163 research outputs found
Supplemental Material, RESUBMISSION_ONLINE_SUPPLEMENTARY_FILES_9.2.19 - Six-Minute Walk Distance After Critical Illness: A Systematic Review and Meta-Analysis
No full textSupplemental Material, RESUBMISSION_ONLINE_SUPPLEMENTARY_FILES_9.2.19 for Six-Minute Walk Distance After Critical Illness: A Systematic Review and Meta-Analysis by Selina M. Parry, Swaroopa R. Nalamalapu, Krishidhar Nunna, Anahita Rabiee, Lisa Aronson Friedman, Elizabeth Colantuoni, Dale M. Needham and Victor D. Dinglas in Journal of Intensive Care Medicine</p
Home Visiting Reach and Engagement of Pregnant Women Who Screen Positive for Substance Use Risk
No full textBackground: Evidence-based home visiting (EBHV) is a strategy for supporting expectant families and families with young children to promote healthy family functioning, positive parenting, and child health and development. EBHV has the potential to serve many families with substance use issues. However, reaching and engaging these families presents unique challenges. Understanding how EBHV programs reach and engage families with substance use issues is key to fulfilling its potential to support such families by meeting their needs and improving family outcomes. This multimethod dissertation investigates the extent to which EBHV services in New Jersey reach and engage pregnant women who screen positive for substance use risk.
Methods: Aims 1 and 2 quantitatively examined differences in reach and engagement indicators by substance use risk status using multilevel multivariate logistic regression models fit on data from the statewide Central Intake system. Aim 3 used reflexive thematic analysis to qualitatively explore EBHV engagement experiences among 11 women identified as positive for substance use risk by their home visitors.
Results: Aim 1 results indicate Central Intake was overall more likely to attempt to contact and refer women to EBHV who screened positive for substance use risk prenatally than those who screened negative. There were no overall significant differences in contact success or enrollment by substance use risk. However, interaction analyses highlight how housing stability, race/ethnicity, parenting experience, and social support modify the association of substance use risk with reach indicators. In Aim 2, there was not a statistically significant difference in receipt of a high dose of services by substance use risk status. Aim 3 results highlight the importance of trusted referral sources, tailored provision of functional supports both related and unrelated to substance use recovery, and trusting relationships with home visitors as key to participants’ engagement.
Discussion: This dissertation’s findings suggest that New Jersey EBHV’s efforts to reach and engage women with substance use issues were successful in some areas, while identifying areas for improvement. Grounded in the Home Visiting Precision Paradigm, discussion focuses on program design and implementation strategies to improve reach and engagement and ultimately to strengthen program effectiveness
Home Visiting Reach and Engagement of Pregnant Women Who Screen Positive for Substance Use Risk
No full textBackground: Evidence-based home visiting (EBHV) is a strategy for supporting expectant families and families with young children to promote healthy family functioning, positive parenting, and child health and development. EBHV has the potential to serve many families with substance use issues. However, reaching and engaging these families presents unique challenges. Understanding how EBHV programs reach and engage families with substance use issues is key to fulfilling its potential to support such families by meeting their needs and improving family outcomes. This multimethod dissertation investigates the extent to which EBHV services in New Jersey reach and engage pregnant women who screen positive for substance use risk.
Methods: Aims 1 and 2 quantitatively examined differences in reach and engagement indicators by substance use risk status using multilevel multivariate logistic regression models fit on data from the statewide Central Intake system. Aim 3 used reflexive thematic analysis to qualitatively explore EBHV engagement experiences among 11 women identified as positive for substance use risk by their home visitors.
Results: Aim 1 results indicate Central Intake was overall more likely to attempt to contact and refer women to EBHV who screened positive for substance use risk prenatally than those who screened negative. There were no overall significant differences in contact success or enrollment by substance use risk. However, interaction analyses highlight how housing stability, race/ethnicity, parenting experience, and social support modify the association of substance use risk with reach indicators. In Aim 2, there was not a statistically significant difference in receipt of a high dose of services by substance use risk status. Aim 3 results highlight the importance of trusted referral sources, tailored provision of functional supports both related and unrelated to substance use recovery, and trusting relationships with home visitors as key to participants’ engagement.
Discussion: This dissertation’s findings suggest that New Jersey EBHV’s efforts to reach and engage women with substance use issues were successful in some areas, while identifying areas for improvement. Grounded in the Home Visiting Precision Paradigm, discussion focuses on program design and implementation strategies to improve reach and engagement and ultimately to strengthen program effectiveness
EVALUATION OF COVARIATES ADJUSTMENT APPROACHES ON ESTIMANDS OF TIME-TO-EVENT OUTCOMES
No full textIn clinical research, the hazard ratio (HR) is widely used to interpret treatment effects, yet it can produce misleading results due to its non-collapsible nature in non-linear models. To circumvent this issue, we recommend alternative measures like the differences and ratios of restricted mean survival times (RMST) and milestone probabilities, which are both logically coherent and collapsible, making them more interpretive and clinically meaningful. Our research involves a detailed simulation study of various statistical methods in the context of two-group randomized clinical trials, emphasizing the importance of adjusting for baseline covariates to improve analysis accuracy. We examine methods such as adjusted Cox regression, Kaplan-Meier estimators with Inverse Probability Weight (IPW), and Targeted Minimum Loss-based Estimation (TMLE), comparing them against standard unadjusted techniques. Our findings indicate that while all adjusted methods tend to slightly underestimate effects and show over-coverage, they maintain adequate statistical power. Specifically for milestone probabilities, all adjusted methods gave unbiased estimates except for TMLE, which displayed notable bias and lower power. In situations with imbalanced subgroups, adjusted methods surpassed unadjusted ones in performance. This study's insights are further illustrated using data from the OAK trial, showcasing the practical application and relevance of these adjusted methods in real-world clinical trials
Prediction of Intervention Effects in Health Systems: Johns Hopkins HealthCare Diabetes Case Study
No full textWe use administrative, claims, and clinical data from Johns Hopkins HealthCare (JHHC) to investigate (1) plan members' health states and health state trajectories, and (2) optimal interventions to improve population health at more affordable costs. Our study population consists of 56,349 members, 27,636 (49%) of whom have an ICD-10 diabetes diagnosis.
We use a simulation-based approach to predict intervention effects and their uncertainty. Our prediction of intervention effects (PIE) model is composed of seven component models corresponding to member enrollment, health state, probability of positive expenditure, size of positive expenditure, and disenrollment due to death, changing plan, or other reasons. We apply our PIE model to two interventions targeted to diabetic members: (1) reducing the effect of diabetes on health state and expenditure by 0-5% and (2) reducing patients' plasma glucose concentration (HbA1c) by 0-1%.
We find that diabetic patients have worse health states, higher probability of positive expenditure, and greater magnitude positive expenditure than otherwise similar non-diabetic patients. Diabetic members have lower hazard of disenrollment due to death, changing plan given survival, and disenrolling for other reasons given survival and not changing plan than otherwise similar non-diabetic patients.
In our first intervention, we predict 200 in monthly savings per Medicare Advantage member if we reduce HbA1c by 0.7%.
We propose our PIE model as a decision-support tool to quantitatively evaluate the relative merits of different interventions. One of its strengths is its flexibility; the component models can be adapted to the scientific question of interest without changing the overarching PIE model structure
Enhancing DataTrail Data Science Education: The BaltimoreTrails R Package and Shiny Web-App
No full textThis thesis presents BaltimoreTrails, a tool designed to enhance data science education within the DataTrail curriculum. By integrating Baltimore-specific datasets into an interactive Shiny Web-App, BaltimoreTrails provides unique data access and an interactive learning experience for students and educators.
Additionally, the tool aims to foster a deeper understanding of data science concepts through interactive data exploration and visualization, thereby enhancing the overall learning experience.
The development of this tool fills a knowledge gap by providing a software tool that enables the easy incorporation of many Baltimore-specific data sets into a graphical user interface to be used by students to support the teaching goals of the DataTrail program and adds directly to the curriculum by providing additional interactive tutorials and exercises on each DataTrail chapter
Quantifying the Impact of Changes in Clinical Practice in the Medical ICU on Outcomes of COVID-19 Patients with ARDS: A Causal Analysis
No full textThe COVID-19 pandemic resulted in substantial changes to many intensive care units (ICUs), including changes in clinical practice. Studies have documented increased use of sedatives for critically ill COVID-19 patients compared to historical controls. Further, compared to non-COVID-19 ICU patients, COVID-19 patients have had longer durations of mechanical ventilation with increased risk for delirium and death. To understand whether differences in outcomes are attributable to changes in clinical practice or to underlying COVID-19-related disease processes, we compare outcomes of COVID-19 patients with acute hypoxic respiratory failure treated in the Medical ICU (MICU) at Johns Hopkins Hospital (JHH) to counterfactual outcomes had these patients received care in the JHH MICU for non-COVID-19-related acute hypoxic respiratory failure during a concurrent pandemic period (March 2020 - January 2021), or during a historical pre-pandemic period (March 2019 - February 2020), after accounting for the dynamic time-varying nature of severity of illness and medical interventions. Counterfactual data were obtained by projecting the COVID-19 patients through each potential day of their MICU stay, via random forest prediction models, as if they had been a concurrent or historical control with acute hypoxic respiratory failure. Compared to being a concurrent non-COVID-19 control or historical control, the COVID-19 acute hypoxic respiratory failure patients had a higher risk of MICU mortality (absolute risk difference of 4 to 7\%) and, on average, 1 to 2 additional days alive in the MICU within 5 and 10 days of admission, respectively. Within 5 and 10 days of MICU admission, the COVID-19 patients had on average 3 and 4.5 days alive free of coma in the MICU respectively, estimated to be 0.3 and 1 additional day alive free of coma compared to the expectation if the same patients received care during the historical control period. The COVID-19 patients had on average 2 and 2.4 days alive free of coma and delirium within 5 and 10 days of MICU admission, respectively, with negligible differences estimated under the counterfactual conditions. The findings are applicable only to the JHH MICU but the methodology can be applied to any clinical setting
Quantifying the Impact of Changes in Clinical Practice in the Medical ICU on Outcomes of COVID-19 Patients with ARDS: A Causal Analysis
No full textThe COVID-19 pandemic resulted in substantial changes to many intensive care units (ICUs), including changes in clinical practice. Studies have documented increased use of sedatives for critically ill COVID-19 patients compared to historical controls. Further, compared to non-COVID-19 ICU patients, COVID-19 patients have had longer durations of mechanical ventilation with increased risk for delirium and death. To understand whether differences in outcomes are attributable to changes in clinical practice or to underlying COVID-19-related disease processes, we compare outcomes of COVID-19 patients with acute hypoxic respiratory failure treated in the Medical ICU (MICU) at Johns Hopkins Hospital (JHH) to counterfactual outcomes had these patients received care in the JHH MICU for non-COVID-19-related acute hypoxic respiratory failure during a concurrent pandemic period (March 2020 - January 2021), or during a historical pre-pandemic period (March 2019 - February 2020), after accounting for the dynamic time-varying nature of severity of illness and medical interventions. Counterfactual data were obtained by projecting the COVID-19 patients through each potential day of their MICU stay, via random forest prediction models, as if they had been a concurrent or historical control with acute hypoxic respiratory failure. Compared to being a concurrent non-COVID-19 control or historical control, the COVID-19 acute hypoxic respiratory failure patients had a higher risk of MICU mortality (absolute risk difference of 4 to 7\%) and, on average, 1 to 2 additional days alive in the MICU within 5 and 10 days of admission, respectively. Within 5 and 10 days of MICU admission, the COVID-19 patients had on average 3 and 4.5 days alive free of coma in the MICU respectively, estimated to be 0.3 and 1 additional day alive free of coma compared to the expectation if the same patients received care during the historical control period. The COVID-19 patients had on average 2 and 2.4 days alive free of coma and delirium within 5 and 10 days of MICU admission, respectively, with negligible differences estimated under the counterfactual conditions. The findings are applicable only to the JHH MICU but the methodology can be applied to any clinical setting
DESIGN CONSIDERATIONS FOR QUALITY IMPROVEMENT STUDIES WITH NO CONCURRENT CONTROLS
No full textOveruse of diagnostic tests in hospital settings can affect the patient's experience, increase costs, and even lead to unnecessary antibiotic use and resistance. Literature suggests that diagnostic stewardship programs can reduce testing, with some studies highlighting no negative impacts to the patient's health. Researchers should inform the design of diagnostic stewardship programs in anticipation of measuring the impact of the intervention on patient outcomes. Different scenarios may require different assumptions and definitions of the treatment effect (estimand). Our goal was to conduct a simulation study to assess different data assumptions and estimand definitions using a previously conducted diagnostic stewardship study that resulted in reductions in blood culture rates and antibiotic use. Percent bias and power calculations were assessed to observe the estimands under various data distributions and to review the power of these estimands under the various distributions and estimands
DESIGN CONSIDERATIONS FOR QUALITY IMPROVEMENT STUDIES WITH NO CONCURRENT CONTROLS
No full textOveruse of diagnostic tests in hospital settings can affect the patient's experience, increase costs, and even lead to unnecessary antibiotic use and resistance. Literature suggests that diagnostic stewardship programs can reduce testing, with some studies highlighting no negative impacts to the patient's health. Researchers should inform the design of diagnostic stewardship programs in anticipation of measuring the impact of the intervention on patient outcomes. Different scenarios may require different assumptions and definitions of the treatment effect (estimand). Our goal was to conduct a simulation study to assess different data assumptions and estimand definitions using a previously conducted diagnostic stewardship study that resulted in reductions in blood culture rates and antibiotic use. Percent bias and power calculations were assessed to observe the estimands under various data distributions and to review the power of these estimands under the various distributions and estimands
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