The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The dataset contains counts of inpatient visits leading to a discharge to hospice care. Inpatient visits included in the counts consist of individuals aged 18 or over with a discharge disposition leading to home or facility hospice care. The total counts per each individual year can be viewed based on different patient characteristics, including patient age groups, individual counties of residence, primary payer type, diagnosis category, and patient sex/race/ethnicity. The disease categories include circulatory conditions, diabetes, malignant/benign neoplasms, malnutrition, neurodegenerative disease, renal failure or other kidney diagnoses, respiratory conditions and circulatory conditions. The categories represent common groupings of diagnoses seen in other studies related to hospice care and were created by grouping together relevant medical MSDRG codes in the HCAI inpatient data.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

M, male; F, female; TBI, traumatic brain injury; EDH, epidural hematoma; SDH, subdural hematoma; ICH, intracerebral hematoma; BG, basal ganglion; F, frontal; T, temporal; P, parietal; DC, decompressive craniectomy; Uni+HR, unilateral craniectomy+removal of hematoma; Bil+HR, bilateral craniectomy+removal of hematoma; CIS, cranial index of symmetry; CAD, computer-assisted design.

The COVID-19 pandemic and subsequent expansion of telehealth may be exacerbating inequities in ambulatory care access due to institutional and structural barriers. We conduct a repeat cross-sectional analysis of ambulatory patients to evaluate for demographic disparities in the utilization of telehealth modalities. The ambulatory patient population at Oregon Health & Science University (Portland, OR) is examined from June 1 through September 30, in 2019 (reference period) and in 2020 (study period). We first assess for changes in demographic representation and then evaluate for disparities in the utilization of telephone and video care modalities using logistic regression. Between the 2019 and 2020 periods, patient video utilization increased from 0.2% to 31%, and telephone use increased from 2.5% to 25%. There was also a small but significant decline in the representation males, Asians, Medicaid, Medicare, and non-English speaking patients. Amongst telehealth users, adjusted odds of video participation were significantly lower for those who were Black, American Indian, male, prefer a non-English language, have Medicaid or Medicare, or older. A large portion of ambulatory patients shifted to telehealth modalities during the pandemic. Seniors, non-English speakers, and Black patients were more reliant on telephone than video for care. The differences in telehealth adoption by vulnerable populations demonstrate the tendency towards disparities that can occur in the expansion of telehealth and suggest structural biases. Organizations should actively monitor the utilization of telehealth modalities and develop best-practice guidelines in order to mitigate the exacerbation of inequities.

Methods A repeat cross-sectional study was conducted of patients who utilized the ambulatory clinics at Oregon Health & Science University (OHSU) from June 1 through September 30, in 2019 (reference period) and 2020 (study period). The study period was chosen because it exhibited a relatively stable rate of in-person, telephone, and video ambulatory visits. The initial months of the pandemic in March through May 2020 were marked by shifting state and institutional policies that affected appointment availability. By the summer of 2020, clinics were more open to scheduling in-person visits. We chose to investigate a later, more stable time-frame for disparities because we believe that the analysis would be more indicative of ongoing trends.

Unique patient counts were extracted from ambulatory provider-led visits, defined as outpatient visits with physicians, nurse practitioners, or physician assistants. Visits modalities included in-person, video, or telephone, the latter two comprising telehealth. Patient demographics included ethnicity, race, preferred language, payer, age, and sex. The encounter-level data was aggregated by unique patient identifier into patient counts for the study period of June 1 through Sept 30, 2020. Table 1 displays unique patient counts of ambulatory care modality utilization (in-person, video, telephone, and any telehealth) for each demographic group (race, ethnicity, sex, preferred language, insurance, and age). There is also a column for total patients in that demographic group. In the main article, we performed logistic regression to evaluate the association of patient demographics with telehealth utilization. Table 2 displays unique patient counts of ambulatory care modality utilization for each demographic group only within primary care clinics.

Table 3 displays unique patient counts for each demographic group within the time periods before and during the COVID-19 pandemic: June 1 through Sept 30, 2019 and June 1 through Sept 30, 2020. In the study, we compared the proportional representation of demographic groups between before and during the pandemic to assess for overall changes in our patient population.

*Mean ankle brachial indices were all greater than 1 despite one patient having arterial disease; this was due to this same patient also having diabetes mellitus.

Childhood Allergies: Prevalence, Diagnosis, and Treatment Outcomes

Investigating Allergy Prevalence, Treatment Outcomes, and Patient Demographics

By [source]

About this dataset

This dataset contains the power to help us better understand the prevalence and treatment outcomes of childhood allergies over an extended period of time. Not only does it publicize the number of individuals currently suffering from asthma, atopic dermatitis, allergic rhinitis and food allergies through retrospective data as reported by healthcare providers - but it also features a set of columns which allow us to gain valuable insights into how these outcomes differ across different demographics such as gender, race and ethnicity. By further examining this data, we can start to recognize patterns in trends among the diagnosed cases - paving way for new treatments and prevention strategies that could prevent severe allergic reactions for many children all around the world

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Assess what kind of questions you want to answer using this data - do you want to focus on one particular type of allergy or analyze them together? Do you want a descriptive analysis or would an analysis that looks for correlations between conditions be more appropriate?

• Once you have determined your research question(s), identify what variables from the dataset are pertinent to your inquiry and assess any outliers that might need further investigation or filtering out during your analysis. Also consider any independent variables or confounding factors which might affect your results as well as any existing hypotheses related to the topic that might help guide your research project expectations

• Be aware of potential sources of bias when using self-reported healthcare provider information such as difficulties in disease identification (i.e allergies may be misdiagnosed). Additionally note that many allergy cases may go unreported/unrecorded due issues such as lack access/awareness about healthcare etc). A good way combat bias is by sample size - use largest possible datasets whenever available!

• Begin collecting relevant data from columns pertaining medical history (allergy diagnosis start & end date etc.), patient demographic information (gender factor ,ethnicity factor etc.), treatment trends & outcomes( first Asthma RX date , last asthma RX date , NUM asthma rx etc ). To get the most insights outta thisdata all these factors must be taken into account – if there isn’t enough evidence then explore other reliable sources too

• Structure & organize collected data so they can me easily accessed later – maybe create separate sheets/tabs with different categories i.e patient/treatment information OR create individual sheets for each subject depending upon how much info needs collecting .Designing formulaic functions will not only make life easier but critically save time & energy when it comes analyzing vast amounts data stored within workbook ! Remember larger sample sizes provide more

Research Ideas

• Use the dataset to identify risk factors or patterns in childhood allergies that can inform preventative and treatment measures.
• Investigate the correlation between demographic characteristics (e.g., age, gender) and diagnosis or severity of childhood allergies by using cross-tabs or other statistical techniques on the data provided in this dataset.
• Analyze longitudinal trends in treatment outcomes for various types of childhood allergy, such as asthma, atopic dermatitis and food allergy by comparing patient results over time (i.e., looking at pre-treatment diagnosis and post-treatment diagnoses)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: food-allergy-analysis-Zenodo.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------------| | BIRTH_YEAR | Year of birth of the patient. (Integer) | | GENDER_FACTOR ...

The National Hospital Discharge Survey (NHDS) collects medical and demographic information annually from a sample of hospital discharge records. Variables include patients' demographic characteristics (sex, age, race, marital status), dates of admission and discharge, source and type of admission, status at discharge, final diagnoses, surgical and nonsurgical procedures, dates of surgeries, and sources of payment. Information on hospital characteristics such as bed size, ownership, and region of the country is also included. This collection includes data for non-newborns for 1979-1989 (Dataset 1), non-newborns for 1990-2006 (Dataset 2) and newborns for 1979-2006 (Dataset 3). The medical information is coded using the INTERNATIONAL CLASSIFICATION OF DISEASES, 9TH REVISION, CLINICAL MODIFICATION (ICD-9-CM). In addition, there are several Excel files that contain information needed to calculate relative standard errors (RSEs) and to compute utilization rates based on Census population estimates (POPs).

The rising incidence of chronic diseases such as cancer, diabetes, and cardiovascular diseases is propelling the adoption of pPatient rRegistry sSoftware. The growing adoption of electronic health records (EHR) is driving the market size to surpass USD 1.71 Billion in 2023 to reach a valuation of around USD 4.85 Billion by 2031.

In addition to this, tThe stringent regulation to implement electronic patient records is spurring up the adoption of pPatient rRegistry sSoftware. Increasing pressure to improve the quality of care and reduce healthcare costs, rising adoption of EHRs and other eHealth solutions is enabling the market to grow at a CAGR of 13.89% from 2024 to 2031.

Patient Registry Software Market: Definition/ Overview

Patient registry software is a database management system designed to capture comprehensive information about patients with a particular condition, disease, or characteristic. This information typically includes demographic data, medical history, clinical outcomes, treatment patterns, and follow-up data collected longitudinally from multiple sources, such as electronic health records (EHRs), administrative databases, and patient-reported outcomes.

Patient registry software facilitates disease surveillance efforts by systematically collecting and analyzing data on disease incidence, prevalence, risk factors, and outcomes. Registries help public health agencies, researchers, and policymakers monitor disease trends, identify emerging health threats, and evaluate the effectiveness of prevention and control measures.Patientmeasures. Patient registry data informs health policy decisions, healthcare planning, resource allocation, and healthcare financing strategies. Registries provide evidence to support clinical guidelines development, coverage decisions, reimbursement policies, and population health management initiatives aimed at improving healthcare access, equity, and affordability.

The future of patient registry software lies in leveraging advanced analytics, machine learning, and predictive modeling techniques to extract actionable insights from registry data. Predictive analytics can identify at-risk patient populations, predict disease progression, and personalize treatment approaches, leading to more targeted and effective interventions.

Patient demographic data and variable features.

NOTE: This dataset is no longer being updated but is being kept for historical reference. For current data on respiratory illness visits and respiratory laboratory testing data please see Influenza, COVID-19, RSV, and Other Respiratory Virus Laboratory Surveillance and Inpatient, Emergency Department, and Outpatient Visits for Respiratory Illnesses. In Illinois, influenza associated Intensive Care Unit (ICU) hospitalizations are reportable as soon as possible, but within 24 hours. Influenza associated ICU hospitalizations are defined as individuals hospitalized in an ICU with a positive laboratory test for influenza A or B, including specimens identified as influenza A/H3N2, A/H1N1pdm09, and specimens not subtyped (e.g., influenza positive cases by PCR or any rapid test such as EIA). This dataset represents weekly aggregated information for influenza-associated ICU hospitalizations among Chicago residents, which is a reportable condition in Illinois. Information includes demographics, influenza laboratory results, vaccination status, and death status. Column names containing "REPORTED" indicate the number of cases for which the indicated data element was reported. This, rather than the total number of cases, is used to calculate the corresponding percentage. All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

The purpose of the Health Interview Survey is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. There are five types of records in this core survey, each in a separate data file. The variables in the Household File (Part 1) in this collection include type of living quarters, size of family, and geographic region. The Person File (Part 2) variables include sex, age, race, marital status, veteran status, education, income, occupation, and limits on activity. The Condition File (Part 3) contains variables on the incidence of illness or injury within the past year. The Hospital Episode File (Part 4) contains variables on the incidence of hospitalizations and presence of chronic conditions. The Doctor Visit File (Part 5) includes variables regarding frequency of doctor visits, type of doctor seen, and reasons for each visit. A sixth, seventh, eighth, and ninth file have been provided. The Disability Supplement File (Part 6) contains variables on the need for help, services, and environment modifications. The H1 Supplement File (Part 7) includes basic demographic variables, medical information, health variables, doctor visits, medical insurance, work days lost, and activity level variables. The Special Aids Supplement File (Part 8)includes basic demographic variables, special aids onset and amount needed, medical information, health variables, and doctor visits. The Influenza Supplement File (Part 9) includes basic demographic variables, flu, grippe, or fever onset, work and school days lost, hospital visits, length of stay, and cost of care.

Abstract (en): This collection provides information on live births in the United States during the calendar year 2011. The natality data in these files are a component of the vital statistics collection effort maintained by the federal government. Birth data is limited to births occurring in the United States to United States residents and nonresidents. Births occurring to United States citizens outside of the United States are not included in this data collection. Dataset 1 contains data on births occurring within the United States, while Dataset 2 contains data on births occurring in the United States territories of Puerto Rico, the U.S. Virgin Islands, Guam, American Samoa, and the Commonwealth of the Northern Mariana Islands. Variables describe the place of delivery, who was in attendance, and medical and health data such as the method of delivery, prenatal care, tobacco use during pregnancy, pregnancy history, medical risk factors, and infant health characteristics. Birth rates, fertility rates, and other aggregate statistics can be found in the Detailed Technical Notes section of the ICPSR User Guide. Demographic information includes the child's sex and month and year of birth, the parents' ages, races, ethnicities, education levels, as well as the mother's marital status and residency status. This report presents detailed data on numbers and characteristics of births in 2011, birth and fertility rates, maternal demographic and health characteristics, place and attendant at birth, and infant health characteristics within the United States and its territories. The data are not weighted and no weight variables are present in the collection. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Live births in the United States and its territories during calendar year 2011. Smallest Geographic Unit: County One-hundred percent of birth certificates in calendar year 2011. record abstractsThe territories file, which includes data on births occurring in Puerto Rico, the U.S. Virgin Islands, Guam, American Samoa, and the Commonwealth of the Northern Marianas Islands, includes limited geographic detail. Information identifying individual territories and counties with populations of 100,000 or more by place of occurrence and residence are available in this file.This collection includes data based on both the 1989 Revision of the U.S. Standard Certificate of Live Birth (unrevised) and the 2003 Revision of the U.S. Standard Certificate of Live Birth (revised). However, in general, only data comparable between 1989 and 2003 revisions and data exclusive to the 2003 revision are included. Beginning with the 2005 data year, the micro-data natality file no longer includes geographic detail (e.g., state or county of birth). Information on the NCHS data release policy is available through the National Center for Health Statistics Web site. Tabulations of birth data by state and for counties with populations of 100,000 or more may be made using VitalStats. Procedures for requesting micro-data files with geographic detail are provided in the National Center for Health Statistics data release policy.Beginning with the 2007 data year, data items such as maternal anemia, ultrasound, and alcohol use are no longer available in public use files.Beginning with the 2011 data year, unrevised data for educational attainment, prenatal care, and type of vaginal and cesarean delivery are no longer included in the data files. Data for these items from the 1989 revision are not comparable with data from the 2003 revision. For additional information on the Natality Detail File Series, please visit the National Center for Health Statistics Web site.

The table HI- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 767560 rows across 699 variables.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data. The complete Data Set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

This data asset was created in response to House Report 117-401, which stated, "The Committee directs the USAID Administrator, in consultation with the Director of the Office of Personnel Management and the Director of the Office of Management and Budget, to submit a report to the appropriate congressional committees, not later than 180 days after enactment of this Act, on USAID's workforce data that includes disaggregated demographic data and other information regarding the diversity of the workforce of USAID. Such report shall include the following data to the maximum extent practicable and permissible by law: 1) demographic data of USAID workforce disaggregated by grade or grade-equivalent; 2) assessment of agency compliance with the Equal Employment Opportunity Commission Management Directive 715; and 3) data on the overall number of individuals who are part of the workforce, including all U.S. Direct Hires, personnel under personal services contracts, and Locally Employed staff at USAID. The report shall also be published on a publicly available website of USAID in a searchable database format." This data asset fulfills the final part of this requirement, to publish the data in a searchable database format. The data are compiled from USAID's 2021 MD-715 report, available at https://www.usaid.gov/reports/md-715. The original data source is the system National Finance Center Insight owned by the Treasury Department.

This dataset includes aggregated weekly influenza virus laboratory data that the Chicago Department of Public Health (CDPH) uses to monitor influenza activity and assess which influenza types and subtypes are circulating in Chicago. The data represent weekly positive influenza PCR tests voluntarily reported by network of several hospital laboratories in Chicago as well as two commercial laboratories serving Chicago facilities. The data includes positive test results by influenza type (influenza A and influenza B) as well as influenza A subtype (H3N2, H1N1pdm09) when available. These data do not include patient demographic or geographic information and represent both Chicago and non-Chicago residents tested by the reporting facility. Influenza laboratory data are available from the 2010-2011 season to present. Two percentage fields are available in the dataset. Percentages are calculated for each characteristic group as follows: The percentage of influenza types is calculated as the total number of positives tests for each influenza type divided by the total number of positive influenza tests reported (e.g., Influenza A/Influenza Positive). The percentage fields describe the percent of positive tests by influenza type each week (count_pct) and for the entire season to date (count_cum_pct). The percentage of influenza A subtypes is calculated as the total number of positive tests for each influenza A subtype divided by the total number of positive influenza A tests reported (Influenza A Subtype/Influenza A). The percentage fields describe the percent of influenza A positive tests by subtype each week (count_pct) and for the entire season to date (count_cum_pct). The percentage for characteristic group ‘Total Positive’ will always be 100% and does not represent influenza test positivity. For data on influenza test positivity see: https://data.cityofchicago.org/Health-Human-Services/Influenza-COVID-19-RSV-and-Other-Respiratory-Virus/qgdz-d5m4/about_data. All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014.