A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database
ImportanceThe United States (US) Medicare claims files are valuable sources of national healthcare utilization data with over 45 million beneficiaries each year. Due to their massive sizes and costs involved in obtaining the data, a method of randomly drawing a representative sample for retrospectiv...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1257163/full |
| _version_ | 1797336099866017792 |
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| author | Timothy L. McMurry Jennifer M. Lobo Soyoun Kim Soyoun Kim Hyojung Kang Min-Woong Sohn |
| author_facet | Timothy L. McMurry Jennifer M. Lobo Soyoun Kim Soyoun Kim Hyojung Kang Min-Woong Sohn |
| author_sort | Timothy L. McMurry |
| collection | DOAJ |
| description | ImportanceThe United States (US) Medicare claims files are valuable sources of national healthcare utilization data with over 45 million beneficiaries each year. Due to their massive sizes and costs involved in obtaining the data, a method of randomly drawing a representative sample for retrospective cohort studies with multi-year follow-up is not well-documented.ObjectiveTo present a method to construct longitudinal patient samples from Medicare claims files that are representative of Medicare populations each year.DesignRetrospective cohort and cross-sectional designs.ParticipantsUS Medicare beneficiaries with diabetes over a 10-year period.MethodsMedicare Master Beneficiary Summary Files were used to identify eligible patients for each year in over a 10-year period. We targeted a sample of ~900,000 patients per year. The first year's sample is stratified by county and race/ethnicity (white vs. minority), and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county population size with oversampling of minorities. Patients who were alive, did not move between counties, and stayed enrolled in Medicare fee-for-service (FFS) were retained in the sample for subsequent years. Non-retained patients (those who died or were dropped from Medicare) were replaced with a sample of patients in their first year of Medicare FFS eligibility or patients who moved into a sampled county during the previous year.ResultsThe resulting sample contains an average of 899,266 ± 408 patients each year over the 10-year study period and closely matches population demographics and chronic conditions. For all years in the sample, the weighted average sample age and the population average age differ by <0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. Rates of 21 comorbidities estimated from the samples for all 10 years were within 0.12% of the population rates. Longitudinal cohorts based on samples also closely resembled the cohorts based on populations remaining after 5- and 10-year follow-up.Conclusions and relevanceThis sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries or other national claims files for longitudinal follow-up with possible oversampling of sub-populations. |
| first_indexed | 2024-03-08T08:49:11Z |
| format | Article |
| id | doaj.art-496b9e34872c41fc8e1798840ae5e2db |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-08T08:49:11Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-496b9e34872c41fc8e1798840ae5e2db2024-02-01T12:19:28ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-02-011210.3389/fpubh.2024.12571631257163A sampling strategy for longitudinal and cross-sectional analyses using a large national claims databaseTimothy L. McMurry0Jennifer M. Lobo1Soyoun Kim2Soyoun Kim3Hyojung Kang4Min-Woong Sohn5Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United StatesDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, United StatesDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, United StatesDepartment of Social Welfare, Ewha Womans University, Seoul, Republic of KoreaDepartment of Kinesiology and Community Health, University of Illinois, Champaign, IL, United StatesDepartment of Health Management and Policy, University of Kentucky, Lexington, KY, United StatesImportanceThe United States (US) Medicare claims files are valuable sources of national healthcare utilization data with over 45 million beneficiaries each year. Due to their massive sizes and costs involved in obtaining the data, a method of randomly drawing a representative sample for retrospective cohort studies with multi-year follow-up is not well-documented.ObjectiveTo present a method to construct longitudinal patient samples from Medicare claims files that are representative of Medicare populations each year.DesignRetrospective cohort and cross-sectional designs.ParticipantsUS Medicare beneficiaries with diabetes over a 10-year period.MethodsMedicare Master Beneficiary Summary Files were used to identify eligible patients for each year in over a 10-year period. We targeted a sample of ~900,000 patients per year. The first year's sample is stratified by county and race/ethnicity (white vs. minority), and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county population size with oversampling of minorities. Patients who were alive, did not move between counties, and stayed enrolled in Medicare fee-for-service (FFS) were retained in the sample for subsequent years. Non-retained patients (those who died or were dropped from Medicare) were replaced with a sample of patients in their first year of Medicare FFS eligibility or patients who moved into a sampled county during the previous year.ResultsThe resulting sample contains an average of 899,266 ± 408 patients each year over the 10-year study period and closely matches population demographics and chronic conditions. For all years in the sample, the weighted average sample age and the population average age differ by <0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. Rates of 21 comorbidities estimated from the samples for all 10 years were within 0.12% of the population rates. Longitudinal cohorts based on samples also closely resembled the cohorts based on populations remaining after 5- and 10-year follow-up.Conclusions and relevanceThis sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries or other national claims files for longitudinal follow-up with possible oversampling of sub-populations.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1257163/fulldiabetesMedicare claimssamplelongitudinal analysiscross-sectional design |
| spellingShingle | Timothy L. McMurry Jennifer M. Lobo Soyoun Kim Soyoun Kim Hyojung Kang Min-Woong Sohn A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database Frontiers in Public Health diabetes Medicare claims sample longitudinal analysis cross-sectional design |
| title | A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database |
| title_full | A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database |
| title_fullStr | A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database |
| title_full_unstemmed | A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database |
| title_short | A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database |
| title_sort | sampling strategy for longitudinal and cross sectional analyses using a large national claims database |
| topic | diabetes Medicare claims sample longitudinal analysis cross-sectional design |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1257163/full |
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