The Resident Assessment Instrument/Minimum Data Set (RAI/MDS) is a comprehensive assessment and care planning process used by the nursing home industry since 1990 as a requirement for nursing home participation in the Medicare and Medicaid programs. The RAI/MDS provides data for monitoring changes in resident status that are consistent and reliable over time. The VA commitment to quality propelled the implementation of the RAI/MDS in its nursing homes now known as VA Community Living Centers (CLC). In addition to providing consistent clinical information, the RAI/MDS can be used as a measure of both quality and resource utilization, thereby serving as a benchmark for quality and cost data within the VA as well as with community based nursing facilities. Workload based on RAI/MDS can be calculated electronically by the interactions of the elements of the MDS data and grouped into 53 categories referred to as Resource Utilization Groups (RUG-IV). Residents are assessed quarterly. The data is grouped for analysis at the Austin Information Technology Center (AITC). Conversion to electronic data entry and transmission to the AITC was completed system-wide by year-end 2000. In 2010, the Centeres for Medicare and Medicaide Services released a significantly upgraded version, MDS 3.0, to begin to be implemented on October 1, 2011 in VHA CLCs. Training is underway currently. The MDS 3.0 will generate a new set of Quality Indicators and Quality Monitors as well the RUGs will increase to 64 RUGs from the current 53 RUG groups.

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 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.

Abstract (en): The Treatment Episode Data Set -- Admissions (TEDS-A) is a national census data system of annual admissions to substance abuse treatment facilities. TEDS-A provides annual data on the number and characteristics of persons admitted to public and private substance abuse treatment programs that receive public funding. The unit of analysis is a treatment admission. TEDS consists of data reported to state substance abuse agencies by the treatment programs, which in turn report it to SAMHSA. A sister data system, called the Treatment Episode Data Set -- Discharges (TEDS-D), collects data on discharges from substance abuse treatment facilities. The first year of TEDS-A data is 1992, while the first year of TEDS-D is 2006. TEDS variables that are required to be reported are called the "Minimum Data Set (MDS)", while those that are optional are called the "Supplemental Data Set (SuDS)". Variables in the MDS include: information on service setting, number of prior treatments, primary source of referral, gender, race, ethnicity, education, employment status, substance(s) abused, route of administration, frequency of use, age at first use, and whether methadone was prescribed in treatment. Supplemental variables include: diagnosis codes, presence of psychiatric problems, living arrangements, source of income, health insurance, expected source of payment, pregnancy and veteran status, marital status, detailed not in labor force codes, detailed criminal justice referral codes, days waiting to enter treatment, and the number of arrests in the 30 days prior to admissions (starting in 2008). Substances abused include alcohol, cocaine and crack, marijuana and hashish, heroin, nonprescription methadone, other opiates and synthetics, PCP, other hallucinogens, methamphetamine, other amphetamines, other stimulants, benzodiazepines, other non-benzodiazepine tranquilizers, barbiturates, other non-barbiturate sedatives or hypnotics, inhalants, over-the-counter medications, and other substances. Created variables include total number of substances reported, intravenous drug use (IDU), and flags for any mention of specific substances. 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: Performed consistency checks.; Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. State laws require certain substance abuse treatment programs to report all of their admissions and discharges to the State. In all States, treatment programs receiving any public funds are required to provide the data on both publicly and privately funded clients. In some States, programs that do not receive public funds are required to provide data as well. On the other hand, there are some instances in which information is provided only for clients whose treatment is funded through public monies. TEDS collects this data from the States on all admissions and discharges aged 12 or older. TEDS-A presents only the admission data. Smallest Geographic Unit: Core-Based Statistical Area (CBSA) 2013-11-27 Updated and released variable-level ddi file.2012-07-19 The recodes for the variables DETNLF and DETCRIM have been revised to provide greater utility in using these variables in analysis. Also, in Appendix B of the codebook the recode table now shows the original percentages of each value for select variables.2011-05-04 Cases where age was missing have been excluded from the dataset. Minor changes to some variable labels, value labels, and question text were made to better align the variables with the information presented in the TEDS Admissions manual.2010-04-12 Improvements were made to align the data and question text with information provided in the admissions manual. Also, changes were made in the recoding of the variables for age, race, and pregnancy status. The variable SERVSET was renamed to SERVSETA to distinguish it from the service setting variable in the TEDS Discharge data.2006-12-12 A new variable (alcdrug) has been added. The width of a few other variables ...

Objective Ketamine, as a sedative, has been administered during mechanical ventilation in critically ill patients; however, its impact on survival outcomes in this patient population remains uncertain. Methods This retrospective cohort study extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) database, version 3.0. Patients were categorized into the ketamine group and the control group based on whether ketamine was administered during mechanical ventilation. Propensity score matching was performed to adjust for demographic variables and coexisting conditions. The primary outcome was 28-day mortality. Secondary outcomes included 14-day and 90-day mortality rates, as well as hospital and ICU lengths of stay. Results The study included a total of 8569 patients, with 330 in the ketamine group and 8239 in the control group. After propensity score matching, significant differences in mechanical ventilation duration and the proportion of patients with acute respiratory distress syndrome remained between groups. No significant differences were observed in 28-day and 90-day mortality rates between the groups. Subgroup analysis indicated that ketamine was associated with lower 14-day mortality rates among younger patients, those with acute respiratory distress syndrome, and norepinephrine users. Ketamine administration was also found to correlate with increased lengths of stay in both the hospital and ICU. Conclusions Ketamine was more frequently selected for patients requiring prolonged mechanical ventilation. The administration of ketamine was associated with reduced 14-day but not with 28-day or 90-day mortality rates.

This dataset presents the footprint of the percentage of distinct clients to alcohol and other drug treatment services (AODTS) by treatment type. The AODTS data is based on data reported to the …Show full descriptionThis dataset presents the footprint of the percentage of distinct clients to alcohol and other drug treatment services (AODTS) by treatment type. The AODTS data is based on data reported to the AODTS National Minimum Dataset (NMDS). The data spans the financial year of 2016-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The AODTS data accompanies the Alcohol and other drug treatment services in Australia 2016-17 Report. For further information about this dataset, please visit: Australian Institute of Health and Welfare - AODTS Data Tables. Alcohol and other drug treatment services NMDS 2016-17 Quality Statement. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The PHN of the client was assigned based on the reported postcode of the client using the Australian Bureau of Statistics' (ABS) Postal Area 2013 to Primary Health Network 2015 concordance file. Clients with an invalid postcode were assigned to the PHN group 'PHN Unallocated' and removed from the analysis. Treatment type refers to the type of activity used to treat the client's alcohol or other drug problem. Main treatment type is the principal activity that is determined at assessment by the treatment provider to be necessary for the completion of the treatment plan for the client's alcohol or other drug problem for their principal drug of concern. One main treatment type is reported for each treatment episode. Assessment only, support and case management only, and information and education only can only be reported as main treatment types. The AODTS NMDS also collects data on a client's other treatment types; however this variable is not included the data. Due to the nuances of data collection systems in Western Australia and Victoria, caution should be used when comparing the reported number of episodes by main treatment type in these states' PHNs to others. Western Australia's and Victoria's data collection systems do not collect an additional treatment type. Instead, a new treatment episode is opened for any additional treatment a client receives, and the additional treatment is recorded as the main treatment. This may inflate the reported number of episodes provides in the collection year. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

The Treatment Episode Data Set -- Discharges (TEDS-D) is a national census data system of annual discharges from substance abuse treatment facilities. TEDS-D provides annual data on the number and characteristics of persons discharged from public and private substance abuse treatment programs that receive public funding. Data collected both at admission and at discharge is included. The unit of analysis is a treatment discharge. TEDS-D consists of data reported to state substance abuse agencies by the treatment programs, which in turn report it to SAMHSA. A sister data system, called the Treatment Episode Data Set -- Admissions (TEDS-A), collects data on admissions to substance abuse treatment facilities. The first year of TEDS-A data is 1992, while the first year of TEDS-D is 2006. TEDS-D variables that are required to be reported are called the "Minimum Data Set (MDS)", while those that are optional are called the "Supplemental Data Set (SuDS)". Variables unique to TEDS-D, and not part of TEDS-A, are the length of stay, reason for leaving treatment, and service setting at time of discharge. TEDS-D also provides many of the same variables that exist in TEDS-A. This includes information on service setting, number of prior treatments, primary source of referral, gender, race, ethnicity, education, employment status, substance(s) abused, route of administration, frequency of use, age at first use, and whether methadone was prescribed in treatment. Supplemental variables include: diagnosis codes, presence of psychiatric problems, living arrangements, source of income, health insurance, expected source of payment, pregnancy and veteran status, marital status, detailed not in labor force codes, detailed criminal justice referral codes, days waiting to enter treatment, and the number of arrests in the 30 days prior to admissions (starting in 2008) . Substances abused include alcohol, cocaine and crack, marijuana and hashish, heroin, nonprescription methadone, other opiates and synthetics, PCP, other hallucinogens, methamphetamine, other amphetamines, other stimulants, benzodiazepines, other non-benzodiazepine tranquilizers, barbiturates, other non-barbiturate sedatives or hypnotics, inhalants, over-the-counter medications, and other substances. Created variables include total number of substances reported, intravenous drug use (IDU), and flags for any mention of specific substances.

Title:TWO PHOTON FLUORESCENCE AND SECOND HARMONIC GENERATION CHARACTERIZATION OF EXTRACELLULAR MATRIX REMODELING IN POST-INJURY MURINE TEMPOROMANDIBULAR JOINT OSTEOARTHRITISSupplemental data for Figure 3Orientation data in radians measured from second harmonic generation microscopynon-surgical control (NSC),4-week (E4), 8-week (E8), 12-week (E12), 16-week (E6), and 16-week sham control (S16)

BackgroundHousehold food insecurity is a major public health problem in Ethiopia despite the presence of various interventions implemented by the government. However, there is a dearth of evidence regarding the prevalence and responsible factors in Ethiopia, specifically in the South Ari district. This study, therefore, aimed to assess household food insecurity and associated factors in South Ari district, Southern Ethiopia.MethodsA community-based cross-sectional study was employed from March 11 to April 11, 2021, at South Ari district, Southern Ethiopia. A two-stage sampling technique was used to draw a sample of 717 households. Data were checked and entered into Epi-Data V3.2., and exported to SPSS V25.0 for data exploration and analysis. Variables with a p-value

Sexual and Reproductive Health and Rights (SRHR) aim to enhance quality of life through safe sexual experiences, reproductive autonomy, and protection against gender-based violence. However, existing SRHR research and interventions in low- and middle-income countries like Bangladesh predominantly focus on women, often understating men and neglecting the nuanced contextual issues faced by married couples. This study contributes to filling this gap by examining SRHR dynamics among newlyweds in rural and poor urban areas of Bangladesh, especially focusing on marital satisfaction, fertility preferences, and post-marriage adaptation mechanisms. Employing a prospective cohort design across four Health and Demographic Surveillance Systems (HDSS) managed by icddr,b, the study spans from November 2021 to March 2025, with data collection starting in December 2022. Of the 2011 newlywed couples identified, 666 who met eligibility criteria (married for ≤6 months, first marriage, and no pregnancy history) were enrolled. Participants will undergo six quantitative interview sessions over a two-year period. Additionally, 44 in-depth qualitative interviews were conducted with 22 purposefully selected couples. Demographic data reveal that a significant proportion of husbands (67.3% in rural areas, 71.8% in poor urban areas) are aged 20–29 years, while a majority of wives (67.9% in rural areas, 84.8% in poor urban areas) are adolescents. Education levels varied, with a higher proportion of poor urban husbands lack formal education compared to their rural counterparts (7.2% vs. 3.0%), while no significant variation was observed among wives (0.6% vs 1.0%). Arranged marriages are more common among rural couples (80%) compared to those in poor urban areas (50%). Moreover, poor urban participants tend to marry at a younger age than the rural participants, with poor urban wives marrying earlier than rural wives (60.4% vs 39.7%). This pioneering study provides valuable insights into the SRHR needs of newlywed couples in Bangladesh. The findings will be instrumental for designing targeted interventions aimed at improving SRHR service utilization and enhancing overall well-being, particularly in rural and poor urban areas of the country.