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2.
Nat Commun ; 13(1): 1678, 2022 03 30.
Article in English | MEDLINE | ID: covidwho-1768824

ABSTRACT

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.


Subject(s)
COVID-19 , Algorithms , COVID-19/epidemiology , Confidentiality , Databases, Factual , Humans , Linear Models
4.
Ann Intern Med ; 175(2): 179-190, 2022 02.
Article in English | MEDLINE | ID: covidwho-1579932

ABSTRACT

BACKGROUND: Although most patients with SARS-CoV-2 infection can be safely managed at home, the need for hospitalization can arise suddenly. OBJECTIVE: To determine whether enrollment in an automated remote monitoring service for community-dwelling adults with COVID-19 at home ("COVID Watch") was associated with improved mortality. DESIGN: Retrospective cohort analysis. SETTING: Mid-Atlantic academic health system in the United States. PARTICIPANTS: Outpatients who tested positive for SARS-CoV-2 between 23 March and 30 November 2020. INTERVENTION: The COVID Watch service consists of twice-daily, automated text message check-ins with an option to report worsening symptoms at any time. All escalations were managed 24 hours a day, 7 days a week by dedicated telemedicine clinicians. MEASUREMENTS: Thirty- and 60-day outcomes of patients enrolled in COVID Watch were compared with those of patients who were eligible to enroll but received usual care. The primary outcome was death at 30 days. Secondary outcomes included emergency department (ED) visits and hospitalizations. Treatment effects were estimated with propensity score-weighted risk adjustment models. RESULTS: A total of 3488 patients enrolled in COVID Watch and 4377 usual care control participants were compared with propensity score weighted models. At 30 days, COVID Watch patients had an odds ratio for death of 0.32 (95% CI, 0.12 to 0.72), with 1.8 fewer deaths per 1000 patients (CI, 0.5 to 3.1) (P = 0.005); at 60 days, the difference was 2.5 fewer deaths per 1000 patients (CI, 0.9 to 4.0) (P = 0.002). Patients in COVID Watch had more telemedicine encounters, ED visits, and hospitalizations and presented to the ED sooner (mean, 1.9 days sooner [CI, 0.9 to 2.9 days]; all P < 0.001). LIMITATION: Observational study with the potential for unobserved confounding. CONCLUSION: Enrollment of outpatients with COVID-19 in an automated remote monitoring service was associated with reduced mortality, potentially explained by more frequent telemedicine encounters and more frequent and earlier presentation to the ED. PRIMARY FUNDING SOURCE: Patient-Centered Outcomes Research Institute.


Subject(s)
COVID-19/therapy , Remote Consultation/methods , Text Messaging , Adult , Aged , COVID-19/mortality , Comparative Effectiveness Research , Emergency Service, Hospital , Female , Home Care Services , Hospitalization , Humans , Male , Middle Aged , Retrospective Studies , United States/epidemiology
5.
JCO Clin Cancer Inform ; 5: 1134-1140, 2021 10.
Article in English | MEDLINE | ID: covidwho-1518337

ABSTRACT

PURPOSE: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19. METHODS: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP. RESULTS: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4). CONCLUSION: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics.


Subject(s)
COVID-19 , Neoplasms , Text Messaging , Female , Humans , Middle Aged , Neoplasms/diagnosis , SARS-CoV-2 , Triage
7.
J Grad Med Educ ; 13(4): 515-525, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1360853

ABSTRACT

BACKGROUND: The COVID-19 pandemic forced numerous unprecedented systemic changes within residency programs and hospital systems. OBJECTIVE: We explored how the COVID-19 pandemic, and associated changes in clinical and educational experiences, were related to internal medicine residents' well-being in the early months of the pandemic. METHODS: Across 4 internal medicine residency programs in the Northeast United States that have previously participated in the iCOMPARE study, all 394 residents were invited to participate in a study with open-ended survey prompts about well-being approximately every 2 weeks in academic year 2019-2020. In March and April 2020, survey prompts were refocused to COVID-19. Content analysis revealed themes in residents' open-ended responses to 4 prompts. RESULTS: One hundred and eighty-six residents expressed interest, and 88 were randomly selected (47%). There were 4 main themes: (1) in early days of the pandemic, internal medicine residents reported fear and anxiety about uncertainty and lack of personal protective equipment; (2) residents adapted and soon were able to reflect, rest, and pursue personal wellness; (3) communication from programs and health systems was inconsistent early in the pandemic but improved in clarity and frequency; (4) residents appreciated the changes programs had made, including shorter shifts, removal of pre-rounding, and telemedicine. CONCLUSIONS: COVID-19 introduced many challenges to internal medicine residency programs and to resident well-being. Programs made structural changes to clinical schedules, educational/conference options, and communication that boosted resident well-being. Many residents hoped these changes would continue regardless of the pandemic's course.


Subject(s)
COVID-19 , Internship and Residency , Anxiety , Humans , Pandemics , SARS-CoV-2
8.
JAMA Netw Open ; 4(6): e2112842, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1274639

ABSTRACT

Importance: Black patients hospitalized with COVID-19 may have worse outcomes than White patients because of excess individual risk or because Black patients are disproportionately cared for in hospitals with worse outcomes for all. Objectives: To examine differences in COVID-19 hospital mortality rates between Black and White patients and to assess whether the mortality rates reflect differences in patient characteristics by race or by the hospitals to which Black and White patients are admitted. Design, Setting, and Participants: This cohort study assessed Medicare beneficiaries admitted with a diagnosis of COVID-19 to 1188 US hospitals from January 1, 2020, through September 21, 2020. Exposure: Hospital admission for a diagnosis of COVID-19. Main Outcomes and Measures: The primary composite outcome was inpatient death or discharge to hospice within 30 days of admission. We estimated the association of patient-level characteristics (including age, sex, zip code-level income, comorbidities, admission from a nursing facility, and days since January 1, 2020) with differences in mortality or discharge to hospice among Black and White patients. To examine the association with the hospital itself, we adjusted for the specific hospitals to which patients were admitted. We used simulation modeling to estimate the mortality among Black patients had they instead been admitted to the hospitals where White patients were admitted. Results: Of the 44 217 Medicare beneficiaries included in the study, 24 281 (55%) were women; mean (SD) age was 76.3 (10.5) years; 33 459 participants (76%) were White, and 10 758 (24%) were Black. Overall, 2634 (8%) White patients and 1100 (10%) Black patients died as inpatients, and 1670 (5%) White patients and 350 (3%) Black patients were discharged to hospice within 30 days of hospitalization, for a total mortality-equivalent rate of 12.86% for White patients and 13.48% for Black patients. Black patients had similar odds of dying or being discharged to hospice (odds ratio [OR], 1.06; 95% CI, 0.99-1.12) in an unadjusted comparison with White patients. After adjustment for clinical and sociodemographic patient characteristics, Black patients were more likely to die or be discharged to hospice (OR, 1.11; 95% CI, 1.03-1.19). This difference became indistinguishable when adjustment was made for the hospitals where care was delivered (odds ratio, 1.02; 95% CI, 0.94-1.10). In simulations, if Black patients in this sample were instead admitted to the same hospitals as White patients in the same distribution, their rate of mortality or discharge to hospice would decline from the observed rate of 13.48% to the simulated rate of 12.23% (95% CI for difference, 1.20%-1.30%). Conclusions and Relevance: This cohort study found that Black patients hospitalized with COVID-19 had higher rates of hospital mortality or discharge to hospice than White patients after adjustment for the personal characteristics of those patients. However, those differences were explained by differences in the hospitals to which Black and White patients were admitted.


Subject(s)
African Americans/statistics & numerical data , COVID-19/ethnology , COVID-19/mortality , Hospital Mortality/ethnology , /statistics & numerical data , Aged , Aged, 80 and over , Cohort Studies , Comorbidity , Female , Health Status Disparities , Healthcare Disparities/statistics & numerical data , Hospice Care/statistics & numerical data , Hospitalization/statistics & numerical data , Hospitals , Humans , Male , Medicare , SARS-CoV-2 , United States/epidemiology
9.
J Med Internet Res ; 23(6): e29395, 2021 06 09.
Article in English | MEDLINE | ID: covidwho-1262585

ABSTRACT

BACKGROUND: In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies. OBJECTIVE: This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy. METHODS: We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information. RESULTS: The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data. CONCLUSIONS: Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.


Subject(s)
Consumer Behavior/statistics & numerical data , Consumer Health Information/statistics & numerical data , Data Collection/ethics , Datasets as Topic/supply & distribution , Interviews as Topic , Privacy/psychology , Qualitative Research , Adolescent , Adult , Cohort Studies , Female , Humans , Male , Middle Aged , United States , Young Adult
10.
J Clin Med ; 10(9)2021 May 03.
Article in English | MEDLINE | ID: covidwho-1224040

ABSTRACT

OBJECTIVE: Patients requiring hospital care for COVID-19 may be stable for discharge soon after admission. This study sought to describe patient characteristics associated with short-stay hospitalization for COVID-19. METHODS: We performed a retrospective cohort study of patients with COVID-19 admitted to five United States hospitals from March to December 2020. We used multivariable logistic regression to identify patient characteristics associated with short hospital length-of-stay. RESULTS: Of 3103 patients, 648 (20.9%) were hospitalized for less than 48 h. These patients were significantly less likely to have an age greater than 60, diabetes, chronic kidney disease; emergency department vital sign abnormalities, or abnormal initial diagnostic testing. For patients with no significant risk factors, the adjusted probability of short-stay hospitalization was 62.4% (95% CI 58.9-69.6). CONCLUSION: Identification of candidates for early hospital discharge may allow hospitals to streamline throughput using protocols that optimize the efficiency of hospital care and coordinate post-discharge monitoring.

11.
JAMA Intern Med ; 181(8): 1134, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1176223
12.
JAMA Intern Med ; 181(4): 471-478, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-985875

ABSTRACT

Importance: It is unknown how much the mortality of patients with coronavirus disease 2019 (COVID-19) depends on the hospital that cares for them, and whether COVID-19 hospital mortality rates are improving. Objective: To identify variation in COVID-19 mortality rates and how those rates have changed over the first months of the pandemic. Design, Setting, and Participants: This cohort study assessed 38 517 adults who were admitted with COVID-19 to 955 US hospitals from January 1, 2020, to June 30, 2020, and a subset of 27 801 adults (72.2%) who were admitted to 398 of these hospitals that treated at least 10 patients with COVID-19 during 2 periods (January 1 to April 30, 2020, and May 1 to June 30, 2020). Exposures: Hospital characteristics, including size, the number of intensive care unit beds, academic and profit status, hospital setting, and regional characteristics, including COVID-19 case burden. Main Outcomes and Measures: The primary outcome was the hospital's risk-standardized event rate (RSER) of 30-day in-hospital mortality or referral to hospice adjusted for patient-level characteristics, including demographic data, comorbidities, community or nursing facility admission source, and time since January 1, 2020. We examined whether hospital characteristics were associated with RSERs or their change over time. Results: The mean (SD) age among participants (18 888 men [49.0%]) was 70.2 (15.5) years. The mean (SD) hospital-level RSER for the 955 hospitals was 11.8% (2.5%). The mean RSER in the worst-performing quintile of hospitals was 15.65% compared with 9.06% in the best-performing quintile (absolute difference, 6.59 percentage points; 95% CI, 6.38%-6.80%; P < .001). Mean RSERs in all but 1 of the 398 hospitals improved; 376 (94%) improved by at least 25%. The overall mean (SD) RSER declined from 16.6% (4.0%) to 9.3% (2.1%). The absolute difference in rates of mortality or referral to hospice between the worst- and best-performing quintiles of hospitals decreased from 10.54 percentage points (95% CI, 10.03%-11.05%; P < .001) to 5.59 percentage points (95% CI, 5.33%-5.86%; P < .001). Higher county-level COVID-19 case rates were associated with worse RSERs, and case rate declines were associated with improvement in RSERs. Conclusions and Relevance: Over the first months of the pandemic, COVID-19 mortality rates in this cohort of US hospitals declined. Hospitals did better when the prevalence of COVID-19 in their surrounding communities was lower.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/therapy , Cohort Studies , Critical Care , Female , Hospital Mortality , Humans , Male , Middle Aged , United States , Young Adult
15.
Health Aff (Millwood) ; 39(6): 1097, 2020 06.
Article in English | MEDLINE | ID: covidwho-457352
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