scholarly journals Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e029373
Author(s):  
Mathew V Kiang ◽  
Nancy Krieger ◽  
Caroline O Buckee ◽  
Jukka Pekka Onnela ◽  
Jarvis T Chen
Keyword(s):  
Health Policy ◽  
Geographic Variation ◽  
Risk Ratio ◽  
Household Income ◽  
Spatial Models ◽  
Premature Mortality ◽  
County Level ◽  
Median Household Income ◽  
Relative Risks ◽  
The Us

ObjectiveDecompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy.SettingAll 50 US states and the District of Columbia, 2010 to 2015.ParticipantsA total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents.Primary and secondary outcome measuresThe race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups.ResultsThere is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income.ConclusionUnderstanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.

Surgical density and its effect on esophageal cancer (EC) and gastric (GC) mortality.

2011 ◽  
Vol 29 (4_suppl) ◽  
pp. 16-16
Author(s):  
M. Y. Ho ◽  
J. S. Albarrak ◽  
W. Y. Cheung
Keyword(s):  
Household Income ◽  
Population Level ◽  
Multimodality Treatment ◽  
General Surgeon ◽  
County Level ◽  
Median Household Income ◽  
Hospital Beds ◽  
Beta Coefficient ◽  
General Surgeons ◽  
The Impact

16 Background: Surgical resection plays an integral role in the multimodality treatment of patients with EC or GC. The distribution of thoracic and general surgeons at the county level varies widely across the US. The impact of the allocation of these surgeons on cancer outcomes is unclear. Our aims were to 1) examine the effect of surgeon density on EC or GC mortality, 2) compare the relative roles of thoracic and general surgeons on EC and GC outcomes and 3) determine other county characteristics associated with cancer mortality. Methods: Using county-level data from the Area Resources File, U.S. Census and National Cancer Institute, we constructed regression models to explore the effect of thoracic and general surgeon density on EC and GC mortality, respectively. Multivariate analyses controlled for incidence rate, county demographics (population aged 65+, proportion eligible for Medicare, education attainment, metropolitan vs. rural), socioeconomic factors (median household income) and healthcare resources (number of general practitioners, number of hospital beds). Results: In total, 332 and 402 counties were identified for EC and GC, respectively: mean EC/GC incidence = 5.29/6.83; mean EC/GC mortality=4.70/3.92; 91% were metropolitan and 9% were rural; mean thoracic and general surgeon densities were 10 and 63 per 100,000 people, respectively. When compared to counties with no thoracic surgeons, those with at least 1 thoracic surgeon had reduced EC mortality (beta coefficient -0.031). For GC, counties with 1 or more general surgeons also had decreased number of deaths (beta coefficient -0.095) when compared with those without any surgeons. While increasing the density of surgeons beyond 10 only yielded minimal improvements in EC mortality, it resulted in significant further reductions in GC mortality. Other county characteristics, such as increased number of hospital beds and higher median household income, were correlated with improved outcomes. Conclusions: Mortality from GC appears to be more susceptible to the benefits of increased surgeon density. For EC, a strategic policy of allocating health resources and distributing the workforce across counties will be best able to optimize outcomes at the population-level. No significant financial relationships to disclose.

scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model

10.2196/23902 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e23902
Author(s):  
Kevin L McKee ◽  
Ian C Crandell ◽  
Alexandra L Hanlon
Keyword(s):  
United States ◽  
Differential Equation ◽  
Population Density ◽  
Household Income ◽  
The United States ◽  
Policy Impact ◽  
County Level ◽  
Median Household Income ◽  
Social Distancing

Background Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. Objective We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. Methods A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. Results Mobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=–0.37 and r=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. Conclusions Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

Abstract P118: County-Level Disparities in the Availability of Farmers Markets in the United States: An Ecological Study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Chelsea Singleton ◽  
Olivia Affuso ◽  
Bisakha Sen
Keyword(s):  
At Risk ◽  
Household Income ◽  
Ecological Study ◽  
Negative Association ◽  
Farmers Markets ◽  
Grocery Stores ◽  
County Level ◽  
Median Household Income ◽  
Recreation Centers ◽  
Per Capita

Introduction: Farmers markets (FM) have been hypothesized to be a potential community-level obesity prevention strategy for populations at risk for chronic diseases because they provide a mechanism for communities to purchase healthy locally grown produce. This study aimed to identify county-level factors associated with FM availability in an effort to determine if disparities in availability exist in the US. Hypothesis: Increased FM availability will be associated with higher median household income, lower % minority residents, lower % obese residents and a higher number of grocery stores and recreation centers per 100,000 residents. Methods: An ecological study was conducted using 2009 data from the USDA Food Environment Atlas on 3,135 US counties. Crude and multivariable adjusted logistic regression models where used to determine associations between having at least one FM available and county-level variables such as % African American (AA) residents, % Hispanic residents, median household income, % WIC participants, % adults obese, % adults with diabetes, per capita grocery stores, per capita supercenters and per capita recreation centers. All analyses were stratified by metro county status and adjusted to address data clustering at the state-level. Results: There were 1,088 and 2,047 counties labeled metro and non-metro respectively. Metro Results : Median household income (p = 0.002) and per capita recreation centers (p < 0.0001) were positively associated with FM availability while % WIC residents (p = 0.008), per capita grocery stores (p = 0.02) and % adults with diabetes (p < 0.0001) showed a negative association. Non-Metro Results: Median household income (p < 0.0001), per capita recreation centers (p < 0.0001) and per capita supercenters (p < 0.0001) were positively associated with FM availability while % WIC residents (p = 0.02), per capita grocery stores (p < 0.0001) and % adults with diabetes (p < 0.03) showed a negative association. The % AA residents appeared to be negatively associated with FM availability but did not achieve statistical significance. County obesity prevalence was not associated with FM availability in both metro and non-metro counties. Conclusion: Results showed that counties with more recreation centers and a higher median household income have increased FM availability while counties with more WIC participants and residents with diabetes have less availability. More information on the association between FM access, diet and obesity in at risk populations should be collected at the individual level.

scholarly journals Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jennifer L. Moss ◽  
Norman J. Johnson ◽  
Mandi Yu ◽  
Sean F. Altekruse ◽  
Kathleen A. Cronin
Keyword(s):  
Socioeconomic Status ◽  
Census Tract ◽  
Household Income ◽  
County Level ◽  
Individual Level ◽  
Census Tract Level ◽  
Level Data ◽  
The Us ◽  
Mortality Disparities ◽  
Household Incomes

Abstract Background Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

scholarly journals Mass Deportations Would Impoverish US Families and Create Immense Social Costs

2017 ◽  
Vol 5 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Robert Warren ◽  
Donald Kerwin
Keyword(s):  
United States ◽  
Household Income ◽  
The United States ◽  
Median Household Income ◽  
Poverty Level ◽  
Executive Summary ◽  
College Degrees ◽  
The Us ◽  
Us Census ◽  
Mixed Status

Executive Summary1 This paper provides a statistical portrait of the US undocumented population, with an emphasis on the social and economic condition of mixed-status households - that is, households that contain a US citizen and an undocumented resident. It is based primarily on data compiled by the Center for Migration Studies (CMS). Major findings include the following: • There were 3.3 million mixed-status households in the United States in 2014. • 6.6 million US-born citizens share 3 million households with undocumented residents (mostly their parents). Of these US-born citizens, 5.7 million are children (under age 18). • 2.9 million undocumented residents were 14 years old or younger when they were brought to the United States. • Three-quarters of a million undocumented residents are self-employed, having created their own jobs and in the process, creating jobs for many others. • A total of 1.3 million, or 13 percent of the undocumented over age 18, have college degrees. • Of those with college degrees, two-thirds, or 855,000, have degrees in four fields: engineering, business, communications, and social sciences. • Six million undocumented residents, or 55 percent of the total, speak English well, very well, or only English. • The unemployment rate for the undocumented was 6.6 percent, the same as the national rate in January 2014.2 • Seventy-three percent had incomes at or above the poverty level. • Sixty-two percent have lived in the United States for 10 years or more. • Their median household income was $41,000, about $12,700 lower than the national figure of $53,700 in 2014 (US Census Bureau 2015). Based on this profile, a massive deportation program can be expected to have the following major consequences: • Removing undocumented residents from mixed-status households would reduce median household income from $41,300 to $22,000, a drop of $19,300, or 47 percent, which would plunge millions of US families into poverty. • If just one-third of the US-born children of undocumented residents remained in the United States following a mass deportation program, which is a very low estimate, the cost of raising those children through their minority would total $118 billion. • The nation's housing market would be jeopardized because a high percentage of the 1.2 million mortgages held by households with undocumented immigrants would be in peril. • Gross domestic product (GDP) would be reduced by 1.4 percent in the first year, and cumulative GDP would be reduced by $4.7 trillion over 10 years. CMS derived its population estimates for 2014 using a series of statistical procedures that involved the analysis of data collected by the US Census Bureau's American Community Survey (ACS). The privacy of all respondents in the survey is legally mandated, and, for the reasons listed in the Appendix, the identity of undocumented residents cannot be derived from the data. A detailed description of the methodology used to develop the estimates is available at the CMS website.3

scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model (Preprint)

2020 ◽  
Author(s):  
Kevin L McKee ◽  
Ian C Crandell ◽  
Alexandra L Hanlon
Keyword(s):  
United States ◽  
Differential Equation ◽  
Population Density ◽  
Household Income ◽  
The United States ◽  
Policy Impact ◽  
County Level ◽  
Median Household Income ◽  
Social Distancing

BACKGROUND Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. OBJECTIVE We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. METHODS A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. RESULTS Mobility dynamics show moderate correlations with two census covariates: population density (Spearman <i>r</i> ranging from 0.11 to 0.31) and median household income (Spearman <i>r</i> ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (<i>r</i>=–0.37 and <i>r</i>=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (<i>r</i>=0.51, <i>r</i>=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. CONCLUSIONS Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

Abstract 280: Association of Bystander CPR Rates with County-Level Median Household Income in Pennsylvania

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Kam Ching Li ◽  
Julie E Griff ◽  
Elizabeth K Heisler ◽  
Anne V Grossestreuer ◽  
Marion Leary ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Population Density ◽  
Household Income ◽  
Census Data ◽  
Education Level ◽  
County Level ◽  
Median Household Income ◽  
Related Factors ◽  
Poverty Status ◽  
Bystander Cpr

Background: Each year, >300,000 people suffer from out-of-hospital cardiac arrest (OHCA) in the U.S. Studies have shown that bystander CPR (BCPR) can greatly increase a victim’s chance of survival, yet the frequency of BCPR may vary as a byproduct of various demographic factors. Over 10,000 OHCAs occur in Pennsylvania each year across 67 counties. Objective: We sought to determine the association of bystander CPR rate with county-level median household income, as well as related factors, such as education level, persons below poverty status, and population density. Methods: Data were obtained from the Cardiac Arrest Registry to Enhance Survival (CARES) for Pennsylvania from 1/2012-12/2013 to determine rate of BCPR and return of spontaneous circulation (ROSC) by county. Demographic information regarding education level, median household income and population density were obtained from 2010 U.S. Census data. Counties were grouped into quartiles by lowest to highest rates of BCPR. Results from the lowest-performing were compared to the highest-performing quartile, with statistical analysis using STATA v11 (StataCorp, College Station, Texas). Results: A total of 7137 cases were included distributed across 47 counties. Mean age of patients was 63.7 years, 40.1% were female, and VF/VT was the initial rhythm in 19.7%. Median BCPR rate for the lowest-performing quartile was 7% (IQR 0%-26.5%). Median BCPR rate for the highest-performing quartile was 50.4% (IQR 46.95%-56%). Median ROSC rate for the lowest-performing quartile was 1.5% (IQR 0%-20%). Median ROSC rate for the highest-performing quartile was 23.3% (IQR 20.8%-33.3%). Median household income in the lowest-performing quartile was significantly lower than in the highest-performing quartile ($43611 ± $5038 v $50225 ± $6661, p = 0.023). Education level, persons below poverty status, and population density were not shown to have a significant association with BCPR rate. BCPR rate was positively associated with ROSC rate (p=0.001). Conclusions: BCPR rates are significantly higher in counties with higher median household income. Higher BCPR rates are associated with higher rates of ROSC. These findings have important implications for statewide public health efforts to improve arrest survival.

scholarly journals Socioeconomic Health Disparities in Canadian Regions

2014 ◽  
Vol 1 (1) ◽  
pp. 90-95 ◽  
Author(s):  
Jalil Safaei
Keyword(s):  
Health Disparities ◽  
Household Income ◽  
Premature Mortality ◽  
Individual Data ◽  
Median Household Income ◽  
Avoidable Mortality ◽  
Average Income ◽  
Inequality Index ◽  
Canadian Health

Purpose: Numerous studies have estimated health disparities along socioeconomic dimensions using individual data from sample surveys. Disparities between communities or regions cannot be estimated without a consistent set of individual data across communities. This study uses data at the health region level to estimate the socioeconomic health disparities between health regions in Canada. Methods: Tow measures of income and a measure of education are used for regional socioeconomic ranking along with several health outcomes such as life expectancies, mortality rates, perceived health and obesity. Weighted regressionanalysis is used to estimate the relative inequality index (RII) between Canadian health regions. Results: The findings of the study indicate the existence of health disparities between Canadian health regions along the three socioeconomic markers of average income, median household income and education in favor of regions with higher socioeconomic ranking on those markers. Disparities are more pronounced along the education and average income dimensions, however. Greater inequalities are observed for premature mortality, avoidable mortality and obesity, which are higher for women than men. Conclusion: There are health disparities between Canadian health regions along education and income dimensions. Such disparities signify the role of socioeconomic factors as important instruments in reducing health disparities.

scholarly journals Clinical Implications of Nonbiological Factors With Colorectal Cancer Patients Younger Than 45 Years

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Liu ◽  
Ruoxin Zhang ◽  
Qingguo Li ◽  
Xinxiang Li
Keyword(s):  
Colorectal Cancer ◽  
Marital Status ◽  
Household Income ◽  
Rank Test ◽  
Clinical Implications ◽  
Insurance Status ◽  
County Level ◽  
Median Household Income ◽  
Increased Risk ◽  
Cox Proportional Hazard Regression

BackgroundTo evaluate the clinical implications of non-biological factors (NBFs) with colorectal cancer (CRC) patients younger than 45 years.MethodsIn the present study, we have conducted Cox proportional hazard regression analyses to evaluate the prognosis of different prognostic factors, the hazard ratios (HRs) were shown with 95% confidence intervals (CIs). Kaplan–Meier method was utilized to compare the prognostic value of different factors with the log-rank test. NBF score was established according to the result of multivariate Cox analyses.ResultsIn total, 15129 patients before 45 years with known NBFs were identified from the SEER database. Only county-level median household income, marital status and insurance status were NBFs that significantly corelated with the cause specifical survival in CRC patients aged less than 45 years old (P &lt; 0.05). Stage NBF 1 showed 50.5% increased risk of CRC-specific mortality (HR = 1.505, 95% CI = 1.411-1.606, P &lt; 0.001). Stage NBF 0 patients were associated with significantly increased CRC-specific survival (CCSS) when compared with the stage NBF 1 patients in different AJCC TNM stages.ConclusionsNBF stage (defined by county-level median household income, marital status and insurance status) was strongly related to the prognosis of CRC patients. NBFs should arouse enough attention of us in clinical practice of patients younger than 45 years.

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