Considerations for Identifying Social Needs in Health Care Systems: A Commentary on the Role of Predictive Models in Supporting a Comprehensive Social Needs Strategy No abstract available

Chronic Disease Medication Adherence After Initiation of Buprenorphine for Opioid Use Disorder Background: Although buprenorphine is an evidence-based treatment for opioid use disorder (OUD), it is unknown whether buprenorphine use may affect patients’ adherence to treatments for chronic, unrelated conditions. Objectives: To quantify the effect of buprenorphine treatment on patient adherence to 5 therapeutic classes: (1) antilipids; (2) antipsychotics; (3) antiepileptics; (4) antidiabetics; and (5) antidepressants. Research Design: This was a retrospective cohort study. Subjects: We started with 12,719 commercially ensured individuals with a diagnosis of OUD and the buprenorphine initiation between January 2011 and June 2015 using Truven Health’s MarketScan data. Individuals using any of the 5 therapeutic classes of interest were included. Measures: Within the 180-day period post buprenorphine initiation, we derived 2 daily indicators: having buprenorphine and having chronic medication on hand for each therapeutic class of interest. We applied logistic regression to assess the association between these 2 daily indicators, adjusting for demographics, morbidity, and baseline adherence. Results: Across the 5 therapeutic classes, the probability with a given treatment on hand was always higher on days when buprenorphine was on hand. After adjustment for demographics, morbidity, and baseline adherence, buprenorphine was associated with a greater odds of adherence to antilipids [odds ratio (OR), 1.27; 95% confidence interval (CI), 1.04–1.54], antiepileptics (OR, 1.22; CI, 1.10–1.36) and antidepressants (OR, 1.42; CI, 1.32–1.60). Conclusions: Using buprenorphine to treat OUD may increase adherence to treatments for chronic unrelated conditions, a finding of particular importance given high rates of mental illness and other comorbidities among many individuals with OUD.

Health Care Utilization After Paraprofessional-administered Substance Use Screening, Brief Intervention, and Referral to Treatment: A Multi-level Cost-offset Analysis Background: Authorities recommend universal substance use screening, brief intervention, and referral to treatment (SBIRT) for all (ie, universal) adult primary care patients. Objective: The objective of this study was to examine long-term (24-mo) changes in health care utilization and costs associated with receipt of universal substance use SBIRT implemented by paraprofessionals in primary care settings. Research Design: This study used a difference-in-differences design and Medicaid administrative data to assess changes in health care use among Medicaid beneficiaries receiving SBIRT. The difference-in-differences estimates were used in a Monte Carlo simulation to estimate potential cost-offsets associated with SBIRT. Subjects: The treatment patients were Medicaid beneficiaries who completed a 4-question substance use screen as part of an SBIRT demonstration program between 2006 and 2011. Comparison Medicaid patients were randomly selected from matched clinics in Wisconsin. Measures: The study includes 4 health care utilization measures: outpatient days; inpatient length of stay; inpatient admissions; and emergency department admissions. Each outcome was assigned a unit cost based on mean Wisconsin Medicaid fee-for-service reimbursement amounts. Results: We found an annual increase of 1.68 outpatient days (P=0.027) and a nonsignificant annual decrease in inpatient days of 0.67 days (P=0.087) associated with SBIRT. The estimates indicate that the cost of a universal SBIRT program could be offset by reductions in inpatient utilization with an annual net cost savings of $782 per patient. Conclusions: Paraprofessional-delivered universal SBIRT is likely to yield health care cost savings and is a cost-effective mechanism for integrating behavioral health services in primary care settings.

Medical Practice Consolidation and Physician Shared Patient Network Size, Strength, and Stability Background: Properties of social networks and shared patient networks of physicians are associated with important outcomes, including costs, quality, information exchange, and organizational effectiveness. Objectives: To determine whether practice consolidation affects size, strength, and stability of US practice-based physician shared patient networks. Research Design: We used a dynamic difference-in-differences (event study) design to determine how 2 types of vertical consolidation (hospital and health system practice acquisition) and 2 types of horizontal consolidation (medical group membership and practice-practice mergers) affect individual shared patient network characteristics, controlling for physician fixed effects and geographic market (metropolitan statistical area). Subjects: Practice-based US physicians whose practices consolidated 2009–2014 are identified via health system, hospital, and medical group affiliation information and appearance/disappearance of listed practice affiliations in the SK&A Physician Database. Measures: Outcomes measured were network size (number of individual physicians with whom a physician shares patients within 30 d), strength (average number of shared patients within those relationships), and stability (percent of shared patient relationships that persist in the current and prior year), all generated from Medicare Shared Patient Patterns (30-d) data. Results: Shared patient network stability increases significantly after acquisition of practices by horizontal practice-practice mergers [βt=1=0.041 (P<0.001), βt=2=0.047 (P<0.001), βt=3=0.041 (P<0.001), βt=4=0.031 (P<0.05), where t is the number of years after the consolidation event]. These effects were robust to sensitivity analyses. Shared patient network size and strength are not observably associated with practice consolidation events. Conclusions: Practice consolidation can increase the stability of physician networks, which may have positive implications for organizational effectiveness.

Individual Nurse Productivity in Preparing Patients for Discharge Is Associated With Patient Likelihood of 30-Day Return to Hospital Objective: Applied to value-based health care, the economic term “individual productivity” refers to the quality of an outcome attributable through a care process to an individual clinician. This study aimed to (1) estimate and describe the discharge preparation productivities of individual acute care nurses and (2) examine the association between the discharge preparation productivity of the discharging nurse and the patient’s likelihood of a 30-day return to hospital [readmission and emergency department (ED) visits]. Research Design: Secondary analysis of patient-nurse data from a cluster-randomized multisite study of patient discharge readiness and readmission. Patients reported discharge readiness scores; postdischarge outcomes and other variables were extracted from electronic health records. Using the structure-process-outcomes model, we viewed patient readiness for hospital discharge as a proximal outcome of the discharge preparation process and used it to measure nurse productivity in discharge preparation. We viewed hospital return as a distal outcome sensitive to discharge preparation care. Multilevel regression analyses used a split-sample approach and adjusted for patient characteristics. Subjects: A total 522 nurses and 29,986 adult (18+ y) patients discharged to home from 31 geographically diverse medical-surgical units between June 15, 2015 and November 30, 2016. Measures: Patient discharge readiness was measured using the 8-item short form of Readiness for Hospital Discharge Scale (RHDS). A 30-day hospital return was a categorical variable for an inpatient readmission or an ED visit, versus no hospital return. Results: Variability in individual nurse productivity explained 9.07% of variance in patient discharge readiness scores. Nurse productivity was negatively associated with the likelihood of a readmission (−0.48 absolute percentage points, P<0.001) and an ED visit (−0.29 absolute percentage points, P=0.042). Conclusions: Variability in individual clinician productivity can have implications for acute care quality patient outcomes.

Changes in Hospital Referral Patterns to Skilled Nursing Facilities Under the Hospital Readmissions Reduction Program Background: The Hospital Readmissions Reduction Program (HRRP) penalizes hospitals for higher-than-expected readmission rates. Almost 20% of Medicare fee-for-service (FFS) patients receive postacute care in skilled nursing facilities (SNFs) after hospitalization. SNF patients have high readmission rates. Objective: The objective of this study was to investigate the association between changes in hospital referral patterns to SNFs and HRRP penalty pressure. Design: We examined changes in the relationship between penalty pressure and outcomes before versus after HRRP announcement among 2698 hospitals serving 6,936,393 Medicare FFS patients admitted for target conditions: acute myocardial infarction, heart failure, or pneumonia. Hospital-level penalty pressure was the expected penalty rate in the first year of the HRRP multiplied by Medicare discharge share. Outcomes: Informal integration measured by the percentage of referrals to hospitals’ most referred SNF; formal integration measured by SNF acquisition; readmission-based quality index of the SNFs to which a hospital referred discharged patients; referral rate to any SNF. Results: Hospitals facing the median level of penalty pressure had modest differential increases of 0.3 percentage points in the proportion of referrals to the most referred SNF and a 0.006 SD increase in the average quality index of SNFs referred to. There were no statistically significant differential increases in formal acquisition of SNFs or referral rate to SNF. Conclusions: HRRP did not prompt substantial changes in hospital referral patterns to SNFs, although readmissions for patients referred to SNF differentially decreased more than for other patients, warranting investigation of other mechanisms underlying readmissions reduction.

Identifying Common Predictors of Multiple Adverse Outcomes Among Elderly Adults With Type-2 Diabetes Objective: As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels. Research Design and Methods: Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k=5) of the combined outcome (without death) within the validation set. Results: The most significant predictors across the 7 outcomes were: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15–7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71–0.80). Conclusions: Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes.

Early Impact of the State Innovation Models Initiative on Diagnosed Diabetes Prevalence Among Adults and Hospitalizations Among Diagnosed Adults Background: The State Innovation Models (SIM) Initiative invested $254 million in 6 states in Round 1 to accelerate delivery system and payment reforms. Objective: The objective of this study was to examine the association of early SIM implementation and diagnosed diabetes prevalence among adults and hospitalization rates among diagnosed adults. Research Design: Quasi-experimental design compares diagnosed diabetes prevalence and hospitalization rates before SIM (2010–2013) and during early implementation (2014) in 6 SIM states versus 6 comparison states. County-level, difference-in-differences regression models were estimated. Subjects: The annual average of 4.5 million adults aged 20+ diagnosed with diabetes with 1.4 million hospitalizations in 583 counties across 12 states. Measures: Diagnosed diabetes prevalence among adults and hospitalization rates per 1000 diagnosed adults. Results: Compared with the pre-SIM period, diagnosed diabetes prevalence increased in SIM counties by 0.65 percentage points (from 10.22% to 10.87%) versus only 0.10 percentage points (from 9.64% to 9.74%) in comparison counties, a difference-in-differences of 0.55 percentage points. The difference-in-differences regression estimates ranged from 0.49 to 0.53 percentage points (P<0.01). Regression results for ambulatory care-sensitive condition and all-cause hospitalization rates were inconsistent across models with difference-in-differences estimates ranging from −5.34 to −0.37 and from −13.16 to 0.92, respectively. Conclusions: SIM Round 1 was associated with higher diagnosed diabetes prevalence among adults after a year of implementation, likely because of SIM’s emphasis on detection and care management. SIM was not associated with lower hospitalization rates among adults diagnosed with diabetes, but the SIM’s long-term impact on hospitalizations should be assessed.

Health Care Provider Communication and the Duration of Time Loss Among Injured Workers: A Prospective Cohort Study Background: In addition to providing injured workers with biomedical treatment, health care providers (HCPs) can promote return to work (RTW) through various communications. Objectives: To test the effect of several types of HCP communications on time loss following injury. Research Design: The authors analyzed survey and administrative claims data from a total of 730 injured workers in Victoria, Australia. Survey responses were collected around 5 months postinjury and provided data on HCP communication and confounders. Administrative claim records provided data on compensated time loss postsurvey. The authors conducted multivariate zero-inflated Poisson regressions to determine both the odds of having future time loss and its duration. Measures: Types of HCP communications included providing an estimated RTW date, discussing types of activities the injured worker could do or ways to prevent a recurrence, and contacting other RTW stakeholders. Each was measured in isolation as well as modified by a low-stress experience with the HCP. Time loss was the count of cumulative compensated work absence in weeks, accrued postsurvey. Results: RTW dates reduced the odds of future time loss [odds ratio, 0.26; 95% confidence interval (CI), 0.09–0.82] regardless of the stressfulness of the experience. Communications that predicted shorter durations of time loss only did so with low-stress experiences: RTW date [incidence rate ratio (IRR), 0.56; 95% CI, 0.50–0.63], stakeholder contact (IRR, 0.78; 95% CI, 0.70–0.87), and prevention discussions (IRR, 0.87; 95% CI, 0.78–0.98). Conclusions: HCPs may reduce time loss through several types of communication, particularly when stress is minimized. RTW dates had the largest and most robust effect.

Disparities in Receipt of Bariatric Surgery in Canada: An Analysis of Data From an Ontario Bariatric Surgery Referral Center Background: Patients with lower socioeconomic status (SES) in the United States have reduced access to many health services including bariatric surgery. It is unclear whether disparities in bariatric surgery exist in countries with government-sponsored universal health benefits. The authors used data from a large regional Canadian bariatric surgery referral center to examine the relationship between SES and receipt of bariatric surgery. Methods: The Toronto Western Hospital bariatric surgery registry was used to identify all adults referred for bariatric surgery assessment from 2010 to 2017. The authors compared demographics, SES measures, and clinical measures among patients who did not and did undergo bariatric surgery (Roux-en-Y or sleeve gastrectomy). Multiple logistic regression was used to examine differences in receipt of bariatric surgery according to patient demographic characteristics and SES factors. Results: Among 2417 patients included in the study, 646 (26.7%) did not receive surgery and 1771 patients (73.2%) did. Patients who did not undergo surgery were more likely to be male individual (29.1% vs. 19.3%; P<0.001), black (12.1% vs. 8.3%; P=0.005), South Asian/Middle Eastern (8.2% vs. 4.5%; P<0.001), and less likely to be white (68.9% vs. 76.7%; P<0.001). In multiple logistic regression, factors associated with not receiving surgery were male sex, Black and South Asian/Middle Eastern ethnicity, being single, lack of employment, and history of psychiatric illness. Conclusions: Among patients referred for bariatric surgery, those who were male individuals, nonwhite, single, and unemployed were less likely to undergo surgery. Our results suggest that even with equal insurance, there are disparities in receipt of bariatric surgery.