Behavioral health conditions and potentially preventable diabetes-related hospitalizations in the United States: Findings from a national sample of commercial claims data

Effects of Mental Illness Amongst Adults in the United States Living With Diabetes Mellitus on Hospital Admissions

OBJECTIVE

To examine the influence of comorbid mental illness on hospitalization among adults reporting diabetes mellitus.

METHODS

This cross-sectional observational study used National Health Interview Survey (NHIS) data from 2000-2018 to examine hospitalization. Mental illness was defined as no to low psychological distress (NLPD), moderate psychological distress (MPD), and serious psychological distress (SPD) as per the Kessler-6 scale. Socio-demographic factors and health status were added as covariates in binary logistic regression.

RESULTS

This study involved 48,807 survey participants and reflected an estimated population of 17,524,418 adults with diabetes in the United States, of whom 19.9% were hospitalized in the year prior to the survey. Among those who were hospitalized, 71.5% exhibited None to Low Psychological Distress (NLPD), 17.7% reported Moderate Psychological Distress (MPD), and 10.8% reported Serious Psychological Distress (SPD). Conversely, among non-hospitalized individuals, the percentages were as follows: 83.2% had NLPD, 11.4% had MPD, and 5.3% had SPD. The odds ratio (OR) for hospitalization was found to be OR=1.31 (95% CI: 1.20, 1.43, p<0.0001) for MPD and OR=1.42 (95% CI: 1.28, 1.58, p<0.0001) for SPD, in comparison to those with no or low psychological distress.

CONCLUSION

Among adults with diabetes mellitus, those with mental illness were more likely to be hospitalized than those without mental illness. Programs and policies to improve care among adults with both mental illness and diabetes may help to reduce hospitalizations.

Untreated substance use disorder affects glycemic control: Results in patients with type 2 diabetes served within a network of community-based healthcare centers in Florida

Introduction

Patients with diabetes and comorbid substance use disorders (SUD) experience poor diabetes management, increased medical complications and mortality. However, research has documented that patients engaged in substance abuse treatment have better management of their comorbid conditions. The current study examines diabetes management among patients with type 2 diabetes, with and without comorbid SUD, receiving care at Florida-based Federally Qualified Health Centers (FQHC) of Health Choice Network (HCN).

Methods

A retrospective analysis was conducted using deidentified electronic health records of 37,452 patients with type 2 diabetes who received care at a HCN site in Florida between 2016 and 2019. A longitudinal logistic regression analysis examined the impact of SUD diagnosis on achievement of diabetes management [HbA1c < 7.0% (53 mmol/mol)] over time. A secondary analysis evaluated, within those with an SUD diagnosis, the likelihood of HbA1c control between those with and without SUD treatment.

Results

The longitudinal assessment of the relationship between SUD status and HbA1c control revealed that those with SUD (N = 6,878, 18.4%) were less likely to control HbA1c over time (OR = 0.56; 95% CI = 0.49-0.63). Among those with SUD, patients engaged in SUD treatment were more likely to control HbA1c (OR = 5.91; 95% CI = 5.05-6.91).

Discussion

Findings highlight that untreated SUD could adversely affect diabetes control and sheds light on the opportunity to enhance care delivery for patients with diabetes and co-occurring SUD.

The impact of comorbid severe mental illness and common chronic physical health conditions on hospitalisation: A systematic review and meta-analysis

Background

People with severe mental illness (SMI) are at higher risk of physical health conditions compared to the general population, however, the impact of specific underlying health conditions on the use of secondary care by people with SMI is unknown. We investigated hospital use in people managed in the community with SMI and five common physical long-term conditions: cardiovascular diseases, COPD, cancers, diabetes and liver disease.

Methods

We performed a systematic review and meta-analysis (Prospero: CRD42020176251) using terms for SMI, physical health conditions and hospitalisation. We included observational studies in adults under the age of 75 with a diagnosis of SMI who were managed in the community and had one of the physical conditions of interest. The primary outcomes were hospital use for all causes, physical health causes and related to the physical condition under study. We performed random-effects meta-analyses, stratified by physical condition.

Results

We identified 5,129 studies, of which 50 were included: focusing on diabetes (n = 21), cardiovascular disease (n = 19), COPD (n = 4), cancer (n = 3), liver disease (n = 1), and multiple physical health conditions (n = 2). The pooled odds ratio (pOR) of any hospital use in patients with diabetes and SMI was 1.28 (95%CI:1.15–1.44) compared to patients with diabetes alone and pooled hazard ratio was 1.19 (95%CI:1.08–1.31). The risk of 30-day readmissions was raised in patients with SMI and diabetes (pOR: 1.18, 95%CI:1.08–1.29), SMI and cardiovascular disease (pOR: 1.27, 95%CI:1.06–1.53) and SMI and COPD (pOR:1.18, 95%CI: 1.14–1.22) compared to patients with those conditions but no SMI.

Conclusion

People with SMI and five physical conditions are at higher risk of hospitalisation compared to people with that physical condition alone. Further research is warranted into the combined effects of SMI and physical conditions on longer-term hospital use to better target interventions aimed at reducing inappropriate hospital use and improving disease management and outcomes.

Healthcare resource use and costs for people with type 2 diabetes mellitus with and without severe mental illness in England: longitudinal matched-cohort study using the Clinical Practice Research Datalink

BACKGROUND

Approximately 60 000 people in England have coexisting type 2 diabetes mellitus (T2DM) and severe mental illness (SMI). They are more likely to have poorer health outcomes and require more complex care pathways compared with those with T2DM alone. Despite increasing prevalence, little is known about the healthcare resource use and costs for people with both conditions.

AIMS

To assess the impact of SMI on healthcare resource use and service costs for adults with T2DM, and explore the predictors of healthcare costs and lifetime costs for people with both conditions.

METHOD

This was a matched-cohort study using data from the Clinical Practice Research Datalink linked to Hospital Episode Statistics for 1620 people with comorbid SMI and T2DM and 4763 people with T2DM alone. Generalised linear models and the Bang and Tsiatis method were used to explore cost predictors and mean lifetime costs respectively.

RESULTS

There were higher average annual costs for people with T2DM and SMI (£1930 higher) than people with T2DM alone, driven primarily by mental health and non-mental health-related hospital admissions. Key predictors of higher total costs were older age, comorbid hypertension, use of antidepressants, use of first-generation antipsychotics, and increased duration of living with both conditions. Expected lifetime costs were approximately £35 000 per person with both SMI and T2DM. Extrapolating nationally, this would generate total annual costs to the National Health Service of around £250 m per year.

CONCLUSIONS

Our estimates of resource use and costs for people with both T2DM and SMI will aid policymakers and commissioners in service planning and resource allocation.

Behavioral Health in Primary Care: Expanding Applications in Practice

Chronic diseases continue to contribute to the leading causes of death in Missouri, and behavioral health plays a key role in their management. Patients receive most of their healthcare in primary care, suggesting primary care providers may serve an important role in the patients' chronic disease management and the behavioral health sequalae. This paper overviews the behavioral health impact of three chronic diseases impacting Missourians and reviews evidence for behavioral health interventions for each disease.

Disparities in diabetes-related avoidable hospitalization among diabetes patients with disability using a nationwide cohort study

Diabetes is an ambulatory care sensitive condition that quality of care can prevent complications development and hospitalization needs. However, diabetes patients with disability face greater challenges with receiving quality diabetes care than those without disabilities. This study examined diabetes-related avoidable hospitalizations (DRAH) focusing on the association with disability. We used nationally representative health insurance cohort data from 2002 to 2013. The study population is people who were newly diagnosed with type 2 diabetes. We measured the cumulated number of DRAH using the Prevention Quality Indicators (PQIs). The variables of interest were disability severity and type. We performed a recurrent events analysis using Cox proportional hazard regression model. Among 49,410 type 2 diabetes patients, 12,231 (24.8%) experienced DRAHs at least once during the follow-up period. Among the total population, 5924 (12.0%) diabetes patients were registered as disabled. The findings report that disability severity was significantly associated with higher risks for DRAH, where severely disabled diabetes patients showed the highest hazard ratio of 2.24 (95% CI 1.80-2.79). Among three DRAH indicators, severely disabled diabetes patients showed increased risks for long-term (AHR 2.21, 95% CI 1.89-2.60) and uncontrolled (AHR 2.28, 95% CI 1.80-2.88) DRAH. In addition, intellectual (AHR 5.52, 95% CI 3.78-8.05) and mental (AHR 3.97, 95% CI 2.29-6.89) disability showed higher risks than other types of disability. In conclusion, diabetes patients with disability are at higher risk for DRAH compared to those without disabilities, and those with intellectual and mental disabilities were more likely to experience DRAH compared to those with physical or other types of disability. These findings call for action to find the more appropriate interventions to improve targeted diabetes primary care for patients with disability. Further research is needed to better understand determinants of increasing risks of DRAH.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Potentially preventable hospitalisations for physical health conditions in community mental health service users: a population-wide linkage study

AIMS

Mental health (MH) service users have increased prevalence of chronic physical conditions such as cardio-respiratory diseases and diabetes. Potentially Preventable Hospitalisations (PPH) for physical health conditions are an indicator of health service access, integration and effectiveness, and are elevated in long term studies of people with MH conditions. We aimed to examine whether PPH rates were elevated in MH service users over a 12-month follow-up period more suitable for routine health indicator reporting. We also examined whether MH service users had increased PPH rates at a younger age, potentially reflecting the younger onset of chronic physical conditions.

METHODS

A population-wide data linkage in New South Wales (NSW), Australia, population 7.8 million. PPH rates in 178 009 people using community MH services in 2016-2017 were compared to population rates. Primary outcomes were crude and age- and disadvantage-standardised annual PPH episode rate (episodes per 100 000 population), PPH day rate (hospital days per 100 000) and adjusted incidence rate ratios (AIRR).

RESULTS

MH service users had higher rates of PPH admission (AIRR 3.6, 95% CI 3.5-3.6) and a larger number of hospital days (AIRR 5.2, 95% CI 5.2-5.3) than other NSW residents due to increased likelihood of admission, more admissions per person and longer length of stay. Increases were greatest for vaccine-preventable conditions (AIRR 4.7, 95% CI 4.5-5.0), and chronic conditions (AIRR 3.7, 95% CI 3.6-3.7). The highest number of admissions and relative risks were for respiratory and metabolic conditions, including chronic obstructive airways disease (AIRR 5.8, 95% CI 5.5-6.0) and diabetic complications (AIRR 5.4, 95% CI 5.1-5.8). One-quarter of excess potentially preventable bed days in MH service users were due to vaccine-related conditions, including vaccine-preventable respiratory illness. Age-related increases in risk occurred earlier in MH service users, particularly for chronic and vaccine-preventable conditions. PPH rates in MH service users aged 20-29 were similar to population rates of people aged 60 and over. These substantial differences were not explained by socio-economic disadvantage.

CONCLUSIONS

PPHs for physical health conditions are substantially increased in people with MH conditions. Short term (12-month) PPH rates may be a useful lead indicator of increased physical morbidity and less accessible, integrated or effective health care. High hospitalisation rates for vaccine-preventable respiratory infections and hepatitis underline the importance of vaccination in MH service users and suggests potential benefits of prioritising this group for COVID-19 vaccination.

Assessment of Out-of-Network Billing for Privately Insured Patients Receiving Care in In-Network Hospitals

IMPORTANCE

Although surprise medical bills are receiving considerable attention from lawmakers and the news media, to date there has been little systematic study of the incidence and financial consequences of out-of-network billing.

OBJECTIVE

To examine out-of-network billing among privately insured patients with an inpatient admission or emergency department (ED) visit at in-network hospitals.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective analysis using data from the Clinformatics Data Mart database (Optum), which includes health insurance claims for individuals from all 50 US states receiving private health insurance from a large commercial insurer was conducted of all inpatient admissions (n = 5 457 981) and ED visits (n = 13 579 006) at in-network hospitals between January 1, 2010, and December 31, 2016. Data were collected and analyzed in March 2019.

EXPOSURES

Receipt of a bill for care from at least 1 out-of-network physician or medical transport service associated with patient admission or ED visit.

MAIN OUTCOMES AND MEASURES

The incidence of out-of-network billing and the potential amount of patients' financial liability associated with out-of-network bills from the admission or visit.

RESULTS

Of 5 457 981 inpatient admissions and 13 579 006 ED admissions between 2010 and 2016, the percentage of ED visits with an out-of-network bill increased from 32.3% to 42.8% (P < .001) during the study period, and the mean (SD) potential financial responsibility for these bills increased from $220 ($420) to $628 ($865) (P < .001; all dollar values in 2018 US$). Similarly, the percentage of inpatient admissions with an out-of-network bill increased from 26.3% to 42.0% (P < .001), and the mean (SD) potential financial responsibility increased from $804 ($2456) to $2040 ($4967) (P < .001).

CONCLUSIONS AND RELEVANCE

Out-of-network billing appears to have become common for privately insured patients even when they seek treatment at in-network hospitals. The mean amounts billed appear to be sufficiently large that they may create financial strain for a substantial proportion of patients.