The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments (CAAs) are part of this process, and provide the foundation upon which a resident's individual care plan is formulated. MDS assessments are completed for all residents in certified nursing homes, regardless of source of payment for the individual resident. MDS assessments are required for residents on admission to the nursing facility, periodically, and on discharge. All assessments are completed within specific guidelines and time frames. In most cases, participants in the assessment process are licensed health care professionals employed by the nursing home. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. When reviewing the MDS 3.0 Frequency files, some common software programs e.g., ‘Microsoft Excel’ might inaccurately strip leading zeros from designated code values (i.e., "01" becomes "1") or misinterpret code ranges as dates (i.e., O0600 ranges such as 02-04 are misread as 04-Feb). As each piece of software is unique, if you encounter an issue when reading the CSV file of Frequency data, please open the file in a plain text editor such as ‘Notepad’ or ‘TextPad’ to review the underlying data, before reaching out to CMS for assistance.

The purpose of the project is to detect unreported Supplemental Security Income (SSI) recipient admissions to Title XIX institutions. A file containing SSN's of SSI recipients (all eligible individuals and members of eligible couples in current pay) will be matched against the Health Care Financing Administration's (HCFA) Minimum Data Set (MDS) database which contains admission, discharge, re-entry and assessment information about persons in Title XIX facilities for all 50 States and Washington, D.C. This database is updated monthly. The match will produce an output file containing MDS data pertinent to SSI eligibility on matched records. This data will be compared back to the SSR data to generate alerts to the Field Offices for their actions.

This dataset contains quality measures displayed on Nursing Home Compare, based on the resident assessments that make up the nursing home Minimum Data Set (MDS). Each row contains a specific measure for a nursing home and includes the four-quarter score average and scores for individual quarter.

The Nursing Home Affiliated Entity Performance Measures dataset provides select quality and performance measures from Care Compare for groups of nursing homes that share common individual or organizational owners, officers, or entities with operational/managerial control. The data include measures such as average health and staffing star ratings, staffing measures, average quality star ratings, select enforcement remedies, claims-based and Minimum Data Set (MDS) measures, average Skilled Nursing Facility Quality Reporting Program (SNF QRP) metrics, and COVID-19 vaccination rates.

The Facility-Level Minimum Data Set (MDS) Frequency dataset provides information for active nursing home residents on topics, such as race/ethnicity, age, or marital status; discharge dispositions; hearing, speech, and vision; cognitive patterns; mood; functional abilities and goals; bladder and bowel; active diagnoses; health conditions; swallowing/nutritional status; oral/dental status; skin conditions; medications; special treatments, procedures, and programs; restraints and alarms; and participation in assessment and goal setting.

Note: The MDS dataset contains more records than most spreadsheet programs can handle. The use of a database or statistical software is generally required. The dataset can be filtered to a more manageable size for use in a spreadsheet program by clicking on the “View Data” button. Additional filter information can be found in the methodology, if needed.

Background: In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis.FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2020 Participant and Program Characteristics (PC2020) is the 17th to be completed using the prototype PC reporting system. In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Processing methods and equipment used: Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2020. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency. Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2020.Study spatial scale (size of replicates and spatial scale of study area): In April 2020, there were 89 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and 33 Indian Tribal Organizations (ITOs).Level of true replication: UnknownSampling precision (within-replicate sampling or pseudoreplication):State Agency Data Submissions. PC2020 is a participant dataset consisting of 7,036,867 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data.PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public.National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 70,368 records. The distribution by category is 5,469 pregnant women, 6,131 breastfeeding women, 4,373 postpartum women, 16,817 infants, and 37,578 children.Level of subsampling (number and repeat or within-replicate sampling): The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample.Study design (before–after, control–impacts, time series, before–after-control–impacts): None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertaken: Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers.Description of any gaps in the data or other limiting factors: All State agencies provided data on a census of their WIC participants.Resources in this dataset:Resource Title: WIC PC 2020 National Sample File Public Use Codebook.; File Name: PC2020 National Sample File Public Use Codebook.docx; Resource Description: WIC PC 2020 National Sample File Public Use CodebookResource Title: WIC PC 2020 Public Use CSV Data.; File Name: wicpc2020_public_use.csv; Resource Description: WIC PC 2020 Public Use CSV DataResource Title: WIC PC 2020 Data Set SAS, R, SPSS, Stata.; File Name: PC2020 Ag Data Commons.zipResource; Description: WIC PC 2020 Data Set SAS, R, SPSS, Stata One dataset in multiple formats

IntroductionThere is a need to develop harmonized procedures and a Minimum Data Set (MDS) for cross-border Multi Casualty Incidents (MCI) in medical emergency scenarios to ensure appropriate management of such incidents, regardless of place, language and internal processes of the institutions involved. That information should be capable of real-time communication to the command-and-control chain. It is crucial that the models adopted are interoperable between countries so that the rights of patients to cross-border healthcare are fully respected.ObjectiveTo optimize management of cross-border Multi Casualty Incidents through a Minimum Data Set collected and communicated in real time to the chain of command and control for each incident. To determine the degree of agreement among experts.MethodWe used the modified Delphi method supplemented with the Utstein technique to reach consensus among experts. In the first phase, the minimum requirements of the project, the profile of the experts who were to participate, the basic requirements of each variable chosen and the way of collecting the data were defined by providing bibliography on the subject. In the second phase, the preliminary variables were grouped into 6 clusters, the objectives, the characteristics of the variables and the logistics of the work were approved. Several meetings were held to reach a consensus to choose the MDS variables using a Modified Delphi technique. Each expert had to score each variable from 1 to 10. Non-voting variables were eliminated, and the round of voting ended. In the third phase, the Utstein Style was applied to discuss each group of variables and choose the ones with the highest consensus. After several rounds of discussion, it was agreed to eliminate the variables with a score of less than 5 points. In phase four, the researchers submitted the variables to the external experts for final assessment and validation before their use in the simulations. Data were analysed with SPSS Statistics (IBM, version 2) software.ResultsSix data entities with 31 sub-entities were defined, generating 127 items representing the final MDS regarded as essential for incident management. The level of consensus for the choice of items was very high and was highest for the category ‘Incident’ with an overall kappa of 0.7401 (95% CI 0.1265–0.5812, p 0.000), a good level of consensus in the Landis and Koch model. The items with the greatest degree of consensus at ten were those relating to location, type of incident, date, time and identification of the incident. All items met the criteria set, such as digital collection and real-time transmission to the chain of command and control.ConclusionsThis study documents the development of a MDS through consensus with a high degree of agreement among a group of experts of different nationalities working in different fields. All items in the MDS were digitally collected and forwarded in real time to the chain of command and control. This tool has demonstrated its validity in four large cross-border simulations involving more than eight countries and their emergency services.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Between 2008 en 2016, 53 different research projects have contributed data to this initiative, resulting in a pooled dataset with cross-sectional and (partly) longitudinal data of >43,000 older persons and >9,000 informal caregivers. Out of these numbers, a number of 7,600 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.The 'TOPICS-MDS NPO caregiver’ dataset contains no care receiver (older person) data, only informal caregiver data.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme, that is specifically aimed at improving the quality of life for people with dementia and the care and support provided to them. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled database with cross-sectional and (partly) longitudinal data of 1,400 older persons with early onset or advanced dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.More background information on both NPO and Memorabel 1-4 can be found in the overall information on TOPICS-MDS under the tab ‘Data files’ in DANS EASY (doi.org/10.17026/dans-xvh-dbbf).The 'TOPICS-MDS Memorabel 1-4 care receiver' dataset, as part of the Memorabel 1-4 database, contains no informal caregiver data, only care receiver (older person) data. The dataset includes data on age, gender, country of birth, level of education, marital status and living situation of the care receiver, as well as data on physical and emotional health and well-being, quality of life, daily functioning and use of care, such as GP visits, home care, day care/treatment and admittance in a hospital, home for the aged or nursing home.Although the TOPICS-MDS survey instrument for the care receiver was updated in 2017, the same initial version of the instrument was used in both NPO and Memorabel 1-4 projects. The TOPICS-MDS care receiver data from NPO and Memorabel 1-4 can therefore be easily merged.

Treatment with the hypomethylating agent 5-azacytidine (AZA) increases survival in high-risk (HR) myelodysplastic syndrome (MDS) patients, but predicting patient response and overall survival remains challenging. To address these issues, we analyzed mutational and transcriptional profiles in CD34+ hematopoietic stem/progenitor cells (HSPCs) before and following AZA therapy in MDS patients. AZA treatment led to a greater reduction in the mutational burden in both blast and hematological responders than non-responders. Blast and hematological responders showed transcriptional evidence of pre-treatment enrichment for pathways such as oxidative phosphorylation, MYC targets, and mTORC1 signaling. While blast non-response was associated with TNFa signaling and leukemia stem cell signature, hematological non-response was associated with cell-cycle related pathways. AZA induced similar transcriptional responses in MDS patients regardless of response type. Comparison of blast responders and non-responders to normal controls, allowed us to generate a transcriptional classifier that could predict AZA response and survival. This classifier outperformed a previously developed gene signature in a second MDS patient cohort, but signatures of hematological responses were unable to predict survival. Overall, these studies characterize the molecular consequences of AZA treatment in MDS HSPCs and identify a potential tool for predicting AZA therapy responses and overall survival prior to initiation of therapy.

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The Payroll Based Journal (PBJ) Nurse Staffing and Non-Nurse Staffing datasets provide information submitted by nursing homes including rehabilitation services on a quarterly basis. The data include the hours staff are paid to work each day, for each facility. Examples of reporting categories include Director of Nursing, Administrative Registered Nurses, Registered Nursing, Administrative Licensed Practice Nurses, Licensed Practice Nurses, Certified Nurse Aides, Certified Medication Aides, and Nurse Aides in Training. There are also other non-nurse staff categories provided in the data such as Respiratory Therapist, Occupational Therapist, and Social Worker. The datasets also include a facility’s daily census calculated using the Minimum Data Set (MDS) submission. The Payroll Based Journal (PBJ) Employee Detail Nursing Home Staffing datasets and technical information have been moved to a new location. Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme, that is specifically aimed at improving the quality of life for people with dementia and the care and support provided to them. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled database with cross-sectional and (partly) longitudinal data of 1,400 older persons with early onset or advanced dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.More background information on both NPO and Memorabel 1-4 can be found in the overall information on TOPICS-MDS under the tab ‘Data files’ in DANS EASY (doi.org/10.17026/dans-xvh-dbbf).At the moment, 3 different research projects have collected data for TOPICS-MDS Memorabel 7.The 'TOPICS-MDS Memorabel 7 care receiver' dataset, as part of the Memorabel 5 database, contains no informal caregiver data, only care receiver (older person) data. The dataset includes data on age, gender, country of birth, level of education, marital status and living situation of the care receiver, as well as data on physical and emotional health and well-being, quality of life, daily functioning and use of care, such as GP visits, home care, day care/treatment and admittance in a hospital, home for the aged or nursing home. Date Submitted: 2023-10-05

This is Aqua AIRS-MODIS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MODIS. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collections is AIRS_MDS_IND

990 unique validated variants from patients with MDS/AML detected in seven public datasets (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411). Databases and web services were accessed for variant annotation and classification on September 7, 2020.

Treatment with the hypomethylating agent 5-azacytidine (AZA) increases survival in high-risk (HR) myelodysplastic syndrome (MDS) patients, but predicting patient response and overall survival remains challenging. To address these issues, we analyzed mutational and transcriptional profiles in CD34+ hematopoietic stem/progenitor cells (HSPCs) before and following AZA therapy in MDS patients. AZA treatment led to a greater reduction in the mutational burden in both blast and hematological responders than non-responders. Blast and hematological responders showed transcriptional evidence of pre-treatment enrichment for pathways such as oxidative phosphorylation, MYC targets, and mTORC1 signaling. While blast non-response was associated with TNFa signaling and leukemia stem cell signature, hematological non-response was associated with cell-cycle related pathways. AZA induced similar transcriptional responses in MDS patients regardless of response type. Comparison of blast responders and non-responders to normal controls, allowed us to generate a transcriptional classifier that could predict AZA response and survival. This classifier outperformed a previously developed gene signature in a second MDS patient cohort, but signatures of hematological responses were unable to predict survival. Overall, these studies characterize the molecular consequences of AZA treatment in MDS HSPCs and identify a potential tool for predicting AZA therapy responses and overall survival prior to initiation of therapy.

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MDS: COVID-19: Lethality: North: Pará data was reported at 2.100 % in 15 Feb 2025. This stayed constant from the previous number of 2.100 % for 14 Feb 2025. MDS: COVID-19: Lethality: North: Pará data is updated daily, averaging 2.200 % from Mar 2020 (Median) to 15 Feb 2025, with 1797 observations. The data reached an all-time high of 10.000 % in 12 May 2020 and a record low of 0.000 % in 31 Mar 2020. MDS: COVID-19: Lethality: North: Pará data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA005: Disease Outbreaks: COVID-19: Lethality. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

The Patient Assessment File (PAF) database compiles the results of the Patient Assessment Instrument (PAI) questionnaire filled out for intermediate care Veterans Health Administration (VHA) patients. The PAI is filled out within two weeks of admission. It is also completed semi-annually on April 1st and October 1st for each patient by a registered nurse familiar with the patient. The PAI questions cover medical treatments, conditions, selected diagnoses, activities of daily living, behaviors, some rehabilitation therapies, and chronic respiratory support. The database is managed by the Geriatrics & Extended Care Strategic Health Care Group in the Office of Patient Care Services. It is currently running at the Austin Information Technology Center (AITC) and is stored in flat files. PAF's primary customer is the Allocation Resource Center (ARC) in Braintree MA. The ARC receives the data from AITC and combines it with data from the Patient Treatment File (PTF) which contains more detailed demographic and treatment information. The ARC builds ORACLE tables, assigning RUG II (Resource Utilization Group II) scores and weighted work units reflecting the level and type of care needed. The 16 different weighted work units, ranging from 479 to 1800, are a factor in the resource allocation and budget decisions on long-term care, and are used to measure efficiency. The data is also used in other reports to Central Office, the Veterans Integrated Service Networks, and the facilities. Several other units also use PAF information including the Decision Support System (DSS). Currently, PAF is in the process of being replaced by the Resident Assessment Instrument/Minimum Data Set (RAI/MDS). RAI/MDS uses a much more extensive questionnaire as its source of information. The RAI/MDS provides clinical data and care protocols in addition to the newer RUG III scores, and is required by the Centers for Medicare and Medicaid Service funded hospitals.

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