Reviewing Racial Disparities in Living Donor Kidney Transplantation: a Socioecological Approach

Despite kidney transplantation having superior outcomes to dialytic therapies, disparities continue to exist among rates of kidney transplantation between Black and non-Hispanic White patients, which cannot be explained by differences in individual characteristics. To better evaluate the persistent Black/White disparities in living kidney transplantation, we review the extant literature and include the critical factors and recent development in living kidney transplantation in the socioecological approach. We also emphasize the potential vertical and hierarchical associations among factors in the socioecological model. Specifically, this review explores the possibility that the relatively low living kidney transplantation among Blacks may be a consequence of individual, interpersonal, and structural inequalities in various social and cultural dimensions. At the individual level, the Black/White differences in socioeconomic conditions and transplant knowledge may account for the low transplantation rates among Blacks. Interpersonally, the relatively weak social support and poor communication between Black patients and their providers may contribute to disparities. At the structural level, the race-based glomerular filtration rate (GFR) calculation that is widely used to screen Black donors is a barrier to receiving living kidney transplantation. This factor is directly related to structural racism in the health care system but its potential impact on living donor transplantation is underexplored. Finally, this literature review emphasizes the current perspective that a race-free GFR should be considered and a multidisciplinary and interprofessional perspective is necessary to devise strategies and interventions to reduce the Black/White disparities in living donor kidney transplantation in the U.S.

Neighborhood Features and Cognitive Function: Moderating Roles of Individual Socioeconomic Status

INTRODUCTION

There is an interest in exploring the associations between neighborhood characteristics and individual cognitive function; however, little is known about whether these relationships can be modified by individual socioeconomic status, such as educational attainment and income.

METHODS

Drawing from the 2010-2018 Health and Retirement Study, this study analyzed 10,621 older respondents (aged 65+) with a total of 33,931 person-waves. These respondents did not have dementia in 2010 and stayed in the same neighborhood throughout the study period. Cognitive function was measured with a 27-point indicator biennially, and neighborhood characteristics (i.e., walkability, concentrated disadvantage, and social isolation) were assessed in 2010. All analyses were performed in 2023.

RESULTS

Cognitive function is positively associated with neighborhood walkability and negatively related to concentrated disadvantage, suggesting that exposures to these neighborhood characteristics have long-lasting impacts on cognitive function. Furthermore, individual socioeconomic status modifies the relationship between neighborhood characteristics and cognitive function. Compared with those graduating from college, respondents without a bachelor's degree consistently have lower cognitive function but the educational gap in cognitive function narrows with increases in walkability (b= -0.152, SE=0.092), and widens when neighborhood concentrated disadvantage (b=0.212, SE=0.070) or social isolation (b=0.315, SE=0.125) rises. The income gap in cognitive function shrinks with increases in walkability (b= -0.063, SE=0.027).

CONCLUSIONS

The moderating role of socioeconomic status indicates that low-socioeconomic status older adults who also live in disadvantaged neighborhoods face a higher risk of poor cognitive function. Low-education and low-income aging adults may have the most to gain from investments to improve neighborhood characteristics.

Race, disadvantage, and violence: A spatial exploration of macrolevel covariates of police-involved homicides within and between US counties

Efforts to explore the macrolevel determinants of police-involved homicides have expanded in recent years due in part to increased scrutiny and media attention to such events, and increased data availability of these events through crowdsourced databases. However, little empirical research has examined the spatial determinants of such events. The present study extends the extant macrolevel research on police-involved homicides by employing an underutilized spatial econometric model, the spatial Durbin model (SDM), to assess the direct and indirect county effects of racial threat, economic threat, social disorganization, and community violence on police killings within and between US counties from 2013 through 2020. Results indicate a direct inverse relationship between racial threat and police-involved homicides, no support for economic threat, and a direct positive association with two measures of social disorganization. Additionally, we find firearm availability exhibits significant direct and indirect spatial dependence on focal county police-involved homicides, reflecting spatial spillover processes. In essence, as firearm availability in neighboring counties increases, police-involved homicides within a focal county increase. The implications of these findings for racial threat, economic threat, social disorganization, and community violence are discussed.

Social Vulnerability and the Prevalence of Opioid Use Disorder Among Older Medicare Beneficiaries in U.S. Counties

OBJECTIVES

Recent research has investigated the factors associated with the prevalence of opioid use disorder (OUD) among older adults (65+), which has rapidly increased in the past decade. However, little is known about the relationship between social vulnerability and the prevalence of OUD, and even less is about whether the correlates of the prevalence of OUD vary across the social vulnerability spectrum. This study aims to fill these gaps.

METHODS

We assemble a county-level data set in the contiguous United States (U.S.) by merging 2021 Medicare claims with the CDC's social vulnerability index and other covariates. Using the total number of older beneficiaries with OUD as the dependent variable and the total number of older beneficiaries as the offset, we implement a series of nested negative binomial regression models and then analyze by social vulnerability quartiles.

RESULTS

Higher social vulnerability is associated with higher prevalence of OUD in U.S. counties. This association cannot be fully explained by the differences in the characteristics of older Medicare beneficiaries (e.g., average age) and/or other social conditions (e.g., social capital) across counties. Moreover, the group comparison tests indicate correlates of the prevalence of OUD vary across social vulnerability quartiles in that the average number of mental disorders is positively related to OUD prevalence in the least and the most vulnerable counties and social capital benefits the less vulnerable counties.

DISCUSSION

A perspective drawing upon contextual factors, especially social vulnerability, may be more effective in reducing OUD among older adults in U.S. counties than a one-size-fits-all approach.

Cross-sectional study of food insecurity and medical expenditures by race and ethnicity

OBJECTIVE

Food insecurity is a risk factor for morbidity and mortality leading to high medical expenditures, but race/ethnicity was used as adjustments in the literature. The study sought to use race/ethnicity as a key predictor to compare racial differences in associations between food insecurity and expenditures of seven health services among non-institutionalized adults.

DESIGN

This cross-sectional study used Medical Expenditure Panel Survey that collects information on food insecurity in 2016 (n=24,179) and 2017 (n=22,539). We examined the association between race/ethnicity and food insecurity status and documented the extent to which impacts of food insecurity on medical expenditures varied by race/ethnicity. We fit multivariable models for each racial group, adjusting for states, age, gender, insurance, and education. Adults older than 18 years were included.

RESULTS

The results show that blacks experienced an inter-racial disparity in food insecurity whereas Hispanics experienced intra-racial disparity. A higher percentage of blacks (28.7%) reported at least one type of food insecurity (11.2% of whites). Around 20% of blacks reported being worried about running out of food and the corresponding number is 8.4% among whites. Hispanics reported more food insecurity issues than whites. Moreover, food insecurity is positively associated with expenditures on emergency room utilization (99% increase for other races vs. 51% increase for whites) but is negatively associated with dental care utilization (43% decrease for blacks and 44% for whites). Except for Hispanics, prescription expenditure has the most positive association with food insecurity, and food insecure blacks are the only group that did not significantly use home health.

CONCLUSION

The study expanded our understanding of food insecurity by investigating how it affected seven types of medical expenditures for each of four racial populations. An interdisciplinary effort is needed to enhance the food supply for minorities. Policy interventions to address intra-racial disparities among Hispanics and inter-racial disparities among African Americans are imperative to close the gap.

Living Alone During Old Age and the Risk of Dementia: Assessing the Cumulative Risk of Living Alone

OBJECTIVES

This study examines the association between living alone during old age and dementia. Whereas most previous studies on this topic utilize measures of living alone status that were obtained at a single point in time, we compare this typical approach to one that measures long-term exposure to living alone among older adults and assesses whether dementia is more likely to occur within individuals with more accumulated time living alone.

METHODS

Data come from the Health and Retirement Study, with a follow-up period of 2000-2018. A total of 18,171 older adults were followed during this period, resulting in 78,490 person-waves analyzed in a series of multi-level logistic models. Contemporaneous living alone was recorded when a respondent's household size was equal to 1 in a given wave. Cumulative living alone was calculated by adding the number of living alone statuses up to a given wave.

RESULTS

Contemporaneous living alone was either not associated (male-only subsample), or inversely associated (female-only subsample) with dementia. By contrast, a one-unit (i.e., one wave) increase in cumulative living alone was associated with about a 10% increase in the odds of dementia for both men (odds ratio [OR] = 1.111) and women (OR = 1.088), net of several covariates, including marital status, age, social activities, and social support.

DISCUSSION

Living alone during late life is an important risk factor for dementia, but the cognitive effects of solitary living probably do not take hold immediately for most older adults and potentially demonstrate a dose-response relationship.

Neighborhood characteristics and opioid use disorder among older Medicare beneficiaries: An examination of the role of the COVID-19 pandemic

This study investigates how the associations between residential characteristics and the risk of opioid user disorder (OUD) among older Medicare beneficiaries (age≥65) are altered by the COVID-19 pandemic. Applying matching techniques and multilevel modeling to the Medicare fee-for-service claims data, this study finds that county-level social isolation, concentrated disadvantage, and residential stability are significantly associated with OUD among older adults (N = 1,080,350) and that those living in counties with low levels of social isolation and residential stability experienced a heightened risk of OUD during the pandemic. The results suggest that the COVID-19 pandemic has aggravated the impacts of residential features on OUD.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties

INTRODUCTION

This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.

METHODS

This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022.

RESULTS

The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local.

CONCLUSIONS

The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

Multiscale dimensions of county-level disparities in opioid use disorder rates among older Medicare beneficiaries

Background

Opioid use disorder (OUD) among older adults (age ≥ 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates.

Methods

Data are from a variety of national sources including the Centers for Medicare & Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016-2020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely "level of influence" (the percentage of older Medicare beneficiaries affected by a certain determinant), "scalability" (the spatial process of a determinant as global, regional, or local), and "specificity" (the determinant that has the strongest association with the OUD rate).

Results

The results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others.

Conclusions

Our findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process.

County social isolation and opioid use disorder among older adults: A longitudinal analysis of Medicare data, 2013-2018

This study aims to fill three knowledge gaps: (1) unclear role of ecological factors in shaping older adults' risk of opioid use disorder (OUD), (2) a lack of longitudinal perspective in OUD research among older adults, and (3) underexplored racial/ethnic differences in the determinants of OUD in older populations. This study estimates the effects of county-level social isolation, concentrated disadvantage, and income inequality on older adults' risk of OUD using longitudinal data analysis. We merged the 2013-2018 Medicare population (aged 65+) data to the American Community Survey 5-year county-level estimates to create a person-year dataset (N = 47,291,217 person-years) and used conditional logit fixed-effects modeling to test whether changes in individual- and county-level covariates alter older adults' risk of OUD. Moreover, we conducted race/ethnicity-specific models to compare how these associations vary across racial/ethnic groups. At the county-level, a one-unit increase in social isolation (mean = -0.197, SD = 0.511) increased the risk of OUD by 5.5 percent (OR = 1.055; 95% CI = [1.018, 1.094]) and a one-percentage-point increase in the working population employed in primary industry decreases the risk of OUD by 1 percent (OR = 0.990; 95% CI = [0.985, 0.996]). At the individual-level, increases in the Medicare Hierarchical Condition Categories risk score, physical comorbidity, and mental comorbidity all elevate the risk of OUD. The relationship between county-level social isolation and OUD is driven by non-Hispanic whites, while Hispanic beneficiaries are less sensitive to the changes in county-level factors than any other racial ethnic groups. Between 2013 and 2018, US older adults' risk of OUD was associated with both ecological and individual factors, which carries implications for intervention. Further research is needed to understand why associations of individual factors with OUD are comparable across racial/ethnic groups, but county-level social isolation is only associated with OUD among non-Hispanic white beneficiaries.

Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties

Purpose

Research on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.

Methods

Analyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups—High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk—and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors.

Results

The key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana.

Conclusions

The COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Social isolation, residential stability, and opioid use disorder among older Medicare beneficiaries: Metropolitan and non-metropolitan county comparison

Research has shown that the prevalence of opioid use disorder (OUD) may rise substantially as society ages, but this issue receives the least attention in the literature. To address this gap, this study utilizes county-level data from multiple data sources (1) to investigate whether social isolation is associated with OUD prevalence among older Medicare beneficiaries, (2) to examine whether and how residential stability moderates the association between social isolation and OUD prevalence in US counties, and (3) to determine if there are any differences in these associations between metropolitan and non-metropolitan counties. The results show that social isolation is a significant factor for county-level OUD prevalence, regardless of metropolitan status. In addition, counties with high residential stability have low prevalence of OUD among older adults and this association is stronger in metropolitan than in non-metropolitan counties. Nonetheless, high levels of residential stability reinforce the positive relationship between social isolation and OUD prevalence. As a result, when developing policies and interventions aimed at reducing OUD among older adults, place of residence must be taken into account.

Indigenous Peoples, concentrated disadvantage, and income inequality in New Mexico: a ZIP code-level investigation of spatially varying associations between socioeconomic disadvantages and confirmed COVID-19 cases

BACKGROUND

The coronavirus disease pandemic has disproportionately affected poor and racial/ethnic minority individuals and communities, especially Indigenous Peoples. The object of this study is to understand the spatially varying associations between socioeconomic disadvantages and the number of confirmed COVID-19 cases in New Mexico at the ZIP code level.

METHODS

We constructed ZIP code-level data (n=372) using the 2014-2018 American Community Survey and COVID-19 data from the New Mexico Department of Health (as of 24 May 2020). The log-linear Poisson and geographically weighted Poisson regression are applied to model the number of confirmed COVID-19 cases (total population as the offset) in a ZIP code.

RESULTS

The number of confirmed COVID-19 cases in a ZIP code is positively associated with socioeconomic disadvantages-specifically, the high levels of concentrated disadvantage and income inequality. It is also positively associated with the percentage of American Indian and Alaskan Native populations, net of other potential confounders at the ZIP code level. Importantly, these associations are spatially varying in that some ZIP codes suffer more from concentrated disadvantage than others.

CONCLUSIONS

Additional attention for COVID-19 mitigation effort should focus on areas with higher levels of concentrated disadvantage, income inequality, and higher percentage of American Indian and Alaska Native populations as these areas have higher incidence of COVID-19. The findings also highlight the importance of plumbing in all households for access to clean and safe water, and the dissemination of educational materials aimed at COVID-19 prevention in non-English language including Indigenous languages.

[Effectiveness of the "14 plus 7 day quarantine" and "nucleic acid plus total antibody testing" strategy for screening imported patients with COVID-19 in Xiamen]

Objective: To analysis effectiveness of the "14 plus 7 day quarantine" and "nucleic acid plus total antibody testing" strategy (combined screening strategy) for screenin the imported patients with COVID-19 in Xiamen. Methods: The study populations were overseas travelers arriving in Xiamen from March 17 to December 31, 2020, and overseas travelers who had quarantine outside Xiamen for less than 21 days from July 18 to December 31, 2020. Data were collected and analyzed on the timing of detection, pathways, and test results of the imported patients with COVID-19 after implementing combined screening strategy. Results: A total of 304 imported patients with COVID-19 were found from 174 628 overseas travelers and 943 overseas travelers from other cities. A total of 163 cases (53.6%) were diagnosed by multitime, multisite intensive nucleic acid testing after positive finding in total antibody testing. Among them, 27 (8.9%) were first positive for nucleic acid in 14 plus 7 day quarantine and 136 were first positive for nucleic acid in 14-day quarantine. Only 8 of these individuals were tested positive for nucleic acid after positive total antibody testing. The other 128 individuals were tested positive for nucleic acid after being negative for average 2.3 times (maximum of 6 times). Aditional 155 cases might be detected by using the combined "14 plus 7 day quarantine" and " nucleic acid plus total antibody testing" strategy compared with "14-day quarantine and nucleic acid testing" strategy, accounting for 51.0% of the total inbound infections. So the combined screening strategy doubled the detection rate for imported patients with COVID-19. No second-generation case caused by overseas travelers had been reported in Xiamen as of February 26, 2021. Conclusions: Xiamen's combined screening strategy can effectively screen the imported patients with COVID-19 who were first positive for nucleic acid after 14 day quarantine. Compared with "14 day quarantine and nucleic acid testing", the combined screening strategy improved detection rate and further reduced the risk of the secondary transmission caused by the imported patients with COVID-19.

Trends in Opioid Use Disorder Among Older Adults: Analyzing Medicare Data, 2013-2018

INTRODUCTION

Opioid use disorder has grown rapidly over the years and is a public health crisis in the U.S. Although opioid use disorder is widely studied, relatively little is known about it among older adults. The goal of this study is to gain a better understanding of opioid use disorder among older Medicare beneficiaries over time and across several sociodemographic dimensions.

METHODS

Data from the 2013-2018 Centers for Medicare & Medicaid Services Master Beneficiary Summary Files were analyzed in 2020 to examine the trends in opioid use disorder prevalence among Fee-for-Service Medicare beneficiaries aged ≥65 years. Utilizing the overarching opioid use disorder flag, trends in opioid use disorder prevalence were examined for the following sociodemographic dimensions: age, sex, race/ethnicity, and dual eligibility status (i.e., enrolled in both Medicare and Medicaid owing to low income). Chi-square tests were used to compare opioid use disorder prevalence across groups.

RESULTS

Since 2013, estimated rates of opioid use disorder among older adults have increased by >3-fold overall in the U.S. Estimated opioid use disorder is more prevalent among the young-old (i.e., ages 65-69 years) beneficiaries than among other older adults, and dually eligible beneficiaries have consistently shared a heavier burden of opioid use disorder than Medicare-only beneficiaries. Regarding race/ethnicity, Blacks and American Indians/Alaskan Natives are more vulnerable to opioid use disorder than other groups.

CONCLUSIONS

The descriptive trends between 2013 and 2018 indicate that estimated opioid use disorder prevalence has increased greatly over the study period in all sociodemographic subgroups of older adults, highlighting an urgent challenge for public health professionals and gerontologists.

Income inequality and opioid prescribing rates: Exploring rural/urban differences in pathways via residential stability and social isolation

While opioid prescribing rates have drawn researchers' attention, little is known about the mechanisms through which income inequality affects opioid prescribing rates and even less focuses on whether there is a rural/urban difference in mediating pathways. Applying mediation analysis techniques to a unique ZIP code level dataset from several sources maintained by the Centers for Medicare and Medicaid Services, we explicitly examine two mechanisms through residential stability and social isolation by rural/urban status and find that (1) income inequality is not directly related to opioid prescribing rates, but it exerts its influence on opioid prescribing via poor residential stability and elevated social isolation; (2) social isolation accounts for two-thirds of the mediating effect of income inequality on opioid prescribing rates among urban ZIP codes, but the proportion halves among rural ZIP codes; (3) residential stability plays a larger role in understanding how income inequality matters in rural than in urban ZIP codes; and (4) beneficiary characteristics only matter in urban ZIP codes. These findings offer nuanced insight into how income inequality affects opioid prescribing rates and suggests that the determinants of opioid prescribing rates vary by rural/urban status. Future research may benefit from identifying place-specific factors for opioid prescribing rates.

Are Older Populations at a Disadvantage? County-Level Analysis of Confirmed COVID-19 Cases in Urban and Rural America

Objectives

This study examines how areas with older populations are affected by COVID-19 and whether urban and rural counties face different challenges.

Methods

Applying negative binomial regression to a dataset of U.S. counties (N = 3,042), we estimated the relationship between older population ratios and the number of confirmed COVID-19 cases, and how this relationship changes over time in urban and rural counties, respectively.

Results

Although low-ratio counties show the highest number of confirmed cases of COVID-19 at the beginning of the pandemic, confirmed cases in high-ratio counties (more than 25% of the total population is 65 and older) increase exponentially with time in urban areas. High-ratio rural counties hit their peak later and recover more slowly compared to low- and medium-ratio rural counties.

Discussion

Both urban and rural counties with larger older populations are more vulnerable and their disadvantages in COVID-19 infections are more rapidly exacerbated over time in urban areas. This underscores the importance of early action in those counties for effective intervention and prevention.

A simple method for moving source depth estimation applied to the SWellEx96 data

Existing methods for source localization in an ocean waveguide generally require simultaneous range and depth estimation as they are intrinsically coupled in the acoustic field. Using beam intensities averaged over ranges longer than the mode cycle distances, it is shown that the range-averaged vertical beam distribution is insensitive to the source range and sensitive to the source depth. An incoherent matched beam processing method is presented and applied to the SWellEx96 moving source data to estimate source depth based on the 127 and 130 Hz signals with good results.

Structural Inequalities Established the Architecture for COVID-19 Pandemic Among Native Americans in Arizona: a Geographically Weighted Regression Perspective

Native Americans are disproportionately affected by COVID-19. The present study explores whether areas with high percentages of Native American residents are experiencing the equal risks of contracting COVID-19 by examining how the relationships between structural inequalities and confirmed COVID-19 cases spatially vary across Arizona using a geographically weighted regression (GWR). GWR helps with the identification of areas with high confirmed COVID-19 cases in Arizona and with understanding of which predictors of social inequalities are associated with confirmed COVID-19 cases at specific locations. We find that structural inequality indicators and presence of Native Americans are significantly associated with higher confirmed COVID-19 cases; and the relationships between structural inequalities and confirmed COVID-19 cases are significantly stronger in areas with high concentration of Native Americans, particular on Tribal lands. The findings highlight the negative effects that lack of infrastructure (i.e., housing with plumbing, transportation, and accessible health communication) may have on individual and population health, and, in this case, associated with the increase of confirmed COVID-19 cases.

COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation

OBJECTIVE

To investigate how racial/ethnic density and residential segregation shape the uneven burden of COVID-19 in US counties and whether (if yes, how) residential segregation moderates the association between racial/ethnic density and infections.

DESIGN

We first merge various risk factors from federal agencies (e.g. Census Bureau and Centers for Disease Control and Prevention) with COVID-19 cases as of June 13th in contiguous US counties (N = 3,042). We then apply negative binomial regression to the county-level dataset to test three interrelated research hypotheses and the moderating role of residential segregation is presented with a figure.

RESULTS

Several key results are obtained. (1) Counties with high racial/ethnic density of minority groups experience more confirmed cases than those with low levels of density. (2) High levels of residential segregation between whites and non-whites increase the number of COVID-19 infections in a county, net of other risk factors. (3) The relationship between racial/ethnic density and COVID-19 infections is enhanced with the increase in residential segregation between whites and non-whites in a county.

CONCLUSIONS

The pre-existing social structure like residential segregation may facilitate the spread of COVID-19 and aggravate racial/ethnic health disparities in infections. Minorities are disproportionately affected by the novel coronavirus and focusing on pre-existing social structures and discrimination in housing market may narrow the uneven burden across racial/ethnic groups.

Regional variation in COVID-19 disparities: connections with immigrant and Latinx communities in U.S. counties

PURPOSE

To evaluate associations between counties' COVID-19 cases and racial-ethnic and nativity composition, considering heterogeneity across Latin American-origin subgroups and regions of the United States.

METHODS

Using county-level data and multilevel negative binomial models, we evaluate associations between COVID-19 cases and percentages of residents that are foreign-born, Latinx, Black, or Asian, presenting estimates for all counties combined and stratifying across regions. Given varying risk factors among Latinx, we also evaluate associations for percentages of residents from specific Latin American-origin groups.

RESULTS

Percentage of foreign-born residents is positively associated with COVID-19 case rate (IRR = 1.106; 95% CI: 1.074-1.139). Adjusted associations for percentage Latinx are nonsignificant for all counties combined, but this obscures heterogeneity. Counties with more Central Americans have higher case rates (IRR = 1.130; 95% CI: 1.067-1.197). And, in the Northeast and Midwest, counties with more Puerto Ricans have higher case rates. Associations with percentage Asians are nonsignificant after adjusting for percentage foreign-born. With the confirmation of prior evidence, the percentage of Black residents is positively and robustly associated with COVID-19 case rate (IRR = 1.031; 95% CI: 1.025-1.036).

CONCLUSIONS

Counties with more immigrants, as well as more Central American or Black residents, have more COVID-19 cases. In the Northeast and Midwest, counties with more Puerto Rican residents also have more COVID-19 cases.

Beam-time delay domain deconvolved scheme for high-resolution active localization of underwater targets

Matched filter (MF) and conventional beamforming (CBF) are widely used in active sonar; the performance of the former (temporal resolution) is limited by the signal bandwidth, and that of the latter (angular resolution) is limited by the array aperture. Previous work has shown that angular resolution can be significantly improved by deconvolving the CBF outputs. In this paper, deconvolution is extended to the time domain by deconvolving the MF outputs, and a high-resolution two-dimensional deconvolution method is proposed to simultaneously improve the temporal and angular resolution. Numerical simulations and experimental tank data show that angular resolutions are improved 26 times, and temporal resolutions are improved 10 times compared with the conventional MF and CBF methods. Reverberations are much suppressed to allow target echoes to be detected from the received time series data.

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE

This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States.

METHODS

We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence.

RESULTS

The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties.

CONCLUSIONS

Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Exploring heterogeneities with geographically weighted quantile regression: An enhancement based on the bootstrap approach

Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are established based on asymptotic approximation, which may suffer computation difficulties or yield incorrect estimates with finite samples. In this paper, we suggest a bootstrap approach to address this limitation. Our bootstrap enhancement is first validated by a simulation experiment and then illustrated with an empirical US mortality data. The results show that the bootstrap provides a practical alternative for inference in GWQR and enhances the utilization of GWQR.

Deconvolution of decomposed conventional beamforming

A recently proposed deconvolution method applied to conventional beamforming (CBF) shows a much higher array gain (AG) than CBF in theory, thereby providing the possibility for detecting a weak signal with a much lower signal-to-noise ratio (SNR). However, simulated data processing shows an effective AG that decreases with decreasing SNR. The reason for the performance loss is analyzed. A method based on deconvolution of the signal subspace of the CBF outputs is shown to recover most of the AG loss. It is used to trace a weak signal in bearing and time.

Neighborhood Poverty and Physical Health at Midlife: The Role of Life-Course Exposure

Studies of the effect of neighborhood poverty on health are dominated by research designs that measure neighborhood poverty at a single point in time, ignoring the potential influence of exposure to neighborhood poverty over the life course. Applying latent class analysis to restricted residential history data from the National Longitudinal Survey of Youth, 1979 Cohort, we identify four trajectories of life-course exposure to high-poverty neighborhoods between adolescence and midlife and then examine how these groups differ in their physical health conditions (SF-12 score) and self-rated health at around age 40. Linear and logistic regression analyses show that life-course exposure to high-poverty neighborhoods is a stronger predictor of midlife physical health than are point-in-time measures of neighborhood poverty observed during either adolescence or midlife. Our findings suggest that a life-course approach can enhance our understanding of how neighborhood poverty affects physical health.

Rural/Urban Differences in the Predictors of Opioid Prescribing Rates Among Medicare Part D Beneficiaries 65 Years of Age and Older

PURPOSE

While research has been done comparing rural/urban differences in opioid prescribing to the disabled Medicare Part D population, research on opioid prescribing among the aged Medicare Part D population is lacking. This study aims to fill this gap by exploring the predictors of opioid prescribing to aged Medicare Part D beneficiaries and investigating whether these predictors vary across rural and urban areas.

METHODS

This is an analysis of ZIP Codes in the continental United States (18,126 ZIP Codes) utilizing 2017 data from Centers for Medicare & Medicaid Services. The analytic approach includes aspatial descriptive analysis, exploratory spatial analysis with geographically weighted regression, and explanatory analysis with spatial error regime modeling.

FINDINGS

Both beneficiary and prescriber characteristics play an important role in determining opioid prescribing rates in urban ZIP Codes, but most of them fail to explain the opioid prescribing rates in rural ZIP Codes.

CONCLUSION

We identify potential spatial nonstationarity in opioid prescribing rates, indicating the complex nature of opioid-related issues. This means that the same stimulus may not lead to the same change in opioid prescribing rates, because the change may be place specific. By understanding the rural/urban differences in the predictors of opioid prescribing, place-specific policies can be developed that can guide more informed opioid prescribing practices and necessary interventions.

Matched beam-intensity processing for a deep vertical line array

A vertical line array can be deployed in deep water below the critical depth, the depth where the sound speed equals the sound speed at the surface, to take advantage of the lower ambient noise level (compared with above the critical depth) for target detection. To differentiate a submerged source from a surface source, a Fourier transform based method [McCargar and Zurk, J. Acoust. Soc. Am. 133, EL320-325 (2013)] was proposed for a narrowband signal that exploits the depth-related harmonic (oscillation) feature of the beam power time series associated with the target arrival. In this paper, incoherent matched beam processing is used to estimate the target depth. Where the replica (calculated) beam intensity or amplitude time series best matches that of the data is used to estimate the source depth. This method is shown, based on simulated data, to provide a better depth resolution in general and better ability to estimate the depth of a very shallow source (say at 10 m) and can be used to complement the Fourier transform based method. It can be extended to process (random) broadband signals and to environments where the Lloyd's mirror theory is not valid.

Evaluating Residential Segregation's Relation to the Clustering of Poor Health across American Cities

Residential segregation by race/ethnicity is widely recognized as a leading source of health disparities. Not clear from past research, however, is the overall health burden cities face due to clustering brought about by segregation. This study builds on previous research by directly measuring how spatially unequal health outcomes are within segregated cities. Utilizing Census-tract data from the Center for Disease Control and Prevention's 500 Cities project, we examine how different dimensions of spatial segregation are associated with the clustering of poor self-rated health in cities. We make novel usage of the Global Moran's I statistic to measure the spatial clustering of poor health within cities. We find spatial segregation is associated with poor health clustering, however the race/ethnicity and dimension of segregation matter. Our study contributes to existing research on segregation and health by unpacking the localized associations of residential segregation with poor health clustering in U.S. cities.

Racial/ethnic segregation and health disparities: Future directions and opportunities

Health researchers have investigated the association between racial segregation and racial health disparities with multilevel approaches. This study systematically reviews these multilevel studies and identifies broad trends and potential directions for future research on racial segregation and health disparities in the US. After searching databases including CINAHL and MEDLINE, we identified and systematically reviewed 66 articles published between 2003 and 2019 and found four major gaps in racial/ethnic segregation and health disparities: (a) the concept of segregation was rarely operationalized at the neighborhood level, (b) except for the evenness and exposure dimension, other dimensions of segregation are overlooked, (c) little attention was paid to the segregation between whites and non-black minorities, particularly Hispanics and Asians, and (d) mental health outcomes were largely absent. Future directions and opportunities include: First, other segregation dimensions should be explored. Second, the spatial scales for segregation measures should be clarified. Third, the theoretical frameworks for black and non-black minorities should be tested. Fourth, mental health, substance use, and the use of mental health care should be examined. Fifth, the long-term health effect of segregation has to be investigated, and finally, other competing explanations for why segregation matters at the neighborhood level should be answered.

Joint towed array shape and direction of arrivals estimation using sparse Bayesian learning during maneuvering

Large aperture towed arrays are widely used underwater to detect weak targets. During maneuvering, the beamformer performance degrades significantly if a wrong array configuration is assumed. Currently, engineering sensors and/or (augmented) acoustic sources are used to estimate the array element positions. The results are often inadequate depending on the number of measurements available. In this paper, an adaptive bow (AB) sparse Bayesian learning (SBL) algorithm is proposed, called ABSBL. Assuming the towed array follows a parabola shape during slow turns and treating the array bow as a hyperparameter in SBL, the bow and directions of arrival (DOAs) of the signals can be jointly estimated from the received acoustic data. Simulations show that ABSBL yields accurate estimates of the bow and target DOAs if the turning direction is known. ABSBL is applied to the MAPEX2000 data. The estimated array bow and DOA agrees with that estimated from relative time delays measured from acoustic pings and SBL, better than that estimated from the GPS data using the water-pulley model. The method can potentially be applied without engineering sensors.

Channel distortion on target scattering amplitude in shallow water

Bistatic acoustic scattering uses echo returns from a target to estimate the scattered intensity or amplitude as a function of angle and frequency (acoustic color) for target classification. In a shallow water waveguide, the echo return from a target at a distance, such as a mine, is modified by the waveguide and deviates significantly from that at close range or in free space. The channel effect or distortion on the echo return is studied using simulated data. A simple frequency domain signal processing method is proposed to equalize the channel effect. The frequency and angle distribution of the estimated scattering function is compared with that of the original function. It is found to be an adequate representation of the original function with a high correlation coefficient (∼0.88).

Source localization in range-dependent and time-varying shallow water: The Shallow Water 2006 experimental results

In a range-dependent and time-varying environment, such as at the Shallow Water 2006 (SW06) experimental site, matched field processing often has difficulty localizing a moving source emitting a narrowband signal when signal mismatch is difficult to mitigate given only the nominal sound speed profile and bottom properties along the source track. Based on the range-averaged mode wavenumbers and depth functions estimated from data received on a vertical line array by synthetic beamforming (without any environmental information) using Doppler shift as a reference, a method is proposed in this paper to search for the source depth first and then the source range. Source localization is demonstrated with the SW06 data for two source runs along and oblique to the shelf. Robustness is achieved by minimizing/breaking the coupling between range and depth, when one of them can be estimated using non-environment-related input.

Array gain of coprime arrays

A coprime array of M + N - 1 elements is designed, using product processing (PP), to yield the same beam width as that of a full array of MN elements using conventional beamforming (CBF), while at the same time suppressing the grating lobes. The array gain (AG) for isotropic noise using conventional PP is slightly less than that obtained using CBF, namely, 10log(M + N - 1). It is shown that using the coherent product processing, the coprime array yields an AG equal to that of the full array of MN elements, 10log(MN). The superior gain is often referred to as supergain.

Racial/ethnic disparities in depression: Investigating how sources of support and types of integration matter

Although social support and social integration are key predictors of depression and exhibit racial/ethnic patterns in the US, previous research has not examined how they shape racial/ethnic disparities in depression. Applying hybrid models to data from the Americans' Changing Lives study from 1986 to 2002, this study analyzes how sources of social support (spouse and friend/relative) and types of social integration (informal/formal) explain black-white and Hispanic-white disparities in depression. We find that strong social support and high social integration are negatively associated with depression and that the patterns of social support and integration vary by race/ethnicity. The results of hybrid models show that social support from one's spouse and friend/relative account for over 25 percent of the black-white disparity, whereas formal social integration including religious groups widens the black-white differential by roughly 10 percent. However, Hispanic-white disparities in depression are mostly a result of the difference in socioeconomic status. The change in spousal support is the most powerful predictor for the change in depression across race/ethnicity groups. Our findings suggest that the racial/ethnic differences in sources of social support and types of social integration play important roles in shaping racial/ethnic disparities in depression.

Lost in the Black Belt South: health outcomes and transportation infrastructure

The importance of transportation infrastructure to health outcomes has been increasingly recognized. However, the relationship between transportation and health is underexplored in rural areas. This study fills the gap by investigating rural health outcomes in association with two transportation infrastructures-highways and airports-in the Black Belt counties of the USA, a region characterized as predominantly rural and black and as having high poverty and unemployment. Spatial regression models are applied to analyze the 2010 data. The results suggest Black Belt counties have poorer health outcomes than their non-Black Belt counterparts, and the difference increases as the percentage of blacks increases. The results also show that the higher accessibility to an airport a county has, the better its health outcomes. Highways, however, do not have a statistically significant association with health outcomes. The poor health outcomes in the Black Belt counties are also influenced by poverty, rurality, unemployment, and low educational attainment. This research was the first to study transportation, especially airports, in the rural US South with relation to health outcomes. Our findings shed new light on removing the health disadvantages accumulated in the Black Belt.

Modeling the Importance of Within- and Between-County Effects in an Ecological Study of the Association Between Social Capital and Mental Distress

INTRODUCTION

Levels of mental distress in the United States are a health policy concern. The association between social capital and mental distress is well documented, but evidence comes primarily from individual-level studies. Our objective was to examine this association at the county level with advanced spatial econometric methods and to explore the importance of between-county effects.

METHODS

We used County Health Rankings and Roadmaps data for 3,106 counties of the contiguous United States. We used spatial Durbin modeling to assess the direct (within a county) and indirect (between neighboring counties) effects of social capital on mental distress. We also examined the spatial spillover effects from neighboring counties based on higher-order spatial weights matrices.

RESULTS

Counties with the highest prevalence of mental distress were found in regional clusters where levels of social capital were low, including the Black Belt, central/southern Appalachia, on the Mississippi River, and around some Indian Reservations. Most of the association between social capital and mental distress was indirect, from the neighboring counties, although significant direct effects showed the within-county association. Models also confirmed the importance of county-level socioeconomic status.

CONCLUSION

We found that county social capital is negatively related to mental distress. Counties are not isolated places and are often part of wider labor and housing markets, so understanding spatial dependencies is important in addressing population-level mental distress.

Analyzing Hospital Choices of Colon Cancer Patients in Four States in Appalachia

While geographic disparities in cancer treatment are documented, little research has investigated patients' willingness to travel longer distances for treatment at higher-quality hospitals. Even fewer studies have compared metropolitan and non-metropolitan patients on this dimension. Using population-based data from the Appalachian counties of four states, we identified all hospitals within a plausible driving distance of each newly diagnosed colon cancer patient and estimated conditional logit models of hospital choices for cancer surgery. Two sets of important findings emerged. First, both metropolitan and non-metropolitan patients chose higher-quality over lower-quality hospitals, and were willing to travel farther to use high-quality facilities. Second, non-metropolitan patients were not willing to travel farther than their metropolitan counterparts to hospitals that were more desirable along most dimensions, but non-metropolitan patients were willing to travel farther to high-volume hospitals. These findings show how travel distances to high-quality hospitals contribute to geographic disparities in colon cancer treatment.

Superdirective beamforming applied to SWellEx96 horizontal arrays data for source localization

Arrays employing superdirective beamforming can provide the same directivity and directivity index (or array gain) with less aperture as a large size array using conventional beamforming (CBF). Superdirective arrays offer a practical and significant improvement in the reception of low frequency signals and is useful for many applications where the array size is limited, such as a miniature microphone array or an underwater acoustic array with a limited aperture. Deconvolving the CBF is a method of superdirective beamforming. The improvement in directivity (beam width) and array gain is studied/illustrated using the SWellEx96 horizontal array data where only sub-arrays are used.

The association between body mass index, primary healthcare use and morbidity in early childhood: findings from the Born In Bradford cohort study

OBJECTIVES

The objective of the article was to examine the association between body mass index (BMI), health and general practice (GP) healthcare use in early childhood.

STUDY DESIGN

This study is a prospective cohort study.

METHODS

Multivariate Poisson and logistic regression models were used to explore the association between BMI and health outcomes using data from the Born In Bradford cohort study, linked to routine data capturing objective measures of BMI at age 5 years, alongside GP appointment rates, GP prescriptions and specific morbidities in the subsequent 3-year period.

RESULTS

Compared with healthy weight, children who were obese at the age of 5 years had significantly higher rates of GP appointments (incident rate ratio 1.14, 95% confidence interval [CI]: 1.06-1.23), GP prescriptions (incident rate ratio 1.15, 95% CI: 1.04-1.27), asthma (odds ratio 1.46, 95% CI: 1.21-1.77), sleep apnoea (odds ratio 2.50, 95% CI: 1.36-4.58), infections (incident rate ratio 1.19, 95% CI: 1.08-1.30), antibiotic prescriptions (incident rate ratio 1.25, 95% CI: 1.10-1.42) and accidents (incident rate ratio 1.20, 95% CI: 1.01-1.42) in the subsequent 3 years. Underweight children were found to have higher rates of GP appointments (incident rate ratio 1.25, 95% CI: 1.04-1.52), but there were no differences between overweight and healthy weight children.

CONCLUSIONS

Childhood obesity was found to be associated with increased primary healthcare use and a range of poorer health outcomes at the age of 8 years, underlining the importance of reducing childhood obesity in early childhood.

Neighborhood effects on body mass: Temporal and spatial dimensions

Research examining the effects of neighborhood characteristics on obesity and excess body weight has generally neglected the influence of both life-course exposure and geographically-proximate communities. Using data on 9357 respondents to the National Longitudinal Survey of Youth, 1979 Cohort, in conjunction with tract-level data from the 1980-2010 U.S. censuses, this study examines how black, Hispanic, and white individuals' cumulative exposure to varying levels of neighborhood poverty and co-ethnic density from their mid-teens through mid-adulthood, as well as the levels of poverty and co-ethnic density in nearby, or "extralocal," neighborhoods, are associated with their body mass index (BMI). Fixed-effect regression models show that, among Hispanics and whites, cumulative exposure to co-ethnic neighbors is a stronger positive predictor of BMI than the co-ethnic density of the immediate, point-in-time neighborhood. Among whites, cumulative exposure to neighborhood poverty is a stronger positive predictor of BMI than is the poverty rate of the current neighborhood of residence. And among both blacks and whites, the distance-weighted poverty rate of extralocal neighborhoods is significantly and inversely related to BMI, suggesting that relative affluence in nearby neighborhoods engenders relative deprivation among residents of the focal neighborhood, leading to increased BMI. Overall, the results suggest that greater attention to both the temporal and spatial dimensions of neighborhood effects has the potential to enhance our understanding of how neighborhoods affect obesity and related health outcomes.

Neighborhood ethnic density and self-rated health: Investigating the mechanisms through social capital and health behaviors

While living with co-ethnics benefits minorities' health, the so-called ethnic density effect, little is known about the mechanisms through which neighborhood ethnic density influences self-rated health. We examine two pathways, namely neighborhood social capital and health behaviors, with a 2010 survey collected in Philadelphia (2297 blacks and 492 Hispanics). The mediation analysis indicates that (1) living with co-ethnics is beneficial to both blacks' and Hispanics' self-rated health, (2) neighborhood social capital and health behaviors mediate almost 15% of the ethnic density effect for blacks, and (3) the two mechanisms do not explain why living with co-ethnics improves Hispanics' health.

Linking perceived discrimination during adolescence to health during mid-adulthood: Self-esteem and risk-behavior mechanisms

RATIONALE

The literature on the effect of perceived discrimination on health has three gaps. First, the long-term relationship between perceived discrimination and health is underexplored. Second, the mechanisms through which perceived discrimination affects health remain unclear. Third, most studies focus on racial/ethnic discrimination, and other aspects of discrimination are overlooked.

OBJECTIVE

This study aims to fill these gaps by testing a research framework that links the discriminatory experience during adolescence to an individual's health during mid-adulthood via self-esteem and risk behaviors at early adulthood.

METHOD

Structural equation modeling is applied to the National Longitudinal Survey of Youth, 1979 Cohort (N = 6478).

RESULTS

The discriminatory experience during adolescence imposes an adverse impact on health during mid-adulthood even after accounting for other potential covariates, a detrimental effect lasting for over 30 years. In addition, while perceived discrimination reduces self-esteem at early adulthood, it affects only mental health during mid-adulthood, rather than general health. Finally, the discriminatory experience promotes risk behaviors at early adulthood and the risk behaviors subsequently compromise health during mid-adulthood.

CONCLUSIONS

Using a life course perspective, we find that the effect of perceived discrimination is more profound than the literature suggested and that risk behaviors may account for approximately 17% of the total effect of perceived discrimination on health. Our findings highlight the importance of early interventions in coping with perceived discrimination during adolescence.

Differential investments and opportunities: How do neighborhood conditions moderate the relationship between perceived housing discrimination and social capital?

Though the adverse consequences of perceived housing discrimination have been documented, little is known about whether such experience undermines one's social capital in a neighborhood and even less is about whether and how this relationship is altered by neighborhood features. We proposed a framework that simultaneously considers within-individual and between-neighborhood processes. We applied multilevel structural equation models to data from Philadelphia (n = 9987) and found that (a) perceived housing discrimination was negatively associated with one's social capital even after other confounders were considered, (b) this negative association could be partly explained by the proliferated daily stress and anxiety mechanisms, (c) differential exposures to neighborhood social disadvantage accounted for the variation in social capital across neighborhoods, and (d) the adverse association between perceived housing discrimination and social capital could be attenuated by neighborhood stability. The findings suggested that appropriate interventions should buffer the negative association of perceived housing discrimination with social capital.

Physics-based coastal current tomographic tracking using a Kalman filter

Ocean acoustic tomography can be used based on measurements of two-way travel-time differences between the nodes deployed on the perimeter of the surveying area to invert/map the ocean current inside the area. Data at different times can be related using a Kalman filter, and given an ocean circulation model, one can in principle now cast and even forecast current distribution given an initial distribution and/or the travel-time difference data on the boundary. However, an ocean circulation model requires many inputs (many of them often not available) and is unpractical for estimation of the current field. A simplified form of the discretized Navier-Stokes equation is used to show that the future velocity state is just a weighted spatial average of the current state. These weights could be obtained from an ocean circulation model, but here in a data driven approach, auto-regressive methods are used to obtain the time and space dependent weights from the data. It is shown, based on simulated data, that the current field tracked using a Kalman filter (with an arbitrary initial condition) is more accurate than that estimated by the standard methods where data at different times are treated independently. Real data are also examined.

A multi-group path analysis of the relationship between perceived racial discrimination and self-rated stress: how does it vary across racial/ethnic groups?

OBJECTIVE

The objective of this study was to answer three questions: (1) Is perceived discrimination adversely related to self-rated stress via the social capital and health care system distrust pathways? (2) Does the relationship between perceived discrimination and self-rated stress vary across race/ethnicity groups? and (3) Do the two pathways differ by one's race/ethnicity background?

DESIGN

Using the Philadelphia Health Management Corporation's Southeastern Pennsylvania Household Survey, we classified 9831 respondents into 4 race/ethnicity groups: non-Hispanic White (n = 6621), non-Hispanic Black (n = 2359), Hispanic (n = 505), and non-Hispanic other races (n = 346). Structural equation modeling was employed to simultaneously estimate five sets of equations, including the confirmatory factor analysis for both social capital and health care distrust and both direct and indirect effects from perceived discrimination to self-rated stress.

RESULTS

The key findings drawn from the analysis include the following: (1) in general, people who experienced racial discrimination have higher distrust and weaker social capital than those without perceived discrimination and both distrust and social capital are ultimately related to self-rated stress. (2) The direct relationship between perceived discrimination and self-rated stress is found for all race/ethnicity groups (except non-Hispanic other races) and it does not vary across groups. (3) The two pathways can be applied to non-Hispanic White and Black, but for Hispanic and non-Hispanic other races, we found little evidence for the social capital pathway.

CONCLUSIONS

For non-Hispanic White, non-Hispanic Black, and Hispanic, perceived discrimination is negatively related to self-rated stress. This finding highlights the importance of reducing interpersonal discriminatory behavior even for non-Hispanic White. The health care system distrust pathway can be used to address the racial health disparity in stress as it holds true for all four race/ethnicity groups. On the other hand, the social capital pathway seems to better help non-Hispanic White and Black to mediate the adverse effect of perceived discrimination on stress.