Importance In 2023, the Massachusetts Medicaid and Children’s Health Insurance Program (MassHealth) required accountable care organizations (ACOs) to increase payments to primary care practices and shift to monthly payments, currently calibrated to historical revenues and enhanced practice capabilities, such as being staffed to address behavioral health needs. To prevent rewarding practices for avoiding difficult patients, future payments to primary care practices should reflect their patients’ apparent need. Objective To describe MassHealth’s initiative and a complexity-adjusted payment model. Design, Setting, and Participants This cross-sectional study of payment model development and performance was conducted between February 2022 and November 2024. Participants included all 2019 Massachusetts Medicaid managed-care eligible members who were enrolled for 183 days or longer. Exposures Medical and social complexity. Main Outcomes and Measures For each member, the primary care activity level (PCAL) outcome proxies the resources that primary care clinicians need to provide comprehensive, coordinated care. Models were evaluated via R 2 and through ratios of observed-to-expected (ie, estimated by the model) outcomes for selected subgroups, which will be approximately 1.0 when payments and expected costs are well matched. The implications of paying practices using PCAL (vs a model based only on age and sex) were explored by examining financial and practice-level characteristics in high and low deciles of practice-level estimated mean. Results Among 1 092 742 MassHealth members enrolled in 3602 primary care practices (1 014 252 person-years; mean [SD] age, 25.9 [18.4] years; 538 065 [53.1%] female), the PCAL model achieved R 2 = 69.6% and estimates within 10% of observed PCAL spending for high-risk populations (mental health disorders, substance use disorders, complex chronic conditions, and disabilities) and across racial and ethnic groups. Age-adjusted and sex-adjusted payments would overpay practices in the lowest-need decile by 10% and underpay those in the highest-need decile by 34%, while the PCAL model would match payment to estimated need almost exactly in the lowest decile and underpay by just 6% in the highest decile. Conclusions and Relevance MassHealth’s 2023 reform invests in primary care. This cross-sectional study developed a risk model that can adjust primary care payments to patient needs. Neither age and sex adjustments nor inflated historical payments would provide adequate resources to primary care practices caring for the most complex patients.

Letters Health AffairsVol. 42, No. 5: Markets, Payments & More Risk Adjustment And Health Equity: The Authors ReplyJ. Michael McWilliams, Gabe Weinreb, Chima D. Ndumele, and Jacob Wallace Affiliations Harvard University and Brigham and Women’s Hospital Boston, Massachusetts Harvard University Boston, Massachusetts Yale University New Haven, ConnecticutPUBLISHED:May 2023No Accesshttps://doi.org/10.1377/hlthaff.2023.00297AboutSectionsView articleView Full TextSupplemental MaterialView PDFPermissions ShareShare onFacebookTwitterLinked InRedditEmail ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions View article"Risk Adjustment And Health Equity: The Authors Reply." Health Affairs, 42(5), p. 732TOPICSRisk adjustmentHealth equityPayment Loading Comments... Please enable JavaScript to view the comments powered by Disqus. DetailsExhibitsReferencesRelated Supplemental Materials Article Metrics History Published online 1 May 2023 Information© 2023 Project HOPE—The People-to-People Health Foundation, Inc.PDF download

ImportanceThe first MassHealth Social Determinants of Health payment model boosted payments for groups with unstable housing and those living in socioeconomically stressed neighborhoods. Improvements were designed to address previously mispriced subgroups and promote equitable payments to MassHealth accountable care organizations (ACOs).ObjectiveTo develop a model that ensures payments largely follow observed costs for members with complex health and/or social risks.Design, Setting, and ParticipantsThis cross sectional study used administrative data for members of the Massachusetts Medicaid program MassHealth in 2016 or 2017. Participants included members who were eligible for MassHealth’s managed care, aged 0 to 64 years, and enrolled for at least 183 days in 2017. A new total cost of care model was developed and its performance compared with 2 earlier models. All models were fit to 2017 data (most recent available) and validated on 2016 data. Analyses were begun in February 2019 and completed in January 2023.ExposuresModel 1 used age-sex categories, a diagnosis-based morbidity relative risk score (RRS), disability, serious mental illness, substance use disorder, housing problems, and neighborhood stress. Model 2 added an interaction for unstable housing with RRS. Model 3 added rurality and updated diagnosis-based RRS, medication-based RRS, and interactions between sociodemographic characteristics and morbidity.Main Outcome and MeasuresTotal 2017 annual cost was modeled and overall model performance (R2) and fair pricing of subgroups evaluated using observed-to-expected (O:E) ratios.ResultsAmong 1 323 424 members, mean (SD) age was 26.4 (17.9) years, 53.4% were female (46.6% male), and mean (SD) 2017 cost was $5862 ($15 417). The R2 for models 1, 2, and 3 was 52.1%, 51.5%, and 60.3%, respectively. Earlier models overestimated costs for members without behavioral health conditions (O:E ratios 0.94 and 0.93 for models 1 and 2, respectively) and underestimated costs for those with behavioral health conditions (O:E ratio >1.10); model 3 O:E ratios were near 1.00. Model 3 was better calibrated for members with housing problems, those with children, and those with high morbidity scores. It reduced underpayments to ACOs whose members had high medical and social complexity. Absolute and relative model performance were similar in 2016 data.Conclusions and RelevanceIn this cross-sectional study of data from Massachusetts Medicaid, careful modeling of social and medical risk improved model performance and mitigated underpayments to safety-net systems.

The objective of this study was to determine the prevalence and predictors of testing for sexually transmitted infections (STIs) under an accountable care model of health care delivery. Data sources were claims and encounter records from the Massachusetts Medicaid and Children’s Health Insurance Program (MassHealth) for enrollees aged 13 to 64 years in 2019. This cross-sectional study examines the one-year prevalence of STI testing and evaluates social determinants of health and other patient characteristics as predictors of such testing in both primary care and other settings. We identified visits with STI testing using procedure codes and primary care settings from provider code types. Among 740,417 members, 55% were female, 11% were homeless or unstably housed, and 15% had some level of disability. While the prevalence of testing in any setting was 20% (N = 151,428), only 57,215 members had testing performed in a primary care setting, resulting in an 8% prevalence of testing by primary care clinicians (PCCs). Members enrolled in a managed care organization (MCO) were significantly less likely to be tested by a primary care provider than those enrolled in accountable care organization (ACO) plans that have specific incentives for primary care practices to coordinate care. Enrollees in a Primary Care ACO had the highest rates of STI testing, both overall and by primary care providers. Massachusetts’ ACO delivery systems may be able to help practices increase STI screening with explicit incentives for STI testing in primary care settings.

On March 1, 2018, the Massachusetts Medicaid and Children's Health Insurance Program (MassHealth) launched an ambitious accountable care organization (ACO) program that sought to integrate care across the physical, behavioral, functional, and social services continuum while holding ACOs accountable for cost and quality. The study objective was to describe changes in health care utilization among MassHealth members during the pre-ACO baseline (2015-2017) and post-implementation periods (2018 and 2019). Using MassHealth administrative data, the authors conducted a repeated cross-sectional study of MassHealth members enrolled in ACOs during 2015-2019. Rates of primary care visits, all-cause and primary-care sensitive emergency department (ED) visits, ED boarding, hospitalizations, acute unplanned admissions, and readmissions were reported during the baseline period (2015-2017) and year 1 (2018) and year 2 (2019). Primary care visit rates increased for adult members throughout the study period from a baseline mean of 7.2-9.2 per member per year (observed-to-expected [O:E]: 1.16) in 2019. Observed all-cause hospitalization rates fell below expected values with O:E ratios of 0.96 among adults and 0.79 among children in 2018, and 0.96 and 0.92 among adults and children, respectively, in 2019. All-cause ED visit rates increased slightly, and rates of pediatric asthma-related admissions, unplanned admissions for adults with ambulatory care sensitive conditions, and unplanned admissions and ED boarding for adults with substance use disorder and serious mental illness all declined for the study period. These findings are suggestive of utilization shifts to higher-value, lower-cost care under Massachusetts's innovative and comprehensive ACO model.

Background:Suicide remains the 10th leading cause of death in the United States. Many patients presenting to healthcare settings with suicide risk are not identified and their risk mitigated during routine care. Our aim is to describe the planned methodology for studying the implementation of the Zero Suicide framework, a systems-based model designed to improve suicide risk detection and treatment, within a large healthcare system. Methods:We planned to use a stepped wedge design to roll-out the Zero Suicide framework over 4 years with a total of 39 clinical units, spanning emergency department, inpatient, and outpatient settings, involving ∼310,000 patients. We used Lean, a widely adopted a continuous quality improvement (CQI) model, to implement improvements using a centralize "hub" working with smaller "spoke" teams comprising CQI personnel, unit managers, and frontline staff. Results:Over the course of the study, five major disruptions impacted our research methods, including a change in The Joint Commission's safety standards for suicide risk mitigation yielding massive system-wide changes and the COVID-19 pandemic. What had been an ambitious program at onset became increasingly challenging because of the disruptions, requiring significant adaptations to our implementation approach and our study methods. Conclusions:Real-life obstacles interfered markedly with our plans. While we were ultimately successful in implementing Zero Suicide, these obstacles led to adaptations to our approach and timeline and required substantial changes in our study methodology. Future studies of quality improvement efforts that cut across multiple units and settings within a given health system should avoid using a stepped-wedge design with randomization at the unit level if there is the potential for sentinel, system-wide events.

Huntington's disease (HD) is a devastating, autosomal dominant neurodegenerative disease caused by a trinucleotide repeat expansion in the huntingtin (HTT) gene. Inactivation of the mutant allele by clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 based gene editing offers a possible therapeutic approach for this disease, but permanent disruption of normal HTT function might compromise adult neuronal function. Here, we use a novel HD mouse model to examine allele-specific editing of mutant HTT (mHTT), with a BAC97 transgene expressing mHTT and a YAC18 transgene expressing normal HTT. We achieve allele-specific inactivation of HTT by targeting a protein coding sequence containing a common, heterozygous single nucleotide polymorphism (SNP). The outcome is a marked and allele-selective reduction of mHTT protein in a mouse model of HD. Expression of a single CRISPR-Cas9 nuclease in neurons generated a high frequency of mutations in the targeted HD allele that included both small insertion/deletion (InDel) mutations and viral vector insertions. Thus, allele-specific targeting of InDel and insertion mutations to heterozygous coding region SNPs provides a feasible approach to inactivate autosomal dominant mutations that cause genetic disease.

IMPORTANCE:Better patient management can reduce emergency department (ED) use. Performance measures should reward plans for reducing utilization by predictably high-use patients, rather than rewarding plans that shun them.OBJECTIVE:The objective of this study was to develop a quality measure for ED use for people diagnosed with serious mental illness or substance use disorder, accounting for both medical and social determinants of health (SDH) risks.DESIGN:Regression modeling to predict ED use rates using diagnosis-based and SDH-augmented models, to compare accuracy overall and for vulnerable populations.SETTING:MassHealth, Massachusetts' Medicaid and Children's Health Insurance Program.PARTICIPANTS:MassHealth members ages 18-64, continuously enrolled for the calendar year 2016, with a diagnosis of serious mental illness or substance use disorder.EXPOSURES:Diagnosis-based model predictors are diagnoses from medical encounters, age, and sex. Additional SDH predictors describe housing problems, behavioral health issues, disability, and neighborhood-level stress.MAIN OUTCOME AND MEASURES:We predicted ED use rates: (1) using age/sex and distinguishing between single or dual diagnoses; (2) adding summarized medical risk (DxCG); and (3) further adding social risk (SDH).RESULTS:Among 144,981 study subjects, 57% were women, 25% dually diagnosed, 67% White/non-Hispanic, 18% unstably housed, and 37% disabled. Utilization was higher by 77% for those dually diagnosed, 50% for members with housing problems, and 18% for members living in the highest-stress neighborhoods. SDH modeling predicted best for these high-use populations and was most accurate for plans with complex patients.CONCLUSION:To set appropriate benchmarks for comparing health plans, quality measures for ED visits should be adjusted for both medical and social risks.

Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA The authors declare no conflict of interest. Correspondence to: Eric O. Mick, ScD, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605. E-mail: [email protected].

OBJECTIVES:The Massachusetts Medicaid and Children's Health Insurance Program, MassHealth, offers comprehensive Senior Care Options (SCO) plans to its Medicare-eligible members 65 years and older. Historically, MassHealth has paid a fixed per-person capitation rate for any "nursing home-certifiable" SCO member despite considerable heterogeneity of need. Our objective was to develop a model to predict long-term services and supports (LTSS) costs for community-dwelling SCO members.STUDY DESIGN:Concurrent predictive modeling.METHODS:We studied nursing home-certifiable SCO members who were enrolled for at least 183 days during 2016-2017 and used linear models to predict annual cost of community-based LTSS from demographic, medical, social determinants of health, and functional characteristics. We evaluated model performance using predictive performance (R2) and predictive ratios (observed costs divided by predicted costs) for various vulnerable subgroups.RESULTS:The modeling population included 35,259 enrollees. Mean (SD) annualized LTSS cost was $14,071 ($13,174). Functional status (ie, activities of daily living [ADLs] and instrumental ADLs) accounted for most of the variability in community LTSS cost (R2 = 18.4%) explainable by available variables. The Massachusetts SCO (MA-SCO) model (R2 = 21.6%) predicts accurately for several high-cost, vulnerable subgroups. Compared with fixed per-member capitation payments for all, the MA-SCO model reduces, for example, the payment to one plan by 28% and increases that to another by 35%.CONCLUSIONS:Predictive models using administrative data and functional status information can appropriately allocate payments for subgroups of members with LTSS needs that differ substantially from average. Calibrating payment to need mitigates incentives for population skimming and promotes the sustainability of mission-oriented organizations.

Small interfering RNAs (siRNAs) are a new class of drugs, exhibiting sequence-driven, potent, and sustained silencing of gene expression in vivo . We recently demonstrated that siRNA chemical architectures can be optimized to provide efficient delivery to the CNS. Many genetically-defined neurodegenerative disorders are autosomal dominant favoring selective silencing of the mutant allele. In some cases, successful targeting of the mutant allele requires targeting of a single nucleotide polymorphism (SNP) heterozygosity. Using Huntington’s disease as a model, we demonstrate allele-specific RNAi-based silencing of gene expression in vivo and in neurons differentiated from HD patient-derived iPSCs. A series of in vitro screens, with chemical and thermodynamic optimization, identified compounds with >50-fold selectivity for the mutant HD-causing allele, based on a single nucleotide difference. The optimized compound exhibits selective silencing of mutant huntingtin (HTT) protein in patient derived cells and throughout the HD mouse brain, providing a demonstration of SNP-based allele-specific RNAi silencing of gene expression in vivo in the CNS. The ability to target a disease-causing allele using RNAi-based therapies could be applied to a wide range of dominant CNS disorders, where maintenance of wild-type expression is essential.### Competing Interest StatementFC, EP, NA and AK are inventors of a patent application related to SNP-based discrimination. AK and NA own stock in Atalanta Therapeutics. Other authors declare no conflict of interest.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 diagnosed ADHD cases and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, revealing new and important information on the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions, loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies; a cohort of diagnosed ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-spcific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits. 28,29 and other neurodevelopmental traits 29 in the population. Genetic studies of quantitative ADHD symptom scores in children further support the hypothesis that ADHD is the extreme of a quantitative trait 30 . Here we present a genome-wide meta-analysis identifying the first genome-wide significant loci for ADHD using a combined sample of 55,374 individuals from an international collaboration. We also strengthen the case that the clinical diagnosis of ADHD is the extreme expression of one or more heritable quantitative traits, at least as it pertains to common variant genetic risk, by integrating our results with previous GWAS of ADHD-related behavior in the general population. significant educational phenotypes are omitted due to substantial overlap with years of schooling. Genetic correlation is presented as a dot and error bars indicate 95% confidence limits.

BACKGROUND:This study measured serial plasma human immunodeficiency virus (HIV)-1-specific antibody (Ab) levels in children who initiated antiretroviral therapy (ART) prior to 2 years of age, and evaluated their relationship to peripheral blood HIV-1 RNA and DNA levels. METHODS:We studied 46 HIV-1-infected children, stratified by age at ART initiation (<3 mo, early therapy [ET]; >3 mo-2 years, late therapy [LT]) and by virologic response (R) or non-response (NR), before and up to 4 years following ART. We studied 20 HIV-1-uninfected children born to HIV-1-infected mothers (seroreverters [SR]) as controls. Plasma immunoglobulin G (IgG) Ab levels directed against HIV-1 envelope (gp160, gp41), gag (capsid, p24; matrix, p17), reverse transcriptase (p66/51), and integrase (p31) were serially measured using quantitative enzyme-linked immunosorbent assays. HIV-1 Ab rates of decline were estimated over the first 15 months of the study. RESULTS:The HIV-1 Ab rates of decline in the ET-R group were similar to those in the SR group for all Ab specificities, except for p17 (P = .01). Ab decline rates in the LT-R group and the NR group were significantly slower than in the SR group for all tested Ab specificities. After 1 year of age, Ab levels to p31 and p17 were significantly associated with HIV-1 RNA levels (P < .001); Ab levels to gp160 (P < .001) and gp41 (P < .001) were significantly associated with cell-associated HIV-1 DNA levels. CONCLUSIONS:Quantitative HIV-1-specific Ab levels may be useful for screening children on ART for viral suppression or for residual, cell-associated HIV-1 DNA levels. CLINICAL TRIALS REGISTRATION:NCT00000872.

The presence of nonhuman RNAs in man has been questioned and it is unclear if food-derived miRNAs cross into the circulation. In a large population study, we found nonhuman miRNAs in plasma by RNA sequencing and validated a small number of pine-pollen miRNAs by RT-qPCR in 2,776 people. The presence of these pine-pollen miRNAs associated with hay fever and not with overt cardiovascular or pulmonary disease. Using in vivo and in vitro models, we found that transmission of pollen-miRNAs into the circulation occurs via pulmonary transfer and this transfer was mediated by platelet-pulmonary vascular cell interactions and platelet pollen-DNA uptake. These data demonstrate that pollen-derived plant miRNAs can be horizontally transferred into the circulation via the pulmonary system in humans. Although these data suggest mechanistic plausibility for pulmonary-mediated plant-derived miRNA transfer into the human circulation, our large observational cohort data do not implicate major disease or risk factor association.

Viral infections associate with disease risk and select families of viruses encode miRNAs that control an efficient viral cycle. The association of viral miRNA expression with disease in a large human population has not been previously explored. We sequenced plasma RNA from 40 participants of the Framingham Heart Study (FHS, Offspring Cohort, Visit 8) and identified 3 viral miRNAs from 3 different human Herpesviridae. These miRNAs were mostly related to viral latency and have not been previously detected in human plasma. Viral miRNA expression was then screened in the plasma of 2763 participants of the remaining cohort utilizing high-throughput RT-qPCR. All 3 viral miRNAs associated with combinations of inflammatory or prothrombotic circulating biomarkers (sTNFRII, IL-6, sICAM1, OPG, P-selectin) but did not associate with hypertension, coronary heart disease or cancer. Using a large observational population, we demonstrate that the presence of select viral miRNAs in the human circulation associate with inflammatory biomarkers and possibly immune response, but fail to associate with overt disease. This study greatly extends smaller singular observations of viral miRNAs in the human circulation and suggests that select viral miRNAs, such as those for latency, may not impact disease manifestation.

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

The prevalence and severity of depression differ in women and men and across racial groups. Psychosocial factors such as chronic stress have been proposed as contributors, but causes of this variation are not fully understood. Allostatic load, a measure of the physiological burden of chronic stress, is known to be associated with depression. Using data from the National Health and Nutrition Examination Survey 2005–2010, we examined the associations of nine allostatic load biomarkers with depression among US black and white adults aged 18–64 years (n = 6431). Depressive symptoms were assessed using the Patient Health Questionaire-9; logistic models estimated adjusted odds of depression based on allostatic load biomarkers. High-risk levels of c-reactive protein were significantly associated with increased odds of depression among white women (adjusted odds ratio (aOR) = 1.7, 95% CI: 1.1–2.5) and men (aOR = 1.8, 95% CI: 1.1–2.8) but not black women (aOR = 0.8, 95% CI: 0.6–1.1) or men (aOR = 0.9, 95% CI: 0.5–1.5). Among black men, hypertension (aOR = 1.7, 95% CI: 1.1–2.7) and adverse serum albumin levels (aOR = 1.7, 95% CI: 1.0–2.9) predicted depression, while high total cholesterol was associated with depression among black women (aOR = 1.6, 95% CI: 1.0–2.7). The associations between allostatic load biomarkers and depression varies with gendered race, suggesting that, despite consistent symptomatology, underlying disease mechanisms may differ between these groups.

Objective: Conceptually, access to primary care (through insurance) should reduce emergency department (ED) visits for primary care sensitive (PCS) conditions. We sought to identify characteristics of insured Massachusetts residents associated with PCS ED use, and compare such use for public versus private insurees. Population and Setting: People under age 65 in the Massachusetts All-Payer Claims Data, 2011-2012. Study Design: Retrospective, observational analysis of PCS ED use with nonurgent, urgent/ primary care treatable, and urgent/ potentially avoidable visits being considered PCS. We predicted utilization in 2012 using multivariable regression models and data available in 2011 administrative records. Principal Findings: Among 2,269,475 nonelderly Massachusetts residents, 40% had public insurance. Among public insurees, PCS ED use was higher than for private (mean, 36.5 vs. 9.0 per 100 persons; adjusted risk ratio, 2.53; 95% confidence limits, 2.49-2.56), while having any primary care visit was less common (70% vs. 83%), as was having any visit to one's own (attributed) primary care provider (38% vs. 44%). Conclusions: Public insurance was associated with less access to primary care and more PCS ED use; statewide labor shortages and low reimbursement rates from public insurance may have provided inadequate access to care that might otherwise have helped reduce PCS ED use.

Background and Purpose— There is increasing interest in extracellular RNAs (ex-RNAs), with numerous reports of associations between selected microRNAs (miRNAs) and a variety of cardiovascular disease phenotypes. Previous studies of ex-RNAs in relation to risk for cardiovascular disease have investigated small numbers of patients and assayed only candidate miRNAs. No human studies have investigated links between novel ex-RNAs and stroke. Methods— We conducted unbiased next-generation sequencing using plasma from 40 participants of the FHS (Framingham Heart Study; Offspring Cohort Exam 8) followed by high-throughput polymerase chain reaction of 471 ex-RNAs. The reverse transcription quantitative polymerase chain reaction included 331 of the most abundant miRNAs, 43 small nucleolar RNAs, and 97 piwi-interacting RNAs in 2763 additional FHS participants and explored the relations of ex-RNAs and prevalent (n=63) and incident (n=51) stroke and coronary heart disease (prevalent=286, incident=69). Results— After adjustment for multiple cardiovascular disease risk factors, 7 ex-RNAs were associated with stroke prevalence or incidence; there were no ex-RNA associated with prevalent or incident coronary heart disease. Statistically significant ex-RNA associations with stroke were specific, with no overlap between prevalent and incident events. Conclusions— This is the largest study of ex-RNAs in relation to stroke using an unbiased approach in an observational cohort and the first large study to examine human small noncoding RNAs beyond miRNAs. These results demonstrate that when studied in a large observational cohort, extracellular miRNAs are associated with stroke risk.

Importance Managed care payment formulas commonly allocate more money for medically complex populations, but ignore most social determinants of health (SDH). Objective To add SDH variables to a diagnosis-based payment formula that allocates funds to managed care plans and accountable care organizations. Design, Setting, and Participants Using data from MassHealth, the Massachusetts Medicaid and Children's Health Insurance Program, we estimated regression models predicting Medicaid spending using a diagnosis-based and SDH-expanded model, and compared the accuracy of their cost predictions overall and for vulnerable populations. MassHealth members enrolled for at least 6 months in 2013 in fee-for-service (FFS) programs (n = 357 660) or managed care organizations (MCOs) (n = 524 607). Exposures We built cost prediction models from a fee-for-service program. Predictors in the diagnosis-based model are age, sex, and diagnoses from claims. The SDH model adds predictors describing housing instability, behavioral health issues, disability, and neighborhood-level stressors. Main Outcomes and Measures Overall model explanatory power and overpayments and underpayments for subgroups of interest for all Medicaid-reimbursable expenditures excepting long-term support services (mean annual cost = $5590 per member). Results We studied 357 660 people who were FFS participants and 524 607 enrolled in MCOs with a combined 806 889 person-years of experience. The FFS program experience included more men (49.6% vs 43.6%), older patients (mean age of 26.1 years vs 21.6 years), and sicker patients (mean morbidity score of 1.16 vs 0.89) than MCOs. Overall, the SDH model performed well, but only slightly better than the diagnosis-based model, explaining most of the spending variation in the managed care population (validatedR2 = 62.4) and reducing underpayments for several vulnerable populations. For example, raw costs for the quintile of people living in the most stressed neighborhoods were 9.6% ($537 per member per year) higher than average. Since greater medical morbidity accounts for much of this difference, the diagnosis-based model underpredicts costs for the most stressed quintile by about 2.1% ($130 per member per year). The expanded model eliminates the neighborhood-based underpayment, as well as underpayments of 72% for clients of the Department of Mental Health (observed costs of about $30 000 per year) and of 7% for those with serious mental illness (observed costs of about $16 000 per year). Conclusions and Relevance Since October 2016, MassHealth has used an expanded model to allocate payments from a prespecified total budget to managed care organizations according to their enrollees’ social and medical risk. Extra payments for socially vulnerable individuals could fund activities, such as housing assistance, that could improve health equity.