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Quality improvement (QI) may be aimed at improving care for all patients, or it may be targeted at only certain patient groups. Health care providers have little guidance when determining when targeted QI may be preferred.The aim was to develop a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which we apply to the performance of health plans for different patient groups.Retrospective analysis of 7 Health Care Effectiveness Data and Information Set (HEDIS) measures of clinical care quality.All Medicare Advantage (MA) beneficiaries eligible for any of 7 HEDIS measures 2015-2018.MA plans with higher overall performance tended to be less inconsistent in their performance (r=-0.2) across groups defined by race-and-ethnicity and low-income status (ie, dual eligibility for Medicaid or receipt of Low-Income Subsidy). Plan characteristics were usually associated with only small differences in inconsistency. The characteristics associated with differences in consistency [eg, size, Health Maintenance Organization (HMO) status] were also associated with differences in overall performance. We identified 9 (of 363) plans that had large inconsistency in performance across groups (0.8 SD) and investigated the reasons for inconsistency for 2 example plans.This newly developed inconsistency metric may help those designing and evaluating QI efforts to appropriately determine when targeted QI is preferred. It can be used in settings where performance varies across groups, which can be defined by patient characteristics, geographic areas, hospital wards, etc. Effectively targeting QI efforts is essential in today's resource-constrained health care environment.

OBJECTIVE: To investigate (a) the magnitude of the independent associations of neighborhood‐level and person‐level social risk factors (SRFs) with quality, (b) whether neighborhood‐level SRF associations may be proxies for person‐level SRF associations, and (c) how the association of person‐level SRFs and quality varies by neighborhood‐level SRFs. DATA SOURCES: 2015–2016 Medicare Advantage HEDIS data, Medicare beneficiary administrative data, and 2016 American Community Survey (ACS). STUDY DESIGN: Mixed effects linear regression models (1) estimated overall inequities by neighborhood‐level and person‐level SRFs, (2) compared neighborhood‐level associations to person‐level associations, and (3) tested the interactions of person‐level SRFs with corresponding neighborhood‐level SRFs. DATA COLLECTION/EXTRACTION METHODS: Beneficiary‐level SES and disability administrative data and five‐year ACS neighborhood‐level SRF information were each linked to HEDIS data. PRINCIPAL FINDINGS: For all or nearly all HEDIS measures, quality was worse in neighborhoods lower in SES and in neighborhoods with higher proportions of residents with a disability. Quality by neighborhood racial and ethnic composition was mixed. Accounting for corresponding person‐level SRFs reduced neighborhood SRF associations by 25% for disability, 43% for SES, and 74%–102% for racial and ethnic groups. Person‐level SRF coefficients were not consistently reduced in models that added neighborhood‐level SRFs. In 19 of 35 instances, there were significant (p

BACKGROUND Each year, about 10% of Medicare Advantage (MA) enrollees voluntarily switch to another MA contract, while another 2% voluntarily switch from MA to fee-for-service Medicare. Voluntary disenrollment from MA plans is related to beneficiaries' negative experiences with their plan, disrupts the continuity of care, and conflicts with goals to reduce Medicare costs. Little is known about racial/ethnic disparities in voluntary disenrollment from MA plans. OBJECTIVE The objective of this study was to investigate differences in rates of voluntary disenrollment from MA plans by race/ethnicity. SUBJECTS A total of 116,770,319 beneficiaries enrolled in 736 MA plans in 2015. METHODS Differences in rates of disenrollment across racial/ethnic groups [Asian or Pacific Islander (API), Black, Hispanic, and White] were summarized using 4 types of logistic regression models: adjusted and unadjusted models estimating overall differences and adjusted and unadjusted models estimating within-plan differences. Unadjusted overall models included only racial/ethnic group probabilities as predictors. Adjusted overall models added age, sex, dual eligibility, disability, and state of residence as control variables. Between-plan differences were estimated by subtracting within-plan differences from overall differences. RESULTS Adjusted rates of disenrollment were significantly (P

Health care quality varies by patient factors, including race-and-ethnicity and preferred language. Addressing inequities requires identifying them and incentivizing equity.We apply an approach first implemented in the Medicare Advantage setting to measure equity in patient experiences by race-and-ethnicity [Asian American and Native Hawaiian or Pacific Islander (AA and NHPI), Black, Hispanic, vs. White] and language preference (English-preferring vs. another-language-preferring). We identify characteristics of hospitals providing high-quality equitable care.We estimated, standardized, and combined performance measures into a Health Equity Summary Score (HESS) using 2016-2019 Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey data. The HCAHPS HESS considered current cross-sectional performance, within-hospital improvement, and overall improvement by race-and-ethnicity and language preference.A total of 3333 US hospitals with 2019 HCAHPS Star Ratings.The HCAHPS HESS was calculable for 44% of hospitals. High-scoring (4-5 diamonds on a 1-diamond to 5-diamond scale) hospitals tended to be smaller than intermediate-scoring [3 diamonds (14% of high-scoring hospitals had100 beds vs. 7% of intermediate-scoring hospitals, P0.001) and were less often for-profit (20% vs. 31%, P0.001)]. While a significant percentage (29%) of patients served by high-scoring hospitals were AA and NHPI, Black, or Hispanic, and 9% were another-language-preferring, there were smaller proportions of Black and Hispanic patients in high-scoring versus other hospitals. HESS performance was negatively associated with the percentage of patients preferring another language to English. HESS scores were moderately correlated with overall Star Ratings ( r =0.70).The HCAHPS HESS and practices of high-scoring hospitals could promote more equitable patient experiences.

Background Payers and policymakers are rewarding high-performing health care providers on the basis of summaries of overall quality performance, and the methods they use for measuring performance are increasingly important. Objective To develop and evaluate a measure that ranks health care systems by ambulatory care quality. Design Systems were ranked using a composite model that summarizes individual measures of quality, accounts for their correlation, and does not require health care systems to report every measure. The composite measure's suitability was evaluated by examining whether it captured the quality indicated by component measures (validity), whether differences in rank between health care systems were larger than statistical noise (reliability), and whether year-to-year changes in rank were small (stability). Setting California and Minnesota, 2014 to 2016. Participants 55 health care systems. Measurements Publicly reported measures of ambulatory care quality. Results The composite measure was valid in that it was broadly representative of the component measures and was not dominated by any single measure. The measure was reliable because the ranks for 93% of California systems and 80% of Minnesota systems were unlikely to be more than 2 places lower or higher. The measure was stable because fewer than half of systems changed ranks by more than 2 ranks from year to year. Limitation The analysis is limited to available measures of ambulatory care quality and includes only 2 states. Conclusion This composite measure uses publicly reported data to produce valid, reliable, and stable ranks of ambulatory care quality for health care systems in Minnesota and California, and this approach could be used in other applications. Primary funding source Agency for Healthcare Research and Quality.

The use of surrogate markers to examine the effectiveness of a treatment has the potential to decrease study length and identify effective treatments more quickly. Most available methods to investigate the usefulness of a surrogate marker involve restrictive parametric assumptions and tend to focus on settings where the surrogate is measured at a single point in time. However, in many clinical settings, the potential surrogate marker is often measured repeatedly over time, and thus, the surrogate marker information is a trajectory of measurements. In addition, it is often difficult in practice to correctly specify the relationship between a treatment, primary outcome, and surrogate marker trajectory. In this paper, we propose a model-free definition for the proportion of the treatment effect on the primary outcome that is explained by the treatment effect on the longitudinal surrogate markers. We propose three novel flexible methods to estimate this proportion, develop the asymptotic properties of our estimators, and investigate the robustness of the estimators under multiple settings via a simulation study. We apply our proposed procedures to an AIDS clinical trial dataset to examine a trajectory of CD4 counts as a potential surrogate.

Functional impairment predicts mortality and health care utilization. However, validated measures of functional impairment are not routinely collected during clinical encounters and are impractical to use for large-scale risk-adjustment or targeting interventions. This study’s purpose was to develop and validate claims-based algorithms to predict functional impairment using Medicare Fee-for-Service (FFS) 2014–2017 claims data linked with post-acute care (PAC) assessment data and weighted to better represent the overall Medicare FFS population. Using supervised machine learning, predictors were identified that best predicted two functional impairment outcomes measured in PAC data—any memory limitation and a count of 0–6 activity/mobility limitations. The memory limitation algorithm had moderately high sensitivity and specificity. The activity/mobility limitations algorithm performed well in identifying beneficiaries with five or more limitations, but overall accuracy was poor. This dataset shows promise for use in PAC populations, though generalizability to broader older adult populations remains a challenge.

OBJECTIVES: To evaluate whether sleep disturbances vary along a continuum of functional limitations in a large nationally representative sample of US adults. METHODS: Using 2014–2015 National Health Interview Survey data (n=33,424), we considered associations between each of five sleep disturbance measures (duration, trouble falling asleep, trouble staying asleep, use of sleep medications, waking rested) and Functional Limitations Index (FLI) score, which distinguishes among adults with little-or-no (least-limited), moderate (somewhat-limited), and high functional limitations (most-limited). RESULTS: Somewhat-limited and most-limited respondents reported significantly worse sleep health for all sleep disturbance measures than people with little-or-no limitations, even controlling for body mass index, psychological distress, and 14 health indicators. CONCLUSIONS: People with significant self-reported limitations in physical functioning, independent of specific disabilities or disabling condition, report more sleep disturbances. Clinicians may want to evaluate the sleep health of patients with functional limitations.

Summary When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference—namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

By reproducing differential expression analysis simulation results presented by Li et al, we identified a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested by Li et al. Please see related Research article with DOI 10.1186/s13059-022-02648-4.

BACKGROUND: Social risk factors (SRFs) such as minority race-and-ethnicity or low income are associated with quality-of-care, health, and healthcare outcomes. Organizations might prioritize improving care for easier-to-treat groups over those with SRFs, but measuring, reporting, and further incentivizing quality-of-care for SRF groups may improve their care. OBJECTIVE: To develop, as a proof-of-concept, a Health Equity Summary Score (HESS): a succinct, easy-to-understand score that could be used to promote high-quality care to those with SRFs in Medicare Advantage (MA) health plans, which provide care for almost twenty million older and disabled Americans and collect extensive quality measure and SRF data. DESIGN: We estimated, standardized, and combined performance scores for two sets of quality measures for enrollees in 2013–2016 MA health plans, considering both current levels of care, within-plan improvement, and nationally benchmarked improvement for those with SRFs (specifically, racial-and-ethnic minority status and dual-eligibility for Medicare and Medicaid). PARTICIPANTS: All MA plans with publicly reported quality scores and 500 or more 2016 enrollees. MAIN MEASURES: Publicly reported clinical quality and patient experience measures. KEY RESULTS: Almost 90% of plans measured for MA Star Ratings received a HESS; plans serving few patients with SRFs were excluded. The summary score was moderately positively correlated with publicly reported overall Star Ratings (r = 0.66–0.67). High-scoring plans typically had sizable enrollment of both racial-and-ethnic minorities (38–42%) and dually eligible beneficiaries (29–38%). CONCLUSIONS: We demonstrated the feasibility of developing and estimating a HESS that is intended to promote and incentivize excellent care for racial-and-ethnic minorities and dually eligible MA enrollees. The HESS measures SRF-specific performance and does not simply duplicate overall plan Star Ratings. It also identifies plans that provide excellent care to large numbers of those with SRFs. Our methodology could be extended to other SRFs, quality measures, and settings.

Background General population surveys are increasingly offering broader response options for questions on sexual orientation-for example, not only gay or lesbian, but also "something else" (SE) and "don't know" (DK). However, these additional response options are potentially confusing for those who may not know what the terms mean. Researchers studying sexual orientation-based disparities face difficult methodological trade-offs regarding how best to classify respondents identifying with the SE and DK categories. Objectives Develop respondent-level probabilities of sexual minority orientation without excluding or misclassifying the potentially ambiguous SE and DK responses. Compare 3 increasingly inclusive analytic approaches for estimating health disparities using a single item: (a) omitting SE and DK respondents; (b) classifying SE as sexual minority and omitting DK; and (c) a new approach classifying only SE and DK respondents with >50% predicted probabilities of being sexual minorities as sexual minority. Materials and methods We used the sociodemographic information and follow-up questions for SE and DK respondents in the 2013-2014 National Health Interview Survey to generate predicted probabilities of identifying as a sexual minority adult. Results About 94% of the 144 SE respondents and 20% of the 310 DK respondents were predicted to identify as a sexual minority adult, with higher probabilities for younger, wealthier, non-Hispanic white, and urban-dwelling respondents. Using a more specific definition of sexual minority orientation improved the precision of health and health care disparity estimates. Conclusions Predicted probabilities of sexual minority orientation may be used in this and other surveys to improve representation and categorization of those who identify as a sexual minority adult.

SummaryState-of-the-art methods for single-cell RNA sequencing (scRNA-seq) Differential Expression Analysis (DEA) often rely on strong distributional assumptions that are difficult to verify in practice. Furthermore, while the increasing complexity of clinical and biological single-cell studies calls for greater tool versatility, the majority of existing methods only tackle the comparison between two conditions. We propose a novel, distribution-free, and flexible approach to DEA for single-cell RNA-seq data. This new method, called ccdf, tests the association of each gene expression with one or many variables of interest (that can be either continuous or discrete), while potentially adjusting for additional covariates. To test such complex hypotheses, ccdf uses a conditional independence test relying on the conditional cumulative distribution function, estimated through multiple regressions. We provide the asymptotic distribution of the ccdf test statistic as well as a permutation test (when the number of observed cells is not sufficiently large). ccdf substantially expands the possibilities for scRNA-seq DEA studies: it obtains good statistical performance in various simulation scenarios considering complex experimental designs (i.e. beyond the two condition comparison), while retaining competitive performance with state-of-the-art methods in a two-condition benchmark. We apply ccdf to a large publicly available scRNA-seq dataset of 84,140 SARS-CoV-2 reactive CD8+ T cells, in order to identify the diffentially expressed genes across 3 groups of COVID-19 severity (mild, hospitalized, and ICU) while accounting for seven different cellular subpopulations.

The US health care system is experiencing a sharp increase in opioid-related adverse events and spending, and opioid overprescription may be a key factor in this crisis. Ambient opioid exposure within households is one of the known major dangers of overprescription.To quantify the association between the postsurgical initiation of prescription opioid use in opioid-naive patients and the subsequent prescription opioid misuse and chronic opioid use among opioid-naive family members.This cohort study was conducted using administrative data from the database of a US commercial insurance provider with more than 35 million covered individuals. Participants included pairs of patients who underwent surgery from January 1, 2008, to December 31, 2016, and their family members within the same household. Data were analyzed from January 1 to November 30, 2018.Duration of opioid exposure and refills of opioid prescriptions received by patients after surgery.Risk of opioid misuse and chronic opioid use in family members were calculated using inverse probability weighted Cox proportional hazards regression models.The final cohort included 843 531 pairs of patients and family members. Most pairs included female patients (445 456 [52.8%]) and male family members (442 992 [52.5%]), and a plurality of pairs included patients in the 45 to 54 years age group (249 369 [29.6%]) and family members in the 15 to 24 years age group (313 707 [37.2%]). A total of 3894 opioid misuse events (0.5%) and 7485 chronic opioid use events (0.9%) occurred in family members. In adjusted models, each additional opioid prescription refill for the patient was associated with a 19.2% (95% CI, 14.5%-24.0%) increase in hazard of opioid misuse in family members. The risk of opioid misuse appeared to increase only in households in which the patient obtained refills. Family members in households with any refill had a 32.9% (95% CI, 22.7%-43.8%) increased adjusted hazard of opioid misuse. When patients became chronic opioid users, the hazard ratio for opioid misuse among family members was 2.52 (95% CI, 1.68-3.80), and similar patterns were found for chronic opioid use.This cohort study found that opioid exposure was a household risk. Family members of a patient who received opioid prescription refills after surgery had an increased risk of opioid misuse and chronic opioid use.

Objective: Examine care integration—efforts to unify disparate parts of health care organizations to generate synergy across activities occurring within and between them—to understand whether and at which organizational level health systems impact care quality and staff experience. Data Sources: Surveys administered to one practice manager (56/59) and up to 26 staff (828/1360) in 59 practice sites within 24 physician organizations within 17 health systems in four states (2017-2019). Study Design: We developed manager and staff surveys to collect data on organizational, social, and clinical process integration, at four organizational levels: practice site, physician organization, health system, and outside health systems. We analyzed data using descriptive statistics and regression. Principal Findings: Managers and staff perceived opportunity for improvement across most types of care integration and organizational levels. Managers/staff perceived little variation in care integration across health systems. They perceived better care integration within practice sites than within physician organizations, health systems, and outside health systems—up to 38 percentage points (pp) lower (P

We developed and pilot-tested an eight-session community-based cognitive behavior therapy group intervention to improve coping with intersectional stigma, address medical mistrust, and improve antiretroviral treatment adherence. Seventy-six HIV-positive Latinx sexual minority men (SMM; 38 intervention, 38 wait-list control) completed surveys at baseline, and 4- and 7-months post-baseline. Adherence was electronically monitored. Intention-to-treat, repeated-measures regressions showed improved adherence in the intervention vs. control group from baseline to follow-up [electronically monitored: b (95% CI) 9.24 (- 0.55, 19.03), p = 0.06; self-reported: b (95% CI) 4.50 (0.70, 8.30), p = .02]. Intervention participants showed marginally decreased negative religious coping beliefs in response to stigma [b (95% CI) = - 0.18 (- 0.37, 0.01), p = .06], and significantly lower medical mistrust [b (95% CI) = - 0.47 (- 0.84, - 0.09), p = .02]. Our intervention holds promise for improving HIV outcomes by empowering Latinx SMM to leverage innate resilience resources when faced with stigma.ClinicalTrials.gov ID (TRN): NCT03432819, 01/31/2018.Hemos desarrollado un estudio piloto para poner a prueba un programa de ocho-sesiones de terapia cognitivo-conductual basado en un grupo de comunidad para abordar el estigma interseccional, la desconfianza médica y mejorar la adherencia al tratamiento antirretroviral. Setenta y seis hombres Latinos de minorías sexuales VIH positivos (38 en el grupo de intervención, 38 en el grupo de control de lista de espera) completaron encuestas al inicio, y cuatro y siete meses después de la línea de base. La adherencia fue medida electrónicamente. Los resultados del análisis mostraron una mejor adherencia en el grupo de intervención en comparación al grupo de control desde el inicio hasta el seguimiento [monitoreado electrónicamente: b (95% IC) 9.24 (− 0.55, 19.03), p = .06; y autoreporte: b (95% IC) 4.50 (0.70, 8.30), p = .02]. Los participantes del grupo de intervención mostraron una disminución marginal en las creencias negativas de afrontamiento religioso al estigma [b (95% IC) − 0.18 (− 0.37, 0.01), p = .06], y significativamente menor desconfianza médica [b (95% IC) − 0.47 (− 0.84, − 0.09), p = .02]. Nuestra intervención es prometedora para mejorar los resultados del VIH al empoderar a hombres Latinos de minorías sexuales para tomar ventaja de los recursos de resiliencia innatos cuando se enfrentan al estigma.

Objectives To develop an easy-to-interpret, patient-reported Functional Limitations Index (FLI) that can be used to assess and monitor the full spectrum of functioning in a community-dwelling population. Study design Observational design using nationally representative survey data. Methods We used self-rated health as a criterion for empirically assigning weights to 5 National Health Interview Survey items assessing difficulty with seeing, hearing, walking, cognition, and self-care. In addition to succinctly summarizing cumulative limitations, we addressed 2 main questions: (1) Which limitations have stronger associations with self-rated health? and (2) How does severity (from 0, no difficulty, to 3, unable to do) relate to self-rated health? We generated a respondent-level summary score based on a model predicting self-rated health from the 5 linearly scored (0-3) items and used splines to account for nonlinear severity-self-rated health associations. Results The strongest association of specific functional limitations with self-rated health involved mobility; the weakest associations involved sensory limitations. The association of severity with self-rated health was nonlinear and largest moving from no difficulty to somewhat difficult. Nationally, 5% of noninstitutionalized adults were considered most limited, 8% somewhat limited, and 87% least limited. Great mobility limitations (defined as a lot of difficulty or unable to do) most distinguished limitation groups (present in 0% of least limited, 25% of somewhat limited, and 70% of most limited). Conclusions The FLI is an easy-to-administer, easy-to-interpret, and valid summary measure of disability that health plans and health care organizations can use for quality-of-care monitoring across a variety of settings to improve care for patients with disabilities.

Objective To describe differences in patient experiences of hospital care by preferred language within racial/ethnic groups. Data source 2014-2015 HCAHPS survey data. Study design We compared six composite measures for seven languages (English, Spanish, Russian, Portuguese, Chinese, Vietnamese, and Other) within applicable subsets of five racial/ethnic groups (Hispanics, Asian/Pacific Islanders, American Indian/Alaska Natives, Blacks, and Whites). We measured patient-mix adjusted overall, between- and within-hospital differences in patient experience by language, using linear regression. Data collection methods Surveys from 5 480 308 patients discharged from 4517 hospitals 2014-2015. Principal findings Within each racial/ethnic group, mean reported experiences for non-English-preferring patients were almost always worse than their English-preferring counterparts. Language differences were largest and most consistent for Care Coordination. Within-hospital differences by language were often larger than between-hospital differences and were largest for Care Coordination. Where between-hospital differences existed, non-English-preferring patients usually attended hospitals whose average patient experience scores for all patients were lower than the average scores for the hospitals of their English-preferring counterparts. Conclusions Efforts should be made to increase access to better hospitals for language minorities and improve care coordination and other facets of patient experience in hospitals with high proportions of non-English-preferring patients, focusing on cultural competence and language-appropriate services.

OBJECTIVES To investigate whether health care experiences of adult Medicaid beneficiaries differ by race/ethnicity and rural/urban status. DATA SOURCES A total of 270 243 respondents to the 2014-2015 Nationwide Adult Medicaid Consumer Assessment of Healthcare Providers and Systems Survey. STUDY DESIGN Linear regression was used to estimate case mix adjusted differences in patient experience between racial/ethnic minority and non-Hispanic white Medicaid beneficiaries, and between beneficiaries residing in small urban areas, small towns, and rural areas vs large urban areas. Dependent measures included getting needed care, getting care quickly, doctor communication, and customer service. PRINCIPAL FINDINGS Compared with white beneficiaries, American Indian/Alaska Native (AIAN) and Asian/Pacific Islander (API) beneficiaries reported worse experiences, while black beneficiaries reported better experiences. Deficits for AIAN beneficiaries were 6-8 points on a 0-100 scale; deficits for API beneficiaries were 13-22 points (P's

Objectives. To describe the types of social services provided at community health centers (CHCs), characteristics of CHCs providing these services, and the association between on-site provision and health care quality.Methods. We surveyed CHCs in 12 US states and the District of Columbia during summer 2017 (n = 208) to identify referral to and provision of services to address 8 social needs. Regression models estimated factors associated with the provision of social services by CHCs and the association between providing services and health care quality (an 8-item composite).Results. CHCs most often offered on-site assistance for needs related to food or nutrition (43%), interpersonal violence (32%), and housing (30%). Participation in projects with community-based organizations was associated with providing services on-site (odds ratio = 2.48; P = .018). On-site provision was associated with better performance on measures of health care quality (e.g., each additional social service was associated with a 4.3 percentage point increase in colorectal cancer screenings).Conclusions. Some CHCs provide social services on-site, and this was associated with better performance on measures of health care quality.Public Health Implications. Health care providers are increasingly seeking to identify and address patients’ unmet social needs, and on-site provision of services is 1 strategy to consider.

IMPORTANCE: Following reductions in US ambulatory care early in the pandemic, it remains unclear whether care consistently returned to expected rates across insurance types and services. OBJECTIVE: To assess whether patients with Medicaid or Medicare-Medicaid dual eligibility had significantly lower than expected return to use of ambulatory care rates than patients with commercial, Medicare Advantage, or Medicare fee-for-service insurance. DESIGN, SETTING, AND PARTICIPANTS: In this retrospective cohort study examining ambulatory care service patterns from January 1, 2019, through February 28, 2021, claims data from multiple US payers were combined using the Milliman MedInsight research database. Using a difference-in-differences design, the extent to which utilization during the pandemic differed from expected rates had the pandemic not occurred was estimated. Changes in utilization rates between January and February 2020 and each subsequent 2-month time frame during the pandemic were compared with the changes in the corresponding months from the year prior. Age- and sex-adjusted Poisson regression models of monthly utilization counts were used, offsetting for total patient-months and stratifying by service and insurance type. EXPOSURES: Patients with Medicaid or Medicare-Medicaid dual eligibility compared with patients with commercial, Medicare Advantage, or Medicare fee-for-service insurance, respectively. MAIN OUTCOMES AND MEASURES: Utilization rates per 100 people for 6 services: emergency department, office and urgent care, behavioral health, screening colonoscopies, screening mammograms, and contraception counseling or HIV screening. RESULTS: More than 14.5 million US adults were included (mean age, 52.7 years; 54.9% women). In the March-April 2020 time frame, the combined use of 6 ambulatory services declined to 67.0% (95% CI, 66.9%-67.1%) of expected rates, but returned to 96.7% (95% CI, 96.6%-96.8%) of expected rates by the November-December 2020 time frame. During the second COVID-19 wave in the January-February 2021 time frame, overall utilization again declined to 86.2% (95% CI, 86.1%-86.3%) of expected rates, with colonoscopy remaining at 65.0% (95% CI, 64.1%-65.9%) and mammography at 79.2% (95% CI, 78.5%-79.8%) of expected rates. By the January-February 2021 time frame, overall utilization returned to expected rates as follows: patients with Medicaid at 78.4% (95% CI, 78.2%-78.7%), Medicare-Medicaid dual eligibility at 73.3% (95% CI, 72.8%-73.8%), commercial at 90.7% (95% CI, 90.5%-90.9%), Medicare Advantage at 83.2% (95% CI, 81.7%-82.2%), and Medicare fee-for-service at 82.0% (95% CI, 81.7%-82.2%; P

Sub-Saharan Africans infected with HIV-1C make up the largest AIDS patient population in the world and exhibit large heterogeneity in disease progression before initiating antiretroviral therapy. To identify host variants associated with HIV disease progression, we performed genome-wide association studies on a total of 556 treatment-naive HIV-infected individuals in Botswana. We characterized the pattern of HIV disease progression using a novel functional principal component analysis, which can better capture longitudinal CD4 and viral load (VL) trajectories. Two single-nucleotide polymorphisms (SNPs) near HCG22 (chr6, peak variant rs2535307, combined p = 3.72 × 10−7, minor allele as risky allele) and CCNG1 (chr5, peak variant kgp22385164, combined p = 1.88 × 10−6, minor allele as risky allele) were significantly associated with CD4 and VL dynamics. Inspection of SNPs in these gene regions in a third Botswana cohort (using GWATCH) also revealed a strong association of HCG22 with HIV-1C acquisition, suggesting that this region is associated with infection as well as disease progression. Our study uncovered two genetic regions that are significant and have specific effects on HIV-1C acquisition or progression in sub-Saharan Africans, and the result suggested new potential targets for AIDS prevention and treatment. In addition, our results also indicate the possibility of using genetic markers as HIV disease progression indicators in sub-Saharan Africans to prioritize fast progressors for antiretroviral treatment.

RESEARCH OBJECTIVE: About one‐tenth of Medicare Advantage (MA) enrollees voluntarily disenroll from their health plans annually. Voluntary disenrollment is related to beneficiaries’ negative experiences with their plan, disrupts continuity of care, and conflicts with goals to reduce Medicare costs. Research on racial and ethnic differences in voluntary disenrollment is important, as it may inform efforts to reduce racial and ethnic disparities in care. The few prior studies of this topic have had significant limitations, including reliance on administrative data on race and ethnicity that are often missing or inaccurate, failure to control for potential confounding variables such as dual eligibility for Medicare and Medicaid, and use of data that is more than 20 years old. Our investigation of racial and ethnic differences in rates of disenrollment from MA plans overcomes these limitations. STUDY DESIGN: Disenrollment data came from Medicare’s enrollment system. Social Security Administration data on race and ethnicity were augmented with surname, address, and other Medicare administrative data to calculate probabilities of membership in different racial and ethnic groups. We summarized differences in rates of disenrollment across racial and ethnic groups (Asian or Pacific Islander [API], black, Hispanic, and white) using four types of logistic regression models: adjusted and unadjusted models estimating overall differences and adjusted and unadjusted models estimating within‐plan differences. Unadjusted overall models included only racial and ethnic group probabilities as predictors. Adjusted overall models added age, gender, dual eligibility, disability, and state of residence as control variables. For each model, we calculated the difference from whites for each racial and ethnic minority group. We also estimated differences in disenrollment rates that are attributable to within‐plan effects. Between‐plan differences were estimated by subtracting within‐plan differences from overall differences. POPULATION STUDIED: 16 770 319 Medicare beneficiaries enrolled in 736 MA plans nationwide in 2015. PRINCIPAL FINDINGS: Adjusted rates of disenrollment were significantly (P

RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We presentdearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show thatdearseqcontrols the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.

The growth in healthcare spending is an important topic in the United States, and preterm and low-birthweight infants have some of the highest healthcare expenditures of any patient population. We performed a retrospective cohort study of spending in this population using a large, national claims database of commercially insured individuals. A total of 763,566 infants with insurance coverage through Aetna, Inc. for the first 6 months of post-natal life were included, and received approximately $8.4 billion (2016 USD) in healthcare services. Infants with billing codes indicating preterm status (n = 50,511) incurred medical expenditures of $76,153 on average, while low-birthweight status (n = 418) had the highest per infant average expenditures of $603,778. Understanding the drivers of variation in costs within gestational age and birthweight bands is an important target for future studies.

Introduction: As hospitals and physician organizations increasingly vertically integrate, there is an important opportunity to use health systems to improve performance. Prior research has largely relied on secondary data sources, but little is known about how health systems are organized “on the ground” and what mechanisms are available to influence physician practice at the front line of care.Methods: We collected in-depth information on eight health systems through key informant interviews, descriptive surveys, and document review. Qualitative data were systematically coded. We conducted analyses to identify organizational structures and mechanisms through which health systems influence practice.Results: As expected, we found that health systems vary on multiple dimensions related to organizational structure (e.g., size, complexity) which reflects history, market and mission. With regard to levers of influence, we observed within-system variation both in mechanisms (e.g., employment of physicians, system-wide EHR, standardization of service lines) and level of influence. Concepts such as “core” versus “peripheral” were more salient than “ownership” versus “contract.”Discussion: Data from secondary sources can help identify and map health systems, but they do not adequately describe them or the variation that exists within and across systems. To examine the degree to which health systems can influence performance, more detailed and nuanced information on health system characteristics is necessary.Conclusion: The mixed-methods data accrual approach used in this study provides granular qualitative data that enables researchers to describe multi-layered health systems, grasp the context in which they operate, and identify the key drivers of performance.

Background Adolescents respond differentially to substance use treatment based on their individual needs and goals. Providers may benefit from guidance (via decision rules) for personalizing aspects of treatment, such as level-of-care (LOC) placements, like choosing between outpatient or inpatient care. The field lacks an empirically-supported foundation to inform the development of an adaptive LOC-placement protocol. This work begins to build the evidence base for adaptive protocols by estimating them from a large observational dataset. Methods We estimated two-stage LOC-placement protocols adapted to individual adolescent characteristics collected from the Global Appraisal of Individual Needs assessment tool (n = 10,131 adolescents). We used a modified version of Q-learning, a regression-based method for estimating personalized treatment rules over time, to estimate four protocols, each targeting a potentially distinct treatment goal: one primary outcome (a composite of ten positive treatment outcomes) and three secondary (substance frequency, substance problems, and emotional problems). We compared the adaptive protocols to non-adaptive protocols using an independent dataset. Results Intensive outpatient was recommended for all adolescents at intake for the primary outcome, while low-risk adolescents were recommended for no further treatment at followup while higher-risk patients were recommended to inpatient. Our adaptive protocols outperformed static protocols by an average of 0.4 standard deviations (95 % confidence interval 0.2−0.6) of the primary outcome. Conclusions Adaptive protocols provide a simple one-to-one guide between adolescents’ needs and recommended treatment which can be used as decision support for clinicians making LOC-placement decisions.

To date, few programs that integrate traditional practices with evidence-based practices have been developed, implemented, and evaluated with urban American Indians/Alaska Natives (AI/ANs) using a strong research design. The current study recruited urban AI/AN teens across northern, central, and southern California during 2014-2017 to participate in a randomized controlled trial testing two cultural interventions that addressed alcohol and other drug (AOD) use. Adolescents were 14-18years old (inclusive), and either verbally self-identified as AI/AN or were identified as AI/AN by a parent or community member. We tested the added benefit of MICUNAY (Motivational Interviewing and Culture for Urban Native American Youth) to a CWG (Community Wellness Gathering). MICUNAY was a group intervention with three workshops that integrated traditional practices with motivational interviewing. CWGs were cultural events held monthly in each city. AI/AN urban adolescents (N=185) completed a baseline survey, were randomized to MICUNAY + CWG or CWG only, and then completed a three- and six-month follow-up. We compared outcomes on AOD use, spirituality, and cultural identification. Overall, AOD use remained stable over the course of the study, and we did not find significant differences between these two groups over time. It may be that connecting urban AI/AN adolescents to culturally centered activities and resources is protective, which has been shown in other work with this population. Given that little work has been conducted in this area, longer term studies of AOD interventions with urban AI/AN youth throughout the U.S. are suggested to test the potential benefits of culturally centered interventions.

American Indian and Alaska Native (AI/AN) youth exhibit multiple health disparities, including high rates of alcohol and other drug (AOD) use, violence and delinquency, and mental health problems. Approximately 70% of AI/AN youth reside in urban areas, where negative outcomes on behavioral health and well-being are often high. Identity development may be particularly complex in urban settings, where youth may face more fragmented and lower density AI/AN communities, as well as mixed racial-ethnic ancestry and decreased familiarity with AI/AN lifeways. This study examines racial-ethnic and cultural identity among AI/AN adolescents and associations with behavioral health and well-being by analyzing quantitative data collected from a baseline assessment of 185 AI/AN urban adolescents from California who were part of a substance use intervention study. Adolescents who identified as AI/AN on their survey reported better mental health, less alcohol and marijuana use, lower rates of delinquency, and increased happiness and spiritual health.

Key Points Question Is the adoption of more advanced electronic health record (EHR) capabilities associated with better ambulatory clinical quality of care? Findings In cross-sectional analyses of 3 states from 2014 to 2017, ambulatory clinics (769-972 per year) with more advanced EHR capabilities had higher scores on a composite measure of ambulatory clinical quality than did other clinics, which translated into an approximately 9% difference in rank order of quality. Across the much smaller number of clinics that gained advanced EHR capabilities between 2014 and 2017 (n = 72), the composite of ambulatory clinical quality improved more than for other clinics, but the difference was not statistically significant. Meaning This study suggests that ambulatory clinics with advanced EHR capabilities were associated with a better performance on a composite measure of ambulatory clinical quality than clinics with less-advanced EHR capabilities; clinics that adopted advanced EHR capabilities during a 3-year period were not associated with significant increases in ambulatory clinical quality performance., Importance Electronic health records (EHRs) are widely promoted to improve the quality of health care, but information about the association of multifunctional EHRs with broad measures of quality in ambulatory settings is scarce. Objective To assess the association between EHRs with different degrees of capabilities and publicly reported ambulatory quality measures in at least 3 clinical domains of care. Design, Setting, and Participants This cross-sectional and longitudinal study was conducted using survey responses from 1141 ambulatory clinics in Minnesota, Washington, and Wisconsin affiliated with a health system that responded to the Healthcare Information and Management Systems Society Annual Survey and reported performance measures in 2014 to 2017. Statistical analysis was performed from July 10, 2019, through February 26, 2021. Main Outcomes and Measures A composite measure of EHR capability that considered 50 EHR capabilities in 7 functional domains, grouped into the following ordered categories: no functional EHR, EHR underuser, EHR, neither underuser or superuser, EHR superuser; as well as a standardized composite of ambulatory clinical performance measures that included 3 to 25 individual measures (median, 13 individual measures). Results In 2014, 381 of 746 clinics (51%) were EHR superusers; this proportion increased in each subsequent year (457 of 846 clinics [54%] in 2015, 510 of 881 clinics [58%] in 2016, and 566 of 932 clinics [61%] in 2017). In each cross-sectional analysis year, EHR superusers had better clinical quality performance than other clinics (adjusted difference in score: 0.39 [95% CI, 0.12-0.65] in 2014; 0.29 [95% CI, −0.01 to 0.59] in 2015; 0.26 [95% CI, –0.05 to 0.56] in 2016; and 0.20 [95% CI, –0.04 to 0.45] in 2017). This difference in scores translates into an approximately 9% difference in a clinic’s rank order in clinical quality. In longitudinal analyses, clinics that progressed to EHR superuser status had only slightly better gains in clinical quality between 2014 and 2017 compared with the gains in clinical quality of clinics that were static in terms of their EHR status (0.10 [95% CI, −0.13 to 0.32]). In an exploratory analysis, different types of EHR capability progressions had different degrees of associated improvements in ambulatory clinical quality (eg, progression from no functional EHR to a status short of superuser, 0.06 [95% CI, −0.40 to 0.52]; progression from EHR underuser to EHR superuser, 0.18 [95% CI, −0.14 to 0.50]). Conclusions and Relevance Between 2014 and 2017, ambulatory clinics in Minnesota, Washington, and Wisconsin with EHRs having greater capabilities had better composite measures of clinical quality than other clinics, but clinics that gained EHR capabilities during this time had smaller increases in clinical quality that were not statistically significant., This cross-sectional study assesses the association between electronic health records with different degrees of capabilities and publicly reported ambulatory quality measures in at least 3 clinical domains of care.

This cross-sectional study examines changes in low-value care use and spending from 2014 to 2018 among the fee-for-service Medicare population., Key Points Question Have low-value care use and spending decreased over time with increasing focus on reducing waste in the US health care system? Findings In this cross-sectional study of more than 21 million individuals with fee-for-service Medicare, the percentage receiving any of 32 measured low-value services decreased marginally from 2014 to 2018. Claim line–level spending on low-value care per 1000 individuals did not decrease substantially over this period. Meaning This study found that among individuals with fee-for-service Medicare receiving any of 32 measured services, low-value care use and spending decreased marginally from 2014 to 2018, despite a national education campaign to address low-value care and increased attention on reducing health care waste., Importance Low-value care, defined as care offering no net benefit in specific clinical scenarios, is associated with harmful outcomes in patients and wasteful spending. Despite a national education campaign and increasing attention on reducing health care waste, recent trends in low-value care delivery remain unknown. Objective To assess national trends in low-value care use and spending. Design, Setting, and Participants In this cross-sectional study, analyses of low-value care use and spending from 2014 to 2018 were conducted using 100% Medicare fee-for-service enrollment and claims data. Included individuals were aged 65 years or older and continuously enrolled in Medicare parts A, B, and D during each measurement year and the previous year. Data were analyzed from September 2019 through December 2020. Exposure Being enrolled in fee-for-service Medicare for a period of time, in years. Main Outcomes and Measures The Milliman MedInsight Health Waste Calculator was used to assess 32 claims-based measures of low-value care associated with Choosing Wisely recommendations and other professional guidelines. The calculator designates services as wasteful, likely wasteful, or not wasteful based on an absence of indication of appropriate use in the claims history; calculator-designated wasteful services were defined as low-value care. Spending was calculated as claim-line level (ie, spending on the low-value service) and claim level (ie, spending on the low-value service plus associated services), adjusting for inflation. Results Among 21 045 759 individuals with fee-for-service Medicare (mean [SD] age, 77.4 [7.9] years; 12 515 915 [59.5%] women), the percentage receiving any of 32 low-value services decreased from 36.3% (95% CI, 36.3%-36.4%) to 33.6% (95% CI, 33.6%-33.6%) from 2014 to 2018. Uses of low-value services per 1000 individuals decreased from 677.8 (95% CI, 676.2-679.5) to 632.7 (95% CI, 632.6-632.8) from 2014 to 2018. Three services comprised approximately two-thirds of uses among 32 low-value services per 1000 individuals: preoperative laboratory testing decreased from 213.8 (95% CI, 213.4-214.2) to 166.2 (95% CI, 166.2-166.2), while opioids for back pain increased from 154.4 (95% CI, 153.6-155.2) to 182.1 (95% CI, 182.1-182.1) and antibiotics for upper respiratory infections increased from 75.0 (95% CI, 75.0-75.1) to 82 (95% CI, 82.0-82.0). Spending per 1000 individuals on low-value care also decreased, from $52 765.5 (95% CI, $51 952.3-$53 578.6) to $46 921.7 (95% CI, $46 593.7-$47 249.7) at the claim-line level and from $160 070.4 (95% CI, $158 999.8-$161 141.0) to $144 741.1 (95% CI, $144 287.5-$145 194.7) at the claim level. Conclusions and Relevance This cross-sectional study found that among individuals with fee-for-service Medicare receiving any of 32 measured services, low-value care use and spending decreased marginally from 2014 to 2018, despite a national education campaign in collaboration with clinician specialty societies and increased attention on low-value care. While most use of low-value care came from 3 services, 1 of these was opioid prescriptions, which increased over time despite the harms associated with their use. These findings may represent several opportunities to prevent patient harm and lower spending.

The extent to which disparities affect all sexual minority women (SMW) versus specific subgroups of lesbian, bisexual, or other women remains unclear, in part due to inconsistent analysis of available data. We propose an integrated approach that aggregates subgroups to maximize power to detect broadly applicable disparities, then tests for subgroup heterogeneity, exercising caution with disaggregation when there is no direct evidence of heterogeneity.Multivariate analyses of 2014-2015 National Health Interview Survey data examined six barriers to care. We compared heterosexual women (n = 36,474) with SMW (n = 1048) overall and tested for heterogeneous outcomes among subgroups of SMW compared with heterosexual women and with each other.Controlling for sociodemographics and health status, aggregated analyses showed that SMW were more likely than heterosexual peers to have trouble finding a provider (adjusted percentages 5.1% vs. 3.2%, p 0.01) and no optimal usual place of care (14.5% vs. 11.2%, p 0.01). There was no evidence of subgroup heterogeneity for either barrier, suggesting uniform disparities for SMW. In contrast, only lesbian/gay women were more likely than heterosexual women to be told their health insurance was not accepted (p = 0.03); this was the only outcome for which the adjusted difference between bisexual and lesbian/gay women was significant (2.8% vs. 6.3%, p = 0.02).Analyses that only disaggregated data would have understated overall sexual minority disparities and perhaps overstated subgroup differences; an integrated approach can more accurately characterize disparities experienced by all SMW versus those specific to certain subgroups. Large national surveys should include sexual orientation questions to support adequately powered comparisons.

OBJECTIVES: There are limited public health data on urban American Indian/Alaska Native (AI/AN) populations, particularly adolescents. The current study attempted to address gaps by providing descriptive information on experiences of urban AI/AN adolescents across northern, central, and southern California. DESIGN: We describe demographics and several behavioral health and cultural domains, including: alcohol and other drug (AOD) use, risky sexual behavior, mental and physical health, discrimination experiences, involvement in traditional practices, and cultural pride and belonging. We recruited 185 urban AI/AN adolescents across northern, central, and southern California from 2014 to 2017 who completed a baseline survey as part of a randomized controlled intervention trial. RESULTS: Average age was 15.6 years; 51% female; 59% of adolescents that indicated AI/AN descent also endorsed another race or ethnicity. Rates of AOD use in this urban AI/AN sample were similar to rates for Monitoring the Future. About one-third of adolescents reported ever having sexual intercourse, with 15% reporting using alcohol or drugs before sex. Most reported good mental and physical health. Most urban AI/AN adolescents participated in traditional practices, such as attending Pow Wows and learning their tribal history. Adolescents also reported discrimination experiences, including being a victim of racial slurs and discrimination by law enforcement. CONCLUSIONS: This study describes a select sample of California urban AI/AN adolescents across several behavioral health and cultural domains. Although these adolescents reported numerous discrimination experiences and other stressors, findings suggest that this sample of urban AI/AN teens may be particularly resilient with regard to behavioral health.

The Improving Medicare Post-Acute Care Transformation Act of 2014 mandates that the Centers for Medicare & Medicaid Services (CMS) use standardized patient assessment data elements across post-acute care settings. RAND was tasked by CMS with developing and testing the data elements. This report, Volume 2 of a series, focuses on the design and methods for the National Beta Test.

The Improving Medicare Post-Acute Care Transformation Act of 2014 mandates that the Centers for Medicare & Medicaid Services (CMS) use standardized patient assessment data elements across post-acute care settings. CMS tasked RAND with developing and testing the data elements. This report, Volume 8 of a series, presents test data and interpretations of the results for data elements developed for non-communicative patients.

We propose an improved estimator of the complier average causal effect (CACE). Researchers typically choose a presumably-unbiased estimator for the CACE in studies with noncompliance, when many other lower-variance estimators may be available. We propose a synthetic estimator that combines information across all available estimators, leveraging the efficiency in lower-variance estimators while maintaining low bias. Our approach minimizes an estimate of the mean squared error of all convex combinations of the candidate estimators. We derive the asymptotic distribution of the synthetic estimator and demonstrate its good performance in simulation, displaying a robustness to inclusion of even high-bias estimators.

The Improving Medicare Post-Acute Care Transformation Act of 2014 mandates that the Centers for Medicare & Medicaid Services (CMS) use standardized patient assessment data elements across post-acute care settings. CMS tasked RAND with developing and testing the data elements. This report, Volume 7 of a series, presents test data and interpretations of the results for care preferences, medication reconciliation, and global health data elements.

The Improving Medicare Post-Acute Care Transformation Act of 2014 mandates that the Centers for Medicare & Medicaid Services (CMS) use standardized patient assessment data elements across post-acute care settings. RAND was tasked by CMS with developing and testing the data elements. This report, Volume 6 of a series, presents results for data elements in the categories of (1) impairments and (2) special services, treatments, and interventions.

The Improving Medicare Post-Acute Care Transformation Act of 2014 mandates that the Centers for Medicare & Medicaid Services (CMS) use standardized patient assessment data elements across post-acute care settings. RAND was tasked by CMS with developing and testing the data elements. This report, Volume 5 of a series, presents test data and interpretations of the results for data elements in the clinical categories of mental status and pain.

ObjectiveWe investigated the presence of non-neuromuscular phenotypes in patients affected by Spinal Muscular Atrophy (SMA), a disorder caused by a mutation in the Survival of Motor Neuron (SMN) gene, and whether these phenotypes may be clinically detectable prior to clinical signs of neuromuscular degeneration and therefore independent of muscle weakness.MethodsWe utilized a de-identified database of insurance claims to explore the health of 1,038 SMA patients compared to controls. Two analyses were performed: (1) claims from the entire insurance coverage window; and (2) for SMA patients, claims prior to diagnosis of any neuromuscular disease or evidence of major neuromuscular degeneration to increase the chance that phenotypes could be attributed directly to reduced SMN levels. Logistic regression was used to determine whether phenotypes were diagnosed at significantly different rates between SMA patients and controls and to obtain covariate-adjusted odds ratios.ResultsResults from the entire coverage window revealed a broad spectrum of phenotypes that are differentially diagnosed in SMA subjects compared to controls. Moreover, data from SMA patients prior to their first clinical signs of neuromuscular degeneration revealed numerous non-neuromuscular phenotypes including defects within the cardiovascular, gastrointestinal, metabolic, reproductive, and skeletal systems. Furthermore, our data provide evidence of a potential ordering of disease progression beginning with these non-neuromuscular phenotypes.ConclusionsOur data point to a direct relationship between early, detectable non-neuromuscular symptoms and SMN deficiency. Our findings are particularly important for evaluating the efficacy of SMN-increasing therapies for SMA, comparing the effectiveness of local versus systemically delivered therapeutics, and determining the optimal therapeutic treatment window prior to irreversible neuromuscular damage.