Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.

BACKGROUND: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. OBJECTIVE: We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. METHODS: We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. RESULTS: The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. CONCLUSIONS: Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Estimating The Impact Of Out-Of-Pocket Cost Changes On Abandonment Of HIV Pre-Exposure Prophylaxis.

Oral HIV pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV. Several different developments in the US either threaten to increase or promise to decrease PrEP out-of-pocket costs and access in the coming years. In a sample of 58,529 people with a new insurer-approved PrEP prescription, we estimated risk-adjusted percentages of patients who abandoned (did not fill) their initial prescription across six out-of-pocket cost categories. We then simulated the percentage of patients who would abandon PrEP under hypothetical changes to out-of-pocket costs, ranging from $0 to more than $500. PrEP abandonment rates of 5.5 percent at $0 rose to 42.6 percent at more than $500; even a small increase from $0 to $10 doubled the rate of abandonment. Conversely, abandonment rates that were 48.0 percent with out-of-pocket costs of more than $500 dropped to 7.3 percent when those costs were cut to $0. HIV diagnoses were two to three times higher among patients who abandoned PrEP prescriptions than among those who filled them. These results imply that recent legal challenges to the provision of PrEP with no cost sharing could substantially increase PrEP abandonment and HIV rates, upending progress on the HIV/AIDS epidemic.

Risk of Major Bleeding Associated with Concomitant Direct-Acting Oral Anticoagulant and Clopidogrel Use: A Retrospective Cohort Study.

BACKGROUND AND AIM: Combined anticoagulant-antiplatelet therapy is often indicated in adults with cardiovascular disease and atrial fibrillation or venous thromboembolism. The study aim was to assess the comparative risk of bleeding between rivaroxaban and apixaban when combined with clopidogrel. METHODS: We conducted a retrospective cohort study of commercially insured US adults newly treated with a combination of rivaroxaban+clopidogrel or apixaban+clopidogrel (2015-2018) using Merative™ Marketscan Research Databases. We used propensity score-based inverse probability of treatment weighting (IPTW) to balance the treatment groups. Weighted Cox proportional hazards regression was used to estimate the risk of major bleeding. RESULTS: The study cohort included 2895 rivaroxaban+clopidogrel users and 3628 apixaban+clopidogrel users. The median (range) duration of follow up was 61 (73) days. Rivaroxaban+clopidogrel users had a similar risk of major bleeding compared with apixaban+clopidogrel users (IPTW incidence rate per 100 person-years 7.96 vs 7.38; IPTW hazard ratio [HR] 1.13 [95% CI 0.78-1.63]). In the subcohort of adults who were treated with DOAC or clopidogrel monotherapy prior to the combined therapy, the risk of major bleeding did not differ by the drug of monotherapy (IPTW HR for rivaroxaban+clopidogrel group: 0.66 [95% CI 0.33-1.32]; IPTW HR for apixaban+clopidogrel group: 1.10 [95% CI 0.55-2.23]) CONCLUSIONS: In our study of commercially insured US adults, the concomitant use of rivaroxaban+clopidogrel and apixaban+clopidogrel conferred a similar risk of major bleeding. DOAC versus clopidogrel monotherapy prior to the concomitant therapy did not influence the risk of major bleeding.

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.

Assessing the Added Value of Vital Signs Extracted from Electronic Health Records in Healthcare Risk Adjustment Models.

Purpose: Patient vital signs are related to specific health risks and outcomes but are underutilized in the prediction of health-care utilization and cost. To measure the added value of electronic health record (EHR) extracted Body Mass Index (BMI) and blood pressure (BP) values in improving healthcare risk and utilization predictions. Patients and Methods: A sample of 12,820 adult outpatients from the Johns Hopkins Health System (JHHS) were identified between 2016 and 2017, having high data quality and recorded values for BMI and BP. We evaluated the added value of BMI and BP in predicting health-care utilization and cost through a retrospective cohort design. BMI, mean arterial pressure (MAP), systolic and diastolic BPs were summarized as annual aggregated values. Concurrent annual BMI and MAP changes were quantified as the difference between maximum and minimum recorded values. Model performance estimates consisted of repeated 10-fold cross validation, compared to base model point estimates for demographic and diagnostic, coded events: (1) patient age and sex, (2) age, sex, and the Charlson weighted index, (3) age, sex and the Johns Hopkins ACG system's DxPM risk score. Results: Both categorical BMI and BP were progressively indicative of disease comorbidity, but not uniformly related to health-care utilization or cost. Annual change in BMI and MAP improved predictions for most concurrent year outcomes when compared to base models. Conclusion: When a healthcare system lacks relevant diagnostic or risk assessment information for a patient, vital signs may be useful for a simple estimation of disease risk, cost and utilization.

Claims-based pharmacy markers for comprehensive medication management program case identification: Validation against concurrent and prospective healthcare costs and utilization.

BACKGROUND: Three claims-based pharmacy markers (complex, costly and risky medications) were developed to help automatically identify patients for comprehensive medication management. OBJECTIVE: To evaluate the association between newly-developed markers and healthcare outcomes. METHODS: This was a two-year retrospective cohort study using PharMetrics Plus patient-level administrative claims in 2014 and 2015. We included all claims from 1,541,873 individuals with: (1) 24-month medical and pharmacy enrollment in 2014 and 2015, (2) aged between 18 and 63 in 2014, and (3) known gender. Independent/control variables came from 2014 while outcomes came from 2014 (concurrent analysis) and 2015 (prospective analysis). Three pharmacy markers, separately or together, were added to four base models to predict concurrent and prospective healthcare costs (total, medical, and pharmacy) and utilization (having any hospitalization, having any emergency department visit, and having any readmission). We applied linear regression for costs while logistic regression for utilization. Measures of model performances and coefficients were derived from a 5-fold cross-validation repeated 20 times. RESULTS: Individuals with 1+ complex, risky or costly medication markers had higher comorbidity, healthcare costs and utilization than their counterparts. Nine binary risky category markers performed the best among the three types of risky medication markers; the Medication Complexity Score and three-level complex category both outperformed a simpler complex medication indicator. Adding three novel pharmacy markers separately or together into the base models provided the greatest improvement in explaining pharmacy costs, compared with medical (non-medication) costs. These pharmacy markers also added value in explaining healthcare utilization among the simple base models. CONCLUSIONS: Three claims-based pharmacy indicators had positive associations with healthcare outcomes and added value in predicting them. This initial study suggested that these novel markers can be used by pharmacy case management programs to help identify potential high-risk patients most likely to benefit from clinical pharmacist review and other interventions.

Treatment of Opioid Use Disorder With Buprenorphine Among US Adolescents and Young Adults During the Early COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic's impact on buprenorphine treatment for opioid use disorder among adolescents and young adults (AYAs) is unknown. METHODS: We used IQVIA Longitudinal Prescription Claims, including US AYAs aged 12-29 with at least 1 buprenorphine fill between January 2018 and August 2020, stratifying by age group and insurance. We compared buprenorphine prescriptions in March-August 2019 to March-August 2020. RESULTS: The monthly buprenorphine prescription rate increased 8.3% among AYAs aged 12-17 but decreased 7.5% among 18- to 24-year-olds and decreased 5.1% among 25- to 29-year-olds. In these age groups, Medicaid prescriptions did not significantly change, whereas commercial insurance prescriptions decreased 12.9% among 18- to 24-year-olds and 11.8% in 25- to 29-year-olds, and cash/other prescriptions decreased 18.7% among 18- to 24-year-olds and 19.9% in 25- to 29-year-olds (p < .001 for all). DISCUSSION: Buprenorphine prescriptions paid with commercial insurance or cash among young adults significantly decreased early in the pandemic, suggesting a possible unmet treatment need among this group.

Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology.

BACKGROUND: A small proportion of high-need patients persistently use the bulk of health care services and incur disproportionate costs. Population health management (PHM) programs often refer to these patients as persistent high utilizers (PHUs). Accurate PHU prediction enables PHM programs to better align scarce health care resources with high-need PHUs while generally improving outcomes. While prior research in PHU prediction has shown promise, traditional regression methods used in these studies have yielded limited accuracy. OBJECTIVE: We are seeking to improve PHU predictions with an ensemble approach in a retrospective observational study design using insurance claim records. METHODS: We defined a PHU as a patient with health care costs in the top 20% of all patients for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in next 24 months. Our study population included 165,595 patients in the Johns Hopkins Health Care plan, with 8359 (5.1%) patients identified as PHUs in 2014 and 2015. We assessed the performance of several standalone machine learning methods and then an ensemble approach combining multiple models. RESULTS: The candidate ensemble with complement naïve Bayes and random forest layers produced increased sensitivity and positive predictive value (PPV; 49.0% and 50.3%, respectively) compared to logistic regression (46.8% and 46.1%, respectively). CONCLUSIONS: Our results suggest that ensemble machine learning can improve prediction of care management needs. Improved PPV implies reduced incorrect referral of low-risk patients. With the improved sensitivity/PPV balance of this approach, resources may be directed more efficiently to patients needing them most.

Comparing and validating medication complexity from insurance claims against electronic health records.

BACKGROUND: Patient effort to comply with complex medication instructions is known to be related to nonadherence and subsequent medical complications or health care costs. A widely used Medication Regimen Complexity Index (MRCI) has been used with electronic health records (EHRs) to identify patients who could benefit from pharmacist intervention. A similar claims-derived measure may be better suited for clinical decision support, since claims offer a more complete view of patient care and health utilization. OBJECTIVE: To define and validate a novel insurance claims-based medication complexity score (MCS) patterned after the widely used MRCI, derived from EHRs. METHODS: Insurance claims and EHR data were provided by HealthPartners (N = 54,988) (Bloomington, Minnesota) and The Johns Hopkins Health System (N = 28,589) (Baltimore, Maryland) for years 2013 and 2017, respectively. Yearly measures of medication complexity were developed for each patient and evaluated with one another using rank correlation within different clinical subgroupings. Indicators for the presence of individually complex prescriptions were also developed and assessed using exact agreement. Complexity measures were then correlated with select covariates to further validate the concordance between MCS and MRCI with respect to clinical metrics. These included demographic, comorbidity, and health care utilization markers. Prescribed medications in each system's EHR were coded using the previously validated MRCI weighting rules. Insurance claims for retail pharmacy medications were coded using our novel MCS, which closely followed MRCI scoring rules. RESULTS: EHR-based MRCI and claims-based MCS were significantly correlated with one another for most clinical subgroupings. Likewise, both measures were correlated with several covariates, including count of active medications and chronic conditions. The MCS was, in most cases, more associated with key health covariates than was MRCI, although both were consistently significant. We found that the highest correlation between MCS and MRCI is obtained with patients who have similar counts of pharmacy records between EHRs and claims (HealthPartners: P = 0.796; Johns Hopkins Health System: P = 0.779). CONCLUSIONS: The findings suggest good correspondence between MCS and MRCI and that claims data represent a useful resource for assessing medication complexity. Claims data also have major practical advantages, such as interoperability across health care systems, although they lack the detailed clinical context of EHRs. DISCLOSURES: The Johns Hopkins University holds the copyright to the Adjusted Clinical Groups (ACG) system and receives royalties from the global distribution of the ACG system. This revenue supports a portion of the authors' salary. No additional or external funding supported this work. The authors have no conflict of interest to disclose.

Assessing the Added Value of Blood Pressure Information Derived from Electronic Health Records in Predicting Health Care Cost and Utilization.

Health care providers are increasingly using clinical measures derived from electronic health records (EHRs) for risk stratification and predictive modeling. EHR-specific data elements such as prescriptions, laboratory results, and vital signs have been shown to improve risk prediction models. In this study, the value of EHR-based blood pressure (BP) values was assessed in predicting health care costs (ie, total, medical, and pharmacy) and key utilization end points (ie, hospitalization, emergency department use, and being among the highest utilizers). The study population included 37,451 patients of a large integrated delivery system in the mid-western United States with complete EHR data files, who were 18-64 years old, had continuous insurance at an affiliated health plan, and had eligible BP records. Both EHRs and insurance claims of the study population were used to extract the predictors (ie, demographics, diagnosis, and BP values) and outcomes (ie, costs and utilizations). Predictors were extracted from 2012 data, whereas concurrent and prospective outcomes were extracted from 2012 to 2013 data. Three base models (BMs) were constructed to predict each of the outcomes. The first BM no. 1 used demographics. The second BM no. 2 added the Charlson comorbidity index to BM no. 1, whereas the third BM no. 3 added the Adjusted Clinical Group Dx-PM case-mix score to BM no. 1. BP was specified as means, ranges, and classes. Adding BP ranges to BM no. 1 and BM no. 2 showed the greatest improvements when predicting costs and utilization. More specifically, adjusted R2 and area under the curve of BM no. 2 improved by 32.9% and 14.1% when BP ranges were added to predict concurrent total cost and hospitalization, respectively. The effect of BP measures on improving the risk stratification models was diminished when predicting prospective outcomes after adding the measures to BM no. 3 (ie, the more comprehensive diagnostic model), specifically when represented as BP means. Given the increasing availability of BP information, this research suggests that these data should be integrated into provider-based population health analytic activities. Future research should focus on subpopulations that benefit the most from incorporating vital signs such as BP measures in risk stratification models.

Novel population-level proxy measures for suboptimal HIV preexposure prophylaxis initiation and persistence in the USA.

OBJECTIVE: In the United States (USA), HIV preexposure prophylaxis (PrEP) use is suboptimal. Population-level metrics on PrEP use are limited and focus on prescriptions issued rather than how much prescriptions are picked up. We introduce PrEP reversals, defined as when patients fail to pick up PrEP prescriptions at the pharmacy point-of-sale, as a proxy for PrEP initiation and persistence. DESIGN: We analysed PrEP pharmacy claims and HIV diagnoses from a Symphony Health Solutions dataset across all US states from 1 October 2015 to 30 September 2019. METHODS: We calculated the percentage of individuals who were newly prescribed PrEP and who reversed (i.e. patient did not pick up an insurance-approved prescription and pharmacy withdrew the claim), delayed (reversed and then picked up within 90 days), very delayed (reversed and then picked up between 90 and 365 days) or abandoned (not picked up within 365 days), and subsequent HIV diagnosis within 365 days. RESULTS: Of 59 219 individuals newly prescribed PrEP, 19% reversed their index prescription. Among those, 21% delayed initiation and 8% had very delayed initiation. Seventy-one percent of patients who reversed their initial prescription abandoned it, 6% of whom were diagnosed with HIV---three times higher than those who persisted on PrEP. CONCLUSION: Nearly one in five patients newlyprescribed PrEP reversed initial prescriptions, leading to delayed medication access, being lost to PrEP care, and dramatically higher HIV risk. Reversals could be used for real-time nationwide PrEP population-based initiation and persistence tracking, and for identifying patients that might otherwise be lost to care.

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data.

BACKGROUND: A high proportion of health care services are persistently utilized by a small subpopulation of patients. To improve clinical outcomes while reducing costs and utilization, population health management programs often provide targeted interventions to patients who may become persistent high users/utilizers (PHUs). Enhanced prediction and management of PHUs can improve health care system efficiencies and improve the overall quality of patient care. OBJECTIVE: The aim of this study was to detect key classes of diseases and medications among the study population and to assess the predictive value of these classes in identifying PHUs. METHODS: This study was a retrospective analysis of insurance claims data of patients from the Johns Hopkins Health Care system. We defined a PHU as a patient incurring health care costs in the top 20% of all patients' costs for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in 2014-2015. We applied latent class analysis (LCA), an unsupervised clustering approach, to identify patient subgroups with similar diagnostic and medication patterns to differentiate variations in health care utilization across PHUs. Logistic regression models were then built to predict PHUs in the full population and in select subpopulations. Predictors included LCA membership probabilities, demographic covariates, and health utilization covariates. Predictive powers of the regression models were assessed and compared using standard metrics. RESULTS: We identified 164,221 patients with continuous enrollment between 2013 and 2015. The mean study population age was 19.7 years, 55.9% were women, 3.3% had ≥1 hospitalization, and 19.1% had 10+ outpatient visits in 2013. A total of 8359 (5.09%) patients were identified as PHUs in both 2014 and 2015. The LCA performed optimally when assigning patients to four probability disease/medication classes. Given the feedback provided by clinical experts, we further divided the population into four diagnostic groups for sensitivity analysis: acute upper respiratory infection (URI) (n=53,232; 4.6% PHUs), mental health (n=34,456; 12.8% PHUs), otitis media (n=24,992; 4.5% PHUs), and musculoskeletal (n=24,799; 15.5% PHUs). For the regression models predicting PHUs in the full population, the F1-score classification metric was lower using a parsimonious model that included LCA categories (F1=38.62%) compared to that of a complex risk stratification model with a full set of predictors (F1=48.20%). However, the LCA-enabled simple models were comparable to the complex model when predicting PHUs in the mental health and musculoskeletal subpopulations (F1-scores of 48.69% and 48.15%, respectively). F1-scores were lower than that of the complex model when the LCA-enabled models were limited to the otitis media and acute URI subpopulations (45.77% and 43.05%, respectively). CONCLUSIONS: Our study illustrates the value of LCA in identifying subgroups of patients with similar patterns of diagnoses and medications. Our results show that LCA-derived classes can simplify predictive models of PHUs without compromising predictive accuracy. Future studies should investigate the value of LCA-derived classes for predicting PHUs in other health care settings.

Assessing the association between area deprivation index on COVID-19 prevalence: a contrast between rural and urban U.S. jurisdictions.

BACKGROUND: The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S. OBJECTIVES: To understand the spread and impact of COVID-19 across the U.S., county level data for confirmed cases of COVID-19 were examined by Area Deprivation Index (ADI) and Metropolitan vs. Nonmetropolitan designations from the National Center for Health Statistics (NCHS). These designations were the basis for making comparisons between Urban and Rural jurisdictions. METHOD: Kendall's Tau-B was used to compare effect sizes between jurisdictions on select ADI composites and well researched social determinants of health (SDH). Spearman coefficients and stratified Poisson modeling was used to explore the association between ADI and COVID-19 prevalence in the context of county designation. RESULTS: Results show that the relationship between area deprivation and COVID-19 prevalence was positive and higher for rural counties, when compared to urban ones. Family income, property value and educational attainment were among the ADI component measures most correlated with prevalence, but this too differed between county type. CONCLUSIONS: Though most Americans live in Metropolitan Areas, rural communities were found to be associated with a stronger relationship between deprivation and COVID-19 prevalence. Models predicting COVID-19 prevalence by ADI and county type reinforced this observation and may inform health policy decisions.

Development of measurable criteria to identify and prioritize patients for inclusion in comprehensive medication management programs within primary care settings.

BACKGROUND: Pharmacists optimize medication use and ensure the safe and effective delivery of pharmacotherapy to patients using comprehensive medication management (CMM). Identifying and prioritizing individual patients who will most likely benefit from CMM can be challenging. Health systems have far more candidates for CMM than there are clinical pharmacists to provide this service. Furthermore, current evidence lacks widely accepted standards or automated mechanisms for identifying patients who would likely benefit from a pharmacist consultation. Existing tools to prioritize patients for pharmacist review often require manual chart review by a pharmacist or other clinicians or data collection by patient survey. OBJECTIVES: To (1) create new medication risk markers for identifying and prioritizing patients within a population and (2) identify patients who met these new markers, assess their clinical characteristics, and compare them with criteria that are widely used for medication therapy management (MTM). METHODS: Along with published literature, a panel of subject matter experts informed the development of 3 medication risk markers. To assess the prevalence of markers developed, we used Multum, a medication database, for medication-level characteristics, and for patient-level characteristics, we used QuintilesIMS, an administrative claims database derived from health plans across the United States, with data for 1,541,873 eligible individuals from 2014-2015. We compared the health care costs, utilization, and medication gap among patients identified through MTM criteria (both broad and narrow, as these are provided as ranges) and our new medication management score markers. RESULTS: We developed 3 claims-derivable markers: (1) instances when a patient filled a medication with high complexity that could affect adherence, (2) instances where a patient filled a medication defined as costly within a therapeutic category that could affect access, and (3) instances when a patient filled a medication defined as risky that could increase incidence of adverse drug events. In the QuintilesIMS database, individuals with 2 new medication risk markers plus at least 3 conditions and more than $3,017 in medication costs when compared with individuals meeting narrow MTM eligibility criteria (≥ 8 medications, ≥ 3 conditions, and > $3,017 medication costs) had increased costs ($36,000 vs $26,100 total; $24,800 vs 21,400 medical; $11,300 vs $4,800 pharmacy); acute care utilization (0.328 vs 0.256 inpatient admissions and 0.627 vs 0.579 emergency department visits); and 1 or more gaps in medication adherence(41.5% vs 34.7%). CONCLUSIONS: We identified novel markers of medication use risk that can be determined using insurance claims and can be useful to identify patients for CMM programs and prioritize patients who would benefit from clinical pharmacist intervention. These markers were associated with higher costs, acute care utilization, and gaps in medication use compared with the overall population and within certain subgroups. Providing CMM to these patients may improve health system performance in relevant quality measures. Evaluation of CMM services delivered by a pharmacist using these markers requires further investigation. DISCLOSURES: No outside funding supported this study. All authors are Johns Hopkins employees. The Johns Hopkins University receives royalties for nonacademic use of software based on the Johns Hopkins Adjusted Clinical Group (ACG) methodology. Chang, Kitchen, Weiner, and Kharrazi receive a portion of their salary support from this revenue. The authors have no conflicts of interests relevant to this study.

Dipeptidyl peptidase-4 inhibitor cardiovascular safety in patients with type 2 diabetes, with cardiovascular and renal disease: a retrospective cohort study.

Clinical trials investigating cardiovascular safety of dipeptidyl peptidase-IV inhibitors (DPP-4i) among patients with cardiovascular and renal disease rarely recruit patients with renal impairment, despite associations with increased risk for major adverse cardiovascular events (MACE). We investigated the risk of MACE associated with the use of DPP-4i among these high-risk patients. Using a new-user, retrospective, cohort design, we analyzed 2010-2015 IBM MarketScan Commercial Claims and Encounters for patients with diabetes, comorbid with cardiovascular disease and/or renal impairment. We compared time to first MACE for DPP-4i versus sulfonylurea and versus metformin. Of 113,296 individuals, 9146 (8.07%) were new DPP-4i users, 17,481 (15.43%) were new sulfonylurea users, and 88,596 (78.20%) were new metformin users. Exposure groups were not mutually exclusive. DPP-4i was associated with lower risk for MACE than sulfonylurea (aHR 0.84; 95% CI 0.74, 0.93) and similar risk for MACE to metformin (aHR 1.07; 95% CI [1.04, 1.16]). DPP-4i use was associated with lower risk for MACE compared to sulfonylureas and similar risk for MACE compared to metformin. This association was most evident in the first year of therapy, suggesting that DPP-4i is a safer choice than sulfonylurea for diabetes treatment initiation in high-risk patients.

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.

Association of antidiabetic therapies with lower extremity amputation, mortality and healthcare cost from a nationwide retrospective cohort study in Taiwan.

We compared risks of clinical outcomes, mortality and healthcare costs among new users of different classes of anti-diabetic medications. This is a population-based, retrospective, new-user design cohort study using the Taiwan National Health Insurance Database between May 2, 2015 and September 30, 2017. An individual was assigned to a medication group based on the first anti-diabetic prescription on or after May 1, 2016: SGLT-2 inhibitors, DPP-4 inhibitors, GLP-1 agonists or older agents (metformin, etc.). Clinical outcomes included lower extremity amputation, peripheral vascular disease, critical limb ischemia, osteomyelitis, and ulcer. We built three Cox proportional hazards models for clinical outcomes and mortality, and three regression models with a log-link function and gamma distribution for healthcare costs, all with propensity-score weighting and covariates. We identified 1,222,436 eligible individuals. After adjustment, new users of SGLT-2 inhibitors were associated with 73% lower mortality compared to those of DPP-4 inhibitors or users of older agents, while 36% lower total costs against those of GLP-1 agonists. However, there was no statistically significant difference in the risk of lower extremity amputation across medication groups. Our study suggested that SGLT-2 inhibitors is associated with lower mortality compared to DPP 4 inhibitors and lower costs compared to GLP-1 agonists.

Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models.

Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 ± 17.8, .624 ± .310, and .810 ± .270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.

Impact of Area Deprivation Index on the Performance of Claims-Based Risk-Adjustment Models in Predicting Health Care Costs and Utilization.

Traditionally, risk-adjustment models do not address the characteristics of minority populations, such as race or socioeconomic status. This study aimed to evaluate the added value of place-based social determinants on risk-adjustment models in explaining health care costs and utilization. Statewide commercial claims from the Maryland Medical Care Database were used, including 1,150,984 Maryland residents aged 18 to 63 with ≥6 months enrollment in 2013 and 2014. Area Deprivation Index (ADI) was assigned to individuals through zip code. The authors examined the addition of ADI to predictive models of concurrent and prospective costs and utilization; linear regression was adopted for costs and logistic regression for utilization markers. Performance measures included R2 for costs (total, pharmacy, and medical costs) and the area under the curve (AUC) for utilization (being top 5% top users, having any hospitalization, having any emergency room [ER] visit, having any avoidable ER visit, and having any readmission). All performance measures were derived from the bootstrapping analysis with 200 iterations. Study subjects were ∼48% male with a mean age of ∼41 years. Adding ADI to the demographics or claims-based models generally did not improve performance except in predicting the probability of having any ER or any avoidable ER visit; for example, AUC of avoidable ER visits increased significantly from .610 to .613 when using ADI rank deciles in claims-based models. Future research should focus on patients with a higher need for social services, assess more granular place-based determinants (eg, Census block group), and evaluate the added value of individual social variables.