Disparities in wellbeing in the USA by race and ethnicity, age, sex, and location, 2008-21: an analysis using the Human Development Index.
The Human Development Index (HDI)-a composite metric encompassing a population's life expectancy, education, and income-is used widely for assessing and comparing human development and wellbeing at the country level, but does not account for within-country inequality. In this study of the USA, we aimed to adapt the HDI framework to measure the HDI at an individual level to examine disparities in the distribution of wellbeing by race and ethnicity, sex, age, and geographical location.
We used individual-level data on adults aged 25 years and older from the 2008-21 American Community Survey (ACS) Public Use Microdata Sample. We extracted information on race and ethnicity, age, sex, location (Public Use Microdata Areas), educational attainment, and household income and size. We merged these data with estimated life tables by race and ethnicity, sex, age, location (county), and year, generated using Bayesian small-area estimation models applied to death certificate data from the National Vital Statistics System. For each individual in the ACS, we used these combined data to estimate years of education, household consumption, and expected lifespan; converted each of these three features into an index using a percentile score; and calculated the HDI as the geometric mean of these three indices. Finally, we grouped individuals into yearly HDI deciles.
Years of education, household consumption, and expected lifespan-and thus the HDI-varied considerably among adults in the USA during the 2008-21 period. For most race and ethnicity and sex groups, the mean HDI increased gradually from 2008 to 2019, then declined in 2020 due to declines in expected lifespan, although there were systematic differences in the distribution of the HDI by race and ethnicity and sex. In the lowest HDI decile, there was over-representation (ie, >10% of the total population of a given race and ethnicity and sex group) of American Indian and Alaska Native (AIAN) males (50% [SE 0·2] in decile, mean annual population in decile 0·37 million [SE 0·002]), Black males (40% [<0·1], 4·67 million [0·006]), AIAN females (23% [0·1], 0·19 million [0·001]), Latino males (21% [<0·1], 3·27 million [0·006]), Black females (14% [<0·1], 1·86 million [0·004]), and Latina females (13% [<0·1], 2·07 million [0·006]). Given differences in total population size, however, White males were the largest population group in the lowest decile (27% [<0·1] of the lowest decile, 5·87 million [0·012]), followed by Black males (22% [<0·1]) and Latino males (15% [<0·1]). There were notable differences in these patterns by age group: for example, for the 25-44 years age group, the lowest HDI decile had even greater over-representation of AIAN males (66% [0·2] in decile, 0·22 million [0·001]) and Black males (46% [<0·1], 2·52 million [0·005]) than the 85 years and older age group (22% [1·1], <0·01 million [<0·001]; and 20% [0·3], 0·03 million [<0·001]). By contrast, the lowest decile had an under-representation of Asian females (2% [<0·1], 0·06 million [<0·001]) in the 25-44 years age group, but an over-representation in the 85 years and older age group (25% [0·3], 0·03 million [<0·001]). The lowest HDI decile for the 25-44 years age group was primarily male (76% [<0·1], 6·44 million [0·009]) whereas for age 85 years and older it was predominantly female (71% [0·1], 0·42 million [0·002]). In the highest HDI decile, shifts in the composition of the population by age were particularly large for White males, who made up 5% (0·1; 0·39 million [0·001]) of this decile in the 25-44 years age group, but 49% (0·2; 0·29 million [0·001]) in the 85 years and older age group. From 2012 to 2021, the proportion of the population living in the lowest HDI decile varied substantially by location, and a disproportionately high share of the population living in locations in much of the southern half of the USA, Appalachia, and Rust Belt states were in the lowest HDI decile.
Substantial disparities in wellbeing exist within the USA and are heavily influenced by race and ethnicity (due to racism), sex, age, and geographical location. These disparities are not immutable, but improvement is not a given, and gains can be fleeting in the face of a crisis such as the COVID-19 pandemic. Sustained action to ensure that everyone has meaningful access to a high-quality education, the means to earn a sufficient income, and the opportunity to live a long and healthy life is needed to reduce these disparities and should focus on the populations and locations that are worst off.
State of Washington and National Institute on Minority Health and Health Disparities.
Dwyer-Lindgren L
，Kendrick P
，Baumann MM
，Li Z
，Schmidt C
，Sylte DO
，Daoud F
，La Motte-Kerr W
，Aldridge RW
，Bisignano C
，Hay SI
，Mokdad AH
，Murray CJL
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Examining racial and ethnic disparities in diagnosis and access to care in infantile atopic dermatitis in the United States: a retrospective cohort study.
Atopic dermatitis (AD) is an inflammatory skin disorder that is common in children and associated with medical and psychosocial comorbidities. Previous studies have shown that there exist significant racial disparities in healthcare utilization in children with AD; however, literature on disparities in dermatology access is limited.
The primary aim of this study was to identify differences in diagnosis of AD and access to dermatologic care by race and ethnicity in infants with AD.
We conducted a retrospective chart review of infants diagnosed with AD at Boston Children's Hospital from January 1, 2015 - December 31, 2019. Race and ethnicity were categorized as Native American or Alaska Native, Asian, non-Hispanic Black, Hispanic, Native Hawaiian or Other Pacific Islander, non-Hispanic white, and other. Outcomes included time to diagnosis and dermatology visit from rash onset and were analyzed utilizing a Kruskal-Wallis test. Severity of presentation at first dermatology visit, presentation to the emergency department (ED), medications prescribed, and follow up were analyzed using Chi-squared tests.
Significantly more non-Hispanic white infants received a prescription by their pediatrician for AD than Hispanic infants (p = 0.002). Non-Hispanic Black and Asian infants waited longer to see a dermatologist after receiving a prescription for AD by their pediatrician compared to non-Hispanic white patients (p < 0.001; p = 0.007). Significantly more non-Hispanic Black and Hispanic infants presented to the ED for AD within the first year of life than non-Hispanic white patients (p < 0.001; p = 0.003).
Our study suggests disparities in diagnosis and access to care for non-Hispanic Black and Hispanic infants with AD, with differences in prescriptions, time to see a dermatologist, and presentation to the ED compared to non-Hispanic white infants.
Servattalab SE
，Lee M
，Hlobik M
，Song H
，Huang J
... -
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Does the Stopping Opioids After Surgery Score Perform Well Among Racial and Socioeconomic Subgroups?
The Stopping Opioids After Surgery (SOS) score is a validated tool that was developed to determine the risk of sustained opioid use after surgical interventions, including orthopaedic procedures. Despite prior investigations validating the SOS score in diverse contexts, its performance across racial, ethnic, and socioeconomic subgroups has not been assessed.
In a large, urban, academic health network, did the performance of the SOS score differ depending on (1) race and ethnicity or (2) socioeconomic status?
This retrospective investigation was conducted using data from an internal, longitudinally maintained registry of a large, urban, academic health system in the Northeastern United States. Between January 1, 2018, and March 31, 2022, we treated 26,732 adult patients via rotator cuff repair, lumbar discectomy, lumbar fusion, TKA, THA, ankle or distal radius open reduction and internal fixation, or ACL reconstruction. We excluded 1% of patients (274 of 26,732) because of missing length of stay information, 0.06% (15) for missing discharge information, 1% (310) for missing medication information related to loss to follow-up, and 0.07% (19) who died during their hospital stay. Based on these inclusion and exclusion criteria, 26,114 adult patients were left for analysis. The median age in our cohort was 63 years (IQR 52 to 71), and most patients were women (52% [13,462 of 26,114]). Most patients self-reported their race and ethnicity as non-Hispanic White (78% [20,408 of 26,114]), but the cohort also included non-Hispanic Black (4% [939]), non-Hispanic Asian (2% [638]), and Hispanic (1% [365]) patients. Five percent (1295) of patients were of low socioeconomic status, defined by prior SOS score investigations as patients with Medicaid insurance. Components of the SOS score and the observed frequency of sustained postoperative opioid prescriptions were abstracted. The performance of the SOS score was compared across racial, ethnic, and socioeconomic subgroups using the c-statistic, which measures the capacity of the model to differentiate between patients with and without sustained opioid use. This measure should be interpreted on a scale between 0 and 1, where 0 represents a model that perfectly predicts the wrong classification, 0.5 represents performance no better than chance, and 1.0 represents perfect discrimination. Scores less than 0.7 are generally considered poor. The baseline performance of the SOS score in past investigations has ranged from 0.76 to 0.80.
The c-statistic for non-Hispanic White patients was 0.79 (95% CI 0.78 to 0.81), which fell within the range of past investigations. The SOS score performed worse for Hispanic patients (c-statistic 0.66 [95% CI 0.52 to 0.79]; p < 0.001), where it tended to overestimate patients' risks of sustained opioid use. The SOS score for non-Hispanic Asian patients did not perform worse than in the White patient population (c-statistic 0.79 [95% CI 0.67 to 0.90]; p = 0.65). Similarly, the degree of overlapping CIs suggests that the SOS score did not perform worse in the non-Hispanic Black population (c-statistic 0.75 [95% CI 0.69 to 0.81]; p = 0.003). There was no difference in score performance among socioeconomic groups (c-statistic 0.79 [95% CI 0.74 to 0.83] for socioeconomically disadvantaged patients; 0.78 [95% CI 0.77 to 0.80] for patients who were not socioeconomically disadvantaged; p = 0.92).
The SOS score performed adequately for non-Hispanic White patients but performed worse for Hispanic patients, where the 95% CI nearly included an area under the curve value of 0.5, suggesting that the tool is no better than chance at predicting sustained opioid use for Hispanic patients. In the Hispanic population, it commonly overestimated the risk of opioid dependence. Its performance did not differ among patients of different sociodemographic backgrounds. Future studies might seek to contextualize why the SOS score overestimates expected opioid prescriptions for Hispanic patients and how the utility performs among more specific Hispanic subgroups.
The SOS score is a valuable tool in ongoing efforts to combat the opioid epidemic; however, disparities exist in terms of its clinical applicability. Based on this analysis, the SOS score should not be used for Hispanic patients. Additionally, we provide a framework for how other predictive models should be tested in various lesser-represented populations before implementation.
Crawford AM
，Striano BM
，Gong J
，Simpson AK
，Schoenfeld AJ
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ResearchGate
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