Using MEPS Data for Trend Analysis Sample Clauses

Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2004-05), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.
Sample 1Sample 2Sample 3See All (45)
AutoNDA by SimpleDocs
Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The adjustment to the weight described in 3.2.3 above based on inpatient discharges potentially could affect some analyses of trends. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in early 2014 at the start of the final rounds of data collection for 2013. This effort likely resulted in improved data quality and a reduction in underreporting in 2013, but could have some modest impact on analyses involving trends in utilization across years. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2012-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.
Sample 1Sample 2Sample 3See All (25)
Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, beginning with the 2007 data, the rules MEPS uses to identify outlier prices for prescription medications became much less stringent than in prior years. Starting with the 2007 Prescribed Medicines file, there was: less editing of prices and quantities reported by pharmacies, more variation in prices for generics, lower mean prices for generics, higher mean prices for brand name drugs, greater differences in prices between generic and brand name drugs, and a somewhat lower proportion of spending on drugs by families, as opposed to third-party payers. Starting with the 2008 Prescribed Medicines file, improvements in the data editing changed the distribution of payments by source: (1) more spending on Medicare beneficiaries is by private insurance, rather than Medicare, and (2) less out-of-pocket payments and more Medicaid payments among Medicaid enrollees. Starting with the 2009 data, additional improvements increased public program amounts and reduced out-of-pocket payments and, for Medicare beneficiaries with both Part D and Medicaid, decreased Medicare payments and increased Medicaid and other state and local government payments. Therefore, users should be cautious in the types of comparisons they make about prescription drug spending before and after 2007, 2008, and 2009. In addition, some therapeutic class codes have changed over time. In 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in 2014. This effort likely improved data quality and reduced underreporting in 2014 and could modestly affect analyses involving trends in utilization across years. There are also statistical factors to cons...
Sample 1See All (7)
Using MEPS Data for Trend Analysis. MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, there are a variety of methodological and statistical considerations when examining trends over time using MEPS. Tests of statistical significance should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. In 2013 MEPS survey operations introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in 2014. This effort resulted in improved data quality and a reduction in underreporting in the second half of 2013 and throughout 2014. Respondents tended to report more visits, especially non-physician visits, by sample members and the new approach appeared particularly effective among those subgroups with relatively large numbers of visits, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. Reported spending on visits also tended to increase, especially for such subgroups. The aforementioned change in the NHIS sample design in 2016 could also potentially affect trend analyses. The new NHIS sample design is based on more up-to-date information related to the distribution of housing units across the U.S. As a result, it can be expected to better cover the full U.S. civilian, noninstitutionalized population, the target population for MEPS, as well as many of its subpopulations. Better coverage of the target population helps to reduce the potential for bias in both NHIS and MEPS estimates. Another change with the potential to affect trend analyses involved major modifications to the MEPS instrument design and data collection process, particularly in the events sections of the instrument. These were introduced in the Spring of 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Since the Full Year 2017 PUFs were established from data collected in Rounds 1-3 of Panel 22 and Rounds 3-5 of Panel 21, they reflected two different in...
Sample 1See All (6)

Related to Using MEPS Data for Trend Analysis

  • Data Analysis In the meeting, the analysis that has led the College President to conclude that a reduction- in-force in the FSA at that College may be necessary will be shared. The analysis will include but is not limited to the following: ● Relationship of the FSA to the mission, vision, values, and strategic plan of the College and district ● External requirement for the services provided by the FSA such as accreditation or intergovernmental agreements ● Annual instructional load (as applicable) ● Percentage of annual instructional load taught by Residential Faculty (as applicable) ● Fall 45th-day FTSE inclusive of dual enrollment ● Number of Residential Faculty teaching/working in the FSA ● Number of Residential Faculty whose primary FSA is the FSA being analyzed ● Revenue trends over five years for the FSA including but not limited to tuition and fees ● Expenditure trends over five years for the FSA including but not limited to personnel and capital ● Account balances for any fees accounts within the FSA ● Cost/benefit analysis of reducing all non-Residential Faculty plus one Residential Faculty within the FSA ● An explanation of the problem that reducing the number of faculty in the FSA would solve ● The list of potential Residential Faculty that are at risk of layoff as determined by the Vice Chancellor of Human Resources ● Other relevant information, as requested

  • Loop Testing/Trouble Reporting 2.1.6.1 NOW will be responsible for testing and isolating troubles on the Loops. NOW must test and isolate trouble to the BellSouth portion of a designed/non-designed unbundled Loop (e.g., UVL-SL2, UCL-D, UVL-SL1, UCL-ND, etc.) before reporting repair to the UNE Customer Wholesale Interconnection Network Services (CWINS) Center. Upon request from BellSouth at the time of the trouble report, NOW will be required to provide the results of the NOW tests which indicate a problem on the BellSouth provided Loop.

  • DATA COLLECTION AND ANALYSIS The goal of this task is to collect operational data from the project, to analyze that data for economic and environmental impacts, and to include the data and analysis in the Final Report. The Recipient shall: • Develop a data collection plan. • Troubleshoot any issues identified. • Collect at least six months of data, including: o Throughput, usage, and operations data o Normal operating hours, up time, down time, and explanations of variations o Feedstock supply summary o Maximum capacity of the new fuel production system in diesel gallon equivalents (DGE) and ordinary units o Gallons of gasoline and/or diesel fuel displaced (with associated mileage information), along with value converted into DGE o Record of wastes from production processes (wastewater, solid waste, criteria emissions, etc.) o Expected air emissions reduction, for example:  Non-methane hydrocarbons  Oxides of nitrogen  Non-methane hydrocarbons plus oxides of nitrogen  Particulate Matter  Formaldehyde o Duty cycle of the current fleet and the expected duty cycle of future vehicle acquisitions, if applicable o Specific jobs and economic development resulting from this project o Levelized cost of fuel and finished fuel price o Analysis of total facility costs, operation and maintenance costs, marginal abatement costs • Comply with the Petroleum Industry Information Reporting Act (PIIRA) and complete CEC Form M810E and CEC Form M13 on a monthly basis for submission to the California Energy Commission’s PIIRA Data Collection Unit. • Provide a written record of registering with the Low Carbon Fuel Standard and Renewable Fuel Standard programs. • Identify any current and planned use of renewable energy at the facility. • Describe any energy efficiency measures used in the facility that may exceed Title 24 standards in Part 6 of the California Code Regulations. • Provide data on potential job creation, economic development, and increased state revenue as a result of expected future expansion. • Provide a quantified estimate of the project’s carbon intensity values or provide an Air Resources Board approved pathway carbon intensity. • Estimate annual life-cycle greenhouse gas emission reduction. • Compare any project performance and expectations provided in the proposal to Energy Commission with actual project performance and accomplishments. • Collect data, information, and analysis described above and include in the Final Report.

  • Statistical Sampling Documentation a. A copy of the printout of the random numbers generated by the “Random Numbers” function of the statistical sampling software used by the IRO.

Time is Money Join Law Insider Premium to draft better contracts faster.