Risk factors for inpatient facility admission among home health care patients with diabetes

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Abstract

Background:

Home health care (HHC) patients with diabetes are at high risk for inpatient admissions.

Purpose:

To identify variables associated with inpatient admissions among adults age ≥50 with diabetes receiving HHC in the community and in assisted living (AL).

Methods:

Retrospective HHC data (collected October 2021 to March 2022 in the Southern United States) from the Outcome and Assessment Information Set D were analyzed with logistic regression ( n = 5,308 patients).

Discussion:

The inpatient admission rate was 29.5%. For community-dwelling patients, multiple hospitalizations, depression, limited cognitive function, decreased activities of daily living (ADL) performance, and unhealed pressure ulcer or injury ≥stage 2 were significantly associated with inpatient admission. For those in AL, multiple prior hospitalizations and decreased ability to perform ADLs were associated with inpatient admission.

Conclusion:

Understanding risk factors for inpatient admissions among patients with diabetes can support the identification of at-risk patients and inform interventions.

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Most cited references 42

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Revisiting the Behavioral Model and Access to Medical Care: Does it Matter?

Ronald Andersen (1995)

The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.

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Economic Costs of Diabetes in the U.S. in 2017

(2018)

OBJECTIVE This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2017. RESEARCH DESIGN AND METHODS We use a prevalence-based approach that combines the demographics of the U.S. population in 2017 with diabetes prevalence, epidemiological data, health care cost, and economic data into a Cost of Diabetes Model. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, insurance coverage, medical condition, and health service category. Data sources include national surveys, Medicare standard analytical files, and one of the largest claims databases for the commercially insured population in the U.S. RESULTS The total estimated cost of diagnosed diabetes in 2017 is $327 billion, including $237 billion in direct medical costs and $90 billion in reduced productivity. For the cost categories analyzed, care for people with diagnosed diabetes accounts for 1 in 4 health care dollars in the U.S., and more than half of that expenditure is directly attributable to diabetes. People with diagnosed diabetes incur average medical expenditures of ∼$16,750 per year, of which ∼$9,600 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures ∼2.3 times higher than what expenditures would be in the absence of diabetes. Indirect costs include increased absenteeism ($3.3 billion) and reduced productivity while at work ($26.9 billion) for the employed population, reduced productivity for those not in the labor force ($2.3 billion), inability to work because of disease-related disability ($37.5 billion), and lost productivity due to 277,000 premature deaths attributed to diabetes ($19.9 billion). CONCLUSIONS After adjusting for inflation, economic costs of diabetes increased by 26% from 2012 to 2017 due to the increased prevalence of diabetes and the increased cost per person with diabetes. The growth in diabetes prevalence and medical costs is primarily among the population aged 65 years and older, contributing to a growing economic cost to the Medicare program. The estimates in this article highlight the substantial financial burden that diabetes imposes on society, in addition to intangible costs from pain and suffering, resources from care provided by nonpaid caregivers, and costs associated with undiagnosed diabetes.

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A simulation study of the number of events per variable in logistic regression analysis.

P Peduzzi, J Concato, E. Kemper … (1996)

We performed a Monte Carlo study to evaluate the effect of the number of events per variable (EPV) analyzed in logistic regression analysis. The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality; the number of events per predictive variable was (252/7 =) 36 for the full sample. For the simulations, at values of EPV = 2, 5, 10, 15, 20, and 25, we randomly generated 500 samples of the 673 patients, chosen with replacement, according to a logistic model derived from the full sample. Simulation results for the regression coefficients for each variable in each group of 500 samples were compared for bias, precision, and significance testing against the results of the model fitted to the original sample. For EPV values of 10 or greater, no major problems occurred. For EPV values less than 10, however, the regression coefficients were biased in both positive and negative directions; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients; the 90% confidence limits about the estimated values did not have proper coverage; the Wald statistic was conservative under the null hypothesis; and paradoxical associations (significance in the wrong direction) were increased. Although other factors (such as the total number of events, or sample size) may influence the validity of the logistic model, our findings indicate that low EPV can lead to major problems.

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Author and article information

Journal

Journal ID (nlm-journal-id): 0401075

Journal ID (pubmed-jr-id): 6152

Journal ID (nlm-ta): Nurs Outlook

Journal ID (iso-abbrev): Nurs Outlook

Title: Nursing outlook

ISSN (Print): 0029-6554

ISSN (Electronic): 1528-3968

Publication date Nihms-submitted: 17 December 2023

Publication date (Print): Nov-Dec 2023

Publication date (Electronic): 25 September 2023

Publication date PMC-release: 23 January 2024

Volume: 71

Issue: 6

Page: 102050

Affiliations

[a ]Indiana University School of Nursing, Indianapolis, IN

[b ]Department of Nursing, University of Texas MD Anderson Cancer Center, Houston, TX

[c ]Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN

[d ]Department of Community & Health Systems, Indiana University School of Nursing, Bloomington, IN

[e ]Aveanna Healthcare, Atlanta, GA

[f ]CenterWell Home Health, Atlanta, GA

[g ]Department of Science of Nursing Care, Indiana University School of Nursing, Indianapolis, IN

Author notes

[* ]Corresponding author: K.E. Webster-Dekker, Indiana University School of Nursing, 600 Barnhill Drive, Indianapolis, IN 46220. katwebst@ 123456iu.edu (K.E. Webster-Dekker).

Article

Manuscript ID: NIHMS1935385

DOI: 10.1016/j.outlook.2023.102050

PMC ID: 10804840

PubMed ID: 37757614

SO-VID: 4bfa1f3e-8565-4bf7-9fcb-a65ad2dcffc1

License:

This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-nc/4.0/).

Risk factors for inpatient facility admission among home health care patients with diabetes

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Abstract

Background:

Purpose:

Methods:

Discussion:

Conclusion:

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Karger: Nephrology

Most cited references 42

Revisiting the Behavioral Model and Access to Medical Care: Does it Matter?

Economic Costs of Diabetes in the U.S. in 2017

A simulation study of the number of events per variable in logistic regression analysis.

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