Algorithm for Identifying Nursing Home Days Using Medicare Claims and Minimum Data Set Assessment Data

Wei, Yu‐Jung; Simoni, Jane M.; Zuckerman, Ilene H.; Brandt, Nicole; Lucas, Judith A.

doi:10.1097/mlr.0000000000000109

Cited by 41 publications

(56 citation statements)

References 14 publications

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“…A benefit of nonpharmacological interventions is their utility in identifying and resolving these underlying causes . Second, exclusion of NH short‐stay residents from the study sample may obscure observation of short‐term mortality risk associated with study medication use, although different disease acuity and goals of services in general characterize short‐stay residents (short‐term rehabilitation and acute illness recovery) as a separate population from long‐stay residents (long‐term assistance with ADLs and management of chronic disabilities) . For the purpose of this study, short stays did not allow a sufficient observation period for behavioral symptoms or treatment initiation.…”

Section: Discussionmentioning

confidence: 99%

“…An algorithm using Medicare administrative claims data and MDS 2.0 files to identify NH long‐stay residents was specifically developed for this study. Details of the algorithm can be found elsewhere . Medicare beneficiaries who joined a Medicare Advantage/Health Maintenance Organization insurance plan during the study period were excluded due to their lack of medical claims to detect ADRD and comorbidities.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Treated Behavioral Symptoms and Mortality in Medicare Beneficiaries in Nursing Homes with Alzheimer's Disease and Related Dementias

Huang

Wei

Moyo

et al. 2015

J American Geriatrics Society

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Treated Behavioral Symptoms and Mortality in Medicare Beneficiaries in Nursing Homes with Alzheimer's Disease and Related Dementias

Huang

Wei

Moyo

et al. 2015

J American Geriatrics Society

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“…We focused on long-stay residents because they require long-term assistance to manage their chronic disabilities (average length of stay of ~2 years). [49] In contrast, short-stay residents or those receiving skilled nursing facility (SNF) care are predominately recovering from acute illnesses and are normally in the nursing home for a limited period of time before being discharged to the community (average length of stay of ~40 days). [49]…”

Section: Methodsmentioning

confidence: 99%

“…We additionally excluded long-stay residents who received SNF care at or within 30 days of the index date because of differences between those needing SNF care and other long-stay residents highlighted above (n=24,947). [49] For the remaining eligible residents, we randomly chose an index assessment for those with multiple eligible assessments (1,488,790 unique residents). Of these residents, we further excluded those who were comatose (n=5,194), <18 years old (n=1,436), or missing data on important covariates (n=94,755).…”

Section: Methodsmentioning

confidence: 99%

Pain and pharmacologic pain management in long-stay nursing home residents

Hunnicutt¹,

Ulbricht²,

Tjia³

et al. 2017

Pain

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Prior studies estimate that >40% of long-stay nursing home (NH) residents experience persistent pain, with 20% of residents in pain receiving no analgesics. Strengthened NH surveyor guidance and improved pain measures on the Minimum Data Set (MDS) 3.0 were introduced in March 2009 and October 2010, respectively. This study aimed to provide estimates after these important initiatives of: 1) prevalence and correlates of persistent pain; and 2) prevalence and correlates of untreated or undertreated persistent pain. We identified 1,387,405 long-stay residents in United States NHs between 2011–2012 with 2 MDS assessments 90 days apart. Pain was categorized as persistent (pain on both assessments), intermittent (pain on either assessment), or none. Pharmacologic pain management was classified as untreated pain (no scheduled or as needed medications received) or potentially undertreated (no scheduled received). Modified Poisson models adjusting for resident clustering within NHs provided adjusted prevalence ratios estimates (APR) and 95% confidence intervals (CI). The prevalence of persistent and intermittent pain was 19.5% and 19.2% respectively but varied substantially by age, gender, race/ethnicity, cognitive impairment, and cancer. Of residents in persistent pain, 6.4% and 32.0% were untreated or undertreated. Racial/ethnic minorities (non-Hispanic blacks vs. whites, APR=1.19, 95% CI: 1.13–1.25) and severely cognitively impaired residents (severe vs. no/mild APR=1.51, 95% CI: 1.44–1.57) had an increased prevalence of untreated and undertreated pain. One in five NH residents has persistent pain. Although this estimate is greatly improved, many residents may be undertreated. The disturbing disparities in untreated and undertreated pain need to be addressed.

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“…We used the MDS reason for assessment fields (AA8A, AA8B) and MDS admission and discharge dates in combination with Skilled Nursing Facility (SNF) claims in the MedPAR file to define the duration of nursing home episodes 16–18 and distinguish SNF and non-SNF stays. Specifically, we first identified all MDS forms with a primary reason for assessment (AA8A) equal to “admission assessment required by day 14” and an MDS admission date between 1/1/2010 and 9/30/2010 (n=41,674).…”

Section: Methodsmentioning

confidence: 99%

Anticholinergic Prescribing in Medicare Part D Beneficiaries Residing in Nursing Homes: Results from a Retrospective Cross-Sectional Analysis of Medicare Data

et al. 2017

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Background Prescribing of medications with anticholinergic properties in older nursing home residents is relatively common, despite an association with increased risk for falls, delirium and other outcomes. Few studies have investigated what factors influence different levels of prescribing of these agents. Objectives The primary objective was to identify factors associated with low- and high-level anticholinergic burden in nursing home residents. A secondary objective was to examine in detail the contribution of different medications to low versus high burden, to aid in determining drugs to target in interventions. Design Retrospective, cross-sectional analysis. Setting National sample of 2009–2010 Medicare Part A and B claims, Part D prescription drug events, and Minimum Data Set (MDS) v2.0 assessments. Participants The cohort included 4730 Medicare beneficiaries age ≥65 with continuous Medicare Parts A, B, and D enrollment, admitted for non-skilled stays ≥14 days between 01/01/2010–09/30/2010. Measurements Anticholinergic burden was defined using the Anticholinergic Cognitive Burden(ACB) scale. Medication scores were summed at the patient-level and categorized as high (score ≥3), low (score 1–2), or none. Baseline predisposing (age, sex, race/ethnicity), enabling (prior year hospital, emergency department, primary care, specialist visits; region; Medicaid/low-income subsidy), and medical need factors (dementia severity, anti-dementia medication, Charlson co-morbidity index [CCI], select comorbidities) were evaluated for association with anticholinergic burden using multinomial logistic regression. Results Overall, 29.6% had high-level anticholinergic burden, and 35.2% had low-level burden. High-level burden was most often (72%) due to one highly anticholinergic medication rather than cumulative effect. In adjusted analyses, factors associated with increased risk of both low and high anticholinergic burden included: comorbidity, antidementia medication, depression, hypertension, and prior year hospitalization. Older age was associated with decreased odds of high anticholinergic burden. Urinary incontinence and prior year specialist visit were associated with increased odds of high anticholinergic burden. Severe and non-severe dementia were associated with decreased odds of low-level burden, but increased odds of high-level burden. Conclusion Almost two-thirds of nursing home patients have some degree of anticholinergic burden. Several medical need variables are significantly associated with increased risk for low-level and high-level anticholinergic burden. Interventions should be developed to optimize prescribing for residents at increased risk of receiving medications with anticholinergic properties. Future study is needed to evaluate the difference in the risk of adverse outcomes associated with various levels of anticholinergic burden.

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Algorithm for Identifying Nursing Home Days Using Medicare Claims and Minimum Data Set Assessment Data

Abstract: Our MDS/SNF algorithm allows the differentiation of long-stay and short-stay residents, resulting in an NH group more precise than using Medicare claims data only.

Cited by 41 publications

References 14 publications

Treated Behavioral Symptoms and Mortality in Medicare Beneficiaries in Nursing Homes with Alzheimer's Disease and Related Dementias

Treated Behavioral Symptoms and Mortality in Medicare Beneficiaries in Nursing Homes with Alzheimer's Disease and Related Dementias

Pain and pharmacologic pain management in long-stay nursing home residents

Anticholinergic Prescribing in Medicare Part D Beneficiaries Residing in Nursing Homes: Results from a Retrospective Cross-Sectional Analysis of Medicare Data

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