Expiration of State Licensure Waivers and Out-of-State Telemedicine Relationships.

This cross-sectional study compares the use of telemedicine in states where COVID-19 pandemic­related licensure waivers expired vs states where waivers continued.

The health effects of nursing home specialization in post-acute care.

Nursing homes serve both long-term care and post-acute care (PAC) patients, two groups with distinct financing mechanisms and requirements for care. We examine empirically the effect of nursing home specialization in PAC using 2011-2018 data for Medicare patients admitted to nursing homes following a hospital stay. To address patient selection into specialized nursing homes, we use an instrumental variables approach that exploits variation over time in the distance from the patient's residential ZIP code to the closest nursing home with different levels of PAC specialization. We find that patients admitted to nursing homes more specialized in PAC have lower hospital readmissions and mortality, longer nursing home stays, and higher Medicare spending for the episode of care, suggesting that specialization improves patient outcomes but at higher costs.

Pneumonia is not just pneumonia: Differences in utilization and costs with common comorbidities.

We sought to explore the heterogeneity among patients hospitalized with pneumonia, a condition targeted in payment reform. In a retrospective cohort study of Medicare beneficiaries hospitalized for pneumonia, we compared postacute care utilization and costs of 90-day episodes of care among patients with and without comorbidities of chronic obstructive pulmonary disease (COPD) and/or heart failure. Of the 1,926,674 discharges, 28.1% had COPD, 14.3% had heart failure, and 14.6% carried both diagnoses. Patients with pneumonia were more likely to be discharged to a facility than those with pneumonia and COPD with or without heart failure, though less likely than those with pneumonia and heart failure only. Compared to patients with pneumonia only, patients with COPD and/or heart failure had higher episode payments. Acute conditions such as pneumonia may hold promise for episode-based care payment reform; however, the heterogeneity within this diagnosis indicates the need to consider other patient characteristics in interventions to improve value-based care.

Training Health Policy Researchers on Policy Engagement and Research Translation for Greater Impact: Evaluation of the Amplify@LDI Program.

BACKGROUND: Few researchers receive formal training in research translation and dissemination or policy engagement. We created Amplify@LDI, a training program for health services and health policy researchers, to equip them with skills to increase the visibility of their research through translation and dissemination activities. AIMS: To describe the program's participants and curriculum, and evaluate the first 2 years of the program. SETTING: The Leonard Davis Institute (LDI) at the University of Pennsylvania (Penn). PARTICIPANTS: An annual cohort of 12 LDI Senior Fellows (Penn faculty) from multiple schools, disciplines, and ranks at Penn. PROGRAM DESCRIPTION: The Amplify@LDI curriculum includes 6 sessions on different aspects of research translation and dissemination, including media and social engagement, writing Op-Eds, and policy engagement. PROGRAM EVALUATION: Participants reported measurable increases in time spent on translation and dissemination activities, as well as new enthusiasm for and confidence in policy engagement. Participants' reach (as measured by Altmetric) increased during the program, compared to smaller increases or reductions in reach for two comparator groups. DISCUSSION: In our preliminary evaluation of Amplify@LDI, we find strong evidence of positive impact from participant evaluations, and suggestive evidence that participation in the program is associated with significant increases in the reach of their research.

Variation in Risk-Standardized Acute Admission Rates Among Patients With Heart Failure in Accountable Care Organizations: Implications for Quality Measurement.

Background Accountable care organizations (ACOs) aim to improve health care quality and reduce costs, including among patients with heart failure (HF). However, variation across ACOs in admission rates for patients with HF and associated factors are not well described. Methods and Results We identified Medicare fee-for-service beneficiaries with HF who were assigned to a Medicare Shared Savings Program ACO in 2017 and survived ≥30 days into 2018. We calculated risk-standardized acute admission rates across ACOs, assigned ACOs to 1 of 3 performance categories, and examined associations between ACO characteristics and performance categories. Among 1 232 222 beneficiaries with HF, 283 795 (mean age, 81 years; 54% women; 86% White; 78% urban) were assigned to 1 of 467 Medicare Shared Savings Program ACOs. Across ACOs, the median risk-standardized acute admission rate was 87 admissions per 100 people, ranging from 61 (minimum) to 109 (maximum) admissions per 100 beneficiaries. Compared to the overall average, 13% of ACOs performed better on risk-standardized acute admission rates, 72% were no different, and 14% performed worse. Most ACOs with better performance had fewer Black beneficiaries and were not hospital affiliated. Most ACOs that performed worse than average were large, located in the Northeast, had a hospital affiliation, and had a lower proportion of primary care providers. Conclusions Admissions are common among beneficiaries with HF in ACOs, and there is variation in risk-standardized acute admission rates across ACOs. ACO performance was associated with certain ACO characteristics. Future studies should attempt to elucidate the relationship between ACO structure and characteristics and admission risk.

Study protocol: Type III hybrid effectiveness-implementation study implementing Age-Friendly evidence-based practices in the VA to improve outcomes in older adults.

BACKGROUND: Unmet care needs among older adults accelerate cognitive and functional decline and increase medical harms, leading to poorer quality of life, more frequent hospitalizations, and premature nursing home admission. The Department of Veterans Affairs (VA) is invested in becoming an "Age-Friendly Health System" to better address four tenets associated with reduced harm and improved outcomes among the 4 million Veterans aged 65 and over receiving VA care. These four tenets focus on "4Ms" that are fundamental to the care of older adults, including (1) what Matters (ensuring that care is consistent with each person's goals and preferences); (2) Medications (only using necessary medications and ensuring that they do not interfere with what matters, mobility, or mentation); (3) Mentation (preventing, identifying, treating, and managing dementia, depression, and delirium); and (4) Mobility (promoting safe movement to maintain function and independence). The Safer Aging through Geriatrics-Informed Evidence-Based Practices (SAGE) Quality Enhancement Research Initiative (QUERI) seeks to implement four evidence-based practices (EBPs) that have shown efficacy in addressing these core tenets of an "Age-Friendly Health System," leading to reduced harm and improved outcomes in older adults. METHODS: We will implement four EBPs in 9 VA medical centers and associated outpatient clinics using a type III hybrid effectiveness-implementation stepped-wedge trial design. We selected four EBPs that align with Age-Friendly Health System principles: Surgical Pause, EMPOWER (Eliminating Medications Through Patient Ownership of End Results), TAP (Tailored Activities Program), and CAPABLE (Community Aging in Place - Advancing Better Living for Elders). Guided by the Pragmatic Robust Implementation and Sustainability Model (PRISM), we are comparing implementation as usual vs. active facilitation. Reach is our primary implementation outcome, while "facility-free days" is our primary effectiveness outcome across evidence-based practice interventions. DISCUSSION: To our knowledge, this is the first large-scale randomized effort to implement "Age-Friendly" aligned evidence-based practices. Understanding the barriers and facilitators to implementing these evidence-based practices is essential to successfully help shift current healthcare systems to become Age-Friendly. Effective implementation of this project will improve the care and outcomes of older Veterans and help them age safely within their communities. TRIAL REGISTRATION: Registered 05 May 2021, at ISRCTN #60,657,985. REPORTING GUIDELINES: Standards for Reporting Implementation Studies (see attached).

Multidisciplinary teams, efficient communication, procedure services, and telehealth improve cirrhosis care: A qualitative study.

BACKGROUND: Cirrhosis care and outcomes are improved with access to subspecialty gastroenterology and hepatology care. In qualitative interviews, we investigated clinicians' perceptions of factors that optimize or impede cirrhosis care. METHODS: We conducted 24 telephone interviews with subspecialty clinicians at 7 Veterans Affairs medical centers with high- and low-complexity services. Purposive sampling stratified Veterans Affairs medical centers on timely post-hospitalization follow-up, a quality measure. We asked open-ended questions about facilitators and barriers of care coordination, access to appointments, procedures, transplantation, management of complications, keeping up to date with medical knowledge, and telehealth use. RESULTS: Key themes that facilitated care were structural: multidisciplinary teams, clinical dashboards, mechanisms for appointment tracking and reminders, and local or virtual access to transplant and liver cancer specialists through the "specialty care access network extension for community health care outcomes" program. Coordination and efficient communication between transplant and non-transplant specialists and between transplant and primary care facilitated timely care. Same-day access to laboratory, procedural, and clinical services is an indicator of high-quality care. Barriers included lack of on-site procedural services, clinician turnover, patient social needs related to transportation, costs, and patient forgetfulness due to HE. Telehealth enabled lower complexity sites to obtain recommendations for complex patient cases. Barriers to telehealth included lack of credit (eg, VA billing equivalent), inadequate staff, lack of audiovisual technology support, and patient and staff discomfort with technology. Telehealth was optimal for return visits, cases where physical examination was nonessential, and where distance and transportation precluded in-person care. Rapid telehealth uptake during the COVID-19 pandemic was a positive disruptor and facilitated use. CONCLUSIONS: We identify multi-level factors related to structure, staffing, technology, and care organization to optimize cirrhosis care delivery.

The Home Care Workforce Has Not Kept Pace With Growth In Home And Community-Based Services.

Home and community-based services (HCBS) are the predominant approach to delivering long-term services and supports in the US, but there are growing numbers of reports of worker shortages in this industry. Medicaid, the primary payer for long-term services and supports, has expanded HCBS coverage, resulting in a shift in the services' provision out of institutions and into homes. Yet it is unknown whether home care workforce growth has kept up with the increased use of these services. Using data from the American Community Survey and the Henry J. Kaiser Family Foundation, we compared trends in the size of the home care workforce with data on Medicaid HCBS participation between 2008 and 2020. The home care workforce grew from approximately 840,000 to 1.22 million workers between 2008 and 2013. After 2013, growth slowed, ultimately reaching 1.42 million workers in 2019. In contrast, the number of Medicaid HCBS participants grew continuously from 2008 to 2020, with accelerated growth between 2013 and 2020. As a consequence, the number of home care workers per 100 HCBS participants declined by 11.6 percent between 2013 and 2019, with preliminary estimates suggesting that further declines occurred in 2020. Improving access to HCBS will require not just expanded insurance coverage but also new workforce investments.

Trends in Supply of Nursing Home Beds, 2011-2019.

Importance: Nursing homes play a vital role in providing postacute and long-term care for individuals whose needs cannot be met in the home or community. Whether the supply of nursing home beds and, specifically, the supply of high-quality beds has kept pace with the growth of the older adult population is unknown. Objective: To describe changes in the supply of population-adjusted nursing home beds from 2011 to 2019. Design, Setting, and Participants: This cross-sectional study examines changes in the population-adjusted supply of nursing home beds across all US counties from 2011 to 2019 and describes county and nursing home characteristics where the supply of nursing home beds has increased vs decreased. Main Outcomes and Measures: Number of nursing home beds adjusted per 10 000 adults aged 65 years and older. Results: The population-adjusted supply of nursing home beds declined from 2011 to 2019 for 86.4% of US counties, by a mean (SD) of 129.9 (123.8) beds per 10 000 adults aged 65 years or older per county from a baseline mean (SD) of 552.5 (274.4) beds per 10 000 adults aged 65 years or older per county in 2011. The share of beds that were high quality (4- or 5-star ratings) also declined, which was driven by a small number of counties where nursing home bed supply increased due to a proliferation of lower-quality beds. Simultaneously, metropolitan counties with declining numbers of nursing home beds also experienced declining number of senior housing residential beds (-11.3 [54.6] beds per 10 000 adults aged 65 years or older per county from a baseline mean [SD] of 354.8 [222.3]). Conclusions and Relevance: The findings of this cross-sectional study suggest that the supply of nursing home beds, specifically high-quality nursing home beds, and senior residential housing beds have not kept pace with the demographics of an aging population. Understanding the supply of high-quality nursing home beds and associated geographic variation can inform targeted policies to best support older adults requiring nursing home care.

Risk of Discharge to Lower-Quality Nursing Homes Among Hospitalized Older Adults With Alzheimer Disease and Related Dementias.

Importance: Individuals with Alzheimer disease and related dementias (ADRD) frequently require skilled nursing facility (SNF) care following hospitalization. Despite lower SNF incentives to care for the ADRD population, knowledge on how the quality of SNF care differs for those with vs without ADRD is limited. Objective: To examine whether persons with ADRD are systematically admitted to lower-quality SNFs. Design, Setting, and Participants: Cross-sectional analysis of Medicare beneficiaries hospitalized between January 1, 2017, and December 31, 2019, was conducted. Data analysis was performed from January 15 to May 30, 2022. Participants were discharged to a Medicare-certified SNF from a general acute hospital. Patients younger than 65 years, enrolled in Medicare Advantage, and with prior SNF or long-term nursing home use within 1 year of hospitalization were excluded. Exposures: The quality level of all SNFs available at the patient's discharge, measured using publicly reported 5-star staffing ratings. The 5-star ratings were grouped into 3 levels (1-2 stars [reference category, low-quality], 3 stars [average-quality], and 4-5 stars [high-quality]). Main Outcomes and Measures: The outcome was the SNF a patient entered among the possible SNF destinations available at discharge. Differences in the association between SNF quality and SNF entry for patients with and without ADRD were assessed using a conditional logit model, which simultaneously controls for differences in discharging hospital, residential neighborhood, and the other characteristics (eg, postacute care specialization) of all SNFs available at discharge. Results: The sample included 2 619 464 patients (mean [SD] age, 81.3 [8.6] years; 61% women; 87% were White; 8% were Black; 22% with ADRD). The probability of discharge to higher quality SNFs was lower for patients with ADRD. If the star rating of an SNF was high instead of low, the log-odds of being discharged to it increased by 0.31 for patients with ADRD and by 0.47 for those without ADRD (difference, -0.16; P < .001). The weaker association between quality and entry for patients with ADRD indicates that they are less likely to be discharged to high-quality SNFs. Conclusions and Relevance: The findings of this study suggest that patients with ADRD are more likely to be discharged to lower-quality SNFs. Targeted reforms, such as ADRD-specific compensation adjustments, may be needed to improve access to better SNFs for patients with ADRD.

Racial and ethnic disparities in access to and enrollment in high-quality Medicare Advantage plans.

OBJECTIVE: Racial and ethnic minority enrollees in Medicare Advantage (MA) plans tend to be in lower-quality plans, measured by a 5-star quality rating system. We examine whether differential access to high-rated plans was associated with this differential enrollment in high-rated plans by race and ethnicity among MA enrollees. DATA SOURCES: The Medicare Master Beneficiary Summary File and MA Landscape File for 2016. STUDY DESIGN: We first examined county-level MA plan offerings by race and ethnicity. We then examined the association of racial and ethnic differences in enrollment by star rating by controlling for the following different sets of covariates: (1) individual-level characteristics only, and (2) individual-level characteristics and county-level MA plan offerings. DATA COLLECTION/EXTRACTION METHODS: Not applicable PRINCIPAL FINDINGS: Racial and ethnic minority enrollees had, on average, more MA plans available in their counties of residence compared to White enrollees (16.1, 20.8, 20.2, vs. 15.1 for Black, Asian/Pacific Islander, Hispanic, and White enrollees), but had fewer number of high-rated plans (4-star plans or higher) and/or more number of low-rated plans (3.5-star plans or lower). While racial and ethnic minority enrollees had lower enrollment in 4-4.5 star plans than White enrollees, this difference substantially decreased after accounting for county-level MA plan offerings (-9.1 to -0.5 percentage points for Black enrollees, -15.9 to -5.0 percentage points for Asian/Pacific Islander enrollees, and -12.7 to 0.6 percentage points for Hispanic enrollees). Results for Black enrollees were notable as the racial difference reversed when we limited the analysis to those who live in counties that offer a 5-star plan. After accounting for county-level MA plan offerings, Black enrollees had 3.2 percentage points higher enrollment in 5-star plans than White enrollees. CONCLUSIONS: Differences in enrollment in high-rated MA plans by race and ethnicity may be explained by limited access and not by individual characteristics or enrollment decisions.

Association of Medicare Advantage Star Ratings With Racial and Ethnic Disparities in Hospitalizations for Ambulatory Care Sensitive Conditions.

BACKGROUND: Enrollment in high-quality Medicare Advantage (MA) plans, measured by a 5-star quality rating system, was lower among racial and ethnic minority enrollees than White enrollees partly due to fewer high-quality plans available in their counties of residence. This may contribute to racial and ethnic disparities in ambulatory care sensitive condition (ACSC) hospitalizations. OBJECTIVE: We examined whether there were racial and ethnic disparities in ACSC hospitalizations among MA enrollees overall and by star rating. METHODS: Using the Medicare enrollment and claims data for 2016, we identified White, Black, Hispanic, and Asian/Pacific Islander enrollees in MA plans. We estimated racial and ethnic disparities in ACSC hospitalizations (per 10,000 enrollees) overall and by star rating. RESULTS: We found that the adjusted rates of ACSC hospitalizations were significantly higher among Black enrollees than White enrollees overall [39.4 (95% confidence interval: 36.3-42.5)]. However, no significant disparities were found among Hispanic and Asian/Pacific Islander enrollees. The adjusted rates of ACSC hospitalizations were higher in lower-rated plans than higher-rated plans in all racial and ethnic groups. The significant disparities in ACSC hospitalizations by star rating were the most pronounced between White and Black enrollees. We found suggestive evidence that enrollment in lower-rated plans was associated with higher disparities in ACSC hospitalizations between White and Black enrollees. CONCLUSIONS: Substantial disparities in ACSC hospitalizations exist between White and Black enrollees in MA plans, especially for lower-rated plans. Policies aimed at reducing racial disparities in ACSC hospitalizations could include improving access to high-rated plans.

SARS-CoV-2 Sequelae and Postdischarge Health Care Visits Over 5 Months Follow-up Among Children Hospitalized for COVID-19 or MIS-C.

Although post-acute sequelae of COVID-19 among adult survivors has gained significant attention, data in children hospitalized for severe acute respiratory syndrome coronavirus 2 is limited. This study of commercially insured US children shows that those hospitalized with COVID-19 or multisystem inflammatory syndrome in children have a substantial burden of severe acute respiratory syndrome coronavirus 2 sequelae and associated health care visits postdischarge.

What matters when it comes to measuring Age-Friendly Health System transformation.

Thousands of health systems are now recognized as "Age-Friendly Health Systems," making this model one of the most widely disseminated - and most promising- models to redesign care delivery for older adults. Sustaining these gains will require demonstrating the impact on care delivery and outcomes of older adults. We propose a new measurement model to more tightly link Age-Friendly Health System transformation to outcomes within each "M" (What Matters, Medications, Mobility, and Mentation). We evaluated measures based on the following characteristics: (1) conceptual responsiveness to changes brought about by practicing "4Ms" care; (2) degree to which they represent outcomes that matter to older adults; and (3) how they can be feasibly, reliably, and validly measured. We offer specific examples of how novel measures are currently being used where available. Finally, we present measures that could capture system-level effects across "M"s. We tie these suggestions together into a conceptual measurement model for AFHS transformation, with the intent to spur discussion, debate, and iterative improvement in measures over time.

Association of Extreme Heat and Cardiovascular Mortality in the United States: A County-Level Longitudinal Analysis From 2008 to 2017.

BACKGROUND: Extreme-heat events are increasing as a result of climate change. Prior studies, typically limited to urban settings, suggest an association between extreme heat and cardiovascular mortality. However, the extent of the burden of cardiovascular deaths associated with extreme heat across the United States and in different age, sex, or race and ethnicity subgroups is unclear. METHODS: County-level daily maximum heat index levels for all counties in the contiguous United States in summer months (May-September) and monthly cardiovascular mortality rates for adults ≥20 years of age were obtained. For each county, an extreme-heat day was identified if the maximum heat index was ≥90 °F (32.2 °C) and in the 99th percentile of the maximum heat index in the baseline period (1979-2007) for that day. Spatial empirical Bayes smoothed monthly cardiovascular mortality rates from 2008 to 2017 were the primary outcome. A Poisson fixed-effects regression model was estimated with the monthly number of extreme-heat days as the independent variable of interest. The model included time-fixed effects and time-varying environmental, economic, demographic, and health care-related variables. RESULTS: Across 3108 counties, from 2008 to 2017, each additional extreme-heat day was associated with a 0.12% (95% CI, 0.04%-0.21%; P=0.004) higher monthly cardiovascular mortality rate. Extreme heat was associated with an estimated 5958 (95% CI, 1847-10 069) additional deaths resulting from cardiovascular disease over the study period. In subgroup analyses, extreme heat was associated with a greater relative increase in mortality rates among men compared with women (0.20% [95% CI, 0.07%-0.33%]) and non-Hispanic Black compared with non-Hispanic White adults (0.19% [95% CI, 0.01%-0.37%]). There was a greater absolute increase among elderly adults compared with nonelderly adults (16.6 [95% CI, 14.6-31.8] additional deaths per 10 million individuals per month). CONCLUSIONS: Extreme-heat days were associated with higher adult cardiovascular mortality rates in the contiguous United States between 2008 and 2017. This association was heterogeneous among age, sex, race, and ethnicity subgroups. As extreme-heat events increase, the burden of cardiovascular mortality may continue to increase, and the disparities between demographic subgroups may widen.

dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling.

OBJECTIVE: To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling. MATERIALS AND METHODS: The GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied. RESULTS: The proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data. CONCLUSION: The dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.

Association of Extreme Heat With All-Cause Mortality in the Contiguous US, 2008-2017.

Importance: The number of extreme heat events is increasing because of climate change. Previous studies showing an association between extreme heat and higher mortality rates generally have been limited to urban areas, and whether there is heterogeneity across different populations is not well studied; understanding whether this association varies across different communities, particularly minoritized racial and ethnic groups, may allow for more targeted mitigation efforts. Objective: To the assess the association between extreme heat and all-cause mortality rates in the US. Design, Setting, and Participants: This cross-sectional study involved a longitudinal analysis of the association between the number of extreme heat days in summer months from 2008 to 2017 (obtained from the Centers for Disease Control and Prevention's Environmental Public Health Tracking Program) and county-level all-cause mortality rates (obtained from the National Center for Health Statistics), using a linear fixed-effects model across all counties in the contiguous US among adults aged 20 years and older. Data analysis was performed from September 2021 to March 2022. Exposures: The number of extreme heat days per month. Extreme heat was identified if the maximum heat index was greater than or equal to 90 °F (32.2 °C) and in the 99th percentile of the maximum heat index in the baseline period (1979 to 2007). Main Outcomes and Measures: County-level, age-adjusted, all-cause mortality rates. Results: There were 219 495 240 adults aged 20 years and older residing in the contiguous US in 2008, of whom 113 294 043 (51.6%) were female and 38 542 838 (17.6%) were older than 65 years. From 2008 to 2017, the median (IQR) number of extreme heat days during summer months in all 3108 counties in the contiguous US was 89 (61-122) days. After accounting for time-invariant confounding, secular time trends, and time-varying environmental and economic measures, each additional extreme heat day in a month was associated with 0.07 additional death per 100 000 adults (95% CI, 0.03-0.10 death per 100 000 adults; P = .001). In subgroup analyses, greater increases in mortality rates were found for older vs younger adults (0.19 death per 100 000 individuals; 95% CI, 0.04-0.34 death per 100 000 individuals), male vs female adults (0.12 death per 100 000 individuals; 95% CI, 0.05-0.18 death per 100 000 individuals), and non-Hispanic Black vs non-Hispanic White adults (0.11 death per 100 000 individuals; 95% CI, 0.02-0.20 death per 100 000 individuals). Conclusions and Relevance: These findings suggest that from 2008 to 2017, extreme heat was associated with higher all-cause mortality in the contiguous US, with a greater increase noted among older adults, men, and non-Hispanic Black individuals. Without mitigation, the projected increase in extreme heat due to climate change may widen health disparities between groups.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.