Medical Care最新文献

Background: Although the recent proliferation of telemental health care has transformed delivery of outpatient mental health care for many patients, little is known about population-level access to telehealth, hybrid, and in-person outpatient mental health care in the US.

Objectives: The objective of this report is to characterize patterns of all telehealth, hybrid, and all in-person outpatient mental health care by US adults.

Methods: An analysis is presented of 2021-2022 Medical Expenditure Panel Survey data (n=39,561) focusing on annual percentages of adults receiving all telehealth, hybrid, and all in-person outpatient mental health care. Results are presented overall and stratified by sociodemographic characteristics. Differences are reported in average marginal estimates from logistic regressions for each sociodemographic characteristic controlling for age group, sex, and psychological distress (Kessler-6).

Results: Approximately 12.0% of adults annually received outpatient mental health care, including 3.3% all telemental health care, 2.6% hybrid, and 6.1% all in-person mental health care. After controlling for age, sex, and distress, unemployed adults 65 years of age or younger were less likely than employed adults to receive all mental health care (-1.0 percentage points, 95% CI: -1.6 to -0.4), and uninsured individuals were less likely than those with private insurance (-2.8 percentage points, 95% CI: -3.6 to -1.9). By contrast, college graduates were 3.2 percentage points (95% CI: 2.3-4.0) more likely than those with less than a high school diploma, higher-income individuals were 1.6 percentage points (95% CI: 0.8-2.30) more likely than those below the poverty level, and urban residents were 1.9 percentage points (95% CI: 1.1-2.7) more likely than rural residents to receive all telemental health care.

Conclusions: These national patterns highlight differences in US telemental health care access across employment, education, income, insurance, and geographic groups.

Background: An important consideration in the design stage of randomized controlled trials is whether individuals within each site should or can be randomized to study arms (an individually randomized controlled trial) or whether entire sites should be randomized (a cluster randomized trial) where the clusters are sites. Recently, cluster randomized trials have grown in popularity; however, investigators have expressed a need for guidelines related to selection of the best design-especially when considering more complex designs such as stepped wedge. This commentary attempts to provide such guidance.

Methods: In this commentary, we address common misconceptions surrounding the appropriate use of cluster randomization and crossover designs (such as the stepped wedge design). The intracluster correlation coefficients for different levels of clustering are presented, and the impact of different designs on sample size discussed. We provide practical recommendations that are accessible to investigators from all disciplines, regardless of their level of statistical training. On the basis of these recommendations, we present a flowchart to help researchers identify a suitable trial design for their study. In addition, we provide a table of commonly used statistical definitions related to cluster randomized trials and a table of design considerations for more complex designs to support effective consultation with a statistician during the design phase.

Conclusion: On the basis of these guidelines, it is recommended that researchers prioritize the use of individual randomization and a parallel trial design when the goal of the study is to assess the efficacy or effectiveness of an intervention.

Objective: To advance patient-centered care for high-need homeless-experienced patients, we identified the rates of various personal health goals, the broader domains that underlie these goals, and associations between these domains and the health-related needs of this population.

Method: The sample consisted of 176 veterans from 3 VA Medical Centers who were enrolled in primary care, on VA's Homeless Registry, and high utilizers of acute care. An interview was conducted with each participant to collect information on their personal health goals and health-related needs. Exploratory factor analysis was used to identify broad domains underlying endorsement of personal health goals. Associations between these broad goal domains and health-related needs (substance use, mental and physical health, treatment engagement, and psychosocial) were examined using an exploratory structural equation modeling-within-confirmatory factor analysis approach.

Results: Three broad domains were found to underlie the personal health goals of the sample: social functioning, health promotion, and substance use. Social functioning and health promotion were highly correlated, whereas substance use was weakly correlated with both social functioning and health promotion. All substance use-related needs were positively associated with substance use goals. Mental and physical health needs were primarily associated with health promotion goals. Treatment engagement and psychosocial needs demonstrated associations across all 3 goal domains.

Conclusions: Findings highlight the high value that many high-need homeless-experienced patients place on their social well-being and the potential benefits to measuring both deficiency and growth needs in this population. Clinical implications and future directions for research are discussed.

Objective: To examine changes in persistent emergency department (ED) use by adult frequent ED (FED) users over time comparing prepandemic versus peripandemic periods, and to identify distinct characteristics of individuals who remained FED users over time during the pandemic versus the prepandemic era.

Methods: We conducted a retrospective, secondary, longitudinal analysis in Texas, following 2 cohorts of ED users over 36 months (cohort 1: March 2017-February 2020; cohort 2: March 2019-February 2022). The main outcomes were whether a baseline FED (≥9 ED visits/12 mo) user remained a FED user persistently over the next 24 months and whether they were persistent FED users prepandemic or peripandemic. Multivariable regressions estimated changes in persistent FED use across cohorts and assessed differences between individuals who remained persistent FED users during the pandemic compared with the prepandemic period.

Setting: All-payer Texas Hospital ED Research Data File from 2016 to 2022.

Results: About 1 million individuals had at least one ED visit in the baseline years, totaling 4.6 million visits. Overall, FED users accounted for 8% of all ED users but over 40% of visits. Although more than half of FED users at baseline exhibited persistent FED use over the next 24 months, there was a 7% decline in the proportion of return FED users after the onset of COVID-19 which was statistically significant (AOR=0.82; 95% CI=0.80-0.84), and this effect was more pronounced when patients with any COVID-19-related ED visit during the study period were excluded (AOR=0.10; 95% CI=0.09-0.11). Individuals aged 45 years or older, those with congestive heart failure and diabetes with complications, and those with public insurance (when COVID-19-related visits were excluded) had higher odds of remaining persistent FED users during the pandemic.

Conclusions: Many FED users will seek ED care for multiple years, given their medical complexity. Future research should identify heterogeneous subgroups among persistent FED users to tailor interventions towards their needs.

Background: There is a significant shortage of psychiatrists compared with the demand for their services. We studied Medicare Part D claims of psychiatrists and similar clinician prescribers to identify possible shifts in medication prescribing to address this gap.

Objective: This pharmacoepidemiology study aimed to identify prescribing patterns among psychiatrists, family physicians, internists, physician associates/assistants (PAs), and nurse practitioners (NPs), as well as trends.

Design: An observational, retrospective cohort study of Medicare Part D claims from 2013 to 2022 was conducted. Psychiatrists' prescription patterns were compared with those of non-psychiatrists, using the National Provider Identifier (NPI). Diagnosis information was not available in the dataset.

Participants: In 2022, more than 32,400 psychiatrists' prescriptions were submitted for more than 36.6 million Medicare Part D claims for various medications.

Main measures: Prescribers and medication claim counts were analyzed to determine the most commonly prescribed drugs by psychiatrists. Total psychiatrist prescription counts and trends were compared with those of primary care physicians, PAs, and NPs.

Key results: Family practice physicians wrote more than 55 million prescriptions in 2022, nearly twice the number written by psychiatrists. From 2013 to 2022, prescriptions written by PAs and NPs showed a strong upward trend. In 2020, prescription claims by NPs surpassed psychiatrists, and by 2022, they had exceeded those of internal medicine physicians.

Conclusions: Claims by NPs increased from 8.6% to 24.8%, whereas psychiatrists' claims decreased from 24.4% to 18.0%. As of 2023, NPs and PAs comprise a significant portion of the workforce that addresses gaps in mental health medication care for Medicare patients.

Background: To identify patients at the highest risk for acute care utilization, health care systems have developed "hot spotter" programs. Homelessness is a robust social determinant of acute care utilization.

Objectives: To describe the prevalence, patterns, and correlates of meeting criteria for a hot spotter program among housing-insecure adults in the US Veterans Health Administration (VHA).

Research design: Among veterans on the VHA Homeless Registry in Fiscal Years 2018-2022 (N=1,469,893), we identified those who met criteria for a Hot Spotter Report [ie, ≥1 hospital admissions and/or ≥2 emergency department (ED) visits in at least one quarter], described their patterns of acute care use, and examined differences in patient characteristics and outpatient service use between those who met report criteria in multiple quarters (vs. one).

Results: Thirty percent (N=446,974) met report criteria in at least one quarter; most (56%) met report criteria in ≥2 quarters. Diagnoses of depression (58%) and/or a substance use disorder (51%) were common; however, the rate of hospitalization in an acute medical setting during the cohort period was twice that of being hospitalized in an acute mental health setting (50% vs. 25%). Being on the Hot Spotter Report in multiple quarters (vs. one) was associated with more chronic conditions (M=5.08 vs. 3.29), higher rates of suicidality (23.7% vs. 11.7%), and higher likelihood of all types of outpatient care ( P <0.0001).

Conclusions: Given rates of chronic medical conditions and medical hospitalizations, it may behoove hot spotter programs to increase care coordination with medical respite programs to support patients in the postacute phase.

Background: Chronic conditions affect over 60% of US adults and drive nearly 90% of the nation's $4.9 trillion in annual health care costs. Nurse practitioners (NPs), particularly in Full Practice Authority (FPA) states, may be critical to improving outcomes and reducing health care burdens.

Objectives: To evaluate whether nurse practitioner FPA reduces hospital readmissions and emergency department visits related to chronic conditions across the United States.

Research design: A secondary data analysis using restricted Medical Expenditure Panel Survey (MEPS) data (2010-2019) was performed on site at the Agency of Health Research and Quality. We applied incidence rate ratios (IRRs) and difference-in-difference (DiD) models.

Measures: Primary outcomes included readmission and emergency visit rates for five chronic conditions: high cholesterol (n=33,409), high blood pressure (n=38,858), diabetes (n=13,075), emphysema (n=2,509), and asthma (n=17,018). Covariates included county-level socioeconomic factors and rurality.

Results: States with FPA had modestly lower IRRs for high cholesterol (0.9863), high blood pressure (0.9758), diabetes (0.9746), and asthma (0.9710) compared with restricted states. DiD models showed inconsistent effects, with most FPA*Post coefficients lacking statistical significance. However, rural FPA counties frequently showed significantly lower readmission rates, notably for diabetes and high cholesterol.

Conclusions: NP FPA is associated with slight reductions in chronic condition readmissions, particularly in rural areas. While DiD models showed limited policy-specific impact, IRR findings support FPA as a promising strategy to enhance chronic disease management and access to care. Future research should address model limitations and explore causal pathways.

Objectives: To compare predictive accuracy of 3-step theory of suicide (3ST) factor scores derived from natural language processing of Veterans Health Administration (VHA) clinical progress notes versus a model that underlies VHA's Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET) program retrained to predict the combined outcome of suicide attempt or suicide death, and to compare characteristics of patients accurately predicted by both approaches.

Background: As health systems incorporate risk prediction models to guide suicide prevention efforts, it is important to evaluate their predictive accuracy and to consider the benefits of different modeling approaches.

Methods: A comparative cohort design in which both risk prediction approaches were evaluated for the same random sample (n=162,132) of VHA patients alive on May 1, 2018, who had clinical encounters during the 4 weeks before that date.

Results: At the highest risks (top 1%-5%), the model based on REACH VET variables outperformed the 3ST approach in terms of positive predictive value and false-negative rate. Among patients who attempted or died by suicide, uniquely identified by the 3ST approach and not by the retrained REACH VET model, none had attempted suicide during the prior 6 months, emergency department visits during the prior month, discharges from mental health hospitalizations during the prior 12 months, or a diagnosis of bipolar disorder during the prior 24 months.

Conclusions: Additional research is recommended to further prepare 3ST factor scores based on NLP of clinical progress notes for use in clinical decision-making.

Background and objective: Low volume has been recognized as a problem when benchmarking hospitals due to outcome rate instability. We asked if low-volume hospital outcomes, using matching to control for many clinical and sociodemographic characteristics, would expose quality problems not observed with CMS methods.

Research design: Matched cohort study. Grades derive from mortality differences between all patients at the low-volume hospital and their matched controls.

Subjects: Medicare patients admitted with Acute Myocardial Infarction, Heart Failure and Pneumonia in 78 low-volume Pennsylvania acute care hospitals (combined condition volume=75≤N≤750 for the 3 y, 2017-2019), using Medicare's Virtual Research Data Center.

Measures: Thirty-day mortality.

Results: Using matching, 10 of 78 reportable low-volume hospitals had significantly higher mortality versus matched typical controls and 16 low-volume hospitals displayed significantly higher mortality versus well-resourced controls. In contrast, Medicare reported that only 3 of these same 78 hospitals had significantly higher mortality than "the national rate" on AMI, HF, or pneumonia.

Conclusions: We find that some low-volume hospitals performed well. Other low-volume hospitals had significantly worse outcomes than both well-resourced and typical hospitals; and some displayed significantly worse mortality compared with well-resourced controls but did not reach significant differences from typical controls. In short, performing "no different from the national rate," as is almost always reported for low-volume hospitals when using CMS methods, does not imply a low-volume hospital has acceptable outcomes. Reports based on matching can expose low-volume hospital quality problems not apparent using standard methods. Low-volume hospitals have more quality problems than generally reported.

Background: Administrative data used to predict unplanned hospital readmissions often lack patient-reported symptoms and functional status. Integrating patient-reported outcome measures (PROMs) may improve risk prediction.

Objectives: To assess the incremental value of PROMs in predicting unplanned readmissions to inform postdischarge monitoring and ongoing care management.

Methods: This population-based retrospective cohort study used linked administrative and PROMs data from British Columbia, Canada. Adults discharged from acute care who provided response to the EQ-5D-5L and Veterans RAND 12-Item Health Survey (VR-12) within 60 days were included. Aggregated Cox proportional hazards models were fitted to estimate unplanned readmission risk across 30-, 180-, and 360-day horizons. The primary prediction horizons were 30 and 180 days. The 360-day horizon was a secondary focus. Model performance was assessed using the concordance statistics and calibration, with subgroup analysis for Ambulatory Care Sensitive Conditions (ACSC).

Results: Among 11,177 individuals, observed unplanned readmission rates within 30, 180, and 360 days of discharge were 5.6%, 18.4%, and 25.0%, respectively. Conditional on surviving to weekly landmarks (23-60 days postdischarge), PROMs modestly improved discrimination. For the 180-day horizon following landmarks, the C-index was 0.762 (95% CI, 0.761-0.763) using predictors from administrative data alone, increasing to 0.774 (95% CI, 0.773-0.774) with EQ-5D-5L and 0.782 (95% CI, 0.781-0.783) with VR-12. Similar gains in discrimination were observed at 30-day and 360-day horizons. All models showed adequate calibration. Among patients with ACSCs, including PROMs improved discrimination by 2.4%-3.0%.

Conclusions: PROMs added predictive value for unplanned hospital readmissions, particularly among patients with ACSCs.