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.
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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 (26)
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. Specifically, beginning with the 2007 data, the rules used to identify outlier prices for prescription medications became much less stringent than in prior years. Starting with the 2007 Prescribed Medicines file, there is 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 is 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. 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 t...
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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. Examining changes over longer periods of time can provide a more complete picture of underlying trends. 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 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. The impacts of these efforts are important to consider. Respondents reported more visits, especially non-physician visits, by sample members and the increase in the number of reported visits was especially large at the tail of the distribution. Consequently, there is a break in trend among people who are more likely to be in that tail, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. In turn, spending on visits also increased, especially in the tail and for these subgroups. These increases in service use and expenditures were not uniform throughout the country, and respondents in the West South Central Census Division reported less increase in use, especially for non-minority sample members. Data users comparing service use and expenditures across states, regions, or racial and ethnic groups, particularly before and after 2013, may take this lack of uniformity into account by working in data centers, which provide access to restricted data files containing the Census Division variable. See: xxxx.xxxx.xxx/xxxx_xxxxx/xxxxxx_xxxxxxxxxx.xxx Changes to the MEPS survey instrument should also be considered when analyzing trends. For example, users should refer to the documentation for the prescription drug file (HC-168A) when analyzing prescription drug spending before and after 2010 and 2011. Similarly, as a result of improved methods for collecting priority conditions data implemented in 2007, prevalence measures prior to 2007 are not comparable to those...
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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

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  • 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.

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