We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing COVID-19 pandemic. Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from seven COVID-19 models, customize a large set of parameters, examine trends in testing and hospitalization, and download forecast data.

The data and scripts contained herein are used to generate Figure 1 of the associated manuscript, which presents general forms of the models used by our application and presents results for each model across time.

Graph and download economic data for All Other Non-Patient Care Revenue for Offices of Other Health Practitioners, All Establishments, Employer Firms (OOOHPAONCRA46213) from 2015 to 2020 about employer firms, revenue, health, establishments, and USA.

Graph and download economic data for Patient Out-Of-Pocket From Patients and Their Families for Offices of Physicians, Mental Health Specialists, All Establishments, Employer Firms (OOPMHSPOFPA4621112) from 2015 to 2022 about mental health, physicians, employer firms, establishments, family, and USA.

PLACES is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC). Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES provides health data for small areas across the country. This allows local health departments and jurisdictions, regardless of population size and rurality, to better understand the burden and geographic distribution of health measures in their areas and assist them in planning public health interventions. PLACES provides model-based, population-level analysis and community estimates of health measures to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTAs) across the United States.

This dataset describes the number and density of health care services in each census tract in the United States. The data includes counts, per capita densities, and area densities per tract for many types of businesses in the health care sector, including doctors, dentists, mental health providers, nursing homes, and pharmacies.

The Decennial Census provides population estimates and demographic information on residents of the United States.

The Census Summary Files contain detailed tables on responses to the decennial census. Data tables in Summary File 1 provide information on population and housing characteristics, including cross-tabulations of age, sex, households, families, relationship to householder, housing units, detailed race and Hispanic or Latino origin groups, and group quarters for the total population. Summary File 2 contains data tables on population and housing characteristics as reported by housing unit.

Researchers at NYU Langone Health can find guidance for the use and analysis of Census Bureau data on the Population Health Data Hub (listed under "Other Resources"), which is accessible only through the intranet portal with a valid Kerberos ID (KID).

This summary statistics data file contains a complete or 100-percent count of all persons in group quarters by sex and age, including ages under 1 to 74 with a category for ages 75 and over, as well as the total. The distribution is repeated for 18 race/Hispanic groups. Population in group quarters includes persons in institutional group quarters such as homes, schools, hospitals, or wards for the physically and mentally handicapped, hospitals or wards for mental, tubercular, or chronically ill patients, homes for unwed mothers, nursing, convalescent, and rest homes for the aged and dependent, orphanages, and correctional institutions. Noninstitutional group quarters include rooming and boarding houses, general hospitals, including nurses' and interns' dormitories, college students' dormitories, religious group quarters, and similar housing. Demographic items specify age, sex, state of birth, race, ethnicity, marital status, education, income, and type of group quarters lived in. Data are available for all counties and independent cities in the United States.

ObjectivesOf the Social Determinants of Health (SDoH), we evaluated socioeconomic and neighborhood-related factors which may affect children with medical complexity (CMC) admitted to a Pediatric Intensive Care Unit (PICU) in Shelby County, Tennessee with severe sepsis and their association with PICU length of stay (LOS). We hypothesized that census tract-level socioeconomic and neighborhood factors were associated with prolonged PICU LOS in CMC admitted with severe sepsis in the underserved community.MethodsThis single-center retrospective observational study included CMC living in Shelby County, Tennessee admitted to the ICU with severe sepsis over an 18-month period. Severe sepsis CMC patients were identified using an existing algorithm incorporated into the electronic medical record at a freestanding children's hospital. SDoH information was collected and analyzed using patient records and publicly available census-tract level data, with ICU length of stay as the primary outcome.Results83 encounters representing 73 patients were included in the analysis. The median PICU LOS was 9.04 days (IQR 3.99–20.35). The population was 53% male with a median age of 4.1 years (IQR 1.96–12.02). There were 57 Black/African American patients (68.7%) and 85.5% had public insurance. Based on census tract-level data, about half (49.4%) of the CMC severe sepsis population lived in census tracts classified as suffering from high social vulnerability. There were no statistically significant relationships between any socioeconomic and neighborhood level factors and PICU LOS.ConclusionPediatric CMC severe sepsis patients admitted to the PICU do not have prolonged lengths of ICU stay related to socioeconomic and neighborhood-level SDoH at our center. A larger sample with the use of individual-level screening would need to be evaluated for associations between social determinants of health and PICU outcomes of these patients.

Characteristics of census tracts that are projected to have high (groups: 250-399 and 400+) and low (groups: 50-99 and 100-249) MHC utilization for COVID-19 vaccination before March 31, 2021. Median values with IQR and p-values for significance difference in the medians are provided.

As of 1/13/2022, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Census-Tract/ekim-wqrr COVID-19 Vaccinations by Census Tract and Age Groups, including Ages 16+, Ages 16-44, Ages 45-64, and Ages 65+. CT Vaccination Program (COVP) data obtained from CTWiZ. COVP Coverage data suppressed if the any of the following conditions were met: -Coefficient of Variation of Denominator is > 30% -Numerator is 30%). Coverage estimates over 100% are shown as 100%. We suggest that the data are used primarily to identify areas that require additional attention rather than to establish and track the exact level of vaccine coverage. All analyses are provisional and subject to change. Caution should be used when interpreting coverage estimates for towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while studying remotely in his/her hometown, the student may be counted as a vaccine recipient in that town.

The PDB is a database of U.S. housing, demographic, socioeconomic and operational statistics based on select 2010 Decennial Census and select 5-year American Community Survey (ACS) estimates. Data are provided at the census tract level of geography. These data can be used for many purposes, including survey field operations planning.

Graph and download economic data for Patient Out-Of-Pocket From Patients and Their Families for Offices of Other Health Practitioners, All Establishments, Employer Firms (OOOHPPOFPAT46213) from 2015 to 2022 about employer firms, health, establishments, family, and USA.

The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.

The purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.

AHA Annual Survey Database™ for Fiscal Year 2022 is a comprehensive hospital database for peer comparisons, market analysis, and health services research. It is produced primarily from the AHA Annual Survey of Hospitals, which has been administered by the American Hospital Association (AHA) since 1946. The survey responses are supplemented by data drawn the U.S. Census Bureau, hospital accrediting bodies, and other organizations.

This census, designed by the Bureau of Justice Statistics and conducted by the United States Census Bureau, includes all state correctional facilities known to the Census Bureau in 1979. Each facility is classified into one of ten categories such as community center, prison farm, road camp, or reception center. Data for 1979 include number of inmates by security classification and by sex, number of full- and part-time staff, number of paid and volunteer staff broken down by position, age, pay, and education, number and age of facilities, type of facilities provided in each cell by size of cell, hospital facilities available, programs provided for the inmates, job training, and inmate IQ scores.

2019 US Census All Counties and County Equivalents geospatial data

U.S. Census Bureau; TIGER/Line Shapefiles 2019 Data accessed from: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2019.html

TIGER/Line Shapefiles do not include demographic data, but they do contain geographic entity codes (GEOIDs) that can be linked to the Census Bureau’s demographic data.

The Geographic Areas Reference Manual (GARM) describes in great detail the basic geographic entities the Census Bureau uses (https://www.census.gov/programs-surveys/acs/geography-acs.html).

TIGER Data Products Guide (https://www.census.gov/programs-surveys/geography/guidance/tiger-data-products-guide.html)

Profile of patients by gender, age and index for relative socioeconomic disadvantage IRSD) versus profile of local population, ATS 4 and 5, July 2009 to June 2016.

The Census Tree is the largest-ever database of record links among the historical U.S. censuses, with over 700 million links for people living in the United States between 1850 and 1940. These links allow researchers to construct a longitudinal dataset that is highly representative of the population, and that includes women, Black Americans, and other under-represented populations at unprecedented rates. Each .csv file consists of a crosswalk between the two years indicated in the filename, using the IPUMS histids. For more information, consult the included Read Me file, and visit https://censustree.org.

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).

About this dataset

Context

The dataset tabulates the North Versailles township population by age. The dataset can be utilized to understand the age distribution and demographics of North Versailles township.

Content

The dataset constitues the following three datasets

• North Versailles Township, Pennsylvania Age Group Population Dataset: A complete breakdown of North Versailles township age demographics from 0 to 85 years, distributed across 18 age groups
• North Versailles Township, Pennsylvania Age Cohorts Dataset: Children, Working Adults, and Seniors in North Versailles township - Population and Percentage Analysis
• North Versailles Township, Pennsylvania Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.