Socioeconomic Inequalities in Premature Cancer Mortality Among U.S. Counties During 1999 to 2018.

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.

Evaluation of Between-County Disparities in Premature Mortality Due to Stroke in the US.

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.

Disparities in Premature Cardiac Death Among US Counties From 1999-2017: Temporal Trends and Key Drivers.

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.

Factors Associated With County-Level Variation in Premature Mortality Due to Noncommunicable Chronic Disease in the United States, 1999-2017.

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.

Evaluating the performance of medicare fee-for-service providers using the health outcomes survey: a comparison of two methods.

BACKGROUND: Health status measures are now being used for evaluating the performance of health care organizations. Trends in SF-36 component scores have previously been examined for Medicare-managed care plans but not for providers serving Medicare fee-for-service (FFS) beneficiaries. We compared 2 methods for evaluating the performance of Medicare FFS providers, the Research Triangle Institute (RTI) and Health Assessment Laboratory (HAL) methods. METHODS: Data were collected from 6547 Medicare FFS beneficiaries in 10 cohorts. SF-36 Physical Health (PCS) and Mental Health (MCS) component scores were computed at baseline and after a 2-year follow-up. The RTI approach predicts follow-up scores based on a standard care regression model. The HAL approach determines the percentage of beneficiaries whose status is the "same or better" at follow-up. Both approaches then compare observed to expected scores for each cohort. RESULTS: The HAL method did not detect any statistically significant differences for the PCS; the RTI method detected a small PCS difference for one cohort. The HAL method identified 4 cohorts that had significantly higher MCS scores; the RTI approach identified one cohort with significantly lower scores. CONCLUSIONS: The 2 approaches provided consistent assessments of provider performance for the PCS but not for the MCS. The differences in the MCS results may have been affected by differing treatment of deaths during follow-up. The HAL approach disregards deaths for the MCS, whereas the RTI method imputes values for death. Implications of using self-reported health status for monitoring provider performance are discussed.

Applying business management models in health care.

Most health care management training programmes and textbooks focus on only one or two models or conceptual frameworks, but the increasing complexity of health care organizations and their environments worldwide means that a broader perspective is needed. This paper reviews five management models developed for business organizations and analyses issues related to their application in health care. Three older, more 'traditional' models are first presented. These include the functional areas model, the tasks model and the roles model. Each is shown to provide a valuable perspective, but to have limitations if used in isolation. Two newer, more 'innovative' models are next discussed. These include total quality management (TQM) and reengineering. They have shown potential for enabling dramatic improvements in quality and cost, but have also been found to be more difficult to implement. A series of 'lessons learned' are presented to illustrate key success factors for applying them in health care organizations. In sum, each of the five models is shown to provide a useful perspective for health care management. Health care managers should gain experience and training with a broader set of business management models.