@article {11216, title = {Identifying cohabiting couples in administrative data: evidence from Medicare address data}, journal = {Health Services and Outcomes Research Methodology}, volume = {21}, year = {2021}, pages = {238-247}, abstract = {Marital status is recognized as an important social determinant of health, income, and social support, but is rarely available in administrative data. We assessed the feasibility of using exact address data and zip code history to identify cohabiting couples using the 2018 Medicare Vital Status file and ZIP codes in the 2011{\textendash}2014 Master Beneficiary Summary Files. Medicare beneficiaries meeting our algorithm displayed characteristics consistent with assortative mating and resembled known married couples in the Health and Retirement Study linked to Medicare claims. Address information represents a promising strategy for identifying cohabiting couples in administrative data including healthcare claims and other data types.}, keywords = {Cohabitation, Couples, Marriage, Medicare}, isbn = {1572-9400}, doi = {10.1007/s10742-020-00229-1}, author = {Matta, Sasmira and Joanne W. Hsu and Theodore J Iwashyna and Micah Y. Baum and Kenneth M. Langa and Lauren Hersch Nicholas} } @article {8820, title = {The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study.}, journal = {J Gen Intern Med}, volume = {32}, year = {2017}, month = {2017 01}, pages = {71-80}, abstract = {

BACKGROUND: Readmission rates after pneumonia, heart failure, and acute myocardial infarction hospitalizations are risk-adjusted for age, gender, and medical comorbidities and used to penalize hospitals.

OBJECTIVE: To assess the impact of disability and social determinants of health on condition-specific readmissions beyond current risk adjustment.

DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of Medicare patients using 1) linked Health and Retirement Study-Medicare claims data (HRS-CMS) and 2) Healthcare Cost and Utilization Project State Inpatient Databases (Florida, Washington) linked with ZIP Code-level measures from the Census American Community Survey (ACS-HCUP). Multilevel logistic regression models assessed the impact of disability and selected social determinants of health on readmission beyond current risk adjustment.

MAIN MEASURES: Outcomes measured were readmissions <=30~days after hospitalizations for pneumonia, heart failure, or acute myocardial infarction. HRS-CMS models included disability measures (activities of daily living [ADL] limitations, cognitive impairment, nursing home residence, home healthcare use) and social determinants of health (spouse, children, wealth, Medicaid, race). ACS-HCUP model measures were ZIP Code-percentage of residents >=65~years of age with ADL difficulty, spouse, income, Medicaid, and patient-level and hospital-level race.

KEY RESULTS: For pneumonia, >=3 ADL difficulties (OR 1.61, CI 1.079-2.391) and prior home healthcare needs (OR 1.68, CI 1.204-2.355) increased readmission in HRS-CMS models (N = 1631); ADL difficulties (OR 1.20, CI 1.063-1.352) and {\textquoteright}other{\textquoteright} race (OR 1.14, CI 1.001-1.301) increased readmission in ACS-HCUP models (N = 27,297). For heart failure, children (OR 0.66, CI 0.437-0.984) and wealth (OR 0.53, CI 0.349-0.787) lowered readmission in HRS-CMS models (N = 2068), while black (OR 1.17, CI 1.056-1.292) and {\textquoteright}other{\textquoteright} race (OR 1.14, CI 1.036-1.260) increased readmission in ACS-HCUP models (N = 37,612). For acute myocardial infarction, nursing home status (OR 4.04, CI 1.212-13.440) increased readmission in HRS-CMS models (N = 833); {\textquoteright}other{\textquoteright} patient-level race (OR 1.18, CI 1.012-1.385) and hospital-level race (OR 1.06, CI 1.001-1.125) increased readmission in ACS-HCUP models (N = 17,496).

CONCLUSIONS: Disability and social determinants of health influence readmission risk when added to the current Medicare risk adjustment models, but the effect varies by condition.

}, keywords = {Activities of Daily Living, Cognitive Dysfunction, Comorbidity, Disability Evaluation, Female, Heart Failure, Humans, Logistic Models, Male, Myocardial Infarction, Patient Readmission, Pneumonia, Retrospective Studies, Risk Adjustment, Social determinants of health}, issn = {1525-1497}, doi = {10.1007/s11606-016-3869-x}, url = {http://link.springer.com/10.1007/s11606-016-3869-xhttp://link.springer.com/content/pdf/10.1007/s11606-016-3869-x.pdfhttp://link.springer.com/content/pdf/10.1007/s11606-016-3869-x.pdfhttp://link.springer.com/article/10.1007/s11606-016-3869-x/fulltext.html}, author = {Meddings, Jennifer and Reichert, Heidi and Shawna N Smith and Theodore J Iwashyna and Kenneth M. Langa and Timothy P Hofer and Laurence F McMahon} } @article {9248, title = {Individual and health system variation in rehospitalizations the year after pneumonia.}, journal = {Medicine (Baltimore)}, volume = {96}, year = {2017}, month = {2017 Aug}, pages = {e7695}, abstract = {Little is known about variation in patterns of recovery among patients discharged alive from hospitalizations for pneumonia.The aim of the is observational cohort study was to characterize the variation in patterns of hospital readmission and survival in the year after discharge for pneumonia in 3 different health systems.The 3 cohorts consisted of (1) the Health and Retirement Study participants enrolled in Fee-for-service Medicare (FFS), (2) Veterans Administration (VA) Healthcare system, and (3) Kaiser Permanente of Northern California (KPNC). The 365-day survival and re-hospitalizations were determined for each cohort. Multinomial logistic regression was used to identify potential contributors to the different patterns.We identified 2731, 23,536, and 39,147 hospitalizations for pneumonia in FFS Medicare, VA, and KPNC, respectively, of whom 88.1\%, 92.8\%, and 89.7\% survived to hospital discharge. The median patient survived to 1 year and was rehospitalized twice in FFS (9.0\%), once in VA (14.1\%) and KPNC (9.1\%). Of the patients who survived the hospitalization, 33.3\% (FFS), 30.2\% (VA), and 26.8\% (KPNC) died during the subsequent year. Of those who survived, 29.8\% (FFS), 35.9\% (VA), and 46.1\% (KPNC) were never rehospitalized. 11.9\% (FFS), 11.9\% (VA), and 11.7\% (KPNC) had greater than 3 hospitalizations. Age, race, gender, comorbidity, ICU use, and hospital length stay collectively explained little (5-7\%) of the variation in the recovery pattern.There is significant variation in the year after the hospitalization for pneumonia across individuals, but less so across health systems. There may be important opportunities to better classify these heterogeneous individual-level pathways.}, keywords = {Hospitalization}, issn = {1536-5964}, doi = {10.1097/MD.0000000000007695}, author = {Elizabeth M Viglianti and Hallie C Prescott and Liu, Vincent and Gabriel J. Escobar and Theodore J Iwashyna} } @article {8115, title = {Increased 1-year healthcare use in survivors of severe sepsis.}, journal = {Am J Respir Crit Care Med}, volume = {190}, year = {2014}, note = {Times Cited: 1}, month = {2014 Jul 01}, pages = {62-9}, publisher = {190}, abstract = {

RATIONALE: Hospitalizations for severe sepsis are common, and a growing number of patients survive to hospital discharge. Nonetheless, little is known about survivors{\textquoteright} post-discharge healthcare use.

OBJECTIVES: To measure inpatient healthcare use of severe sepsis survivors compared with patients{\textquoteright} own presepsis resource use and the resource use of survivors of otherwise similar nonsepsis hospitalizations.

METHODS: This is an observational cohort study of survivors of severe sepsis and nonsepsis hospitalizations identified from participants in the Health and Retirement Study with linked Medicare claims, 1998-2005. We matched severe sepsis and nonsepsis hospitalizations by demographics, comorbidity burden, premorbid disability, hospitalization length, and intensive care use.

MEASUREMENTS AND MAIN RESULTS: Using Medicare claims, we measured patients{\textquoteright} use of inpatient facilities (hospitals, long-term acute care hospitals, and skilled nursing facilities) in the 2 years surrounding hospitalization. Severe sepsis survivors spent more days (median, 16 [interquartile range, 3-45] vs. 7 [0-29]; P < 0.001) and a higher proportion of days alive (median, 9.6\% [interquartile range, 1.4-33.8\%] vs. 1.9\% [0.0-7.9\%]; P < 0.001) admitted to facilities in the year after hospitalization, compared with the year prior. The increase in facility-days was similar for nonsepsis hospitalizations. However, the severe sepsis cohort experienced greater post-discharge mortality (44.2\% [95\% confidence interval, 41.3-47.2\%] vs. 31.4\% [95\% confidence interval, 28.6-34.2\%] at 1 year), a steeper decline in days spent at home (difference-in-differences, -38.6 d [95\% confidence interval, -50.9 to 26.3]; P < 0.001), and a greater increase in the proportion of days alive spent in a facility (difference-in-differences, 5.4\% [95\% confidence interval, 2.8-8.1\%]; P < 0.001).

CONCLUSIONS: Healthcare use is markedly elevated after severe sepsis, and post-discharge management may be an opportunity to reduce resource use.

}, keywords = {Aged, Female, Health Facilities, Humans, Insurance Claim Review, Long-term Care, Male, Medical Record Linkage, Medicare, Mortality, Outcome Assessment, Health Care, Patient Readmission, Prospective Studies, Sepsis, Skilled Nursing Facilities, Survivors, United States}, issn = {1535-4970}, doi = {10.1164/rccm.201403-0471OC}, author = {Hallie C Prescott and Kenneth M. Langa and Liu, Vincent and Gabriel J. Escobar and Theodore J Iwashyna} }