The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

This dataset comprises Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for the efficient assessment of PV system and wind turbine performance for over 2,000 locations across the U.S. TSYs and TWYs are single synthetic years generated from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) data spanning from 2001 to 2022. These synthetic years represent the long-term average solar and wind resource conditions of a location, respectively. The POWER solar data is derived from satellite observations and has a spatial resolution of 1 degree * 1 degree (latitude/longitude). The meteorological variables are sourced from NASA's Goddard Earth Observing System (GEOS) Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) assimilation model, which features a spatial resolution of 1/2 degree * 5/8 degree (latitude/longitude). The methods for creating TSYs and TWYs are adapted from the Sandia method. Specifically, the weights assigned to different weather parameters have been adjusted, and the final selection step has been omitted. For TSYs, a weight of 0.7 is assigned to daily cumulative GHI, and 0.3 is assigned to daily cumulative DNI. For TWYs, weights of 0.2, 0.2, and 0.6 are assigned to daily median zonal wind speed, daily median meridional wind speed, and daily 0.75 quantile wind speed, respectively. These weights have been optimized based on the simulated solar PV system and wind turbine outputs. 12 representative months are then selected based on their Finkelstein-Schafer (FS) statistics and concatenated into a synthetic year. The paper describing our methodology has been published in Applied Energy and is available via the "Project Publication" resource link below. The TSYs and TWYs are provided for the centroids of all Public Use Microdata Areas (PUMAs) in the U.S. PUMAs are non-overlapping statistical geographic areas that partition each state or equivalent entity into regions containing no fewer than 100,000 people each. The 2,378 PUMAs collectively cover the entire U.S. A file named "PUMA information.csv" is included with the dataset, containing the PUMA number, PUMA name, latitude, longitude, elevation, and time zone of all PUMA centroids. Users can reference this file to find the PUMAs corresponding to their locations of interest. To accommodate different user communities, the data is provided in three formats. The TSYs are available in EPW and SAM weather file formats, while the TWYs are available in EPW, SAM weather file, and CSV formats. The EPW format, developed by the U.S. Department of Energy, is a de facto standard for weather data in building energy modeling and is compatible with various building energy modeling programs, including EnergyPlus, ESP-r, and IESVE. The SAM weather file format is designed for the System Advisor Model (SAM), a renewable energy project evaluation tool developed by the National Renewable Energy Laboratory (NREL). If you use this dataset in your research, please consider citing our paper: Zeng, Z., Stackhouse, P., Kim, J.-H. (Jeannie), & Muehleisen, R. T. (2025). Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S. Applied Energy, 377, 124698. https://doi.org/10.1016/j.apenergy.2024.124698.

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 19 of the 28 states in India which covers 96% of the population. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households.

Kind of data

Sample survey data [ssd]

Sampling procedure

World Health Survey Sampling India has 28 states and seven union territories. 19 of the 28 states were included in the design representing 96% of the population. India used a stratified multistage cluster sample design. Six states were selected in accordance with their geographic location and level of development. Strata were defined by the 6 states:(Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal), and locality (urban or rural). There are 12 strata in total. The 2000 Census demarcation was used as the sampling frame. Two stage and three stage sampling was adopted in rural and urban areas, respectively. In rural areas PSUs(villages) were selected probability proportional to size. The measure of size being the 2001 Census population in the village. SSUs (households) were selected using systematic sampling. TSUs (individuals) were selected using Kish tables. In urban areas, PSUs(city wards) were selected probability proportional to size. SSUs(census enumeration blocks), two were randomly selected from each PSU. TSU (households) were selected using systematic sampling. QSU (individuals) were selected as in rural areas. A sample of 379 EAs was selected as the primary sampling units(PSU).

SAGE Sampling The SAGE sample was pre-determined as all PSUs and households selected for the WHS/SAGE Wave 0 survey were included. Exceptions are three PSUs in Assam which were replaced as they were inaccessible due to flooding. And a further six PSUs were omitted for which the household roster information was not available. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: 1)Households with a WHS/SAGE Wave 0 respondent aged 50-plus: all members aged 50-plus including the WHS/SAGE Wave 0 respondent were eligible for the individual interview. 2)Households with a WHS/SAGE Wave 0 respondent aged 47-49: all members aged 50-plus including the WHS/SAGE Wave 0 respondent aged 47-49 was eligible for the individual interview. 3)Households with a WHS/SAGE Wave 0 female respondent aged 18-46: all females members aged 18-49 including the WHS/SAGE Wave 0 female respondent aged 18-46 were eligible for the individual interview. 4)Households with a WHS/SAGE Wave 0 male respondent aged 18-46: three households were selected using systematic sampling and one male aged 18-49 was eligible for the individual interview. In the households not selected, all members aged 50-plus were eligible for the individual interview.

Stages of selection Strata: State, Locality=12 PSU: EAs=375 surveyed SSU: Households=10424 surveyed TSU: Individual=12198 surveyed

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. A Womans Questionnaire was administered to all females aged 18-49 years identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into Hindi, Assamese, Kanada and Marathi. SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=88% Cooperation rate=92%

Individual: Response rate=68% Cooperation rate=92%

Link worker social prescribing programs are gaining recognition in Australia for addressing health and social needs outside routine medical care. The evaluation of these programs is essential for informing future social prescribing programs, research and evolving policy. However, diverse outcome evaluation measures present challenges for benchmarking across link worker social prescribing programs. An integrative review was conducted to identify and describe outcome domains and measures, and the methodological approaches and evaluation designs of link worker social prescribing programs in Australia. Comprehensive searches of the literature on link worker social prescribing programs in Australia were conducted across 14 electronic databases. In order to reduce the risk of bias, study selection and data extraction were conducted independently by multiple authors, and included studies underwent quality and risk of bias assessment using the standardised Mixed Methods Appraisal Tool. Six studies met the inclusion criteria. Outcome domains were categorised into ‘person-level’, ‘system-level’ and ‘program implementation’ domains. Despite the variation in participant groups, the ‘person-level’ domains of global well-being and social well-being were consistently evaluated. While measurement tools varied significantly, the WHO Quality of Life Brief Assessment and short-form UCLA Loneliness Scale were most commonly applied. At the system level, health service utilisation was primarily evaluated. This integrative review reports on the current state of evidence in Australia, with the potential to track changes and trends over time. Developing a core outcome set, incorporating stakeholder and consumer contributions for benchmarking aligned with the healthcare landscape is recommended. The findings may guide the refining of social prescribing initiatives and future research, ensuring methodological robustness and alignment with individual and community needs.

This map service contains GIS data from the EPA Office of Water 305(b) Assessed Waters Program. The information supporting this service resides in the Reach Address Database (RAD) which is part of the Watershed Assessment, Tracking & Environmental Results System (WATERS).The 305(b) program system provide assessed water data and assessed water features for river segments, lakes, and estuaries designated under Section 305(b) of the Clean Water Act. 305(b) waterbodies are coded onto NHDPlus v2.1 features creating area, point and linear events representing assessed and non-assessed waters. In addition to NHDPlus reach indexed data there may also be custom events (point, line, or area) that are not associated with NHDPlus and are in an EPA standard format that is compatible with EPA's Reach Address Database. These custom events are used to represent locations of 305(b) waterbodies that are not represented well in NHDPlus. To identify the spatial extent of waters listed under the 305(b) program attributed as being assessed in the ATTAINS database, these waters can be linked to the 305(b) information stored in the EPA's Assessment and TMDL Tracking and Implementation System (ATTAINS) for query and display. Use the Source_FeatureID field and Cycle_Year field to link indexed assessed waters to the EPA's ATTAINS Database. For complete metadata, please use EPA's Environmental Data Gateway (EDG): https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B81060F20-4F5C-42E2-BBC7-CD96E442B8FA%7D.

About the Project The objective of this project is to assess potential economic and technical gains that could be realized by utilizing the GCC Interconnector to deliver electricity at least-cost across the GCC. Summary This paper presents datasets that support economic and policy analyses of countries in the Gulf Cooperation Council (GCC). The objective is to provide an overview of the GCC energy systems and serve as a reference for researchers performing quantitative modeling and analysis. The following data have been collected from public sources, using the most recent complete datasets available. We begin by describing the GCC in terms of electricity systems specific to each country. For each system, we compile and present information about how electricity and water are supplied in terms of technologies and fuels. A key point is the linkage of electricity and water production in the GCC. Power plants typically produce a combination of electricity and water, primarily through desalinating seawater using waste heat. This linkage must be considered when analyzing how energy is transformed in the GCC. An assessment of fossil and renewable resources follows in the third section. The GCC states are well endowed with fossil and renewable resources. To date, fossil energy has been exploited for export and domestic consumption while the use of renewable resources has been negligible in terms of total primary energy supply. The fourth section presents government administered fuel prices and electricity tariffs. These provide a context for understanding the composition of the energy and water sectors. Regulated energy prices are a characteristic of the GCC. Administered prices on the supply (electricity production) and demand side (electricity consumption) have been, and continue to be, a key barrier to electricity trade and greater penetration of renewable technologies in the power and water sectors. Ongoing price reforms are expected to improve the prospects of electricity trade and cost-effectiveness of renewables. Existing energy policies, future targets and power sector reforms are covered in the fifth section. GCC countries have announced plans to both diversify electricity production (by deploying renewables and nuclear capacity) and to reduce demand (through efficiency measures). Recently announced targets in all six GCC states suggest that renewable resources and nuclear energy will be a prominent component of the region’s future energy systems. Almost 80 GW of renewables will be installed, around four times the amount of nuclear power that is planned in the region. The accompanying datasets are available on the OpenKAPSARC data portal and will be updated as new data are available.

Automotive data loggers range from basic devices that record basic vehicle data to advanced systems that capture and analyze large volumes of data from multiple sensors. These systems are available in various form factors, such as handheld, in-vehicle, and cloud-based. Recent developments include: June 2023: Klas, a prominent worldwide provider of durable edge technology solutions, has unveiled its latest line of automotive data logging solutions, the TRX D8 2.0, at the ADAS & Autonomous Vehicle Technology Expo in Stuttgart. These solutions have been specifically designed to record the remarkable increase in vision-based data for advanced driver assistance systems (ADAS) and autonomous driving (AD) capabilities. The TRX D8 2.0 is a pioneering solution for vehicle data logging that specifically caters to the requirement for fast storage within current in-car PCIe-based toolchains. It offers the versatility to connect and record data from advanced high-bandwidth ethernet network-based vehicle sensors. The TRX D8 2.0 stands out in terms of its storage capacity of 240TBs and its impressive disk write rates of up to 200Gbps (25GB/s). This makes it unparalleled in capturing large amounts and various types of data during test drives. The TRX D8 ingest station or the integrated high-speed network connections provide for efficient and rapid transfer of data to the HIL/SIL or cloud., In April 2023, Xylon introduced the Xylon Quattro, a state-of-the-art data recording and HIL system designed specifically for the development, testing, and validation of autonomous driving (AD) and vision-based advanced driver assistance systems (ADAS). With its comprehensive and forward-looking range of features, the Xylon Quattro is able to support automotive advancements up to Level 5 autonomy. It complements the current third-generation logiRECORDER data logger, which will continue to be maintained and upgraded for use in automotive developments that prioritize performance and cost optimization. The Xylon Quattro has a remarkable data logging and playback bandwidth of 128Gbps, along with a generous internal data storage capacity of up to 128TB. Additionally, it offers automobile interface capabilities. The testing solution allows for the direct connection of a maximum of 16 video cameras, with resolutions of up to 32MP, using existing LVDS interfaces like as GMSL2 from Analog Devices or FPD-Link III from Texas Instruments. In addition, Xylon Quattro has the ability to work with the current logiRECORDER video I/O modules, allowing for compatibility with various video interfaces.. Key drivers for this market are: Increasing demand for ADAS and autonomous driving Rising adoption of fleet management solutions. Potential restraints include: Cost of implementation and data storage Data privacy and security risks. Notable trends are: The increasing complexity of electronic architecture in modern vehicles drives market growth Artificial Intelligence (AI) and Machine Learning (ML) for data analysis Integration with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication.

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey. Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in all nine provinces in South Africa. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older were selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa used a stratified multistage cluster sample design. Strata were defined by the nine provinces:(Eastern Cape, Free State, Gauteng, Kwa-Zulu Natal, Limpopo, Mpumalanga, North West, Northern Cape and Western Cape), locality (urban or rural), and predominant race group (African/Black, White, Coloured and Indian/Asian), as not all combinations of stratification variables were possible, there were 50 strata in total. The Human Science Research Council's master sample was used as the sampling frame which comprised 1000EAs. A sample of 600 EAs was selected as the primary sampling units(PSU). The number of EAs to be selected from each strata was based on proportional allocation (determined by the number of EAs in each strataspecified on the Master Sample). EAs were then selected from each strata with probability proportional to size; the measure of size being the number of individuals aged 50 years or more in the EA. In each selected EA 30 households were randomly selected from the Master Sample. A listing of the 30 selected households was conducted to classify each household into one of two mutually exclusive categories: (1) households with one or more members aged 50 years or more (defined as '50 plus households'); (2) households which did not include any members aged 50 years or more, but included residents aged 18-49 (defined as '18-49 households'). All 50 plus households were eligible for the household interview, and all 50 plus members of the household were eligible for the individual interview. Two of the remaining 18-49 households were randomly selected for the household interview. In each of these household one person aged 18-49 was eligible for the individual interview, and the individual to be included was selected using a Kish Grid.

Stages of selection Strata: Province, Predominant Race Group, Locality=50 PSU: EAs=408 surveyed SSU: Households=4020 surveyed TSU: Individual=4227 surveyed

Sampling deviation

Originally 600 EAs were drawn into the sample. However due to time and financial contraints only 396 EAs were visited.

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionnaire was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into six of the major languages in South Africa: Afrikaans, IsiZulu, IsiXhosa, Sepedi, Setswana and Xitsonga. All SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=67% Cooperation rate=99%

Individual: Response rate=77% Cooperation rate=99%

Abstract

The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Health Systems and Innovation Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 2 (2014/15) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content: - Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

• Verbal Autopsy questionnaire Section 1: Information on the Deceased and Date/Place of Death Section 1A7: Vital Registration and Certification Section 2: Information on the Respondent Section 3A: Medical History Associated with Final Illness Section 3B: General Signs and Symptoms Associated with Final Illness Section 3E: History of Injuries/Accidents Section 3G: Health Service Utilization Section 4: Background Section 5A: Interviewer Observations

• Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilisation 6000 Social Networks 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

• Proxy Questionnaire Section1 Respondent Characteristics and IQ CODE Section2 Health State Descriptions Section4 Chronic Conditions and Health Services Coverage Section5 Health Care Utilisation

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 31 of the 32 federal states in Mexico. Colima was excluded. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older was selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Mexico strata were defined by locality (metropolitan, urban, rural). All 211 PSUs selected for wave 1 were included in the wave 2 sample. A sub-sample of 211 PSUs was selected from the 797 WHS PSUs for the wave 1 sample. The Basic Geo-Statistical Areas (AGEB) defined by the National Institute of Statistics (INEGI) constitutes a PSU. PSUs were selected probability proportional to three factors: a) (WHS/SAGE Wave 0 50plus): number of WHS/SAGE Wave 0 50-plus interviewed at the PSU, b) (State Population): population of the state to which the PSU belongs, c) (WHS/SAGE Wave 0 PSU at county): number of PSUs selected from the county to which the PSU belongs for the WHS/SAGE Wave 0 The first and third factors were included to reduce geographic dispersion. Factor two affords states with larger populations a greater chance of selection.

All WHS/SAGE Wave 0 individuals aged 50 years or older in the selected rural or urban PSUs and a random sample 90% of individuals aged 50 years or older in metropolitan PSUs who had been interviewed for the WHS/SAGE Wave 0 were included in the SAGE Wave 1 ''primary'' sample. The remaining 10% of WHS/SAGE Wave 0 individuals aged 50 years or older in metropolitan areas were then allocated as a ''replacement'' sample for individuals who could not be contacted or did not consent to participate in SAGE Wave 1. A systematic sample of 1000 WHS/SAGE Wave 0 individuals aged 18-49 across all selected PSUs was selected as the ''primary'' sample and 500 as a ''replacement'' sample.

This selection process resulted in a sample which had an over-representation of individuals from metropolitan strata; therefore, it was decided to increase the number of individuals aged 50 years or older from rural and urban strata. This was achieved by including individuals who had not been part of WHS/SAGE Wave 0 (which became a ''supplementary'' sample), although the household in which they lived included an individual from WHS/SAGE Wave 0. All individuals aged 50 or over were included from rural and urban ''18-49 households'' (that is, where an individual aged 18-49 was included in WHS/SAGE Wave 0) as part of the ''primary supplementary'' sample. A systematic random sample of individuals aged 50 years or older was then obtained from urban and rural households where an individual had already been selected as part of the 50 years and older or 18-49 samples. These individuals then formed part of the ''primary supplementary'' sample and the remainder (that is, those not systematically selected) were allocated to the ''replacement supplementary'' sample. Thus, all individuals aged 50 years or older who lived in households in urban and rural PSUs obtained for SAGE Wave 1 were selected as either a primary or replacement participant. A final ''replacement'' sample for the 50 and over age group was obtained from a systematic sample of all individuals aged 50 or over from households which included the individuals already selected for either the 50 and over or 18-49. This sampling strategy also provided participants who had not been included in WHS/SAGE Wave 0, but lived in a household where an individual had been part of WHS/SAGE Wave 0 (that is, the ''supplementary'' sample), in addition to follow-up of individuals who had been included in the WHS/SAGE Wave 0 sample.

Strata: Locality = 3 PSU: AGEBs = 211 SSU: Households = 6549 surveyed TSU: Individual = 6342 surveyed

Mode of data collection

Face-to-face [f2f], CAPI

Research instrument

The questionnaires were based on the SAGE Wave 1 Questionnaires with some modification and new additions, except for verbal autopsy. SAGE Wave 2 used the 2012 version of the WHO Verbal Autopsy Questionnare. SAGE Wave 1 used an adapted version of the Sample Vital Registration iwth Verbal Autopsy (SAVVY) questionnaire. A Household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to 50 plus households only. In follow-up 50 plus household if the death occured since the last wave of the study and in a new 50 plus household if the death occurred in the

CDC Maternal Health Survey

Attitudes and Experiences Before, During, and After Pregnancy

By Health [source]

About this dataset

The Centers for Disease Control and Prevention (CDC) is proud to present PRAMS, the Pregnancy Risk Assessment Monitoring System. This survey provides valuable insights and analysis on maternal health, mindset, and experiences pre-pregnancy through postpartum phase. Statistically representative data is gathered from mothers all over the United States concerning issues such as abuse, alcohol use, contraception, breastfeeding, mental health, obesity and many more.

This survey provides an invaluable source of information which is key in targeting areas that need improvement when it comes to maternal wellbeing. Armed with PRAMS data state health officials are able to work towards promoting a healthy environment for mothers and their babies during this important period of life. Rich in data points ranging from smoking exposure to infant sleep behavior trends can be identified across states as well as nationally with this unique system supported by CDC's partnership with state health departments.

Here you will find a-mazing datasets containing columns such like Year or LocationAbbr or Response allowing you analyze some really meaningful stuff like: Are women in certain parts of the US more likely compared to others to breastfeed? What about rates at which pregnant mothers take prenatal care? Dive into the 2019 CDC PRAMStat dataset today!

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How to use the dataset

In order to make full use of this dataset it’s important that you understand what each column contains so that you can extract the most relevant data for your purposes. Here are some tips for understanding how to maximize this dataset: - Look through each column carefully – take note of which columns contain numerical information (Data_Value_Unit), categorical responses (Response) or location descriptions (Location Desc). - Make sure that you are aware of any standard errors that may be associated with data values (Data_Value_Std_Err). - It’s useful to know the source(DataSource)of your data so if possible check out who has collected it.
- Check what classifications have been used in BreakOut columns – this can give additional insight into how subjects were divided up within datasets.
- Understand how pregnancies were grouped together geographically by taking a look at LocationAbbr and Geolocation columns - understanding where surveys have been done can help break down regional differences in responses.
With these steps will help you navigate through your dataset so that you can accurately interpret questions posed by pregnant women from different locations across the U.S.

Research Ideas

• Using this dataset, public health officials could analyze maternal attitudes and experiences over a period of time to develop targeted strategies to improve maternal health.
• This dataset can be used to create predictive models of maternal behavior based on the amount of prenatal care received and other factors such as alcohol use, sleep behavior and tobacco use.
• Analyzing this dataset would also allow researchers to identify trends in infant wellbeing outcomes across various states/municipalities with different policies/interventions in place which can then be replicated in other areas with similar characteristics

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv | Column name | Description ...

Introduction

Twenty to 50% of older surgical patients live with frailty, a multidimensional state characterized by increased vulnerability to stressors due to accumulation of age- and disease-related deficits.1,2 Frailty is a strong perioperative risk factor associated with more than a doubling in the odds of postoperative morbidity, patient-reported disability and mortality, as well as increased healthcare resource use.3–5 Importantly, frailty appears to provide novel prognostic information when assessed in addition to risk factors typically identified before surgery (e.g., age, sex, ASA score, procedural risk), or when added to high-performing multivariable risk models.2,6–8 Preoperative frailty assessment may also represent a novel opportunity to drive optimization of underlying physical, nutritional, and cognitive deficits before surgery.9

Accordingly, routine preoperative frailty assessment is recommended by multiple international, multi-specialty best practice guidelines.6,10,11 Identification and communication of frailty status before surgery is associated with decreased postoperative mortality.12 However, routine frailty assessment in older patients appears to be rarely performed in practice.13,14 Barriers to preoperative frailty assessment are likely multifactorial and include the lack of a single or best-performing instrument as well as added time required to perform an assessment. Automated electronic frailty assessment could help to overcome such barriers by applying a prognostically accurate frailty instrument to preoperative electronic health data. While a recent systematic review identified 22 different electronic frailty instruments studied in the perioperative setting, most did not adhere to consensus definitions of frailty and accepted conceptual frameworks.15–17 Furthermore, among multi-dimensional instruments, there were no head-to-head comparisons performed and little data was available to describe predictive accuracy or added accuracy compared to risk factors that are typically assessed. This means that clinicians and health system planners have limited data to guide the development or implementation of an electronic approach to preoperative frailty assessment.

The Frailty Index (FI), Risk Analysis Index (RAI), Adjusted Clinical Groups Frailty Defining Diagnoses indicator (ACG) and Hospital Frailty Risk Score (HFRS) all represent well-studied, multi-dimensional frailty instruments that can be applied to electronic health data. However, they have not been systematically compared when predicting postoperative outcomes relevant to older surgical patients. Therefore, using two population-based, non-cardiac surgery cohorts, our objectives are to: 1) determine the predictive accuracy of each of these frailty instruments in predicting postoperative outcomes (primary: 30-day mortality, secondary: days alive at home (within 30- and 365-days of surgery), length of hospital stay, health systems costs (within 30- and 365-days of surgery), discharge destination and one-year mortality); 2) determine the added predictive accuracy of each instrument beyond that provided by typically assessed risk factors; and 3) compare the predictive accuracy of each instrument head-to-head.

Methods and Analysis

Design and Data Sources This will be a retrospective, population-based cohort study using linked health administrative data from Ontario, Canada. All relevant data will come from ICES, an independent research institute where data are extracted and coded using validated and standardized processes consistent with national health data standards. As ICES data are anonymized and routinely collected, this study is legally exempt from research ethics review based on provincial health privacy legislation. We will use unique, encrypted patient identifiers to deterministically link several databases to re-construct each patient’s perioperative health system episode, including: the Discharge Abstract Database (DAD), which captures demographic and clinical (diagnoses, comorbidities, procedures, admission characteristics) information about all hospitalizations; the Registered Persons Database (RPDB), which captures all deaths and death dates for Ontarians; the Ontario Drug Database (ODB), which captures all prescription drug claims; the Ontario Health Insurance Claims Database (OHIP) which includes physician claims data for inpatient, outpatient and long-term care settings; the Continuing Care Reporting System (CCRS), which captures details of non-hospital institutional care; the Home Care Data (HCD) which contains home care assessments and service data; the Ontario Cancer Registry (OCR) which captures all tissue diagnoses of malignancy; and the Canadian Census which includes sociodemographic statistics. Reporting will follow relevant guidelines.18–20

Study Population We will derive two distinct cohorts of non-cardiac surgery patients. The first will include patients >65 years of age on the date of their first major, elective, non-cardiac surgery (Apr 2012-Mar 2018). These surgeries (gender neutral major orthopedic, vascular, and oncologic) will be identified using validated Canadian Classification of Intervention (CCI) codes.21 The second will include patients >65 years of age on the date of their first emergency general surgery procedure (Apr 2012-Mar 2018), which will be identified using CCI codes for a core set of EGS procedures that account for >80% of deaths and resource use in the United States.22,23

Sample Size As there is no clearly defined minimally important increase in predictive accuracy measures, our sample size considerations focused on ensuring estimated models were stable and would not be overfit. We estimated the minimum number of individuals that would be required for our models using the methods of Riley and colleagues and the related ‘pmsampsize’ package in R.24 For mortality, assuming a conservatively low R2 value of 0.1, mortality rates of 1% and 16 parameters in our model, we would require 2715 individuals. As a population-based study, we will include all eligible individuals, and previous experience with similar data suggest that we will have more than 100,000 elective participants and 50,000 emergency participants.

Exposures Frailty has been defined in electronic perioperative data using at least 22 different instruments.25 However, only a minority align with consensus frailty definitions. Therefore, our study will operationalize frailty exposure in 4 distinct ways: 1) FI,26 2) HFRS,27 3) ACG,28 and 4) RAI.29 As none of the frailty instruments were derived (i.e., weighted) in the data under study, our analyses represent external validation.

Covariates Baseline clinical and demographic characteristics will be collected including age, sex, type of surgery, and ASA score (assigned by the intraoperative anesthesiologist). To support sensitivity analyses we will also collect socioeconomic indicators, cancer diagnoses, preoperative resource use, all Elixhauser comorbidities,30 and preoperative receipt of home- or institution-based support services.

Outcomes Outcomes have been selected based on their valid availability in electronic data and relevance to patients and healthcare systems. The primary outcome will be 30-day mortality. Secondary outcomes will include days alive at home (calculated as the number of days alive within 30 or 365 days of surgery minus time in acute care hospitals (index or readmission) or institutionalized),31,32 length of hospital stay (date of discharge minus date of surgery), health system costs (using validated costing algorithms incorporating direct and indirect costs),33 and non-home discharge (hospital discharge to a non-home location or death in hospital (which is a competing risk)).

Analysis All data manipulation and analyses will be performed using SAS version 9.4 for Windows (SAS Institute, Cary NC). Descriptive statistics will be computed separately in each cohort to compare characteristics between people who did, or did not, die within 30 days of surgery. Differences will be quantified using absolute standardized differences, where a value >0.1 is considered to represent a substantive difference. Agreement between dichotomized representations of each instrument will be quantified using kappa statistics.

To compare predictive accuracy of different frailty instruments, we require a modelling framework that addresses two key considerations. First, we need to identify whether each frailty instrument adds predictive accuracy above that provided by risk factors typically assessed before surgery. While ‘typical’ preoperative variables used for risk assessment will vary, our methods draw on those of the METS study, as well as previous comparisons of clinical frailty instruments.2,34 Specifically, our baseline risk model will include age (as a restricted cubic spline), sex (binary), ASA score (categorical) and procedural risk (categorical using each CCI code). These variables will be used to estimate the ‘typical’ accuracy with which outcomes can be predicted without frailty assessment. Second, we need to compare whether a given frailty instrument adds greater accuracy than comparator instruments. Therefore, we will add each frailty instrument (separately) to the baseline model to estimate the predictive accuracy of the baseline model plus each frailty instrument.

For binary outcomes (death, non-home discharge), logistic regression will be used. The predictive accuracy measures will be: discrimination (c-statistic: whether a model assigns a greater predicted probability of outcome to people who did experience the outcome than those who did not); calibration (calibration plots and integrated calibration index (ICI): extent to which predicted risks match observed outcomes); explained variance (Nagelkerke R2: extent that the model accounts for observed outcome variation); event reclassification (continuous net reclassification index

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. INDEPTH SAGE Wave 1 (2006/7) provides data on the health and well-being of adults in: Ghana, India and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: INDEPTH SAGE's first full round of data collection included persons aged 50 years and older in the health and demographic surveillance sites. All persons aged 50+ years (for example, spouses and siblings) were invited to participate. Standardized SAGE survey instruments were used in all countries consisting of two main parts: 1) household questionnaire; 2) individual questionnaire. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content: - Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

• Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

Kassena-Nankana District of the Upper East region of Ghana.

Analysis unit

households and individuals

Universe

Navrongo Health and Demographic Surveillance Site fifty plus population

Kind of data

Sample survey data [ssd]

Sampling procedure

Single random sample of individuals 50+ years. Sampling frame obtained from demographic surveillance database. No replacement of individuals not met, not found or for refusals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. An Individual questionnaire was administered to eligible respondents identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest. All documents were translated into XX. All INDEPTH SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

A total of 900 were sampled. 593 successful respondents in data set. Response rate (593/900) is 65.9%