The possible total score ranged from zero to 64 and the cumulative incidence function was used to determine the probability of mortality associated with each total score within the first 10 years after the first admission. The area under the ROC curve (AUC) of the first to last time point ranged from 0.760 to 0.738. CONCLUSION:A nine-factor risk score for predicting 10-year mortality in SE patients was developed. Further studies should focus on external validity and including a range seizure types and duration of seizure as the predictors.

Project description:Examining health care system characteristics possibly associated with 30-day readmission may reveal opportunities to improve healthcare quality as well as reduce costs.Examine the relationship between 30-day mental health readmission for persons with schizophrenia and county-level community treatment characteristics.Observational study of 18 state Medicaid programs (N=274 counties, representing 103,967 enrollees with schizophrenia 28,083 of whom received more than 1 mental health hospitalization) using Medicaid administrative and United States Area Health Resource File data from 2005. Medicaid is a federal-state program and major health insurance provider for low income and disabled individuals, and the predominant provider of insurance for individuals with schizophrenia. The Area Health Resource File provides county-level estimates of providers. We first fit a regression model examining the relationship between 30-day mental health readmission and enrollee characteristics (e.g., demographics, substance use disorder [SUD], and general medical comorbidity) from which we created a county-level demographic and comorbidity case-mix adjuster. The case-mix adjuster was included in a second regression model examining the relationship between 30-day readmission and county-level factors: (i) quality (antipsychotic/visit continuity, post-hospital follow-up); (ii) mental health hospitalization (length of stay, admission rates); and (iii) treatment capacity (e.g., population-based estimates of outpatient providers/clinics). We calculated predicted probabilities of readmission for significant patient and county-level variables.Higher county rates of mental health visits within 7-days post-hospitalization were associated with lower readmission probabilities (e.g., county rates of 7-day follow up of 55% versus 85%, readmission predicted probability (PP) [95%CI]=16.1% [15.8%-16.4%] versus 13.3% [12.9%-13.6%]). In contrast, higher county rates of mental health hospitalization were associated with higher readmission probabilities (e.g., country admission rates 10% versus 30%, readmission predicted probability=11.3% [11.0%-11.6%] versus 16.7% [16.4%-17.0%]). Although not our primary focus, enrollee comorbidity was associated with higher predicted probability of 30-day mental health readmission: PP [95%CI] for enrollees with SUD=23.9% [21.5%-26.3%] versus 14.7% [13.9%-15.4%] for those without; PP [95% CI] for those with=three chronic medical conditions=25.1% [22.1%-28.2%] versus none=17.7% [16.3%-19.1].County rates of hospitalization and 7-day follow-up post hospital discharge were associated with readmission, along with patient SUD and general medical comorbidity. This observational design limits causal inference and utilization patterns may have changed since 2005. However, overall funding for U.S. Medicaid programs remained constant since 2005, reducing the likelihood significant changes. Last, our inability to identify community capacity variables associated with readmission may reflect imprecision of some variables as measured in the Area Health Resource File. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE AND FOR HEALTH POLICIES: Healthcare policy and programming to reduce 30-day mental health readmissions should focus on county-level factors that contribute to hospitalization in general and improving transitions to community care, as well as patient comorbidity.Given the likely importance of local care systems, to reduce readmission future research is needed to refine community-level capacity variables that are associated with reduced readmissions; and to evaluate models of care coordination in this population.

Project description:Patient experience plays an essential role in improving clinical effectiveness and patient safety. It's important to identify factors influencing patient experience and to improve quality of healthcare. To identify factors that influence patient experience in hospital wards. We conducted a systematic review including six databases; they were PubMed, CINAHL, Embase, PsycInfo, ProQuest, and Cochrane. Studies were included if they met the inclusion criteria. The JBI checklist was used to perform quality appraisal. We used 5 domains of the ecological model to organize and synthesize our findings to comprehensively understand the multi-level factors influencing the issue. A total of 138 studies were included, and 164 factors were identified. These factors were integrated into 6 domains. All domains but one (survey-related factors) could be mapped onto the attributes of the ecological framework: intrapersonal, interpersonal, institutional, community, and public policy level factors. All factors had mixed effect on patient experience. The intrapersonal level refers to individual characteristics of patients. The interpersonal level refers to interactions between patients and healthcare providers, such as the caring time spent by a nurse. The institutional level refers to organizational characteristics, rules and regulations for operations, such as hospital size and accreditation. The community level refers to relationships among organizations, institutions, and informational networks within defined boundaries, such as a hospital located in a larger population area. Public policy level refers to local, state, national, and global laws and policies, including health insurance policies. The sixth domain, survey-related factors, was added to the framework and included factors such as survey response rate and survey response time. The factors influencing patient experience are comprehensive, ranging from intrapersonal to public policy. Providers should adopt a holistic and integrated perspective to assess patient experience and develop context-specific interventions to improve the quality of care. PROSPERO REGISTRATION NUMBER CRD42023401066.