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PURPOSE To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms. METHODS Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS. RESULTS Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, –4.3 percentage points, 95% CI, –4.8 to –3.6; Hispanic, –3.6 percentage points, 95% CI, –4.4 to –2.9) and long-acting opioids (Black, –3.1 percentage points, 95% CI, –3.6 to –2.8; Hispanic, –2.2 percentage points, 95% CI, –2.7 to –1.7). They also received lower daily doses (Black, –10.5 MMED, 95% CI, –12.8 to –8.2; Hispanic, –9.1 MMED, 95% CI, –12.1 to –6.1) and lower total doses (Black, –210 MMEs, 95% CI, –293 to –207; Hispanic, –179 MMEs, 95% CI, –217 to –142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities. CONCLUSION There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.

This paper develops a satellite account for the US health sector and measures productivity growth in health care for the elderly population between 1999 and 2012. We measure the change in medical spending and health outcomes for a comprehensive set of 80 conditions. Medical care has positive productivity growth over the time period, with aggregate productivity growth of 1.5 percent per year. However, there is significant heterogeneity in productivity growth. Care for cardiovascular disease has had very high productivity growth. In contrast, care for people with musculoskeletal conditions has been costly but has not led to improved outcomes. (JEL E01, H51, I10)

This JAMA Forum discusses the problems facing the health care workforce in the wake of the COVID-19 pandemic, including a shortage of workers and the challenge of increasing wages, and highlights issues that policy makers and leaders may consider to address these problems.

This JAMA Forum discusses some guidelines for discussing deficit reduction and suggests 3 principles for keeping the rates of health insurance coverage high, using averages in an unaverage world, and distinguishing between cost shifting and cost cutting.

Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty. An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity.

Life expectancy in the US increased 3.3 years between 1990 and 2015, but the drivers of this increase are not well understood. We used vital statistics data and cause-deletion analysis to identify ...

The current US drug innovation financing framework rests on the notion that a defined period of marketing exclusivity combined with the expectation of reimbursement for clinically valuable, cost-effective therapies, followed by vigorous price competition from generic drugs and biosimilars ensures a sufficient return on investment (ROI) to incent private sector risk-based investment and research and development activities while providing access for new treatments to patients. While periodically, alternatives such as government prizes, direct purchases or development, and limits on certain incentives have been proposed, the basic approach has remained intact since the 1980s, with incremental provisions addressing specific gaps and priorities, and adding provisions for biosimilar entry. This paper reviews the main elements of the current US system to financing drug innovation and its approach to balancing multiple objectives. In addition, the system for financing drug innovation must be effective over a wide range of potential scientific approaches and economic conditions. It should be predictable for investors and payers making long-term development and coverage decisions, while also encompassing unanticipated new treatment modalities and scientific progress. An important emerging challenge is posed by clinically transformative, high-investment, single-administration therapies, such as gene therapy. Continued experimentation and the input of a range of stakeholders are needed to ensure next-generation therapeutic advances continue to be developed and made available to patients.

Information about an extensive set of health conditions on a well-defined sample of subjects is essential for assessing population health, gauging the impact of various policies, modeling costs, and studying health disparities. Unfortunately, there is no single data source that provides accurate information about health conditions. We combine information from several administrative and survey data sets to obtain model-based dummy variables for 107 health conditions (diseases, preventive measures, and screening for diseases) for elderly (age 65 and older) subjects in the Medicare Current Beneficiary Survey (MCBS) over the fourteen-year period, 1999–2012. The MCBS has prevalence of diseases assessed based on Medicare claims and provides detailed information on all health conditions but is prone to underestimation bias. The National Health and Nutrition Examination Survey (NHANES), on the other hand, collects self-reports and physical/laboratory measures only for a subset of the 107 health conditions. Neither source provides complete information, but we use them together to derive model-based corrected dummy variables in MCBS for the full range of existing health conditions using a missing data and measurement error model framework. We create multiply imputed dummy variables and use them to construct the prevalence rate and trend estimates. The broader goal, however, is to use these corrected or modeled dummy variables for a multitude of policy analysis, cost modeling, and analysis of other relationships either using them as predictors or as outcome variables.

ImportanceHealth systems play a central role in the delivery of health care, but relatively little is known about these organizations and their performance.ObjectiveTo (1) identify and describe health systems in the United States; (2) assess differences between physicians and hospitals in and outside of health systems; and (3) compare quality and cost of care delivered by physicians and hospitals in and outside of health systems.Evidence ReviewHealth systems were defined as groups of commonly owned or managed entities that included at least 1 general acute care hospital, 10 primary care physicians, and 50 total physicians located within a single hospital referral region. They were identified using Centers for Medicare & Medicaid Services administrative data, Internal Revenue Service filings, Medicare and commercial claims, and other data. Health systems were categorized as academic, public, large for-profit, large nonprofit, or other private systems. Quality of preventive care, chronic disease management, patient experience, low-value care, mortality, hospital readmissions, and spending were assessed for Medicare beneficiaries attributed to system and nonsystem physicians. Prices for physician and hospital services and total spending were assessed in 2018 commercial claims data. Outcomes were adjusted for patient characteristics and geographic area.FindingsA total of 580 health systems were identified and varied greatly in size. Systems accounted for 40% of physicians and 84% of general acute care hospital beds and delivered primary care to 41% of traditional Medicare beneficiaries. Academic and large nonprofit systems accounted for a majority of system physicians (80%) and system hospital beds (64%). System hospitals were larger than nonsystem hospitals (67% vs 23% with >100 beds), as were system physician practices (74% vs 12% with >100 physicians). Performance on measures of preventive care, clinical quality, and patient experience was modestly higher for health system physicians and hospitals than for nonsystem physicians and hospitals. Prices paid to health system physicians and hospitals were significantly higher than prices paid to nonsystem physicians and hospitals (12%-26% higher for physician services, 31% for hospital services). Adjusting for practice size attenuated health systems differences on quality measures, but price differences for small and medium practices remained large.Conclusions and RelevanceIn 2018, health system physicians and hospitals delivered a large portion of medical services. Performance on clinical quality and patient experience measures was marginally better in systems but spending and prices were substantially higher. This was especially true for small practices. Small quality differentials combined with large price differentials suggests that health systems have not, on average, realized their potential for better care at equal or lower cost.

OBJECTIVE: This study explored trends in the quantity of inpatient psychiatry beds and in facility characteristics. METHODS: Using the National Bureau of Economic Research’s Health Systems and Provider Database, the authors examined changes in the number of psychiatric facilities and beds, focusing on system ownership, profit status, facility type (general acute care versus freestanding), and affiliation with psychiatric hospital chains from 2010 to 2016. RESULTS: The number of psychiatric beds was relatively unchanged from 2010 (N=112,182 beds) to 2016 (N=111,184). However, the number of beds operated by systems increased by 39.8% (N=15,803); for-profits, by 56.9% (N=8,572); and chains, by 16.7% (N=6,256). Net increases in beds were primarily concentrated in for-profit freestanding psychiatric hospitals. In 2016, most for-profit beds were part of chains (70.2%) and systems (61.3%). CONCLUSIONS: Inpatient psychiatry has shifted toward increased ownership by systems, for-profits, and chains. Payers and policy makers should safeguard against profiteering, and future research should investigate the implications of these trends on quality of care.

PURPOSE Heightened regulations have decreased opioid prescribing across the United States, yet little is known about trends in opioid access among patients dying of cancer. METHODS Among 270,632 Medicare fee-for-service decedents with poor prognosis cancers, we used part D data to examine trends from 2007 to 2017 in opioid prescription fills and opioid potency (morphine milligram equivalents per day [MMED]) near the end-of-life (EOL), defined as the 30 days before death or hospice enrollment. We used administrative claims to evaluate trends in pain-related emergency department (ED) visits near EOL. RESULTS Between 2007 and 2017, the proportion of decedents with poor prognosis cancers receiving ≥ 1 opioid prescription near EOL declined 15.5% (relative percent difference [RPD]), from 42.0% (95% CI, 41.4 to 42.7) to 35.5% (95% CI, 34.9 to 36.0) and the proportion receiving ≥ 1 long-acting opioid prescription declined 36.5% (RPD), from 18.1% (95% CI, 17.6 to 18.6) to 11.5% (95% CI, 11.1 to 11.9). Among decedents receiving opioids near EOL, the mean daily dose fell 24.5%, from 85.6 MMED (95% CI, 82.9 to 88.3) to 64.6 (95% CI, 62.7 to 66.6) MMED. Overall, the total amount of opioids prescribed per decedent near EOL (averaged across those who did and did not receive an opioid) fell 38.0%, from 1,075 morphine milligram equivalents per decedent (95% CI, 1,042 to 1,109) to 666 morphine milligram equivalents per decedent (95% CI, 646 to 686). Simultaneously, the proportion of patients with pain-related ED visits increased 50.8% (RPD), from 13.2% (95% CI, 12.7 to 13.6) to 19.9% (95% CI, 19.4 to 20.4). Sensitivity analyses demonstrated similar declines in opioid utilization in the 60 and 90 days before death or hospice, and suggested that trends in opioid access were not confounded by secular trends in hospice utilization. CONCLUSION Opioid use among patients dying of cancer has declined substantially from 2007 to 2017. Rising pain-related ED visits suggests that EOL cancer pain management may be worsening.

Tirbanibulin is a novel tubulin polymerization and Src kinase signaling inhibitor. This study was designed to fully characterize tirbanibulin pharmacokinetics (PK) when applied topically under maximal use conditions. This was an open-label, parallel-group PK safety study of tirbanibulin ointment 1% applied to 25 cm2 of the face or balding scalp in adults with actinic keratosis (AK). Eligible subjects self-applied tirbanibulin once-daily for 5 days. PK sampling occurred on days 1, 3 and 4 at 0 hour (before dosing), and on day 5 at prespecified time points up to 24 hours after application. Safety assessments included adverse events and local skin reactions were evaluated up to day 29. Eighteen subjects (face or scalp, n = 9 each) completed the study. Subjects were White (100%), of mean [range] age 66.4 [43-83] years, predominantly men (83.3%) with Fitzpatrick skin type I to III (94.4%); baseline AK lesion count, mean [range] 8.2 [6-14]. All subjects had quantifiable but low plasma concentrations of tirbanibulin. On day 5, overall mean (standard deviation) maximum concentration (Cmax ) was 0.26 (0.23) ng/mL (or 0.60 nM), median time to maximum concentration was 6.91 hours, and mean (standard deviation) area under the plasma concentration-time curve from time 0 to 24 hours was 4.09 (3.15) ng ∙ h/mL. Four subjects experienced a total of 5 treatment-emergent adverse events that resolved. Mild to moderate erythema, flaking, or scaling in the treatment area peaked around day 8 before resolving or returning to baseline by day 29. In conclusion, under maximal use conditions, tirbanibulin ointment 1% for 5 days in the treatment of AK on the face or scalp was well tolerated and resulted in low systemic exposure with subnanomolar plasma concentrations.

There is considerable controversy about the causes of regional variations in health care expenditures. Using vignettes from patient and physician surveys linked to fee-for-service Medicare expenditures, this study asks whether patient demand-side factors or physician supply-side factors explain these variations. The results indicate that patient demand is relatively unimportant in explaining variations. Physician organizational factors matter, but the most important factor is physician beliefs about treatment. In Medicare, we estimate that 35 percent of spending for end-of-life care and 12 percent of spending for heart attack patients (and for all enrollees) is associated with physician beliefs unsupported by clinical evidence. (JEL D83, H75, I11, I18)

Medicare's Hospital Readmissions Reduction Program (HRRP) has been credited with lowering risk-adjusted readmission rates for targeted conditions at general acute care hospitals. However, these reductions appear to be illusory or overstated. This is because a concurrent change in electronic transaction standards allowed hospitals to document a larger number of diagnoses per claim, which had the effect of reducing risk-adjusted patient readmission rates. Prior studies of the HRRP relied upon control groups' having lower baseline readmission rates, which could falsely create the appearance that readmission rates are changing more in the treatment than in the control group. Accounting for the revised standards reduced the decline in risk-adjusted readmission rates for targeted conditions by 48 percent. After further adjusting for differences in pre-HRRP readmission rates across samples, we found that declines for targeted conditions at general acute care hospitals were statistically indistinguishable from declines in two control samples. Either the HRRP had no effect on readmissions, or it led to a systemwide reduction in readmissions that was roughly half as large as prior estimates have suggested.

It is unknown to what extent rising drug costs are due to inflation in the prices of existing drugs versus the entry of new products. We used pricing data from First Databank and pharmacy claims from UPMC Health Plan to quantify the contribution of new versus existing drugs to the changes in costs of oral and injectable drugs used in the outpatient setting in 2008-16. The costs of oral and injectable brand-name drugs increased annually by 9.2 percent and 15.1 percent, respectively, largely driven by existing drugs. For oral and injectable specialty drugs, costs increased 20.6 percent and 12.5 percent, respectively, with 71.1 percent and 52.4 percent of these increases attributable to new drugs. Costs of oral and injectable generics increased by 4.4 percent and 7.3 percent, respectively, driven by new drug entry. The rising costs of generic and specialty drugs were mostly driven by new product entry, whereas the rising costs of brand-name drugs were due to existing drug price inflation.

Background In Haiti, rural populations lack access to specialized care, such as dermatology, yet dermatological conditions are common causes for primary care (PC) visits. Studies on teledermatology from resource-constrained settings demonstrate promising results. We assessed the feasibility of implementing teledermatology in rural Haiti. Methods Patients with dermatological problems were examined by a PC provider in Haiti. Photos and intake evaluations were reviewed by U.S.-based dermatologists. Results Among 101 patients with dermatological problems, atopic disease and fungal infections were most common. Average diagnostic concordance between Haitian providers and U.S. dermatologists was 68.9%. The average time from intake to "case-closed" was 1.67 days. Discussion Diagnostic concordance and turn-around time were comparable to similar studies, demonstrating that teledermatology is well-suited for rural Haiti and could increase access to specialized care. Conclusion In Haiti and similar settings, telemedicine should be considered a viable option for meeting the health care needs of underserved populations.

1584 Background: There has been increasing hospital and health system ownership of physician practices in recent years, particularly in oncology. However, relatively little is known about how this impacts care delivery for patients with cancer, who use many hospital-based services that may be impacted by integration. We evaluated the impact of physician-hospital integration in oncology on spending and quality of care for Medicare beneficiaries with cancer. Methods: We used Medicare Fee-for-Service claims from 2005-2019 linked with a unique Health System and Provider Database, developed by National Bureau of Economic Research and Harvard University researchers, to track practice ownership relationships over time. We used a stacked event study to assess outcomes for patients three years before and after oncologists move from independent practices to hospital- or system- owned practices. We compared outcomes to a control group with oncologists who shifted from independent to hospital- or system-owned practices in later years. We focused on two cohorts of patients. The first cohort included cancer patients with presumed incident or recurrent cancer based on ≥2 visits to an oncologist and no visit in the past year. For these patients, we evaluated the impact of physician-hospital integration on the likelihood of receiving chemotherapy following the visit. The second cohort included 6-month episodes for patients receiving chemotherapy. For these patients we evaluated the impact of physician-hospital integration on spending, utilization, and quality. Quality measures included receipt of timely chemotherapy (within 60 days) following surgery, inpatient readmissions, non-use of tamoxifen + strong CYPD26 inhibitors, and end-of-life intensity of care measures. Results: There was no change in the likelihood of receiving chemotherapy with an initial oncology consultation following an oncologist’s transition to hospital-based employment. Total spending during six-month chemotherapy episodes increased by $1391 (95%CI: $465, $2316). The primary contributors to this growth were increases in spending on inpatient care, chemotherapy administration, and office visits. Spending growth, where observed, was driven primarily by higher Medicare prices for care in hospital outpatient settings. We found no positive impact of physician-hospital integration on timeliness of chemotherapy initiation, readmissions, concurrent use of tamoxifen+strong CYPD26 inhibitors, or intensity of end-of-life care. Conclusions: Physician-hospital integration resulted in higher prices and thus higher spending, but had limited impact on utilization and no detectable impacts on measures of quality. These results suggest that claims of quality improvements and concerns regarding overuse associated with physician-hospital integration may be overstated. Our results also support continued movement towards site-neutral payments.

Background and purpose: Paclitaxel is a widely used anti-neoplastic agent but has low oral bioavailability due to gut extrusion by P-glycoprotein (P-gp). Oral paclitaxel could be more convenient, less resource intensive, and more tolerable than intravenous administration. Encequidar (HM30181A) is a novel, minimally absorbed gut specific P-gp inhibitor. We tested whether administration of oral paclitaxel with encequidar (oPac+E) achieved comparable AUC to intravenous paclitaxel (IVP) 80mg/m2. Experimental approach: We conducted a multi-centre randomised crossover study with two treatment periods. Patients (pts) with advanced cancer received either oral paclitaxel 615mg/m2 divided over three days and encequidar 15mg orally one-hour prior, followed by IVP 80mg/m2, or the reverse sequence. PK blood samples were taken up to day 9 for oPac+E and day 5 for IVP. Key Results: 42 pts were enrolled; 35 completed both treatment periods. AUC0-∞was 5033.5 +/- 1401.1 ng.h/mL for oPac+E and 5595.9 +/- 1264.1 ng.h/mL with IVP. The geometric mean ratio (GMR) for AUC was 89.5% (90% CI 83.9-95.5). Mean absolute bioavailability of oPac+E was 12%. PK parameters did not change meaningfully after 4 weeks administration of oPac+E in an extension study. G3 treatment emergent adverse events occurred in 7 (18%) pts with oPac+E and 2 (5%) with IVP. 75% of pts preferred oPac+E over IVP. Conclusion and Implications: GMR for AUC was within the predefined acceptable range of 80%-125% for demonstrating equivalence. oPac+E is tolerable and there is no evidence of P-gp induction with repeat administration. With further study, oPac+E is a candidate to replace IVP.

A substantial literature has studied the influence of malpractice pressure on physician behavior. However, these studies generally focus on malpractice pressure stemming from state laws that govern liability exposure, which may be unknown or not salient to physicians. We test how physicians respond to malpractice allegations made directly against them. Our sample is Emergency Department physicians in Florida, where we have the universe of data on patients and how they are treated along with a census of malpractice complaints. We find that physicians oversee 9% fewer discharges after malpractice allegations and treat each discharge 4% more expensively after an allegation. These effects are true for both allegations that result in money paid and allegations which are dropped. Further, the increase in treatment is generalized, i.e., not limited to patients with conditions similar to what the physician is reported for. The results suggest significant, if modest, impacts of malpractice claims on medical practice.

IMPORTANCE: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use. OBJECTIVE: To provide insights into which EHR activities clinicians spend their time doing, the EHR tools they use, the system messages they receive, and the amount of time they spend using the EHR after hours. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed the deidentified metadata of ambulatory care health systems in the US, Canada, Northern Europe, Western Europe, the Middle East, and Oceania from January 1, 2019, to August 31, 2019. All of these organizations used the EHR software from Epic Systems and represented most of Epic Systems’s ambulatory customer base. The sample included all clinicians with scheduled patient appointments, such as physicians and advanced practice practitioners. EXPOSURES: Clinician EHR use was tracked by deidentified and aggregated metadata across a variety of clinical activities. MAIN OUTCOMES AND MEASURES: Descriptive statistics for clinician EHR use included time spent on clinical activities, note documentation (as measured by the percentage of characters in the note generated by automated or manual data entry source), messages received, and time spent after hours. RESULTS: A total of 371 health systems were included in the sample, of which 348 (93.8%) were located in the US and 23 (6.2%) were located in other countries. US clinicians spent more time per day actively using the EHR compared with non-US clinicians (mean time, 90.2 minutes vs 59.1 minutes; P

The landscape of the U.S. healthcare industry is changing dramatically as healthcare providers expand both within and across markets. While federal antitrust agencies have mounted several challenges to same-market combinations, they have not challenged any non-horizontal affiliations – including vertical integration of providers along the value chain of production. The Clayton Act prohibits combinations that “substantially lessen” competition; few empirical studies have focused on whether this is the source of harm from vertical combinations. We examine whether hospitals that are vertically integrated with skilled nursing facilities (SNFs) lessen competition among SNFs by foreclosing rival SNFs from access to the most lucrative referrals. Exploiting a plausibly exogenous shock to Medicare reimbursement for SNFs, we find that a 1 percent increase in a patient’s expected profitability to a SNF increases the probability that a hospital self-refers that patient (i.e., to a co-owned SNF) by 2.5 percent. We find no evidence that increased self-referrals improve patient outcomes or change post-discharge Medicare spending. Additional analyses show that when integrated SNFs are divested by their parent hospitals, independent rivals are less likely to exit. Together, the results suggest vertical integration in this setting may reduce downstream competition without offsetting benefits to patients or payers.

Introduction: Integrated care has long been put forward as a way to both decrease costs and improve the quality and outcomes. To date however, this hypothesis has not been formally evaluated in nationally representative data. In this study, using 100% Medicare data and a novel enhanced database (EDB), we aim to determine the association between integrated inpatient and outpatient care after acute myocardial infarction (AMI) and quality and outcomes. Hypothesis: There will be no difference in quality or outcomes after AMI between integrated and non-integrated inpatient and outpatient care. Methods: Using 100% Medicare data and a novel EDB that combines claims data, tax and financial records and proprietary datasets, we classified all hospitals and providers in the US as: “part of a health system” or “not part of a health system.” We then determined whether AMI patients had “integrated” (hospital and outpatient provider in the same system) or “non-integrated” (hospital and outpatient provider not in the same system) care after AMI. Multivariable linear regression was used to determine the association between care integration between hospital and outpatient provider and quality/outcomes. Results: The differences in quality and outcomes, based on care integration, are displayed below ( Table ). There were no differences in short-term quality or follow up. Integrated care was associated with significantly higher rates of cardiac rehab enrollment. Long-term quality metric performance and outcomes were uniformly better among patients who received integrated care. Conclusions: Integrated inpatient and outpatient care after AMI is associated with higher rates of cardiac rehab enrollment, better long-term quality and improved patient outcomes.

How quickly the 70 million infants, children, and adolescents living in the United States can reach appropriate levels of hospital care for potentially life-threatening illnesses or injuries is a critical, yet understudied, aspect of our nation’s pediatric public health preparedness.1,2 Although US health planning and policy efforts would benefit from a national understanding of how accessible general types of pediatric hospital services (newborn, emergency, and pediatric inpatient) and their more advanced counterparts (NICU, pediatric emergency, and PICU services, respectively) are, available studies have … Address correspondence to Alyna T. Chien, MD, MS, Division of General Pediatrics, Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115. E-mail: alyna.chien{at}childrens.harvard.edu

OBJECTIVE: To characterize physician health system membership in four states between 2012 and 2016 and to compare primary care quality and cost between in‐system providers and non‐system providers for the commercially insured population. DATA SOURCES: Physician membership in health systems was obtained from a unique longitudinal database on health systems and matched at the provider level to 2014 all‐payer claims data from Colorado, Massachusetts, Oregon, and Utah. STUDY DESIGN: Using an observational study design, we compared physicians in health systems to non‐system physicians located in the same state and geography on average cost of care (risk‐adjusted using the Johns Hopkins’ Adjusted Clinical Grouper), five HEDIS quality measures, one measure of developmental screening, and two Prevention Quality Indicator Measures. DATA COLLECTION/EXTRACTION METHODS: Patients in commercial health plans were attributed to a primary care physician accounting for the plurality of office visits. A cohort for each quality measure was constructed based on appropriate measure specifications. PRINCIPAL FINDINGS: The share of physicians in health systems increased steadily from 2012 to 2016 and ranged from 48% in Colorado to 63% in Utah in 2016. Compared to physicians not in a system, system physicians performed similarly on most HEDIS quality metrics compared to non‐system physicians. Patients attributed to in‐system physicians had about 40% higher rates (P

Reports of pain differ markedly across socioeconomic groups and are correlated with outcomes such as functional limitations and disability insurance receipt. This paper examines the differential experience of pain by education. We focus on knee pain, the most common musculoskeletal complaint. Comparing clinical interpretation of knee x-rays of people with and without pain, there are few differences in presence or clinical severity of arthritis across education groups. In contrast, less educated people report more pain for any given objective measure of arthritis. After confirming that reported pain maps to objective measures like walking speed and range of motion, we test four theories for differential experience of pain: differences in obesity, physically demanding occupations, psychological factors, and medical treatment differences. We find that physical demands on the job and obesity each explain about one-third of the education gradient in knee pain. There is an interaction between the two; physical requirements on the job are associated with knee pain primarily in those who are obese. In contrast, psychological traits and access to medical care explain little of the difference in reported pain by education level. These findings imply that educational gradients in pain are likely to persist or even widen as the need for physically demanding occupations—like home health aides and personal service workers—grows in importance with the aging population, and the working population continues to be obese.

Life expectancy in the US increased 3.3 years between 1990 and 2015, but the drivers of this increase are not well understood. We used vital statistics data and cause-deletion analysis to identify the conditions most responsible for changing life expectancy and quantified how public health, pharmaceuticals, other (nonpharmaceutical) medical care, and other/unknown factors contributed to the improvement. We found that twelve conditions most responsible for changing life expectancy explained 2.9 years of net improvement (85 percent of the total). Ischemic heart disease was the largest positive contributor to life expectancy, and accidental poisoning or drug overdose was the largest negative contributor. Forty-four percent of improved life expectancy was attributable to public health, 35 percent was attributable to pharmaceuticals, 13 percent was attributable to other medical care, and -7 percent was attributable to other/unknown factors. Our findings emphasize the crucial role of public health advances, as well as pharmaceutical innovation, in explaining improving life expectancy.

Estimating medical care productivity is a central economic challenge. This paper develops a satellite account for the US health sector that appropriately measures health care productivity and applies that to the elderly population between 1999 and 2012. The central output of the satellite account is health. The primary input is medical care; we also examine the impact of behavioral risk factors. Our empirical work measures the change in medical spending and health outcomes for a comprehensive set of 80 conditions. We estimate that medical care has positive productivity as a whole, with aggregate productivity growth of 9% over the time period. However, there is significant heterogeneity in productivity by condition. At the upper end, care for cardiovascular disease has been extremely productive. In contrast, care for people with mental illness and musculoskeletal conditions has been costly but not productive.

Key Points Question How do reported incidence, knowledge, and behaviors regarding coronavirus disease 2019 vary across sociodemographic characteristics in the US? Findings In this survey study, the largest differences in coronavirus disease 2019–related knowledge and behaviors were associated with race/ethnicity, sex, and age. African American participants, men, and people younger than 55 years were less likely to know how the disease is spread, were less likely to know the symptoms of coronavirus disease 2019, washed their hands less frequently, and left the home more often. Meaning These findings suggest that more effort is needed to increase accurate information and encourage appropriate behaviors among minority communities, men, and younger people., This survey study examines the association of sociodemographic characteristics with reported incidence, knowledge, and behavior regarding coronavirus disease 2019 among US adults., Importance Data from the coronavirus disease 2019 (COVID-19) pandemic in the US show large differences in hospitalizations and mortality across race and geography. However, there are limited data on health information, beliefs, and behaviors that might indicate different exposure to risk. Objective To determine the association of sociodemographic characteristics with reported incidence, knowledge, and behavior regarding COVID-19 among US adults. Design, Setting, and Participants A US national survey study was conducted from March 29 to April 13, 2020, to measure differences in knowledge, beliefs, and behavior about COVID-19. The survey oversampled COVID-19 hotspot areas. The survey was conducted electronically. The criteria for inclusion were age 18 years or older and residence in the US. Data analysis was performed in April 2020. Main Outcomes and Measures The main outcomes were incidence, knowledge, and behaviors related to COVID-19 as measured by survey response. Results The survey included 5198 individuals (mean [SD] age, 48 [18] years; 2336 men [45%]; 3759 white [72%], 830 [16%] African American, and 609 [12%] Hispanic). The largest differences in COVID-19–related knowledge and behaviors were associated with race/ethnicity, sex, and age, with African American participants, men, and people younger than 55 years showing less knowledge than other groups. African American respondents were 3.5 percentage points (95% CI, 1.5 to 5.5 percentage points; P = .001) more likely than white respondents to report being infected with COVID-19, as were men compared with women (3.2 percentage points; 95% CI, 2.0 to 4.4 percentage points; P

BackgroundData from the COVID-19 pandemic in the United States show large differences in hospitalizations and mortality across race and geography. However, there is limited data on health information, beliefs, and behaviors that might indicate different exposure to risk.MethodsA sample of 5,198 respondents in the United States (80% population representative, 20% oversample of hotspot areas in New York City, Seattle, New Orleans, and Detroit) was conducted from March 29th to April 13th to measure differences in knowledge, beliefs and behavior regarding COVID-19. Linear regression was used to understand racial, geographic, political, and socioeconomic differences in COVID-19 reported incidence knowledge, and behaviors after adjusting for state-specific and survey date fixed effects.ResultsThe largest differences in COVID-19 knowledge and behaviors are associated with race/ethnicity, gender, and age. African-Americans, men, and people ConclusionsThere are wide gaps in COVID-19 reported incidence, knowledge regarding disease spread and symptoms, and in social distancing behavior. The findings suggest more effort is needed to increase accurate information and encourage appropriate behaviors among minority communities, men, and younger people.

Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients' pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients' experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients' pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm's ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients' pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.