Publications

@article{nokey,

title = { The T-MSIS Analytic Files (TAF) Analysis Reporting Checklist},

author = {William L. Schpero and John McConnell and Greta Bushnell and Alina Denham and Patience M. Dow and Shashi N. Kapadia and Stephan R. Lindner and Hillary Samples and Lindsay Shea and Kelsey Watson and Sarah H. Gordon},

doi = {10.1001/jamahealthforum.2025.3622},

year  = {2025},

date = {2025-10-24},

urldate = {2025-10-24},

journal = {JAMA Health Forum },

volume = {6},

issue = {10},

abstract = {Importance: Medicaid is the single largest source of health care coverage in the US, but health policy research on the Medicaid program has historically lagged research on Medicare due to limited availability of high-quality administrative claims data across states. In 2019, the US Centers for Medicare & Medicaid Services released the T-MSIS Analytic Files (TAF), a new-generation federal Medicaid claims dataset that has catalyzed policy-relevant research on the Medicaid program. TAF data are highly complex, however, with meaningful differences in quality across states, years, and data elements. There is an urgent need for standardized reporting guidelines to ensure TAF-based research is high quality and reproducible.



Objective: To develop a checklist to guide reporting of research using the TAF data.



Evidence review: The development of the TAF Analysis Reporting Checklist was led by a subcommittee of the Medicaid Data Learning Network (MDLN), a national consortium of research teams focused on developing and disseminating best practices for conducting health services research with the TAF data. The subcommittee first created a draft checklist drawing from published technical guidance on proper use of the TAF data, as well as published analyses of TAF data quality. This draft was iteratively refined based on feedback from (1) MDLN members; (2) the MDLN Advisory Group, composed of leaders in academia, government, and industry with Medicaid claims experience; (3) editors of health policy journals; and (4) the broader Medicaid research community.



Findings: The final checklist includes 4 categories of items that are recommended for reporting in studies using the TAF data. This includes (1) details on the specific data used (files, years, release versions, and size of the data extract), (2) how the analytic sample was defined (eligibility criteria, enrollment span, and scope of benefits), (3) what states and/or territories were excluded from the analysis based on data quality concerns (and the exclusion criteria used to do so), and (4) additional information on special considerations, including use of spending data and changes in data quality over time.



Conclusions and relevance: The TAF Analysis Reporting Checklist represents a consensus effort to identify items researchers should report to promote transparency and reproducibility in TAF-based studies. This reporting is a key step in safeguarding the quality of research used to inform Medicaid policy.},

keywords = {Drivers of Health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = { Social Determinants of Health and Geographic Variation in Medicare per Beneficiary Spending},

author = {Yongkang Zhang and Jing Li and Jiani Yu and Robert Tyler Braun and Lawrence P. Casalino},

doi = {https://doi.org/10.1001/jamanetworkopen.2021.13212},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-01},

journal = {JAMA Network Open},

volume = {4},

issue = {6},

abstract = {Importance: Despite substantial geographic variation in Medicare per beneficiary spending in the US, little is known about the extent to which social determinants of health (SDoH) are associated with this variation.



Objective: To determine the associations between SDoH and county-level price-adjusted Medicare per beneficiary spending.



Design, setting, and participants: This cross-sectional study used county-level data on 2017 Medicare fee-for-service (FFS) spending, patient demographic characteristics (eg, age and gender) and clinical risk score, supply of health care resources (eg, number of hospital beds), and SDoH measures (eg, median income and unemployment rate) from multiple sources. Multivariable regressions were used to estimate the association of the variation in spending across quintiles with SDoH.



Main outcomes and measures: 2017 county-level price-adjusted Medicare Parts A and B spending per beneficiary. SDoH measures included socioeconomic position, race/ethnicity, social relationships, and residential and community context.



Results: Among 3038 counties with 33 495 776 Medicare FFS beneficiaries (18 352 336 [54.8%] women; mean [SD] age, 72 [1.5] years), mean Medicare price-adjusted per beneficiary spending for counties in the highest spending quintile was $3785 (95% CI, $3706-$3862) higher, or 49% higher, than spending for bottom-quintile counties (mean [SD] spending per beneficiary, $11 464 [735] vs $7679 [522]; P < .001). The total contribution (including through both direct and indirect pathways) of SDoH was 37.7% ($1428 of $3785) of this variation, compared with 59.8% ($2265 of $3785) by patient clinical risk, 14.5% ($549 of $3785) by supply of health care resources, and 19.8% ($751 of $3785) by patient demographic characteristics. When all factors were included within the same model, the direct contribution of SDoH was associated with 5.8% of the variation, compared with 4.6% by supply, 4.7% by patient demographic characteristics, and 62.0% by patient clinical risk.



Conclusions and relevance: These findings suggest social determinants of health are associated with considerable proportions of geographic variation in Medicare spending. Policies addressing SDoH for disadvantaged patients in certain regions have the potential to contain health care spending and improve the value of health care; patient SDoH may need to be accounted for in publicly reported physician performance, and in value-based purchasing incentive programs for health care professionals.},

keywords = {Drivers of Health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Association Between Patient Social Risk and Physician Performance Scores in the First Year of the Merit-based Incentive Payment System},

author = {Dhruv Khullar and William L. Schpero and Amelia M. Bond and Yuting Qian and Lawrence P. Casalino},

doi = {https://doi.org/10.1001/jama.2020.13129},

year  = {2020},

date = {2020-09-08},

urldate = {2020-09-08},

journal = {JAMA},

volume = {324},

issue = {10},

pages = {975-983},

abstract = {Importance: The US Merit-based Incentive Payment System (MIPS) is a major Medicare value-based payment program aimed at improving quality and reducing costs. Little is known about how physicians' performance varies by social risk of their patients.



Objective: To determine the relationship between patient social risk and physicians' scores in the first year of MIPS.



Design, setting, and participants: Cross-sectional study of physicians participating in MIPS in 2017.



Exposures: Physicians in the highest quintile of proportion of dually eligible patients served; physicians in the 3 middle quintiles; and physicians in the lowest quintile.



Main outcomes and measures: The primary outcome was the 2017 composite MIPS score (range, 0-100; higher scores indicate better performance). Payment rates were adjusted -4% to 4% based on scores.



Results: The final sample included 284 544 physicians (76.1% men, 60.1% with ≥20 years in practice, 11.9% in rural location, 26.8% hospital-based, and 24.6% in primary care). The mean composite MIPS score was 73.3. Physicians in the highest risk quintile cared for 52.0% of dually eligible patients; those in the 3 middle risk quintiles, 21.8%; and those in the lowest risk quintile, 6.6%. After adjusting for medical complexity, the mean MIPS score for physicians in the highest risk quintile (64.7) was lower relative to scores for physicians in the middle 3 (75.4) and lowest (75.9) risk quintiles (difference for highest vs middle 3, -10.7 [95% CI, -11.0 to -10.4]; highest vs lowest, -11.2 [95% CI, -11.6 to -10.8]; P < .001). This relationship was found across specialties except psychiatry. Compared with physicians in the lowest risk quintile, physicians in the highest risk quintile were more likely to work in rural areas (12.7% vs 6.4%; difference, 6.3 percentage points [95% CI, 6.0 to 6.7]; P < .001) but less likely to care for more than 1000 Medicare beneficiaries (9.4% vs 17.8%; difference, -8.3 percentage points [95% CI, -8.7 to -8.0]; P < .001) or to have more than 20 years in practice (56.7% vs 70.6%; difference, -13.9 percentage points [95% CI, -14.4 to -13.3]; P < .001). For physicians in the highest risk quintile, several characteristics were associated with higher MIPS scores, including practicing in a larger group (mean score, 82.4 for more than 50 physicians vs 46.1 for 1-5 physicians; difference, 36.2 [95% CI, 35.3 to 37.2]; P < .001) and reporting through an alternative payment model (mean score, 79.5 for alternative payment model vs 59.9 for reporting as individual; difference, 19.7 [95% CI, 18.9 to 20.4]; P < .001).



Conclusions and relevance: In this cross-sectional analysis of physicians who participated in the first year of the Medicare MIPS program, physicians with the highest proportion of patients dually eligible for Medicare and Medicaid had significantly lower MIPS scores compared with other physicians. Further research is needed to understand the reasons underlying the differences in physician MIPS scores by levels of patient social risk.},

keywords = {Drivers of Health, Payment Reform and Health Care Incentives},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = { Nursing Home Characteristics Associated With COVID-19 Deaths in Connecticut, New Jersey, and New York},

author = {Mark Aaron Unruh and Hyunkyung Yun and Yongkang Zhang and Hye-Young Jung and Robert Tyler Braun},

doi = {https://doi.org/10.1016/j.jamda.2020.06.019},

year  = {2020},

date = {2020-06-15},

urldate = {2020-06-15},

journal = {JAMDA},

volume = {21},

issue = {7},

pages = {1001-1003},

keywords = {Drivers of Health},

pubstate = {published},

tppubtype = {article}

}