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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.

ObjectiveTo 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.MethodsBased 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.ResultsOverall, 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.ConclusionsThis 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.

ObjectivesTo (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 source4893 adult patients were treated in the ED of 16 hospitals across the United States in 2018.Study designThe study utilized a cross-sectional survey.Data collectionSurvey 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 findingsAnalyses 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.ConclusionsThe 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.

Background and objectivesPatients with a primary mental health condition account for nearly 10% of pediatric hospitalizations nationally, but little is known about the quality of care provided for them in hospital settings. Our objective was to develop and test medical record-based measures used to assess quality of pediatric mental health care in the emergency department (ED) and inpatient settings.MethodsWe drafted an evidence-based set of pediatric mental health care quality measures for the ED and inpatient settings. We used the modified Delphi method to prioritize measures; 2 ED and 6 inpatient measures were operationalized and field-tested in 2 community and 3 children's hospitals. Eligible patients were 5 to 19 years old and diagnosed with psychosis, suicidality, or substance use from January 2012 to December 2013. We used bivariate and multivariate models to examine measure performance by patient characteristics and by hospital.ResultsEight hundred and seventeen records were abstracted with primary diagnoses of suicidality (n = 446), psychosis (n = 321), and substance use (n = 50). Performance varied across measures. Among patients with suicidality, male patients (adjusted odds ratio: 0.27, P < .001) and African American patients (adjusted odds ratio: 0.31, P = .02) were less likely to have documentation of caregiver counseling on lethal means restriction. Among admitted suicidal patients, 27% had documentation of communication with an outside provider, with variation across hospitals (0%-38%; P < .001). There was low overall performance on screening for comorbid substance abuse in ED patients with psychosis (mean: 30.3).ConclusionsThese new pediatric mental health care quality measures were used to identify sex and race disparities and substantial hospital variation. These measures may be useful for assessing and improving hospital-based pediatric mental health care quality.

There is a widely held belief that children’s general and psychological health benefits from owning and/or interacting with pets. In our study, we aimed to determine whether children who live with a dog or cat in their home have better mental and physical health outcomes compared with children without such a pet. Our study design consisted of a secondary analysis of household survey data from the 2003 California Health Interview Survey. Children in pet-owning households (n = 2,236 households with a dog or cat) were compared with children in non-pet owning households (n = 2,955 house-holds) using a weighted propensity score regression approach. Double robust regression analyses were used to examine the association between living with a dog or cat and health outcomes, while accounting for confounding factors. Our results demonstrated strong confounding effects. Unadjusted analyses found that children in pet-owning households were significantly healthier than children in non-owning households in terms of, for example, better general health, higher activity level, and less concern from parents regarding mood, behavior, and learning ability. However, when estimates were adjusted using the double robust approach, the effects were smaller and no longer statistically significant. The results indicate that the benefits of owning pets observed in this study were largely explained by confounding factors.

There is conflicting evidence about whether living with pets results in better mental and physical health outcomes, with the majority of the empirical research evidence being inconclusive due to methodological limitations. We briefly review the research evidence, including the hypothesized mechanisms through which pet ownership may influence health outcomes. This study examines how pet and non-pet owners differ across a variety of socio-demographic and health measures, which has implications for the proper interpretation of a large number of correlational studies that attempt to draw causal attributions. We use a large, population-based survey from California administered in 2003 (n = 42,044) and find that pet owners and non-pet owners differ across many traits, including gender, age, race/ethnicity, living arrangements, and income. We include a discussion about how the factors associated with the selection into the pet ownership group are related to a range of mental and physical health outcomes. Finally, we provide guidance on how to properly model the effects of pet ownership on health to accurately estimate this relationship in the general population.

Determining whether a surrogate marker can be used to replace a primary outcome in a clinical study is complex. While many statistical methods have been developed to formally evaluate a surrogate marker, they generally do not provide a way to examine heterogeneity in the utility of a surrogate marker. Similar to treatment effect heterogeneity, where the effect of a treatment varies based on a patient characteristic, heterogeneity in surrogacy means that the strength or utility of the surrogate marker varies based on a patient characteristic. The few methods that have been recently developed to examine such heterogeneity cannot accommodate censored data. Studies with a censored outcome are typically the studies that could most benefit from a surrogate because the follow‐up time is often long. In this paper, we develop a robust nonparametric approach to assess heterogeneity in the utility of a surrogate marker with respect to a baseline variable in a censored time‐to‐event outcome setting. In addition, we propose and evaluate a testing procedure to formally test for heterogeneity at a single time point or across multiple time points simultaneously. Finite sample performance of our estimation and testing procedure are examined in a simulation study. We use our proposed method to investigate the complex relationship between change in fasting plasma glucose, diabetes, and sex hormones using data from the diabetes prevention program study. [ABSTRACT FROM AUTHOR]

In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate the utility of the mean and maximum Absolute Standardized Mean Difference (ASMD) as a test of balance between a synthetic control unit and treated unit, and provide guidance on what constitutes a poor fit when using a synthetic control. We explore and compare other potential metrics using a simulation study. We provide an application of our proposed balance metric to the 2013 Los Angeles (LA) Firearm Study [9]. Using Uniform Crime Report data, we apply the SCM to obtain a counterfactual for the LA firearm-related crime rate based on a weighted combination of control units in a donor pool of cities. We use this counterfactual to estimate the effect of the LA Firearm Study intervention and explore the impact of changing the donor pool and pre-intervention duration period on resulting matches and estimated effects. We demonstrate how decision-making about the quality of a synthetic control can be improved by using ASMD. The mean and max ASMD clearly differentiate between poor matches and good matches. Researchers need better guidance on what is a meaningful imbalance between synthetic control and treated groups. In addition to the use of gap plots, the proposed balance metric can provide an objective way of determining fit.

The expanded use of clinical process-of-care measures to assess the quality of health care in the context of public reporting and pay-for-performance applications has led to a desire to demonstrate the value of such efforts in terms of improved patient outcomes. The inability to observe associations between improved delivery of clinical processes and improved clinical outcomes in practice has raised concerns about the value of holding providers accountable for delivery of clinical processes of care. Analyses that attempt to investigate this relationship are fraught with many challenges, including selection of an appropriate outcome, the proximity of the outcome to the receipt of the clinical process, limited power to detect an effect, small expected effect sizes in practice, potential bias due to unmeasured confounding factors, and difficulties due to changes in measure specification over time. To avoid potentially misleading conclusions about an observed or lack of observed association between a clinical process of care and an outcome in the context of observational studies, individuals conducting and interpreting such studies should carefully consider, evaluate, and acknowledge these types of challenges.

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. [ABSTRACT FROM AUTHOR]