Browsing by Author "Carnahan, Jennifer"

Now showing 1 - 4 of 4
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    Characterizing Avoidability of Nursing Home Residents: Comparing the Claims-Based Algorithm and Nurse Assessment
    (Oxford University Press, 2022-12-20) Blackburn, Justin; Carnahan, Jennifer; Hickman, Susan; Sachs, Greg; Unroe, Kathleen; Health Policy and Management, School of Public Health
    The elevated risks associated with transferring nursing home residents to the hospital are problematic, but identifying which transfers can be avoided is complex. Using billing claims to determine “avoidability” based on hospital discharge diagnostic codes ignores resource constraints, clinical comorbidities, and asymmetrical information between nursing home staff making the transfer decision at the onset of clinical changes and hospital billing departments following treatment and diagnostic procedures. Conversely, relying on clinical staff assessments at the time of transfer may be an impractical and resource-intensive strategy to drive payment reform and improve quality. Using Medicare claims data representing emergency department and hospitalization transfers from 38 nursing facilities in Indiana from 2016-2020, we compared classification of transfers using a claims-based algorithm and trained nurse assessments of avoidability. Among 960 transfers, nurses judged 48.4% were potentially avoidable while 30.8% were classified as such using claims data. Of concordant assessments, 15.3% were avoidable and 36.0% as not avoidable. Of discordant assessments, 33.1% were judged avoidable by nurses only and 15.5% via the claims-based algorithm (Kappa=0.0153). Discordance was most frequent among transfers with heart failure (64%, n=42), psychosis (74.5%, n=34), acute renal disease (50%, n=28); and lowest among urinary tract infections (31.3%, n=64). No resident demographic or clinical characteristics were associated with discordance (age, race, sex, cognitive function scale, activities of daily living, or CHESS scale). High discordance in determining avoidability may be driven by presentation of symptoms or other condition-specific factors. Policies to reduce avoidable hospitalizations must not rely on overly simplistic approaches for identification.
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    Effects of Dementia Diagnosis on Hospital Readmission After Discharge From a Skilled Nursing Home
    (Oxford University Press, 2024-12-31) Carnahan, Jennifer; Slaven, James; Fox Ludden, Emily; Bowditch, Corinne; Ryan, Healey; Jiangqiong, Li; Tu, Wanzhu; Torke, Alexia; Medicine, School of Medicine
    High acute care utilization has been associated with dementia, but the risk of repeat acute care use following a skilled nursing home (SNH) to home transition is not well understood. We examined the association of dementia with 30-day hospital readmission after leaving a SNH. Data from the Health and Retirement Study from 2000-2018 was linked to Medicare and SNH claims. We analyzed the events of hospital readmission following SNH discharge using logistic regression while controlling for repeated within-subject readmissions. There were 5,912 discharges to the community from an SNH and 941 hospital readmissions. ICD codes for dementia were present with 1,754 of the SNH-to-home transitions and 314 of the hospital readmissions. In bivariate analysis, people with dementia were more likely to be readmitted (p=0.0074). Post-SNH readmissions were also more likely among people who were Black, male, dual eligible beneficiaries, longer pre-SNH hospital stays, higher Charlson Comorbidity Index, and greater activities of daily living deficits. In the multivariable logistic regression model, the odds of readmissions were still greater among persons with dementia, but significance was attenuated (OR 1.09; 0.90, 1.32; p=0.4065). Significant associations with readmissions include the Charlson Comorbidity Index (OR 1.07; 1.04, 1.09; p< 0.0001) and activities of daily living deficits (OR 1.11 (1.04, 1.17; p=0.0005). People with dementia may be at greater risk of readmission after SNH discharge but not when controlling for factors such as socioeconomic status, activities of daily living, and other comorbidities.
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    Getting to 100%: Research Priorities and Unanswered Questions to Inform the US Debate on Universal Health Insurance Coverage
    (Springer, 2022-01-21) Cram, Peter; Selker, Harry; Carnahan, Jennifer; Romero-Brufau, Santiago; Fischer, Michael A.; Medicine, School of Medicine
    A majority of Americans favor universal health insurance, but there is uncertainty over how best to achieve this goal. Whatever the insurance design that is implemented, additional details that must be considered include breadth of services covered, restrictions and limits on volumes of services, cost-sharing for individuals, and pricing. In the hopes that research can inform this ongoing debate, we review evidence supporting different models for achieving universal coverage in the US and identify areas where additional research and stakeholder input is needed. Key areas in need of further research include how care should be organized, how costs can be reduced, and what healthcare services universal insurance should cover.
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    Mental Health Utilization Among Transgender Veterans
    (American Medical Association, 2025-01-02) Lee, Joy L.; Hirsh, Adam; Radhakrishnan, Archana; Jasuja, Guneet K.; Taylor, Stanley; Dickinson, Stephanie; Mineo, Jocelyn; Carnahan, Jennifer; Weiner, Michael; Psychology, School of Science
    Importance: Compared with cisgender (CG) individuals, transgender and gender-diverse (TGD) individuals experience substantial social and economic disparities that can result in adverse mental health consequences. It is critical to understand potential barriers to care and to address the causes of the disparities in the future. Objective: To characterize mental health care utilization among TGD veterans with depression. Design, setting, and participants: This cohort study used electronic health record data from the US Department of Veterans Affairs (VA) to create a 1:3 age group-matched and VA facility-matched nationwide cohort of TGD and CG veterans with documentation of depression during 2018 to 2020. Data analysis was performed from January to November 2023. Exposure: TGD identity was ascertained by diagnosis of a gender identity disorder. Main outcomes and measures: The primary outcome was mental health care utilization, including counts of outpatient (in specialty care and primary care settings), telehealth, emergency department, and inpatient visits in this cohort. Descriptive statistics were used to characterize counts of mental health utilization, and statistically significant differences between TGD and CG veterans were tested using χ2 and Fisher exact tests. Wilcoxon rank-sum tests were used to test for differences in utilization between the 2 groups. Adjusted logistic regression, controlling for age group, administrative sex, race, Charlson Comorbidity Index, and number of mental health medications (eg, antidepressant, antipsychotic, and anxiolytic medications), was also used to compare utilization between TGD and CG veterans. Results: Among 10 564 veterans with depression (mean [SD] age, 46.4 [15.2] years; 8050 male [76.2%]), 2643 TGD veterans were matched with 7921 CG veterans. TGD veterans had 6 more specialty mental health visits per year than CG veterans (mean [SD], 13.93 [20.08] vs 8.46 [14.96] visits a year; median [range], 7.14 [0.00-246.30] vs 3.76 [0.00-202.38] visits per year). In adjusted models, compared with CG veterans, TGD veterans were 2.6 times more likely to have an outpatient mental-health visit (odds ratio, 2.60; 95% CI, 2.16-3.15). Conclusions and relevance: In this cohort study of veterans with depression, TGD veterans had significantly higher utilization of mental health services compared with CG veterans, even after adjusting for several relevant health factors. Different health system resources may be required to meet the needs of this population. Further studies are needed to understand the determinants of these disparities and subsequently how to address them.