Evaluating Hospital Readmissions for Persons With Serious and Complex Illness: A Competing Risks Approach

May, Peter; Garrido, Melissa M.; Fabbro, Egidio Del; Noreika, Danielle; Normand, Charles; Skoro, Nevena; Cassel, J. Brian

doi:10.1177/1077558718823919

Cited by 15 publications

(9 citation statements)

References 47 publications

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“…Since mortality and dropping out are not independent of our predictors, simply treating these outcomes as missing data increases the risk of bias. [29][30][31] We treated these outcomes as competing risks; that is, events that potentially prevent occurrence of the primary outcome of interest but which should not be treated as missing in analysis. 32 At wave 2, if a participant was in the top 10% of costs then they were deemed to have the outcome of interest, if the participant had died or did not participate they were deemed to have the competing risk, and if they had neither then they were retained to examine outcomes at wave 3, and so on.…”

Section: Methodsmentioning

confidence: 99%

Do Mini–Mental State Examination and Montreal Cognitive Assessment predict high‐cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

May

Looze

Feeney

et al. 2022

Int J Geriat Psychiatry

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Objectives Policymakers want to better identify in advance the 10% of people who account for approximately 75% of health care costs. We evaluated how well Mini–Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) predicted high costs in Ireland. Methods/Design We used five waves from The Irish Longitudinal Study on Ageing, a biennial population‐representative survey of people aged 50+ (2010–2018). We used competing risks analysis where our outcome of interest was “high costs” (top 10% at any wave) and the competing outcome was dying or loss to follow‐up without first having the high‐cost outcome. Our binary predictors of interest were a ‘low score’ (bottom 10% in the sample) in MMSE (≤25 pts) and MoCA (≤19 pts) at baseline, and we calculated sub‐hazard ratios after controlling for sociodemographic, clinical and functional factors. Results Of 5856 participants, 1427 (24%) had the ‘high cost’ outcome; 1463 (25%) had a competing outcome; and 2966 (51%) completed eight years of follow‐up without either outcome. In multivariable regressions a low MoCA score was associated with high costs (SHR: 1.38 (95% CI: 1.2–1.6) but a low MMSE score was not. Low MoCA score at baseline had a higher true positive rate (40%) than did low MMSE score (35%). The scores had similar association with exit from the study. Conclusions MoCA had superior predictive accuracy for high costs than MMSE but the two scores identify somewhat different types of high‐cost user. Combining the approaches may improve efforts to identify in advance high‐cost users.

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Section: Methodsmentioning

confidence: 99%

Do Mini–Mental State Examination and Montreal Cognitive Assessment predict high‐cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

May

Looze

Feeney

et al. 2022

Int J Geriat Psychiatry

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“…The second major problem faced by these studies is systematic selection bias. Those who receive palliative care have a higher illness burden and different preferences for high-intensity treatment that routine data do not capture [ 22 ]. Observational studies often find higher mortality rates in the palliative care group than the comparison group [ 22 , 23 ], but this effect is unobserved in trials [ 8 ] except in rare cases where palliative care had a positive survival effect [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…Those who receive palliative care have a higher illness burden and different preferences for high-intensity treatment that routine data do not capture [ 22 ]. Observational studies often find higher mortality rates in the palliative care group than the comparison group [ 22 , 23 ], but this effect is unobserved in trials [ 8 ] except in rare cases where palliative care had a positive survival effect [ 24 ]. This unobserved mortality risk may bias cost estimates in either direction: proximity to death is often associated with rising costs [ 25 ], so a treatment group with high mortality risk may have higher costs than observed covariates explain; but a clear prognosis that patients are entering the end-of-life phase may precipitate a move away from intensive curative treatment towards supportive care [ 26 ], lowering costs in a high-mortality-risk treatment group.…”

Section: Methodsmentioning

confidence: 99%

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Using predicted length of stay to define treatment and model costs in hospitalized adults with serious illness: an evaluation of palliative care

et al. 2021

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Background Economic research on hospital palliative care faces major challenges. Observational studies using routine data encounter difficulties because treatment timing is not under investigator control and unobserved patient complexity is endemic. An individual’s predicted LOS at admission offers potential advantages in this context. Methods We conducted a retrospective cohort study on adults admitted to a large cancer center in the United States between 2009 and 2015. We defined a derivation sample to estimate predicted LOS using baseline factors (N = 16,425) and an analytic sample for our primary analyses (N = 2674) based on diagnosis of a terminal illness and high risk of hospital mortality. We modelled our treatment variable according to the timing of first palliative care interaction as a function of predicted LOS, and we employed predicted LOS as an additional covariate in regression as a proxy for complexity alongside diagnosis and comorbidity index. We evaluated models based on predictive accuracy in and out of sample, on Akaike and Bayesian Information Criteria, and precision of treatment effect estimate. Results Our approach using an additional covariate yielded major improvement in model accuracy: R2 increased from 0.14 to 0.23, and model performance also improved on predictive accuracy and information criteria. Treatment effect estimates and conclusions were unaffected. Our approach with respect to treatment variable yielded no substantial improvements in model performance, but post hoc analyses show an association between treatment effect estimate and estimated LOS at baseline. Conclusion Allocation of scarce palliative care capacity and value-based reimbursement models should take into consideration when and for whom the intervention has the largest impact on treatment choices. An individual’s predicted LOS at baseline is useful in this context for accurately predicting costs, and potentially has further benefits in modelling treatment effects.

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“…43 This modifies the Elixhauser scoring system, attributing each of the 31 conditions a score (range: −7 to +12) that captures each condition’s association with hospital death. In the context of well-known problems controlling for mortality in seriously-ill populations with routine data, 44 we considered this a superior approach to excluding or explicitly controlling for observed hospital death in the sample due to endogeneity concerns. 45…”

Section: Methodsmentioning

confidence: 99%

Economic Analysis of Hospital Palliative Care: Investigating Heterogeneity by Noncancer Diagnoses

May

Normand

Fabbro

et al. 2019

MDM Policy & Practice

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Background. Single-disease-focused treatment and hospital-centric care are poorly suited to meet complex needs in an era of multimorbidity. Understanding variation in palliative care’s association with treatment choices is essential to optimizing interdisciplinary decision making in care of complex patients. Aim. To estimate the association between palliative care and hospital costs by primary diagnosis and multimorbidity for adults with one of six life-limiting conditions: heart failure, chronic obstructive pulmonary disease (COPD), liver failure, kidney failure, neurodegenerative conditions including dementia, and HIV/AIDS. Methods. Data from four studies (2002–2015) were pooled to provide an analytic dataset of 73,304 participants with mean costs $10,483, of whom 5,348 (7%) received palliative care. We estimated average effect of palliative care on direct hospital costs among the treated, using propensity scores to control for observed confounding. Results. Palliative care was associated with a statistically significant reduction in total direct costs for heart failure (estimated treatment effect: −$2666; 95% confidence interval [CI]: −$3440 to −$1892), neurodegenerative conditions (−$3523; −$4394 to −$2651), COPD (−$1613; −$2217 to −$1009), kidney failure (−$3589; −$5132 to −$2045), and liver failure (−$7574; −$9232 to −$5916). The association for liver failure patients was statistically significantly larger than for any other disease group. Cost-saving associations were also statistically larger for patients with multimorbidity than single disease for two of the six groups: neurodegenerative and liver failure. Conclusions. Heterogeneity in treatment effect estimates was observable in assessing association between palliative care and hospital costs for adults with serious life-limiting illnesses other than cancer. The results illustrate the importance of careful definition of palliative care populations in research and practice, and raise further questions about the role of interdisciplinary decision making in treatment of complex medical illness.

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Evaluating Hospital Readmissions for Persons With Serious and Complex Illness: A Competing Risks Approach

Cited by 15 publications

References 47 publications

Do Mini–Mental State Examination and Montreal Cognitive Assessment predict high‐cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

Do Mini–Mental State Examination and Montreal Cognitive Assessment predict high‐cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

Using predicted length of stay to define treatment and model costs in hospitalized adults with serious illness: an evaluation of palliative care

Economic Analysis of Hospital Palliative Care: Investigating Heterogeneity by Noncancer Diagnoses

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