Background

Federal regulations in the United States have shaped Institutional Review Boards (IRBs) to focus on protecting individual human subjects. Health services research studies focusing on healthcare institutions such as hospitals or clinics do not have individual human subjects. Since U.S. federal regulations are silent on what type of review, if any, these studies require, different IRBs may approach similar studies differently, resulting in undesirable variation in the review of studies focusing on healthcare institutions. Further, although these studies do not focus on individual human subjects, they may pose risks to participating institutions, as well as individuals who work at those institutions, if identifying information becomes public.

Discussion

Using two recent health services research studies conducted in the U.S. as examples, we discuss variations in the level of IRB oversight for studies focusing on institutions rather than individual human subjects. We highlight how lack of IRB guidance poses challenges for researchers who wish to both protect their subjects and work appropriately with the public, journalists or the legal system in the U.S. Competing interests include the public's interest in transparency, the researcher's interest in their science, and the research participants' interests in confidentiality. Potential solutions that may help guide health services researchers to balance these competing interests include: 1) creating consensus guidelines and standard practices that address confidentiality risk to healthcare institutions and their employees; and 2) expanding the IRB role to conduct a streamlined review of health services research studies focusing on healthcare institutions to balance the competing interest of stakeholders on a case-by-case basis.

Summary

For health services research studies focusing on healthcare institutions, we outline the competing interests of researchers, healthcare institutions and the public. We propose solutions to decrease undesirable variations in the review of these studies.

Because the pathologic processes that underlie Alzheimer's disease (AD) appear to start 10 to 20 years before symptoms develop, there is currently intense interest in developing techniques to accurately predict which individuals are most likely to become symptomatic. Several AD risk prediction strategies - including identification of biomarkers and neuroimaging techniques and development of risk indices that combine traditional and non-traditional risk factors - are being explored. Most AD risk prediction strategies developed to date have had moderate prognostic accuracy but are limited by two key issues. First, they do not explicitly model mortality along with AD risk and, therefore, do not differentiate individuals who are likely to develop symptomatic AD prior to death from those who are likely to die of other causes. This is critically important so that any preventive treatments can be targeted to maximize the potential benefit and minimize the potential harm. Second, AD risk prediction strategies developed to date have not explored the full range of predictive variables (biomarkers, imaging, and traditional and non-traditional risk factors) over the full preclinical period (10 to 20 years). Sophisticated modeling techniques such as hidden Markov models may enable the development of a more comprehensive AD risk prediction algorithm by combining data from multiple cohorts. As the field moves forward, it will be critically important to develop techniques that simultaneously model the risk of mortality as well as the risk of AD over the full preclinical spectrum and to consider the potential harm as well as the benefit of identifying and treating high-risk older patients.

BACKGROUND: Although older men value maintaining independence and avoiding functional decline, little is known about their functional trajectories with receipt of prostate radiation. METHODS: We performed a retrospective cohort study including veterans age 65+ with localized prostate cancer who resided in a VA nursing facility while receiving prostate radiation from 2005 to 2015. We evaluated the change in Minimum Data Set (MDS) activities of daily living (ADL) score during 6 months from the start of treatment. Because prior studies have shown Charlson Comorbidity Index (CCI) to be a strong predictor of treatment-related toxicity, analysis included interaction with CCI. RESULTS: We identified 487 patients with median age 73 (range 65-94). For the average patient in our cohort, the predicted MDS-ADL score worsened from 2.9 (95% CI 2.4-3.6) at the start of radiation to 3.8 (95% CI 3.1-4.8) at 3 months and then 4.5 (95% CI 3.5-5.7) at month 6. Patients with greater comorbidity (CCI ≥ 4) had worse functional outcomes in months 0-3 compared to patients with less comorbidity (CCI 0-3). MDS-ADL score worsened by 1.9 in the CCI ≥4 patients compared to 0.3 in the CCI 0-3 group During months 3-6, patients in both Charlson groups experienced similar worsening of MDS-ADL score. CONCLUSIONS: In a vulnerable population of older patients with localized prostate cancer, radiation was associated with a decline in functional independence. Patients with higher comorbidity experienced more severe functional decline within the first 3 months of radiation therapy. In all comorbidity levels, functional status had not returned to baseline by 6 months.

Background: Guidelines recommend incorporating life expectancy (LE) into clinical decision-making for preventive interventions such as cancer screening. Previous research focused on mortality risk (e.g. 28% at 4 years) which is more difficult to interpret

Background

Numerous studies indicate that the use of feeding tubes (FT) in persons with advanced cognitive impairment (CI) does not improve clinical outcomes or survival, and results in higher rates of hospitalization and emergency department (ED) visits. It is not clear, however, whether such risk varies by resident level of CI and whether these ED visits and hospitalizations are potentially preventable. The objective of this study was to determine the rates of ED visits, hospitalizations and potentially preventable ambulatory care sensitive (ACS) ED visits and ACS hospitalizations for long-stay NH residents with FTs at differing levels of CI.

Methods

We linked Centers for Medicare and Medicaid Services inpatient & outpatient administrative claims and beneficiary eligibility data with Minimum Data Set (MDS) resident assessment data for nursing home residents with feeding tubes in a 5% random sample of Medicare beneficiaries residing in US nursing facilities in 2006 (n = 3479). Severity of CI was measured using the Cognitive Performance Scale (CPS) and categorized into 4 groups: None/Mild (CPS = 0-1, MMSE = 22-25), Moderate (CPS = 2-3, MMSE = 15-19), Severe (CPS = 4-5, MMSE = 5-7) and Very Severe (CPS = 6, MMSE = 0-4). ED visits, hospitalizations, ACS ED visits and ACS hospitalizations were ascertained from inpatient and outpatient administrative claims. We estimated the risk ratio of each outcome by CI level using over-dispersed Poisson models accounting for potential confounding factors.

Results

Twenty-nine percent of our cohort was considered "comatose" and "without any discernible consciousness", suggesting that over 20,000 NH residents in the US with feeding tubes are non-interactive. Approximately 25% of NH residents with FTs required an ED visit or hospitalization, with 44% of hospitalizations and 24% of ED visits being potentially preventable or for an ACS condition. Severity of CI had a significant effect on rates of ACS ED visits, but little effect on ACS hospitalizations.

Conclusions

ED visits and hospitalizations are common in cognitively impaired tube-fed nursing home residents and a substantial proportion of ED visits and hospitalizations are potentially preventable due to ACS conditions.

Introduction

For older adults with type 2 diabetes, the American Diabetes Association (ADA) Framework uses comorbidities and functional status to categorize patients by estimated life expectancy to guide individualization of glycemic treatment. We evaluated whether modifying the ADA Framework by removing three comorbidities and incorporating age could improve life expectancy stratification and better identify patients likely to benefit from intensive treatment.

Research design and methods

We examined 3166 Health and Retirement Study participants aged ≥65 with diabetes from 1998 to 2004, using a prospective cohort design with mortality follow-up through 2016. We classified participants into one of three ADA Framework categories: Healthy, Intermediate Health, and Poor Health. We created modified categories by excluding comorbidities weakly associated with mortality (hypertension, arthritis, and incontinence). Using Gompertz regression, we estimated life expectancy across age strata for both original and modified ADA Framework categories.

Results

The original ADA Framework classified 34% as Healthy (likely to benefit from intensive treatment), 50% as Intermediate Health, and 16% as Poor Health (unlikely to benefit from intensive treatment). Our comorbidity modification reclassified 20% of participants from Intermediate Health to Healthy. Using the modified ADA Framework, median life expectancy of the Healthy varied greatly by age (aged 65-69: 16.3 years; aged ≥80: 7.6 years), indicating differing likelihood of benefit. Additionally, age ≥80 made extended life expectancy unlikely (median life expectancy for Healthy 7.6 years, Intermediate Health 5.9 years, Poor Health 2.5 years), suggesting adults ≥80 are unlikely to benefit from intensive treatment.

Conclusions

Modifying the ADA Framework by incorporating age and focusing on comorbidities associated with mortality improved life expectancy stratification, resulting in different treatment recommendations for many older adults.

Background: Numerous studies indicate that the use of feeding tubes (FT) in persons with advanced cognitive impairment (CI) does not improve clinical outcomes or survival, and results in higher rates of hospitalization and emergency department (ED) visits.

Objectives

Although there is increasing interest in using functional status to guide clinical decision making, function is seldom routinely assessed in primary care. We explored clinician perspectives on barriers and facilitators to routine measurement of older adults' functional status in primary care settings.

Design

Qualitative study using semistructured interviews.

Setting

Primary care and geriatrics clinics at six Veterans Affairs Medical Centers.

Participants

Twenty-four primary care providers, including 17 from primary care clinics and 7 from geriatrics clinics.

Measurements

We conducted interviews to elicit clinician perspectives about functional status measurement, including barriers and facilitators to routine assessment. We analyzed transcripts iteratively using a hybrid inductive and deductive thematic approach.

Results

Interviews revealed three distinct aspects to measuring function: screening and assessment, documentation, and use of data to inform care. Barriers and facilitators to screening and assessment included time availability, clinic processes, and degree of interdisciplinary environment. Barriers and facilitators to documentation included the usability and integration of electronic instruments into workflows and the availability of a standardized location to document function in the electronic medical record. Barriers and facilitators to use of data included the availability of a standardized location to retrieve data on function, the availability of appropriate referrals and services, and provider knowledge of available resources to address functional impairments. To address these barriers, providers emphasized the critical importance of connecting measurement of function directly to improved patient care.

Conclusion

Although clinicians emphasized the importance of measuring function, they also cautioned against additional workload burdens, cumbersome electronic documentation, and measurement of function without ensuring that these data are used to improve care. Approaches to functional status measurement must address these barriers to improve care and outcomes for older adults. J Am Geriatr Soc 67:493-502, 2019.

Background/objective

Guidelines recommend less intensive glycemic treatment and less frequent glucose monitoring for nursing home (NH) residents. However, little is known about the frequency of fingerstick (FS) glucose monitoring in this population. Our objective was to examine the frequency of FS glucose monitoring in Veterans Affairs (VA) NH residents with diabetes mellitus, type II (T2DM).

Design and setting

National retrospective cohort study in 140 VA NHs.

Participants

NH residents with T2DM and older than 65 years admitted to VA NHs between 2013 and 2015 following discharge from a VA hospital.

Measurements

NH residents were classified into five groups based on their highest hypoglycemia risk glucose-lowering medication (GLM) each day: no GLMs; metformin only; sulfonylureas; long-acting insulin; and any short-acting insulin. Our outcome was a daily count of FS measurements.

Results

Among 17,474 VA NH residents, mean age was 76 (standard deviation (SD) = 8) years and mean hemoglobin A1c was 7.6% (SD = 1.5%). On day 1 after NH admission, 49% of NH residents were on short-acting insulin, decreasing slightly to 43% at day 90. Overall, NH residents had an average of 1.9 (95% confidence interval (CI) = 1.8-1.9) FS measurements on NH day 1, decreasing to 1.4 (95% CI = 1.3-1.4) by day 90. NH residents on short-acting insulin had the most frequent FS measurements, with 3.0 measurements (95% CI = 2.9-3.0) on day 1, decreasing to 2.6 measurements (95% CI = 2.5-2.7) by day 90. Less frequent FS measurements were seen for NH residents receiving long-acting insulin (2.1 (95% CI = 2.0-2.2) on day 1) and sulfonylureas (1.7 (95% CI = 1.5-1.8) on day 1). Even NH residents on metformin monotherapy had 1.1 (95% CI = 1.1-1.2) measurements on day 1, decreasing to 0.5 (95% CI = 0.4-0.6) measurements on day 90.

Conclusion

Although guidelines recommend less frequent glucose monitoring for NH residents, we found that many VA NH residents receive frequent FS monitoring. Given the uncertain benefits and potential for substantial patient burdens and harms, our results suggest decreasing FS monitoring may be warranted for many low hypoglycemia risk NH residents.

Background and objective

Most methods for developing clinical prognostic models focus on identifying parsimonious and accurate models to predict a single outcome; however, patients and providers often want to predict multiple outcomes simultaneously. As an example, for older adults one is often interested in predicting nursing home admission as well as mortality. We propose and evaluate a novel predictor-selection computing method for multiple outcomes and provide the code for its implementation.

Methods

Our proposed algorithm selected the best subset of common predictors based on the minimum average normalized Bayesian Information Criterion (BIC) across outcomes: the Best Average BIC (baBIC) method. We compared the predictive accuracy (Harrell's C-statistic) and parsimony (number of predictors) of the model obtained using the baBIC method with: 1) a subset of common predictors obtained from the union of optimal models for each outcome (Union method), 2) a subset obtained from the intersection of optimal models for each outcome (Intersection method), and 3) a model with no variable selection (Full method). We used a case-study data from the Health and Retirement Study (HRS) to demonstrate our method and conducted a simulation study to investigate performance.

Results

In the case-study data and simulations, the average Harrell's C-statistics across outcomes of the models obtained with the baBIC and Union methods were comparable. Despite the similar discrimination, the baBIC method produced more parsimonious models than the Union method. In contrast, the models selected with the Intersection method were the most parsimonious, but with worst predictive accuracy, and the opposite was true in the Full method. In the simulations, the baBIC method performed well by identifying many of the predictors selected in the baBIC model of the case-study data most of the time and excluding those not selected in the majority of the simulations.

Conclusions

Our method identified a common subset of variables to predict multiple clinical outcomes with superior balance between parsimony and predictive accuracy to current methods.