The Area Health Resources Files (AHRF) provide current as well as historic data for more than 6,000 variables for each of the nation's counties, as well as state and national data. They contain information on health facilities, health professions, measures of resource scarcity, health status, economic activity, health training programs, and socioeconomic and environmental characteristics. In addition, the basic file contains geographic codes and other metadata which enable it to be linked to other files.

The Area Resource File is a database of health resources data, measured at the county level for over 6,000 indicators. The information includes measures of employment in various health professions including numbers of professionals registered as M.D., D.O., DDS, R.N., L.P.N., veterinarian, pharmacist, optometrist, podiatrist, and dental hygienist; availability of health facilities including hospital size, type, utilization, staffing and services, and nursing home data; frequency of utilization including utilization rates, inpatient days, outpatient visits and operations data; hospital and Medicare expenditures and demographic and geographic indicators by county. The information is collected from several sources which are noted in the technical documentation and provided by the Bureau of Health Professions, Health Resources and Services Administration of the US Department of Health and Human Services. The data here are from the 2005 Area Resource File that was purchased by the Woodruff Library in 2006. The data are available in Stata 13 (.dta) and comma-delimited (.csv) formats and are available for use by Emory students, faculty and staff. They are also provided as a whole, and in smaller datasets that are divided into broad subjects (Professions, Population, Facilities and Expenditures and Utilization). The files are available in both "wide" (one row per county) and "long" (one row per county-year) formats. We are also providing lists of variables for both the wide and long data, along with the original data and documentation and SAS code. The data cover the years 1940-2004. Note, however, that the data are not purely annual, as the vast majority of the variables are only available for selected years. For more recent ARF data, see https://data.hrsa.gov/topics/health-workforce/ahrf. County-level, state-level, and national data are freely available to download. Older ARF data are also available via the ICPSR.

All credit for variables in AHRQ_included_variables.csv is attributed to

• AHRF: Area Health Resources Files (AHRF) 2009-2018. US Department of Health and
  Human Services, Health Resources and Services Administration, Bureau of Health Workforce,
  Rockville, MD.
• AHRQ SDOH Database (v1), from American Hospital Association Annual Survey Data,
  https://www.ahadata.com/,
• AHRQ SDOH Database (v1), from Health Resources and Services Administration. (2021, Nov 1).
  Area Health Resources Files [Database]. U.S. Department of Health and Human Services, Health
  Resources and Services Administration, Bureau of Health Workforce.
  https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from the Foundation for AIDS Research. Opioid & Health Indicators
  Database. https://opioid.amfar.org/about/download
• AHRQ SDOH Database (v1), from United States Census Bureau. (n.d.). County Adjacency File
  [Data file]. https://www.census.gov/geographies/reference-files/2010/geo/countyadjacency.html.
• AHRQ SDOH Database (v1), from United States Census Bureau. County Business Patterns [Data
  series]. U.S. Department of Commerce, Bureau of the Census, Data User Services Division.
  https://www.census.gov/programs-surveys/cbp/data/tables.html.
• AHRQ SDOH Database (v1), from Local Education Agency (School District) Finance Survey (F-33)
  Data, v.1a—Provisional Common Core of Data (CCD) - CCD Data Files (ed.gov). Retrieved March
  2021 from https://nces.ed.gov/ccd/files.asp#Fiscal:1,LevelId:5,Page:1.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, Division for Heart
  Disease and Stroke Prevention, Interactive Atlas of Heart Disease and Stroke,
  http://nccd.cdc.gov/DHDSPAtlas.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention. NCHHSTP
  AtlasPlus. Updated 2017. https://www.cdc.gov/nchhstp/atlas/index.htm.
• AHRQ SDOH Database (v1), from PLACES: Local Data for Better Health. Center for Disease
  Control and Prevention (2020 and 2021 Release). Retrieved January 2022 from:
  https://chronicdata.cdc.gov/browse?category=500+Cities+%26+Places&sortBy=newest&utf8.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, National Center for
  Health Statistics. Multiple Cause of Death, 1999–2019, in CDC WONDER Online Database. (2021).
  Data are from the Multiple Cause of Death Files, 1999–2019, as compiled from data provided by
  the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program.
  http://wonder.cdc.gov/mcd-icd10.html.
• United States Census Bureau. Cartographic Boundary Files – Shapefile.
  https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
• AHRQ SDOH Database (v1), from Robert Wood Johnson Foundation, & University of Wisconsin
  Population Health Institute. (2020). County Health Rankings & Roadmaps.
  https://www.countyhealthrankings.org.
• AHRQ SDOH Database (v1), from U.S. Department of Education. (n.d.). Civil Rights Data
  Collection. Retrieved September 2021, from https://ocrdata.ed.gov.
• AHRQ SDOH Database (v1), from Community Resilience Estimates Social, Economic, and
  Housing Statistics Division U.S. Census Bureau. Retrieved September 2021 from
  https://www.census.gov/programs-surveys/community-resilience-estimates.html.
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency. Outdoor Air
  Quality Data. Air Quality Statistics Report. Retrieved March 2022 from:
  https://www.epa.gov/outdoor-air-quality-data/air-quality-statistics-report
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency (EPA) National
  Walkability Index (2021). Retrieved June 2021 from: https://catalog.data.gov/dataset/walkabilityindex.
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture, Economic Research Service.
  2010 Rural-Urban Commuting Area codes, from https://www.ers.usda.gov/data-products/ruralurban-commuting-area-codes.
• AHRQ SDOH Database (v1), from Economic Research Service (ERS), U.S. Department of
  Agriculture (USDA). Food Access Research Atlas, Retrieved November 2021 from
  https://www.ers.usda.gov/data-products/food-access-research-atlas/
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture (USDA), Economic Research
  Service (ERS) 2020. Food Environment Atlas. Retrieved September 2021 from:
  https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-anddocumentation-downloads/.
• AHRQ SDOH Database (v1), from LTCFocus Public Use Data sponsored by the National Institute
  on Aging (P01 AG027296) through a cooperative agreement with the Brown University School of
  Public Health. Available at www.ltcfocus.org. https://doi.org/10.26300/h9a2-2c26.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Home Health
  Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• AHRQ SDOH Database (v1), from the Department of Homeland Security. Homeland Infrastructure
  Foundation-Level Data (HIFLD)—Urgent Care Facilities. 2018. Available: https://hifldgeoplatform.opendata.arcgis.com/datasets/urgent-care-facilities.
• AHRQ SDOH Database (v1), from the Indian Health Service. IHS facilities. 2021. Available:
  https://www.ihs.gov/locations/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June). MA
  State/County Penetration, from https://www.cms.gov/Research-Statistics-DataandSystems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/MA-State-CountyPenetration.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June).
  Medicare Geographic Variation Public Use File, from https://www.cms.gov/Research-StatisticsData-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention; U.S.
  Department of Health and Human Services; Agency for Toxic Substances and Disease Registry.
  Minority Health Social Vulnerability Index 2018. Retrieved July 2021 from
  https://www.minorityhealth.hhs.gov/minority-health-svi/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services, Office of Minority
  Health. (2020). Medicare Mapping Disparities Tool, from https://data.cms.gov/tools/mappingmedicare-disparities-by-population.
• AHRQ SDOH Database (v1), from the Health Resources and Services Administration. (2021).
  Medically Underserved Areas, from https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from Ingram, D. D., & Franco, S. J. (2014, April). 2013 NCHS urban–
  rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and
  Methods Research. Number 166, 1–73. https://pubmed.ncbi.nlm.nih.gov/24776070.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention. (2020).
  National Environmental Public Health Tracking Network from https://www.cdc.gov/ephtracking.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (n.d.). Nursing
  Home Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C.,
  Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for
  U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251.
  Retrieved Oct 29, 2021, from https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-
  0248.1.xml.
  AHRQ SDOH Database (v1), from Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke;
  Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014):
  NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center.
  doi:10.7289/V5M32STR. Retrieved from: https://www.ncei.noaa.gov/data/climdiv/access/.
• AHRQ SDOH Database (v1), from National Center for Environmental Information (NCEI), National
  Environmental Satellite, Data and Information Service (NESDIS) and National Oceanic and
  Atmospheric Administration (NOAA), Storm Events Database 2009-2020 Retrieved August 2021
  from https://www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/.
• AHRQ SDOH Database (v1), from U.S. Census Bureau, Opportunity Insights. Opportunity Atlas [All
  Outcomes by Census Tract Race, Gender, and Parental Income Percentile, All Outcomes by
  County Race, Gender, and Parental Income Percentile] Retrieved October 2021 from:
  https://www.c
• AHRQ SDOH Database (v1), from National Center for Education Statistics. U.S. Skills Map: State
  and County Indicators of Adult Literacy and Numeracy. Retrieved March 2021 from:
  https://nces.ed.gov/surveys/piaac/skillsmap/.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Physician
  Compare. https://data.cms.gov/provider-data/topics/doctors-clinicians.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Provider of
  Services File. https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-PublicUse-Files/Provider-of-Services.
• AHRQ SDOH Database (v1), from Small Area Health Insurance Estimates. (n.d.). U.S. Census
  Bureau. https://www.census.gov/data/datasets/time-series/demo/sahie/estimates-acs.html
• AHRQ SDOH Database (v1), from Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D.,
  Jang, H., &

Univariate linear regression of admission variables of AHRF patients associated with ICU length of stay in patients who survived at discharge (n = 1291).

Multivariate logistic regression of patient admission variables associated with AHRF ICU mortality.

Cognitive measure descriptive statistics, independent sample t-tests evaluating group differences in sex (female vs. male), ethnicity (Hispanic vs. Non-Hispanic), and respiratory distress (ARDS/AHRF vs. SOB/None), as well as Pearson’s r correlations testing associations with COVID-19 clinical severity (NEWS2), depression (PHQ-9), anxiety (GAD 7), sleep disturbance (PSQI), and brain fog (BFQ) total scores.

A dataset of chest X-rays for the diagnosis of congestive heart failure and pneumonia.

COVID-19 manifests a spectrum of respiratory symptoms, with the more severe often requiring hospitalization. To identify markers for disease progression, we analyzed longitudinal gene expression data from patients with confirmed SARS-CoV-2 infection admitted to the intensive care unit (ICU) for acute hypoxic respiratory failure (AHRF) as well as other ICU patients with or without AHRF and correlated results of gene set enrichment analysis with clinical features. The results were then compared with a second dataset of COVID-19 patients separated by disease stage and severity. Transcriptomic analysis revealed that enrichment of plasma cells (PCs) was characteristic of all COVID-19 patients whereas enrichment of interferon (IFN) and neutrophil gene signatures was specific to patients requiring hospitalization. Furthermore, gene expression results were used to divide AHRF COVID-19 patients into 2 groups with differences in immune profiles and clinical features indicative of severe disease. Thus, transcriptomic analysis reveals gene signatures unique to COVID-19 patients and provides opportunities for identification of the most at-risk individuals.

IntroductionABO blood type A was reported to correlate with an increased risk of acute respiratory distress syndrome (ARDS) in white patients with severe sepsis and major trauma compared with patients with other blood types. Information regarding ABO phenotypes and major outcomes in patients with ARDS is unavailable. The primary aim was to determine the relationship between ABO blood type A and intensive care unit (ICU) mortality in patients with acute hypoxemic respiratory failure (AHRF). The secondary aim was to describe the association between ABO blood type A and ICU length of stay (LOS) in this study population.MethodsIn a multicenter, retrospective cohort study, we collected the clinical records of patients admitted from January 2012 to December 2014 in five ICUs of Northern Italy. We included adult white patients admitted to the ICU who were diagnosed with AHRF requiring mechanical ventilation.ResultsThe electronic records of 1732 patients with AHRF were reviewed. The proportion of patients with ABO blood type A versus other blood types was 39.9% versus 60.1%. ICU mortality (25%) and ICU LOS (median [interquartile range], 5 [2–12] days) were not different when stratified by ABO blood type (ICU mortality, overall p value = 0.905; ICU LOS, overall p value = 0.609). SAPSII was a positive predictor of ICU mortality (odds ration [OR], 32.80; 95% confidence interval [CI], 18.80–57.24; p < 0.001) and ICU LOS (β coefficient, 0.55; 95% CI, 0.35–0.75; p < 0.001) at multivariate analyses, whereas ABO blood type did not predict ICU outcome when forced into the model.ConclusionABO blood type did not correlate with ICU mortality and ICU LOS in adult patients with AHRF who were mechanically ventilated.

背景：限制驾驶压力 （DP） 可以降低急性呼吸窘迫综合征 （ARDS） 患者的死亡率。然而，尚不确定这种方法是否可以降低急性低氧性呼吸衰竭（AHRF）患者的死亡率。因此，本研究旨在确定初始 DP 设置与 AHRF 患者首次入住重症监护病房 （ICU） 时的临床结果之间的相关性。方法：采用重症监护IV期医疗信息市场（MIMIC-IV）数据库检索AHRF患者资料，以180天死亡率为主要结局。随后进行多元回归分析，以评估初始 DP 浓度与 180 天死亡率之间的相关性。使用受限三次样条曲线和相互作用研究验证了结果的可靠性。结果：本研究入组收治了907例患者，其中生存组581例（64.06%），非生存组（NSG）326例（35.94%），入院180天后随访。结果显示，初始DP升高与180天死亡率显著相关（HR 1.071（95%CI 1.040,1.102）），尤其是当初始DP超过12 cmH2O时。初始 DP > 12 cmH2O 的 AHRF 患者在 28 天 （p = 0.0082）、90 天 （p = 0.0083） 和 180 天 （p = 0.0039） 的死亡率显著高于初始 DP ≤12 cmH2O 的患者。在重症 AHRF 患者中，初始 DP > 12 cmH2O 的组的 180 天死亡率显著高于初始 DP ≤ 12 cmH2O 的组 （p = 0.029）。初始 DP < 12 cmH2O 的患者住院时间 （LOS） 明显长于初始 DP > 12 cmH2O 的患者 （p = 0.029）。在初始DP>12 cmH2O的AHRF患者中，生存组的ICU LOS明显高于NSG（p = 0.00026）。结论：初始 DP 设置与入住 ICU 的 AHRF 患者的 180 天死亡率相关。特别是对于重症 AHRF 患者，优先实施早期限制性 DP 通气作为降低死亡率的手段至关重要。

Multivariate regressions of incidence and mortality with PCP density and co-variates.

Cox proportional hazard survival analysis, by HPSA status.

Background: Patients with acute respiratory failure (ARF) may need mechanical ventilation (MV), which can lead to diaphragmatic dysfunction and muscle wasting, thus making difficult the weaning from the ventilator. Currently, there are no biomarkers specific for respiratory muscle and their function can only be assessed trough ultrasound or other invasive methods. Previously, the fast and slow isoform of the skeletal troponin I (fsTnI and ssTnI, respectively) have shown to be specific markers of muscle damage in healthy volunteers. We aimed therefore at describing the trend of skeletal troponin in mixed population of ICU patients undergoing weaning from mechanical ventilation and compared the value of fsTnI and ssTnI with diaphragmatic ultrasound derived parameters.Methods: In this prospective observational study we enrolled consecutive patients recovering from acute hypoxemic respiratory failure (AHRF) within 24 h from the start of weaning. Every day an arterial blood sample was collected to measure fsTnI, ssTnI, and global markers of muscle damage, such as ALT, AST, and CPK. Moreover, thickening fraction (TF) and diaphragmatic displacement (DE) were assessed by diaphragmatic ultrasound. The trend of fsTnI and ssTnI was evaluated during the first 3 days of weaning.Results: We enrolled 62 consecutive patients in the study, with a mean age of 67 ± 13 years and 43 of them (69%) were male. We did not find significant variations in the ssTnI trend (p = 0.623), but fsTnI significantly decreased over time by 30% from Day 1 to Day 2 and by 20% from Day 2 to Day 3 (p < 0.05). There was a significant interaction effect between baseline ssTnI and DE [F(2) = 4.396, p = 0.015], with high basal levels of ssTnI being associated to a higher decrease in DE. On the contrary, the high basal levels of fsTnI at day 1 were characterized by significant higher DE at each time point.Conclusions: Skeletal muscle proteins have a distinctive pattern of variation during weaning from mechanical ventilation. At day 1, a high basal value of ssTnI were associated to a higher decrease over time of diaphragmatic function while high values of fsTnI were associated to a higher displacement at each time point.

County-level descriptive statistics, 2008–2012.

Clinical characteristics in total patients.

Multivariate analysis of the apnea-hypopnea index.

Multivariate logistic regression analysis for risk of death in the hospital and ICU.

Unadjusted and model-adjusted risk of primary and secondary outcomes.

Demographic, laboratory characteristics, and outcomes by corticosteroids.

Clinical and laboratory indices of patients with and without composite primary outcome (ICU transfer or intubation or death).