Structural Factors and Racial/Ethnic Inequities in Travel Times to Acute Care Hospitals in the Rural US South, 2007-2018

Urology on a changing planet: links between climate change and urological disease

Urological diseases and their varied forms of management warrant special attention in the setting of climate change. Regarding urological cancers, climate change will probably increase the incidence and severity of cancer diagnoses through exposures to certain environmental risk factors, while simultaneously disrupting cancer care delivery and downstream outcomes. Regarding benign urological diseases, a burgeoning body of work exists on climate-related heat waves, dehydration, urolithiasis, renal injury and infectious and vector-borne diseases. Adding to the potential effect on disease pathogenesis, many patients with urological diseases undergo high-tech, resource-intensive interventions, such as robotic surgery, and entail intensive longitudinal assessments over many years. These features incur a considerable carbon footprint, generate substantial waste, and can introduce vulnerabilities to climate-related weather events. Links exist between planetary health (the health of humans and the natural systems that support our health), climate change and urological disease and urological care providers face many challenges in the era of anthropogenic climate change. The next steps and priorities for research, management, and health care delivery include identification and prioritization of health care delivery strategies to minimize waste and carbon emissions, while supporting climate resilience. Examples include supporting telemedicine, limiting low-value care, and building resilience to minimize impacts of climate-related disasters to prepare for the challenges ahead.

Ten Americas: a systematic analysis of life expectancy disparities in the USA

BACKGROUND

Nearly two decades ago, the Eight Americas study offered a novel lens for examining health inequities in the USA by partitioning the US population into eight groups based on geography, race, urbanicity, income per capita, and homicide rate. That study found gaps of 12·8 years for females and 15·4 years for males in life expectancy in 2001 across these eight groups. In this study, we aimed to update and expand the original Eight Americas study, examining trends in life expectancy from 2000 to 2021 for ten Americas (analogues to the original eight, plus two additional groups comprising the US Latino population), by year, sex, and age group.

METHODS

In this systematic analysis, we defined ten mutually exclusive and collectively exhaustive Americas comprising the entire US population, starting with all combinations of county and race and ethnicity, and assigning each to one of the ten Americas based on race and ethnicity and a variable combination of geographical location, metropolitan status, income, and Black-White residential segregation. We adjusted deaths from the National Vital Statistics System to account for misreporting of race and ethnicity on death certificates. We then tabulated deaths from the National Vital Statistics System and population estimates from the US Census Bureau and the National Center for Health Statistics from Jan 1, 2000, to Dec 31, 2021, by America, year, sex, and age, and calculated age-specific mortality rates in each of these strata. Finally, we constructed abridged life tables for each America, year, and sex, and extracted life expectancy at birth, partial life expectancy within five age groups (0-4, 5-24, 25-44, 45-64, and 65-84 years), and remaining life expectancy at age 85 years.

FINDINGS

We defined the ten Americas as: America 1-Asian individuals; America 2-Latino individuals in other counties; America 3-White (majority), Asian, and American Indian or Alaska Native (AIAN) individuals in other counties; America 4-White individuals in non-metropolitan and low-income Northlands; America 5-Latino individuals in the Southwest; America 6-Black individuals in other counties; America 7-Black individuals in highly segregated metropolitan areas; America 8-White individuals in low-income Appalachia and Lower Mississippi Valley; America 9-Black individuals in the non-metropolitan and low-income South; and America 10-AIAN individuals in the West. Large disparities in life expectancy between the Americas were apparent throughout the study period but grew more substantial over time, particularly during the first 2 years of the COVID-19 pandemic. In 2000, life expectancy ranged 12·6 years (95% uncertainty interval 12·2-13·1), from 70·5 years (70·3-70·7) for America 9 to 83·1 years (82·7-83·5) for America 1. The gap between Americas with the lowest and highest life expectancies increased to 13·9 years (12·6-15·2) in 2010, 15·8 years (14·4-17·1) in 2019, 18·9 years (17·7-20·2) in 2020, and 20·4 years (19·0-21·8) in 2021. The trends over time in life expectancy varied by America, leading to changes in the ordering of the Americas over this time period. America 10 was the only America to experience substantial declines in life expectancy from 2000 to 2019, and experienced the largest declines from 2019 to 2021. The three Black Americas (Americas 6, 7, and 9) all experienced relatively large increases in life expectancy before 2020, and thus all three had higher life expectancy than America 10 by 2006, despite starting at a lower level in 2000. By 2010, the increase in America 6 was sufficient to also overtake America 8, which had a relatively flat trend from 2000 to 2019. America 5 had relatively similar life expectancy to Americas 3 and 4 in 2000, but a faster rate of increase in life expectancy from 2000 to 2019, and thus higher life expectancy in 2019; however, America 5 experienced a much larger decline in 2020, reversing this advantage. In some cases, these trends varied substantially by sex and age group. There were also large differences in income and educational attainment among the ten Americas, but the patterns in these variables differed from each other and from the patterns in life expectancy in some notable ways. For example, America 3 had the highest income in most years, and the highest proportion of high-school graduates in all years, but was ranked fourth or fifth in life expectancy before 2020.

INTERPRETATION

Our analysis confirms the continued existence of different Americas within the USA. One's life expectancy varies dramatically depending on where one lives, the economic conditions in that location, and one's racial and ethnic identity. This gulf was large at the beginning of the century, only grew larger over the first two decades, and was dramatically exacerbated by the COVID-19 pandemic. These results underscore the vital need to reduce the massive inequity in longevity in the USA, as well as the benefits of detailed analyses of the interacting drivers of health disparities to fully understand the nature of the problem. Such analyses make targeted action possible-local planning and national prioritisation and resource allocation-to address the root causes of poor health for those most disadvantaged so that all Americans can live long, healthy lives, regardless of where they live and their race, ethnicity, or income.

FUNDING

State of Washington, Bloomberg Philanthropies, Bill & Melinda Gates Foundation.

Delays in Care by Race, Ethnicity, and Gender Before and During the COVID-19 Pandemic Using Cross-Sectional Data From the National Institutes of Health's All of Us Research Program

PURPOSE

Delays in receiving medical care are an urgent problem. This study aims to determine whether the odds of, and reasons for, experiencing care delays differ by gender, race-ethnicity, and survey completion before versus during the COVID-19 pandemic.

METHODS

We conducted a cross-sectional analysis of survey data from participants age ≥18 in the National Institutes of Health's All of Us Research Program collected from May 6, 2018, to January 1, 2022. Logistic regressions were performed to assess the association of gender, race-ethnicity, and survey completion date with any of nine reasons for delaying care in the past 12 months.

RESULTS

Of 119,983 participants, 37.8% reported delaying care in the past 12 months. After adjusting for employment status, education, income, marital status, health insurance, and age, women of every race-ethnicity and Black and other race-ethnicity men were more likely than white men to report delays in care: Asian women (odds ratio [OR] 1.23; 95% confidence interval [CI] [1.13, 1.34]), Black men (OR 1.15; 95% CI [1.05, 1.25]) and women (OR 1.46; 95% CI [1.38, 1.54]), Hispanic women (OR 1.36; 95% CI [1.28, 1.44]), white women (OR 1.55; 95% CI [1.50, 1.60]), and other race-ethnicity men (OR 1.15; 95% CI [1.05, 1.27]) and women (OR 1.79; 95% CI [1.67, 1.91]). A small but statistically significant difference was seen in reports of care delays for non-pandemic-related reasons during versus before the COVID-19 pandemic (OR 0.88; 95% CI [0.83, 0.93]).

CONCLUSIONS

In this study of a diverse group of U.S. participants, women and Black and other race-ethnicity men were more likely than white men to report delays in care, both before and during COVID-19. Addressing care delays may be necessary to ameliorate health disparities by race-ethnicity and gender.

Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area

Introduction

We examined the geographic distribution and sociodemographic and economic characteristics of chronic disease prevalence in the US. Understanding disease prevalence and its impact on communities is crucial for effective public health interventions.

Methods

Data came from the American Community Survey, the American Hospital Association Survey, and the Centers for Disease Control and Prevention's PLACES. We used quartile thresholds for 10 chronic diseases to assess chronic disease prevalence by Zip Code Tabulation Areas (ZCTAs). ZCTAs were scored from 0 to 20 based on their chronic disease prevalence quartile. Three prevalence categories were established: least prevalent (score ≤6), moderately prevalent (score 7-13), and highest prevalence (score ≥14). Community characteristics were compared across categories and spatial analyses to identify clusters of ZCTAs with high disease prevalence.

Results

Our study showed a high prevalence of chronic disease in the southeastern region of the US. Populations in ZCTAs with the highest prevalence showed significantly greater socioeconomic disadvantages (ie, lower household income, lower home value, lower educational attainment, and higher uninsured rates) and barriers to health care access (lower percentage of car ownership and longer travel distances to hospital-based intensive care units, emergency departments, federally qualified health centers, and pharmacies) compared with ZCTAs with the lowest prevalence.

Conclusion

Socioeconomic disparities and health care access should be addressed in communities with high chronic disease prevalence. Carefully directed resource allocation and interventions are necessary to reduce the effects of chronic disease on these communities. Policy makers and clinicians should prioritize efforts to reduce chronic disease prevalence and improve the overall health and well-being of affected communities throughout the US.