The Washington State Department of Health presents this information as a service to the public. This includes information on the work status, practice characteristics, education, and demographics of healthcare providers, provided in response to the Washington Health Workforce Survey.

This is a complete set of data across all of the responding professions. The data dictionary identifies questions that are specific to an individual profession and aren't common to all surveys. The dataset is provided without identifying information for the responding providers.

More information on the Washington Health Workforce Survey can be found at www.doh.wa.gov/workforcesurvey

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by week of testing. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/trends/antibody-by-week.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by sex. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-sex.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.

These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.

For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data

Description

The DoH Internet Servers dataset comprises a verified list of Internet servers offering DNS over HTTPS (DoH). This is an updated 10.17632/ny4m53g6bw.1 The list was created through the aggregation of a previously existing, but incomplete, list of DoH servers. The servers in this dataset went through a verification phase where it was confirmed they were active and working as advertised. The verification was done between May 1st, 2022, and May 4th, 2022. The dataset contains a total of 254 unique DoH servers, out of which 136 are over IPv4 and 118 over IPv6. The DoH servers belong to 59 unique Autonomous Systems and are associated with a total of 106 unique domain names.

The following public lists of existing DoH servers were used to create this dataset:

• https://developers.google.com/speed/public-dns/docs/doh/json
• https://blog.nightly.mozilla.org/2018/06/01/improving-dns-privacy-in-firefox/
• https://github.com/curl/curl/wiki/DNS-over-HTTPS
• https://help.keenetic.com/hc/en-us/articles/360007687159-DNS-over-TLS-and-DNS-over-HTTPS-proxy-servers-for-DNS-requests-encryption
• https://dnsprivacy.org/wiki/display/DP/DNS+Privacy+Public+Resolvers
• https://kb.adguard.com/en/general/dns-providers
• https://applied-privacy.net/services/dns/
• https://www.pacnog.org/pacnog24/presentations/DoT-DoH-DNS-Privacy.pdf
• https://www.privacytools.io/providers/dns/

The verification of the DoH servers was performed using a custom-made python script. The script is available at: https://github.com/stratosphereips/DoH-Research/tree/main/validation-script

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.
These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.
For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates. For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

The Department of Health, Philippines (DoH) is planning to expand a health care complex in Cortes, Bohol, the Philippines.The project involves the construction of a 301-bed capacity (225-bed to 526-bed) healthcare complex on 5ha of land. It involves the construction of five three-story buildings, a six-story main building a two-story hospital command center and deck. It also includes the construction of an outpatient department, an emergency unit, surgery units, patient rooms, treatment rooms, an intensive care unit, an administrative space, laboratories, storage units, installation of elevators, safety and security systems, parking and related facilities.ADCE Builders Development Corporation has been appointed as architect. In September 2013, the DoH announced its commitment to fund the project. In August 2014, the National Economic Development Authority approved the funding of the project.n February 2016, Senate Committee on Health and Demography has approved the increase to 526-bed capacity and thereby recommended for National Economic and Development Authority (NEDA) approval in August 2016.The project secured approvals from Investment Coordination Committee (ICC) in September and then followed with National Economic and Development Authority (NEDA) approval. The project was awaiting issuance of Notice to Proceed for the project in the third/fourth quarter of 2016 with a groundbreaking ceremony held in January 2017.In January 2017, EM Cuerpo has been appointed as construction contractor for the project and a groundbreaking ceremony was held on the site.Pre-construction activities are underway. Read More

Please refer to the original data article for further data description: Jeřábek & Hynek et al., Collection of datasets with DNS over HTTPS traffic In: Data in Brief Journal ,DOI:10.1016/j.dib.2022.108310

Dataset of DNS over HTTPS traffic from Firefox (AdGuard, AhaDNS, BlahDNS, BraveDNS, CloudFlare)
The dataset contains DoH and HTTPS traffic that was captured in a virtualized environment (Docker) and generated automatically by Firefox browser with enabled DoH towards 5 different DoH servers (AdGuard, AhaDNS, BlahDNS, BraveDNS, CloudFlare) and a web page loads towards a sample of web pages taken from Majestic Million dataset. The data are provided in the form of PCAP files. However, we also provided TLS enriched flow data that are generated with opensource [ipfixprobe](https://github.com/CESNET/ipfixprobe) flow exporter. Other than TLS related information is not relevant since the dataset comprises only encrypted TLS traffic. The TLS enriched flow data are provided in the form of CSV files with the following columns:

The DoH resolvers in the dataset can be identified by IP addresses written in doh_resolver_ip.csv file.

The main part of the dataset is located in DoH-Gen-F-AABBC.tar.gz and has the following structure:

.
└─── data          | - Main directory with data
   └── generated     | - Directory with generated captures
     ├── pcap      | - Generated PCAPs
     │  └── firefox
     └── tls-flow-csv  | - Generated CSV flow data
       └── firefox

Total stats of generated data:

DoH Server information

Please cite the original article:

@article{Jerabek2022,
title = {Collection of datasets with DNS over HTTPS traffic},
journal = {Data in Brief},
volume = {42},
pages = {108310},
year = {2022},
issn = {2352-3409},
doi = {https://doi.org/10.1016/j.dib.2022.108310},
url = {https://www.sciencedirect.com/science/article/pii/S2352340922005121},
author = {Kamil Jeřábek and Karel Hynek and Tomáš Čejka and Ondřej Ryšavý}
}

This is a report for all the relevant columns of DOH - The Amount Allocated, Obligated and Paid broken down by federal agency, program, vendor, project, county, and municipality.

The Washington State Department of Health presents this information as a service to the public. True and correct copies of legal disciplinary actions taken after July 1998 are available on our Provider Credential Search site. These records are considered certified by the Department of Health.

This includes information on health care providers.

Please contact our Customer Service Center at 360-236-4700 for information about actions before July 1998. The information on this site comes directly from our database and is updated daily at 10:00 a.m.. This data is a primary source for verification of credentials and is extracted from the primary database at 2:00 a.m. daily.

News releases about disciplinary actions taken against Washington State healthcare providers, agencies or facilities are on the agency's Newsroom webpage.

Disclaimer The absence of information in the Provider Credential Search system doesn't imply any recommendation, endorsement or guarantee of competence of any healthcare professional. The presence of information in this system doesn't imply a provider isn't competent or qualified to practice. The reader is encouraged to carefully evaluate any information found in this data set.

The Public Health Activities and Services (PHAS) data measures what public health does in the state and how much of it is done across all 35 local health agencies and the Department of Health in Washington State each year. Activities measured fall under the following broad categories: Access To Care Assessment Communicable Disease Communicable Disease: Immunization Emergency Preparedness Environmental Health Healthy Families Prevention and Wellness More PHAS data is available at https://fortress.wa.gov/doh/phip/PHIP/Home.mvc

Dialysis Center in Hawaii

This dataset is an additional DoH dataset used for researching DoH traffic data drift phenomena. It contains anonymized packet captures (pcaps) from the following days:

2022-11-28

2022-12-05

2022-12-12

2022-12-19

2022-12-26

The traffic was captured on the CESNET2 network and anonymized. The packet capturing and anonymization follow the methodology described in [1]. The list of IP addresses used for DoH recognition is also included within the dataset in doh_resolver_ip.csv file. The structure of the dataset is as follows:

. ├── doh_resolver_ip.csv ├── pcap │ ├── 2022-11-28 │ │ ├── DoH-20221128180002.pcapng │ │ └── HTTPS-20221128180002.pcapng │ ├── 2022-12-05 │ │ ├── DoH-20221205180001.pcapng │ │ └── HTTPS-20221205180001.pcapng │ ├── 2022-12-12 │ │ ├── DoH-20221212180001.pcapng │ │ └── HTTPS-20221212180001.pcapng │ ├── 2022-12-19 │ │ ├── DoH-20221219180001.pcapng │ │ └── HTTPS-20221219180001.pcapng │ └── 2022-12-26 │ ├── DoH-20221226180001.pcapng │ └── HTTPS-20221226180001.pcapng └── README.md

[1] Jeřábek, K., Hynek, K., Čejka, T., & Ryšavý, O. (2022). Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42, 108310. https://www.sciencedirect.com/science/article/pii/S2352340922005121

Included in the data set is :-Local Health Department-Planning Organization-Director of Health (DoH) Name- DoH title-DoH Degree-Doh Email-LHD Status (District, Part time, Full Time)-LHD Phone-Agency Fax

On January 21, 2020, the U.S. Centers for Disease Control and Prevention (CDC) and Washington State Department of Health (DOH) announced the first case of 2019 Novel Coronavirus (COVID-19) in the United States, in Washington state. The link below provides access to DOH daily updates of confirmed Washington State COVID-19 cases and deaths, along with essential information about the virus and guidance on prevention and risk management. The link includes Frequently Asked Questions, as well as resources for specific groups such as parents, caregivers, employers, schools and health care providers.

This is a report for all the relevant columns of DOH - The Amount Allocated, Obligated and Paid broken down by federal agency, program, vendor, project, county, and municipality.

Included in the data set is :-Local Health Department-Planning Organization-Director of Health (DoH) Name- DoH title-DoH Degree-Doh Email-LHD Status (District, Part time, Full Time)-LHD Phone-Agency Fax