Association of Area-Level Socioeconomic Deprivation With Hypoglycemic and Hyperglycemic Crises in US Adults With Diabetes.

Importance: Social determinants of health play a role in diabetes management and outcomes, including potentially life-threatening complications of severe hypoglycemia and diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar state (HHS). Although several person-level socioeconomic factors have been associated with these complications, the implications of area-level socioeconomic deprivation are unknown. Objective: To examine the association between area-level deprivation and the risks of experiencing emergency department visits or hospitalizations for hypoglycemic and hyperglycemic crises (ie, DKA or HHS). Design, Setting, and Participants: This cohort study used deidentified administrative claims data for privately insured individuals and Medicare Advantage beneficiaries across the US. The analysis included adults with diabetes who met the claims criteria for diabetes between January 1, 2016, and December 31, 2017. Data analyses were performed from November 17, 2020, to November 11, 2021. Exposures: Area deprivation index (ADI) was derived for each county for 2016 and 2017 using 17 county-level indicators from the American Community Survey. ADI values were applied to patients who were living in each county based on their index dates and were categorized according to county-level ADI quintile (with quintile 1 having the least deprivation and quintile 5 having the most deprivation). Main Outcomes and Measures: The numbers of emergency department visits or hospitalizations related to the primary diagnoses of hypoglycemia and DKA or HHS (ascertained using validated diagnosis codes in the first or primary position of emergency department or hospital claims) between 2016 and 2019 were calculated for each ADI quintile using negative binomial regression models and adjusted for patient age, sex, health plan type, comorbidities, glucose-lowering medication type, and percentage of White residents in the county. Results: The study population included 1 116 361 individuals (563 943 women [50.5%]), with a mean (SD) age of 64.9 (13.2) years. Of these patients, 343 726 (30.8%) resided in counties with the least deprivation (quintile 1) and 121 810 (10.9%) lived in counties with the most deprivation (quintile 5). Adjusted rates of severe hypoglycemia increased from 13.54 (95% CI, 12.91-14.17) per 1000 person-years in quintile 1 counties to 19.13 (95% CI, 17.62-20.63) per 1000 person-years in quintile 5 counties, corresponding to an incidence rate ratio of 1.41 (95% CI, 1.29-1.54; P < .001). Adjusted rates of DKA or HHS increased from 7.49 (95% CI, 6.96-8.02) per 1000 person-years in quintile 1 counties to 8.37 (95% CI, 7.50-9.23) per 1000 person-years in quintile 5 counties, corresponding to an incidence rate ratio of 1.12 (95% CI, 1.00-1.25; P = .049). Conclusions and Relevance: This study found that living in counties with a high area-level deprivation was associated with an increased risk of severe hypoglycemia and DKA or HHS. The concentration of these preventable events in areas of high deprivation signals the need for interventions that target the structural barriers to optimal diabetes management and health.

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices.

Importance: Diabetes management operates under a complex interrelationship between behavioral, social, and economic factors that affect a patient's ability to self-manage and access care. Objective: To examine the association between 2 complementary area-based metrics, area deprivation index (ADI) score and rurality, and optimal diabetes care. Design, Setting, and Participants: This cross-sectional study analyzed the electronic health records of patients who were receiving care at any of the 75 Mayo Clinic or Mayo Clinic Health System primary care practices in Minnesota, Iowa, and Wisconsin in 2019. Participants were adults with diabetes aged 18 to 75 years. All data were abstracted and analyzed between June 1 and November 30, 2020. Main Outcomes and Measures: The primary outcome was the attainment of all 5 components of the D5 metric of optimal diabetes care: glycemic control (hemoglobin A1c <8.0%), blood pressure (BP) control (systolic BP <140 mm Hg and diastolic BP <90 mm Hg), lipid control (use of statin therapy according to recommended guidelines), aspirin use (for patients with ischemic vascular disease), and no tobacco use. The proportion of patients receiving optimal diabetes care was calculated as a function of block group-level ADI score (a composite measure of 17 US Census indicators) and zip code-level rurality (calculated using Rural-Urban Commuting Area codes). Odds of achieving the D5 metric and its components were assessed using logistic regression that was adjusted for demographic characteristics, coronary artery disease history, and primary care team specialty. Results: Among the 31 934 patients included in the study (mean [SD] age, 59 [11.7] years; 17 645 men [55.3%]), 13 138 (41.1%) achieved the D5 metric of optimal diabetes care. Overall, 4090 patients (12.8%) resided in the least deprived quintile (quintile 1) of block groups and 1614 (5.1%) lived in the most deprived quintile (quintile 5), while 9193 patients (28.8%) lived in rural areas and 2299 (7.2%) in highly rural areas. The odds of meeting the D5 metric were lower for individuals residing in quintile 5 vs quintile 1 block groups (odds ratio [OR], 0.72; 95% CI, 0.67-0.78). Patients residing in rural (OR, 0.84; 95% CI, 0.73-0.97) and highly rural (OR, 0.81; 95% CI, 0.72-0.91) zip codes were also less likely to attain the D5 metric compared with those in urban areas. Conclusions and Relevance: This cross-sectional study found that patients living in more deprived and rural areas were significantly less likely to attain high-quality diabetes care compared with those living in less deprived and urban areas. The results call for geographically targeted population health management efforts by health systems, public health agencies, and payers.

Adherence to Asthma Biologics: Implications for Patient Selection, Step Therapy, and Outcomes.

BACKGROUND: Little is known about adherence to asthma biologics. RESEARCH QUESTION: Is adherence to inhaled corticosteroid (ICS) associated with subsequent asthma biologic adherence? STUDY DESIGN AND METHODS: We analyzed individuals with asthma who started asthma biologics in the OptumLab Data Warehouse and used that data until October 2019. We calculated proportion days covered (PDC) for ICS ± long-acting ß-agonists in the 6 months before and after asthma biologics were started and asthma biologic PDC for the first 6 months of use. We performed a multivariable analysis to identify factors associated with asthma biologic PDC ≥0.75, ICS PDC ≥0.75 during the 6-month period after asthma biologic were started, and achievement of a ≥50% reduction in asthma exacerbations during the first 6 months of asthma biologic use. RESULTS: We identified 5,319 people who started asthma biologics. The mean PDC for asthma biologics was 0.76 (95% CI, 0.75-0.77) in the first 6 months after starting, higher than the mean PDCs for ICS in the 6 months before (0.44 [95% CI, 0.43-0.45]) and after (0.40 [95% CI, 0.39-0.40]) starting the asthma biologic. PDC ≥0.75 for ICS 6 months before index biologic use is associated with PDC for asthma biologics ≥0.75 (OR, 1.25; 95% CI, 1.10-1.43) and for ICS during the first 6 months of biologic use (OR, 9.93; 95% CI, 8.55-11.53). Neither ICS PDC ≥0.75 (OR, 0.92; 95% CI, 0.74-1.14) nor asthma biologic PDC ≥0.75 (OR, 1.15; 95% CI, 0.97-1.36) is associated with a statistically significant reduction in asthma exacerbations during the first 6 months of asthma biologic use among people with any exacerbation in the 6 months before first use. INTERPRETATION: Adherence to asthma biologic is higher than to ICS and is associated with different factors.

Place, poverty and prescriptions: a cross-sectional study using Area Deprivation Index to assess opioid use and drug-poisoning mortality in the USA from 2012 to 2017.

OBJECTIVE: To identify the relationships between county-level area deprivation and patterns of both opioid prescriptions and drug-poisoning mortality. DESIGN, SETTING AND PARTICIPANTS: For this retrospective cross-sectional study, we used the IQVIA Xponent data to capture opioid prescriptions and Centres for Disease Control and Prevention National Vital Statistics System to assess drug-poisoning mortality. The Area Deprivation Index (ADI) is a composite measure of social determinants of health comprised of 17 US census indicators, spanning four socioeconomic domains. For all US counties with available opioid prescription (2712 counties) and drug-poisoning mortality (3133 counties) data between 2012 and 2017, we used negative binomial regression to examine the association between quintiles of county-level ADI and the rates of opioid prescriptions and drug-poisoning mortality adjusted for year, age, race and sex. PRIMARY OUTCOME MEASURES: County-level opioid prescription fills and drug-poisoning mortality. RESULTS: Between 2012 and 2017, overall rates of opioid prescriptions decreased from 96.6 to 72.2 per 100 people, while the rates of drug-poisoning mortality increased from 14.3 to 22.8 per 100 000 people. Opioid prescription and drug-poisoning mortality rates were consistently higher with greater levels of deprivation. The risk of filling an opioid prescription was 72% higher, and the risk of drug-poisoning mortality was 36% higher, for most deprived compared with the least deprived counties (both p<0.001). DISCUSSION: Counties with greater area-level deprivation have higher rates of filled opioid prescriptions and drug-poisoning mortality. Although opioid prescription rates declined across all ADI quintiles, the rates of drug-poisoning mortality continued to rise proportionately in each ADI quintile. This underscores the need for individualised and targeted interventions that consider the deprivation of communities where people live.

Trends and Disparities in Asthma Biologic Use in the United States.

BACKGROUND: From 2003 to 2015, only 1 biologic was approved for the treatment of moderate to severe asthma in the United States. Since 2015, 4 new asthma biologics were approved by the US Food and Drug Administration. OBJECTIVE: To describe trends and disparities of asthma biologic use in the United States from 2003 to 2018. METHODS: We conducted a retrospective analysis using a cohort developed from the OptumLabs Data Warehouse. Prevalent and incident asthma biologic users were identified, and characteristics of users and nonusers were analyzed using regression analysis. Clinician prescribing behavior was described. RESULTS: Use of biologic medications remains uncommon among individuals with asthma, with prevalence peaking in 2006 at 3 in 1000 individuals with asthma. Several factors are associated with a higher likelihood of asthma biologic use: middle age, higher income, commercial insurance, and access to a specialist. Most clinicians (65%) in the cohort prescribed only 1 biologic. CONCLUSIONS: We report low overall use of asthma biologics and evidence of disparities in access to asthma biologics.

Risk of Gastrointestinal Bleeding Increases With Combinations of Antithrombotic Agents and Patient Age.

BACKGROUND & AIMS: The safety of different antithrombotic strategies for patients with 1 or more indication for antithrombotic drugs has not been determined. We investigated the risk and time frame for gastrointestinal bleeding (GIB) in patients prescribed different antithrombotic regimens. We proposed that risk would increase over time and with combination regimens, especially among elderly patients. METHODS: We performed a retrospective analysis of nationwide claims data from privately insured and Medicare Advantage enrollees who received anticoagulant and/or antiplatelet agents from October 1, 2010, through May 31, 2017. Patients were stratified by their prescriptions (anticoagulant alone, antiplatelet alone, or a combination) and by their primary diagnosis (atrial fibrillation, ischemic heart disease, or venous thromboembolism). The 1-year GIB risk was estimated using parametric time-to-event survival models and expressed as annualized risk and number needed to harm (NNH). RESULTS: Our final analysis included 311,211 patients (mean ages, 67 years for monotherapy and 69.8 years for combination antithrombotic therapy). There was no significant difference in the proportion of patients with bleeding after anticoagulant or antiplatelet monotherapy (∼3.5%/year). Combination antithrombotic therapy increased GIB risk compared with anticoagulant (NNH, 29) or antiplatelet (NNH, 31) monotherapy, regardless of the patients' diagnosis or time point analyzed. Advancing age was associated with increasing 1-year probability of GIB. Patients prescribed combination therapy were at the greatest risk for GIB, especially after the age of 75 years (GIB occurred in 10%-17.5% of patients/y). CONCLUSIONS: In an analysis of nationwide insurance and Medicare claims data, we found GIB to occur in a higher proportion of patients prescribed combinations of anticoagulant and antiplatelet agents compared with monotherapy. Among all drug exposure categories and cardiovascular conditions, the risk of GIB increased with age, especially among patients older than 75 years.

What is the effect of a decision aid in potentially vulnerable parents? Insights from the head CT choice randomized trial.

OBJECTIVE: To test the hypotheses that use of the Head CT Choice decision aid would be similarly effective in all parent/patient dyads but parents with high (vs low) numeracy experience a greater increase in knowledge while those with low (vs high) health literacy experience a greater increase in trust. METHODS: This was a secondary analysis of a cluster randomized trial conducted at seven sites. One hundred seventy-two clinicians caring for 971 children at intermediate risk for clinically important traumatic brain injuries were randomized to shared decision making facilitated by the DA (n = 493) or to usual care (n = 478). We assessed for subgroup effects based on patient and parent characteristics, including socioeconomic status (health literacy, numeracy and income). We tested for interactions using regression models with indicators for arm assignment and study site. RESULTS: The decision aid did not increase knowledge more in parents with high numeracy (P for interaction [Pint ] = 0.14) or physician trust more in parents with low health literacy (Pint  = 0.34). The decision aid decreased decisional conflict more in non-white parents (decisional conflict scale, -8.14, 95% CI: -12.33 to -3.95; Pint  = 0.05) and increased physician trust more in socioeconomically disadvantaged parents (trust in physician scale, OR: 8.59, 95% CI: 2.35-14.83; Pint  = 0.04). CONCLUSIONS: Use of the Head CT Choice decision aid resulted in less decisional conflict in non-white parents and greater physician trust in socioeconomically disadvantaged parents. Decision aids may be particularly effective in potentially vulnerable parents.

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. OBJECTIVES: To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. DESIGN: The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize >100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. SUMMARY: This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms.

Identifying high-cost episodes in lower extremity joint replacement.

OBJECTIVES: To evaluate the ability of claims-based risk adjustment and incremental components of clinical data to identify 90-day episode costs among lower extremity joint replacement (LEJR) patients according to the Centers for Medicare & Medicaid Services (CMS) Comprehensive Care for Joint Replacement (CJR) program provisions. DATA SOURCES: Medicare fee-for-service (FFS) data for qualifying CJR episodes in the United States, and FFS data linked with clinical data from CJR-qualifying LEJR episodes performed at High Value Healthcare Collaborative (HVHC) and Mayo Clinic in 2013. HVHC and Mayo Clinic populations are subsets of the total FFS population to assess the additive value of additional pieces of clinical data in correctly assigning patients to cost groups. STUDY DESIGN: Multivariable logistic models identified high-cost episodes. DATA COLLECTION/EXTRACTION METHODS: Clinical data from participating health care systems merged with Medicare FFS data. PRINCIPAL FINDINGS: Our three populations consisted of 363 621 patients in the CMS population, 4881 in the HVHC population, and 918 in the Mayo population. When modeling per CJR specifications, we observed low to moderate model performance (CMS C-Stat = 0.714; HVHC C-Stat = 0.628; Mayo C-Stat = 0.587). Adding CMS-HCC categories improved identification of patients in the top 20% of episode costs (CMS C-Stat = 0.758, HVHC C-Stat = 0.692, Mayo C-Stat = 0.677). Clinical variables, particularly functional status in the population for which this was available (Mayo C-Stat = 0.783), improved ability to identify patients within cost groups. CONCLUSIONS: Policy makers could use these findings to improve payment adjustments for bundled LEJR procedures and in consideration of new data elements for reimbursement.

Impact of a Shared Decision Making Intervention on Health Care Utilization: A Secondary Analysis of the Chest Pain Choice Multicenter Randomized Trial.

BACKGROUND: Patients at low risk for acute coronary syndrome are frequently admitted for observation and cardiac testing, resulting in substantial burden and cost to the patient and the health care system. OBJECTIVES: The purpose of this investigation was to measure the effect of the Chest Pain Choice (CPC) decision aid on overall health care utilization as well as utilization of specific services both during the index emergency department (ED) visit and in the subsequent 45 days. METHODS: This was a planned secondary analysis of data from a pragmatic multicenter randomized trial of shared decision making in adults presenting to the ED with chest pain who were being considered for observation unit admission for cardiac stress testing or coronary computed tomography angiography. The trial compared an intervention group engaged in shared decision making facilitated by the CPC decision aid to a control group receiving usual care. Hospital-level billing data were used to measure utilization for the index ED visit and during the following 45 days. Patients in both groups also were asked to keep a diary recording health care utilization over the same 45-day period. Outcomes assessed included length of time in the ED and observation, ED visits, office visits, hospitalizations, testing, imaging, and procedures. RESULTS: Of the 898 patients included in the original trial, we were able to contact 834 (92.9%) patients for 45-day health care diary review. There was no difference in patient-reported health care utilization between the study arms. Hospital-level billing data were obtained for all 898 (100%) patients. During the initial ED visit the length of stay (LOS) was similar, and there was no difference in the frequency of observation unit admission between study arms. However, the mean observation unit LOS was 95 minutes (95% confidence interval [CI] = 40.8-149.8) shorter in the CPC arm and the mean number of tests was lower in the CPC arm (decrease in 19.4 imaging studies per 100 patients, 95% CI = 15.5-23.3). When evaluating the entire encounter and follow-up period, the intervention arm underwent fewer tests (decrease in 125.6 tests per 100 patients, 95% CI = 29.3-221.6). More specifically, there were fewer advanced cardiac imaging tests completed (25.8 fewer per 100 patients, 95% CI = 3.74-47.9) in the intervention arm. CONCLUSIONS: Shared decision making in low-risk chest pain can lead to decreased diagnostic testing without worsening outcomes measured over 45 days.

Impact of sociodemographic patient characteristics on the efficacy of decision AIDS: a patient-level meta-analysis of 7 randomized trials.

BACKGROUND: Decision aids (DAs) increase patient knowledge, reduce decisional conflict, and promote shared decision making (SDM). The extent to which they do so across diverse sociodemographic patient groups is unknown. METHODS AND RESULTS: We conducted a patient-level meta-analysis of 7 randomized trials of DA versus usual care comprising 771 encounters between patients and clinicians discussing treatment options for chest pain, myocardial infarction, diabetes mellitus, and osteoporosis. Using a random effects model, we examined the impact of sociodemographic patient characteristics (age, sex, education, income, and insurance status) on the outcomes of knowledge transfer, decisional conflict, and patient involvement in SDM. Because of small numbers of people of color in the study population, we were not powered to investigate the role of race. Most patients were aged ≥65 years (61%), white (94%), and women (59%); two thirds had greater than a high school education. Compared with usual care, DA patients gained knowledge, were more likely to know their risk, and had less decisional conflict along with greater involvement in SDM. These gains were largely consistent across sociodemographic patient groups, with DAs demonstrating similar efficacy when used with vulnerable patients such as the elderly and those with less income and less formal education. Differences in efficacy were found only in knowledge of risk in 1 subgroup, with greater efficacy among those with higher education (35% versus 18%; P=0.02). CONCLUSIONS: In this patient-level meta-analysis of 7 randomized trials, DAs were efficacious across diverse sociodemographic groups as measured by knowledge transfer, decisional conflict, and patient involvement in SDM. To the extent that DAs increase patient knowledge and participation in SDM, they have potential to impact health disparities related to these factors.