Risk Compensation After Initiation of Daily Oral Pre-exposure Prophylaxis Among Sexual and Gender Minorities in Nigeria.

Pre-exposure prophylaxis (PrEP) use may be associated with risk compensation. We enrolled and provided PreP to sexual and gender minorities (SGM) in Abuja, Nigeria between April 2018 and May 2019. Behavioral information and samples for urogenital and anorectal Chlamydia trachomatis and Neisseria gonorrhoeae sexually transmitted infections (STIs) were collected at baseline. Blood samples for PrEP assay and self-reported adherence were collected at three-monthly follow-up visits. STIs were detected using Aptima Combo2 assay. We estimated the odds ratios (ORs) of PCR-diagnosed bacterial STIs and self-reported behavioral outcomes (condomless anal intercourse [CAI] and concurrent sexual relationships) with conditional logistic regression. Of 400 SGM who initiated PrEP, 206 were eligible for analysis, and had a median age of 24 years (IQR 22-27). In multivariable analysis, participants in the PrEP period had decreased odds of CAI (adjusted OR: 0.49, 95% CI 0.28, 0.84). PrEP use was not associated with risk compensation.

Validation of the Passive Surveillance Stroke Severity Score in Three Canadian Provinces.

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. CONCLUSION: PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.

Quality of life outcomes for informal carers of long-term care service users in Austria, England and Finland.

PURPOSE: 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. CONCLUSION: 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.

Reweighting and validation of the hospital frailty risk score using electronic health records in Germany: a retrospective observational study.

BACKGROUND: In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany. METHODS: The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models. RESULTS: Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men. CONCLUSIONS: Our data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting.

Comparing risk-adjusted inpatient fall rates internationally: validation of a risk-adjustment model using multicentre cross-sectional data from hospitals in Switzerland and Austria.

BACKGROUND: Inpatient falls in hospitals are an acknowledged indicator of quality of care. International comparisons could highlight quality improvement potential and enable cross-national learning. Key to fair cross-national comparison is the availability of a risk adjustment model validated in an international context. This study aimed to 1) ascertain that the variables of the inpatient fall risk adjustment model do not interact with country and thus can be used for risk adjustment, 2) compare the risk of falling in hospitals between Switzerland and Austria after risk adjustment. METHODS: The data on inpatient falls from Swiss and Austrian acute care hospitals were collected on a single measurement day in 2017, 2018 and 2019 as part of an international multicentre cross-sectional study. Multilevel logistic regression models were used to screen for interaction effects between the patient-related fall risk factors and the countries. The risks of falling in hospital in Switzerland and in Austria were compared after applying the risk-adjustment model. RESULTS: Data from 176 hospitals and 43,984 patients revealed an inpatient fall rate of 3.4% in Switzerland and 3.9% in Austria. Two of 15 patient-related fall risk variables showed an interaction effect with country: Patients who had fallen in the last 12 months (OR 1.49, 95% CI 1.10-2.01, p = 0.009) or had taken sedatives/psychotropic medication (OR 1.40, 95% CI 1.05-1.87, p = 0.022) had higher odds of falling in Austrian hospitals. Significantly higher odds of falling were observed in Austrian (OR 1.38, 95% CI 1.13-1.68, p = 0.002) compared to Swiss hospitals after applying the risk-adjustment model. CONCLUSIONS: Almost all patient-related fall risk factors in the model are suitable for a risk-adjusted cross-country comparison, as they do not interact with the countries. Further model validation with additional countries is warranted, particularly to assess the interaction of risk factors "fall in the last 12 months" and "sedatives/psychotropic medication intake" with country variable. The study underscores the crucial role of an appropriate risk-adjustment model in ensuring fair international comparisons of inpatient falls, as the risk-adjusted, as opposed to the non-risk-adjusted country comparison, indicated significantly higher odds of falling in Austrian compared to Swiss hospitals.

Risk Versus Reward: Hospitals Incentivized More Than Surgeons to Care for Riskier Arthroplasty Patients.

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.

Should Chronological Age be a Consideration in Patients Undergoing Elective Primary Total Knee Arthroplasty?

BACKGROUND: The optimal time for total knee arthroplasty (TKA) requires a balance between patient disability and health state to minimize complications. While chronological age has not been shown to be predictive of complications in elective surgical patients, there is a point beyond which even optimized elderly patients would be at increased risk for complications. The purpose of this study was to examine the impact of chronological age on complications following primary TKA. METHODS: Using an administrative database, the records of 2,129,191 patients undergoing elective unilateral TKA between 2006 and 2021 were reviewed. The primary outcomes of interest were cardiac and pulmonary complications, and their relationship to the Charlson-Deyo Comorbidity Index (CDI) and chronological age. Secondary outcomes included risk of renal, neurologic, infection, and intensive care utilization postoperatively. The results were analyzed using a graphical method. The impact of chronological age as a modifier of overall risk for complications was modeled as a continuous variable. An age cutoff threshold of 80 years was also assigned for clinical convenience. RESULTS: The risk of complications correlated more closely to the CDI (odds ratio (OR) 1.37 to 2.1) than chronological age (OR 1.0 to 1.1) across the various complications [Table 1. However, beyond age 80 years, the risks of cardiac, pulmonary, renal, and cerebrovascular complications were significantly increased for all CDI categories (OR 1.73 to 3.40) compared to patients below age 80 years [Table 2] [Figures 1A and 1B]. CONCLUSIONS: Chronologic age can impact the risk of complications even in well-optimized elderly patients undergoing primary TKA. As arthroplasty continues to transition to outpatient settings and inpatient denials increase, these results can help patients, physicians, and payors mitigate risk while optimizing the allocation of resources.

[Fragility Fractures: Preoperative assessment and optimisation]. / Fragilitätsfraktur beim geriatrischen Patienten: Präoperative Abklärung und Optimierung.

Fragility fractures are associated with high morbidity and mortality. An interdisciplinary collaboration and an individualized, patient-centered approach are essential to ensure an optimized preoperative period and to improve perioperative safety. Preoperative responsibilities of trauma surgery include in the first step the identification of fragility fractures and the necessity for geriatric involvement. Orthogeriatric co-management (OCM) focuses on the identification of the medical, functional and social needs of the patient. In the preoperative period attention is focussed on acute diseases in need of treatment that have a negative impact on the course of further treatment and the prevention of delirium.

Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation.

BACKGROUND: Risk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models. METHODS: We analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO). RESULTS: HGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features. CONCLUSIONS: In this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.

Hospital frailty risk score is superior to legacy comorbidity indices for risk adjustment of in-hospital cirrhosis cases.

Background & Aims: The hospital frailty risk score (HFRS) identifies older patients at risk of poor outcomes and may have value in cirrhosis. We compared the Charlson (CCI), Elixhauser (ECI), and cirrhosis (CirCom) comorbidity indices with the HFRS in predicting outcomes for cirrhosis hospitalisations. Methods: Using the National Inpatient Sample (quarter 4 of 2015-2019), we analysed cirrhosis hospitalisations. For each index, we described the prevalence of comorbid conditions and inpatient mortality. We compared the ability of CCI, ECI, CirCom, and HFRS to predict inpatient mortality. Raw and adjusted models predicting inpatient mortality were compared using the area under the receiver operating characteristic curve and the Akaike information criterion. Results: The cohort's (N = 626,553) median age was 61 years (IQR 52-68 years), 60% were male, cirrhosis was caused by alcohol in 43%, and 38% had ascites. The median comorbidity scores are as follows: ECI 4 (IQR 3-6), CCI 5 (IQR 4-8), and HFRS 5.6 (IQR 3.0-8.6). The most common CirCom score was 0 + 0 (44%). Across the range of values of each index, we observed different mortality ranges: CCI 1.9-13.1%, ECI 3.2-8.7%, CirCom 4.9-13.8%, and HFRS 1.0-15.2%. An adjusted model with HFRS had the highest area under the receiver operating characteristic curve in predicting mortality (HFRS 0.782 vs. ECI 0.689, CCI 0.695, and CirCom 0.692). We observed substantial variation in mortality with HFRS within each level of CCI, ECI, and CirCom. For example, for ECI 4, mortality increased from 0.6 to 16.4%, as HFRS increased from 0 to 15. Conclusions: Comorbidity indices predict inpatient cirrhosis mortality, but HFRS performs better than CCI, ECI, and CirCom. HFRS is an ideal tool for measuring comorbidity burden and disease severity risk adjustment in cirrhosis-related administrative database studies. Impact and Implications: We compared commonly used comorbidity indices to a more recently described risk score (hospital frailty risk score [HFRS]) in patients with cirrhosis using a national sample of hospital records. Comorbid conditions are common in hospitalised patients with cirrhosis. There is significant variability in mortality across the range of each index. HFRS outperforms the Charlson comorbidity index, Elixhauser comorbidity index, and CirCom (cirrhosis-specific comorbidity scoring system) in predicting inpatient mortality. HFRS is a valuable index for risk adjustment in inpatient administrative database studies.

The Neiman Imaging Comorbidity Index: Development and Validation in a National Commercial Claims Database.

OBJECTIVE: To build the Neiman Imaging Comorbidity Index (NICI), based on variables available in claims datasets, which provides good discrimination of an individual's chance of receiving advanced imaging (CT, MR, PET), and thus, utility as a control variable in research. METHODS: This retrospective study used national commercial claims data from Optum's deidentified Clinformatics Data Mart database from the period January 1, 2018 to December 31, 2019. Individuals with continuous enrollment during this 2-year study period were included. Lasso (least absolute shrinkage and selection operator) regression was used to predict the chance of receiving advanced imaging in 2019 based on the presence of comorbidities in 2018. A numerical index was created in a development cohort (70% of the total dataset) using weights assigned to each comorbidity, based on regression ß coefficients. Internal validation of assigned scores was performed in the remaining 30% of claims, with comparison to the commonly used Charlson Comorbidity Index. RESULTS: The final sample (development and validation cohorts) included 10,532,734 beneficiaries, of whom 2,116,348 (20.1%) received advanced imaging. After model development, the NICI included nine comorbidities. In the internal validation set, the NICI achieved good discrimination of receipt of advanced imaging with a C statistic of 0.709 (95% confidence interval [CI] 0.708-0.709), which predicted advanced imaging better than the CCI (C 0.692, 95% CI 0.691-0.692). Controlling for age and sex yielded better discrimination (C 0.748, 95% CI 0.748-0.749). DISCUSSION: The NICI is an easily calculated measure of comorbidity burden that can be used to adjust for patients' chances of receiving advanced imaging. Future work should explore external validation of the NICI.

Comparative Performance of Three Claims-Based Frailty Measures Among Medicare Beneficiaries.

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.

Predicting major clinical events among Canadian adults with laboratory-confirmed influenza infection using the influenza severity scale.

We developed and validated the Influenza Severity Scale (ISS), a standardized risk assessment for influenza, to estimate and predict the probability of major clinical events in patients with laboratory-confirmed infection. Data from the Canadian Immunization Research Network's Serious Outcomes Surveillance Network (2011/2012-2018/2019 influenza seasons) enabled the selecting of all laboratory-confirmed influenza patients. A machine learning-based approach then identified variables, generated weighted scores, and evaluated model performance. This study included 12,954 patients with laboratory-confirmed influenza infections. The optimal scale encompassed ten variables: demographic (age and sex), health history (smoking status, chronic pulmonary disease, diabetes mellitus, and influenza vaccination status), clinical presentation (cough, sputum production, and shortness of breath), and function (need for regular support for activities of daily living). As a continuous variable, the scale had an AU-ROC of 0.73 (95% CI, 0.71-0.74). Aggregated scores classified participants into three risk categories: low (ISS < 30; 79.9% sensitivity, 51% specificity), moderate (ISS ≥ 30 but < 50; 54.5% sensitivity, 55.9% specificity), and high (ISS ≥ 50; 51.4% sensitivity, 80.5% specificity). ISS demonstrated a solid ability to identify patients with hospitalized laboratory-confirmed influenza at increased risk for Major Clinical Events, potentially impacting clinical practice and research.

Improving Statewide Post-Operative Sepsis Performance Measurement Using Hospital Risk Adjustment Within a Surgical Collaborative.

Background: The Georgia Quality Improvement Program (GQIP) surgical collaborative participating hospitals have shown consistently poor performance in the post-operative sepsis category of National Surgical Quality Improvement Program data as compared with national benchmarks. We aimed to compare crude versus risk-adjusted post-operative sepsis rankings to determine high and low performers amongst GQIP hospitals. Patients and Methods: The cohort included intra-abdominal general surgery patients across 10 collaborative hospitals from 2015 to 2020. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) sepsis definition was used among all hospitals for case abstraction and NSQIP data were utilized to train and validate a multivariable risk-adjustment model with post-operative sepsis as the outcome. This model was used to rank GQIP hospitals by risk-adjusted post-operative sepsis rates. Rankings between crude and risk-adjusted post-operative sepsis rankings were compared ordinally and for changes in tertile. Results: The study included 20,314 patients with 595 cases of post-operative sepsis. Crude 30-day post-operative sepsis risk among hospitals ranged from 0.81 to 5.11. When applying the risk-adjustment model which included: age, American Society of Anesthesiology class, case complexity, pre-operative pneumonia/urinary tract infection/surgical site infection, admission status, and wound class, nine of 10 hospitals were re-ranked and four hospitals changed performance tertiles. Conclusions: Inter-collaborative risk-adjusted post-operative sepsis rankings are important to present. These metrics benchmark collaborating hospitals, which facilitates best practice exchange from high to low performers.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

PURPOSE: By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. METHODS: We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. FINDINGS: Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. CONCLUSIONS: US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Prompt access to outpatient care post-incarceration among adults with a history of substance use: Predisposing, enabling, and need-based factors.

INTRODUCTION: As expanded Medicaid coverage reduces financial barriers to receiving health care among formerly incarcerated adults, more information is needed to understand the factors that predict prompt use of health care after release among insured adults with a history of substance use. This study's aim was to estimate the associations between characteristics suggested by the Andersen behavioral model of health service use and measures of health care use during the immediate reentry period and in the presence of Medicaid coverage. METHODS: In this retrospective cohort study, we linked individual-level data from multiple Wisconsin agencies. The sample included individuals aged 18-64 released from a Wisconsin State Correctional Facility between April 2014 and June 2017 to a community in the state who enrolled in Medicaid within one month of release and had a history of substance use. We grouped predictors of outpatient care into variable domains within the Andersen model: predisposing- individual socio-demographic characteristics; enabling characteristics including area-level socio-economic resources, area-level health care supply, and characteristics of the incarceration and release; and need-based- pre-release health conditions. We used a model selection algorithm to select a subset of variable domains and estimated the association between the variables in these domains and two outcomes: any outpatient visit within 30 days of release from a state correctional facility, and receipt of medication for opioid use disorder within 30 days of release. RESULTS: The size and sign of many of the estimated associations differed for our two outcomes. Race was associated with both outcomes, Black individuals being 12.1 p.p. (95 % CI, 8.7-15.4, P < .001) less likely than White individuals to have an outpatient visit within 30 days of release and 1.3 p.p. (95 % CI, 0.48-2.1, P = .002) less likely to receive MOUD within 30 days of release. Chronic pre-release health conditions were positively associated with the likelihood of post-release health care use. CONCLUSIONS: Conditional on health insurance coverage, meaningful differences in post-incarceration outpatient care use still exist across adults leaving prison with a history of substance use. These findings can help guide the development of care transition interventions including the prioritization of subgroups that may warrant particular attention.

Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model.

BACKGROUND: When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis). RESEARCH QUESTION: Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ? STUDY DESIGN AND METHODS: Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split-derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier's scores, and comparisons of predicted vs observed mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vs adjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment. RESULTS: Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and ≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9% of hospitals. INTERPRETATION: The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model can aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

Catheterization for Congenital Heart Disease Adjustment for Risk Method II.

BACKGROUND: Current metrics used to adjust for case mix complexity in congenital cardiac catheterization are becoming outdated due to the introduction of novel procedures, innovative technologies, and expanding patient subgroups. This study aims to develop a risk adjustment methodology introducing a novel, clinically meaningful adverse event outcome and incorporating a modern understanding of risk. METHODS: Data from diagnostic only and interventional cases with defined case types were collected for patients ≤18 years of age and ≥2.5 kg at all Congenital Cardiac Catheterization Project on Outcomes participating centers. The derivation data set consisted of cases performed from 2014 to 2017, and the validation data set consisted of cases performed from 2019 to 2020. Severity level 3 adverse events were stratified into 3 tiers by clinical impact (3a/b/c); the study outcome was clinically meaningful adverse events, severity level ≥3b (3bc/4/5). RESULTS: The derivation data set contained 15 224 cases, and the validation data set included 9462 cases. Clinically meaningful adverse event rates were 4.5% and 4.2% in the derivation and validation cohorts, respectively. The final risk adjustment model included age <30 days, Procedural Risk in Congenital Cardiac Catheterization risk category, and hemodynamic vulnerability score (C statistic, 0.70; Hosmer-Lemeshow P value, 0.83; Brier score, 0.042). CONCLUSIONS: CHARM II (Congenital Heart Disease Adjustment for Risk Method II) risk adjustment methodology allows for equitable comparison of clinically meaningful adverse events among institutions and operators with varying patient populations and case mix complexity performing pediatric cardiac catheterization.

Separating the wheat from the chaff: How to measure hospital quality in routine data?

OBJECTIVE: To measure hospital quality based on routine data available in many health care systems including the United States, Germany, the United Kingdom, Scandinavia, and Switzerland. DATA SOURCES AND STUDY SETTING: We use the Swiss Medical Statistics of Hospitals, an administrative hospital dataset of all inpatient stays in acute care hospitals in Switzerland for the years 2017-2019. STUDY DESIGN: We study hospital quality based on quality indicators used by leading agencies in five countries (the United States, the United Kingdom, Germany, Austria, and Switzerland) for two high-volume elective procedures: inguinal hernia repair and hip replacement surgery. We assess how least absolute shrinkage and selection operator (LASSO), a supervised machine learning technique for variable selection, and Mundlak corrections that account for unobserved heterogeneity between hospitals can be used to improve risk adjustment and correct for imbalances in patient risks across hospitals. DATA COLLECTION/EXTRACTION METHODS: The Swiss Federal Statistical Office collects annual data on all acute care inpatient stays including basic socio-demographic patient attributes and case-level diagnosis and procedure codes. PRINCIPAL FINDINGS: We find that LASSO-selected and Mundlak-corrected hospital random effects logit models outperform common practice logistic regression models used for risk adjustment. Besides the more favorable statistical properties, they have superior in- and out-of-sample explanatory power. Moreover, we find that Mundlak-corrected logits and the more complex LASSO-selected models identify the same hospitals as high or low-quality offering public health authorities a valuable alternative to standard logistic regression models. Our analysis shows that hospitals vary considerably in the quality they provide to patients. CONCLUSION: We find that routine hospital data can be used to measure clinically relevant quality indicators that help patients make informed hospital choices.