This dataset recreates three releases (2015, 2020, and 2022) of The Neighborhood Atlas team’s Area Deprivation Index (ADI) using standardized components. The ADI is a measure that aims to quantify the socioeconomic conditions of census block groups (sometimes used to approximate neighborhoods), originally based on 1990 census tract data and factor loadings. The Neighborhood Atlas team at the University of Wisconsin adapted the ADI to block groups and more recent data, imputing missing data using tract- and county-level data.However, unlike the original index construction method, The Neighborhood Atlas team did not adjust (standardize) individual components before combining them into an overall score. This approach resulted in individual index components measured in dollars, such as income and home value, being overly influential in the final score. This dataset corrects for that by standardizing these components before aggregating, offering a more multi-dimensional view of socioeconomic conditions. The standardized ADI dataset provides continuous rankings for block groups nationwide and decile rankings for block groups within each state.

The table adi_by_county is part of the dataset Area Deprivation Index (ADI), available at https://columbia.redivis.com/datasets/axrk-7jx8wdwc2. It contains 9426 rows across 8 variables.

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.Much of the ADI research and popularity would not be possible without the excellent work of Dr. Amy Kind and colleagues at HIPxChange and at The University of Wisconsin Madison.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery. En savoir plus

ADI: An index of socioeconomic status for communities. Dataset ingested directly from BigQuery.

The area deprivation index (ADI) represents a geographic area-based measure of the socioeconomic deprivation experienced by a neighborhood. Higher index values represent higher levels of deprivation and associated with an increased risk of adverse health and health care. It includes factors for the theoretical domains of income, education, employment, and housing quality.

Relationship between quintiles of neighborhood deprivation as measured by the ADI and COVID-19 rates in Louisiana census tracts (N = 1127).

Area Deprivation Index (ADI) Quintiles (Q) in Louisiana census tracts (N = 1127).

ObjectivesTo compare 2 frequently used area-level socioeconomic deprivation indices: the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI).MethodsIndex agreement was assessed via pairwise correlations, decile score distribution and mean comparisons, and mapping. The 2019 ADI and 2018 SVI indices at the U.S. census tract-level were analyzed.ResultsIndex correlation was modest (R = 0.51). Less than half (44.4%) of all tracts had good index agreement (0–1 decile difference). Among the 6.3% of tracts with poor index agreement (≥6 decile difference), nearly 1 in 5 were classified by high SVI and low ADI scores. Index items driving poor agreement, such as high rents, mortgages, and home values in urban areas with characteristics indicative of socioeconomic deprivation, were also identified.ConclusionsDifferences in index dimensions and agreement indicated that ADI and SVI are not interchangeable measures of socioeconomic deprivation at the tract level. Careful consideration is necessary when selecting an area-level socioeconomic deprivation measure that appropriately defines deprivation relative to the context in which it will be used. How deprivation is operationalized affects interpretation by researchers as well as public health practitioners and policymakers making decisions about resource allocation and working to address health equity.

Median and IQR values of census tract level indicators in Louisiana.

All records used to generate this analysis are shown without identifying information. (XLSX)

IntroductionPrevious studies highlight the negative impact of adverse socioeconomic conditions throughout life on motor skills and cognitive health. Factors such as cognitive activity, physical activity, lifestyle, and socioeconomic position significantly affect general health status and brain health. This pilot study investigates the relationships among the Area Deprivation Index (ADI)—a measure of neighborhood-level socioeconomic deprivation, brain structure (cortical volume and thickness), and cognitive status in adults in Arizona. Identifying measures sensitive to ADI could elucidate mechanisms driving cognitive decline.MethodsThe study included 22 adults(mean age = 56.2 ± 15.2) in Arizona, residing in the area for over 10 years(mean = 42.7 ± 15.8). We assessed specific cognitive domains using the NeuroTrax™ cognitive screening test, which evaluates memory, executive function, visual–spatial processing, attention, information processing speed, and motor function. We also measured cortical thickness and volume in 10 cortical regions using FreeSurfer 7.2. Linear regression tests were conducted to examine the relationships between ADI metrics, cognitive status, and brain health measures.ResultsResults indicated a significant inverse relationship between ADI metrics and memory scores, explaining 25% of the variance. Both national and state ADI metrics negatively correlated with motor skills and global cognition (r’s 

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, environmental justice, health equity, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, U.S. Census Bureau American CommUnity Survey (ACS), and Area Deprivation Index (ADI). The data are particularly useful for studies on rural populations who are under-represented due to the lack of air monitoring sites in rural areas. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by lowering the key barrier to participation in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.

The risk of thromboembolism and bleeding before initiation of oral anticoagulant (OAC) in atrial fibrillation patients is estimated by CHA 2 DS 2 -VASc and HAS-BLED scoring system, respectively. Patients’ socioeconomic status (SES) could influence these risks, but its impact on the two risk scores' predictive performance with respect to clinical events remains unknown. Our objective was to determine if patient SES defined by area deprivation index (ADI), in conjunction with CHA 2 DS 2 -VASc and HAS-BLED scores, could guide oral anticoagulation therapy. Methods and Findings The study cohort included newly diagnosed patients with AF who were treated with warfarin. The cohort was stratified by the time in therapeutic range of INR (TTR), ADI, CHA 2 DS 2 -VASc, and HAS-BLED risk scores. TTR and ischemic and bleeding events during the first year of therapy were compared across subpopulations. Among 7274 patients, those living in the two most deprived quintiles (ADI ≥60%) had a significantly higher risk of ischemic events and those in the most deprived quintile (ADI≥80%) had a significantly increased risk of bleeding events. ADI significantly improved the predictive performance of CHA 2 DS 2 -VASc but not HAS-BLED risk scores. Conclusion ADI can predict increased risk for ischemic and bleeding events in the first year of warfarin therapy in patients with incident AF.

PurposePatient satisfaction surveys are pivotal in evaluating healthcare quality and enhancing patient care. Understanding the factors influencing patient engagement with these surveys in radiation oncology can guide improvements in patient-centered care.MethodsThis retrospective study analyzed data from radiation oncology patients at a large multi-site single-institution center from May 2021 to January 2024. We assessed the influence of demographic, clinical, and socioeconomic factors on the likelihood of survey participation using univariate (UVA) and multivariable (MVA) logistic regression analyses. Factors included age, gender, race, socioeconomic status (SES) via Area Deprivation Index (ADI), language, marital status, smoking, employment, insurance type, mental health disorders (MHD), comorbidity index (CCI), and cancer type.ResultsIn a comprehensive analysis of 11,859 patients, most were female (57.2%), over 65 years old (60.7%), and primarily insured by Medicare (45.9%). MVA showed that higher socioeconomic disadvantage significantly decreased survey participation (ADI third tertile vs. first tertile OR=0.708, p

Associations between individual-level variables and SARS-CoV-2 infection in Baton Rouge and New Orleans.

Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia).Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods.Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020.Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease.

Residing in disadvantaged neighborhoods has been linked to worsened survival among Black women with estrogen receptor-positive (ER+) breast cancer (BC), yet the underlying multiomic alterations remain underexplored. To investigate associations between neighborhood deprivation and pretreatment steroid hormones, untargeted metabolites, inflammatory proteins, and tumoral gene expression in women with primary ER+ BC and cancer-free controls, pretreatment plasma was collected from ER+ BC patients (n = 91) and controls (n = 141) across three Chicago hospitals. Area deprivation index (ADI) was calculated per participant. Plasma was analyzed via targeted steroid hormone and untargeted metabolomics assays, and Olink’s inflammatory protein panel. Tumor samples (n = 71) were analyzed using the Nanostring Breast Cancer 360 panel. Single-omic analysis and multiomics integration were performed. Elevated inflammatory proteins were observed in cases and controls from disadvantaged neighborhoods (p < 0.05), and tumoral gene expression showed upregulation of inflammatory and proliferation-related genes (p < 0.05). Patients from deprived areas exhibited higher inflammation and antioxidant depletion even within the same tumor grade (p < 0.05). Neighborhood deprivation correlates with pro-inflammatory, proliferative multiomic profiles that may underlie worsened outcomes.

BackgroundIntracerebral hemorrhage (ICH) disproportionally affects underserved populations, and coincides with risk factors for cardiovascular events and cognitive decline after ICH. We investigated associations between social determinants of health and management of blood pressure (BP), hyperlipidemia, diabetes, obstructive sleep apnea (OSA), and hearing impairment before and after ICH hospitalization.MethodsSurvivors of the Massachusetts General Hospital longitudinal ICH study between 2016 and 2019 who received healthcare at least 6 months after ICH were analyzed. Measurements of BP, LDL and HbA1c and their management in the year surrounding ICH and referrals for sleep studies and audiology up to 6 months after ICH were gathered from electronic health records. The US-wide area deprivation index (ADI) was used as proxy for social determinants of health.ResultsThe study included 234 patients (mean 71 years, 42% female). BP measurements were performed in 109 (47%) before ICH, LDL measurements were performed in 165 (71%), and HbA1c measurements in 154 (66%) patients before or after ICH. 27/59 (46%) with off-target LDL and 3/12 (25%) with off-target HbA1c were managed appropriately. Of those without history of OSA or hearing impairment before ICH, 47/207 (23%) were referred for sleep studies and 16/212 (8%) to audiology. Higher ADI was associated with lower odds of BP, LDL, and HbA1c measurement prior to ICH [OR 0.94 (0.90–0.99), 0.96 (0.93–0.99), and 0.96 (0.93–0.99), respectively, per decile] but not with management during or after hospitalization.ConclusionSocial determinants of health are associated with pre-ICH management of cerebrovascular risk factors. More than 25% of patients were not assessed for hyperlipidemia and diabetes in the year surrounding ICH hospitalization, and less than half of those with off-target values received treatment intensification. Few patients were evaluated for OSA and hearing impairment, both common among ICH survivors. Future trials should evaluate whether using the ICH hospitalization to systematically address co-morbidities can improve long-term outcomes.

The DRIVES Project collects and processes low frequency and high frequency naturalistic driving data in order to study their association with cognitive decline in older drivers. Both sets of data are obtained daily from an off-the-shelf telematics datalogger that is installed our participants' vehicles. The low frequency data is collected at 1 Hz in 30 second intervals, whereas the high frequency data is collected at 24 Hz in one second intervals. The low frequency data is collected in the form of four CSV files: 1) A breadcrumbs file that contains the periodic driving data, 2) An activity file that provides detailed trip information, 3) An events file that provides detailed information on all adverse events 4) A a summary file that aggregates all daily trips carried out by each vehicle a day. The high frequency data is collected in the form of JSON files; each JSON file contains data for a single trip taken by a single vehicle on a given day. Each JSON is processed into four data tables: 1) A trip_info table that provides the periodic driving data 2) An activity table that details all adverse events that occurred during the trip (i.e. speeding, hard braking, idling etc.) 3) A braking table that details all hard braking events that occurred during the trip, and 4) A idling table that details each time the vehicle was idle during a trip. In addition to naturalistic driving data, the DRIVES Project collects clinical and neuropsychological data from our enrolled participants. Our participants undergo a variety of neuropsychological evaluations from which the DRIVES Project derives this data from (see attached data descriptor for more details). The DRIVES Project also collects data related to social determinants of health (SDoH). In particular, the DRIVES Project uses our participants' primary home addresses to obtain their Area of Deprivation Index (ADI) and Social Vulnerability Index (SVI) rankings. These rankings are provided by the Center of Health Disparities Research at the University of Wisconsin, Madison and the Center for Disease Control’s Agency for Toxic Substances and Disease Registry. The DRIVES Project uses two Python scripts to process the raw data files for the LFD and HFD. The scripts remove data and transforms the raw data files as needed to create the processed tables. In this repository, we provide a short demo of how our scripts processes our raw data in preparation for subsequent analysis or data storage. The demo code provides a walkthrough on how our scripts process 4 LFD CSV files that the DRIVES Project collected on March 31st, 2023 and a single HFD trip JSON that the project collected on March 31st, 2023. In the raw_data folder, we have provided four 'Spring2023' CSV files that contain the combined daily files that we download for the breadcrumbs, activity, events, and summary LFD data from March 1st, 2023 to May 31st, 2023. We've also provided twelve tarballs (.tar.gz files) that contain weekly HFD trip JSONs that we downloaded during the same time period; each tarball corresponds to the HFD trip JSONs we downloaded in a month (i.e. March, April, May). We've included these comprehensive files in case users would like to experiment with our scripts on more data. See attached metadata file for an explanation on the features for each table

Additional file 2: Table S2.1. Characteristics of study participants at time point 1 (T1) stratified by Area Deprivation Index (ADI). Results: Summary statistics displaying variable distribution, mean, standard deviation, median, and 1st and 3rd quartiles. We tested for differences between low and high ADI groups using chi-square, T, or Wilcoxon rank-sum tests as appropriate. Analysis performed on Area Deprivation Index-stratified (ADI-stratified) T1 data (Low ADI, N = 102); High ADI, N = 104). Table S2.2. Characteristics of study participants at time point 2 (T2) stratified by Area Deprivation Index (ADI). Results: Summary statistics displaying variable distribution, mean, standard deviation, median, and 1st and 3rd quartiles. We tested for differences between low and high ADI groups using chi-square, T, or Wilcoxon rank-sum tests as appropriate. Analysis performed on Area Deprivation Index-stratified (ADI-stratified) T2 data (Low ADI, N = 143); High ADI, N = 123).