The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

Drug Use By Age

This directory contains data behind the story How Baby Boomers Get High. It covers 13 drugs across 17 age groups.

Source: National Survey on Drug Use and Health from the Substance Abuse and Mental Health Data Archive.

1) Data Introduction • The EHR Vendor count Dataset is a compilation of what Electronic Medical Records (EHR) vendor products were used by healthcare institutions (outpatient clinics and non-federal inpatient hospitals) participating in the U.S. Medicare EHR incentive program between 2011 and 2016, and the cumulative number of institutions introduced by vendor by year, institution type, and product version.

2) Data Utilization (1) EHR Vendor count Dataset has characteristics that: • It consists of developer (vendor name), provider_type (outpatient/hospitalization), program_year, toot_proves_report_developer (accumulated number of applicable vendor products used), toot_proves_report_edition/2015_edition/2014_edition/2011_edition, and product_type (commercial/self-developed) by year, institution type, and vendor to compare market share and technology upgrade status. (2) EHR Vendor count Dataset can be used to: • EHR Market Share and Introduction Trends Analysis: Using the number of institutions introduced by year, institution type, vendor, and certification version, U.S. healthcare institutions can analyze changes in market share and new product upgrades by EHR vendors. • EHR adoption patterns and policy assessment by institution: By comparing and analyzing the current status of commercial/self-developed products by institutional type, such as outpatient clinics and inpatient hospitals, it can be used to assess EHR adoption strategies based on the effectiveness of medical IT policies and institutional characteristics.

NOTE: This dataset has been retired and marked as historical-only.

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

Cash-Flow-Per-Share Time Series for Biomarin Pharmaceutical Inc. BioMarin Pharmaceutical Inc., a biotechnology company, engages in the development and commercialization of therapies for life-threatening rare diseases and medical conditions in the United States, Europe, Latin America, the Middle East, the Asia Pacific, and internationally. The company's products include VIMIZIM, an enzyme replacement therapy for the treatment of mucopolysaccharidosis (MPS) IV type A, a lysosomal storage disorder; VOXZOGO, a once daily injection analog of c-type natriuretic peptide (CNP) for the treatment of achondroplasia; NAGLAZYME, a recombinant form of N- acetylgalactosamine 4-sulfatase for patients with MPS VI; and PALYNZIQ, a PEGylated recombinant phenylalanine (Phe) ammonia lyase enzyme delivered through subcutaneous injection to reduce blood Phe concentrations. It also develops BRINEURA, a recombinant human tripeptidyl peptidase 1 for the treatment of patients with ceroid lipofuscinosis type 2, a form of Batten disease; ALDURAZYME, a purified protein designed to be identical to a naturally occurring form of the human enzyme alpha-L-iduronidase; KUVAN, a proprietary synthetic oral form of 6R-BH4 that is used to treat patients with phenylketonuria, an inherited metabolic disease; and ROCTAVIAN, an adeno associated virus vector for the treatment of severe hemophilia A. The company's products under development include BMN 333, a longer-acting CNP for the treatment of multiple growth disorders, such as achondroplasia and hypochondroplasia; BMN 349, an oral therapeutic for the treatment of liver disease associated with alpha-1 antitrypsin deficiency; and BMN 351, an oligonucleotide for the treatment of duchenne muscular dystrophy. It serves specialty pharmacies, hospitals, non-U.S. government agencies, distributors, and pharmaceutical wholesalers. The company has license and collaboration agreements with Catalyst Pharmaceutical Partners, Inc., and Ares Trading S.A. The company was incorporated in 1996 and is based in San Rafael, California.

Explore North America Artificial Intelligence in Healthcare Market, including size, share, growth, trends, and industry analysis, with forecasts extending to 2033.

*Based on 4,168 persons who completed the Eight-item Patient Health Questionnaire (PHQ-8) depression scale; †Male-to-female or female-to-male.wgt. row% = weighted row %; 95% CI = 95% confidence intervals.Responses to the PHQ-8 were used to define “Major depression” and “Other depression” according to criteria from the Diagnostic and Statistical Manual of Mental Disorders, 4 th Edition. Any depression is the presence of either major depression or other depression.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for IQIs generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The IQIs are a set of measures that provide a perspective on hospital quality of care using hospital administrative data. These indicators reflect quality of care inside hospitals and include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality. All the IQI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) IQI measures.

The mortality, volume and utilization measures IQIs are presented by hospital as rates or counts. Area-level utilization measures are presented by county as rates. For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The National Hospital Ambulatory Medical Care Survey (NHAMCS), conducted by the National Center for Health Statistics (NCHS), collects annual data on visits to emergency departments to describe patterns of utilization and provision of ambulatory care delivery in the United States. Data are collected from nonfederal, general, and short-stay hospitals from all 50 U.S. states and the District of Columbia, and are used to develop nationally representative estimates. The data include counts and rates of emergency department visits from 2016-2022 for the 10 leading primary diagnoses and reasons for visit, stratified by selected patient and hospital characteristics. Rankings for the 10 leading categories were identified using weighted data from 2022 and were then assessed in prior years.