IMPORTANCE Identifying the factors associated with premature stroke mortality and measuring between-county disparities may provide insight into how to reduce variations and achieve more equitable health outcomes. OBJECTIVE To examine the between-county disparities in premature stroke mortality in the US, investigate county-level factors associated with mortality, and describe differences in mortality disparities by place of death and stroke subtype. DESIGN, SETTING, AND PARTICIPANTS This retrospective cross-sectional study linked the mortality and demographic data of US counties from the Centers for Disease Control and Prevention WONDER database to county-level characteristics from multiple databases. The outcome measure was county-level age-adjusted stroke mortality among adults aged 25 to 64 years in 2637 US counties from 1999 to 2018. This study was conducted from April 1, 2019, to October 31, 2020. Generalized linear Poisson regressions were fitted to investigate 4 sets of factors associated with county-level mortality: demographic composition, socioeconomic status, health care and environmental features, and population health. The Theil index score was calculated to assess the mortality disparities. MAIN OUTCOMES AND MEASURES Stroke mortality was measured as the number of deaths attributed to stroke in the data set. Out-of-stroke-unit death was defined as any death occurring in outpatient or emergency departments or at the pretransport location. Five stroke subtypes were included in the analysis. RESULTS Although mortality did not change substantially from 1999 to 2018 (from 12.62 to 11.81 per 100 000 population), the proportion of deaths occurring out of the stroke unit increased from 23.56% (4328 of 18 369) to 34.57% (6978 of 20 188). A large percentage of stroke of an uncertain cause was reported, with most deaths (55.20%) occurring out of the stroke unit. In the county with the highest premature stroke mortality, the incidence was 20.78 times as high as that in the county with the lowest mortality (65.04 vs 3.13 deaths per 100 000 population). The highest between-county disparities were found for stroke of uncertain cause. For out-of-stroke-unit death, county-level mortality was largely associated with demographic composition (31.6%) and health care and environmental features (25.8%). For in-hospital death, 29.8% of county-level mortality was associated with population health and 28.7% was associated with demographic composition. CONCLUSIONS AND RELEVANCE These findings suggest that strategies addressing specific factors that underlie the mortality disparities among US counties, especially for out-of-stroke-unit death and stroke of uncertain cause, may be useful when tailored to the county-level context before implementing interventions for the neediest counties.

Background: This study investigated socioeconomic inequalities in premature cancer mortality by cancer types, and evaluated the associations between socioeconomic status (SES) and premature cancer mortality by cancer types. Methods: Using multiple databases, cancer mortality was linked to SES and other county characteristics. The outcome measure was cancer mortality among adults ages 25-64 years in 3,028 U.S. counties, from 1999 to 2018. Socioeconomic inequalities in mortality were calculated as a concentration index (CI) by income (annual median household income), educational attainment (% with bachelor's degree or higher), and unemployment rate. A hierarchical linear mixed model and dominance analyses were used to investigate SES associated with county-level mortality. The analyses were also conducted by cancer types. Results: CIs of SES factors varied by cancer types. Low-SES counties showed increasing trends in mortality, while high-SES counties showed decreasing trends. Socioeconomic inequalities in mortality among high-SES counties were larger than those among low-SES counties. SES explained 25.73% of the mortality. County-level cancer mortality was associated with income, educational attainment, and unemployment rate, at -0.24 [95% (CI): -0.36 to -0.12], -0.68 (95% CI: -0.87 to -0.50), and 1.50 (95% CI: 0.92-2.07) deaths per 100,000 population with one-unit SES factors increase, respectively, after controlling for health care environment and population health. Conclusions: SES acts as a key driver of premature cancer mortality, and socioeconomic inequalities differ by cancer types. Impact: Focused efforts that target socioeconomic drivers of mortalities and inequalities are warranted for designing cancer-prevention implementation strategies and control programs and policies for socioeconomically underprivileged groups.

Importance:Progress against premature death due to noncommunicable chronic disease (NCD) has stagnated. In the United States, county-level variation in NCD premature mortality has widened, which has impeded progress toward mortality reduction for the World Health Organization (WHO) 25 × 25 target. Objectives:To estimate variations in county-level NCD premature mortality, to investigate factors associated with mortality, and to present the progress toward achieving the WHO 25 × 25 target by analyzing the trends in mortality. Design, Setting, and Participants:This cross-sectional study focused on NCD premature mortality and its factors from 3109 counties using US mortality data for cause of death from the Centers for Disease Control and Prevention WONDER databases and county-level characteristics data from multiple databases. Data were collected from January 1, 1999, through December 31, 2017, and analyzed from April 1 through October 28, 2019. Exposures:County-level factors, including demographic composition, socioeconomic features, health care environment, and population health status. Main Outcomes and Measures:Variations in county-level, age-adjusted NCD mortality in the US residents aged 25 to 64 years and associations between mortality and the 4 sets of county-level factors. Results:A total of 6 794 434 deaths due to NCD were recorded during the study period (50.58% women; 16.49% aged 65 years or older). Mortality decreased by 4.30 (95% CI, -4.54 to -4.08) deaths per 100 000 person-years annually from 1999 to 2010 (P < .001) and decreased annually at a rate of 0.90 (95% CI, -1.13 to -0.73) deaths per 100 000 person-years annually from 2010 to 2017 (P < .001). Mortality in the county with the highest mortality was 10.40 times as high as that in the county with the lowest mortality (615.40 vs 59.20 per 100 000 population) in 2017. Geographic inequality was decomposed by between-state and within-state differences, and within-state differences accounted for most inequality (57.10% in 2017). County-level factors were associated with 71.83% variation in NCD mortality. Association with intercounty mortality was 19.51% for demographic features, 23.34% for socioeconomic composition, 16.40% for health care environment, and 40.75% for health-status characteristics. Conclusions and Relevance:Given the stagnated trend of decline and increasing variations in NCD premature mortality, these findings suggest that the WHO 25 × 25 target appears to be unattainable, which may be related to broad failure by United Nations members to follow through on commitments of reducing socioeconomic inequalities. The increasing inequalities in mortality are alarming and warrant expanded multisectoral efforts to ameliorate socioeconomic disparities.

Background Disparities in premature cardiac death (PCD) might stagnate the progress toward the reduction of PCD in the United States and worldwide. We estimated disparities across US counties in PCD rates and investigated county‐level factors related to the disparities. Methods and Results We used US mortality data for cause‐of‐death and demographic data from death certificates and county‐level characteristics data from multiple databases. PCD was defined as any death that occurred at an age between 35 and 74 years with an underlying cause of death caused by cardiac disease based on International Classification of Diseases, Tenth Revision (ICD‐10), codes. Of the 1 598 173 PCDs that occurred during 1999–2017, 60.9% were out of hospital. Although the PCD rates declined from 1999–2017, the proportion of out‐of‐hospital PCDs among all cardiac deaths increased from 58.3% to 61.5%. The geographic disparities in PCD rates across counties widened from 1999 (Theil index=0.10) to 2017 (Theil index=0.23), and within‐state differences accounted for the majority of disparities (57.4% in 2017). The disparities in out‐of‐hospital PCD rates (and in‐hospital PCD rates) associated with demographic composition were 36.51% (and 37.51%), socioeconomic features were 18.64% (and 18.36%), healthcare environment were 18.64% (and 13.90%), and population health status were 23.73% (and 30.23%). Conclusions Disparities in PCD rates exist across US counties, which may be related to the decelerated trend of decline in the rates among middle‐aged adults. The slower declines in out‐of‐hospital rates warrants more precision targeting and sustained efforts to ensure progress at better levels of health (with lower PCD rates) against PCD.

Although the federal electronic health record (EHR) incentive program has ended, the need to effectively implement and use EHRs has not. The advent of the federal Quality Payment Program (QPP) has made effective use of EHRs more critical than ever, especially for clinical quality measurement and improvement. However, practices continue to face challenges in successfully implementing and using EHRs to achieve these aims. We used a multiple case study approach to understand how physician practices were using EHR data to measure and improve quality. We interviewed a variety of physicians and staff at multiple practices of diverse sizes and settings. Our findings suggest specific approaches that can help practices better harness their EHR data to measure and improve the quality of care while reducing or preventing staff dissatisfaction and burnout. These lessons can help practices better leverage their EHRs to succeed in the QPP.

Cardiovascular diseases (CVDs) remain the biggest cause of deaths worldwide. More than 17 million people die from CVDs annually. The HeartRescue Global Program is a five-year international healthcare program, sponsored by Medtronic Philanthropy and its partners, that supports community-based demonstration projects specifically designed to expand access to life saving interventions for acute cardiovascular events. Both global in scope and local in nature, HeartRescue focuses on working with healthcare partners in select communities in China, India, and Russia. The Heart Rescue Global Program will draw upon important lessons learned and expertise from within the on-going Heart Rescue-US Project. HeartRescue China has begun operating in select districts of China. This article focuses on introduces in details of the specific aims of the project, major components and activities, Expected Measurable Outcomes of the HeartRescue Program. This article focuses on and introduces in detail.

OBJECTIVE:To examine the impact of the Medicare Physician Group Practice (PGP) demonstration on expenditure, utilization, and quality outcomes.DATA SOURCE:Secondary data analysis of 2001-2010 Medicare claims for 1,776,387 person years assigned to the ten participating provider organizations and 1,579,080 person years in the corresponding local comparison groups.STUDY DESIGN:We used a pre-post comparison group observational design consisting of four pre-demonstration years (1/01-12/04) and five demonstration years (4/05-3/10). We employed a propensity-weighted difference-in-differences regression model to estimate demonstration effects, adjusting for demographics, health status, geographic area, and secular trends.PRINCIPAL FINDINGS:The ten demonstration sites combined saved $171 (2.0%) per assigned beneficiary person year (p<0.001) during the five-year demonstration period. Medicare paid performance bonuses to the participating PGPs that averaged $102 per person year. The net savings to the Medicare program were $69 (0.8%) per person year. Demonstration savings were achieved primarily from the inpatient setting. The demonstration improved quality of care as measured by six of seven claims-based process quality indicators.CONCLUSIONS:The PGP demonstration, which used a payment model similar to the Medicare Accountable Care Organization (ACO) program, resulted in small reductions in Medicare expenditures and inpatient utilization, and improvements in process quality indicators. Judging from this demonstration experience, it is unlikely that Medicare ACOs will initially achieve large savings. Nevertheless, ACOs paid through shared savings may be an important first step toward greater efficiency and quality in the Medicare fee-for-service program.

This book provides a balanced assessment of pay for performance (P4P), addressing both its promise and its shortcomings. P4P programs have become widespread in health care in just the past decade and have generated a great deal of enthusiasm in health policy circles and among legislators, despite limited evidence of their effectiveness. On a positive note, this movement has developed and tested many new types of health care payment systems and has stimulated much new thinking about how to improve quality of care and reduce the costs of health care.

This book provides a balanced assessment of pay for performance (P4P), addressing both its promise and its shortcomings. P4P programs have become widespread in health care in just the past decade and have generated a great deal of enthusiasm in health policy circles and among legislators, despite limited evidence of their effectiveness. On a positive note, this movement has developed and tested many new types of health care payment systems and has stimulated much new thinking about how to improve quality of care and reduce the costs of health care. The current interest in P4P echoes earlier enthusiasms in health policy—such as those for capitation and managed care in the 1990s—that failed to live up to their early promise. The fate of P4P is not yet certain, but we can learn a number of lessons from experiences with P4P to date, and ways to improve the designs of P4P programs are becoming apparent. We anticipate that a “second generation” of P4P programs can now be developed that can have greater impact and be better integrated with other interventions to improve the quality of care and reduce costs.

OBJECTIVE The purpose of this research was to understand the roles of family members in dialysis care and to identify information gaps that renal professionals and organizations can address to better meet family member needs. METHODS Twelve triads were conducted with 41 family members. Triads explored caregiving roles and challenges, sources of dialysis information, and information needs across stages of dialysis care and a range of topics related to dialysis. RESULTS Resources and guidance for nutrition management was the most frequently reported information need. It was also the most common challenge and the role family members most frequently experienced with dialysis care. Other roles included providing emotional support, medication management, and transportation. Results also suggested that the information needs of family members may change over time. Stages included: (1) understanding the patient's diagnosis; (2) managing dialysis and its effects; and (3) understanding the long-term effects of dialysis. DISCUSSION Family members' information needs parallel the roles they play in caring for the patient, and these needs can change over time. Renal providers and professionals should acknowledge and address these needs of family members, whether they are new to dialysis or have years of experience. Informational materials tailored to or distributed during different stages of dialysis and greater access to family-member support opportunities are likely to be beneficial.

The purpose of this study was to examine motivators for and barriers to family-based detection for hereditary hemochromatosis (HH). HH patients ( n = 60) and HH siblings ( n = 25) participated in one-on-one or group interviews. Patients and siblings understood that HH “runs in families,” but not that siblings are at higher HH risk than other family members. Patient motivators included concern for siblings’ health, seriousness of untreated HH, and doctor’s encouragement to tell siblings that they need to seek diagnostic testing. Siblings were motivated by the seriousness of HH. Barriers included lack of symptoms, belief that HH was rare, and assumption that their doctor would have mentioned the risk of HH. Family-based detection continues to be a feasible part of an overall public health strategy to promote early detection of HH. Greater awareness of HH and its potential consequences, especially among high-risk groups, provides an additional potential avenue for public health action.

We studied Medicare's Dialysis Facility Compare (DFC) Web site, which publicly reports patient survival (mortality) data. We conducted qualitative research to evaluate how well patients and family members understand the patient survival data as it is currently explained and presented on DFC, and their view of its value. We also tested potential improvements using alternative language and display formats. Overall, participants responded positively to the patient survival data, indicating that publicly reporting this type of information has value for patients and family members. Participants could identify facilities with better performance but had difficulty understanding the statistical differences between patient survival ratings.