Temporal and Geographic variation in the validity and internal consistency of the Nursing Home Resident Assessment Minimum Data Set 2.0

<p>Abstract</p> <p>Background</p> <p>The Minimum Data Set (MDS) for nursing home resident assessment has been required in all U.S. nursing homes since 1990 and has been universally computerized since 1998. Initially intended to structure clinical care planning, uses of the MDS expanded to include policy applications such as case-mix reimbursement, quality monitoring and research. The purpose of this paper is to summarize a series of analyses examining the internal consistency and predictive validity of the MDS data as used in the "real world" in all U.S. nursing homes between 1999 and 2007.</p> <p>Methods</p> <p>We used person level linked MDS and Medicare denominator and all institutional claim files including inpatient (hospital and skilled nursing facilities) for all Medicare fee-for-service beneficiaries entering U.S. nursing homes during the period 1999 to 2007. We calculated the sensitivity and positive predictive value (PPV) of diagnoses taken from Medicare hospital claims and from the MDS among all new admissions from hospitals to nursing homes and the internal consistency (alpha reliability) of pairs of items within the MDS that logically should be related. We also tested the internal consistency of commonly used MDS based multi-item scales and examined the predictive validity of an MDS based severity measure viz. one year survival. Finally, we examined the correspondence of the MDS discharge record to hospitalizations and deaths seen in Medicare claims, and the completeness of MDS assessments upon skilled nursing facility (SNF) admission.</p> <p>Results</p> <p>Each year there were some 800,000 new admissions directly from hospital to US nursing homes and some 900,000 uninterrupted SNF stays. Comparing Medicare enrollment records and claims with MDS records revealed reasonably good correspondence that improved over time (by 2006 only 3% of deaths had no MDS discharge record, only 5% of SNF stays had no MDS, but over 20% of MDS discharges indicating hospitalization had no associated Medicare claim). The PPV and sensitivity levels of Medicare hospital diagnoses and MDS based diagnoses were between .6 and .7 for major diagnoses like CHF, hypertension, diabetes. Internal consistency, as measured by PPV, of the MDS ADL items with other MDS items measuring impairments and symptoms exceeded .9. The Activities of Daily Living (ADL) long form summary scale achieved an alpha inter-consistency level exceeding .85 and multi-item scale alpha levels of .65 were achieved for well being and mood, and .55 for behavior, levels that were sustained even after stratification by ADL and cognition. The Changes in Health, End-stage disease and Symptoms and Signs (CHESS) index, a summary measure of frailty was highly predictive of one year survival.</p> <p>Conclusion</p> <p>The MDS demonstrates a reasonable level of consistency both in terms of how well MDS diagnoses correspond to hospital discharge diagnoses and in terms of the internal consistency of functioning and behavioral items. The level of alpha reliability and validity demonstrated by the scales suggest that the data can be useful for research and policy analysis. However, while improving, the MDS discharge tracking record should still not be used to indicate Medicare hospitalizations or mortality. It will be important to monitor the performance of the MDS 3.0 with respect to consistency, reliability and validity now that it has replaced version 2.0, using these results as a baseline that should be exceeded.</p

Least Squared Simulated Errors

Estimation by minimizing the sum of squared residuals is a common method for parameters of regression functions; however, regression functions are not always known or of interest. Maximizing the likelihood function is an alternative if a distribution can be properly specified. However, cases can arise in which a regression function is not known, no additional moment conditions are indicated, and we have a distribution for the random quantities, but maximum likelihood estimation is difficult to implement. In this article, we present the least squared simulated errors (LSSE) estimator for such cases. The conditions for consistency and asymptotic normality are given. Finite sample properties are investigated via Monte Carlo experiments on two examples. Results suggest LSSE can perform well in finite samples. We discuss the estimator’s limitations and conclude that the estimator is a viable option. We recommend Monte Carlo investigation of any given model to judge bias for a particular finite sample size of interest and discern whether asymptotic approximations or resampling techniques are preferable for the construction of tests or confidence intervals

Quantitative trait loci for resistance to pre-harvest sprouting in US hard white winter wheat Rio Blanco

Pre-harvest sprouting (PHS) of wheat is a major problem that severely limits the end-use quality of flour in many wheat-growing areas worldwide. To identify quantitative trait loci (QTLs) for PHS resistance, a population of 171 recombinant inbred lines (RILs) was developed from the cross between PHS-resistant white wheat cultivar Rio Blanco and PHS-susceptible white wheat breeding line NW97S186. The population was evaluated for PHS in three greenhouse experiments and one Weld experiment. After 1,430 pairs of simple sequence repeat (SSR) primers were screened between the two parents and two bulks, 112 polymorphic markers between two bulks were used to screen the RILs. One major QTL, QPhs.pseru-3AS, was identified in the distal region of chromosome 3AS and explained up to 41.0% of the total phenotypic variation in three greenhouse experiments. One minor QTL, QPhs.pseru-2B.1, was detected in the 2005 and 2006 experiments and for the means over the greenhouse experiments, and explained 5.0–6.4% of phenotypic variation. Another minor QTL, QPhs.pseru-2B.2, was detected in only one greenhouse experiment and explained 4.5% of phenotypic variation for PHS resistance. In another RIL population developed from the cross of Rio Blanco/NW97S078, QPhs.pseru-3AS was significant for all three greenhouse experiments and the means over all greenhouse experiments and explained up to 58.0% of phenotypic variation. Because Rio Blanco is a popular parent used in many hard winter wheat breeding programs, SSR markers linked to the QTLs have potential for use in high-throughput marker-assisted selection of wheat cultivars with improved PHS resistance as well as fine mapping and map-based cloning of the major QTL QPhs.pseru-3AS

The Impact of Medicaid Payer Status on Hospitalizations in Nursing Homes

ObjectivesTo examine the association between payer status (Medicaid vs. private-pay) and the risk of hospitalizations among long-term stay nursing home (NH) residents who reside in the same facility.Data and study populationThe 2007-2010 National Medicare Claims and the Minimum Data Set were linked. We identified newly admitted NH residents who became long-stayers and then followed them for 180 days.AnalysesThree dichotomous outcomes-all-cause, discretionary, and nondiscretionary hospitalizations during the follow-up period-were defined. Linear probability model with facility fixed-effects and robust SEs were used to examine the within-facility difference in hospitalizations between Medicaid and private-pay residents. A set of sensitivity analyses were performed to examine the robustness of the findings.ResultsThe prevalence of all-cause hospitalization during a 180-day follow-up period was 23.3% among Medicaid residents compared with 21.6% among private-pay residents. After accounting for individual characteristics and facility effects, the probability of any all-cause hospitalization was 1.8-percentage point (P&lt;0.01) higher for Medicaid residents than for private-pay residents within the same facility. We also found that Medicaid residents were more likely to be hospitalized for discretionary conditions (5% increase in the likelihood of discretionary hospitalizations), but not for nondiscretionary conditions. The findings from the sensitivity analyses were consistent with the main analyses.ConclusionsWe observed a higher hospitalization rate among Medicaid NH residents than private-pay residents. The difference is in part driven by the financial incentives NHs have to hospitalize Medicaid residents

Continuity of care and health care cost among community‐dwelling older adult veterans living with dementia

ObjectivesTo estimate the causal impact of continuity of care (COC) on total, institutional, and noninstitutional cost among community‐dwelling older veterans with dementia.Data SourcesCombined Veterans Health Administration (VHA) and Medicare data in Fiscal Years (FYs) 2014‐2015.Study DesignFY 2014 COC was measured by the Bice‐Boxerman Continuity of Care (BBC) index on a 0‐1 scale. FY 2015 total combined VHA and Medicare cost, institutional cost of acute inpatient, emergency department [ED], long‐/short‐stay nursing home, and noninstitutional long‐term care (LTC) cost for medical (like skilled‐) and social (like unskilled‐) services were assessed controlling for covariates. An instrumental variable for COC (change of residence by more than 10 miles) was used to account for unobserved health confounders.Data CollectionCommunity‐dwelling veterans with dementia aged 66 and older, enrolled in Traditional Medicare (N = 102 073).Principal FindingsMean BBC in FY 2014 was 0.32; mean total cost in FY 2015 was $35 425. A 0.1 higher BBC resulted in (a)$4045 lower total cost; (b) $1597 lower acute inpatient cost,$119 lower ED cost, $4368 lower long‐stay nursing home cost; (c)$402 higher noninstitutional medical LTC and $764 higher noninstitutional social LTC cost. BBC had no impact on short‐stay nursing home cost.ConclusionsCOC is an effective approach to reducing total health care cost by supporting noninstitutional care and reducing institutional care.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167831/1/hesr13541.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167831/2/hesr13541-sup-0001-Authormatrix.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167831/3/hesr13541_am.pd

Hospital Readmissions Among Post-acute Nursing Home Residents: Does Obesity Matter?

ObjectivesTo explore profiles of obese residents who receive post-acute care in nursing homes (NHs) and to assess the relationship between obesity and hospital readmissions and how it is modified by individual comorbidities, age, and type of index hospitalizations.DesignRetrospective cohort study.Setting and participantsMedicare fee-for-service beneficiaries who were newly admitted to free-standing US NHs after an acute inpatient episode between 2011 and 2014 (N&nbsp;=&nbsp;2,323,019).MeasuresThe Minimum Data Set 3.0 were linked with Medicare data. The outcome variable was 30-day hospital readmission from an NH. Residents were categorized into 3 groups based on their body mass index (BMI): nonobese, mildly obese, moderate-to-severely obese. We tested the relationship between obesity and 30-day readmissions by fixed-effects logit models and stratified analyses by the type of index hospitalization and residents' age.ResultsForty percent of the identified residents were admitted after a surgical episode, and the rest were admitted after a medical episode. The overall relationship between obesity and readmissions suggested that obesity was associated with higher risks of readmission among the oldest old (≥85&nbsp;years) residents but with lower risks of readmission among the youngest group (65-74&nbsp;years). After accounting for individual co-covariates, the association between obesity and readmissions among the oldest old residents became weaker; the adjusted odds ratio was 1.061 (P&nbsp;=&nbsp;.049) and 1.004 (P&nbsp;=&nbsp;.829) for moderate-to-severely obese patients with surgical and medical index hospitalizations, respectively. The protective effect of obesity among younger residents reduced after adjusting for covariates.Conclusions/relevanceThe relationship between obesity and hospital readmission among post-acute residents could be affected by comorbidities, age, and the type of index hospitalization. Further studies are also warranted to understand how to effectively measure NH quality outcomes, including hospital readmissions, so that policies targeting at quality improvement can successfully achieve their goals without unintended consequences

Obesity among Nursing Home Residents: Association with Potentially Avoidable Hospitalizations

Background/objectivesStudies show that in nursing homes (NHs), the prevalence of moderate-to-severe obesity has doubled in the last decade and continues to increase. Obese residents are often complex and costly, and this increase in prevalence has come at a time when NHs struggle to decrease hospitalizations, particularly those that are potentially avoidable. This study examined the association between obesity and hospitalizations.DesignWe linked 2011-2014 national data using Medicare NH assessments, hospital claims, and the NH Compare.Setting and participantsIndividuals aged ≥65 years, newly admitted to NHs, who became long-term residents between July 1, 2011 and March 26, 2014. The analytical sample included 490,086 residents.MethodsNH-originating hospitalization was the outcome; a categorical variable defined as no hospitalization, potentially avoidable hospitalization (PAH), and other hospitalization (non-PAH). The main independent variable was body mass index (BMI) defined as normal weight (30 &gt;BMI ≥18.5&nbsp;kg/m2), mildly obese (35 &gt;BMI ≥30&nbsp;kg/m2), or moderately-to-severely obese (BMI ≥35&nbsp;kg/m2). Covariates included individual and NH characteristics. Multinomial models with NH random effects and state dummies were estimated.ResultsAfter adjusting for individual level covariates, the risk of non-PAH for the mildly and moderate/severely obese was not different from normal weight residents. But the risk of PAH remained significantly higher for the moderate/severely obese (relative risk ratio&nbsp;= 1.055; 95% confidence interval 1.018, 1.094). Several NH-level factors also influenced hospitalization risk.Conclusions and implicationsObese residents are more likely to experience PAH but not non-PAH. Efforts to improve care for these residents may need to broadly consider the ability of NHs to commit additional resources to fully integrate care for this growing segment of the population