Prediction of rehospitalization and mortality risks for skilled nursing facilities using a dimension reduction approach.

BACKGROUND: Hospitals are incentivized to reduce rehospitalization rates, creating an emphasis on skilled nursing facilities (SNFs) for post-hospital discharge. How rehospitalization rates vary depending on patient and SNF characteristics is not well understood, in part because these characteristics are high-dimensional. We sought to estimate rehospitalization and mortality risks by patient and skilled nursing facility (SNF) leveraging high-dimensional characteristics. METHODS: Using 1,060,337 discharges from 13,708 SNFs of Medicare patients residing or visiting a provider in Wisconsin, Iowa, and Illinois, factor analysis was performed to reduce the number of patient and SNF characteristics. K-means clustering was applied to SNF factors to categorize SNFs into groups. Rehospitalization and mortality risks within 60 days of discharge was estimated by SNF group for various values of patient factors. RESULTS: Patient and SNF characteristics (616 in total) were reduced to 12 patient factors and 4 SNF groups. Patient factors reflected broad conditions. SNF groups differed in beds and staff capacity, off-site services, and physical and occupational therapy capacity; and in mortality and rehospitalization rates for some patients. Patients with cardiac, orthopedic, and neuropsychiatric conditions are associated with better outcomes when assigned to SNFs with greater on-site capacity (i.e. beds, staff, physical and occupational therapy), whereas patients with conditions related to cancer or chronic renal failure are associated with better outcomes when assigned to SNFs with less on-site capacity. CONCLUSIONS: Risks of rehospitalization and mortality appear to vary significantly by patient and SNF, with certain SNFs being better suited for some patient conditions over others.

A digital self-report survey of mood for bipolar disorder.

OBJECTIVES: Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity. METHODS: We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure. RESULTS: We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R2  = 0.47) and SIGH-D (R2  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores. CONCLUSIONS: While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

The Average Effect of Emergency Department Admission on Readmission and Mortality for Older Adults With Chest Pain.

BACKGROUND: Many older adults (65+) present to the Emergency Department (ED) with chest pain, but do not have otherwise clear clinical indication of whether they should be admitted or discharged. This uncertainty leads to decisions that are highly variable-in addition to already being costly-which could have adverse consequences, since older adults are particularly vulnerable from hospitalization. OBJECTIVE: The objective of this study was to determine whether admitting versus discharging an older adult presenting to the ED with chest pain reduces risk of mortality and readmission. STUDY DESIGN: Electronic health records were curated from an academic hospital system between January 1, 2014, and September 27, 2018. Average effects of admission on 30-day readmission and mortality were estimated using a new causal inference approach based on a latent-variable model of the admission process. Additional analyses assessed moderators and robustness of estimates. SUBJECTS: Older patients (n=3090) presenting to University of Wisconsin Hospital ED. MEASURES: Readmission and mortality within 25, 30, and 35 days of discharge from the ED for discharged patients or the hospital for admitted patients RESULTS:: For older chest pain patients, admission is estimated to lower the 30-day risk of readmission by 42.8% (95% confidence interval: 41.0%-44.6%) but increase the 30-day risk of mortality by 0.8% (95% confidence interval: 0.4%-1.2%). Individuals with higher hierarchical conditional category scores or diabetes with complications have both lower 30-day risk of readmission and higher 30-day risk of mortality compared with their counterparts (P≤0.02). CONCLUSIONS: Our findings suggest ED admission may prevent readmission at the cost of increasing mortality risk for older chest pain patients, especially those with comorbidity. Additional studies are needed to validate these findings.

A latent variable approach to potential outcomes for emergency department admission decisions.

In emergency departments (EDs), care providers continuously weigh admissions against continued monitoring and treatment often without knowing their condition and health needs. To understand the decision process and its causal effect on outcomes, an observational study must contend with unobserved/missing information and a lack of exchangeability between admitted and discharged patients. Our goal was to provide a general framework to evaluate admission decisions from electronic healthcare records (EHRs). We describe admission decisions as a decision-making process in which the patient's health needs is a binary latent variable. We estimate latent health needs from EHR with only partial knowledge of the decision process (ie, initial evaluation, admission decision, length of stay). Estimated latent health needs are then used to understand the admission decision and the decision's causal impact on outcomes. For the latter, we assume potential outcomes are stochastically independent from the admission decision conditional on latent health needs. As a case study, we apply our approach to over 150 000 patient encounters with the ED from the University of Michigan Health System collected from August 2012 through July 2015. We estimate that while admitting a patient with higher latent needs reduces the 30-day risk of revisiting the ED or later being admitted through the ED by over 79%, admitting a patient with lower latent needs actually increases these 30-day risks by 3.0% and 7.6%, respectively.

Newborn Screening Collection and Delivery Processes in Michigan Birthing Hospitals: Strategies to Improve Timeliness.

Objectives This study aimed to determine which steps in the newborn screening collection and delivery processes contribute to delays and identify strategies to improve timeliness. Methods Data was analyzed from infants (N = 94,770) who underwent newborn screening at 83 hospitals in Michigan between April 2014 and March 2015. Linear mixed effects models estimated effects of hospital and newborn characteristics on times between steps in the process, whereas simulation explored how to improve timeliness through adjustments to schedules for the state laboratory and for specimen pickup from hospitals. Results Time from collection to receipt of arrival to the state laboratory varied greatly with collection timing (P < 0.001), with specimens collected on Friday or Saturday delayed an average of 9-12 h compared to other specimens. Simulation estimates shifting specimen pickup from 6 p.m. Sunday-Friday to 9 p.m. Sunday-Friday could lead to an additional 12.6% of specimens received by the Michigan laboratory within 60 h of birth. Conclusions for Practice The time between when a specimen is collected and received by the laboratory can be a significant bottleneck in the newborn screening process. Modifying hospital pickup schedules appears to be a simple way to improve timeliness.

The association between attitudes and the provision of medications for opioid use disorder (MOUD) in United States jails.

Background: Opioid use disorder is prevalent among individuals who are incarcerated, yet medications for opioid use disorder (MOUD) are not widely available in United States jails and prisons. Negative staff attitudes across the criminal legal system may prevent MOUD from being provided. We sought to determine if staff attitudes are associated with the provision of MOUD in prisons or jails. Methods: 227 staff members of 43 jails and partnering community-based treatment providers answered questions on the effectiveness and acceptability of methadone, buprenorphine, and naltrexone. Response patterns were summarized with principal component analysis. Mixed-effects regression was performed to determine if attitudes toward MOUD were associated with the number of individuals screened and diagnosed with an OUD, referred to treatment, provided MOUD and referred to treatment after release. Results: Sites whose staff had negative attitudes towards methadone and positive attitudes towards naltrexone were associated with fewer people being screened (Mean ratio [MR] = 0.84, 95 % CI: [0.72, 0.97]), diagnosed (MR = 0.85, 95 % CI: [0.73, 0.99]), referred (MR = 0.76, 95 % CI: [0.65, 0.89]), provided MOUD (MR = 0.70, 95 % CI: [0.58, 0.84]), and referred after release (MR = 0.82, 95 % CI: [0.72, 0.94]). Sites with overall positive attitudes towards all MOUD were associated with more people being screened (MR = 1.16, 95 % CI: [1.01, 1.34]), diagnosed (MR = 1.37, 95 % CI: [1.18, 1.60]), and referred to treatment (MR = 1.41, 95 % CI: [1.20, 1.65]). Conclusions: Attitudinal barriers exist in the criminal legal system and are associated with the provision of MOUD.

Association between advanced image ordered in the emergency department on subsequent imaging for abdominal pain patients.

BACKGROUND: Abdominal pain is associated with high rates of emergency department (ED) imaging utilization and revisits. While imaging often improves diagnosis, a better understanding is needed on when the decision to image is justified and how it influences subsequent resource utilization and outcomes for patients in the ED presenting with abdominal pain. We evaluated the association between advanced ED imaging on subsequent outpatient imaging and on revisits among abdominal pain patients discharged from the ED. METHODS: A retrospective, observational study was conducted using electronic health record data from an academic ED in the U.S. Midwest. A sample of Medicare patients with a chief complaint of abdominal pain from January 2013 to December 2016 following ED evaluation were included in the analysis. Logistic regression was used to estimate associations between receiving advanced imaging in the ED and subsequent outpatient imaging within 7-, 14-, and 28-day windows after discharge, and 30-day revisit rates to the study ED and to any ED. RESULTS: Of the 1385 ED visits with abdominal pain chief complaint and discharged home from the ED, individuals who were not imaged in the ED had significantly higher adjusted odds of being imaged outside the ED within 7 days (adjusted odds ratio [aOR] 6.65, 95% confidence interval [CI] 3.96-11.17, p < 0.001), 14 days (aOR 4.69, 95% CI 3.11-7.07, p < 0.001), and 28 days (aOR 3.1, 95% CI 2.25-4.27, p < 0.001) of being discharged and had a significantly higher adjusted odds of revisiting the study ED (aOR 1.65, 95% CI 1.29-2.12, p < 0.001) and revisiting any ED (aOR 1.47, 95% CI 1.16-1.86, p = 0.001) within 30 days of being discharged. CONCLUSIONS: Abdominal imaging in the ED was associated with significantly lower imaging utilization after discharge and 30-day revisit rates, suggesting that imaging in the ED may replace downstream outpatient imaging.

Impact of a community-policing initiative promoting substance use disorder treatment over criminal charges on arrest recidivism.

BACKGROUND: Overdose deaths, addiction, and drug-related crime have increased in the United States over the past decade. Treatment improves outcomes, including reducing crime, but few individuals with addiction receive treatment. Here, we determine whether the Madison Addiction Recovery Initiative (MARI), a community policing program implemented by the City of Madison (Wisconsin) Police Department (MPD) that diverts adults who committed a non-violent, drug use-related crime from criminal prosecution to addiction treatment, reduces the risk of recidivism (i.e., an arrest) in the 6-month period following the index crime. METHODS: Observational data were collected by the MPD for 12 months before through 6 months after an index crime from participants in the MARI program (n = 263) who referred to MARI between September 1, 2017 and August 31, 2020 and a Historical Comparison group (n = 52) who committed a comparable crime between September 1, 2015 and August 31, 2016. Average effects were estimated using intention-to-treat (ITT), a per-protocol, and a complier average causal effects (CACE) analyses, adjusted for covariates. RESULTS: ITT analysis did not show that MARI assignment lowered adjusted odds of 6-month recidivism (aOR = 0.59 [0.32, 1.12], p = 0.11). Per-protocol analysis showed that completing MARI lowered the adjusted odds of 6-month recidivism (aOR = 0.23 [0.10, 0.52], p < 0.001). CACE analysis indicated that assignment to MARI among individuals who would complete the MARI program if assigned to the program lowered the adjusted odds of 6-month recidivism (aOR = 0.85 [0.80, 0.90], p < 0.001). CONCLUSIONS: Diverting adults who committed a non-violent, drug use-related crime from criminal prosecution to addiction treatment may reduce 6-month recidivism.

An Innovative Framework to Improve Efficiency of Interhospital Transfer of Children in Respiratory Failure.

RATIONALE: High mortality and resource use burden are associated with hospitalization of critically ill children transferred from level II pediatric intensive care units (PICUs) to level I PICUs for escalated care. Guidelines urge transfer of the most severely ill children to level I PICUs without specification of either the criteria or the best timing of transfer to achieve good outcomes. OBJECTIVES: To identify factors associated with transfer, develop a modeling framework that uses those factors to determine thresholds to guide transfer decisions, and test these thresholds against actual patient transfer data to determine if delay in transfer could be reduced. METHODS: A multistep approach was adopted, with initial identification of factors associated with transfer status using data from a prior case-control study conducted with children with respiratory failure admitted to six level II PICUs between January 1, 1997, and December 31, 2007. To identify when to transfer a patient, thresholds for transfer were created using generalized estimating equations and discrete event simulation. The transfer policies were then tested against actual transfer data. MEASUREMENTS AND MAIN RESULTS: Multivariate logistic regression revealed that the absolute difference of a patient's pediatric logistic organ dysfunction score from the admission value, high-frequency oscillatory ventilation use, antibiotic use, and blood transfusions were all significantly associated with transfer status. The resulting threshold policies led to average transfer delay reduction ranging from 0.5 to 2.3 days in the testing dataset. CONCLUSIONS: Current transfer guidelines are devoid of criteria to identify critically ill children who might benefit from transfer and when the best time to transfer might be. In this study, we used innovative methods to create thresholds of transfer that might reduce delay in transfer.