Common use of Using MEPS Data for Trend Analysis Clause in Contracts

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 from 2007 and beyond for many of these conditions. Users should refer to the documentation for the conditions file (HC-170) for details. Data users should review relevant sections of the documentation for descriptions of these types of changes before undertaking trend analyses. Analysts may also wish to consider using statistical techniques to 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, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, 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. References

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 Respondents tended to consider. Respondents reported report more visits, especially non-physician visits, by sample members and the increase in the number new approach appeared particularly effective among those subgroups with relatively large numbers 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 tailvisits, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. In turn, Reported 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 usetended to increase, especially for non-minority sample memberssuch subgroups. 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 from 2007 and beyond for many of these conditions. Users should refer to the documentation for the conditions file (HC-170) for details. Data users should review relevant sections of the documentation for descriptions of these types of changes before undertaking trend analyses. Analysts may also wish to consider using statistical techniques to smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-13), 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, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, 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. References.

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 Respondents tended to consider. Respondents reported report more visits, especially non-physician visits, by sample members and the increase in the number new approach appeared particularly effective among those subgroups with relatively large numbers 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 tailvisits, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. In turn, Reported 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 usetended to increase, especially for non-minority sample memberssuch subgroups. 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 from 2007 and beyond for many of these conditions. Users should refer to the documentation for the conditions file (HC-170) for details. Data users should review relevant sections of the documentation for descriptions of these types of changes before undertaking trend analyses. Analysts may also wish to consider using statistical techniques to smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), 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, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, 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. References.

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 Tests of statistical significance should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The length of time can provide a more complete picture of underlying trendsbeing 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. The impacts of these efforts are important Respondents tended to consider. Respondents reported report more visits, especially non-physician visits, by sample members and the increase in the number new approach appeared particularly effective among those subgroups with relatively large numbers 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 tailvisits, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. In turn, Reported spending on visits also increasedtended to increase, especially for such subgroups. The aforementioned change in the tail 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 for these subgroupsMEPS 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 increases 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 instrument designs. In order to mitigate the effect of such differences within the same full year file, the Panel 22, Round 3 data and the Panel 21 Round 5 data were transformed to make them as consistent as possible with data collected under the previous design. The changes in the instrument were designed to make the data collection effort more efficient and easy to administer. In addition, expectations were that data on some items, such as those related to health care events, would be more complete with the potential of identifying more events. Increases in service use and expenditures were not uniform throughout reported since 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 from 2007 and beyond for many implementation of these conditionschanges are consistent with these expectations. Users should refer There are also statistical factors to the documentation for the conditions file (HC-170) for details. Data users should review relevant sections of the documentation for descriptions of these types of changes before undertaking consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may also wish to consider using statistical techniques to smooth evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), working with moving averages averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, 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. References.

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 from 2007 and beyond for many of these conditions. Users should refer to the documentation for the conditions file (HC-170) for details. Data users should review relevant sections of the documentation for descriptions of these types of changes before undertaking trend analyses. Analysts may also wish to consider using statistical techniques to 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, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, 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. References.

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 considerconsider when assessing trends. Respondents For example, respondents reported more visits, especially non-physician visits, by sample members and the members. This increase in the number of reported visits was especially large at the tail for those that tend to have relatively large numbers of the distribution. Consequently, there is a break in trend among people who are more likely to be in that tail, visits such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. In turnThis had a corresponding impact on expenditures, spending on visits also increased, especially in the tail and for these particularly among such subgroups. These increases Thus, the interpretation of trends in service use both visits 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 membershas been affected. 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-168AHC-178A) 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 from 2007 and beyond for many of these conditions. Users should refer to the documentation for the conditions file (HC-170HC-180) for details. Data users should review relevant sections of the documentation for descriptions of these types of changes before undertaking trend analyses. Analysts may also wish to consider using statistical techniques to smooth or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 20122011-1312), 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, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, 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. References