Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina.

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.

Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective: To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting: The entire United States. Participants: None. Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results: From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records.

BACKGROUND: Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications. METHODS: We perform a retrospective case-control study using Electronic Health Records (EHRs) to measure differences in the risks for COVID-19 severe outcomes amongst those with diabetes complications. Using multiple logistic regression, we calculate adjusted odds ratios (OR) for COVID-19 infection, hospitalization, and in-hospital mortality of the case group (patients with diabetes complications) compared to a control group (patients without diabetes). We also calculate adjusted mean difference in length of stay between the case and control groups using multiple linear regression. RESULTS: Adjusting demographics and comorbidities, diabetes patients with renal complications have the highest odds for COVID-19 infection (OR = 1.85, 95% CI = [1.71, 1.99]) while those with metabolic complications have the highest odds for COVID-19 hospitalization (OR = 5.58, 95% CI = [3.54, 8.77]) and in-hospital mortality (OR = 2.41, 95% CI = [1.35, 4.31]). The adjusted mean difference (MD) of hospital length-of-stay for diabetes patients, especially those with cardiovascular (MD = 0.94, 95% CI = [0.17, 1.71]) or peripheral vascular (MD = 1.72, 95% CI = [0.84, 2.60]) complications, is significantly higher than non-diabetes patients. African American patients have higher odds for COVID-19 infection (OR = 1.79, 95% CI = [1.66, 1.92]) and hospitalization (OR = 1.62, 95% CI = [1.39, 1.90]) than White patients in the general diabetes population. However, White diabetes patients have higher odds for COVID-19 in-hospital mortality. Hispanic patients have higher odds for COVID-19 infection (OR = 2.86, 95% CI = [2.42, 3.38]) and shorter mean length of hospital stay than non-Hispanic patients in the general diabetes population. Although there is no significant difference in the odds for COVID-19 hospitalization and in-hospital mortality between Hispanic and non-Hispanic patients in the general diabetes population, Hispanic patients have higher odds for COVID-19 hospitalization (OR = 1.83, 95% CI = [1.16, 2.89]) and in-hospital mortality (OR = 3.69, 95% CI = [1.18, 11.50]) in the diabetes population with no complications. CONCLUSIONS: The presence of diabetes complications increases the risks of COVID-19 infection, hospitalization, and worse health outcomes with respect to in-hospital mortality and longer hospital length of stay. We show the presence of health disparities in COVID-19 outcomes across demographic groups in our diabetes population. One such disparity is that African American and Hispanic diabetes patients have higher odds of COVID-19 infection than White and Non-Hispanic diabetes patients, respectively. Furthermore, Hispanic patients might have less access to the hospital care compared to non-Hispanic patients when longer hospitalizations are needed due to their diabetes complications. Finally, diabetes complications, which are generally associated with worse COVID-19 outcomes, might be predominantly determining the COVID-19 severity in those infected patients resulting in less demographic differences in COVID-19 hospitalization and in-hospital mortality.

Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub.

Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.

Vaccinating children against COVID-19 is crucial to protect schools and communities.

To evaluate the joint impact of childhood vaccination rates and school masking policies on community transmission and severe outcomes due to COVID-19, we utilized a stochastic, agent-based simulation of North Carolina to test 24 health policy scenarios. In these scenarios, we varied the childhood (ages 5 to 19) vaccination rate relative to the adult's (ages 20 to 64) vaccination rate and the masking relaxation policies in schools. We measured the overall incidence of disease, COVID-19-related hospitalization, and mortality from 2021 July 1 to 2023 July 1. Our simulation estimates that removing all masks in schools in January 2022 could lead to a 31% to 45%, 23% to 35%, and 13% to 19% increase in cumulative infections for ages 5 to 9, 10 to 19, and the total population, respectively, depending on the childhood vaccination rate. Additionally, achieving a childhood vaccine uptake rate of 50% of adults could lead to a 31% to 39% reduction in peak hospitalizations overall masking scenarios compared with not vaccinating this group. Finally, our simulation estimates that increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 83% and 87% across all masking scenarios compared to the scenarios where no children are vaccinated. Our simulation suggests that high vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.

Checklists in Healthcare: Operational Improvement of Standards using Safety Engineering - Project CHOISSE - A framework for evaluating the effects of checklists on surgical team culture.

The CHOISSE multi-stage framework for evaluating the effects of electronic checklist applications (e-checklists) on surgical team members' perception of their roles, performance, communication, and understanding of checklists is introduced via a pilot study. A prospective interventional cohort study design was piloted to assess the effectiveness of the framework and the sociotechnical effects of the e-checklist. A Delphi process was used to design the stages of the framework based on literature and expert consensus. The CHOISSE framework was applied to guide the implementation and evaluation of e-checklists on team culture for ten pilot teams across the US over a 24-week period. The pilot results revealed more engagement by surgeons than non-surgeons, and significant increases in surgeons' perception of communication and engagement during surgery with a small sample. Mixed methods analysis of the data and lessons learned were used to identify iterative improvements to the CHOISSE framework and to inform future studies.

The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions-A Case Study on the Microvascular Complications of Diabetes.

Over 34 million people in the US have diabetes, a major cause of blindness, renal failure, and amputations. Machine learning (ML) models can predict high-risk patients to help prevent adverse outcomes. Selecting the 'best' prediction model for a given disease, population, and clinical application is challenging due to the hundreds of health-related ML models in the literature and the increasing availability of ML methodologies. To support this decision process, we developed the Selection of Machine-learning Algorithms with ReplicaTions (SMART) Framework that integrates building and selecting ML models with decision theory. We build ML models and estimate performance for multiple plausible future populations with a replicated nested cross-validation technique. We rank ML models by simulating decision-maker priorities, using a range of accuracy measures (e.g., AUC) and robustness metrics from decision theory (e.g., minimax Regret). We present the SMART Framework through a case study on the microvascular complications of diabetes using data from the ACCORD clinical trial. We compare selections made by risk-averse, -neutral, and -seeking decision-makers, finding agreement in 80% of the risk-averse and risk-neutral selections, with the risk-averse selections showing consistency for a given complication. We also found that the models that best predicted outcomes in the validation set were those with low performance variance on the testing set, indicating a risk-averse approach in model selection is ideal when there is a potential for high population feature variability. The SMART Framework is a powerful, interactive tool that incorporates various ML algorithms and stakeholder preferences, generalizable to new data and technological advancements.

Can vaccine prioritization reduce disparities in COVID-19 burden for historically marginalized populations?

SARS-CoV-2 vaccination strategies were designed to reduce COVID-19 mortality, morbidity, and health inequities. To assess the impact of vaccination strategies on disparities in COVID-19 burden among historically marginalized populations (HMPs), e.g. Black race and Hispanic ethnicity, we used an agent-based simulation model, populated with census-tract data from North Carolina. We projected COVID-19 deaths, hospitalizations, and cases from 2020 July 1 to 2021 December 31, and estimated racial/ethnic disparities in COVID-19 outcomes. We modeled 2-stage vaccination prioritization scenarios applied to sub-groups including essential workers, older adults (65+), adults with high-risk health conditions, HMPs, or people in low-income tracts. Additionally, we estimated the effects of maximal uptake (100% for HMP vs. 100% for everyone), and distribution to only susceptible people. We found strategies prioritizing essential workers, then older adults led to the largest mortality and case reductions compared to no prioritization. Under baseline uptake scenarios, the age-adjusted mortality for HMPs was higher (e.g. 33.3%-34.1% higher for the Black population and 13.3%-17.0% for the Hispanic population) compared to the White population. The burden on HMPs decreased only when uptake was increased to 100% in HMPs; however, the Black population still had the highest relative mortality rate even when targeted distribution strategies were employed. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve. The vaccination strategies publicly outlined were insufficient, exacerbating disparities between racial and ethnic groups. Strategies targeted to increase vaccine uptake among HMPs are needed to ensure equitable distribution and minimize disparities in outcomes.

Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality.

Importance: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and COVID-19 morbidity and mortality. The relative importance of vaccination strategies and nonpharmaceutical interventions (NPIs) is not well understood. Objective: To assess the association of simulated COVID-19 vaccine efficacy and coverage scenarios with and without NPIs with infections, hospitalizations, and deaths. Design, Setting, and Participants: An established agent-based decision analytical model was used to simulate COVID-19 transmission and progression from March 24, 2020, to September 23, 2021. The model simulated COVID-19 spread in North Carolina, a US state of 10.5 million people. A network of 1 017 720 agents was constructed from US Census data to represent the statewide population. Exposures: Scenarios of vaccine efficacy (50% and 90%), vaccine coverage (25%, 50%, and 75% at the end of a 6-month distribution period), and NPIs (reduced mobility, school closings, and use of face masks) maintained and removed during vaccine distribution. Main Outcomes and Measures: Risks of infection from the start of vaccine distribution and risk differences comparing scenarios. Outcome means and SDs were calculated across replications. Results: In the worst-case vaccination scenario (50% efficacy, 25% coverage), a mean (SD) of 2 231 134 (117 867) new infections occurred after vaccination began with NPIs removed, and a mean (SD) of 799 949 (60 279) new infections occurred with NPIs maintained during 11 months. In contrast, in the best-case scenario (90% efficacy, 75% coverage), a mean (SD) of 527 409 (40 637) new infections occurred with NPIs removed and a mean (SD) of 450 575 (32 716) new infections occurred with NPIs maintained. With NPIs removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared with the worst-case scenario (mean [SD] absolute risk reduction, 13% [1%] and 8% [1%], respectively). Conclusions and Relevance: Simulation outcomes suggest that removing NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared with more efficacious vaccines at lower coverage. These findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many prepandemic activities can be resumed.

The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation.

Background: Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and morbidity and mortality due to COVID-19. This modeling study simulated the comparative and joint impact of COVID-19 vaccine efficacy and coverage with and without non-pharmaceutical interventions (NPIs) on total infections, hospitalizations, and deaths. Methods: An agent-based simulation model was employed to estimate incident SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths over 18 months for the State of North Carolina, a population of roughly 10.5 million. Vaccine efficacy of 50% and 90% and vaccine coverage of 25%, 50%, and 75% (at the end of a 6-month distribution period) were evaluated. Six vaccination scenarios were simulated with NPIs (i.e., reduced mobility, school closings, face mask usage) maintained and removed during the period of vaccine distribution. Results: In the worst-case vaccination scenario (50% efficacy and 25% coverage), 2,231,134 new SARS-CoV-2 infections occurred with NPIs removed and 799,949 infections with NPIs maintained. In contrast, in the best-case scenario (90% efficacy and 75% coverage), there were 450,575 new infections with NPIs maintained and 527,409 with NPIs removed. When NPIs were removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared to the worst-case scenario (absolute risk reduction 13% and 8%, respectively). Conclusion: Simulation results suggest that premature lifting of NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared to more efficacious vaccines at lower coverage. Our findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many pre-pandemic activities can be resumed.

The value of missing information in severity of illness score development.

OBJECTIVE: We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes. STUDY DESIGN AND SETTING: We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed. RESULTS: We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables. CONCLUSION: When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.

Simulation model of the relationship between cesarean section rates and labor duration.

Cesarean delivery is the most common major abdominal surgery in many parts of the world, and it accounts for nearly one-third of births in the United States. For a patient who requires a C-section, allowing prolonged labor is not recommended because of the increased risk of infection. However, for a patient who is capable of a successful vaginal delivery, performing an unnecessary C-section can have a substantial adverse impact on the patient's future health. We develop two stochastic simulation models of the delivery process for women in labor; and our objectives are (i) to represent the natural progression of labor and thereby gain insights concerning the duration of labor as it depends on the dilation state for induced, augmented, and spontaneous labors; and (ii) to evaluate the Friedman curve and other labor-progression rules, including their impact on the C-section rate and on the rates of maternal and fetal complications. To use a shifted lognormal distribution for modeling the duration of labor in each dilation state and for each type of labor, we formulate a percentile-matching procedure that requires three estimated quantiles of each distribution as reported in the literature. Based on results generated by both simulation models, we concluded that for singleton births by nulliparous women with no prior complications, labor duration longer than two hours (i.e., the time limit for labor arrest based on the Friedman curve) should be allowed in each dilation state; furthermore, the allowed labor duration should be a function of dilation state.

Not all organ dysfunctions are created equal - Prevalence and mortality in sepsis.

PURPOSE: While organ dysfunctions within sepsis have been widely studied, interaction between measures of organ dysfunction remains an understudied area. The objective of this study is to quantify the impact of organ dysfunction on in-hospital mortality in infected population. MATERIALS AND METHODS: Descriptive and multivariate analyses of retrospective data including patients (age ≥ 18 years) hospitalized at the study hospital from July 2013 to April 2016 who met the criteria for an infection visit (62,057 unique visits). RESULTS: The multivariate logistic regression model had an area under the curve of 0.9. Highest odds ratio (OR) associated with increased mortality risk was identified as fraction of inspired oxygen (FiO2) > 21% (OR = 5.8 and 95% Confidence Interval (CI) 1.8-35.6), and elevated lactate >2.0 mmol/L (OR = 2.45 (95% CI = 2.1-2.8)). Most commonly observed measures of organ dysfunction within mortality visits included elevated lactate (> 2.0 mmol/L), mechanical ventilation, and oxygen saturation (SpO2)/FiO2 ratio (< 421) at least once within 48 h prior to or 24 h after anti-infective administration. CONCLUSION: There exist differences in measures of organ dysfunction occurrence and their association with mortality. These findings support increased clinical efforts to identify sepsis patients to inform diagnostic decisions.

A stochastic model of acute-care decisions based on patient and provider heterogeneity.

The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue. Hospitals have instituted rapid response systems or teams (RRT) to provide timely critical care for APD, with thresholds that trigger the involvement of critical care expertise. The National Early Warning Score (NEWS) was developed to define these thresholds. However, current triggers are inconsistent and ignore patient-specific factors. Further, acute care is delivered by providers with different clinical experience, resulting in quality-of-care variation. This article documents a semi-Markov decision process model of APD that incorporates patient and provider heterogeneity. The model allows for stochastically changing health states, while determining patient subpopulation-specific RRT-activation thresholds. The objective function minimizes the total time associated with patient deterioration and stabilization; and the relative values of nursing and RRT times can be modified. A case study from January 2011 to December 2012 identified six subpopulations. RRT activation was optimal for patients in "slightly concerning" health states (NEWS > 0) for all subpopulations, except surgical patients with low risk of deterioration for whom RRT was activated in "concerning" states (NEWS > 4). Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.

Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients.

AIM: While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models. METHODS: The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer). RESULTS: Statistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains (P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty. CONCLUSIONS: EWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized.

Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups.

This study quantifies breast cancer mortality in the presence of competing risks for complex patients. Breast cancer behaves differently in different patient populations, which can have significant implications for patient survival; hence these differences must be considered when making screening and treatment decisions. Mortality estimation for breast cancer patients has been a significant research question. Accurate estimation is critical for clinical decision making, including recommendations. In this study, a competing risks framework is built to analyze the effect of patient risk factors and cancer characteristics on breast cancer and other cause mortality. To estimate mortality probabilities from breast cancer and other causes as a function of not only the patient's age or race but also biomarkers for estrogen and progesterone receptor status, a nonparametric cumulative incidence function is formulated using data from the community-based Carolina Mammography Registry. Based on the log(-log) transformation, confidence intervals are constructed for mortality estimates over time. To compare mortality probabilities in two independent risk groups at a given time, a method with improved power is formulated using the log(-log) transformation.

The association of breast density with breast cancer mortality in African American and white women screened in community practice.

The effect of breast density on survival outcomes for American women who participate in screening remains unknown. We studied the role of breast density on both breast cancer and other cause of mortality in screened women. Data for women with breast cancer, identified from the community-based Carolina Mammography Registry, were linked with the North Carolina cancer registry and NC death tapes for this study. Cause-specific Cox proportional hazards models were developed to analyze the effect of several covariates on breast cancer mortality-namely, age, race (African American/White), cancer stage at diagnosis (in situ, local, regional, and distant), and breast density (BI-RADS( ® ) 1-4). Two stratified Cox models were considered controlling for (1) age and race, and (2) age and cancer stage, respectively, to further study the effect of density. The cumulative incidence function with confidence interval approximation was used to quantify mortality probabilities over time. For this study, 22,597 screened women were identified as having breast cancer. The non-stratified and stratified Cox models showed no significant statistical difference in mortality between dense tissue and fatty tissue, while controlling for other covariate effects (p value = 0.1242, 0.0717, and 0.0619 for the non-stratified, race-stratified, and cancer stage-stratified models, respectively). The cumulative mortality probability estimates showed that women with dense breast tissues did not have significantly different breast cancer mortality than women with fatty breast tissue, regardless of age (e.g., 10-year confidence interval of mortality probabilities for whites aged 60-69 white: 0.056-0.090 vs. 0.054-0.083). Aging, African American race, and advanced cancer stage were found to be significant risk factors for breast cancer mortality (hazard ratio >1.0). After controlling for cancer incidence, there was not a significant association between mammographic breast density and mortality, adjusting for the effects of age, race, and cancer stage.

A conceptual framework for future research on mode of delivery.

Our goal was to develop a comprehensive conceptual research framework on mode of delivery and to identify research priorities in this topic area through a Delphi process. We convened a multidisciplinary team of 16 experts (North Carolina Collaborative on Mode of Delivery) representing the fields of obstetrics and gynecology, neonatology, midwifery, epidemiology, psychometrics, decision sciences, bioethics, health care engineering, health economics, health disparities, and women's studies. We finalized the conceptual framework after multiple iterations, including revisions during a one-day in-person conference. The conceptual framework illustrates the causal pathway for mode of delivery and the complex interplay and relationships among patient, fetal, family, provider, cultural, and societal factors as drivers of change from intended to actual mode of delivery. This conceptual framework on mode of delivery will help put specific research ideas into a broader context and identify important knowledge gaps for future investigation.