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The United States spends more than any other country on health care. The poor relative performance of the US compared to other high-income countries has attracted attention and raised questions about the performance of the US health system. An important dimension to poor national performance is the large disparities in life expectancy.
We applied a...
Citations
... However, large disparities continue, with Texas still having 20% of their population uninsured while Massachusetts had an uninsurance rate of 3% in 2021. 1 Similarly, health status, as measured by life expectancy, has decreased for the third year in a row for the United States, and inequality, with regard to this same metric, has only become worse. [2][3][4] Most recently, these trends have been attributed mainly to COVID-19 and the aging population in the United States, yet the underlying causes and drivers of these inequalities remain complicated and are poorly understood. 5,6 Murray et al 7 examined inequality in the United States in their 2006 paper titled, "Eight Americas: Investigating Mortality Disparities across Race, Counties, and Race-Counties in the U.S." Using data across 3144 US counties over the period 1982-2001, they showed significant disparity in the race-county combinations they created and termed these combinations the "Eight Americas." ...
Health and health care access in the United States are plagued by high inequality. While machine learning (ML) is increasingly used in clinical settings to inform health care delivery decisions and predict health care utilization, using ML as a research tool to understand health care disparities in the United States and how these are connected to health outcomes, access to health care, and health system organization is less common. We utilized over 650 variables from 24 different databases aggregated by the Agency for Healthcare Research and Quality in their Social Determinants of Health (SDOH) database. We used k-means—a non-hierarchical ML clustering method—to cluster county-level data. Principal factor analysis created county-level index values for each SDOH domain and 2 health care domains: health care infrastructure and health care access. Logistic regression classification was used to identify the primary drivers of cluster classification. The most efficient cluster classification consists of 3 distinct clusters in the United States; the cluster having the highest life expectancy comprised only 10% of counties. The most efficient ML clusters do not identify the clusters with the widest health care disparities. ML clustering, using county-level data, shows that health care infrastructure and access are the primary drivers of cluster composition.
... So, even without any change to individual health, it might appear that health inequalities are increasing, that is, the positive coefficient β would increase in magnitude over time. This is an important concern for studies that use characteristics of geographical regions to proxy for lifetime income (see Baker Wang et al., 2013). The approach we use mitigates this problem by allowing Rank to vary over time, following Schwandt (2016a, 2016b). ...
Although Australia maintains relatively high standards of health and healthcare, there exists disparity in health outcomes and longevity among different segments of the population. Internationally, there is growing evidence that life expectancy gains are not being shared equally among the rich and the poor. In this paper we examine the evolution of mortality inequality in Australia between 2001 and 2018. Using a spatial inequality model and combining data from several administrative data sources, we document significant mortality inequality between the rich and the poor in Australia. For most age groups, mortality inequality has remained unchanged over the last 20 years. However, mortality inequality is increasing for middle‐aged men and women. In part, this can be explained by improvements in longevity which favor urban over rural Australians. Another contributing factor we identify is differential access to healthcare in rich and poor regions. Although Australia's socioeconomic gradient of mortality is flatter than in the US, due to universal health coverage, the fact that mortality inequality is increasing for some groups accentuates the importance of safeguarding health care accessibility.
... For the life expectancy, γ, we use the United States average life expectancy as reported by the United Nations 2 , ranging between 1950 and 2020. In order to get the life expectancy divided into gender and socioeconomic status, we used life expectancy gender differences reported by [58] and the socioeconomic status differences reported by [59]. When the socioeconomic status is divided by tenths, this division results in 20 time-series with 12 constraints -10 for the socioeconomic status, one for the gender differences, and one to agree with the average reported life expectancy. ...
Timely pre- and post-diagnosis check-ups are critical for cancer patients, across all cancer types, as these often lead to better outcomes. Several socio-demographic properties have been identified as strongly connected with both cancer's clinical dynamics and (indirectly) with different individual check-up behaviors. Unfortunately, existing check-up policies typically consider only the former association explicitly. In this work, we propose a novel framework, accompanied by a high-resolution computer simulation, to investigate and optimize socio-demographic-based SMS reminder campaigns for cancer check-ups. We instantiate our framework and simulation for the case of bladder cancer, the 10th most prevalent cancer today, using extensive real-world data. Our results indicate that optimizing an SMS reminder campaign based solely on simple socio-demographic features can bring about a statistically significant reduction in mortality rate compared to alternative campaigns by up to 5.8%.
... Additionally, the previous study only compared the SE of the LE estimates obtained using the traditional method and the Bayesian spatial model (8). It is unknown whether or not the LE estimates using the Bayesian spatial or spatio-temporal areas or time points using point estimates or one-tailed tests under the normality assumption (5,(9)(10)(11). ...
The purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatio-temporal models. We hypothesized six scenarios in which all 247 districts of Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013–2017). We generated 1,000 hypothetical datasets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between the two Bayesian spatio-temporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cut-off value for the comparisons. The Bayesian spatio-temporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatio-temporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cut-off value that minimizes false-positive and false-negative rates. We proposed 0.8 as a cut-off value to determine the statistical significance of the difference in life expectancy.
... For decades, the United States has lagged com pa ra bly high-income nations and some mid dle-income countries on major pop u la tion health indi ca tors (Kulkarni et al. 2011;Woolf and Aron 2013), includ ing life expec tancy (Schwandt et al. 2021). Recently, life expec tancy in the United States declined (Case and Deaton 2015), while mortal ity inequalities increased (Chetty et al. 2016;Currie and Schwandt 2014;Dwyer-Lindgren et al. 2016;Ezzati et al. 2008;Montez and Zajacova 2013;Murray et al. 2006;Wang et al. 2013). Understanding lon gev ity disparities across sub pop u la tions is a crit i cal step in addressing America's grow ing health dis ad van tages. ...
... Geography-encompassing phys i cal, social, and pol icy envi ron ments-is a key axis of mor tal ity disparities (Black et al. 2015;Chetty et al. 2016;Dwyer-Lindgren 2016;Ezzati et al. 2008;Murray et al. 2006;Wang et al. 2013;Woolf and Schoomaker Understanding Geographic Disparities in Mortality Using the Mortality Disparities in Amer i can Communities data set, we find important dif fer ences between mea sures of life expec tancy by state of res i dence and state of birth. Overall, we find that the method of aggre gat ing indi vid u als by state of resi dence in later life under es ti mates the extent of geo graphic inequal ity in mor tal ity out comes com pared with the method that aggre gates indi vid u als by state of birth. ...
... Previous lit er a ture has shown that the Amer i can South has the low est lev els of life expec tancy by state of res i dence (Chetty et al. 2016;Murray et al. 2006;Wang et al. 2013, among many other papers). We con firm this pat tern in panel a of Figure 2, where we show male life expec tan cies at age 50 by state of res i dence. ...
A rich literature shows that early-life conditions shape later-life outcomes, including health and migration events. However, analyses of geographic disparities in mortality outcomes focus almost exclusively on contemporaneously measured geographic place (e.g., state of residence at death), thereby potentially conflating the role of early-life conditions, migration patterns, and effects of destinations. We employ the newly available Mortality Disparities in American Communities data set, which links respondents in the 2008 American Community Survey to official death records, and estimate consequential differences based on the method of aggregation we use: the unweighted mean absolute deviation of the difference in life expectancy at age 50 measured by state of birth versus state of residence is 0.58 years for men and 0.40 years for women. These differences are also spatially clustered, and we show that regional inequality in life expectancy is higher based on life expectancies by state of birth, implying that interstate migration mitigates baseline geographic inequality in mortality outcomes. Finally, we assess how state-specific features of in-migration, out-migration, and nonmigration together shape measures of mortality disparities by state (of residence), further demonstrating the difficulty of clearly interpreting these widely used measures.
... The rural southern United States, especially Tennessee, Kentucky, Alabama, and Mississippi, has higher rates of morbidity and mortality compared with other rural and urban areas in the Northeast, Midwest, and Western United States [40,41]. In particular, Appalachia and the Mississippi Delta regions have the lowest life expectancy in the country and the highest mortality rates due to various health issues [42][43][44][45][46]. People from Tennessee and Appalachia are the main contributors to the Bass Donated collection [26]. ...
Age estimation from human skeletal remains is a critical component of the biological profile for unidentified decedents. Using a Bayesian approach, we examine two popular methods (Lovejoy–LJ, and Buckberry zand Chamberlain–BC) for estimating age from the auricular surface of the ilium. Ages of transition are generated from a modern Portuguese skeletal sample (n = 466) and are coupled with an informative prior from historic Spitalfields, London (n = 179) to estimate age in a sample of modern Americans from the Bass Donated collection (n = 639). The Bass collection was challenging to statistically model, potentially due to higher morbidity and mortality characteristics of the central southern United States. The highest posterior density ranges provide a realized accuracy between 84–89% for males and 85–91% for females using the LJ method, and a realized accuracy between 79–82% for males and 65–71% for females using the BC method. Both methods worked well for older individuals. Cumulative binomials showed that both methods significantly underperformed; however, results were better for the LJ method, which also showed lower bias. Reference tables for aging modern American samples are provided, and the data meet Daubert guidelines, i.e., legal criteria for acceptable scientific evidence in a court of law in the United States.
... Mortality inequalities are among the starkest manifestations of inequity in our society. Prior research suggests geographic inequality in mortality has increased over time, and that these inequalities have reached substantial magnitudes (4,(7)(8)(9)(10)(11). Where people live influences what policies are in place, their access to and quality of health care, the social and economic conditions they experience, and what health behaviors they practice. ...
... Where people live influences what policies are in place, their access to and quality of health care, the social and economic conditions they experience, and what health behaviors they practice. These differences are the most commonly proposed explanations for geographic inequalities and their growth over time (4,(7)(8)(9)(10)(11)(12). ...
Background
Geographic inequality in US mortality has increased rapidly over the last 25 years, particularly between metropolitan and nonmetropolitan areas. These gaps are sizeable and rival life expectancy differences between the US and other high-income countries. This study determines the contribution of smoking, a key contributor to premature mortality in the US, to geographic inequality in mortality over the past quarter century.
Methods
We used death certificate and census data covering the entire US population aged 50+ between Jan 1, 1990 and Dec 31, 2019. We categorized counties into 40 geographic areas cross-classified by region and metropolitan category. We estimated life expectancy at age 50 and the index of dissimilarity for mortality, a measure of inequality in mortality, with and without smoking for these areas in 1990–1992 and 2017–2019. We estimated the changes in life expectancy levels and percent change in inequality in mortality due to smoking between these periods.
Results
We find that the gap in life expectany between metros and nonmetros increased by 2.17 years for men and 2.77 years for women. Changes in smoking-related deaths are responsible for 19% and 22% of those increases, respectively. Among the 40 geographic areas, increases in life expectancy driven by changes in smoking ranged from 0.91 to 2.34 years for men while, for women, smoking-related changes ranged from a 0.61-year decline to a 0.45-year improvement. The most favorable trends in years of life lost to smoking tended to be concentrated in large central metros in the South and Midwest, while the least favorable trends occurred in nonmetros in these same regions. Smoking contributed to increases in mortality inequality for men aged 70+, with the contribution ranging from 8 to 24%, and for women aged 50–84, ranging from 14 to 44%.
Conclusions
Mortality attributable to smoking is declining fastest in large cities and coastal areas and more slowly in nonmetropolitan areas of the US. Increasing geographic inequalities in mortality are partly due to these geographic divergences in smoking patterns over the past several decades. Policies addressing smoking in non-metropolitan areas may reduce geographic inequality in mortality and contribute to future gains in life expectancy.
... In contrast, Catawba County (Hickory, North Carolina) recorded the lowest life expectancy during that time (64.6 years) (Figure 1). Spatial variation in life expectancy is on the rise, likely exacerbated by the COVID-19 pandemic, opioid use disorder, obesity rates, and lack of health care access [2,3]. ...
... While such research mainly focuses on the temporal dimension of mortality, it can be argued that a model might be improved by accounting not only for the evolution of mortality across time, but by also accounting for geographical patterns of mortality when they are present. Although there is an emerging body of mortality research being conducted at local spatial scales in the USA (see Wang et al. (2013) and Dwyer-Lindgren et al. (2016)) and Europe (see Rasulo et al. (2007) and Bennett et al. (2018)), the focus on geographical space has so far remained underexamined in the Canadian demographic literature. Considering the vastness of Canada from a geographical standpoint, our current understanding of mortality could stand to be updated through investigations operating at much more refined spatial scales. ...
While studies of mortality have been gaining research attention worldwide, little attempt has been made to understand Canadian mortality rates from a spatial perspective. The objective of this research was to detect patterns in the spatial distribution of mortality rates for the sixty-five and older age group at the Canadian census division level. Specifically, the spatial patterns of mortality rates for Canadian males and females were examined using Moran’s I statistics, local indicators of spatial association, and cluster maps. The global Moran’s I tests of spatial autocorrelation suggest that Canadian mortality rates are spatially clustered, and further revealed the extent to which these spatial patterns remained consistent across the years in the study. These results were validated by local spatial modeling techniques, which were able to detect several areas in Canada where the mortality rates are clustered. This exploratory study revealed several regions of Canada which experience elevated mortality rates compared to the rest of the country, and provides a starting point for more nuanced investigations of mortality in Canada.
... The prior study included 72,823 patients [49] and used a measure of local area surgical practice styles as an instrument (see the description below). As recommended by the literature [62], additional control variables were specified for this type of instrument including county-level life expectancy [63] and county-level adjusted per capita Medicare spending [64]. Inconsistent links between county identifiers across data sources reduced the population in this study to 72,751 patients. ...
Background
Comparative effectiveness research (CER) using observational databases has been suggested to obtain personalized evidence of treatment effectiveness. Inferential difficulties remain using traditional CER approaches especially related to designating patients to reference classes a priori. A novel Instrumental Variable Causal Forest Algorithm (IV-CFA) has the potential to provide personalized evidence using observational data without designating reference classes a priori, but the consistency of the evidence when varying key algorithm parameters remains unclear. We investigated the consistency of IV-CFA estimates through application to a database of Medicare beneficiaries with proximal humerus fractures (PHFs) that previously revealed heterogeneity in the effects of early surgery using instrumental variable estimators.
Methods
IV-CFA was used to estimate patient-specific early surgery effects on both beneficial and detrimental outcomes using different combinations of algorithm parameters and estimate variation was assessed for a population of 72,751 fee-for-service Medicare beneficiaries with PHFs in 2011. Classification and regression trees (CART) were applied to these estimates to create ex-post reference classes and the consistency of these classes were assessed. Two-stage least squares (2SLS) estimators were applied to representative ex-post reference classes to scrutinize the estimates relative to known 2SLS properties.
Results
IV-CFA uncovered substantial early surgery effect heterogeneity across PHF patients, but estimates for individual patients varied with algorithm parameters. CART applied to these estimates revealed ex-post reference classes consistent across algorithm parameters. 2SLS estimates showed that ex-post reference classes containing older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to benefit and more likely to have detriments from higher rates of early surgery.
Conclusions
IV-CFA provides an illuminating method to uncover ex-post reference classes of patients based on treatment effects using observational data with a strong instrumental variable. Interpretation of treatment effect estimates within each ex-post reference class using traditional CER methods remains conditional on the extent of measured information in the data.
