Discontinuation of Buprenorphine Treatment for Opioid Use Disorder During the Coronavirus Disease-2019 Pandemic: A Multilevel Framework.

BACKGROUND: The coronavirus disease-2019 pandemic has been associated with large increases in opioid-related mortality, yet it is unclear whether specific subpopulations were especially likely to discontinue buprenorphine treatment for opioid use disorder as the pandemic ensued. OBJECTIVE: The aim was to assess predictors of buprenorphine discontinuation in the early months of the coronavirus disease-2019 pandemic (April-July 2020) compared with a prepandemic period (April-July 2019). DESIGN: In each time period, we estimated a multilevel regression models to assess risk of discontinuation in April-July for people who started buprenorphine in January-February. Models included person-level, prescriber-level, and area-level covariates. SUBJECTS: Individuals age 18 years or older in the all-payer IQVIA Longitudinal Prescription Claims. MEASURES: The primary outcome was buprenorphine discontinuation (ie, no filled prescriptions during the follow-up periods). RESULTS: Overall, 13.98% of patients discontinued buprenorphine in April-July 2020, less than the 15.71% in 2019 (P<0.001). In 2020, patient-level factors associated with discontinuation included younger age, male sex, shorter baseline possession ratio, and payment by cash. Compared with patients with a primary care physician prescriber, specialties most associated with discontinuation were pain medicine and physician assistant/nurse practitioner. Compared with the South Atlantic region, discontinuation risk was lowest in New England and highest in the West South Central States. The association between patient, prescriber, and geographic variables to risk of discontinuation was very similar in 2019 and 2020. CONCLUSIONS: While clinical and policy interventions may have mitigated opioid use disorder treatment discontinuation following the pandemic, such discontinuation is nevertheless common and varies by identifiable patient, provider and geographic factors.

External Validation of and Factors Associated with the Overuse Index: a Nationwide Population-Based Study from Taiwan.

BACKGROUND: The Overuse Index (OI), previously called the Johns Hopkins Overuse Index, is developed and validated as a composite measure of systematic overuse/low-value care using United States claims data. However, no information is available concerning whether the external validation of the OI is sustained, especially for international application. Moreover, little is known about which supply and demand factors are associated with the OI. OBJECTIVE: We used nationwide population-based data from Taiwan to externally validate the OI and to examine the association of regional healthcare resources and socioeconomic factors with the OI. DESIGN AND PARTICIPANTS: We analyzed 1,994,636 beneficiaries randomly selected from all people enrolled in the Taiwan National Health Insurance in 2013. MAIN MEASURES: The OI was calculated for 2013 to 2015 for each of 50 medical regions. Spearman correlation analysis was applied to examine the association of the OI with total medical costs per capita and mortality rate. Generalized estimating equation linear regression analysis was conducted to examine the association of regional healthcare resources (number of hospital beds per 1000 population, number of physicians per 1000 population, and proportion of primary care physicians [PCPs]) and socioeconomic factors (proportion of low-income people and proportion of population aged 20 and older without a high school diploma) with the OI. RESULTS: Higher scores of the OI were associated with higher total medical costs per capita (ρ = 0.48, P < 0.001) and not associated with total mortality (ρ = - 0.01, P = 0.882). Higher proportions of PCPs and higher proportions of low-income people were associated with lower scores of the OI (ß = - 0.022, P = 0.016 and ß = - 0.224, P < 0.001, respectively). CONCLUSIONS: Our study supported the external validation of the OI by demonstrating a similar association within a universal healthcare system, and it showed the association of a higher proportion of PCPs and a higher proportion of low-income people with less overuse/low-value care.

Early relaxation of community mitigation policies and risk of COVID-19 resurgence in the United States.

This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy. The groups included: (1) High Change (19 states; 7-day prevalence change ≥50th percentile), (2) Low Change (19 states; 7-day prevalence change <50th percentile), (3) No SAH (11 states: did not adopt SAH order), and (4) No SAH End (2 states: did not relax SAH order). We performed regression modeling assessing the association between change in prevalence at the time of SAH order relaxation and COVID-19 prevalence days after the relaxation of SAH order for four selected groups. After adjusting for other factors, compared to the High Change group, counties in the Low Change group had 33.8 (per 100,000 population) fewer cases (standard error (SE): 19.8, p < 0.001) 7 days after the relaxation of SAH order and the difference was larger by time passing. On August 21, 2020, the No SAH End group had 383.1 fewer cases (per 100,000 population) than the High Change group (SE: 143.6, p < 0.01). A measured, evidence-based approach is required to safely relax the community mitigation strategies and practice phased-reopening of the country.

Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States.

BACKGROUND: The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents' mobility across neighborhoods of different levels of socioeconomic disadvantage. METHODS: This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. RESULTS: On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. CONCLUSIONS: Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

Comparative risk of serious infections among real-world users of biologics for psoriasis or psoriatic arthritis.

OBJECTIVE: To examine whether initiation of interleukin (IL)-17, IL-12/23 or tumour necrosis factor (TNF) inhibitor is associated with an increased risk of serious infection among real-world psoriasis (PsO) or psoriatic arthritis (PsA) patients. METHODS: We assembled a retrospective cohort of commercially insured adults in the USA diagnosed with PsO or PsA between 2015 and 2018. Exposure was dispensation for IL-17 (ixekizumab or secukinumab), IL-12/23 (ustekinumab) or TNF (adalimumab, certolizumab pegol, etanercept, golimumab and infliximab). The outcome was infection requiring hospitalisation after biologic initiation. Incidence rates (IRs) per 100 person-years were computed, and hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression models, adjusted for inverse probability of treatment-weighted propensity scores. RESULTS: A total of 11 560 new treatment episodes were included. Overall, 190 serious infections (2% of treatment episodes) were identified in 9264 person-years of follow-up. Class-specific IRs were similar among IL-17 and TNF, yet significantly lower for IL-12/23. After adjustment for propensity scores, there was no increased risk with IL-17 compared with either TNF (HR=0.89, 95% CI 0.48 to 1.66) or IL-12/23 (HR=1.12, 95% CI 0.62 to 2.03). By contrast, IL-23/23 were associated with a lower risk of infections than TNF (HR=0.59, 95% CI 0.39 to 0.90). CONCLUSIONS: Relative to TNF and IL-17, IL-12/23 inhibitors were associated with a reduced risk of serious infection in biologic-naïve patients with PsO or PsA. In biologic-experienced individuals, there was no difference in infection risk across TNF, IL-17 or IL-12/23 inhibitors.

The Impact of Various Risk Assessment Time Frames on the Performance of Opioid Overdose Forecasting Models.

BACKGROUND: An individual's risk for future opioid overdoses is usually assessed using a 12-month "lookback" period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. METHODS: We included 1,014,033 Maryland residents aged 18-80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program-derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. RESULTS: For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. CONCLUSIONS: Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.

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.

Using trajectory models to assess the effect of hydrocodone upscheduling among chronic hydrocodone users.

PURPOSE: In October 2014, the US Drug Enforcement Agency moved hydrocodone combination products (HCPs) from schedule III to II of the Controlled Substances Act, further restricting their access. The aim of the study is to quantify the effect of hydrocodone's "upscheduling" on the use of opioid and nonopioid analgesics among chronic users. METHODS: Using IQVIA LRx LifeLink anonymized pharmacy data 2013 to 2015, we performed interrupted time series analysis and group-based trajectory modeling to characterize the effect of rescheduling on 316 731 long-term hydrocodone users. Main measures were the number of prescriptions, patients, tablets, and morphine milligram equivalents of opioids and nonopioid analgesics pre and post the policy change. We used logistic regression to assess the relationship between sociodemographic characteristics and these measures. RESULTS: The schedule change was associated with significant declines in opioid prescriptions (20.9%, from 421 798 to 333 627) and the number of patients using opioids (11.4%, from 307 974 to 272 804). Majority of hydrocodone users filled prescriptions for nonopioid analgesics with some declines in the number of users after the schedule change (5.2%, from 181 085 to 171 758). Based on group-based trajectory models, majority of patients continued to fill HCP prescriptions consistently after the policy change, while 15.4% showed large declines in HCP use, accounting for two-thirds of the decrease in opioid volume. There was no evidence that the policy change was associated with significant increases in the use of alternative analgesics. CONCLUSIONS: The upscheduling of hydrocodone led to reductions in opioid use, which were concentrated among a small subset of chronic hydrocodone users, without evidence of commensurate increases in the use of alternative pharmacologic pain treatments.

Systemic overuse of health care in a commercially insured US population, 2010-2015.

BACKGROUND: Overuse is a leading contributor to the high cost of health care in the United States. Overuse harms patients and is a definitive waste of resources. The Johns Hopkins Overuse Index (JHOI) is a normalized measure of systemic health care services overuse, generated from claims data, that has been used to describe overuse in Medicare beneficiaries and to understand drivers of overuse. We aimed to adapt the JHOI for application to a commercially insured US population, to examine geographic variation in systemic overuse in this population, and to analyze trends over time to inform whether systemic overuse is an enduring problem. METHODS: We analyzed commercial insurance claims from 18 to 64 year old beneficiaries. We calculated a semiannual JHOI for each of the 375 Metropolitan Statistical Areas and 47 rural regions of the US. We generated maps to examine geographic variation and then analyzed each region's change in their JHOI quintile from January 2011 to June 2015. RESULTS: The JHOI varied markedly across the US. Across the country, rural regions tended to have less systemic overuse than their MSA counterparts (p < 0.01). Regional systemic overuse is positively correlated from one time period to the next (p < 0.001). Between 2011 and 2015, 53.7% (N = 226) of regions remained in the same quintile of the JHOI. Eighty of these regions had a persistently high or persistently low JHOI throughout study duration. CONCLUSIONS: The systemic overuse of health care resources is an enduring, regional problem. Areas identified as having a persistently high rate of systemic overuse merit further investigation to understand drivers and potential points of intervention.

Healthcare costs and utilization associated with high-risk prescription opioid use: a retrospective cohort study.

BACKGROUND: Previous studies on high-risk opioid use have only focused on patients diagnosed with an opioid disorder. This study evaluates the impact of various high-risk prescription opioid use groups on healthcare costs and utilization. METHODS: This is a retrospective cohort study using QuintilesIMS health plan claims with independent variables from 2012 and outcomes from 2013. We included a population-based sample of 191,405 non-elderly adults with known sex, one or more opioid prescriptions, and continuous enrollment in 2012 and 2013. Three high-risk opioid use groups were identified in 2012 as (1) persons with 100+ morphine milligram equivalents per day for 90+ consecutive days (chronic users); (2) persons with 30+ days of concomitant opioid and benzodiazepine use (concomitant users); and (3) individuals diagnosed with an opioid use disorder. The length of time that a person had been characterized as a high-risk user was measured. Three healthcare costs (total, medical, and pharmacy costs) and four binary utilization indicators (the top 5% total cost users, the top 5% pharmacy cost users, any hospitalization, and any emergency department visit) derived from 2013 were outcomes. We applied a generalized linear model (GLM) with a log-link function and gamma distribution for costs while logistic regression was employed for utilization indicators. We also adopted propensity score weighting to control for the baseline differences between high-risk and non-high-risk opioid users. RESULTS: Of individuals with one or more opioid prescription, 1.45% were chronic users, 4.81% were concomitant users, and 0.94% were diagnosed as having an opioid use disorder. After adjustment and propensity score weighting, chronic users had statistically significant higher prospective total (40%), medical (3%), and pharmacy (172%) costs. The increases in total, medical, and pharmacy costs associated with concomitant users were 13%, 7%, and 41%, and 28%, 21% and 63% for users with a diagnosed opioid use disorder. Both total and pharmacy costs increased with the length of time characterized as high-risk users, with the increase being statistically significant. Only concomitant users were associated with a higher odds of hospitalization or emergency department use. CONCLUSIONS: Individuals with high-risk prescription opioid use have significantly higher healthcare costs and utilization than their counterparts, especially those with chronic high-dose opioid use.

Assessing the Impact of Body Mass Index Information on the Performance of Risk Adjustment Models in Predicting Health Care Costs and Utilization.

BACKGROUND: Using electronic health records (EHRs) for population risk stratification has gained attention in recent years. Compared with insurance claims, EHRs offer novel data types (eg, vital signs) that can potentially improve population-based predictive models of cost and utilization. OBJECTIVE: To evaluate whether EHR-extracted body mass index (BMI) improves the performance of diagnosis-based models to predict concurrent and prospective health care costs and utilization. METHODS: We used claims and EHR data over a 2-year period from a cohort of continuously insured patients (aged 20-64 y) within an integrated health system. We examined the addition of BMI to 3 diagnosis-based models of increasing comprehensiveness (ie, demographics, Charlson, and Dx-PM model of the Adjusted Clinical Group system) to predict concurrent and prospective costs and utilization, and compared the performance of models with and without BMI. RESULTS: The study population included 59,849 patients, 57% female, with BMI class I, II, and III comprising 19%, 9%, and 6% of the population. Among demographic models, R improvement from adding BMI ranged from 61% (ie, R increased from 0.56 to 0.90) for prospective pharmacy cost to 29% (1.24-1.60) for concurrent medical cost. Adding BMI to demographic models improved the prediction of all binary service-linked outcomes (ie, hospitalization, emergency department admission, and being in top 5% total costs) with area under the curve increasing from 2% (0.602-0.617) to 7% (0.516-0.554). Adding BMI to Charlson models only improved total and medical cost predictions prospectively (13% and 15%; 4.23-4.79 and 3.30-3.79), and also improved predicting all prospective outcomes with area under the curve increasing from 3% (0.649-0.668) to 4% (0.639-0.665; and, 0.556-0.576). No improvements in prediction were seen in the most comprehensive model (ie, Dx-PM). DISCUSSION: EHR-extracted BMI levels can be used to enhance predictive models of utilization especially if comprehensive diagnostic data are missing.

Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records.

BACKGROUND: Using electronic health records (EHRs), in addition to claims, to systematically identify patients with factors associated with adverse outcomes (geriatric risk) among older adults can prove beneficial for population health management and clinical service delivery. OBJECTIVE: To define and compare geriatric risk factors derivable from claims, structured EHRs, and unstructured EHRs, and estimate the relationship between geriatric risk factors and health care utilization. RESEARCH DESIGN: We performed a retrospective cohort study of patients enrolled in a Medicare Advantage plan from 2011 to 2013 using both administrative claims and EHRs. We defined 10 individual geriatric risk factors and a summary geriatric risk index based on diagnosed conditions and pattern matching techniques applied to EHR free text. The prevalence of geriatric risk factors was estimated using claims, structured EHRs, and structured and unstructured EHRs combined. The association of geriatric risk index with any occurrence of hospitalizations, emergency department visits, and nursing home visits were estimated using logistic regression adjusted for demographic and comorbidity covariates. RESULTS: The prevalence of geriatric risk factors increased after adding unstructured EHR data to structured EHRs, compared with those derived from structured EHRs alone and claims alone. On the basis of claims, structured EHRs, and structured and unstructured EHRs combined, 12.9%, 15.0%, and 24.6% of the patients had 1 geriatric risk factor, respectively; 3.9%, 4.2%, and 15.8% had ≥2 geriatric risk factors, respectively. Statistically significant association between geriatric risk index and health care utilization was found independent of demographic and comorbidity covariates. For example, based on claims, estimated odds ratios for having 1 and ≥2 geriatric risk factors in year 1 were 1.49 (P<0.001) and 2.62 (P<0.001) in predicting any occurrence of hospitalizations in year 1, and 1.32 (P<0.001) and 1.34 (P=0.003) in predicting any occurrence of hospitalizations in year 2. CONCLUSIONS: The results demonstrate the feasibility and potential of using EHRs and claims for collecting new types of geriatric risk information that could augment the more commonly collected disease information to identify and move upstream the management of high-risk cases among older patients.

Impact of Florida's prescription drug monitoring program and pill mill law on high-risk patients: A comparative interrupted time series analysis.

PURPOSE: We quantified the effects of Florida's prescription drug monitoring program and pill mill law on high-risk patients. METHODS: We used QuintilesIMS LRx Lifelink data to identify patients receiving prescription opioids in Florida (intervention state, N: 1.13 million) and Georgia (control state, N: 0.54 million). The preintervention, intervention, and postintervention periods were July 2010 to June 2011, July 2011 to September 2011, and October 2011 to September 2012. We identified 3 types of high-risk patients: (1) concomitant users: patients with concomitant use of benzodiazepines and opioids; (2) chronic users: long-term, high-dose, opioid users; and (3) opioid shoppers: patients receiving opioids from multiple sources. We compared changes in opioid prescriptions between Florida and Georgia before and after policy implementation among high-risk/low-risk patients. Our monthly measures included (1) average morphine milligram equivalent per transaction, (2) total opioid volume across all prescriptions, (3) average days supplied per transaction, and (4) total number of opioid prescriptions dispensed. RESULTS: Among opioid-receiving individuals in Florida, 6.62% were concomitant users, 1.96% were chronic users, and 0.46% were opioid shoppers. Following policy implementation, Florida's high-risk patients experienced relative reductions in morphine milligram equivalent (opioid shoppers: -1.08 mg/month, 95% confidence interval [CI] -1.62 to -0.54), total opioid volume (chronic users: -4.58 kg/month, CI -5.41 to -3.76), and number of dispensed opioid prescriptions (concomitant users: -640 prescriptions/month, CI -950 to -340). Low-risk patients generally did not experience statistically significantly relative reductions. CONCLUSIONS: Compared with Georgia, Florida's prescription drug monitoring program and pill mill law were associated with large relative reductions in prescription opioid utilization among high-risk patients.

Impact of bundled payments on hip fracture outcomes: a nationwide population-based study.

OBJECTIVE: Establishing one price for all bundled services for a particular illness, which has become the key to healthcare reform efforts, is designed to encourage health professionals to coordinate their care for patients. Limited information is available, however, concerning whether bundled payments are associated with changes in patient outcomes. Nationwide longitudinal population-based data were used to examine the effect of bundled payments on hip fracture outcomes. DESIGN: An interrupted time series design with a comparison group. SETTING: General acute care hospitals throughout Taiwan. PARTICIPANTS: A total of 178 586 hip fracture patients admitted over the period 2007-12 identified from the Taiwan's National Health Insurance Research Database. INTERVENTION: Bundled payments for hip fractures were implemented in Taiwan in January 2010. MAIN OUTCOME MEASURES: The 30-day unplanned readmission and postdischarge mortality. Segmented generalized estimating equation regression models were used after adjustment for trends, patient, physician and hospital characteristics to assess the effect of bundled payments on 30-day outcomes for hip fracture compared with a reference condition. RESULTS: The 30-day unplanned readmission rate for hip fracture showed a relative decreasing trend after the implementation of bundled payments compared with the trend before the implementation relative to that of the reference condition. CONCLUSIONS: This finding might imply that the implementation of bundled payments encourages health professionals to coordinate their care, leading to reduced readmission for hip fracture.

Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims.

BACKGROUND: There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. METHODS: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. RESULTS: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. DISCUSSION: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

Effect of Direct-to-Consumer Advertising on Statin Use in the United States.

IMPORTANCE: The value of direct-to-consumer advertising (DTCA) of prescription drugs is widely debated, as is the effect of DTCA on prescription sales and health care utilization. OBJECTIVE: We examined the association between DTCA intensity for statin medications and prescription sales and cholesterol-related health care utilization. DESIGN, SETTING, AND PARTICIPANTS: We conducted an ecological study for 75 designated market areas from 2005 to 2009 in the United States using linked data regarding televised DTCA volume, non-DTCA marketing and promotion, retail, mail order and long-term care prescription drug sales, prescription drug and ambulatory care health care utilization, and contextual factors such as health care density and socioeconomic status. Main outcomes and measures were volume of sales, number of dispensed prescriptions, and high cholesterol-related outpatient visits. Analyses were conducted in 2016. RESULTS: The intensity of rosuvastatin and atorvastatin ad exposures per household varied substantially across designated market areas. After adjustment for socioeconomic, demographic, and clinical characteristics, each 100-unit increase in advertisement viewership was associated with a 2.22% [95% confidence interval (CI), 0.30%-4.19%] increase in statin sales. Similar patterns were observed between DTCA and statin dispensing among the commercially insured. DTCA was associated with increases in high cholesterol-related outpatient visits among adults 18-45 years of age (3.15% increase in visits per 100-unit increase in viewership, 95% CI, 0.98%-5.37%) but not among those 46-65 years of age (0.51%, 95% CI, -1.49% to 2.55%). CONCLUSION: DTCA for statins is associated with increases in statin utilization and hyperlipidemia-related outpatient visits, especially for young adults.

Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype.

BACKGROUND: Fried and colleagues described a frailty phenotype measured in the Cardiovascular Health Study (CHS). This phenotype is manifest when ≥3 of the following are present: low grip strength, low energy, slowed waking speed, low physical activity, or unintentional weight loss. We sought to approximate frailty phenotype using only administrative claims data to enable frailty to be assessed without physical performance measures. STUDY DESIGN: We used the CHS cohort data linked to participants Medicare claims. The reference standard was the frailty phenotype measured at visits 5 and 9. With penalized logistic regression, we developed a parsimonious index for predicting the frailty phenotype using a linear combination of diagnoses, operationalized with claims data. We assessed the predictive validity of frailty index by examining how well it predicted common aging-related outcomes including hospitalization, disability, and death. RESULTS: There were 4454 CHS participants from 4 clinical sites. In total, 84% were white, 58% were women and their mean age was 72 years at enrollment. Approximately 11% of the cohort was frail. The model had an area under the receiver operating curve of 0.75 to concurrently predict a frailty phenotype. This Claims-based Frailty Indicator significantly predicted death (odds ratio, 1.84), time to death (hazards ratio, 1.71), number of hospital admissions (incidence rate ratio, 1.74), and nursing home admission (odds ratio, 1.47) in models adjusted for age and sex. CONCLUSIONS: Claims data alone can be used to classify individuals as frail and nonfrail. The Claims-based Frailty Indicator might be used in research with large datasets for confounding adjustment or risk prediction. The indicator might also be used for emergency preparedness for identification of regions enriched with frail individuals.

Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance.

BACKGROUND: Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models. OBJECTIVE: We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization. METHODS: We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders. RESULTS: The overall, primary 0-7, and 0-30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates. CONCLUSIONS: Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.

Identifying Consistent High-cost Users in a Health Plan: Comparison of Alternative Prediction Models.

BACKGROUND: High-cost users in a period may not incur high-cost utilization in the next period. Consistent high-cost users (CHUs) may be better targets for cost-saving interventions. OBJECTIVES: To compare the characteristics of CHUs (patients with plan-specific top 20% medical costs in all 4 half-year periods across 2008 and 2009) and point high-cost users (PHUs) (top users in 2008 alone), and to build claims-based models to identify CHUs. RESEARCH DESIGN: This is a retrospective cohort study. Logistic regression was used to predict being CHUs. Independent variables were derived from 2007 claims; 5 models with different sets of independent variables (prior costs, medications, diagnoses, medications and diagnoses, medications and diagnoses and prior costs) were constructed. SUBJECTS: Three-year continuous enrollees aged from 18 to 62 years old from a large administrative database with $100 or more yearly costs (N=1,721,992). MEASURES: Correlation, overlap, and characteristics of top risk scorers derived from 5 CHUs models were presented. C-statistics, sensitivity, and positive predictive value were calculated. RESULTS: CHUs were characterized by having increasing total and pharmacy costs over 2007-2009, and more baseline chronic and psychosocial conditions than PHUs. Individuals' risk scores derived from CHUs models were moderately correlated (∼0.6). The medication-only model performed better than the diagnosis-only model and the prior-cost model. CONCLUSIONS: Five models identified different individuals as potential CHUs. The recurrent medication utilization and a high prevalence of chronic and psychosocial conditions are important in differentiating CHUs from PHUs. For cost-saving interventions with long-term impacts or focusing on medication, CHUs may be better targets.

Patient perspectives of care and process and outcome quality measures for heart failure admissions in US hospitals: how are they related in the era of public reporting?

IMPORTANCE: Process quality measure performance has improved significantly with public reporting, requiring reevaluation of process-outcome relationships and the emerging role of patient perspectives on care. OBJECTIVE: To evaluate associations between heart failure patient perspectives of care and publicly reported processes and outcomes. DESIGN: Cross-sectional study, July 2008-June 2011. SETTING: US hospitals in the Press Ganey database. PARTICIPANTS: Heart failure inpatients. MEASURES: Outcomes were Hospital Compare hospital-level risk-adjusted 30-day heart failure mortality and readmissions. Predictors included Hospital Compare heart failure processes of care, a weighted process composite and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) domains for heart failure. Hospital characteristics included volume of heart failure patients and race, health status and education. RESULTS: Among 895 included hospitals, performance on process measures was high (median by hospital for composite, 95.6%); the median HCAHPS overall rating was 86.9. Median mortality was 11.3% and readmissions was 24.8%. No process measures were statistically significantly associated with lower mortality or readmissions in adjusted analyses. Higher ratings on HCAHPS patient perspectives of care were significantly correlated with lower readmissions in adjusted analyses, particularly those publicly reported domains conceptually related to readmissions. The magnitude was small (1.8 points higher on a 100-point scale between the highest and lowest quartiles of hospital readmissions). CONCLUSIONS: Publicly reported process quality measures were no longer associated with outcomes, but higher patient perspectives of care were associated with lower heart failure readmissions. These associations support continued reevaluation of these measures and increased emphasis on patient experience and outcomes, as planned for Value-Based Purchasing.