Medicare Part D Coverage of Anti-obesity Medications: a Call for Forward-Looking Policy Reform.

When the Medicare Part D benefit was constructed, drugs for weight loss were explicitly excluded from coverage, as the limited effectiveness and unfavorable safety profile of medications available at the time failed to justify coverage of drugs perceived to be used for cosmetic purposes. In recent years, drugs activating the glucagon-like peptide-1 receptor (GLP-1R) pathway have proved to achieve significant reductions in body weight with a favorable safety profile. The effectiveness of GLP-1R agonists in reducing weight and improving the metabolic profile warrants the reconsideration of the historical exclusion of weight loss drugs from Part D coverage. In this perspective, we outline policy options to enable Part D coverage of GLP-1R agonists. These include legislative change through the passage of the Treat and Reduce Obesity Act and evaluation of coverage policies under the waiver authority of the Center for Medicare and Medicaid Innovation.

5-Year simulation of diabetes-related complications in people treated with tirzepatide or semaglutide versus insulin glargine.

AIM: This study compared the 5-year incidence rate of macrovascular and microvascular complications for tirzepatide, semaglutide and insulin glargine in individuals with type 2 diabetes, using the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes simulation model. RESEARCH DESIGN AND METHODS: This study was a 5-year SURPASS-2 trial extrapolation, with an insulin glargine arm added as an additional comparator. The 1-year treatment effects of tirzepatide (5, 10 or 15 mg), semaglutide (1 mg) and insulin glargine on glycated haemoglobin, systolic blood pressure, low-density lipoprotein and body weights were obtained from the SUSTAIN-4 and SURPASS-2 trials. We used the BRAVO model to predict 5-year complications for each study arm under two scenarios: the 1-year treatment effects persisted (optimistic) or diminished to none in 5 years (conservative). RESULTS: When compared with insulin glargine, we projected a 5-year risk reduction in cardiovascular adverse events [rate ratio (RR) 0.64, 95% confidence interval (CI) 0.61-0.67] and microvascular composite (RR 0.67, 95% CI 0.64-0.70) with 15 mg tirzepatide, and 5-year risk reduction in cardiovascular adverse events (RR 0.75, 95% CI 0.72-0.79) and microvascular composite (RR 0.79, 95% CI 0.76-0.82) with semaglutide (1 mg) under an optimistic scenario. Lower doses of tirzepatide also had similar, albeit smaller benefits. Treatment effects for tirzepatide and semaglutide were smaller but still significantly higher than insulin glargine under a conservative scenario. The 5-year risk reduction in diabetes-related complication events and mortality for the 15 mg tirzepatide compared with insulin glargine ranged from 49% to 10% under an optimistic scenario, which was reduced by 17%-33% when a conservative scenario was assumed. CONCLUSION: With the use of the BRAVO diabetes model, tirzepatide and semaglutide exhibited potential to reduce the risk of macrovascular and microvascular complications among individuals with type 2 diabetes, compared with insulin glargine in a 5-year window. Based on the current modelling assumptions, tirzepatide (15 mg) may potentially outperform semaglutide (1 mg). While the BRAVO model offered insights, the long-term cardiovascular benefit of tirzepatide should be further validated in a prospective clinical trial.

Incremental burden on health-related quality of life, health service utilization and direct medical expenditures associated with cognitive impairment among non-institutionalized people with diabetes aged 65 years and older.

AIMS: To quantify the incremental health and economic burden associated with cognitive impairment (CI) among non-institutionalized people with diabetes ≥65 years in the United States. MATERIALS AND METHODS: Using 2016-2019 Medical Expenditure Panel Surveys data, we identified participants ≥65 years with diabetes. We used propensity score weighting to quantify the CI-associated incremental burden on health-related quality of life measured by the 12-item Short Form Survey (SF-12), including the mental component summary score, physical component summary score and health utility. We also compared the annual health service utilization and expenditures on ambulatory visits, prescriptions, home care, emergency room (ER), hospitalizations and total annual direct medical expenditures. RESULTS: We included 5094 adults aged ≥65 with diabetes, of whom 804 had CI. After propensity score weighting, CI was associated with a lower mental component summary score (-8.4, p < .001), physical component summary score (-5.2, p < .001) and health utility (-0.12, p < .001). The CI group had more ambulatory visits (+4.4, p = .004) and prescriptions (+9.9, p < .001), with higher probabilities of having home care (+11.3%, p < .001) and ER visits (+8.2%, p = .001). People with CI spent $5441 (p < .001) more annually, $2039 (p = .002) more on prescriptions, $2695 (p < .001) more on home care and $118 (p < .001) more on ER visits. There is no statistically significant difference in the utilization and expenditure of hospitalizations. CONCLUSION: CI was associated with worse health-related quality of life, higher health service utilization and expenditures. Our findings can be used to monitor the health and economic burden of CI in non-institutionalized older persons with diabetes.

Association between low cognitive performance and diabetes-related health indicators across racial and ethnic groups in adults with diabetes.

AIM: To examine the associations between low cognitive performance (LCP) and diabetes-related health indicators (including body mass index [BMI], HbA1c, systolic blood pressure [SBP], low-density lipoprotein [LDL] and self-reported poor physical health) and whether these associations vary across racial/ethnic subgroups. METHODS: We identified adults aged 60 years or older with self-reported diabetes from the 2011-2014 National Health and Nutrition Examination Survey. Individuals with cognitive test scores in the lowest quartile were defined as having LCP. We used regression models to measure the associations of LCP with diabetes-related biometrics (BMI, HbA1c, SBP and LDL); and self-reported poor physical health. Moreover, we explored potential variations in these associations across racial/ethnic subgroups. RESULTS: Of 873 (261 with LCP) adults with diabetes, LCP was associated with higher HbA1c, SBP and LDL (adjusted difference: 0.41%, 5.01 mmHg and 5.08 mg/dL, respectively; P < .05), and greater odds of reporting poor physical health (adjusted odds ratio: 1.59, P < .05). The association between LCP and HbA1c was consistent across racial/ethnic groups, and notably pronounced in Hispanic and Other. BMI worsened with LCP, except for non-Hispanic Black. Excluding the Other group, elevated SBP was observed in people with LCP, with Hispanic showing the most significant association. LDL levels were elevated with LCP for Hispanic and Other. Physical health worsened with LCP for both non-Hispanic Black and Hispanic. CONCLUSIONS: We quantified the association between LCP and diabetes-related health indicators. These associations were more pronounced in Hispanic and Other racial/ethnic groups.

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Racial and Ethnic Inequities in Spatial Access to Pharmacies: A Geographic Information System Analysis.

BACKGROUND: Pharmacy accessibility is crucial for equity in healthcare access because community pharmacists may reach individuals who do not have access to other healthcare providers. OBJECTIVE: To determine whether spatial access to pharmacies differs among racial/ethnic groups across the rural-urban continuum. METHODS: We obtained a 30% random sample of the Research Triangle Institute (RTI) synthetic population, sampled at the census block level. For each individual, we defined optimal pharmacy access as having a driving distance ≤2 miles to the closest pharmacy in urban counties, ≤5 miles in suburban counties, and ≤10 miles in rural counties. We used a logistic regression model to measure the association between race/ethnicity and pharmacy access, while controlling for racial/ethnic composition of the census tract, Area Deprivation Index, income, age, gender, and US region. The model included an interaction between race/ethnicity and urbanicity to evaluate whether racial/ethnic inequities differed across the rural-urban continuum. RESULTS: The sample included 90,749,446 individuals of whom 80.6% had optimal pharmacy access. Racial/ethnic inequities in pharmacy access differed across the rural-urban continuum (p-value for interaction= <0.0001). In rural areas, Black (OR 0.87; 95%CI 0.86-0.87), Hispanic (OR 0.80; 95%CI 0.79-0.80), and Indigenous (OR 0.47; 95%CI 0.47-0.48) individuals had lower odds of optimal pharmacy access, compared to White individuals. Hispanic (OR 0.96; 95%CI 0.96-0.97) and Indigenous individuals (OR 0.75; 95%CI 0.75-0.76) had lower odds of optimal pharmacy access compared to White individuals in suburban areas. In Western states, Asian had lower odds of optimal pharmacy access in suburban (OR 0.88; 95%CI 0.86-0.90) and rural areas (OR 0.91; 95%CI 0.87-0.95) compared to White Individuals. CONCLUSIONS: Racial/ethnic inequities in spatial access to community pharmacies vary between urban and rural communities. Underrepresented racial/ethnic groups have significantly lower pharmacy access in rural and some suburban areas, but not in urban areas.

Heterogeneous treatment effects of metformin on risk of dementia in patients with type 2 diabetes: A longitudinal observational study.

INTRODUCTION: Little is known about the heterogeneous treatment effects of metformin on dementia risk in people with type 2 diabetes (T2D). METHODS: Participants (≥ 50 years) with T2D and normal cognition at baseline were identified from the National Alzheimer's Coordinating Center database (2005-2021). We applied a doubly robust learning approach to estimate risk differences (RD) with a 95% confidence interval (CI) for dementia risk between metformin use and no use in the overall population and subgroups identified through a decision tree model. RESULTS: Among 1393 participants, 104 developed dementia over a 4-year median follow-up. Metformin was significantly associated with a lower risk of dementia in the overall population (RD, -3.2%; 95% CI, -6.2% to -0.2%). We identified four subgroups with varied risks for dementia, defined by neuropsychiatric disorders, non-steroidal anti-inflammatory drugs, and antidepressant use. DISCUSSION: Metformin use was significantly associated with a lower risk of dementia in individuals with T2D, with significant variability among subgroups.

Disparities in Access to Hepatitis C Treatment Among Arizona Medicaid Beneficiaries With Chronic Hepatitis C.

BACKGROUND: High costs of direct-acting antivirals (DAAs) have led to their restricted access for patients with hepatitis C virus (HCV). OBJECTIVE: The aim was to assess how HCV treatment access and predictors of HCV treatment changed in the post-DAA period compared with pre-DAA period. METHODS: A retrospective cohort study using Arizona Medicaid data was conducted for patients with HCV to compare treatment initiation rates between pre-DAA (January 2008-October 2013) and post-DAA (November 2013-December 2018) periods. Multivariable logistic regression was used, controlling for demographic and clinical variables. RESULTS: Twenty-four thousand and ninety and 28,756 patients during the pre-DAA and post-DAA periods were identified. Overall, 12.6% were treated in the post-DAA period compared with 7.8% in the pre-DAA period ( P <0.001). The relative increase in the HCV treatment initiation rate from the pre-DAA to the post-DAA period was significant greater for Black beneficiaries compared with White beneficiaries ( P =0.002). Hispanic beneficiaries were less likely to be treated in the post-DAA period [adjusted odds ratios (aOR): 0.88; CI: 0.79-0.98] compared with White beneficiaries. Those with mental illness (aOR: 0.71; 95% CI: 0.63-0.80) and substance use disorders (aOR: 0.63; 95% CI: 0.58-0.68) were less likely to be treated in the post-DAA period. CONCLUSIONS: Although treatment initiation increased and disparities for Black beneficiaries compared with White beneficiaries attenuated in the post-DAA period, only 13% of Arizona Medicaid patients with HCV received DAA treatment. Disparities in DAA access remained among Hispanic patients and those with mental illness and substance use disorders.

Changes in racial and ethnic disparities in glucose-lowering drug utilization and glycated haemoglobin A1c in US adults with diabetes: 2005-2018.

AIM: To examine changes in racial and ethnic disparities in glucose-lowering drugs (GLD) use and glycated haemoglobin A1c in US adults with diabetes from 2005 to 2018. METHODS: We conducted pooled cross-sectional analysis using data from the 2005-2018 Medical Expenditure Panel Surveys, and the 2005-2018 National Health and Nutrition Examination Survey. Individuals ≥18 years with diabetes were included. Racial and ethnic disparities were measured in (a) newer non-insulin GLD use; (b) insulin analogue use; (c) non-insulin GLDs adherence; (d) insulin adherence; and (e) glucose management, along with (f) the proportion of the disparities explained by potential contributing factors. RESULTS: From 2005 to 2018, racial and ethnic disparities persisted in newer GLD use, non-insulin GLDs adherence, insulin analogue use and glucose management. In 2018, compared with non-Hispanic white adults, non-Hispanic black, Hispanic and other race/ethnicity groups had lower rates of using newer GLDs (adjusted risk ratio: 0.44, 0.52, 0.64, respectively; p < .05 for all) and insulin analogues (adjusted risk ratio: 0.93, 0.89, 0.95, respectively; p < .05 for all except other groups), lower non-insulin GLD adherence (proportion of days covered: -4.5%, -5.6%, -4.3%, respectively; p < .05 for all), higher glycated haemoglobin A1c (0.29%, 0.32%, 0.02%, respectively; p < .05 for all except other group), and similar insulin adherences. Socioeconomic and health status were the main contributors to these disparities. CONCLUSIONS: Our findings provide evidence of racial and ethnic disparities in newer GLD use and quality of care in glucose management. Our study results can inform decision-makers of the status of racial and ethnic disparities and identify ways to reduce these disparities.

COVID-19 pandemic and initiation of treatment for atrial fibrillation: a nationwide analysis of claims data.

BACKGROUND: The COVID-19 pandemic profoundly disrupted the delivery of medical care. It remains unclear whether individuals diagnosed with new onset disease during the pandemic were less likely to initiate treatments after diagnosis. We sought to evaluate changes in the treatment initiation of patients newly diagnosed with atrial fibrillation (AF) after the onset of the COVID-19 pandemic. METHODS: In this retrospective cohort study, we identified individuals with incident AF from 01/01/2016-09/30/2021 using Optum's de-identified Clinformatics® Data Mart Database. The primary outcome was initiation of oral anticoagulation (OAC) within 30 days of AF diagnosis. Secondary outcomes included initiation of OAC within 180 days of diagnosis, initiation of warfarin, direct oral anticoagulants (DOACs), rhythm control medications and electrical cardioversion within 30 days of diagnosis. We constructed interrupted time series analyses to examine changes in the outcomes following the onset of the pandemic. RESULTS: A total of 573,524 patients (age 73.0 ± 10.9 years) were included in the study. There were no significant changes in the initiation of OAC, DOAC, and rhythm control medications associated with the onset of the pandemic. There was a significant decrease in initiation of electrical cardioversion associated with the onset of the pandemic. The rate of electronic cardioversion within 30 days of diagnosis decreased by 4.9% per 1,000 patients after the onset of the pandemic and decreased by about 35% in April 2020, compared to April 2019, from 5.53% to 3.58%. CONCLUSION: The COVID-19 pandemic did not affect the OAC initiation within 30 days of AF diagnosis but was associated with a decline in the provision of procedures for patients newly diagnosed with AF.

Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study.

BACKGROUND: Prior studies suggested that antidepressant use is associated with an increased risk of dementia compared to no use, which is subject to confounding by indication. We aimed to compare the dementia risk among older adults with depression receiving first-line antidepressants (i.e., SSRI/SNRI) versus psychotherapy, which is also considered the first-line therapy for depression. METHODS: This retrospective cohort study was conducted using the US Medical Expenditure Panel Survey from 2010 to 2019. We included adults aged ≥ 50 years diagnosed with depression who initiated SSRI/SNRI or psychotherapy. We excluded patients with a dementia diagnosis before the first record of SSRI/SNRI use or psychotherapy. The exposure was the patient's receipt of SSRI/SNRI (identified from self-report questionnaires) or psychotherapy (identified from the Outpatient Visits or Office-Based Medical Provider Visits files). The outcome was a new diagnosis of dementia within 2 years (i.e., survey panel period) identified using ICD-9/ICD-10 codes from the Medical Conditions file. Using a multivariable logistic regression model, we reported adjusted odds ratios (aORs) with 95% confidence intervals (CIs). We also conducted subgroup analyses by patient sex, age group, race/ethnicity, severity of depression, combined use of other non-SSRI/SNRI antidepressants, and presence of underlying cognitive impairment. RESULTS: Among 2,710 eligible patients (mean age = 61 ± 8, female = 69%, White = 84%), 89% used SSRIs/SNRIs, and 11% received psychotherapy. The SSRI/SNRI users had a higher crude incidence of dementia than the psychotherapy group (16.4% vs. 11.8%), with an aOR of 1.36 (95% CI = 1.06-1.74). Subgroup analyses yielded similar findings as the main analyses, except no significant association for patients who were aged < 65 years (1.23, 95% CI = 0.93-1.62), male (1.34, 95% CI = 0.95-1.90), Black (0.76, 95% CI = 0.48-1.19), had a higher PHQ-2 (1.39, 95% CI = 0.90-2.15), and had underlying cognitive impairment (1.06, 95% CI = 0.80-1.42). CONCLUSIONS: Our findings suggested that older adults with depression receiving SSRIs/SNRIs were associated with an increased dementia risk compared to those receiving psychotherapy.

Disparities in distribution of COVID-19 vaccines across US counties: A geographic information system-based cross-sectional study.

BACKGROUND: The US Centers for Disease Control and Prevention has repeatedly called for Coronavirus Disease 2019 (COVID-19) vaccine equity. The objective our study was to measure equity in the early distribution of COVID-19 vaccines to healthcare facilities across the US. Specifically, we tested whether the likelihood of a healthcare facility administering COVID-19 vaccines in May 2021 differed by county-level racial composition and degree of urbanicity. METHODS AND FINDINGS: The outcome was whether an eligible vaccination facility actually administered COVID-19 vaccines as of May 2021, and was defined by spatially matching locations of eligible and actual COVID-19 vaccine administration locations. The outcome was regressed against county-level measures for racial/ethnic composition, urbanicity, income, social vulnerability index, COVID-19 mortality, 2020 election results, and availability of nontraditional vaccination locations using generalized estimating equations. Across the US, 61.4% of eligible healthcare facilities and 76.0% of eligible pharmacies provided COVID-19 vaccinations as of May 2021. Facilities in counties with >42.2% non-Hispanic Black population (i.e., > 95th county percentile of Black race composition) were less likely to serve as COVID-19 vaccine administration locations compared to facilities in counties with <12.5% non-Hispanic Black population (i.e., lower than US average), with OR 0.83; 95% CI, 0.70 to 0.98, p = 0.030. Location of a facility in a rural county (OR 0.82; 95% CI, 0.75 to 0.90, p < 0.001, versus metropolitan county) or in a county in the top quintile of COVID-19 mortality (OR 0.83; 95% CI, 0.75 to 0.93, p = 0.001, versus bottom 4 quintiles) was associated with decreased odds of serving as a COVID-19 vaccine administration location. There was a significant interaction of urbanicity and racial/ethnic composition: In metropolitan counties, facilities in counties with >42.2% non-Hispanic Black population (i.e., >95th county percentile of Black race composition) had 32% (95% CI 14% to 47%, p = 0.001) lower odds of serving as COVID administration facility compared to facilities in counties with below US average Black population. This association between Black composition and odds of a facility serving as vaccine administration facility was not observed in rural or suburban counties. In rural counties, facilities in counties with above US average Hispanic population had 26% (95% CI 11% to 38%, p = 0.002) lower odds of serving as vaccine administration facility compared to facilities in counties with below US average Hispanic population. This association between Hispanic ethnicity and odds of a facility serving as vaccine administration facility was not observed in metropolitan or suburban counties. Our analyses did not include nontraditional vaccination sites and are based on data as of May 2021, thus they represent the early distribution of COVID-19 vaccines. Our results based on this cross-sectional analysis may not be generalizable to later phases of the COVID-19 vaccine distribution process. CONCLUSIONS: Healthcare facilities in counties with higher Black composition, in rural areas, and in hardest-hit communities were less likely to serve as COVID-19 vaccine administration locations in May 2021. The lower uptake of COVID-19 vaccinations among minority populations and rural areas has been attributed to vaccine hesitancy; however, decreased access to vaccination sites may be an additional overlooked barrier.

Access to community pharmacies: A nationwide geographic information systems cross-sectional analysis.

BACKGROUND: Pharmacy accessibility is key for the emerging role of community pharmacists as providers of patient-centered, medication management services in addition to traditional dispensing roles. OBJECTIVE: To quantify population access to community pharmacies across the United States. METHODS: We obtained addresses for pharmacy locations in the United States from the National Council for Prescription Drug Programs and geocoded each. For a 1% sample of a U.S. synthetic population, we calculated the driving distance to the closest pharmacy using ArcGIS. We estimated the proportion of population living within 1, 2, 5, and 10 miles of a community pharmacy. We quantified the role of chain vs regional franchises or independently owned pharmacies in providing access across degrees of urbanicity. RESULTS: We identified 61,715 pharmacies, including 37,954 (61.5%) chains, 23,521 (38.1%) regional franchises or independently owned pharmacies, and 240 (0.4%) government pharmacies. In large metropolitan areas, 62.8% of the pharmacies were chains; however, in rural areas, 76.5% of pharmacies were franchises or independent pharmacies. Across the overall U.S. population, 48.1% lived within 1 mile of any pharmacy, 73.1% within 2 miles, 88.9% within 5 miles, and 96.5% within 10 miles. Across the United States, 8.3% of counties had at least 50% of residents with a distance greater than 10 miles. These low-access counties were concentrated in Alaska, South Dakota, North Dakota, and Montana. CONCLUSIONS: Community pharmacies may serve as accessible locations for patient-centered, medication management services that enhance the health and wellness of communities. Although chain pharmacies represent the majority of pharmacy locations across the country, access to community pharmacies in rural areas predominantly relies on regional franchises and independently owned pharmacies.

Predicting Mortality Risk After a Hospital or Emergency Department Visit for Nonfatal Opioid Overdose.

BACKGROUND: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose. OBJECTIVE: To predict risk of death after a nonfatal opioid overdose. DESIGN AND PARTICIPANTS: This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period. EXPOSURES, MAIN OUTCOME, AND MEASURES: Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup. KEY RESULTS: Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001). CONCLUSIONS: A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.

Using machine learning to identify diabetes patients with canagliflozin prescriptions at high-risk of lower extremity amputation using real-world data.

AIMS: Canagliflozin, a sodium-glucose cotransporter 2 inhibitor indicated for lowering glucose, has been increasingly used in diabetes patients because of its beneficial effects on cardiovascular and renal outcomes. However, clinical trials have documented an increased risk of lower extremity amputations (LEA) associated with canagliflozin. We applied machine learning methods to predict LEA among diabetes patients treated with canagliflozin. METHODS: Using claims data from a 5% random sample of Medicare beneficiaries, we identified 13 904 diabetes individuals initiating canagliflozin between April 2013 and December 2016. The samples were randomly and equally split into training and testing sets. We identified 41 predictor candidates using information from the year prior to canagliflozin initiation, and applied four machine learning approaches (elastic net, least absolute shrinkage and selection operator [LASSO], gradient boosting machine and random forests) to predict LEA risk after canagliflozin initiation. RESULTS: The incidence rate of LEA was 0.57% over a median 1.5 years follow-up. LASSO produced the best prediction, yielding a C-statistic of 0.81 (95% CI: 0.76, 0.86). Among individuals categorized in the top 5% of the risk score, the actual incidence rate of LEA was 3.74%. Among the 16 factors selected by LASSO, history of LEA [adjusted odds ratio (aOR): 33.6 (13.8, 81.9)] and loop diuretic use [aOR: 3.6 (1.8,7.3)] had the strongest associations with LEA incidence. CONCLUSIONS: Our machine learning model efficiently predicted the risk of LEA among diabetes patients undergoing canagliflozin treatment. The risk score may support optimized treatment decisions and thus improve health outcomes of diabetes patients.

Costs and outcomes of "intermediate" vs "minimal" care for youth-onset type 1 diabetes in six countries.

OBJECTIVE: Data are needed to demonstrate that providing an "intermediate" level of type 1 diabetes (T1D) care is cost-effective compared to "minimal" care in less-resourced countries. We studied these care scenarios in six countries. METHODS: We modeled the complications/costs/mortality/healthy life years (HLYs) associated with "intermediate" care including two blood glucose tests/day (mean HbA1c 9.0% [75 mmol/mol]) in three lower-gross domestic product (GDP) countries (Mali, Tanzania, Pakistan), or three tests/day (mean HbA1c 8.5% [69 mmol/mol]) in three higher-GDP countries (Bolivia, Sri Lanka, Azerbaijan); and compared findings to "minimal" care (mean HbA1c 12.5% [113 mmol/mol]). A discrete time Markov illness-death model with age and calendar-year-dependent transition probabilities was developed, with inputs of 30 years of complications and Standardized Mortality Rate data from the youth cohort in the Pittsburgh Epidemiology of Diabetes Complications Study, background mortality, and costs determined from international and local prices. RESULTS: Cumulative 30 years incidences of complications were much lower for "intermediate care" than "minimal care", for example, for renal failure incidence was 68.1% (HbA1c 12.5%) compared to 3.9% (9%) and 2.4% (8.5%). For Mali, Tanzania, Pakistan, Bolivia, Sri Lanka, and Azerbaijan, 30 years survival was 50.1%/52.7%/76.7%/72.5%/82.8%/89.2% for "intermediate" and 8.5%/10.1%/39.4%/25.8%/45.5%/62.1% for "minimal" care, respectively. The cost of a HLY gained as a % GDP/capita was 141.1%/110.0%/52.3%/41.8%/17.0%/15.6%, respectively. CONCLUSIONS: Marked reductions in complications rates and mortality are achievable with "intermediate" T1D care achieving mean clinic HbA1c of 8.5% to 9% (69-75 mmol/mol). This is also "very cost-effective" in four of six countries according to the WHO "Fair Choices" approach which costs HLYs gained against GDP/capita.

Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen.

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. RESULTS: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1). CONCLUSIONS: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes.

AIMS/HYPOTHESIS: We sought to assess the role of coronary artery calcification (CAC) and its progression in predicting incident coronary artery disease (CAD) in individuals with type 1 diabetes using data from the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study. METHODS: The present study examined 292 participants who had at least one CAC measure and were free from CAD at baseline; 181 (62%) had repeat CAC assessments 4-8 years later and did not develop CAD between the two CAC measures. The HRs of incident CAD events were estimated using Cox models in categorised or in appropriately transformed CAC scores. C statistics and net reclassification improvement (NRI) were used to assess the added predictive value of CAC for incident CAD. RESULTS: At baseline, the mean age of participants was 39.4 years and the mean diabetes duration was 29.5 years. There were 76 participants who experienced a first incident CAD event over an average follow-up of 10.7 years. At baseline, compared with those without CAC (Agatston score = 0), the adjusted HR (95% CI) in groups of 1-99, 100-399 and ≥400 was 3.1 (1.6, 6.1), 4.4 (2.0, 9.5) and 4.8 (1.9, 12.0), respectively. CAC density was inversely associated with incident CAD in those with CAC volume ≥100 (HR 0.3 [95% CI 0.1, 0.9]) after adjusting for volume score. Among participants with repeated CAC measures, annual CAC progression was positively associated with incident CAD after controlling for baseline CAC. The HR (95% CI) for above vs below the median annual CAC volume progression was 3.2 (1.2, 8.5). When compared with a model that only included established risk factors, the addition of CAC improved the predictive ability for incident CAD events in the whole group. CONCLUSIONS/INTERPRETATION: CAC is strongly associated with incident CAD events in individuals with type 1 diabetes; its inclusion in CAD risk models may lead to improvement in prediction over established risk factors.

Serum long-chain n-3 polyunsaturated fatty acids and aortic calcification in middle-aged men: The population-based cross-sectional ERA-JUMP study.

BACKGROUND AND AIM: Few studies have examined the association of long-chain n-3 polyunsaturated fatty acids (LCn-3PUFAs) with the measures of atherosclerosis in the general population. This study aimed to examine the relationship of total LCn-3PUFAs, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) with aortic calcification. METHODS AND RESULTS: In a multiethnic population-based cross-sectional study of 998 asymptomatic men aged 40-49 years (300 US-White, 101 US-Black, 287 Japanese American, and 310 Japanese in Japan), we examined the relationship of serum LCn-3PUFAs to aortic calcification (measured by electron-beam computed tomography and quantified using the Agatston method) using Tobit regression and ordinal logistic regression after adjusting for potential confounders. Overall 56.5% participants had an aortic calcification score (AoCaS) > 0. The means (SD) of total LCn-3PUFAs, EPA, and DHA were 5.8% (3.3%), 1.4% (1.3%), and 3.7% (2.1%), respectively. In multivariable-adjusted Tobit regression, a 1-SD increase in total LCn-3PUFAs, EPA, and DHA was associated with 29% (95% CI = 0.51, 1.00), 9% (95% CI = 0.68, 1.23), and 35% (95% CI = 0.46, 0.91) lower AoCaS, respectively. Results were similar in ordinal logistic regression analysis. There was no significant interaction between race/ethnicity and total LCn-3PUFAs, EPA or DHA on aortic calcification. CONCLUSIONS: This study showed the significant inverse association of LCn-3PUFAs with aortic calcification independent of conventional cardiovascular risk factors among men in the general population. This association appeared to be driven by DHA but not EPA.