Specification curve analysis to identify heterogeneity in risk factors for dementia: findings from the UK Biobank.

BACKGROUND: In 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer's disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes. METHODS: We evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer's disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors). RESULTS: SCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes. CONCLUSIONS: We observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia.

A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas.

Importance: Models predicting health care spending and other outcomes from administrative records are widely used to manage and pay for health care, despite well-documented deficiencies. New methods are needed that can incorporate more than 70 000 diagnoses without creating undesirable coding incentives. Objective: To develop a machine learning (ML) algorithm, building on Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods, that automates development of clinically credible and transparent predictive models for policymakers and clinicians. Design, Setting, and Participants: DXIs were organized into disease hierarchies and assigned an Appropriateness to Include (ATI) score to reflect vagueness and gameability concerns. A novel automated DCG algorithm iteratively assigned DXIs in 1 or more disease hierarchies to DCGs, identifying sets of DXIs with the largest regression coefficient as dominant; presence of a previously identified dominating DXI removed lower-ranked ones before the next iteration. The Merative MarketScan Commercial Claims and Encounters Database for commercial health insurance enrollees 64 years and younger was used. Data from January 2016 through December 2018 were randomly split 90% to 10% for model development and validation, respectively. Deidentified claims and enrollment data were delivered by Merative the following November in each calendar year and analyzed from November 2020 to January 2024. Main Outcome and Measures: Concurrent top-coded total health care cost. Model performance was assessed using validation sample weighted least-squares regression, mean absolute errors, and mean errors for rare and common diagnoses. Results: This study included 35 245 586 commercial health insurance enrollees 64 years and younger (65 901 460 person-years) and relied on 19 clinicians who provided reviews in the base model. The algorithm implemented 218 clinician-specified hierarchies compared with the US Department of Health and Human Services (HHS) hierarchical condition category (HCC) model's 64 hierarchies. The base model that dropped vague and gameable DXIs reduced the number of parameters by 80% (1624 of 3150), achieved an R2 of 0.535, and kept mean predicted spending within 12% ($3843 of $31 313) of actual spending for the 3% of people with rare diseases. In contrast, the HHS HCC model had an R2 of 0.428 and underpaid this group by 33% ($10 354 of $31 313). Conclusions and Relevance: In this study, by automating DXI clustering within clinically specified hierarchies, this algorithm built clinically interpretable risk models in large datasets while addressing diagnostic vagueness and gameability concerns.

Managed competition in the United States: How well is it promoting equity and efficiency?

Managed competition frameworks aim to control healthcare costs and promote access to high-quality health insurance and services through a combination of public policies and market forces. In the United States, managed competition delivery systems are varied and diffused across a patchwork of divided markets and populations. This, coupled with extremely high national health spending per capita, makes a more unified managed competition strategy an appealing alternative to a currently struggling healthcare system. We examine the relative effectiveness of three existing programmes in the U.S. that each rely upon some principles of managed competition: health insurance exchanges instituted by the Affordable Care Act, Medicaid managed care organisations, and Medicare Advantage plans. Although each programme leverages some competitive features, each faces significant hurdles as a candidate for expansion. We highlight these challenges with a survey of academic health economists, and find that provider and insurer consolidation, highly segmented markets, and failing to incentivise competitive efficiencies all dampen the success of existing programmes. Although managed competition for all is a potentially desirable framework for future health reform in the U.S., successful expansion relies on addressing fundamental issues revealed by imperfect existing programmes.

Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison.

In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.

Leveraging tele-mentoring and remote learning to strengthen the emergency care capacity of health workers in Uganda.

Background: A robust emergency care system is a cost-effective method of reducing preventable death and disability, especially in low-and middle-income countries. To scale emergency care expertise across the country, the Uganda Ministry of Health and Seed Global Health established the Emergency Medical Services (EMS) ECHO program. We describe the process of establishing the program in a resource-limited setting, best practices, and lessons learned in Uganda. Methods: Investigators conducted a mixed-methods evaluation to assess the initial 4 months' implementation of the EMS ECHO. We conducted pre/post-program assessments of healthcare worker knowledge, self-efficacy, and professional's satisfaction with the program. The analysis compared the differences between pre/post-test scores descriptively. Results: The EMS ECHO was initiated in November 2021. A phased curriculum was developed with the initial phase focusing on the ABCDE (Airway, Breathing, Circulation, Disability, and Exposure) approach to the emergency patient. This phase reached 2,030 health workers cumulatively across 200 health facilities. The majority of the participants were medical doctors (n = 751, 37%), and nurses (n = 568, 28%). Majority of participants (95%) rated the sessions as informative. On whether the ECHO sessions diminished professional isolation, 66% agreed or strongly agreed. Conclusions: Similar to other ECHO program evaluation results, Uganda's EMS ECHO program improved knowledge, skills, and the development of a virtual community of practice thereby diminishing professional isolation. It also demonstrates that through a planned stepwise process, virtual learning and telementorship can be used efficiently to improve healthcare worker knowledge,skills and multiply the limited number of emergency care experts available in the country.

Dose mediates the protracted effects of adolescent THC exposure on reward and stress reactivity in males relevant to perturbation of the basolateral amygdala transcriptome.

Despite the belief that cannabis is relatively harmless, exposure during adolescence is associated with increased risk of developing several psychopathologies in adulthood. In addition to the high levels of use amongst teenagers, the potency of ∆-9-tetrahydrocannabinol (THC) has increased more than fourfold compared to even twenty years ago, and it is unclear whether potency influences the presentation of THC-induced behaviors. Expanded knowledge about the impact of adolescent THC exposure, especially high dose, is important to delineating neural networks and molecular mechanisms underlying psychiatric risk. Here, we observed that repeated exposure to low (1.5 mg/kg) and high (5 mg/kg) doses of THC during adolescence in male rats produced divergent effects on behavior in adulthood. Whereas low dose rats showed greater sensitivity to reward devaluation and also self-administered more heroin, high dose animals were significantly more reactive to social isolation stress. RNA sequencing of the basolateral amygdala, a region linked to reward processing and stress, revealed significant perturbations in transcripts and gene networks related to synaptic plasticity and HPA axis that were distinct to THC dose as well as stress. In silico single-cell deconvolution of the RNAseq data revealed a significant reduction of astrocyte-specific genes related to glutamate regulation in stressed high dose animals, a result paired anatomically with greater astrocyte-to-neuron ratios and hypotrophic astrocytes. These findings emphasize the importance of dose and behavioral state on the presentation of THC-related behavioral phenotypes in adulthood and dysregulation of astrocytes as an interface for the protracted effects of high dose THC and subsequent stress sensitivity.

Long-Term Outcomes of Adolescent THC Exposure on Translational Cognitive Measures in Adulthood in an Animal Model and Computational Assessment of Human Data.

Importance: Although perceived as relatively harmless and nonaddictive, adolescent cannabis use significantly increases the likelihood of developing cannabis use disorder in adulthood, especially for high-potency cannabis. Risky decision-making is associated with chronic cannabis use, but given confounds of human studies, it remains unclear whether adolescent cannabis exposure and Δ9-tetrahydrocannabinol (THC) potency specifically predicts risky decision-making or influences cognitive response to the drug later in life. Objective: To leverage a human data set of cannabis users and a rat model to evaluate the long-term outcomes of adolescent THC exposure on adult decision-making and impulse control. Design, Setting, and Participants: This translational rat study tested the link between adolescent THC exposure and adulthood decision-making. A reanalysis of a previously published dataset of human chronic cannabis users was conducted to evaluate decision-making phenotypes. Computational modeling assessed the human and animal results in a single framework. Data were collected from 2017 to 2020 and analyzed from 2020 to 2022. Main Outcomes and Measures: Decision-making was measured by the Iowa Gambling Task (IGT) and Rat Gambling Task (rGT). Impulse control was assessed in the rat model. Computational modeling was used to determine reward and punishment learning rates and learning strategy used by cannabis users and THC-exposed rats. Cell-specific molecular measures were conducted in the prefrontal cortex and amygdala. Results: Of 37 participants, 24 (65%) were male, and the mean (SD) age was 33.0 (8.3) years. Chronic cannabis users (n = 22; mean [SE] IGT score, -5.182 [1.262]) showed disadvantageous decision-making compared with controls (n = 15; mean [SE] IGT score, 7.133 [2.687]; Cohen d = 1.436). Risky choice was associated with increased reward learning (mean [SE] IGT score: cannabis user, 0.170 [0.018]; control, 0.046 [0.008]; Cohen d = 1.895) and a strategy favoring exploration vs long-term gains (mean [SE] IGT score: cannabis user, 0.088 [0.012]; control, 0.020 [0.002]; Cohen d = 2.218). Rats exposed to high-dose THC but not low-dose THC during adolescence also showed increased risky decision-making (mean [SE] rGT score: vehicle, 46.17 [7.02]; low-dose THC, 69.45 [6.01]; high-dose THC, 21.97 [11.98]; Cohen d = 0.433) and elevated reward learning rates (mean [SE] rGT score: vehicle, 0.17 [0.01]; low-dose THC, 0.10 [0.01]; high-dose THC, 0.24 [0.06]; Cohen d = 1.541) during task acquisition. These animals were also uniquely susceptible to increased cognitive impairments after reexposure to THC in adulthood, which was correlated with even greater reward learning (r = -0.525; P < .001) and a shift in strategy (r = 0.502; P < .001), similar to results seen in human cannabis users. Molecular studies revealed that adolescent THC dose differentially affected cannabinoid-1 receptor messenger RNA expression in the prelimbic cortex and basolateral amygdala in a layer- and cell-specific manner. Further, astrocyte glial fibrillary acidic protein messenger RNA expression associated with cognitive deficits apparent with adult THC reexposure. Conclusions and Relevance: In this translational study, high-dose adolescent THC exposure was associated with cognitive vulnerability in adulthood, especially with THC re-exposure. These data also suggest a link between astrocytes and cognition that altogether provides important insights regarding the neurobiological genesis of risky cannabis use that may help promote prevention and treatment efforts.

Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment: The Diagnostic Items Classification System.

Importance: Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Objective: To develop an ICD-10-CM-based classification framework for predicting diverse health care payment, quality, and performance outcomes. Design Setting and Participants: Physician teams mapped all ICD-10-CM diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using R 2, mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage. Main Outcomes and Measures: Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000. Results: A total of 65 901 460 person-years were split into 90% estimation/10% validation samples (n = 6 604 259). In all, 3223 DXIs were created: 2435 main effects, 772 modifiers, and 16 scaled items. Stepwise regressions predicting annual health care spending (mean [SD], $5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated R 2 was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs. Conclusions: In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.

Questionable Research Practices, Low Statistical Power, and Other Obstacles to Replicability: Why Preclinical Neuroscience Research Would Benefit from Registered Reports.

Replicability, the degree to which a previous scientific finding can be repeated in a distinct set of data, has been considered an integral component of institutionalized scientific practice since its inception several hundred years ago. In the past decade, large-scale replication studies have demonstrated that replicability is far from favorable, across multiple scientific fields. Here, I evaluate this literature and describe contributing factors including the prevalence of questionable research practices (QRPs), misunderstanding of p-values, and low statistical power. I subsequently discuss how these issues manifest specifically in preclinical neuroscience research. I conclude that these problems are multifaceted and difficult to solve, relying on the actions of early and late career researchers, funding sources, academic publishers, and others. I assert that any viable solution to the problem of substandard replicability must include changing academic incentives, with adoption of registered reports being the most immediately impactful and pragmatic strategy. For animal research in particular, comprehensive reporting guidelines that document potential sources of sensitivity for experimental outcomes is an essential addition.

Prenatal Δ9-Tetrahydrocannabinol Exposure in Males Leads to Motivational Disturbances Related to Striatal Epigenetic Dysregulation.

BACKGROUND: Cannabis remains one of the most widely abused drugs during pregnancy. In utero exposure to its principal psychoactive component, Δ9-tetrahydrocannabinol (THC), can result in long-term neuropsychiatric risk for the progeny. This study investigated epigenetic signatures underlying these enduring consequences. METHODS: Rat dams were exposed daily to THC (0.15 mg/kg) during pregnancy, and adult male offspring were examined for reward and depressive-like behavioral endophenotypes. Using unbiased sequencing approaches, we explored transcriptional and epigenetic profiles in the nucleus accumbens (NAc), a brain area central to reward and emotional processing. An in vitro CRISPR (clustered regularly interspaced short palindromic repeats) activation model coupled with RNA sequencing was also applied to study specific consequences of epigenetic dysregulation, and altered molecular signatures were compared with human major depressive disorder transcriptome datasets. RESULTS: Prenatal THC exposure induced increased motivation for food, heightened learned helplessness and anhedonia, and altered stress sensitivity. We identified a robust increase specific to males in the expression of Kmt2a (histone-lysine N-methyltransferase 2A) that targets H3K4 (lysine 4 on histone H3) in cellular chromatin. Normalizing Kmt2a in the NAc rescued the motivational phenotype of prenatally THC-exposed animals. Comparison of RNA- and H3K4me3-sequencing datasets from the NAc of rat offspring with the in vitro model of Kmt2a upregulation revealed overlapping, significant disturbances in pathways that mediate synaptic plasticity. Similar transcriptional alterations were detected in human major depressive disorder. CONCLUSIONS: These studies provide direct evidence for the persistent effects of prenatal cannabis exposure on transcriptional and epigenetic deviations in the NAc via Kmt2a dysregulation and associated psychiatric vulnerability.

Synaptic Zn2+ potentiates the effects of cocaine on striatal dopamine neurotransmission and behavior.

Cocaine binds to the dopamine (DA) transporter (DAT) to regulate cocaine reward and seeking behavior. Zinc (Zn2+) also binds to the DAT, but the in vivo relevance of this interaction is unknown. We found that Zn2+ concentrations in postmortem brain (caudate) tissue from humans who died of cocaine overdose were significantly lower than in control subjects. Moreover, the level of striatal Zn2+ content in these subjects negatively correlated with plasma levels of benzoylecgonine, a cocaine metabolite indicative of recent use. In mice, repeated cocaine exposure increased synaptic Zn2+ concentrations in the caudate putamen (CPu) and nucleus accumbens (NAc). Cocaine-induced increases in Zn2+ were dependent on the Zn2+ transporter 3 (ZnT3), a neuronal Zn2+ transporter localized to synaptic vesicle membranes, as ZnT3 knockout (KO) mice were insensitive to cocaine-induced increases in striatal Zn2+. ZnT3 KO mice showed significantly lower electrically evoked DA release and greater DA clearance when exposed to cocaine compared to controls. ZnT3 KO mice also displayed significant reductions in cocaine locomotor sensitization, conditioned place preference (CPP), self-administration, and reinstatement compared to control mice and were insensitive to cocaine-induced increases in striatal DAT binding. Finally, dietary Zn2+ deficiency in mice resulted in decreased striatal Zn2+ content, cocaine locomotor sensitization, CPP, and striatal DAT binding. These results indicate that cocaine increases synaptic Zn2+ release and turnover/metabolism in the striatum, and that synaptically released Zn2+ potentiates the effects of cocaine on striatal DA neurotransmission and behavior and is required for cocaine-primed reinstatement. In sum, these findings reveal new insights into cocaine's pharmacological mechanism of action and suggest that Zn2+ may serve as an environmentally derived regulator of DA neurotransmission, cocaine pharmacodynamics, and vulnerability to cocaine use disorders.

SnapShot: Neurobiology of opioid use disorder.

The use of opioid drugs and related overdose deaths, which rose to epidemic proportions over the past decade, have been exacerbated by the COVID pandemic, a time of great uncertainty and isolation. Much is known about opioid pharmacology and related neural circuits that, combined with novel emerging neurobiological insights, can help guide new treatment strategies. To view this SnapShot, open or download the PDF.

Intermittent claudication treatment patterns in the commercially insured non-Medicare population.

OBJECTIVE: Despite published guidelines and data for Medicare patients, it is uncertain how younger patients with intermittent claudication (IC) are treated. Additionally, the degree to which treatment patterns have changed over time with the expansion of endovascular interventions and outpatient centers is unclear. Our goal was to characterize IC treatment patterns in the commercially insured non-Medicare population. METHODS: The IBM MarketScan Commercial Database, which includes more than 8 billion US commercial insurance claims, was queried for patients newly diagnosed with IC from 2007 to 2016. Patient demographics, medication profiles, and open/endovascular interventions were evaluated. Time trends were modeled using simple linear regression and goodness-of-fit was assessed with coefficients of determination (R2). A patient-centered cohort sample and a procedure-focused dataset were analyzed. RESULTS: Among 152,935,013 unique patients in the database, there were 300,590 patients newly diagnosed with IC. The mean insurance coverage was 4.4 years. The median patients age was 58 years and 56% of patients were male. The prevalence of statin use was 48% among patients at the time of IC diagnosis and increased to 52% among patients after one year from diagnosis. Interventions were performed in 14.3%, of whom 20% and 6% underwent two or more and three or more interventions, respectively. The median time from diagnosis to intervention decreased from 230 days in 2008 days to 49 days in 2016 (R2 = 0.98). There were 16,406 inpatient and 102,925 ambulatory interventions for IC over the study period. Among ambulatory interventions, 7.9% were performed in office-based/surgical centers. The proportion of atherectomies performed in the ambulatory setting increased from 9.7% in 2007 to 29% in 2016 (R2 = 0.94). In office-based/surgical centers, 57.6% of interventions for IC used atherectomy in 2016. Atherectomy was used in ambulatory interventions by cardiologists in 22.6%, surgeons in 15.2%, and radiologists in 13.6% of interventions. Inpatient atherectomy rates remained stable over the study period. Open and endovascular tibial interventions were performed in 7.9% and 7.8% of ambulatory and inpatient IC interventions, respectively. Tibial bypasses were performed in 8.2% of all open IC interventions. CONCLUSIONS: There has been shorter time to intervention in the treatment of younger, commercially insured patients with IC, with many receiving multiple interventions. Statin use was low. Ambulatory procedures, especially in office-based/surgical centers, increasingly used atherectomy, which was not observed in inpatient settings.

bbeaR: an R package and framework for epitope-specific antibody profiling.

SUMMARY: Analysis of epitope-specific antibody repertoires has provided novel insights into the pathogenesis of inflammatory disorders, especially allergies. A novel multiplex immunoassay, termed Bead-Based Epitope Assay (BBEA), was developed to quantify levels of epitope-specific immunoglobulins, including IgE, IgG, IgA and IgD isotypes. bbeaR is an open-source R package, developed for the BBEA, provides a framework to import, process and normalize .csv data files exported from the Luminex reader, evaluate various quality control metrics, analyze differential epitope-binding antibodies with linear modeling, visualize results and map epitopes' amino acid sequences to their respective primary protein structures. bbeaR enables streamlined and reproducible analysis of epitope-specific antibody profiles. AVAILABILITY AND IMPLEMENTATION: bbeaR is open-source and freely available from GitHub as an R package: https://github.com/msuprun/bbeaR; vignettes included. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

Importance: On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. Objective: To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants: This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. Exposures: US implementation of ICD-10-CM. Main Outcomes and Measures: Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10 000 eligible members. Results: The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. Conclusions and Relevance: These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

Striatal Rgs4 regulates feeding and susceptibility to diet-induced obesity.

Consumption of high fat, high sugar (western) diets is a major contributor to the current high levels of obesity. Here, we used a multidisciplinary approach to gain insight into the molecular mechanisms underlying susceptibility to diet-induced obesity (DIO). Using positron emission tomography (PET), we identified the dorsal striatum as the brain area most altered in DIO-susceptible rats and molecular studies within this region highlighted regulator of G-protein signaling 4 (Rgs4) within laser-capture micro-dissected striatonigral (SN) and striatopallidal (SP) medium spiny neurons (MSNs) as playing a key role. Rgs4 is a GTPase accelerating enzyme implicated in plasticity mechanisms of SP MSNs, which are known to regulate feeding and disturbances of which are associated with obesity. Compared to DIO-resistant rats, DIO-susceptible rats exhibited increased striatal Rgs4 with mRNA expression levels enriched in SP MSNs. siRNA-mediated knockdown of striatal Rgs4 in DIO-susceptible rats decreased food intake to levels comparable to DIO-resistant animals. Finally, we demonstrated that the human Rgs4 gene locus is associated with increased body weight and obesity susceptibility phenotypes, and that overweight humans exhibit increased striatal Rgs4 protein. Our findings highlight a novel role for involvement of Rgs4 in SP MSNs in feeding and DIO-susceptibility.

Predicting opioid dependence from electronic health records with machine learning.

BACKGROUND: The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence. RESULTS: We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls. CONCLUSIONS: The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room.

Health care utilization and spending among privately insured children with medical complexity.

Children with medical complexity have high health service utilization and health expenditures that can impose significant financial burdens. This study examined these issues for families with children enrolled in US private health plans. Using IBM Watson/Truven Analytics℠ MarketScan® commercial claims and encounters data (2012-2014), we analyzed through regression models, the differences in health care utilization and spending of disaggregated health care services by health plan types and children's medical complexity levels. Children in consumer-driven and high-deductible plans had much higher out-of-pocket spending and cost shares than those in health maintenance organizations and preferred provider organizations (PPOs). Children with complex chronic conditions had higher service utilization and out-of-pocket expenditures while having lower cost shares on various categories of services than those without any chronic condition. Compared to families covered by PPOs, those with high-deductible or consumer-driven plans were 2.7 and 1.7 times more likely to spend over US$1000 out of pocket on their children's medical care, respectively. Families with higher complexity levels were more likely to experience financial burdens from expenditures on children's medical services. In conclusion, policymakers and families with children need to be cognizant of the significant financial burdens that can arise from children's complex medical needs and health plan demand-side cost sharing.

Measuring efficiency of health plan payment systems in managed competition health insurance markets.

Adverse selection in health insurance markets leads to two types of inefficiency. On the demand side, adverse selection leads to plan price distortions resulting in inefficient sorting of consumers across health plans. On the supply side, adverse selection creates incentives for plans to inefficiently distort benefits to attract profitable enrollees. Reinsurance, risk adjustment, and premium categories address these problems. Building on prior research on health plan payment system evaluation, we develop measures of the efficiency consequences of price and benefit distortions under a given payment system. Our measures are based on explicit economic models of insurer behavior under adverse selection, incorporate multiple features of plan payment systems, and can be calculated prior to observing actual insurer and consumer behavior. We illustrate the use of these measures with data from a simulated market for individual health insurance.

Demand elasticities and service selection incentives among competing private health plans.

We examine selection incentives by health plans while refining the selection index of McGuire et al. (2014) to reflect not only service predictability and predictiveness but also variation in cost sharing, risk-adjusted profits, profit margins, and newly-refined demand elasticities across 26 disaggregated types of service. We contrast selection incentives, measured by service selection elasticities, across six plan types using privately-insured claims data from 73 large employers from 2008 to 2014. Compared to flat capitation, concurrent risk adjustment reduces the elasticity by 47%, prospective risk adjustment by 43%, simple reinsurance system by 32%, and combined concurrent risk adjustment with reinsurance by 60%. Reinsurance significantly reduces the variability of individual-level profits, but increases the correlation of expected spending with profits, which strengthens selection incentives.