Lower socioeconomic status may help explain racial disparities in asthma and atopic dermatitis prevalence: A mediation analysis

BACKGROUND

Racial disparities in atopic disease (atopic dermatitis [AD], asthma, and allergies) prevalence are well documented. Despite strong associations between race and socioeconomic deprivation in the United States, and socioeconomic status (SES) and atopic diseases, the extent to which SES explains these disparities is not fully understood.

OBJECTIVE

We sought to identify racial disparities in childhood atopic disease prevalence and determine what proportion of those disparities is mediated by SES.

METHODS

This study used the National Health Interview Survey (2011-2018) to investigate AD, asthma, and respiratory allergy prevalence in Black and White children and the extent to which measures of SES explain any identified disparities.

RESULTS

By race, prevalences were as follows: AD, White 11.8% (95% CI: 11.4%, 12.2%) and Black 17.4% (95% CI: 16.6%, 18.3%); asthma prevalence, White 7.4% (95% CI: 7.0%, 7.7%) and Black 14.3% (95% CI: 13.5%, 15.0%); respiratory allergy, White 11.4% (95% CI: 11.0%, 11.9%) and Black 10.9% (95% CI: 10.3%, 11.6%). The percentage of the disparity between racial groups and disease prevalence explained by a multivariable measure of SES was 25% (95% CI: 15%, 36%) for Black versus White children with AD and 47% (95% CI: 40%, 54%) for Black versus White children with asthma.

CONCLUSIONS

In a nationally representative US population, Black children had higher prevalence of AD and asthma than White children did and similar prevalence of respiratory allergy; a multivariable SES measure explained a proportion of the association between Black versus White race and AD and a much larger proportion for asthma.

Flexible evaluation of surrogate markers with Bayesian model averaging

When long-term follow up is required for a primary endpoint in a randomized clinical trial, a valid surrogate marker can help to estimate the treatment effect and accelerate the decision process. Several model-based methods have been developed to evaluate the proportion of the treatment effect that is explained by the treatment effect on the surrogate marker. More recently, a nonparametric approach has been proposed allowing for more flexibility by avoiding the restrictive parametric model assumptions required in the model-based methods. While the model-based approaches suffer from potential mis-specification of the models, the nonparametric method fails to give desirable estimates when the sample size is small, or when the range of the data does not follow certain conditions. In this paper, we propose a Bayesian model averaging approach to estimate the proportion of treatment effect explained by the surrogate marker. Our procedure offers a compromise between the model-based approach and the nonparametric approach by introducing model flexibility via averaging over several candidate models and maintains the strength of parametric models with respect to inference. We compare our approach with previous model-based methods and the nonparametric method. Simulation studies demonstrate the advantage of our method when surrogate supports are inconsistent and sample sizes are small. We illustrate our method using data from the Diabetes Prevention Program study to examine hemoglobin A1c as a surrogate marker for fasting glucose.

Statistical Methods to Evaluate Surrogate Markers

BACKGROUND

There is tremendous interest in evaluating surrogate markers given their potential to decrease study time, costs, and patient burden.

OBJECTIVES

The purpose of this statistical workshop article is to describe and illustrate how to evaluate a surrogate marker of interest using the proportion of treatment effect (PTE) explained as a measure of the quality of the surrogate marker for: (1) a setting with a general fully observed primary outcome (eg, biopsy score); and (2) a setting with a time-to-event primary outcome which may be censored due to study termination or early drop out (eg, time to diabetes).

METHODS

The methods are motivated by 2 randomized trials, one among children with nonalcoholic fatty liver disease where the primary outcome was a change in biopsy score (general outcome) and another study among adults at high risk for Type 2 diabetes where the primary outcome was time to diabetes (time-to-event outcome). The methods are illustrated using the Rsurrogate package with a detailed R code provided.

RESULTS

In the biopsy score outcome setting, the estimated PTE of the examined surrogate marker was 0.182 (95% confidence interval [CI]: 0.121, 0.240), that is, the surrogate explained only 18.2% of the treatment effect on the biopsy score. In the diabetes setting, the estimated PTE of the surrogate marker was 0.596 (95% CI: 0.404, 0.760), that is, the surrogate explained 59.6% of the treatment effect on diabetes incidence.

CONCLUSIONS

This statistical workshop provides tools that will support future researchers in the evaluation of surrogate markers.

A rank-based approach to evaluate a surrogate marker in a small sample setting

In clinical studies of chronic diseases, the effectiveness of an intervention is often assessed using "high cost" outcomes that require long-term patient follow-up and/or are invasive to obtain. While much progress has been made in the development of statistical methods to identify surrogate markers, that is, measurements that could replace such costly outcomes, they are generally not applicable to studies with a small sample size. These methods either rely on nonparametric smoothing which requires a relatively large sample size or rely on strict model assumptions that are unlikely to hold in practice and empirically difficult to verify with a small sample size. In this paper, we develop a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. The method developed in this paper is motivated by a small study of children with nonalcoholic fatty liver disease (NAFLD), a diagnosis for a range of liver conditions in individuals without significant history of alcohol intake. Specifically, we examine whether change in alanine aminotransferase (ALT; measured in blood) is a surrogate marker for change in NAFLD activity score (obtained by biopsy) in a trial, which compared Vitamin E ($n=50$) versus placebo ($n=46$) among children with NAFLD.

Survey Protocols, Response Rates, and Representation of Underserved Patients: A Randomized Clinical Trial

Importance

Surveys often underrepresent certain patients, such as underserved patients. Methods that improve their response rates (RRs) would help patient surveys better represent their experiences and assess equity and equity-targeted quality improvement efforts.

Objective

To estimate the effect of adding an initial web mode to existing Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey protocols and extending the fielding period on RR and representativeness of underserved patient groups.

Design, Setting, and Participants

This randomized clinical trial included 36 001 patients discharged from 46 US hospitals from May through December 2021. Data analysis was performed from May 2022 to September 2023.

Exposures

Patients were randomized to 1 of 6 survey protocols: 3 standard HCAHPS protocols (mail only, phone only, mail-phone) plus 3 web-enhanced protocols (web-mail, web-phone, web-mail-phone).

Main Outcomes and Measures

RR and number of respondents per 100 survey attempts (yield) were calculated and compared for each of the 6 survey protocols, overall, and by patient age, service line, sex, and race and ethnicity.

Results

A total of 34 335 patients (median age range, 55-59 years; 59.3% female individuals and 40.7% male individuals) were eligible and included in the study. Of the respondents, 6.9% were Asian American or Native Hawaiian or Other Pacific Islander, 0.7% were American Indian or Alaska Native, 11.5% were Black, 17.4% were Hispanic, 61.0% were White, and 2.6% were multiracial. Of the 6 protocols, RRs were highest in web-mail-phone (36.5%), intermediate for the 3 two-mode survey protocols (mail-phone, web-mail, web-phone, 30.3%-31.1%), and lowest for the 2 single-mode protocols (mail only, phone only, 22.1%-24.3%). Web-mail-phone resulted in the highest yield for 3 racial and ethnic groups (Black, Hispanic, and White patients) and second highest for another (multiracial patients). Otherwise, the highest or second highest yield was almost always a 2-mode protocol. Mail only was the lowest-yield protocol for Black, Hispanic, and multiracial patients and phone-only was the lowest-yield protocol for White patients; these 2 protocols tied for lowest-yield for Asian American or Native Hawaiian or Other Pacific Islander patients. Gains from multimode approaches were often 2 to 3 times as large for Asian American or Native Hawaiian or Other Pacific Islander, Black, Hispanic, and multiracial patients as for White patients. Web-mail-phone had the highest RR for 6 of 8 age groups and 4 of 5 combinations of service line and sex.

Conclusions and Relevance

In this randomized clinical trial, web-first multimode survey protocols significantly improved the RR and representativeness of patient surveys. The best-performing protocol based on RR and representativeness was web-mail-phone. Web-phone performed well for young and diverse patient populations, and web-mail for older and less diverse patient populations. The US Centers for Medicare & Medicaid Services will allow hospitals to use the web-mail, web-phone, and web-mail-phone protocols for HCAHPS administration beginning in 2025.

Doubly robust evaluation of high-dimensional surrogate markers

When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

Exploring Which Patients Use Their Closest Emergency Departments Using Geocoded Data

BACKGROUND

Each year, roughly 20% of U.S. adults visit an emergency department (ED), but little is known about patients' choice of ED.

OBJECTIVES

Examine the discretion patients have to choose among EDs, characteristics associated with ED choice, and relationship between ED choice and self-reported care experiences of ED patients.

METHODS

We surveyed adult patients discharged to the community (DTC) in January-March 2018 from 16 geographically dispersed hospital-based EDs, geocoded patient and hospital-based ED addresses within 100 miles of patient addresses, and calculated travel distances. We examined the likelihood of visiting the closest ED based on patient and ED characteristics. Linear regression models examined the association of choosing the closest ED with seven measures of patient experience of care (scaled 0-100), adjusting for patient characteristics.

RESULTS

43.6% of 4647 responding patients visited the ED nearest their home (on average, 5.7 miles away). Patients who chose a farther ED had more urgent conditions, were more educated, and were less likely to be non-Hispanic White. They were significantly more likely to have visited an ED in a higher-rated, metropolitan, network hospital with major teaching status, a cardiac intensive care unit, and a certified trauma center. Patients who chose a farther ED were more likely to recommend that ED, with "medium-to-large" differences in scores (+4.3% more selected "definitely yes", p < 0.05).

CONCLUSIONS

Fewer than half of patients visited the closest ED. Patients who chose a farther ED tended to seek higher-rated hospitals and report more favorable experiences.

Can earlier biomarker measurements explain a treatment effect on diabetes incidence? A robust comparison of five surrogate markers

INTRODUCTION

We measured and compared five individual surrogate markers-change from baseline to 1 year after randomization in hemoglobin A1c (HbA1c), fasting glucose, 2-hour postchallenge glucose, triglyceride-glucose index (TyG) index, and homeostatic model assessment of insulin resistance (HOMA-IR)-in terms of their ability to explain a treatment effect on reducing the risk of type 2 diabetes mellitus at 2, 3, and 4 years after treatment initiation.

RESEARCH DESIGN AND METHODS

Study participants were from the Diabetes Prevention Program study, randomly assigned to either a lifestyle intervention (n=1023) or placebo (n=1030). The surrogate markers were measured at baseline and 1 year, and diabetes incidence was examined at 2, 3, and 4 years postrandomization. Surrogacy was evaluated using a robust model-free estimate of the proportion of treatment effect explained (PTE) by the surrogate marker.

RESULTS

Across all time points, change in fasting glucose and HOMA-IR explained higher proportions of the treatment effect than 2-hour glucose, TyG index, or HbA1c. For example, at 2 years, glucose explained the highest (80.1%) proportion of the treatment effect, followed by HOMA-IR (77.7%), 2-hour glucose (76.2%), and HbA1c (74.6%); the TyG index explained the smallest (70.3%) proportion.

CONCLUSIONS

These data suggest that, of the five examined surrogate markers, glucose and HOMA-IR were the superior surrogate markers in terms of PTE, compared with 2-hour glucose, HbA1c, and TyG index.

Robust approach to combining multiple markers to improve surrogacy

Identifying effective and valid surrogate markers to make inference about a treatment effect on long-term outcomes is an important step in improving the efficiency of clinical trials. Replacing a long-term outcome with short-term and/or cheaper surrogate markers can potentially shorten study duration and reduce trial costs. There is sizable statistical literature on methods to quantify the effectiveness of a single surrogate marker. Both parametric and nonparametric approaches have been well developed for different outcome types. However, when there are multiple markers available, methods for combining markers to construct a composite marker with improved surrogacy remain limited. In this paper, building on top of the optimal transformation framework of Wang et al. (2020), we propose a novel calibrated model fusion approach to optimally combine multiple markers to improve surrogacy. Specifically, we obtain two initial estimates of optimal composite scores of the markers based on two sets of models with one set approximating the underlying data distribution and the other directly approximating the optimal transformation function. We then estimate an optimal calibrated combination of the two estimated scores which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained by the final combined score. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully nonparametric estimation which is subject to the curse of dimensionality. Our identified optimal transformation can also be used to directly quantify the surrogacy of this identified combined score. Theoretical properties of the proposed estimators are derived, and the finite sample performance of the proposed method is evaluated through simulation studies. We further illustrate the proposed method using data from the Diabetes Prevention Program study.

Testing for heterogeneity in the utility of a surrogate marker

In studies that require long-term and/or costly follow-up of participants to evaluate a treatment, there is often interest in identifying and using a surrogate marker to evaluate the treatment effect. While several statistical methods have been proposed to evaluate potential surrogate markers, available methods generally do not account for or address the potential for a surrogate to vary in utility or strength by patient characteristics. Previous work examining surrogate markers has indicated that there may be such heterogeneity, that is, that a surrogate marker may be useful (with respect to capturing the treatment effect on the primary outcome) for some subgroups, but not for others. This heterogeneity is important to understand, particularly if the surrogate is to be used in a future trial to replace the primary outcome. In this paper, we propose an approach and estimation procedures to measure the surrogate strength as a function of a baseline covariate W and thus examine potential heterogeneity in the utility of the surrogate marker with respect to W. Within a potential outcome framework, we quantify the surrogate strength/utility using the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate. We propose testing procedures to test for evidence of heterogeneity, examine finite sample performance of these methods via simulation, and illustrate the methods using AIDS clinical trial data.

Improving the Quality of Written Discharge Instructions: A Multisite Collaborative Project

BACKGROUND AND OBJECTIVES

Written discharge instructions help to bridge hospital-to-home transitions for patients and families, though substantial variation in discharge instruction quality exists. We aimed to assess the association between participation in an Institute for Healthcare Improvement Virtual Breakthrough Series collaborative and the quality of pediatric written discharge instructions across 8 US hospitals.

METHODS

We conducted a multicenter, interrupted time-series analysis of a medical records-based quality measure focused on written discharge instruction content (0-100 scale, higher scores reflect better quality). Data were from random samples of pediatric patients (N = 5739) discharged from participating hospitals between September 2015 and August 2016, and between December 2017 and January 2020. These periods consisted of 3 phases: 1. a 14-month precollaborative phase; 2. a 12-month quality improvement collaborative phase when hospitals implemented multiple rapid cycle tests of change and shared improvement strategies; and 3. a 12-month postcollaborative phase. Interrupted time-series models assessed the association between study phase and measure performance over time, stratified by baseline hospital performance, adjusting for seasonality and hospital fixed effects.

RESULTS

Among hospitals with high baseline performance, measure scores increased during the quality improvement collaborative phase beyond the expected precollaborative trend (+0.7 points/month; 95% confidence interval, 0.4-1.0; P < .001). Among hospitals with low baseline performance, measure scores increased but at a lower rate than the expected precollaborative trend (-0.5 points/month; 95% confidence interval, -0.8 to -0.2; P < .01).

CONCLUSIONS

Participation in this 8-hospital Institute for Healthcare Improvement Virtual Breakthrough Series collaborative was associated with improvement in the quality of written discharge instructions beyond precollaborative trends only for hospitals with high baseline performance.

Association of Hospice Profit Status With Family Caregivers' Reported Care Experiences

Importance

Expansive growth in the US hospice market has been driven almost exclusively by an increase in for-profit hospices. Prior research found that, in contrast to not-for-profit hospices, for-profit hospices focus on delivering care to patients in nursing homes, provide fewer nursing visits, and use less skilled staff. However, prior studies have not reported on the associations of these differences in care patterns with hospice care quality. Patient- and family-centeredness is a core element of hospice care quality that is measured through surveys of care experiences.

Objective

To examine whether differences in profit status are associated with family caregivers' reports of hospice care experiences and assess factors that may be associated with observed differences in care experiences by profit status.

Design, Setting, and Participants

Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey data from 653 208 caregiver respondents, reflecting care received from 3107 hospices between April 2017 and March 2019, were used for a cross-sectional examination of hospice care experiences by profit status. Data analysis was performed from January 2020 to November 2022.

Main Outcomes and Measures

Outcomes were case-mix-adjusted and mode-adjusted top-box scores for 8 measures of hospice care experiences, including communication, timely care, symptom management, and emotional and religious support, as well as a summary score averaging across measures. Linear regression examined the association between profit status and hospice-level scores, adjusting for other organizational and structural hospice characteristics.

Results

There were 906 not-for-profit and 1761 for-profit hospices with mean (SD) time in operation of 25.7 (7.8) years and 13.8 (8.0) years, respectively. Mean (SD) decedent age at death was 82.8 (2.3) years, similar for not-for-profit and for-profit hospices. The mean proportion of patients who were Black, Hispanic, and White was 4.9%, 0.9%, and 91.4% for not-for-profit hospices and 9.0%, 2.2%, and 85.4% for for-profit hospices, respectively. Family caregivers reported worse care experiences at for-profit hospices than at not-for-profit hospices for all measures. Significant differences in average hospice performance by profit status remained after adjusting for hospice characteristics. However, for-profit hospice performance varied, with 548 of 1761 (31.1%) for-profit hospices scoring 3 or more points below the national hospice average of overall performance and 386 of 1761 (21.9%) scoring 3 or more points above the average. In contrast, only 113 of 906 (12.5%) not-for-profit hospices scored 3 or more points below the average, and 305 of 906 (33.7%) scored 3 or more points above the average.

Conclusions and Relevance

In this cross-sectional study of CAHPS Hospice Survey data, caregivers of patients receiving hospice care reported substantially worse care experiences in for-profit than in not-for-profit hospices; however, there was variation in reported experiences among both types of hospices. Public reporting of hospice quality is important.

Using a surrogate with heterogeneous utility to test for a treatment effect

The primary benefit of identifying a valid surrogate marker is the ability to use it in a future trial to test for a treatment effect with shorter follow-up time or less cost. However, previous work has demonstrated potential heterogeneity in the utility of a surrogate marker. When such heterogeneity exists, existing methods that use the surrogate to test for a treatment effect while ignoring this heterogeneity may lead to inaccurate conclusions about the treatment effect, particularly when the patient population in the new study has a different mix of characteristics than the study used to evaluate the utility of the surrogate marker. In this article, we develop a novel test for a treatment effect using surrogate marker information that accounts for heterogeneity in the utility of the surrogate. We compare our testing procedure to a test that uses primary outcome information (gold standard) and a test that uses surrogate marker information, but ignores heterogeneity. We demonstrate the validity of our approach and derive the asymptotic properties of our estimator and variance estimates. Simulation studies examine the finite sample properties of our testing procedure and demonstrate when our proposed approach can outperform the testing approach that ignores heterogeneity. We illustrate our methods using data from an AIDS clinical trial to test for a treatment effect using CD4 count as a surrogate marker for RNA.

Collaborative to Increase Lethal Means Counseling for Caregivers of Youth With Suicidality

BACKGROUND

The number of youth presenting to hospitals with suicidality and/or self-harm has increased substantially in recent years. We implemented a multihospital quality improvement (QI) collaborative from February 1, 2018 to January 31, 2019, aiming for an absolute increase in hospitals' mean rate of caregiver lethal means counseling (LMC) of 10 percentage points (from a baseline mean performance of 68% to 78%) by the end of the collaborative, and to evaluate the effectiveness of the collaborative on LMC, adjusting for secular trends.

METHODS

This 8 hospital collaborative used a structured process of alternating learning sessions and action periods to improve LMC across hospitals. Electronic medical record documentation of caregiver LMC was evaluated during 3 phases: precollaborative, active QI collaborative, and postcollaborative. We used statistical process control to evaluate changes in LMC monthly. Following collaborative completion, interrupted time series analyses were used to evaluate changes in the level and trend and slope of LMC, adjusting for covariates.

RESULTS

In the study, 4208 children and adolescents were included-1314 (31.2%) precollaborative, 1335 (31.7%) during the active QI collaborative, and 1559 (37.0%) postcollaborative. Statistical process control analyses demonstrated that LMC increased from a hospital-level mean of 68% precollaborative to 75% (February 2018) and then 86% (October 2018) during the collaborative. In interrupted time series analyses, there were no significant differences in LMC during and following the collaborative beyond those expected based on pre-collaborative trends.

CONCLUSIONS

LMC increased during the collaborative, but the increase did not exceed expected trends. Interventions developed by participating hospitals may be beneficial to others aiming to improve LMC for caregivers of hospitalized youth with suicidality.

Hospice Care Experiences Among Decedents With Huntington's Disease

CONTEXT

Little is known about the hospice care experiences of those with Huntington's Disease (HD).

OBJECTIVES

Our objective is to provide the first national characterization of hospice care quality for patients with HD and their families.

METHODS

We used national Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey data to examine caregiver-reported experiences of hospice care. We analyzed data from 550 caregivers of patients with HD and 1,098,819 caregivers of patients without HD who died January 2016-June 2019 while receiving hospice care from 3,845 hospices nationwide. Outcomes (on a 0-100 scale) were eight publicly-reported quality of care measures, and four individual survey items about receiving help for specific symptoms. Analyses were propensity-score weighted and adjusted for patient and caregiver characteristics.

RESULTS

Experiences of care among patients with HD were similar to or better than for patients without HD. Across all hospice and care settings, the only significant difference was for Providing Emotional, and Spiritual Support (90.9 [HD] vs. 88.2 [non-HD], a medium effect size, P < 0.01). However, patients with HD more often received care in settings with worse experiences for all patients; within the same hospice and same setting of care, measure scores were significantly higher for patients with HD compared to those without HD (2.3-4.6 points higher on a 0-100 scale) for all measures except Getting Hospice Care Training.

CONCLUSION

Our findings highlight the benefits of hospice care for those with HD and their families and may be useful for patients with HD when making decisions regarding options for end-of-life care.

Development and Testing of an Emergency Department Quality Measure for Pediatric Suicidal Ideation and Self-Harm

OBJECTIVE

To develop and test a new quality measure assessing timeliness of follow-up mental health care for youth presenting to the emergency department (ED) with suicidal ideation or self-harm.

METHODS

Based on a conceptual framework, evidence review, and a modified Delphi process, we developed a quality measure assessing whether youth 5 to 17 years old evaluated for suicidal ideation or self-harm in the ED and discharged to home had a follow-up mental health care visit within 7 days. The measure was tested in 4 geographically dispersed states (California, Pennsylvania, South Carolina, Tennessee) using Medicaid administrative data. We examined measure feasibility of implementation, variation, reliability, and validity. To test validity, adjusted regression models examined associations between quality measure scores and subsequent all-cause and same-cause hospital readmissions/ED return visits.

RESULTS

Overall, there were 16,486 eligible ED visits between September 1, 2014 and July 31, 2016; 53.5% of eligible ED visits had an associated mental health care follow-up visit within 7 days. Measure scores varied by state, ranging from 26.3% to 66.5%, and by youth characteristics: visits by youth who were non-White, male, and living in an urban area were significantly less likely to be associated with a follow-up visit within 7 days. Better quality measure performance was not associated with decreased reutilization.

CONCLUSIONS

This new ED quality measure may be useful for monitoring and improving the quality of care for this vulnerable population; however, future work is needed to establish the measure's predictive validity using more prevalent outcomes such as recurrence of suicidal ideation or deliberate self-harm.

National travel distances for emergency care

Background

Most emergency department (ED) patients arrive by their own transport and, for various reasons, may not choose the nearest ED. How far patients travel for ED treatment may reflect both patients’ access to care and severity of illness. In this study, we aimed to examine the travel distance and travel time between a patient’s home and ED they visited and investigate how these distances/times vary by patient and hospital characteristics.

Methods

We randomly sampled and collected data from 14,812 patients discharged to the community (DTC) between January and March 2016 from 50 hospital-based EDs nationwide. We geocoded and calculated the distance and travel time between patient and hospital-based ED addresses, examined the travel distances/ times between patients’ home and the ED they visited, and used mixed-effects regression models to investigate how these distances/times vary by patient and hospital characteristics.

Results

Patients travelled an average of 8.0 (SD = 10.9) miles and 17.3 (SD = 18.0) driving minutes to the ED. Patients travelled significantly farther to avoid EDs in lower performing hospitals (p < 0.01) and in the West (p < 0.05) and Midwest (p < 0.05). Patients travelled farther when visiting EDs in rural areas. Younger patients travelled farther than older patients.

Conclusions

Understanding how far patients are willing to travel is indicative of whether patient populations have adequate access to ED services. By showing that patients travel farther to avoid a low-performing hospital, we provide evidence that DTC patients likely do exercise some choice among EDs, indicating some market incentives for higher-quality care, even for some ED admissions. Understanding these issues will help policymakers better define access to ED care and assist in directing quality improvement efforts. To our knowledge, our study is the most comprehensive nationwide characterization of patient travel for ED treatment to date.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07743-7.

Estimation of the proportion of treatment effect explained by a high-dimensional surrogate

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long-term follow-up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow-up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high-dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high-dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey-based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets.

Development and validation of a patient experience of care survey for emergency departments

OBJECTIVES

To (1) develop a survey to assess the patient experience of care in hospital-based emergency departments (ED) and (2) evaluate the reliability and validity of composite measures of patient experience using data collected through the experimental implementation of the newly developed Emergency Department Patient Experience of Care (EDPEC) Discharged to Community (DTC) Survey.

DATA SOURCE

4893 adult patients were treated in the ED of 16 hospitals across the United States in 2018.

STUDY DESIGN

The study utilized a cross-sectional survey.

DATA COLLECTION

Survey development activities included a literature review, focus groups, and cognitive interviews with recently discharged ED patients, technical expert panels, and multiple field experiments. Survey development resulted in a 34-item instrument; the analysis reported here focuses on 18 items on patient experience of care. Using data from the EDPEC DTC Survey in the 2018 Feasibility Test, we performed confirmatory factor analysis to group 15 evaluative survey items into composite measures. We examined internal consistency reliability, interunit reliability, and associations between each composite measure and patients' overall rating and willingness to recommend the ED.

PRINCIPAL FINDINGS

Analyses of 15 evaluative items identified four composite measures: Getting Timely Care, How Well Doctors and Nurses Communicate, Communication about Medications, and Communication about Follow-up. Patient-level internal consistency reliability exceeded 0.75 for two of four composites; ED-level internal consistency reliability exceeded 0.83 for all four composites. Interunit reliability estimates indicated that 450 survey completes per ED results in at least 0.70 reliability for all composites. Higher scores on each composite were associated with higher overall ratings and willingness to recommend the ED.

CONCLUSIONS

The composite measures derived from the EDPEC DTC Survey are statistically reliable and valid. These results provide guidance for EDPEC DTC Survey adopters on how to construct meaningful and psychometrically-sound composite measures for monitoring the quality of care they provide.

Racial/Ethnic Differences in Emergency Department Utilization and Experience

BACKGROUND

Previous work has demonstrated racial/ethnic differences in emergency department (ED) utilization, but less is known about racial/ethnic differences in the experience of care received during an ED visit.

OBJECTIVE

To examine differences in self-reported healthcare utilization and experiences with ED care by patients' race/ethnicity.

DESIGN

Adult ED patients discharged to community (DTC) were surveyed (response rate: 20.25%) using the Emergency Department Patient Experience of Care (EDPEC) DTC Survey. Linear regression was used to estimate case-mix-adjusted differences in patient experience between racial/ethnic groups.

PARTICIPANTS

3122 survey respondents who were discharged from the EDs of 50 hospitals nationwide January-March 2016.

MAIN MEASURES

Six measures: getting timely care, doctor and nurse communication, communication about medications, receipt of sufficient information about test results, whether hospital staff discussed the patient's ability to receive follow-up care, and willingness to recommend the ED.

KEY RESULTS

Black and Hispanic patients were significantly more likely than White patients to report visiting the ED for an ongoing health condition (40% Black, 30% Hispanic, 28% White, p<0.001), report having visited an ED 3+ times in the last 6 months (26% Black, 25% Hispanic, 19% White, p<0.001), and report not having a usual source of care (19% Black, 19% Hispanic, 8% White, p<0.001). Compared with White patients, Hispanic patients more often reported that hospital staff talked with them about their ability to receive needed follow-up care (+7.2 percentile points, p=0.038) and recommended the ED (+7.2 points, p=0.037); Hispanic and Black patients reported better doctor and nurse communication (+6.4 points, p=0.008; +4 points, p=0.036, respectively).

CONCLUSIONS

Hispanic and Black ED patients reported higher ED utilization, lacked a usual source of care, and reported better experience with ED care than White patients. Results may reflect differences in care delivery by staff and/or different expectations of ED care among Hispanic and Black patients.

Quantifying the feasibility of shortening clinical trial duration using surrogate markers

The potential benefit of using a surrogate marker in place of a long-term primary outcome is very attractive in terms of the impact on study length and cost. Many available methods for quantifying the effectiveness of a surrogate endpoint either rely on strict parametric modeling assumptions or require that the primary outcome and surrogate marker are fully observed that is, not subject to censoring. Moreover, available methods for quantifying surrogacy typically provide a proportion of treatment effect explained (PTE) measure and do not directly address the important questions of whether and how the trial can be ended earlier using the surrogate marker. In this article, we specifically address these important questions by proposing a PTE measure to quantify the feasibility of ending trials early based on endpoint information collected at an earlier landmark point t 0 in a time-to-event outcome setting. We provide a framework for deriving an optimally predicted outcome for individual patients at t 0 based on a combination of surrogate marker and event time information in the presence of censoring. We propose a non-parametric estimator for the PTE measure and derive the asymptotic properties of our estimators. Finite sample performance of our estimators are illustrated via extensive simulation studies and a real data application examining the potential of hemoglobin A1c and fasting plasma glucose to predict treatment effects on long term diabetes risk based on the Diabetes Prevention Program study.

Quality of Care for Youth Hospitalized for Suicidal Ideation and Self-Harm

OBJECTIVE

To examine performance on quality measures for pediatric inpatient suicidal ideation/self-harm care, and whether performance is associated with reutilization.

METHODS

Retrospective observational 8 hospital study of patients [N = 1090] aged 5 to 17 years hospitalized for suicidal ideation/self-harm between 9/1/14 and 8/31/16. Two medical records-based quality measures assessing suicidal ideation/self-harm care were evaluated, one on counseling caregivers regarding restricting access to lethal means and the other on communication between inpatient and outpatient providers regarding the follow-up plan. Multivariable logistic regression assessed associations between quality measure scores and 1) hospital site, 2) patient demographics, and 3) 30-day emergency department return visits and inpatient readmissions.

RESULTS

Medical record documentation revealed that, depending on hospital site, 17% to 98% of caregivers received lethal means restriction counseling (mean 70%); inpatient-to-outpatient provider communication was documented in 0% to 51% of cases (mean 16%). The odds of documenting receipt of lethal means restriction counseling was higher for caregivers of female patients compared to caregivers of male patients (adjusted odds ratio [aOR] 1.51, 95% confidence interval [CI], 1.07-2.14). The odds of documenting inpatient-to-outpatient provider follow-up plan communication was lower for Black patients compared to White patients (aOR 0.45, 95% CI, 0.24-0.84). All-cause 30-day readmission was lower for patients with documented caregiver receipt of lethal means restriction counseling (aOR 0.48, 95% CI, 0.28-0.83).

CONCLUSIONS

This study revealed disparities and deficits in the quality of care received by youth with suicidal ideation/self-harm. Providing caregivers lethal means restriction counseling prior to discharge may help to prevent readmission.

Hospice Care Experiences Among Cancer Patients and Their Caregivers

BACKGROUND

Little is known about the current quality of care for hospice cancer patients and how it varies across hospice programs in the USA.

OBJECTIVE

To examine hospice care experiences among decedents with a primary cancer diagnosis and their family caregivers, comparing quality across settings of hospice care.

DESIGN

We analyzed data from the Consumer Assessment of Healthcare Providers and Systems Hospice Survey (32% response rate). Top-box outcomes (0-100) were calculated overall and by care setting, adjusting for survey mode and patient case mix.

PARTICIPANTS

Two hundred seventeen thousand five hundred ninety-six caregiver respondents whose family member had a primary cancer diagnosis and died in 2017 or 2018 while receiving hospice care from 2,890 hospices nationwide.

MAIN MEASURES

Outcomes (0-100 scale) included 8 National Quality Forum-endorsed quality measures, as well as responses to 4 survey questions assessing whether needs were met for specific symptoms (pain, dyspnea, constipation, anxiety/sadness).

KEY RESULTS

Quality measure scores ranged from 74.9 (Getting Hospice Care Training measure) to 89.5 (Treating Family Member with Respect measure). The overall score for Getting Help for Symptoms was 75.1 with item scores within this measure ranging from 60.6 (getting needed help for feelings of anxiety or sadness) to 84.5 (getting needed help for pain). Measure scores varied significantly across settings and differences were large in magnitude, with caregivers of decedents who received care in a nursing home (NH) or assisted living facility (ALF) setting consistently reporting poorer quality of care.

CONCLUSIONS

Important opportunities exist to improve hospice care for symptom palliation and providing training for caregivers when their family members are at home or in an ALF setting. Efforts to improve care for cancer patients in the NH and ALF setting are especially needed.

Robust methods to correct for measurement error when evaluating a surrogate marker

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker that is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

Association between Receipt of Emotional Support and Caregivers' Overall Hospice Rating

Background: A major goal of hospice care is to provide individually tailored emotional and spiritual support to caregivers of hospice patients. Objectives: Examine the association between reported emotional support and caregivers' overall rating of hospice care, overall and by race/ethnicity/language. Subjects: We analyzed survey data corresponding to 657,805 decedents/caregivers who received care from 3160 hospice programs during January 2017-December 2018. Measurements: Linear regression models examined the association between caregiver-reported receipt of emotional and spiritual support ("too little" vs. "right amount" vs. "too much") and overall rating of the hospice (0 vs. 100 rating). Interaction terms assessed variation in this association by race/ethnicity/language. Results: "Too much" emotional support was less common than "too little," except for caregivers of Hispanic decedents responding in Spanish. "Too little" support was strongly associated with lower hospice ratings for all groups (compared to "right amount" of support, p < 0.001). In contrast, the negative association between "too much" support and hospice rating was much smaller (p < 0.001) among caregivers of white and black decedents. "Too much" support was associated with more positive ratings among caregivers of Hispanic decedents (p < 0.001). Conclusions: Receipt of "too much" support is a less common and much weaker driver of poor hospice ratings than receipt of "too little" support for all groups, and is not always viewed negatively. This suggests that for hospice evaluation, "too much" support should not be scored equivalently to "too little" support and that providing enough support should be a hospice priority.

Evaluating multiple surrogate markers with censored data

The utilization of surrogate markers offers the opportunity to reduce the length of required follow-up time and/or costs of a randomized trial examining the effectiveness of an intervention or treatment. There are many available methods for evaluating the utility of a single surrogate marker including both parametric and nonparametric approaches. However, as the dimension of the surrogate marker increases, a completely nonparametric procedure becomes infeasible due to the curse of dimensionality. In this paper, we define a quantity to assess the value of multiple surrogate markers in a time-to-event outcome setting and propose a robust estimation approach for censored data. We focus on surrogate markers that are measured at some landmark time, t₀ , which occurs earlier than the end of the study. Our approach is based on a dimension reduction procedure with an option to incorporate weights to guard against potential misspecification of the working model, resulting in three different proposed estimators, two of which can be shown to be double robust. We examine the finite sample performance of the estimators under various scenarios using a simulation study. We illustrate the estimation and inference procedures using data from the Diabetes Prevention Program (DPP) to examine multiple potential surrogate markers for diabetes.

Quantifying the bias due to observed individual confounders in causal treatment effect estimates

It is often of interest to use observational data to estimate the causal effect of a target exposure or treatment on an outcome. When estimating the treatment effect, it is essential to appropriately adjust for selection bias due to observed confounders using, for example, propensity score weighting. Selection bias due to confounders occurs when individuals who are treated are substantially different from those who are untreated with respect to covariates that are also associated with the outcome. A comparison of the unadjusted, naive treatment effect estimate with the propensity score adjusted treatment effect estimate provides an estimate of the selection bias due to these observed confounders. In this article, we propose methods to identify the observed covariate that explains the largest proportion of the estimated selection bias. Identification of the most influential observed covariate or covariates is important in resource-sensitive settings where the number of covariates obtained from individuals needs to be minimized due to cost and/or patient burden and in settings where this covariate can provide actionable information to healthcare agencies, providers, and stakeholders. We propose straightforward parametric and nonparametric procedures to examine the role of observed covariates and quantify the proportion of the observed selection bias explained by each covariate. We demonstrate good finite sample performance of our proposed estimates using a simulation study and use our procedures to identify the most influential covariates that explain the observed selection bias in estimating the causal effect of alcohol use on progression of Huntington's disease, a rare neurological disease.

Evaluation of longitudinal surrogate markers

The use of surrogate markers to examine the effectiveness of a treatment has the potential to decrease study length and identify effective treatments more quickly. Most available methods to investigate the usefulness of a surrogate marker involve restrictive parametric assumptions and tend to focus on settings where the surrogate is measured at a single point in time. However, in many clinical settings, the potential surrogate marker is often measured repeatedly over time, and thus, the surrogate marker information is a trajectory of measurements. In addition, it is often difficult in practice to correctly specify the relationship between a treatment, primary outcome, and surrogate marker trajectory. In this paper, we propose a model-free definition for the proportion of the treatment effect on the primary outcome that is explained by the treatment effect on the longitudinal surrogate markers. We propose three novel flexible methods to estimate this proportion, develop the asymptotic properties of our estimators, and investigate the robustness of the estimators under multiple settings via a simulation study. We apply our proposed procedures to an AIDS clinical trial dataset to examine a trajectory of CD4 counts as a potential surrogate.

Assessing the value of a censored surrogate outcome

Assessing the potential of surrogate markers and surrogate outcomes for replacing a long term outcome is an active area of research. The interest in this topic is partly motivated by increasing pressure from stakeholders to shorten the time required to evaluate the safety and/or efficacy of a treatment or intervention such that treatments deemed safe and effective can be made available to those in need more quickly. Most existing methods in surrogacy evaluation either require strict model assumptions or that primary outcome and surrogate outcome information is available for all study participants. In this paper, we focus on a setting where the primary outcome is subject to censoring and the aim is to quantify the surrogacy of an intermediate outcome, which is also subject to censoring. We define the surrogacy as the proportion of treatment effect on the primary outcome that is explained by the intermediate surrogate outcome information and propose two robust methods to estimate this quantity. We propose both a nonparametric approach that uses a kernel smoothed Nelson-Aalen estimator of conditional survival, and a semiparametric method that derives conditional survival estimates from a landmark Cox proportional hazards model. Simulation studies demonstrate that both approaches perform well in finite samples. Our methodological development is motivated by our interest in investigating the use of a composite cardiovascular endpoint as a surrogate outcome in a randomized study of the effectiveness of angiotensin-converting enzyme inhibitors on survival. We apply the proposed methods to quantify the surrogacy of this potential surrogate outcome for the primary outcome, time to death.

Characteristics of Hospices Providing High-Quality Care

Background: The hospice market has changed substantially, shifting from predominately not-for-profit independent entities to for-profit national chains. Little is known about how hospice organizational characteristics are associated with quality of hospice care. Objective: To examine the association between hospice characteristics and care processes and performance on measures of hospice care quality. Design: Logistic regression models assessed the association between hospice characteristics and processes and hospices being in the top quartile of quality measure performance. Setting/Subjects: U.S. hospices with publicly reported measure scores in 2015-2017. Measurements: Summaries of hospice-level performance on Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey measures (including communication, timely care, symptom management, emotional and spiritual support, respect, training families, overall rating, and willingness to recommend) and Hospice Item Set (HIS) measures (including pain screening and assessment, dyspnea screening and treatment, bowel regimen for patients on opioids, discussion of treatment preferences, and beliefs/values addressed). Results: Of the 2746 hospices that met public reporting requirements, 5.6% were in the top quartile of both CAHPS and HIS performance. Characteristics associated with being in the top quartile for CAHPS included being a nonprofit and nonchain or government hospice, smaller size (<200 patients per year), and serving a rural area. Characteristics associated with being in the top quartile for HIS included being in a for-profit chain, larger size (91+ patients per year), and having <40% of patients in a nursing home. Providing professional staff visits in the last two days of life to a higher proportion of patients was associated with hospices being in the top quartile of HIS and in the top quartile of CAHPS. Conclusions: Hospice characteristics associated with strong performance on HIS measures differ from those associated with strong performance on CAHPS measures. Providing professional staff visits in the last two days of life is associated with high performance on both quality domains.

Differences in Caregiver Reports of the Quality of Hospice Care Across Settings

OBJECTIVES

To examine variation in reported experiences with hospice care by setting.

DESIGN

Consumer Assessment of Healthcare Providers and Systems Hospice (CAHPS®) Survey data from 2016 were analyzed. Multivariate linear regression analysis was used to examine differences in measure scores by setting of care (home, nursing home [NH], hospital, freestanding hospice inpatient unit [IPU], and assisted living facility [ALF]).

SETTING

A total of 2636 US hospices.

PARTICIPANTS

A total of 311 635 primary caregivers of patients who died in hospice.

MEASUREMENTS

Outcomes were seven hospice quality measures, including five composite measures that assess aspects of hospice care important to patients and families, including hospice team communication, timeliness of care, treating family member with respect, symptom management, and emotional and spiritual support, and two global measures of the overall rating of the hospice and willingness to recommend it to friends and family. Analyses were adjusted for mode of survey administration and differences in case-mix between hospices.

RESULTS

Caregivers of decedents who received hospice care in a NH reported significantly worse experiences than caregivers of those in the home for all measures. ALF scores were also significantly lower than home for all measures, except providing emotional and spiritual support. Differences in NH and ALF settings compared to home were particularly large for hospice team communication (ranging from -11 to -12 on a 0-100 scale) and getting help for symptoms (ranging from -7 to -10). Consistently across all care settings, hospice team communication, treating family member with respect, and providing emotional and spiritual support were most strongly associated with overall rating of care.

CONCLUSIONS

Important opportunities exist to improve quality of hospice care in NHs and ALFs. Quality improvement and regulatory interventions targeting the NH and ALF settings are needed to ensure that all hospice decedents and their family receive high-quality, patient- and family-centered hospice care. J Am Geriatr Soc 68:1218-1225, 2020.

Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker

In randomized clinical trials, the primary outcome, Y, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, S, to infer the treatment effect on Y, Δ. Identifying such an S and quantifying the proportion of treatment effect on Y explained by the effect on S are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and may yield biased estimates under model misspecification. Recently proposed nonparametric methods require strong assumptions to ensure that the proportion of treatment effect is in the range [0, 1]. Additionally, optimal use of S to approximate Δ is especially important when S relates to Y nonlinearly. In this paper we identify an optimal transformation of S, g _opt(·), such that the proportion of treatment effect explained can be inferred based on g _opt(S). In addition, we provide two novel model-free definitions of proportion of treatment effect explained and simple conditions for ensuring that it lies within [0, 1]. We provide nonparametric estimation procedures and establish asymptotic properties of the proposed estimators. Simulation studies demonstrate that the proposed methods perform well in finite samples. We illustrate the proposed procedures using a randomized study of HIV patients.

Associations between young adult marijuana outcomes and availability of medical marijuana dispensaries and storefront signage

AIMS

We investigated associations between the density of medical marijuana dispensaries (MMDs) around young adults' homes and marijuana use outcomes.

DESIGN

Secondary data analysis.

SETTING

Los Angeles County, CA, USA.

PARTICIPANTS

A total of 1887 participants aged 18-22 years, surveyed online in 2016-17.

MEASUREMENTS

Outcomes were past-month marijuana use (number of days used, number of times each day), positive expectancies and perceived peer use. Density was measured as the total number of MMDs and number of MMDs with storefront signage indicative of marijuana sales, within 4 miles of respondents' homes.

FINDINGS

Eighty-four per cent of respondents had 10 or more MMDs within 4 miles of their homes. Multiple linear regression analyses that adjusted for individual-level socio-demographic characteristics and neighborhood socio-economic status indicated that living near a higher number of MMDs was associated with greater number of days used in the past month [β = 0.025; 95% confidence interval (CI) = 0.001, 0.049; P = 0.04] and higher positive marijuana expectancies (β = 0.003; 95% CI = 0.001, 0.007; P = 0.04). Living near more MMDs with storefront signage had a four- to six-fold larger effect on number of times used per day and positive expectancies, respectively, compared with associations with the total MMD count. Adjusting for medical marijuana card ownership attenuated the association with number of days used in the past month and positive expectancies, and an unexpected association emerged between higher MMD density and fewer number of times used each day (β = -0.005; 95% CI = -0.009, -0.001; P = 0.03).

CONCLUSIONS

For young adults in Los Angeles County, living near more medical marijuana dispensaries (MMDs) is positively associated with more frequent use of marijuana within the past month and greater expectations of marijuana's positive benefits. MMDs with signage show stronger associations with number of times used each day and positive expectancies.

Dynamic landmark prediction for mixture data

In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Our estimator has improved prediction accuracy over existing estimators that ignore covariate information. It is built within a dynamic landmark prediction framework whereby we can obtain personalized dynamic predictions over time. Compared to current standards, a simple transformation of our estimator provides more efficient estimates of marginal distribution functions in settings where patient-specific predictions are not the main goal. We show our estimator is unbiased and has more predictive accuracy compared to methods that ignore covariate information and landmarking. Applying our method to a Huntington disease study of mortality, we develop dynamic survival prediction curves incorporating gender and familial genetic information.

Dynamic risk prediction for diabetes using biomarker change measurements

Background

Dynamic risk models, which incorporate disease-free survival and repeated measurements over time, might yield more accurate predictions of future health status compared to static models. The objective of this study was to develop and apply a dynamic prediction model to estimate the risk of developing type 2 diabetes mellitus.

Methods

Both a static prediction model and a dynamic landmark model were used to provide predictions of a 2-year horizon time for diabetes-free survival, updated at 1, 2, and 3 years post-baseline i.e., predicting diabetes-free survival to 2 years and predicting diabetes-free survival to 3 years, 4 years, and 5 years post-baseline, given the patient already survived past 1 year, 2 years, and 3 years post-baseline, respectively. Prediction accuracy was evaluated at each time point using robust non-parametric procedures. Data from 2057 participants of the Diabetes Prevention Program (DPP) study (1027 in metformin arm, 1030 in placebo arm) were analyzed.

Results

The dynamic landmark model demonstrated good prediction accuracy with area under curve (AUC) estimates ranging from 0.645 to 0.752 and Brier Score estimates ranging from 0.088 to 0.135. Relative to a static risk model, the dynamic landmark model did not significantly differ in terms of AUC but had significantly lower (i.e., better) Brier Score estimates for predictions at 1, 2, and 3 years (e.g. 0.167 versus 0.099; difference − 0.068 95% CI − 0.083 to − 0.053, at 3 years in placebo group) post-baseline.

Conclusions

Dynamic prediction models based on longitudinal, repeated risk factor measurements have the potential to improve the accuracy of future health status predictions.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0812-y) contains supplementary material, which is available to authorized users.

Understanding Which Teenagers Benefit Most From a Brief Primary Care Substance Use Intervention

BACKGROUND AND OBJECTIVES

The primary care (PC) setting provides an opportunity to address adolescent alcohol and marijuana use. We examined moderators of effectiveness for a PC brief motivational intervention on adolescents' alcohol and marijuana use and consequences 1 year later.

METHODS

We conducted a randomized controlled trial in 4 PC clinics from April 2013 to November 2015 and followed adolescents using Web-based surveys. We examined whether demographic factors and severity of use moderated 12-month outcomes. Adolescents aged 12 through 18 were screened by using the National Institute on Alcohol Abuse and Alcoholism Screening Guide. Those identified as at risk were randomly assigned to the intervention (CHAT) or to usual care (UC).

RESULTS

The sample (n = 294) was 58% female, 66% Hispanic, 17% African American, 12% white, and 5% multiethnic or of other race with an average age of 16 years. After controlling for baseline values of outcomes, teens in CHAT who reported more negative consequences from drinking or had an alcohol use disorder at baseline reported less alcohol use, heavy drinking, and consequences 1 year later compared with teens in UC. Similarly, teens in CHAT with more negative consequences from marijuana use at baseline reported less marijuana use 1 year later compared with teens in UC; however, teens in CHAT who reported fewer marijuana consequences at baseline reported greater marijuana use 1 year later compared with teens in UC.

CONCLUSIONS

A brief intervention can be efficacious over the long-term for adolescents who report problems from alcohol and marijuana use. Findings emphasize the importance of both screening and intervention in at-risk adolescents in PC.

Associations between driving under the influence or riding with an impaired driver and future substance use among adolescents

Objective: Risky driving behaviors among adolescents, such as riding with a drinking or impaired driver (RWID) or driving while under the influence (DUI) of alcohol or drugs, are significant public health concerns. Few studies have examined associations of RWID and DUI with future substance use and problems after controlling for baseline substance use. Given that the DUI/RWDD event may be a teachable moment to prevent future consequences (e.g., when injured or arrested), it is important to understand how this risk behavior relates to subsequent use and problems. This study therefore examined characteristics of adolescents who reported DUI and RWID and assessed their risk of future alcohol and marijuana use and consequences 6 months later. Methods: Participants were 668 adolescents aged 12 to 18 (inclusive) recruited at 1 of 4 primary care clinics in Pittsburgh and Los Angeles as part of a larger randomized controlled trial. They completed surveys about their health behaviors at baseline and 6 months after baseline. We examined baseline characteristics of adolescents who reported DUI and RWID and then assessed whether past-year DUI and RWID at baseline were associated with alcohol and marijuana use and consequences 6 months after baseline. Results: Fifty-eight percent of participants were female, 56% were Hispanic, 23% were Black, 14% were White, 7% were multiethnic or other, and the average age was 16 years (SD = 1.9). At baseline, participants who reported RWID or DUI were more likely to be older, report past-year use of alcohol and marijuana, and more likely to have an alcohol use disorder or cannabis use disorder versus those who did not report RWID or DUI, respectively. At 6-month follow-up and after controlling for baseline demographics and baseline alcohol use, RWID was associated with more frequent drinking episodes in the past 3 months and greater number of drinks in the past month when they drank heavily. DUI at baseline was associated with more frequent heavy drinking episodes and alcohol and marijuana consequences 6 months later. Conclusions: RWID and DUI are significantly associated with greater alcohol and marijuana use over time. This study highlights that teens may be at higher risk for problem substance use in the future even if they ride with someone who is impaired. Prevention and intervention efforts for adolescents need to address both driving under the influence and riding with an impaired driver to prevent downstream consequences.

Using a surrogate marker for early testing of a treatment effect

The development of methods to identify, validate, and use surrogate markers to test for a treatment effect has been an area of intense research interest given the potential for valid surrogate markers to reduce the required costs and follow-up times of future studies. Several quantities and procedures have been proposed to assess the utility of a surrogate marker. However, few methods have been proposed to address how one might use the surrogate marker information to test for a treatment effect at an earlier time point, especially in settings where the primary outcome and the surrogate marker are subject to censoring. In this paper, we propose a novel test statistic to test for a treatment effect using surrogate marker information measured prior to the end of the study in a time-to-event outcome setting. We propose a robust nonparametric estimation procedure and propose inference procedures. In addition, we evaluate the power for the design of a future study based on surrogate marker information. We illustrate the proposed procedure and relative power of the proposed test compared to a test performed at the end of the study using simulation studies and an application to data from the Diabetes Prevention Program.

Utilization and Perceptions of Drop-in Center Services Among Youth Experiencing Homelessness

Drop-in centers offer a range of services to assist unaccompanied youth experiencing homelessness, but little is known about their perceptions of drop-in centers or use of different services. A random sample of 273 youth experiencing homelessness in the Los Angeles area who had ever used a drop-in center was surveyed. Most youth heard about local drop-in centers from peers (65.1%). They generally reported positive perceptions of the drop-in center environment, staff, and clients; overall, 57.8% were "very" or "extremely" satisfied with the services they had received. Nearly all youth cited basic services (e.g., food, showers, clothes) as a reason they went to drop-in centers; far fewer reported going to obtain higher-level services (e.g., case management). Perceptions and utilization did not differ by sexual orientation; however, non-white youth were more likely than Whites to use drop-in centers for certain higher-level services. Strategies for engaging youth in drop-in center services are discussed.

Brief motivational interviewing intervention to reduce alcohol and marijuana use for at-risk adolescents in primary care

OBJECTIVE

The primary care (PC) setting provides a unique opportunity to address adolescent alcohol and other drug (AOD) use.

METHOD

We conducted a randomized controlled trial in 4 PC clinics from April 2013 to November 2015 to determine whether a 15-min brief motivational interviewing (MI) AOD intervention, delivered in PC, reduced alcohol and marijuana use and consequences. Adolescents ages 12-18 who came for an appointment during the 2.5-year study period were asked to be in the study and screened using the National Institute of Alcohol Abuse and Alcoholism Screening Guide. Those identified as at risk were randomized to the CHAT intervention or usual care (UC). Adolescents completed 4 web-based surveys at baseline and 3, 6, and 12 months postbaseline.

RESULTS

The sample (n = 294) was 58% female and 66% Hispanic, 17% Black, 12% White, 5% multiethnic or other, with an average age of 16 years. Compared to UC adolescents, CHAT adolescents reported significantly less perceived peer use of alcohol and marijuana at 3 months (alcohol: p < .0001; marijuana p = .01) and 6 months (alcohol: p = .04; marijuana p = .04). CHAT adolescents also reported marginally fewer negative alcohol consequences experienced at 6 months (p = .08). At 12 months, compared to UC, CHAT adolescents reported less perceived peer alcohol (p = .04) and marijuana (p < .01) use and fewer negative consequences from alcohol (p = .03) and marijuana (p = .04) use.

CONCLUSIONS

A brief MI intervention delivered in PC reduced negative consequences from alcohol and marijuana use 1 year later. Findings emphasize that adolescents can benefit from PC interventions that briefly and effectively address both alcohol and marijuana use. (PsycINFO Database Record

Effects of Caregiver and Decedent Characteristics on CAHPS Hospice Survey Scores

CONTEXT

The Consumer Assessment of Healthcare Providers and Systems (CAHPS^®) Hospice Survey assesses the care experiences of hospice patients and their families. Public reporting of hospice performance on these survey measures began in February 2018.

OBJECTIVES

Develop an appropriate case-mix adjustment (CMA) model to allow for fair comparisons between hospices.

METHODS

We analyzed CAHPS Hospice Survey data reflecting experiences of 915,442 patients who received care from 2513 hospice programs between April 2015 and March 2016. Decedent and caregiver characteristics were identified for inclusion in CMA based on their variation across hospices (as measured by intraclass correlation coefficients [ICCs]) and how predictive they were of responses to survey questions (as assessed by linear regression).

RESULTS

The final CMA model included decedent age, payer for hospice care, primary diagnosis, length of final episode of hospice care, caregiver age, caregiver education, relationship to decedent, survey language/language spoken at home, and response percentile. The characteristics that varied most across hospices were language (ICC = 0.48 for Spanish survey or home language) and payer for hospice care (ICC = 0.42 for Medicare only; ICC = 0.35 for Medicare and private insurance). The characteristics that were most predictive of caregivers' survey responses were payer for hospice care, caregiver education, and survey language/language spoken at home. Lack of appropriate adjustment would incorrectly rank hospices by 1.2-5.4 percentile points.

CONCLUSION

To ensure fair comparisons across hospices, CAHPS Hospice Survey measure scores should be adjusted for several caregiver and decedent characteristics.

Identifying adolescents with alcohol use disorder: Optimal screening using the National Institute on Alcohol Abuse and Alcoholism screening guide

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) screening guide (SG) uses a 2-question screening process to identify adolescents at risk for alcohol use. The aim of this study was to identify the optimal screening rule in terms of identifying adolescents with alcohol use disorder (AUD) using the NIAAA questions by examining whether the cutpoint should vary by gender, race/ethnicity, grade, and/or age. Youth aged 12 through 18 years (N = 1,573; 27% Black, 51% Hispanic) were screened using the NIAAA SG, and then completed a survey. We used receiver operating characteristic curve analyses to identify the optimal cutpoint for the NIAAA screener question on self-use with AUD as the outcome. We compared the resulting screening rule with the NIAAA SG rule. We found that the optimal cutpoint depended on age and grade of adolescents. The resulting screening rule was the same as the NIAAA SG, and thus independently validated the NIAAA SG, with the exception of screening for adolescents 18 years of age, for which a lower cutpoint was indicated. The performance of both screening rules was highly similar when applied to the study sample, with a sensitivity of 0.89 for the optimal screening rule and a sensitivity of 0.87 for the NIAAA SG. In settings in which the cost of a false positive is relatively low (depending on available resources and cost of the intervention), lower cutpoints for older adolescents should be considered, as this may increase sensitivity of identifying these individuals at risk for AUD. (PsycINFO Database Record

Validation of New Care Coordination Quality Measures for Children with Medical Complexity

OBJECTIVES

To validate new caregiver-reported quality measures assessing care coordination services for children with medical complexity (CMC).

METHODS

A cross-sectional analysis of the associations between 20 newly developed Family Experiences with Coordination of Care (FECC) quality measures and 3 validation measures among 1209 caregivers who responded to a telephone or mailed survey from August to November 2013 in Minnesota and Washington. Validation measures included an access composite, a provider rating item, and a care coordination outcome measure, all derived from Consumer Assessments of Healthcare Providers and Systems (CAHPS) survey items. Multivariate regression was used to examine associations between the 3 validation measures and each of the 20 FECC quality measures.

RESULTS

Nineteen of the 20 FECC quality measures were significantly and positively associated with ≥1 of the validation measures. The components of care coordination demonstrating the strongest positive association with provider ratings included: 1) having a care coordinator who was knowledgeable and supportive and advocated for the child's needs (β = 26.4; 95% confidence interval [CI], 20.0-32.8, scaled to reflect change associated with a 0-100 change in the FECC measure score); and 2) receiving a written visit summary that was useful and easy to understand (β = 22.0; 95% CI, 17.1-27.0).

CONCLUSIONS

Nineteen newly developed FECC quality measures demonstrated convergent validity with previously validated CAHPS measures. These new measures are valid for assessing the quality of care coordination services provided to CMC and may be useful for evaluating new models of care focused on improving these services.