Health Insurance Type and Outpatient Specialist Care Among Children With Asthma.

Importance: Although children with asthma are often successfully treated by primary care clinicians, outpatient specialist care is recommended for those with poorly controlled disease. Little is known about differences in specialist use for asthma among children with Medicaid vs private insurance. Objective: To examine differences among children with asthma regarding receipt of asthma specialist care by insurance type. Design, Setting, and Participants: In this cross-sectional study using data from the Massachusetts All Payer Claims Database (APCD) between 2014 to 2020, children with asthma were identified and differences in receipt of outpatient specialist care by whether their insurance was public (Medicaid and the Children's Health Insurance Program) or private were examined. Eligible participants included children with asthma in 2015 to 2020 aged 2 to 17 years. Data analysis was conducted from January 2023 to April 2024. Exposure: Medicaid vs private insurance. Main Outcomes and Measures: The primary outcome was receipt of specialist care (any outpatient visit with a pulmonology, allergy and immunology, or otolaryngology physician). Multivariable logistic regression models estimated differences in receipt of specialist care by insurance type accounting for child and area characteristics including demographics, health status, persistent asthma, calendar year, and zip code characteristics. Additional analyses examined if the associations of specialist care with insurance type varied by asthma persistence and severity, and whether associations varied over time. Results: Among 198 101 unique children, there were 432 455 child-year observations (186 296 female [43.1%] and 246 159 male [56.9%]; 211 269 aged 5 to 11 years [48.9%]; 82 108 [19.0%] with persistent asthma) including 286 408 (66.2%) that were Medicaid insured and 146 047 (33.8%) that were privately insured. Although persistent asthma was more common among child-year observations with Medicaid vs private insurance (57 381 [20.0%] vs 24 727 [16.9%]), children with Medicaid were less likely to receive specialist care. Overall, 64 239 child-year observations (14.9%) received specialist care, with substantially lower rates for children with Medicaid vs private insurance (34 093 child-year observations [11.9%] vs 30 146 child-year observations [20.6%]). Regression-based estimates confirmed these disparities; children with Medicaid had 55% lower odds of receiving specialist care (adjusted odds ratio, 0.45; 95% CI, 0.43 to 0.47) and a regression-adjusted 9.7 percentage point (95% CI, -10.4 percentage points to -9.1 percentage points) lower rate of receipt of specialist care. Compared with children with private insurance, there was an additional 3.2 percentage point (95% CI, 2.0 percentage points to 4.4 percentage points) deficit for children with Medicaid with persistent asthma. Conclusions and Relevance: In this cross-sectional study, children with Medicaid were less likely to receive specialist care, with the largest gaps among those with persistent asthma. These findings suggest that closing this care gap may be one approach to addressing ongoing disparities in asthma outcomes.

Disparities in Palliative Care Use for Patients With Blood Cancer Who Died in the Hospital.

Background: Palliative care can enhance quality of life during a terminal hospitalization. Despite advances in diagnostic and treatment tools, blood cancers lag behind solid malignancies in palliative use. It is not clear what factors affect palliative care use in blood cancer. Methods: We used the 2016 to 2019 National Inpatient Sample to identify demographic and socioeconomic factors associated with receiving palliative care among patients over age 18 with any malignant hematological diagnosis during a terminal hospitalization lasting at least 3 days, excluding those receiving a stem cell transplant. Results: Palliative care use was documented 54% of the time among 49,720 weighted cases (9944 distinct individual hospitalizations), approximately evenly distributed across the years 2016-2019. Palliative care use was lowest in 2016 (51%) and highest in 2018 (58%), and increased with age, reaching 58% for those 80 years and older. Men and women were similarly likely to receive care. Patients of Hispanic ethnicity and African Americans received less palliative care (47% and 49%, respectively), as did those insured by Medicaid (48%), and those admitted to small or rural hospitals (52% and 47%, respectively). Charges for hospitalizations with palliative care were 19% lower than for those without it. Conclusions: This study highlights disparities in palliative care use among blood-cancer patients who died in the hospital. It seems likely that many of the 46% who did not receive palliative care could have benefitted from it. Interventions are likely needed to achieve equitable access to ideal levels of palliative care services in late-stage blood cancer.

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.

Differences in Health Care Utilization for Asthma by Children with Medicaid versus Private Insurance.

Asthma is the most common chronic disease in children, disproportionately affects families with lower incomes, and is a leading reason for acute care visits and hospitalizations. This retrospective cohort study used the Massachusetts All Payer Claims Database (2014-2018) to examine differences in acute care utilization and quality of care for asthma between Medicaid- and privately insured children in Massachusetts. Outcomes included acute care use (emergency department [ED] or hospitalization), ED visits with asthma, routine asthma visits, and filled prescriptions for asthma medications. Multivariable logistic regression was used to account for differences in demographics, ZIP codes, health status, and asthma severity. Overall, 10.0% of Medicaid-insured children and 5.6% of privately insured were classified as having asthma. Among 317,596 child-year observations for children with asthma, 64.4% were insured by Medicaid. Medicaid-insured children had higher rates of any acute care use (50.4% vs. 30.0%) and ED visits with an asthma diagnosis (27.2% vs. 13.3%) compared to privately insured children. Only 65.4% of Medicaid enrollees had at least one routine asthma visit compared to 74.3% of privately insured children. Most children received at least one asthma medication (88.6% Medicaid vs. 83.3% privately insured), but a higher percentage of Medicaid-insured children received at least one rescue medication (84.0% vs. 73.7%), and a lower percentage of Medicaid-insured (46.1% vs. 49.2%) received a controller medication. These results suggest that opportunities for improvement in childhood asthma persist, particularly for children insured by Medicaid.

Practice Site Heterogeneity within and between Medicaid Accountable Care Organizations.

The existing literature has considered accountable care organizations (ACOs) as whole entities, neglecting potentially important variations in the characteristics and experiences of the individual practice sites that comprise them. In this observational cross-sectional study, our aim is to characterize the experience, capacity, and process heterogeneity at the practice site level within and between Medicaid ACOs, drawing on the Massachusetts Medicaid and Children's Health Insurance Program (MassHealth), which launched an ACO reform effort in 2018. We used a 2019 survey of a representative sample of administrators from practice sites participating in Medicaid ACOs in Massachusetts (n = 225). We quantified the clustering of responses by practice site within all 17 Medicaid ACOs in Massachusetts for measures of process change, previous experience with alternative payment models, and changes in the practices' ability to deliver high-quality care. Using multilevel logistic models, we calculated median odds ratios (MORs) and intraclass correlation coefficients (ICCs) to quantify the variation within and between ACOs for each measure. We found greater heterogeneity within the ACOs than between them for all measures, regardless of practice site and ACO characteristics (all ICCs ≤ 0.26). Our research indicates diverse experience with, and capacity for, implementing ACO initiatives across practice sites in Medicaid ACOs. Future research and program design should account for characteristics of practice sites within ACOs.

Strengthening Quality Measurement to Predict Success for Total Knee Arthroplasty: Results from a Nationally Representative Total Knee Arthroplasty Cohort.

BACKGROUND: When performed well on appropriate patients, total knee arthroplasty (TKA) can dramatically improve quality of life. Patient-reported outcome measures (PROMs) are increasingly used to measure outcome following TKA. Accurate prediction of improvement in PROMs after TKA potentially plays an important role in judging the surgical quality of the health-care institutions as well as informing preoperative shared decision-making. Starting in 2027, the U.S. Centers for Medicare & Medicaid Services (CMS) will begin mandating PROM reporting to assess the quality of TKAs. METHODS: Using data from a national cohort of patients undergoing primary unilateral TKA, we developed an original model that closely followed a CMS-proposed measure to predict success, defined as achieving substantial clinical benefit, specifically at least a 20-point improvement on the Knee injury and Osteoarthritis Outcome Score, Joint Arthroplasty (KOOS, JR) at 1 year, and an enhanced model with just 1 additional predictor: the baseline KOOS, JR. We evaluated each model's performance using the area under the receiver operator characteristic curve (AUC) and the ratio of observed to expected (model-predicted) outcomes (O:E ratio). RESULTS: We studied 5,958 patients with a mean age of 67 years; 63% were women, 93% were White, and 87% were overweight or obese. Adding the baseline KOOS, JR improved the AUC from 0.58 to 0.73. Ninety-four percent of those in the top decile of predicted probability of success under the enhanced model achieved success, compared with 34% in its bottom decile. Analogous numbers for the original model were less discriminating: 77% compared with 57%. Only the enhanced model predicted success accurately across the spectrum of baseline scores. The findings were virtually identical when we replicated these analyses on only patients ≥65 years of age. CONCLUSIONS: Adding a baseline knee-specific PROM score to a quality measurement model in a nationally representative cohort dramatically improved its predictive power, eliminating ceiling and floor effects and mispredictions for readily identifiable patient subgroups. The enhanced model neither favors nor discourages care for those with greater knee dysfunction and requires no new data collection. LEVEL OF EVIDENCE: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

The role of primary care providers in testing for sexually transmitted infections in the MassHealth Medicaid program.

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.

Maintaining Program Fidelity in a Changing World: National Implementation of a School-Based HIV Prevention Program.

Large-scale, evidence-based interventions face challenges to program fidelity of implementation. We developed implementation strategies to support teachers implementing an evidence-based HIV prevention program in schools, Focus on Youth in The Caribbean (FOYC) and Caribbean Informed Parents and Children Together (CImPACT) in The Bahamas. We examined the effects of these implementation strategies on teachers' implementation in the subsequent year after the initial implementation during the COVID-19 pandemic. Data were collected from 79 Grade 6 teachers in 24 government elementary schools. Teachers completed training workshops and a pre-implementation questionnaire to record their characteristics and perceptions that might affect their program fidelity. School coordinators and peer mentors provided teachers with monitoring, feedback, and mentoring. In Year 1, teachers on average taught 79.3% of the sessions and 80.8% of core activities; teachers in Year 2 covered 84.2% of sessions and 72.9% of the core activities. Teachers with "good" or "excellent" school coordinators in the second year taught significantly more sessions on average (7.8 vs. 7.0, t = 2.04, P < 0.05) and more core activities (26.3 vs. 23.0, t = 2.41, P < 0.05) than teachers with "satisfactory" coordinators. Teachers who had a "good" or "satisfactory" mentor taught more sessions than teachers who did not have a mentor (7.9 vs. 7.3; t = 2.22; P = 0.03). Two-level mixed-effects model analysis indicated that teachers' program fidelity in Year 1, confidence in the execution of core activities, and school coordinators' performance were significantly associated with Year 2 implementation dose. Implementation of FOYC + CImPACT was significantly associated with improved student outcomes. Teachers maintained high fidelity to a comprehensive HIV prevention program over 2 years during the COVID-19 pandemic. Future program implementers should consider additional implementation support to improve the implementation of school-based programs.

Measuring The Enduring Imprint Of Structural Racism On American Neighborhoods.

A long history of discriminatory policies in the United States has created disparities in neighborhood resources that shape ethnoracial health inequities today. To quantify these differences, we organized publicly available data on forty-two variables at the census tract level within nine domains affected by structural racism: built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth. Using data from multiple sources at several levels of geography, we developed scores in each domain, as well as a summary score that we call the Structural Racism Effect Index. We examined correlations with life expectancy and other measures of health for this index and other commonly used area-based indices. The Structural Racism Effect Index was more strongly associated with each health outcome than were the other indices. Its domain and summary scores can be used to describe differences in social risk factors, and they provide powerful new tools to guide policies and investments to advance health equity.

Changes in Health Care Utilization During the First 2 Years of Massachusetts Medicaid Accountable Care Organizations.

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.

Paying for Medical and Social Complexity in Massachusetts Medicaid.

Importance: The 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). Objective: To develop a model that ensures payments largely follow observed costs for members with complex health and/or social risks. Design, Setting, and Participants: This 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. Exposures: Model 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 Measures: Total 2017 annual cost was modeled and overall model performance (R2) and fair pricing of subgroups evaluated using observed-to-expected (O:E) ratios. Results: Among 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 Relevance: In 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.

Influenza Vaccinations Among Privately and Publicly Insured Children With Asthma.

OBJECTIVE: Annual influenza vaccination rates for children remain well below the Healthy People 2030 target of 70%. We aimed to compare influenza vaccination rates for children with asthma by insurance type and to identify associated factors. METHODS: This cross-sectional study examined influenza vaccination rates for children with asthma by insurance type, age, year, and disease status using the Massachusetts All Payer Claims Database (2014-2018). We used multivariable logistic regression to estimate the probability of vaccination accounting for child and insurance characteristics. RESULTS: The sample included 317,596 child-year observations for children with asthma in 2015-18. Fewer than half of children with asthma received influenza vaccinations; 51.3% among privately insured and 45.1% among Medicaid insured. Risk modeling reduced, but did not eliminate, this gap; privately insured children were 3.7 percentage points (pp) more likely to receive an influenza vaccination than Medicaid-insured children (95% confidence interval [CI]: 2.9-4.5pp). Risk modeling also found persistent asthma was associated with more vaccinations (6.7pp higher; 95% CI: 6.2-7.2pp), as was younger age. The regression-adjusted probability of influenza vaccination in a non-office setting was 3.2pp higher in 2018 than 2015 (95% CI: 2.2-4.2pp), and significantly lower for children with Medicaid. CONCLUSIONS: Despite clear recommendations for annual influenza vaccinations for children with asthma, low rates persist, particularly for children with Medicaid. Offering vaccines in non-office settings such as retail pharmacies may reduce barriers, but we did not observe increased vaccination rates in the first years after this policy change.

Secure Messaging for Diabetes Management: Content Analysis.

BACKGROUND: Secure messaging use is associated with improved diabetes-related outcomes. However, it is less clear how secure messaging supports diabetes management. OBJECTIVE: We examined secure message topics between patients and clinical team members in a national sample of veterans with type 2 diabetes to understand use of secure messaging for diabetes management and potential associations with glycemic control. METHODS: We surveyed and analyzed the content of secure messages between 448 US Veterans Health Administration patients with type 2 diabetes and their clinical teams. We also explored the relationship between secure messaging content and glycemic control. RESULTS: Explicit diabetes-related content was the most frequent topic (72.1% of participants), followed by blood pressure (31.7% of participants). Among diabetes-related conversations, 90.7% of patients discussed medication renewals or refills. More patients with good glycemic control engaged in 1 or more threads about blood pressure compared to those with poor control (37.5% vs 27.2%, P=.02). More patients with good glycemic control engaged in 1 more threads intended to share information with their clinical team about an aspect of their diabetes management compared to those with poor control (23.7% vs 12.4%, P=.009). CONCLUSIONS: There were few differences in secure messaging topics between patients in good versus poor glycemic control. Those in good control were more likely to engage in informational messages to their team and send messages related to blood pressure. It may be that the specific topic content of the secure messages may not be that important for glycemic control. Simply making it easier for patients to communicate with their clinical teams may be the driving influence between associations previously reported in the literature between secure messaging and positive clinical outcomes in diabetes.

Association Between the Modified Frailty Index and Outcomes Following Lobectomy.

INTRODUCTION: Elective thoracic surgery is safe in well-selected elderly patients. The association of frailty with postoperative morbidity in elective-lobectomy patients is understudied. We examined frailty as defined by abbreviated modified frailty index (mFI-5), mFI-11 in the thoracic surgery population, and the correlation between frailty and postoperative complications. METHODS: We studied outcomes of patients in two cohorts, 2010-2012 and 2013-2019, from the National Surgical Quality Improvement Program (NSQIP) database and used multivariable logistic regression models to predict all postoperative morbidity, mortality, and major morbidity. The mFI-5 could be calculated for all subjects (both 2010-2012, and 2013-2019); the mFI-11 could only be calculated for the 2010-2012 cohort. Patient frailty was defined as mFI≥3 (with either index). We used odds ratios (ORs) to examine associations of preoperative characteristics with postoperative complications and C-statistics to assess overall predictive power. RESULTS: Complications were less prevalent in the 2013-2019 cohort (17.9% versus 19.5%, P = 0.008). Open lobectomies were more common in the 2010-2012 cohort (53.9% versus 34.6%) and were strongly associated with postoperative morbidity and mortality (ORs >1.5) in both cohorts. Each frailty measure was associated with morbidity and mortality (ORs >1.4) after adjusting for other significant preoperative factors. Models on the 2010-2012 cohort had nearly identical C-statistics using the mFI-11 versus mFI-5 frailty indices (0.6142 versus 0.6139; P > 0.8). CONCLUSIONS: Frailty, as captured in the mFI-5, is a significant associated factor of postoperative morbidity and mortality following elective lobectomies. As a modifiable risk factor, frailty should be considered in surgical decision-making and when counseling patients regarding perioperative risks.

Predicting Adolescent Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.

Interventions to teach protective behaviors may be differentially effective within an adolescent population. Identifying the characteristics of youth who are less likely to respond to an intervention can guide program modifications to improve its effectiveness. Using comprehensive longitudinal data on adolescent risk behaviors, perceptions, sensation-seeking, peer and family influence, and neighborhood risk factors from 2564 grade 10-12 students in The Bahamas, this study employs machine learning approaches (support vector machines, logistic regression, decision tree, and random forest) to identify important predictors of non-responsiveness for precision prevention. We used 80% of the data to train the models and the rest for model testing. Among different machine learning algorithms, the random forest model using longitudinal data and the Boruta feature selection approach predicted intervention non-responsiveness best, achieving sensitivity of 85.4%, specificity of 78.4% and AUROC of 0.93 on the training data, and sensitivity of 84.3%, specificity of 67.1%, and AUROC of 0.85 on the test data. Key predictors include self-efficacy, perceived response cost, parent monitoring, vulnerability, response efficacy, HIV/AIDS knowledge, communication about condom use, and severity of HIV/STI. Machine learning can yield powerful predictive models to identify adolescents who are unlikely to respond to an intervention. Such models can guide the development of alternative strategies that may be more effective with intervention non-responders.

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.

Predictive algorithm to stratify newborns at-risk for child undernutrition in India: Secondary analysis of the National Family Health Survey-4.

Background: India is at the epicentre of global child undernutrition. Strategies to identify at-risk populations are needed in the context of limited resources. Methods: Data from children under the age of five surveyed in the 2015-2016 National Family Health Survey were used. Child undernutrition was assessed using anthropometric measurements. Predictor variables were identified from the extant literature and included if they could be measured at the time of delivery. Survey-weighted logistic regression was applied to model the outcome. Internal validation of the model was performed using 200 bootstrapped samples representing half of the total data sets. Results: In 2016, 54.4% (95% CI = 54.0%-54.8%) of Indian children were undernourished, according to a composite index of anthropometric failure. The predictive model for overall undernutrition included maternal (height, education, reproductive history, number of antenatal visits), child (sex, birthweight), and household characteristics (district of residence, caste, rural residence, toilet availability, presence of a separate kitchen). The model demonstrated reasonable discrimination ability (optimism-adjusted c = 0.67). The group of children classified in the lowest decile for risk of undernutrition had a prevalence of 25.9%, while the group classified in the highest decile had a prevalence of 77.4%. Conclusions: It is possible to stratify newborns at the time of delivery based on their risk for undernutrition in the first five years of life. The model developed by this study represents a first step in adopting a risk-score based approach for the most vulnerable population to receive services in a timely manner.

Association of Trauma Center Designation With Postdischarge Survival Among Older Adults With Injuries.

Importance: Trauma centers improve outcomes for young patients with serious injuries. However, most injury-related hospital admissions and deaths occur in older adults, and it is not clear whether trauma center care provides the same benefit in this population. Objective: To examine whether 30- and 365-day mortality of injured older adults is associated with the treating hospital's trauma center level. Design, Setting, and Participants: This prospective, population-based cohort study used Medicare claims data from January 1, 2013, to December 31, 2016, for all fee-for-service Medicare beneficiaries 66 years or older with inpatient admission for traumatic injury in 2014 to 2015. Data analysis was performed from January 1 to June 31, 2021. Preinjury health was measured using 2013 claims, and outcomes were measured through 2016. The population was stratified by anatomical injury pattern. Propensity scores for level I trauma center treatment were estimated using the Abbreviated Injury Scale, age, and residential proximity to trauma center and then used to match beneficiaries from each trauma level (I, II, III, and IV/non-trauma centers) by injury type. Exposure: Admitting hospital's trauma center level. Main Outcomes and Measures: Case fatality rates (CFRs) at 30 and 365 days after injury, estimated in the matched sample using multivariable, hierarchical logistic regression models. Results: A total of 433 169 Medicare beneficiaries (mean [SD] age, 82.9 [8.3] years; 68.4% female; 91.5% White) were included in the analysis. A total of 206 275 (47.6%) were admitted to non-trauma centers and 161 492 (37.3%) to level I or II trauma centers. Patients with isolated extremity fracture had the fewest deaths (365-day CFR ranged from 16.1% [95% CI, 11.2%-22.4%] to 17.4% [95% CI, 11.8%-24.6%] by trauma center status). Patients with both hip fracture and traumatic brain injury had the most deaths (365-day CFRs ranged from 33.4% [95% CI, 25.8%-42.1%] to 35.8% [95% CI, 28.9%-43.5%]). Conclusions and Relevance: These findings suggest that older adults do not benefit from existing trauma center care, which is designed with younger patients in mind. There is a critical need to improve trauma care practices to address common injury mechanisms and types of injury in older adults.