OBJECTIVE: The provision and funding of long-term care (LTC) for older people varies between European countries. Despite differences, there is limited information about the comparative performance of LTC systems in Europe. In this study, we compared quality of life (QoL) of informal carers of home care service users in Austria, England and Finland.
METHODS: Informal carers were surveyed in Austria, England and Finland. The study data (n = 835) contained information on social care-related quality of life (SCRQoL) associated with the ASCOT-Carer measure, and characteristics of carers and care recipients from each country. We applied risk-adjustment methods using a fractional regression model to produce risk-adjusted SCRQoL scores for the comparative analysis. In a sensitivity analysis, we applied multiple imputation to missing data to validate our findings.
RESULTS: We found that the mean values of the risk-adjusted SCRQoL of informal carers in England were 1.4-2.9% and 0.3-0.5% higher than in Finland and Austria, and the mean values of the risk-adjusted SCRQoL of carers in Austria were 0.8-2.7% higher than in Finland. Differences in the mean values of the country-specific risk-adjusted SCRQoL scores were small and statistically non-significant. English informal carers were less healthy and co-resided with care resipients more often than carers in Austria or Finland.
CONCLUSIONS: Small differences between the risk-adjusted SCRQoL scores between Austria, England and Finland are consistent with the observation that the countries provide different types of support for informal carers. Our results help local and national decision-makers in these countries to benchmark their informal care support systems.

BACKGROUND: The purpose of this study was to assess the relationship between risk and reimbursement for both surgeons and hospitals among Medicare patients undergoing primary total joint arthroplasty (TJA).
METHODS: The \"2021 Medicare Physician and Other Provider\" and \"2021 Medicare Inpatient Hospitals\" files were used. Patient comorbidity profiles were collected, including the mean patient hierarchal condition category (HCC) risk score. Surgeon data included all primary TJA procedures (inpatient and outpatient) billed to Medicare in 2021, while hospital data included all such inpatient episodes. Surgeon and hospital reimbursements were collected. All episodes were split into a \"sicker cohort\" with an HCC risk score of 1.5 or more and a \"healthier cohort\" with HCC risk scores less than 1.5. Variables were compared across cohorts.
RESULTS: In 2021, 386,355 primary total hip and knee arthroplasty procedures were billed to Medicare and were included. The mean surgeon reimbursement among the sicker cohort was $1,021.91, which was less than for the healthier cohort of $1,060.13 (P < .001). Meanwhile, for the hospital analysis, 112,012 Medicare TJA patients were admitted as inpatients and included. The mean reimbursement to hospitals was significantly greater for the sicker cohort at $13,950.66, compared to the healthier cohort of $8,430.46. For both analyses, the sicker patient cohorts had a significantly higher rate of all comorbidities assessed (P < .001).
CONCLUSIONS: This study demonstrates that mean surgeon reimbursement was lower for primary TJA among sicker patients in comparison to their healthier counterparts, while hospital reimbursement was higher for sicker patients. This represents a discrepancy in the incentivization of care for complex patients, as hospitals receive increased remuneration for taking on extra risk, while surgeons get paid less on average for performing TJA on sicker patients. Such data should inform future policy to assure continued access to arthroplasty care among complex patients.

BACKGROUND: Stroke outcomes research requires risk-adjustment for stroke severity, but this measure is often unavailable. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based stroke severity measure that was developed in Ontario, Canada. We assessed the geographical and temporal external validity of PaSSV in British Columbia (BC), Nova Scotia (NS) and Ontario, Canada.
METHODS: We used linked administrative data in each province to identify adult patients with ischemic stroke or intracerebral hemorrhage between 2014-2019 and calculated their PaSSV score. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models\' discriminative values using Uno\'s c-statistic, comparing models with versus without PaSSV.
RESULTS: We included 86,142 patients (n = 18,387 in BC, n = 65,082 in Ontario, n = 2,673 in NS). The mean and median PaSSV were similar across provinces. A higher PaSSV score, representing lower stroke severity, was associated with a lower hazard of death (hazard ratio and 95% confidence intervals 0.70 [0.68, 0.71] in BC, 0.69 [0.68, 0.69] in Ontario, 0.72 [0.68, 0.75] in NS) and admission to long-term care (0.77 [0.76, 0.79] in BC, 0.84 [0.83, 0.85] in Ontario, 0.86 [0.79, 0.93] in NS). Including PaSSV in the multivariable models increased the c-statistics compared to models without this variable.
CONCLUSIONS: PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.

Frailty is an important predictor of mortality, health care costs and utilization, and health outcomes. Validated measures of frailty are not consistently collected during clinical encounters, making comparisons across populations challenging. However, several claims-based algorithms have been developed to predict frailty and related concepts. This study compares performance of three such algorithms among Medicare beneficiaries. Claims data from 12-month continuous enrollment periods were selected during 2014-2016. Frailty scores, calculated using previously developed algorithms from Faurot, Kim, and RAND, were added to baseline regression models to predict claims-based outcomes measured in the following year. Root mean square error and area under the receiver operating characteristic curve were calculated for each model and outcome combination and tested in subpopulations of interest. Overall, Kim models performed best across most outcomes, metrics, and subpopulations. Kim frailty scores may be used by health systems and researchers for risk adjustment or targeting interventions.

暂无摘要。

Congenital heart surgery (CHS) encompasses a heterogeneous population of patients and surgeries. Risk standardization models that adjust for patient and procedural characteristics can allow for collective study of these disparate patients and procedures.
We sought to develop a risk-adjustment model for CHS using the newly developed Risk Stratification for Congenital Heart Surgery for ICD-10 Administrative Data (RACHS-2) methodology.
Within the Kids\' Inpatient Database 2019, we identified all CHSs that could be assigned a RACHS-2 score. Hierarchical logistic regression (clustered on hospital) was used to identify patient and procedural characteristics associated with in-hospital mortality. Model validation was performed using data from 24 State Inpatient Databases during 2017.
Of 5,902,538 total weighted hospital discharges in the Kids\' Inpatient Database 2019, 22,310 pediatric cardiac surgeries were identified and assigned a RACHS-2 score. In-hospital mortality occurred in 543 (2.4%) of cases. Using only RACHS-2, the mortality mode had a C-statistic of 0.81 that improved to 0.83 with the addition of age. A final multivariable model inclusive of RACHS-2, age, payer, and presence of a complex chronic condition outside of congenital heart disease further improved model discrimination to 0.87 (P < 0.001). Discrimination in the validation cohort was also very good with a C-statistic of 0.83.
We created and validated a risk-adjustment model for CHS that accounts for patient and procedural characteristics associated with in-hospital mortality available in administrative data, including the newly developed RACHS-2. Our risk model will be critical for use in health services research and quality improvement initiatives.

Automated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustment of quality indicators in healthcare. The methods that have been used so far, however, do not account for the fact that patient data are typically nested within hospitals.
Therefore, we aimed to demonstrate how to account for the multilevel structure of hospital data with LASSO and compare the results of this procedure with a LASSO variant that ignores the multilevel structure of the data. We used three different data sets (from acute myocardial infarcation, COPD, and stroke patients) with two dependent variables (one numeric and one binary), on which different LASSO variants with and without consideration of the nested data structure were applied. Using a 20-fold sub-sampling procedure, we tested the predictive performance of the different LASSO variants and examined differences in variable importance.
For the metric dependent variable Duration Stay, we found that inserting hospitals led to better predictions, whereas for the binary variable Mortality, all methods performed equally well. However, in some instances, the variable importances differed greatly between the methods.
We showed that it is possible to take the multilevel structure of data into account in automated predictor selection and that this leads, at least partly, to better predictive performance. From the perspective of variable importance, including the multilevel structure is crucial to select predictors in an unbiased way under consideration of the structural differences between hospitals.

Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery.
This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration.
The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15-0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15-0.42), 0.40% (neurovascular, IQR 0.24-0.78) and 0.49% (trauma neurosurgery, IQR 0.23-1.68).
Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.

The purpose of this study was to assess surgeon reimbursement among total joint arthroplasty (TJA) patients who had differing risk profiles within the Medicare population.
The \"2019 Medicare Physician and Other Provider\" file was utilized. In 2019, 441,584 primary total hip and knee arthroplasty procedures were billed to Medicare Part B. All episodes were included. Patient demographics and comorbidity profiles were collected for all patients. Additionally, mean patient hierarchal condition category (HCC) risk scores and physician reimbursements were collected. All procedure episodes were split into 2 cohorts; those with an HCC risk score of 1.5 or greater, and those with patient HCC risk scores less than 1.5. Variables were averaged for each cohort and compared.
The mean reimbursement across all procedures was $1,068.03. For the sicker patient cohort with a mean HCC risk score of 1.5 or greater, there was a significantly higher rate of all comorbidities compared to the cohort with HCC risk score under 1.5. The mean payment across the sicker cohort was $1,059.21, while the mean payment among the cohort with HCC risk score under 1.5 was 1,073.32 (P = .032).
This study demonstrates that for Medicare patients undergoing primary TJA in 2019, the mean surgeon reimbursement was lower for primary TJA among sick patients in comparison to their healthier counterparts, although it is difficult to ascertain the impact of this discrepancy. As alternative payment models continue to undergo evaluation and development, these data will be important for the potential advancement of more equitable reimbursement models in arthroplasty care, specifically regarding surgeon reimbursement and possible risk adjustment within such models.

UNASSIGNED: Methods for assessing long-term outcome quality of acute care for sepsis are lacking. We investigated a method for measuring long-term outcome quality based on health claims data in Germany.
UNASSIGNED: Analyses were based on data of the largest German health insurer, covering 32% of the population. Cases (aged 15 years and older) with ICD-10-codes for severe sepsis or septic shock according to sepsis-1-definitions hospitalized in 2014 were included. Short-term outcome was assessed by 90-day mortality; long-term outcome was assessed by a composite endpoint defined by 1-year mortality or increased dependency on chronic care. Risk factors were identified by logistic regressions with backward selection. Hierarchical generalized linear models were used to correct for clustering of cases in hospitals. Predictive validity of the models was assessed by internal validation using bootstrap-sampling. Risk-standardized mortality rates (RSMR) were calculated with and without reliability adjustment and their univariate and bivariate distributions were described.
UNASSIGNED: Among 35,552 included patients, 53.2% died within 90 days after admission; 39.8% of 90-day survivors died within the first year or had an increased dependency on chronic care. Both risk-models showed a sufficient predictive validity regarding discrimination [AUC = 0.748 (95% CI: 0.742; 0.752) for 90-day mortality; AUC = 0.675 (95% CI: 0.665; 0.685) for the 1-year composite outcome, respectively], calibration (Brier Score of 0.203 and 0.220; calibration slope of 1.094 and 0.978), and explained variance (R 2 = 0.242 and R 2 = 0.111). Because of a small case-volume per hospital, applying reliability adjustment to the RSMR led to a great decrease in variability across hospitals [from median (1st quartile, 3rd quartile) 54.2% (44.3%, 65.5%) to 53.2% (50.7%, 55.9%) for 90-day mortality; from 39.2% (27.8%, 51.1%) to 39.9% (39.5%, 40.4%) for the 1-year composite endpoint]. There was no substantial correlation between the two endpoints at hospital level (observed rates: ρ = 0, p = 0.99; RSMR: ρ = 0.017, p = 0.56; reliability-adjusted RSMR: ρ = 0.067; p = 0.026).
UNASSIGNED: Quality assurance and epidemiological surveillance of sepsis care should include indicators of long-term mortality and morbidity. Claims-based risk-adjustment models for quality indicators of acute sepsis care showed satisfactory predictive validity. To increase reliability of measurement, data sources should cover the full population and hospitals need to improve ICD-10-coding of sepsis.