Similar Neighborhoods Research Articles

Racial and Ethnic Differences in Obesity in People With Spinal Cord Injury: The Effects of Disadvantaged Neighborhood

ObjectiveTo examine the role of neighborhood in the relation between race and obesity in people with spinal cord injury (SCI). DesignA cross-sectional analysis of survey data from National SCI Database linked with neighborhood data from American Community Survey by census tract. SettingA total of 17 SCI Model Systems centers. ParticipantsIndividuals (N=3385; 2251 non-Hispanic whites, 760 non-Hispanic blacks, 374 Hispanics) who completed a follow-up assessment during 2006-2017 (mean duration of injury, 8.3±9.9y) and resided in 2934 census tracts. InterventionNot applicable. Main Outcome MeasuresBody mass index (BMI) (kg/m2). ResultsThe overall prevalence of obesity was 52.9% (BMI≥25.0) and 23.3% (BMI≥30.0). Hispanics were 67.0% more likely to be obese (BMI≥30.0 kg/m2) relative to non-Hispanic whites (odds ratio, 1.67; 95% confidence interval, 1.27-2.18), after controlling for demographic and injury-related characteristics. Most of the non-Hispanic blacks (66.8%) were living in neighborhoods with high concentrated disadvantaged index (CDI), compared to 35.0% of Hispanics and 9.2% of non-Hispanic whites living in this similar neighborhood status (P<.0001). After accounting for CDI, the odds of being obese in Hispanics decreased (odds ratio, 1.51; 95% confidence interval, 1.15-1.99). Regardless of race and ethnicity, people with SCI from disadvantaged neighborhoods were 42.0%-70.0% more likely to be obese than those from minimal CDI neighborhoods. ConclusionsNeighborhood characteristics partially diminish racial differences in obesity. Weight management for the SCI population should target those who are Hispanic and living in the disadvantaged neighborhoods.

Constructing Matched Groups in Dental Observational Health Disparity Studies for Causal Effects.

Electronic health record (EHR) systems provide investigators with rich data from which to examine actual impacts of care delivery in real-world settings. However, confounding is a major concern when comparison groups are not randomized. This article introduced a step-by-step strategy to construct comparable matched groups in a dental study based on the EHR of the Willamette Dental Group. This strategy was employed in preparation for a longitudinal study evaluating the impact of a standardized risk-based caries prevention and management program across patients with public versus private dental insurance in Oregon. This study constructed comparable dental patient groups through a process of 1) evaluating the need for and feasibility of matching, 2) considering different matching methods, and 3) evaluating matching quality. The matched groups were then compared for their average ratio in the number of decayed, missing, and filled tooth surfaces (DMFS + dmfs) at baseline. This systematic process resulted in comparably matched groups in baseline covariates but with a clear baseline disparity in caries experience between them. The weighted average ratio in our study showed that, at baseline, publicly insured patients had 1.21-times (95% CI: 1.08 to 1.32) and 1.21-times (95% CI: 1.08 to 1.37) greater number of DMFS + dmfs and number of decayed tooth surfaces (DS + ds) than privately insured patients, respectively. Matching is a useful tool to create comparable groups with EHR data to resemble randomized studies, as demonstrated by our study where even with similar demographics, neighborhood and clinic characteristics, publicly insured pediatric patients had greater numbers of DMFS + dmfs and DS + ds than privately insured pediatric patients. This article provides a systematic, step-by-step strategy for investigators to follow when matching groups in a study-in this case, a study based on electronic health record data. The results from this study will provide patients, clinicians, and policy makers with information to better understand the disparities in oral health between comparable publicly and privately insured pediatric patients who have similar values in individual, clinic, and community covariates. Such understanding will help clinicians and policy makers modify oral health care and relevant policies to improve oral health and reduce disparities between publicly and privately insured patients.

A Super Region Approach to Improve Pooled Flood Frequency Analysis

Floods are known as one of the most damaging natural hazards with devastating influence on people and the environment. Accurately estimating flood frequencies is essential for effective design of flood mitigation systems. Estimation of these frequencies is difficult since extreme events are rare and the length of recorded data is often short. In such situations, extreme flow information from a number of similar sites is combined (pooled) to augment the available at-site information. Pooled flood frequency analysis is a well-known approach used to improve the estimation of extreme flow quantiles at sites with short data records. Identification of pooling groups that will effectively transfer extreme flow information is thus essential. The present paper proposes an approach to improve flood quantile estimates through utilizing the concept of super regions integrated with seasonality-based similarity measures to conduct pooled frequency analysis for extreme flow events. To identify homogeneous regions, this study focuses on the region of influence (ROI), or focussed pooling group approach among hydrological neighborhood techniques. To define the hydrologically similar neighborhood of a target site, a single numeric that measures similarity/dissimilarity between sites is usually utilized. This work investigates the effect of employing catchment physiographic-climate characteristics and several flood seasonality statistics as the similarity measures. Moreover, this study explores and establishes a super region technique that in a hierarchical process employs the two types of similarity measures. A large dataset of catchments across Canada was used to compare the proposed method with more traditional approaches. The effectiveness of these techniques both in terms of constructing homogeneous pooling groups and accurately estimating extreme flow quantiles is explored for the catchments under study. The proposed super region approach was shown to form more reliable homogeneous pooling groups. Analyzing confidence intervals of quantile estimates obtained from pooled and at-site estimates revealed promising improvement.

Task modulates ERP effects of orthographic neighborhood for pseudowords but not words

Words and pseudowords from high-density orthographic neighborhoods elicit larger amplitude N400s than similar items from low-density orthographic neighborhoods in the lexical decision task. This pattern could be interpreted as an increase in the amount of lexico-semantic information to be processed or as an increase in difficulty identifying a word (or rejecting a pseudoword) amongst many co-activated alternatives. In order to dissociate between these mechanisms, we compared neighborhood effects between a lexical decision task (LDT) and a letter search task (LST). Behaviorally, we found the standard neighborhood and lexicality effects in the LDT, but no significant effects in the LST. Thus, behavioral responses were sensitive to the decisions required by the respective tasks. Electrophysiologically, we found similar N400 neighborhood effects between tasks for words, but the N400 neighborhood effect for pseudowords was only present in the LDT. Moreover, the effect of neighborhood in the LDT occurred earlier for words than for pseudowords. These nuanced differences in the time course and automaticity of word and pseudoword neighborhood effects lend insight into the processes that underlie N400 effects of orthographic neighborhood and how they unfold over time. We propose that the early neighborhood effects for words across tasks were driven by highly automatized word identification processes that were sensitive to the lateral inhibition generated by orthographic neighbors. In contrast, the later neighborhood effects for pseudowords in the LDT could have been driven by task-specific processes tied to how global lexical activity is used to make a lexical decision.

HIV Status Does Not Affect Rectal Microbiome Composition, Diversity, or Stability over Time: A Chicago Women's Interagency HIV Study.

It remains unclear whether differences in gut microbiota noted between HIV-infected and uninfected individuals are driven by HIV or sexual behavior. We evaluated rectal swab microbiota of HIV-infected and uninfected women with similar demographic, neighborhood, and diet characteristics enrolled in the Chicago Women's Interagency HIV Study (WIHS). DNA was amplified for sequencing of fragments of bacterial small subunit (SSU or 16S) ribosomal RNA (rRNA) genes. HIV-infected and uninfected women did not differ by Shannon diversity index (p = .14), non-metric multidimensional scaling (NMDS) plot of Bray-Curtis indices (p = .488, r = 0.0027), or copy number of individual taxa. Both groups demonstrated marked microbiome stability over time (p = .889).

Automated partial atomic charge assignment for drug-like molecules: a fast knapsack approach

A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly mathcal {NP}-complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We demonstrate that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule from hours or even days to below a second. Our software is openly available.

Evaluation of long-term effect of pedestrian signage with time travel indication in Grenoble city (France)

Background:French population does not achieve recommended physical activity (PA) level. Promoting walking as an active transport mode through urban environment is a recommended strategy to maintain health. This study assessed long-term effect (1 year) of pedestrian signage in the city of Grenoble (France). The first part of our study focused on the attendance of general population living in Grenoble on two important crossroads in a neighborhood equipped with pedestrian signage. The second one studied the receptiveness to this signage of low to moderate socioeconomic status Grenoble citizens living in this neighborhood. In addition, we tried to evidence the potential impact of this signage in the latter population in term of walking activity, cardiometabolic health and motivation toward PA.Methods:For the first part of the study, we compared the attendance (number of passages manual counting) in Grenoble general population on 2 crossroads in a neighborhood equipped with signage (PSN) with 2 crossroads of a similar neighborhood not equipped (CN). For the second part of the study, we planned to specifically recruit 140 subjects (electoral lists, brochures, phone calls and mailing) in these two neighborhoods (pedestrian signage group: PSG; control group: CG). These subjects were proposed to participate in a follow-up of their health during one year. We asked if they have seen the pedestrian signage and compared these groups on daily steps number (pedometer), cardiometabolic risk (SCORE) and motivation toward PA (Self-Regulation Questionnaire for exercise) over one year period.Results:For the first part of the study, the attendance slightly increased between June and December (effect size: 0.5;p &lt; 0.05) in PSN suggesting a transitory effect of the signage in general population. For the second part, only forty-two subjects (22 in PSG and 20 in CG) accepted to participate in this study. We failed to include low to moderate socio-economic citizens. Seventy percent of the recruited subjects did not see the pedestrian signage. This sample did not change its walking activity level, cardiometabolic risk as well as PA motivation.Conclusion:On the basis of attendance study, such a pedestrian signage might be incentive to increase PA in general population but this needs confirmation. The effects of a larger specific communication associated with this urban environment targeting particular population groups deserve to be studied.

Parents and Peers: Parental Neighbourhood- and School-Level Variation in Individual Neighbourhood Outcomes Later in Life

There is a link between the socio-economic outcomes of parents and their children over the life course. Intergenerational transmissions were repeatedly shown for socioeconomic characteristics and (dis)advantage, but recently also for residential neighbourhood status. Previous research from the Netherlands, Sweden, and the US shows that children from disadvantaged parental neighbourhoods experience long-term exposure to similar neighbourhoods as adults. However, there are multiple parallel socio-spatial contexts besides the residential space to which individuals are exposed on a daily basis, such as households, schools, and places of work and leisure, which may also influence their outcomes. For children and adolescents, the school environment may be especially important. This study contributes to the literature by examining the joint influence of the parental background, the parental neighbourhood, and a compositional measure of the school environment, on the neighbourhood trajectories of Dutch adolescents after leaving the parental home. We use longitudinal register data from the Netherlands to study a complete cohort of school-going home-leavers, who were followed from 1999 to 2012. We fit cross-classified multilevel models, in order to split up the variance components of schools and parental residential neighbourhoods over time. We find that poverty concentration in the parental neighbourhood plays an important role in determining their children’s residential outcomes later in life. Some variation in individual neighbourhood outcomes at the level of the secondary school remains unexplained.

Tandem Duplicate Genes in Maize Are Abundant and Date to Two Distinct Periods of Time.

Tandem duplicate genes are proximally duplicated and as such occur in similar genomic neighborhoods. Using the maize B73 and PH207 de novo genome assemblies, we identified thousands of tandem gene duplicates that account for ∼10% of the annotated genes. These tandem duplicates have a bimodal distribution of ages, which coincide with ancient allopolyploidization and more recent domestication. Tandem duplicates are smaller on average and have a higher probability of containing LTR elements than other genes, suggesting origins in nonhomologous recombination. Within relatively recent tandem duplicate genes, ∼26% appear to be undergoing degeneration or divergence in function from the ancestral copy. Our results show that tandem duplicates are abundant in maize, arose in bursts throughout maize evolutionary history under multiple potential mechanisms, and may provide a substrate for novel phenotypic variation.

Identifying spatiotemporal urban activities through linguistic signatures

Identifying the activities that individuals conduct in a city is key to understanding urban dynamics. It is difficult, however, to identify different human activities on a large scale without incurring significant costs. This study focuses on modeling the spatiotemporal patterns of different activity types within cities by employing user-contributed, geosocial content as a proxy for human activities. In this work, we use linguistic topic modeling to analyze georeferenced twitter data in order to differentiate different activity types. We then examine the spatial and temporal patterns of the derived activity types in three U.S. cities: Baltimore, MD., Washington, D.C., and New York City, NY. The linguistic patterns reflect the spatiotemporal context of the places where the social media content is posted. We further construct a method to link what people post online to the activities conducted within a city. We then use these derived activities to profile the characteristics of neighborhoods in the three cities, and apply the activity signatures to discover similar neighborhoods both within and between the cities. This approach represents a novel activity-based method for assessing similarity between neighborhoods.

Alcohol consumption and risk of type 2 diabetes mellitus among males in The Manggis Area, Karangasem, Bali

AbstractBackground and purpose: Diabetes mellitus (DM) is a serious health problem worldwide. Local alcohol consumption is one of Balinese’s traditions considered to be associated with the high incidence of DM. This study aims to determine the pattern of alcohol consumption and risk of type 2 DM in Manggis Subdistrict, Karangasem, Bali.Methods A case control study was conducted in 55 men with type 2 DM as cases and 55 non-diabetic men as controls. Non-diabetic men were defined with fasting glucose level (GDP) &lt;100 mg/dl and without clinical symptoms of DM. Cases were recruited from the patient's register at the public health centre (PHC) in Manggis Sub-District and controls were recruited from similar neighborhood with the cases and matched by age. Data were collected over May-July 2017 by households’ interview using structured questionnaires. Multivariate analysis was employed using logistic regression to identify the risk factors of type 2 DM.Results: The characteristics of cases and controls were similar in terms of age, education and physical activity, but there was a significant difference in employment. Multivariate analysis showed that heavy alcohol consumption (AOR=7.84; 95%CI: 1.46-42.28), frequent consumption of high sugar alcohol drinks (AOR=3.45; 95%CI: 1.16-10.22), history of obesity (AOR=8.82; 95% CI: 2.43-32.01), employed (AOR=5.98; 95%CI: 1.89-18.93) and frequent consumption of sweetened beverages (AOR=39,57; 95%CI: 4.00-391,8) were significantly associated with the incidence of type 2 DM. No significant association was found between the duration of alcohol consumption and the incidence of type 2 DM.Conclusions: Alcohol consumption, a history of obesity, consumption of high sugar alcohol drinks, retirement/unemployment and consumption of sweetened beverages are associated with the incidence of type 2 DM. Education regarding those risk factors should be enhanced to reduce the incidence of type 2 DM.

Alcohol consumption and risk of type 2 diabetes mellitus among males in The Manggis Area, Karangasem, Bali

AbstractBackground and purpose: Diabetes mellitus (DM) is a serious health problem worldwide. Local alcohol consumption is one of Balinese’s traditions considered to be associated with the high incidence of DM. This study aims to determine the pattern of alcohol consumption and risk of type 2 DM in Manggis Subdistrict, Karangasem, Bali.Methods A case control study was conducted in 55 men with type 2 DM as cases and 55 non-diabetic men as controls. Non-diabetic men were defined with fasting glucose level (GDP) &lt;100 mg/dl and without clinical symptoms of DM. Cases were recruited from the patient's register at the public health centre (PHC) in Manggis Sub-District and controls were recruited from similar neighborhood with the cases and matched by age. Data were collected over May-July 2017 by households’ interview using structured questionnaires. Multivariate analysis was employed using logistic regression to identify the risk factors of type 2 DM.Results: The characteristics of cases and controls were similar in terms of age, education and physical activity, but there was a significant difference in employment. Multivariate analysis showed that heavy alcohol consumption (AOR=7.84; 95%CI: 1.46-42.28), frequent consumption of high sugar alcohol drinks (AOR=3.45; 95%CI: 1.16-10.22), history of obesity (AOR=8.82; 95% CI: 2.43-32.01), employed (AOR=5.98; 95%CI: 1.89-18.93) and frequent consumption of sweetened beverages (AOR=39,57; 95%CI: 4.00-391,8) were significantly associated with the incidence of type 2 DM. No significant association was found between the duration of alcohol consumption and the incidence of type 2 DM.Conclusions: Alcohol consumption, a history of obesity, consumption of high sugar alcohol drinks, retirement/unemployment and consumption of sweetened beverages are associated with the incidence of type 2 DM. Education regarding those risk factors should be enhanced to reduce the incidence of type 2 DM.

BASELINE DIFFERENCES IN BRAIN MORPHOMETRY AND IMAGE GRADING OF INDIVIDUALS ON THE CONTINUUM FROM SUBJECTIVE COGNITIVE DECLINE TO AD: RESULTS FROM THE CIMA-Q STUDY

Individuals expressing subjective complaints of cognitive decline (SCD) without objective cognitive impairment may represent a group at risk of developing mild cognitive impairment (MCI), and hence AD. To validate this concept using biomarkers, we compared participants with normal cognitive functioning (CON, n=30), SCD (n=67), early MCI (eMCI, n=22), late MCI (lMCI, n=8), and AD (n=16) from the CIMA-Q study on measures derived from T1w MRIs: 1) Anatomical morphometry (volumes, surfaces, and thicknesses) from 90 brain regions, computed using FreeSurfer and normalized for age, sex, intracranial volume, scanner strength and type; 2) Tissue grading, according to a nonlocal image patch estimator (provided by TPMD) in the hippocampi (HPC) and entorhinal cortices (EC). The patented TPMD technique (see NeuroImage:Clinical 1(1):141-152,2012) grades images by matching patches of voxels within a region to similar neighbourhoods in a pre-labeled set of training images. Each patch is tagged with information known a priori from the selected training images, achieving a grading score expressing the similarity to images of tissue from individuals with probable AD. We hypothesized that there would be gradients of increasing biomarker severity from CON to AD, with SCD showing a small, but significant difference from CON in the EC and HPC. Qualitatively, morphometry results showed a gradient of changes from CON to AD in the EC and HPC (Figure 1), especially for the left EC and right HPC, but with SCD not significantly different from CON. Furthermore, compared to CON, SCD had significant lower right lingual surface (p=.021), left posterior cingulate thickness (p=.026), right rostral anterior cingulate thickness (p=.012), left supramarginal surface (p=.019) and volume (p=.039), however none of those differences resisted FDR correction. Grading results (Figure 2) were similar to morphometry, with a gradient of changes from CON to AD, especially for the left EC and right HPC, but no significant difference between SCD and CON.

Scaling up: How computational models can propel bilingualism research forward

The Multilink model that Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (2018) present is an excellent example that connects empirical patterns obtained from behavioral studies with mechanisms that can be implemented in computational models. We have previously argued that implementation of computational models is important because it forces the researchers to be explicit about assumptions and to specify parameters and variables that may be absent in verbal models. The Multilink model, along with BIA/BIA+ and many other models, provides concrete hypotheses regarding the role of variables such as word frequency, word length, orthographic similarity, and phonological neighborhood for researchers to test and verify against empirical data (see examples in the special issue on computational modeling published in this journal; Li, 2013).

How Durable Are Ethnoracial Segregation and Spatial Disadvantage? Intergenerational Contextual Mobility in France

Building on emerging research into intergenerational contextual mobility, I use longitudinal data from France (1990-2008) to investigate the extent to which second-generation immigrants and the French majority continue to live in similar neighborhood environments during childhood and adulthood. To explore the persistence of ethnoracial segregation and spatial disadvantage, I draw on two measures of neighborhood composition: the immigrant share and the unemployment rate. The analysis explores the individual and contextual factors underpinning intergenerational contextual mobility and variation across immigrant-origin groups. The results document a strong stability of neighborhood environments from childhood to adulthood, especially with regard to the ethnoracial composition of the neighborhood. Individual-level factors are quite weak in accounting for these patterns compared with the characteristics of the city of origin. Moreover, the degree of contextual mobility between childhood and adulthood varies across groups. I find that neighborhood environments are more stable over time for non-European second-generation immigrants. The findings offer important new empirical contributions to the French literature on the residential segregation of immigrants and will more broadly be of interest to scholars of intergenerational spatial and social mobility.

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization

With the rapid development of cloud manufacturing (CMfg), quality-of-service (QoS) prediction becomes increasingly important in CMfg service platform because it turns out to be impractical to acquire all service QoS values. In this paper, we present a neighbourhood enhanced matrix factorization approach to predict missing QoS values. We first systematically consider geographical information, sample set diversity computation and platform context to extend basic Pearson Correlation Coefficient (PCC) similarity and extract neighbourhood information. Then, we integrate neighbourhood information into matrix factorization (MF) and make prediction of missing values. Compared with existing methods, the proposed method has the following new features: (1) entropy information is adopted to derive personal weights for different users or services when computing PCC similarity; (2) location information and sample set similarity are considered to enhance PCC similarity; (3) topology information is introduced to address data sparsity issue; (4) neighbourhood information is incorporated with MF to improve prediction accuracy. We conduct an experiment on a real-world dataset which includes web service invocations from 339 service users on 5825 services to verify the feasibility and efficiency of our method.

Hash based manifold learning technique to generating random fields for image segmentation

The manifold learning technique is a class of machine learning techniques that converts the intrinsic geometry of the data from higher to lower dimensional representation by using the manifold distance and preserved in hamming space. It is an offline learning process, so it requires more time and memory. We proposed a new hash based manifold learning technique to generate random fields for image segmentation. The proposed method is a two-step process, first is to find the similarity neighborhood pixels, the second is to construct the weighted matrix. The proposed method requires less time and space comparatively than the existing method, because the manifold structure is directly reconstructed in hamming space, which has been shown in the experimental results on semantic datasets and our own datasets in the form of tables and graphs.

Using Community-Based Participatory Research to Develop Geospatial Models Toward Improving Community Health for Disadvantaged Hispanic Populations in Charlotte, NC

Hispanic immigrant communities across the U.S. experience persistent health disparities and barriers to primary care. We examined whether community-based participatory research (CBPR) and geospatial modeling could systematically and reproducibly pinpoint neighborhoods in Charlotte, North Carolina with large proportions of Hispanic immigrants who were at-risk for poor health outcomes and health disparities. Using a CBPR framework, we identified 21 social determinants of health measures and developed a geospatial model from a subset of those measures to identify neighborhoods with large proportions of Hispanic immigrant populations at risk for poor health outcomes. The geospatial model included four measures-poverty, English ability, acculturation and violent crime-which comprised our Hispanic Health Risk Index (HHRI). We developed a Primary Care Barrier Index (PCBI) to determine (1) how well the HHRI correlated with a statistically derived composite measure incorporating all 21 measures identified through the CBPR process as being associated with access to primary care; (2) whether the HHRI predicted primary care access as well as the statistically-derived composite measure in a statistical model; and (3) whether the HHRI identified similar neighborhoods as the statisticallyderived composite measure. We collapsed 17 of the 21 social determinants using principal components analysis to develop the PCBI. We determined the correlation of each index with inappropriate emergency department (ED) visits, a proxy for primary care access, using logistic generalized estimating equations. Results from logistic regression models showed positive associations of both the HHRI and the PCBI with the use of the ED for primary care treatable conditions. Enhanced by the knowledge of the local community, the CBPR process with geospatial modeling can guide the multi-tiered validation of social determinants of health and identify neighborhoods that are at-risk for poor health outcomes and health disparities.

Associations of overweight/obesity and socioeconomic status with hypertension prevalence across racial and ethnic groups.

Racial/ethnic disparities in the prevalence of diagnosed hypertension are persistent but may be partially explained by racial/ethnic differences in weight category and neighborhood socioeconomic status. The authors compared hypertension prevalence rates among 4060585 adults with overweight or obesity across 10 healthcare systems by weight category and neighborhood education level in geographically and racially diverse individuals. Data were obtained from electronic health records. Hypertension was defined as at least two outpatient visits or one inpatient hospitalization with a coded diagnosis. Logistic regression, adjusted for age, sex, and site, with two-way interactions between race/ethnicity and weight category or neighborhood education, was used to examine the association between hypertension and race/ethnicity, with whites as the reference. Results documented that odds ratios for hypertension prevalence were greater for blacks, American Indians/Alaskan Natives, Asians, and Native Hawaiians/other Pacific Islanders compared with whites and lower for Hispanics in similar weight categories and neighborhood education levels. Although two-way interactions were statistically significant, the magnitude of the odds of hypertension compared with whites did not substantially vary across weight or neighborhood education. Hypertension odds were almost double relative to whites for blacks and Native Hawaiians/other Pacific Islanders across most weight categories and all neighborhood education levels. Odds of hypertension were about 50% greater for Asians relative to whites across weight categories. Results suggest that other factors might be associated with racial/ethnic disparities in hypertension. More research is needed to understand the many factors that may contribute to variation in diagnosed hypertension across racial/ethnic groups with overweight or obesity.