Data Sources. Client may only upload data related to individuals that originates with or is owned by Client. Client shall not upload data purchased from third parties without Granicus’ prior written consent and list cleansing Services provided by Granicus for an additional fee. Granicus will not sell, use, or disclose any personal information provided by Client for any purpose other than performing Services subject to this Agreement.
Data Sources. Table 10 list the data items used for NAMPO’s implementation of ITHIM. Table 10 Data Sources for ITHIM Implementation by NAMPO Source Calibration Data Item Units Strata Middle TN Transportation and Health Study Per capita mean daily travel distance Miles/person/day Travel mode Per capita mean daily travel time Min/person/day Travel mode Ratio: per capita mean daily active transportation time(reference group: females aged 15–29 years) Dimensionless Walk, bike, age, sex Standard deviation of mean daily active transportation time Min/person/day None Walking speed Miles/hour None Ratio of daily per capita bicycling time to walking time Dimensionless Bicycle, walk Personal auto travel distance and time Miles and hours/day Driver, passenger Travel Demand Model Vehicle miles traveled (VMT) by facility type Miles/day Travel mode and road type4 US Census Distribution of population by age and gender % Age, sex NHANES Per capita weekly non-travel related physical activity MET-hours/week Median of quintile of walk +bicycle METs, by age and sex TN Department of Health Age-sex specific ratio of disease- specific mortality rate between Nashville metro and USA. Dimensionless Disease group5, age, sex Proportion of colon cancers from all colorectal cancers Dimensionless None TN Department of Safety Serious and fatal injuries between a striking vehicle and a victim vehicle in road traffic collisions Injuries Severity, striking mode x victim mode, road type TN Department of Environment and Conservation Emissions of PM2.5 attributable to light-duty vehicles Tons/day None Source: Xxxxxxxxx et al. (2017) The Middle Tennessee Transportation and Health Study (MTTHS) was NAMPO’s regional household travel survey conducted in 2012 (Xxx et al., 2013). The MTTHS contained questions for residents in the MPO area regarding the origins, destinations, purposes, travel modes (including walking and cycling), start time, and end time of all trips in a 24-hour period. The travel distance between a pair of origin and destination was estimated with recommend travel route on Google Maps (Xxxxxxxxx et al., 2017). Vehicle miles traveled by roadway types were obtained from the NAMPO’s travel demand models. The 2010 US Census provided data for the study area population by age and sex. Participation in non-travel related physical activities (i.e., leisure, domestic, and occupational physical activity) were obtained from the National Health and Nutrition Examination Survey 2011–2012 (CDC, 2013). The mo...
Data Sources. Table 14 shows the data items and sources for ITHIM SACOG implementation. Rather than obtaining travel behavior data from regional travel surveys like the implementations in San Francisco Bay Area and NAMPO, Xx et al. (2019) sourced travel behavior data from SACSIM15, which is an activity-based model built and calibrated to produce disaggregate travel data at the individual level (SACOG, 2015). SACSIM15 outputs for the 2016 MTP/SCS were used to estimate the average active transportation (walking and cycling) time (i.e., minutes per day) and average distance (i.e., miles per day) for each demographic group for all analysis scenarios. VMT outputs from SACSIM15 for each travel mode was estimated. Health data for all-cause mortality statistics were obtained from the California Department of Public Health (CDPH) vital records data and statistics (CDPH, 2020). Average annual all-cause mortality rates by age-sex-race/ethnicity and age-sex income level categories were calculated for each county in the SACOG region. Due to small African-American population in some counties, annual all-cause mortality rate for the Black population is only available for the entire region rather than for each county. The U.S. disease burden data for all age-sex categories were derived from the Global Burden of Disease (GBD) database (Institute for Health Metrics and Evaluation, 2017). The California Health Interview Survey (UCLA, 2012) data were used to identify characteristics of non-transport physical activities for residents of SACOG. MET-hours per week are calculated for occupational and exercise physical activity (non-travel METs) in the same way as the San Francisco Bay Area study by Xxxxxxxx et al. (2013). Table 14 Data Sources for ITHIM SACOG Implementation Source Calibration Data Item Units Stratification Sacramento Activity- Based Travel Simulation Model (SACSIM15) (SACOG, 2015) Per capita mean daily travel distance Miles/person/ day Travel mode Per capita mean daily travel time Minutes/person/ day Travel mode Ratio: per capita mean daily active transportation time Walk, bike, age, and sex Standard deviation of mean daily active transportation time Minutes/person/ day Walking speed Miles/hour Ratio of daily per capita bicycling time to walking time Personal auto travel distance and time Miles and hours/day Driver and passenger Vehicle miles traveled (VMT) by facility type Miles/day Travel mode and road type US Census Distribution of population by age and gender % Age and sex...
Data Sources. In the past, Annual Reports have used a Statistics Canada survey, the Survey of Labour and Income Dynamics (SLID), to present societal indicators in LMAPD Annual Reports (e.g., employment income levels of persons with disabilities). As announced by the Government of Canada, SLID data is being replaced by data from the Canadian National Household Income Survey. The federal government was not able to provide Survey data to provinces and territories in time for the required posting of this Report by December 3, 2014. The Ministry will make this data available once received. For reference, 2011 social indicator data is provided in the Appendix of this report. Ontario’s program data is derived from ministry and service provider databases.
Data Sources. This study used a mixed - methods design to gather quantitative and qualitative data simultaneously (Xxxxxxxx, 2012). The questionnaires used open - ended and closed-ended questions with 5 point Likert scales. By answering the questions, the extent of partnership between schools and pedagogy universities in teacher education can be revealed and recognized. In this study, the characteristics of partnership between schools and teacher education universities in teacher education were used as a theoretical framework to set up specific themes and questions for the questionnaires. Accordingly, the questionnaires focus on determining awareness and attitudes implemented by teachers, lecturers and students in practicum process. The questionnaires were discussed intensively several times with other researchers as regarding their words before they were used in practice. In this study, the data come from three main sources: • Teacher questionnaires, • Student questionnaires, • Lecturers questionnaires The questionnaires applied similar content for the questions, with an emphasis on specific, visible and measurable manifestations of the partnership between schools and teacher education universities in teacher education and attitudes about the activities of school and teacher education universities. Specifically, the questions were about: • The importance and level of the coordinating contents between the school and the pedagogy university in practicum to training teachers; • The level of implementation and awareness between the school and the teacher education university in practicum to training teachers;
Data Sources. The Committee may use whatever data sources it deems appropriate, excluding, however, anonymous surveys, provided the data it intends to use in a mid or end cycle review or summative evaluation has been reduced to writing and shared with the Superintendent at least 14 calendar days before the meeting in a timely manner. Due to the unreliability and potential prejudice of anonymous or so-called “360” evaluations, these instruments shall not be solicited or utilized as part of the evaluation procedure.
Data Sources. 1.1 This Agreement covers the following types and sources of data, which may be accessible to the Participant through the Shared Electronic Record:
Data Sources. County-Level Client and Service Data: California requires county mental health programs to report monthly client-level service utilization data to the Client & Service Information (CSI) system, a statewide database maintained by California’s Department of Health Care Services (DHCS). Counties have reported detailed information about client-level utilization of county-funded mental health services such as demographics, the types of services used, admission details, detailed service utilization, and client disposition since 2007. We determined two limitations with using data directly from the CSI system. The first limitation is that counties do not submit SB-82-specific information to the CSI system. This client-level information remains with counties in their respective EHR systems. Second, through correspondence with the counties and experts familiar with CSI we have learned that data in the CSI system varies in quality over time and between counties. To overcome these limitations, we plan to collect the same long-term data directly from counties as data can be exported directly from the county mental health EHR. We will use the structure and data elements contained in the CSI system as a baseline for the datasets we will request from counties. We will request a dataset from the counties containing client-level service data that includes client sociodemographic information such as age, race, ethnicity, education, and employment status. For each client, we will also have a record of service utilization that includes the date of service, the mode of service, service function, units of time for each service, the client’s disposition at the end of service, whether a client was admitted voluntarily, the admission necessity code, place of service, and whether services a client received meets definition of an evidence-based service according to the Substance Abuse and Mental Health Services Administration (SAMHSA). Additionally, we will have information about the client such as whether they have experienced trauma and the type, whether the client has a substance dependence, and the results of an Axis-V Global Assessment of Functioning (GAF) test if the client was assessed. See Appendix 6 for a table of variables and their descriptions. Hence, this dataset will provide a rich set of client- level information and service utilization history for the evaluation team to analyze. Collaboration with the counties to ensure collection of this data is consistent across cou...
Data Sources. The SaaS Services is an integration platform that will aggregate data from various third- party sources to provide the Customer with one user interface to analyze their data. Customer shall obtain any and all licenses, access rights, API Keys, or other such permissions, as needed, from such third parties necessary to provide the SaaS Services. Customer acknowledges and agrees that any such third-party data sources and/or services/solutions are being provided to you by third parties and not by Topcon. Customer further acknowledges and agrees that its access or use of any such data sources and/or services/solutions are not governed by this Agreement but instead are governed by such third-party suppliers’ terms of use or other like agreement. XXXXXX MAKES NO REPRESENTATIONS OR WARRANTIES CONCERNING THE THIRD-PARTY DATA SOURCE OF ANY SORT. TOPCON CANNOT AND SHALL NOT BE RESPONSIBLE FOR, AND EXPRESSLY DISCLAIMS ANY LIABILITY, OF ANY KIND, IN CONNECTION WITH CUSTOMER’S ACCESS TO ANY THIRD-PARTY DATA SOURCES, OR THEREOF, INCLUDING, BUT NOT LIMITED TO CUSTOMER’S ACCESS AND USE OF ITS SUCH DATA SOURCES IN CONNECTION WITH THE SAAS SERVICES.
Data Sources. The major data sources used in this study are the GCCR and Georgia Medicaid enrollment and claims. GCCR is a statewide population-based cancer registry of all incident cancer cases diagnosed in any Georgia county since 1999. It provides data on demographics and disease status for each case. Medicaid enrollment files provide monthly enrollment records of all linked cancer beneficiaries. Medicaid claims files provide data from diagnosis fields coded by the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) on claims as the patient is actually enrolled in Medicaid and received the treatment. By combining these three files together, we were able to identify not only the time gap between the cancer diagnosis and Medicaid enrollment, but also a measure of the possible cancer stage progression over this time period. We also merged county data from the Area Resource File, County Business File and Georgia Department of Community Health to control county factors that could affect individual ability to enroll in Medicaid.
