The traditional model specification of stepped-wedge cluster-randomized trials assumes a homogeneous treatment effect across time while adjusting for fixed-time effects. However, when treatment effects vary over time, the constant effect estimator may be biased. In the general setting of stepped-wedge cluster-randomized trials with multiple interventions, we derive the expected value of the constant effect estimator when the true treatment effects depend on exposure time periods. Applying this result to concurrent and factorial stepped wedge designs, we show that the estimator represents a weighted average of exposure-time-specific treatment effects, with weights that are not necessarily uniform across exposure periods. Extensive simulation studies reveal that ignoring time heterogeneity can result in biased estimates and poor coverage of the average treatment effect. In this study, we examine two models designed to accommodate multiple interventions with time-varying treatment effects: (1) a time-varying fixed treatment effect model, which allows treatment effects to vary by exposure time but remain fixed for each time point, and (2) a random treatment effect model, where the time-varying treatment effects are modeled as random deviations from an overall mean. In the simulations considered in this study, concurrent designs generally achieve higher power than factorial designs under a time-varying fixed treatment effect model, though the differences are modest. Finally, we apply the constant effect model and both time-varying treatment effect models to data from the Prognosticating Outcomes and Nudging Decisions in the Electronic Health Record (PONDER) trial. All three models indicate a lack of treatment effect for either intervention, though they differ in the precision of their estimates, likely due to variations in modeling assumptions.

“Nudges” embedded in the electronic health record (EHR) facilitate desired decisions while preserving autonomy and may provide a scalable strategy to overcome the common implementation barrier of lack of knowledge about a best practice. We sought to test whether EHR-based nudges targeting two intensive care unit (ICU) clinician groups would safely increase evidence-based use of low tidal volume ventilation. We performed a stepped-wedge, cluster randomized, hybrid type 3 effectiveness-implementation trial in 12 ICUs from February 2021 to May 2023 to test three nudges targeting clinicians responsible for order entry and respiratory therapists responsible for operationalizing orders and documentation. A default ventilation order auto-populated a low tidal volume setting; an accountable justification order required a free-text justification to order high tidal volume; and an accountable justification flowsheet required a free-text justification to document delivery of high tidal volume. ICUs were randomly assigned to launch one of the two order nudges on a pre-specified date, followed by the flowsheet nudge six months thereafter. The primary outcome was fidelity to low tidal volume ventilation, defined as percentage of time during the first 72 h of ventilation with low tidal volumes. For additional contextual inquiry, we conducted qualitative interviews with ICU clinicians regarding their perspectives on low tidal volume ventilation and study nudges. The primary analysis included 4412 patients. Unadjusted median fidelity to low tidal volume ventilation was 45.7 https://clinicaltrials.gov/study/NCT04663802

OBJECTIVES:. To understand how strain-process-outcome relationships in patients with sepsis may vary among hospitals. DESIGN:. Retrospective cohort study using a validated hospital capacity strain index as a within-hospital instrumental variable governing ICU versus ward admission, stratified by hospital. SETTING:. Twenty-seven U.S. hospitals from 2013 to 2018. PATIENTS:. High-acuity emergency department patients with sepsis who do not require life support therapies. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. The mean predicted probability of ICU admission across strain deciles ranged from 4.9% (lowest ICU-utilizing hospital for sepsis without life support) to 61.2% (highest ICU-utilizing hospital for sepsis without life support). The difference in the predicted probabilities of ICU admission between the lowest and highest strain deciles ranged from 9.0% (least strain-sensitive hospital) to 45.2% (most strain-sensitive hospital). In pooled analyses, emergency department patients with sepsis (n = 90,150) experienced a 1.3-day longer median hospital length of stay (LOS) if admitted initially to the ICU compared with the ward, but across the 27 study hospitals (n = 517-6,564), this effect varied from 9.0 days shorter (95% CI, –10.8 to –7.2; p < 0.001) to 19.0 days longer (95% CI, 16.7–21.3; p < 0.001). Corresponding ranges for inhospital mortality with ICU compared with ward admission revealed odds ratios (ORs) from 0.16 (95% CI, 0.03–0.99; p = 0.04) to 4.62 (95% CI, 1.16–18.22; p = 0.02) among patients with sepsis (pooled OR = 1.48). CONCLUSIONS:. There is significant among-hospital variation in ICU admission rates for patients with sepsis not requiring life support therapies, how sensitive those ICU admission decisions are to hospital capacity strain, and the association of ICU admission with hospital LOS and hospital mortality. Hospital-level heterogeneity should be considered alongside patient-level heterogeneity in critical and acute care study design and interpretation.

Background: Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes. Objectives: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. Research design: Retrospective cohort study. Subjects: All ICU patients in five hospitals from October 2017 through September 2019. Measures: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots. Results: The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone. Conclusions: Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

Background:Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes.Objective:Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients.Research design:Retrospective cohort study.Patients:ICU patients in 5 hospitals from October 2017 through September 2019.Measures:We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using 4 hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots.Results:The cohort included 13,993 patients and 107,699 ICU days. Across validation hospitals, patient-day-level models including daily LAPS2 (SBS: 0.119-0.235; c-statistic: 0.772-0.878) consistently outperformed models with admission LAPS2 alone in patient-level (SBS: 0.109-0.175; c-statistic: 0.768-0.867) and patient-day-level (SBS: 0.064-0.153; c-statistic: 0.714-0.861) models. Across all predicted mortalities, daily models were better calibrated than models with admission LAPS2 alone.Conclusions:Patient-day-level models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population performs as well or better than models incorporating modified admission LAPS2 alone. The use of daily LAPS2 may offer an improved tool for clinical prognostication and risk adjustment in research in this population.

Many studies encounter clustering due to multicenter enrollment and nonmortality outcomes, such as quality of life, that are truncated due to death-that is, missing not at random and nonignorable. Traditional missing-data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) carried out among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e., the survivor average causal effect (SACE)). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete-case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and software code to support future research.

Rationale: We have previously shown that hospital strain is associated with intensive care unit (ICU) admission and that ICU admission, compared with ward admission, may benefit certain patients with acute respiratory failure (ARF). Objectives: To understand how strain-process-outcomes relationships in patients with ARF may vary among hospitals and what hospital practice differences may account for such variation. Methods: We examined high-acuity patients with ARF who did not require mechanical ventilation or vasopressors in the emergency department (ED) and were admitted to 27 U.S. hospitals from 2013 to 2018. Stratifying by hospital, we compared hospital strain-ICU admission relationships and hospital length of stay (LOS) and mortality among patients initially admitted to the ICU versus the ward using hospital strain as a previously validated instrumental variable. We also surveyed hospital practices and, in exploratory analyses, evaluated their associations with the above processes and outcomes. Results: There was significant among-hospital variation in ICU admission rates, in hospital strain-ICU admission relationships, and in the association of ICU admission with hospital LOS and hospital mortality. Overall, ED patients with ARF (n = 45,339) experienced a 0.82-day shorter median hospital LOS if admitted initially to the ICU compared with the ward, but among the 27 hospitals (n = 224-3,324), this effect varied from 5.85 days shorter (95% confidence interval [CI], 28.84 to 22.86; P, 0.001) to 4.38 days longer (95% CI, 1.86-6.90; P = 0.001). Corresponding ranges for in-hospital mortality with ICU compared with ward admission revealed odds ratios from 0.08 (95% CI, 0.01-0.56; P, 0.007) to 8.89 (95% CI, 1.60-79.85; P = 0.016) among patients with ARF (pooled odds ratio, 0.75). In exploratory analyses, only a small number of measured hospital practices-the presence of a sepsis ED disposition guideline and maximum ED patient capacity-were potentially associated with hospital strain-ICU admission relationships. Conclusions: Hospitals vary considerably in ICU admission rates, the sensitivity of those rates to hospital capacity strain, and the benefits of ICU admission for patients with ARF not requiring life support therapies in the ED. Future work is needed to more fully identify hospital-level factors contributing to these relationships.

BACKGROUND:Despite recommendations to integrate palliative care (PC) into care for critically ill trauma patients, little is known about current PC practices in trauma care to inform opportunities for improvement. OBJECTIVE:Describe patterns of PC delivery among a large, critically ill trauma cohort. SETTING/SUBJECTS:Retrospective cohort study of adult (≥18 years) trauma patients admitted to an intensive care unit (ICU) at an urban, level one trauma center in the United States from March 1, 2017 to March 1, 2019. METHODS:We linked the electronic medical record with the institutional trauma registry. PC process measures included a PC consult order, advance care planning (ACP) note, and hospice use. Unadjusted results are reported for the total population, decedents, and subgroups at risk for poor outcomes (age ≥55 years, Black race ≥1 pre-existing comorbidity, and severe injury) after trauma. RESULTS:Among 1309 eligible admissions, 902 (68.9%) were male, 640 (48.9%) were Black, and 654 (50.0%) were ≥55 years old. Eighty-one (6.2%) patients received a PC consult order, 66 (5.0%) had an ACP note, and 13 (1.1%) were discharged to hospice. Among decedents (N = 91; 7%), 28 (30.8%) received a PC consult order and 36 (39.6%) had an ACP note. For high-risk subgroups, PC consult orders and ACP note rates ranged from 4.5-12.8% and 4.5-11.8%, respectively. CONCLUSION:PC delivery was rare among this cohort, including those at high risk for poor outcomes. Urgent efforts are needed to identify barriers to and develop targeted interventions for high quality PC delivery in trauma ICU care.

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

OBJECTIVES: Prior studies have demonstrated suboptimal adherence to lung protective ventilation among patients with acute respiratory distress syndrome. A common barrier to providing this evidence-based practice is diagnostic uncertainty. We sought to test the hypothesis that patients with acute respiratory distress syndrome due to coronavirus disease 2019, in whom acute respiratory distress syndrome is easily recognized, would be more likely to receive low tidal volume ventilation than concurrently admitted acute respiratory distress syndrome patients without coronavirus disease 2019. DESIGN: Retrospective cohort study. SETTING: Five hospitals of a single health system. PATIENTS: Mechanically ventilated patients with coronavirus disease 2019 or noncoronavirus disease 2019 acute respiratory distress syndrome as identified by an automated, electronic acute respiratory distress syndrome finder in clinical use at study hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 333 coronavirus disease 2019 patients and 234 noncoronavirus disease 2019 acute respiratory distress syndrome patients, the average initial tidal volume was 6.4 cc/kg predicted body weight and 6.8 cc/kg predicted body weight, respectively. Patients had tidal volumes less than or equal to 6.5 cc/kg predicted body weight for a mean of 70% of the first 72 hours of mechanical ventilation in the coronavirus disease 2019 cohort, compared with 52% in the noncoronavirus disease 2019 cohort (unadjusted p < 0.001). After adjusting for height, gender, admitting hospital, and whether or not the patient was admitted to a medical specialty ICU, coronavirus disease 2019 diagnosis was associated with a 21% higher percentage of time receiving tidal volumes less than or equal to 6.5 cc/kg predicted body weight within the first 72 hours of mechanical ventilation (95% CI, 14–28%; p < 0.001). CONCLUSIONS: Adherence to low tidal volume ventilation during the first 72 hours of mechanical ventilation is higher in patients with coronavirus disease 2019 than with acute respiratory distress syndrome without coronavirus disease 2019. This population may present an opportunity to understand facilitators of implementation of this life-saving evidence-based practice.

Background Clustered or correlated outcome data is common in medical research studies, such as the analysis of national or international disease registries, or cluster-randomized trials, where groups of trial participants, instead of each trial participant, are randomized to interventions. Within-group correlation in studies with clustered data requires the use of specific statistical methods, such as generalized estimating equations and mixed-effects models, to account for this correlation and support unbiased statistical inference. Methods We compare different approaches to estimating generalized estimating equations and mixed effects models for a continuous outcome in R through a simulation study and a data example. The methods are implemented through four popular functions of the statistical software R , “geese”, “gls”, “lme”, and “lmer”. In the simulation study, we compare the mean squared error of estimating all the model parameters and compare the coverage proportion of the 95% confidence intervals. In the data analysis, we compare estimation of the intervention effect and the intra-class correlation. Results In the simulation study, the function “lme” takes the least computation time. There is no difference in the mean squared error of the four functions. The “lmer” function provides better coverage of the fixed effects when the number of clusters is small as 10. The function “gls” produces close to nominal scale confidence intervals of the intra-class correlation. In the data analysis and the “gls” function yields a positive estimate of the intra-class correlation while the “geese” function gives a negative estimate. Neither of the confidence intervals contains the value zero. Conclusions The “gls” function efficiently produces an estimate of the intra-class correlation with a confidence interval. When the within-group correlation is as high as 0.5, the confidence interval is not always obtainable.

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 >= 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P= 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P=0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

Anesi, George1; Liu, Vincent2; Chowdhury, Marzana1; Small, Dylan3; Wang, Wei1; Delgado, M1; Bayes, Brian1; Dress, Erich1; Escobar, Gabriel2; Halpern, Scott1 Author Information

Background: The long-term impact of COVID-19 on patient health has been a recent focus. This study aims to determine the persistent symptoms and psychological conditions of patients hospitalized with COVID-19 15 months after onset. The potential risk factors were also explored.Methods: A cohort of COVID-19 patients discharged from February 20, 2020 to March 31, 2020 was recruited. Follow-ups were conducted using validated questionnaires and psychological screening scales at 15 months after onset to evaluate the patients’ health status. The risk factors for long-term health impacts and their associations with disease severity was analyzed.Findings: 534 COVID-19 patients were enrolled. The median age of the patients was 62.0 years old (IQR 52.0-70.0) and 295 were female (55.2%). The median time from onset to follow-up was 460.0 (451.0-467.0) days. Sleep disturbance (18.5%, 99/534) and fatigue (17.2%, 92/534) were the most common persistent symptoms. 6.4% (34/534) of the patients had depression, 9.2% (49/534) were anxious, 13.0% (70/534) had insomnia and 4.7% (25/534) suffered from posttraumatic stress disorder (PTSD). Multivariate adjusted logistic regression analysis showed that glucocorticoid use during hospitalization (OR 3.58, 95% CI 1.12-11.44) was significantly associated with an increased risk of fatigue. The OR values for anxiety and sleep disorders were 2.36 (95% CI 1.07-5.20) and 2.16 (95% CI 1.13-4.14) in females compared with males. The OR value of PTSD was 25.6 (95% CI 3.3-198.4) in patients with persistent symptoms to those without persistent symptoms. No significant associations were observed between fatigue syndrome or adverse mental outcomes and disease severity.Interpretation: 15-month follow-up in this study aroused the need of extended rehabilitation intervention for complete recovery in COVID-19 patients. Funding: None to declare. Declaration of Interest: All the authors declare no competing interests.Ethical Approval: The Research Ethics Committee of Shanghai Changzheng Hospital approved this study (2020SL007).

In a large health system in the United States, investigators examined whether mortality, receipt of mechanical ventilation, and patient acuity changed over time among adult patients with COVID-19–related critical illness admitted to intensive care units.

Background Behavioral economic insights have yielded strategies to overcome implementation barriers. For example, default strategies and accountable justification strategies have improved adherence to best practices in clinical settings. Embedding such strategies in the electronic health record (EHR) holds promise for simple and scalable approaches to facilitating implementation. A proven-effective but under-utilized treatment for patients who undergo mechanical ventilation involves prescribing low tidal volumes, which protects the lungs from injury. We will evaluate EHR-based implementation strategies grounded in behavioral economic theory to improve evidence-based management of mechanical ventilation. Methods The Implementing Nudges to Promote Utilization of low Tidal volume ventilation (INPUT) study is a pragmatic, stepped-wedge, hybrid type III effectiveness implementation trial of three strategies to improve adherence to low tidal volume ventilation. The strategies target clinicians who enter electronic orders and respiratory therapists who manage the mechanical ventilator, two key stakeholder groups. INPUT has five study arms: usual care, a default strategy within the mechanical ventilation order, an accountable justification strategy within the mechanical ventilation order, and each of the order strategies combined with an accountable justification strategy within flowsheet documentation. We will create six matched pairs of twelve intensive care units (ICUs) in five hospitals in one large health system to balance patient volume and baseline adherence to low tidal volume ventilation. We will randomly assign ICUs within each matched pair to one of the order panels, and each pair to one of six wedges, which will determine date of adoption of the order panel strategy. All ICUs will adopt the flowsheet documentation strategy 6 months afterwards. The primary outcome will be fidelity to low tidal volume ventilation. The secondary effectiveness outcomes will include in-hospital mortality, duration of mechanical ventilation, ICU and hospital length of stay, and occurrence of potential adverse events. Discussion This stepped-wedge, hybrid type III trial will provide evidence regarding the role of EHR-based behavioral economic strategies to improve adherence to evidence-based practices among patients who undergo mechanical ventilation in ICUs, thereby advancing the field of implementation science, as well as testing the effectiveness of low tidal volume ventilation among broad patient populations. Trial registration ClinicalTrials.gov , NCT04663802 . Registered 11 December 2020.

Background The population attributable fraction (PAF) is the fraction of disease cases in a sample that can be attributed to an exposure. Estimating the PAF often involves the estimation of the probability of having the disease given the exposure while adjusting for confounders. In many settings, the exposure can interact with confounders. Additionally, the exposure may have a monotone effect on the probability of having the disease, and this effect is not necessarily linear. Methods We develop a semiparametric approach for estimating the probability of having the disease and, consequently, for estimating the PAF, controlling for the interaction between the exposure and a confounder. We use a tensor product of univariate B-splines to model the interaction under the monotonicity constraint. The model fitting procedure is formulated as a quadratic programming problem, and, thus, can be easily solved using standard optimization packages. We conduct simulations to compare the performance of the developed approach with the conventional B-splines approach without the monotonicity constraint, and with the logistic regression approach. To illustrate our method, we estimate the PAF of hopelessness and depression for suicidal ideation among elderly depressed patients. Results The proposed estimator exhibited better performance than the other two approaches in the simulation settings we tried. The estimated PAF attributable to hopelessness is 67.99 % with 95% confidence interval: 42.10 % to 97.42 % , and is 22.36 % with 95% confidence interval: 12.77 % to 56.49 % due to depression. Conclusions The developed approach is easy to implement and supports flexible modeling of possible non-linear relationships between a disease and an exposure of interest.

This cross-sectional study examines disparities in gun homicide rates among neighborhoods of different racial composition for fixed levels of socioeconomic status.

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations. Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes. Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression. Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%1 at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P >= 0.13). Strain was also not meaningfully associated with patient characteristics. Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.