COVID-19 Observational Research Collaboratory - Long-Term Outcomes (LTO) Study
Conditions: COVID-19, SARS-CoV-2 Infection
Sponsor: VA Office of Research and Development

 
 
 
 
 
 
 
 
 
COVID-19 Observational Research Collaboratory 
Funding Agency: VA HSR&D 
Principal Investigator: Dr. Amy S. B. Bohnert, PhD, MHS 
Version 7, December 16, 2024 
 
 
Human Subjects Protocol 
Ann Arbor VA Healthcare System IRB 

 
Abstract 
 
Background: Early evidence suggests that the frequency and type of long-term complications stemming from 
SARS-CoV-2 infection may depend on the severity of initial presentation. However, additional unidentified factors 
may also play a role including those related to the individual, the environment and/or the specific strain of the virus. 
The long-term health impacts of SARS-CoV-2 will have major implications for health care utilization, care processes 
and quality and costs of care for Veterans, both within the VA and in the community.  Over 200,000 Veterans have 
had COVID-19. Although the long-term consequences of this novel illness are unknown, it is clear they will often be 
significant. Our overarching goal in the Long-Term Outcome Study (LTO) is to advance VA HSR&D research into 
the consequences and care of COVID-19 infection by developing and releasing integrated multi-modal data and 
results on long-term outcomes of COVID for use by VA clinical, research and operations communities. We will do so 
via a coordinated program of data production and analysis using the VA electronic health record (EHR) and other 
administrative sources (e.g., Medicare), de novo longitudinal survey, and qualitative inquiry. This work will be 
closely coordinated with the COVID-19 Observational Research Collaboratory (CORC) Coordinating Center (CCC) 
and the burgeoning community of HSR&D-funded COVID researchers.                                                    
 
This study aims to examine the long-term outcomes in Veterans infected and uninfected with SARS-CoV-2.  
Aim 1: Using VINCI-CDW data, patients will be identified who had a SARS-CoV-2 test since February 2020. We will 
use a combination of traditional epidemiologic studies, and machine learning algorithms to determine the long-term, 
multi-systemic and functional adverse outcomes and/or syndromes related to SARS-CoV-2. Members of the 
research team are currently developing methods to identify new onset and exacerbations in comorbidities following 
SARS-CoV-2 illness in Veterans. 
Aim 2: Determine Veteran-Reported Outcome Effects Using Structured Survey on domains such as self- 
reported symptoms, disability, and financial impacts, measured repeatedly over 36  months. 
 

List of Abbreviations 
ACEI  
angiotensin-converting enzyme inhibitors 
ARB 
 
angiotensin receptor blockers 
ARDS  
Acute Respiratory Distress Syndrome 
BMI 
 
Body mass index 
BSW  
Bachelor of Social Work 
CCMR  
Center for Clinical Management Research 
CDC   
Centers for Disease Control and Prevention 
CDW   
Corporate Data Warehouse 
CMS    
Centers for Medicare & Medicaid Services 
Co-I    
Co-Investigator 
COPD  
Chronic obstructive pulmonary disease  
CORC  
COVID-19 Observational Research Collaboratory 
CORC  
COVID-19 Observational Research Collaboratory 
COVID-19   Coronavirus Disease 2019 
COVID-19 
Coronavirus Disease 2019 
CRP 
 
C-reactive protein 
CT 
 
Computed tomography 
DART  
Data Access Request Tracker 
EHR   
Electronic Health Record 
EKG 
 
Electrocardiogram 
HIPAA  
Health Insurance Portability and Accountability Act 
HIPAA  
Health insurance portability and accountability act 
HSR&D  
Health Services Research & Development 
ICU   
Intensive Care Unit 
ICU 
 
Intensive care unit 
IL-6 
 
Interleukin 6 
IRB   
Institutional Review Board 
JAMA  
Journal of the American Medical Association 
LASSO 
Least Absolute Shrinkage and Selection Operator 
LTO   
Long-term outcomes 
MD    
Doctor of Medicine 
ML 
 
Machine Learning 
MPH    
Master of Public Health 
MS    
Master of Science 
MSW   
Master of Social Work 
NHLBI   
National Heart, Lung, and Blood Institute  
NSAIDS 
Nonsteroidal anti-inflammatory drugs 
PD/PI   
Project Director/Principal Investigator 
PhD   Doctor of Philosophy 
PT/INR 
Prothrombin time and international normalized ratio 
ResCU   
REcovey after in HoSpital Cardiac arrest: late outcomes and Utilization 
SARS-CoV-2  Severe Acute Respiratory Syndrome Coronavirus 2 
SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 
US    
United States 
VA   
 
Veterans Affairs 
VAAAHS   
Veterans Affairs Ann Arbor Healthcare System 
VAMC – VA Medical Center 
VHA – Veterans Health Administration 
VINCI-CDW 
VA Informatics and Computing Infrastructure-Corporate Data Warehouse 
VIReC – Veterans Information Resource Center 
VISIN – Veterans Integrated Service Networks  
WHODAS – World Health Organization Disability Assessment Schedule 
 

Contents 
Protocol Title: ............................................................................................................................................................... 5 
1. 
Study Personnel .................................................................................................................................................... 5 
2. 
Introduction ........................................................................................................................................................... 5 
3. 
Objectives ............................................................................................................................................................. 8 
4.  
Resources and Personnel ................................................................................. Error! Bookmark not defined. 
5. 
Study Procedures .................................................................................................................................................. 9 
5.1 Study Design ...................................................................................................................................................... 9 
5.2 Recruitment Methods ........................................................................................................................................ 13 
5.3 Informed Consent Procedures .......................................................................................................................... 15 
5.4 Inclusion/Exclusion Criteria ............................................................................................................................... 15 
5.5 
Risk/Benefit Assessment ............................................................................................................................. 16 
5.6 
Withdrawal of Subjects ................................................................................................................................ 17 
5.7 
Study Evaluations ........................................................................................................................................ 17 
5.8      Data Analysis ............................................................................................................................................... 19 
6. 
Reporting ........................................................................................................................................................... 191 
7. 
Privacy and Confidentiality .................................................................................................................................. 19 
8. 
Communication Plan ......................................................................................................................................... 202 
9. 
Information Security and Data Storage/Movement ........................................................................................... 202 
10. 
References .................................................................................................................................................... 235 
 

 
Protocol Title:  COVID-19 Observational Research Collaboratory 
 
1. 
Introduction 
The majority of patients infected with SARS-CoV-2 develop relatively mild, self-limiting, flu-like symptoms that do 
not require hospitalization. However, a small proportion develop severe viral pneumonia and hypoxemia, which may 
progress to respiratory failure, ARDS, cytokine release syndrome, multiorgan failure and death. These patients 
need hospitalization, frequently require admission to the intensive care unit, often need mechanical ventilation, and 
have a high mortality.  
The COVID-19 pandemic has evolved so quickly that it is still unclear what proportion of infected patients 
experience adverse outcomes such as hospitalization, ICU admission, mechanical ventilation and death. It is also 
unclear why a small minority develop severe, life-threatening COVID-19 while the majority have mild or even 
asymptomatic presentation.  
Furthermore, it is becoming apparent that many patients continue to experience symptoms and adverse effects 
from COVID-19 infection for a long period of time after the acute symptoms subside, a condition known as long 
COVID. A particular interest of this project is to evaluate the long-term manifestations  and outcomes of SARS-CoV-
2.1-9 The literature so far suggests the following: 
• 
Long-term adverse outcomes may occur not only among survivors of severe acute COVID-19 illness, but among 
the much larger group who were asymptomatic or minimally symptomatic at presentation. 
• 
A variety of multi-systemic long-term complications are being described related to direct tissue invasion via 
ACE2 receptors (e.g. lungs, heart, kidneys, CNS), immunologic activation and/or hypercoagulability. 
• 
Early evidence suggests that the frequency and type of long-term complications may depend on the severity of 
initial presentation. However, additional unidentified factors may also play a role including those related to the 
individual, the environment and/or the specific strain of the virus. 
• 
Some individuals appear to have persistent symptoms (e.g., fatigue, joint pain and cognitive dysfunction) after 
clearing the virus (“COVID long haulers”).10,11 It is not known how prevalent this potential new syndrome might 
be, the timeline, extent and reversibility of clinical manifestations.  
• 
A “post-ICU” syndrome has been described  among survivors of sepsis, respiratory failure, and SARS-CoV and 
MERS-CoV including decrements in cognitive and physical function, psychological health, quality of life and loss 
of independence12,13 but has not been well characterized in SARS-CoV-2.  
• 
The long-term health impacts of SARS-CoV-2 will have major implications for health care utilization, care 
processes and quality and costs of care for Veterans, both within the VA and in the community.                                    
 
For Aim 1, We propose to use national VA electronic data derived from the Corporate Data Warehouse to extract 
the electronic health records of all VA patients who tested positive for SARS-CoV-2 as well as appropriate control 
VA enrollees who were in care at the same time but did not test positive for SARS-CoV-2, to determine the rate of 
adverse outcomes, identify independent predictors and develop innovative, state-of-the-art prediction models of 
adverse outcomes, evaluate the effectiveness of pharmacotherapies on adverse outcomes and evaluate the impact 
of SARS-CoV-2 on healthcare utilization. 
Aim 2 is a prospective survey of Veterans that has been funded by national HSR&D as a part of the COVID-19 
Observational Research Collaboratory (CORC). The CORC is an integrated suite of activities includes secondary 
analyses of data, the development of this survey (submitted for review here), and other activities not yet progressed 
to the level of human subjects research and/or being led at other centers. Because the needs of Veterans continue 
to evolve across the epidemic, all CORC research could not be pre-planned—instead, the project had to be divided 

Figure 1. Readmission or death 
after COVID-19 in VA, Spring 2020 
into multiple phases. The CORC team, all the way up to HSR&D Director David Atkins, recognize that this places 
additional burdens on review process, and are grateful for your flexibility. 
The primary scientific question of Aim 2 is “what are the effects of having gotten COVID-19 among Veteran 
survivors?” In this specific proposal, this question will be answered by comparing a random sample of Veteran 
survivors of COVID-19 to carefully matched non-COVID Veterans who had the same risk of acquiring COVID, but 
did not.  Our primary outcomes are disability, health-related quality of life, mobility, caregiving receipt, financial 
hardship, and non-VA healthcare use. 
The human subjects research aspects of this work will be conducted exclusively at VA Ann Arbor by approved study 
team members. 
We wish to emphasize that while the application of these techniques to the specific population of Veterans with 
COVID is highly novel, the techniques themselves are not. They have been developed over the last 10 years at VA 
Ann Arbor in the study of patients who survived in-hospital cardiac arrest, where they have been highly informative. 
These techniques for identifying relevant Veterans, contacting them, seeking verbal informed consent, using a brief 
survey instrument, and following them up over time have all been previously reviewed in detail by AAVA’s RDC and 
IRB as part of IIRs: 1) ResCU I [IIR 13-079] and ResCU II [IIR 17-045], and approved and re-approved as part of 
continuing review. 
Aim 2 will utilize the VA Corporate Data Warehouse (CDW) secondary data analysis from Aim 1 to identify all 
Veterans to be surveyed. These data will come from the CDW and is secured under a HIPAA waiver.  
 
Scientific Background 
Scientifically, it has become increasingly clear that patients who survive the illness caused by SARS-CoV-2—that is, 
COVID-19—have a wide range of ongoing concerns. Whereas once some assumed the COVID-19 would be rapid 
but self-limited illness (ending in either mortality or recovery), it is instead clear that something is still going on with 
these patients. Lay press reports have termed this “Long COVID” or “Long-Haulers”, and have framed it as 
consistent with other post-illness syndromes or as something entirely new and different.6-8 A robust online 
community has evolved (e.g. https:// www . survivorcorps . com) and the CDC has begun doing education about its 
existence ( https:// www .cdc.gov /coronavirus/2019-ncov/long-term-effects.html ). The NIH has announced a major 
call for research into Post Acute Sequelae of COVID.9 By October 2020, del Rio, Collins, and Malani reviewed 
potential long-term consequences in JAMA and noted potential sequelae in nearly every organ.10 The United 
Kingdom’s National Institute for Health and Care Excellence, the Scottish Intercollegiate Guidelines Network, and 
the Royal College of General Practitioners have already endeavored to release guidelines for Long-COVID, and 
these have been critiqued11 as being insufficiently immunologically specific, even as that critique seems to omit any 
awareness of non-immunologically mediated consequences of illness.12 No generally accepted case definition or 
consensus theory of “long-term outcomes” exists yet. 
This may be, in part, because population-based surveillance has been woefully rare to this point, and what is out 
there has recreated nearly every form of bias in measurement described in any epidemiologic text—particularly 
highly selected samples with substantial non-random missingness in outcome 
ascertainment. Within these selected samples, there is often a very 
substantial focus on specific symptoms. 6-month follow-up from Spring 2020 
survivors in Wuhan emphasized fatigue, weakness, and depression, albeit in 
follow-up limited to among those without readmission to the hospital or any 
mobility impairment.13 A January 2021 report from Northern Italy noted 
frequent substantial impairments in respiratory function and mental health 

among survivors of COVID, yet also a 65% refusal-to-participate rate.14 Frequent worsening of mental health and 
limited access to care were noted among respondents to an online convenience sample of Canadians.15 
What U.S.-based population-based data are available raise troubling signals. A multi-state follow-up of patients with 
a positive COVID test from spring 2020 found that a third had not returned to work 2-3 weeks later.16 Similar rates 
are being seen in a new national cohort study funded by NHLBI. (unpublished data) While a report from a single 
home health company in New York City suggested dramatic early improvements17, patients hospitalized at 38 
hospitals in Michigan from the spring 2020 wave were followed for 60-days and found 38% were still unable to 
return to their normal activity, 49% reported mental health effects of their illness, 23% had lost a job because of their 
illness and its sequelae, and 37% reported negative financial impacts of their illness; yet this study was only able to 
follow-up 42% of those alive and whom it attempted to contact.18 We recently have shown in a JAMA article19 that 
Veterans with COVID-19 (n=2,179) have an increased risk of readmission or death in the 10-days but not 60-days 
after discharge compared to matched patients with pneumonia or heart failure. (Figure 1) Of those readmitted, the 
most common readmission diagnoses were COVID-19 (30.2%), sepsis (8.5%), pneumonia (3.1%), and heart failure 
(3.1%). During readmission, 22.6% were treated in ICU, 7.1% were mechanically ventilated, and 7.9% received 
vasopressors. Readmission problems after COVID were of the same order of magnitude as readmission problems 
after pneumonia or heart failure—well-established problems of impact. 
The exponential growth of the COVID-19 literature makes a more comprehensive overview both impossible and 
outdated by the time it is finished. Interpreting the above key findings in the COVID-19 literature in light of existing 
literature on long-term outcomes of other acute infectious diseases1,2,12,20 identifies gaps in the literature not likely to 
be filled by other groups, emphasizing VA HSR&D’s comparative advantages. 
First of all, it makes it clear that measuring the long-term outcomes of COVID-19 among Veterans is not a trivial 
task. Depending on the purpose, that question must be operationalized in different ways. 
• 
For a clinician running a post-COVID clinic, the central issue is: what are the range and expected 
frequencies of health problems that a Veteran coming to the clinic may have? 
• 
For those seeking to understand the natural history of COVID-19, the central issue is: what would the 
patient’s life have been like if they had not been infected with SARS-CoV-2? 
• 
For those studying recovery from acute illness, the central issue is: what is distinctive about COVID-19 
recovery relative to other recoveries from equally severe acute illnesses? 
• 
For those charged with planning for health systems, the central issues may be: what can be done to prevent 
future harm, improve the recovery of those who have already recovered from COVID-19, and plan for the 
ongoing healthcare needs of those still in recovery? 
• 
For Veterans recovering from COVID-19, their families and those who care for them: what are they to expect 
in the future and what is the experience of having long-term complications of COVID-19 like? 
The problems that patients with COVID-19 face represent the culmination of multiple different factors and dynamic 
processes: 
• 
SARS-CoV-2-specific damage and recovery 
• 
Treatments for COVID-19 and associated complications 
• 
2020/2021 surge-related changes in access to and quality of care 
• 
2020/2021 economic and social disruptions 
• 
Their pre-existing health, access to care, and social supports and environment 

Because COVID-19’s distribution is non-random among Veterans—and the fundamental implications of this 
observation21-23 is easy to overlook—identifying the “outcomes” of COVID-19 is not a trivial task. We propose 
approaches to disentangle these factors in ways relevant to the key users above. We also propose that the past 
literature has largely missed a focus on the distinctive issues of health services research. Past work has focused on 
specific symptoms and organ dysfunction, rather than taking a patient-centered approach that puts overall health 
needs and ability to participate in work and life at the center of the exploration. Such organ-specific science is 
incredibly important, but also risks missing the issues that are driving central decrements of quality of life; moreover, 
other groups outside of HSR&D are extensively investing in it. In many cases, not enough time has elapsed yet to 
understand the outcomes of COVID-19. Furthermore, past work on “big picture” recovery has overwhelmingly not 
had an explicit comparison group, or a comparison group matched in a relatively non-specific way. Given the non-
randomness of COVID infection21, including its fundamental racial, ethnic24, geographic, and temporal variation25, 
this means that often past work is unable to disentangle what is caused by COVID and what is caused by the other 
co-incident factors outlined above. We propose to correct some of these deficits. 
 
 
 
 
2. 
Objectives 
 
Aim 1 
1. Determine the long-term multi-systemic and functional adverse outcomes of SARS-CoV-2 infection in the 
Veteran population 
2. Determine risk factors and develop prediction models for the development of long-term adverse outcomes of 
SARS-CoV-2 to support risk stratification, mitigation, targeted outreach, preventive measures and treatment. 
3. Evaluate the effectiveness of pharmacotherapies on the development of short- and long-term adverse outcomes 
of SARS-CoV-2 infection. 
4. Evaluate the impact of SARS-CoV-2 on healthcare utilization, care processes and costs to estimate current and 
future health system needs related to COVID-19. 
Aim 2 
The challenge of COVID-19 is unequaled in magnitude; it is not, however, without precedent. VA clinicians have 
saved—and continue to save—thousands of lives by grounding their care in decades of research on viral 
pneumonia, sepsis, acute respiratory distress syndrome (ARDS), and infection control. While much is unknown, this 
proposal is informed by decades of rigorous research on the long-term consequences of acute infection,1-5 while 
identifying distinctive features of COVID-19. Around Thanksgiving 2020, when we began preparing this proposal, 
the VA estimated approximately 100,000 Veterans had been stricken by COVID-19; that estimate as of President’s 
Day 2021 is now over 220,000. These Veterans deserve research that is rigorous, reproducible, and relevant to 
inform efforts to enhance their long-term recovery. Lessons learned in the VA will benefit the wider nation. We have 
structured our proposal to be able to serve the Veterans already struck with COVID-19 while able to adapt to 
changing scientific priorities as the pandemic continues.  

Our overarching goal in the Long-Term Outcome Study (LTO) is to advance VA HSR&D research into the 
consequences and care of COVID-19 infection by developing and releasing integrated multi-modal data and results 
on long-term outcomes of COVID for use by VA clinical, research and operations communities. We will do so via a 
coordinated program of data production and analysis using the VA electronic health record (EHR) and other 
administrative sources (e.g., Medicare), de novo longitudinal survey (this study), and qualitative inquiry. This work 
will be closely coordinated with the COVID-19 Observational Research Collaboratory (CORC) Coordinating Center 
(CCC) and the burgeoning community of HSR&D-funded COVID researchers. Philosophically, we believe in four 
co-equal metrics of success: (1) novel discoveries published in the scientific literature; (2) usefulness of our work to 
VA operational partners; (3) use of our work as foundation and support for other VA investigators in their own work; 
and (4) giving voice to Veterans experiences and challenges in recovery from COVID-19.  
In this proposal, our driving scientific question throughout is: “What is the effect on Veterans of a COVID-19 
infection compared to not having gotten COVID-19 at that time?” We focus on the first 36 months after COVID-19.  
Aim 2, Determine Veteran-Reported Outcome Effects Using Structured Survey. Examine and compare a population 
of Veterans recovering from COVID illness with a population with no positive COVID test or treatment in the VA 
regarding self-reported symptoms, disability, informal caregiving receipt, and financial impacts, using a structured 
survey approach and linked with routinely collected administrative and clinical data. We will describe effects, 
moderators, and causal effects via repeated surveys.  
 
 
 
 
 
 
 
5. Study Procedures 
 
5.1 Study Design 
Aim 1 
The study design is a retrospective case control study of electronic health record data from the VINCI-CDW. The 
study will obtain data on all patients at VA who were tested for COVID-19 since February 2020 (from the VA 
COVID-19 Shared Data Resource in CDW).  We will derive appropriate controls for our different analyses using 
data for all VA enrollees who were enrolled in VA care since February 2020 from CDW. There is no direct contact 
with patients. 
Aim 2 
We will measure clinical outcomes and sources of their variation using existing secondary data from Aim 1, and 
through a prospective structured survey (Aim 2). For Aim 1 and 2, we will initially conduct descriptive analyses of 
outcomes within Veterans with COVID-19, then subsequently define causal effects and causal moderation for 
subsets of pre-specified outcomes. 
We have used this approach—use the VA secondary data to identify precisely eligible patients (so no screening 
questionnaires are asked of patients who are ultimately ineligible), followed by mail and telephone information and 

recruitment after hospital discharge—successfully in VA Ann Arbor studies including ResCU 1, ResCU 2 (IIR’s 13-
079 and 17-045, approved Protocols 1597244 and 1597246). 
A driving finding in VA research for the last decade has been that there is unequal and incomplete access to the 
benefits of VA care. EHR data by definition are only available for Veterans who have accessed care in the VA, 
including VA community-based care and contracted care. Other Administrative data (e.g., claims data for Medicare, 
Medicaid) are not available in real-time, given our dependency on other entities (i.e., CMS and VIReC) for 
production and linkage. Further, many important measures are not systematically ascertained or entered into the 
EHR in a uniform fashion for all patients. Therefore, prospective primary data collection from Veterans is necessary 
to define measures that are poorly captured in the VA EHR including care received at non-VA sites, caregiving 
receipt, disability, and employment/role function and financial toxicity, and unmet care needs in order to ensure the 
relevance of the work being done here. In general, we will seek to survey Veterans at 12, 18, 24, 30, and 36 months 
after their initial SARS-CoV-2 infection, enrolling new patients as the epidemic continues, and following patients 
longitudinally. The key variables of interest from new survey data for the LTO are shown in Table 2 below. 
Concept 
Instrument 
# of items 
(Approx) 
Activity + Quality of Life 
Activities of Daily Living (ADL) 
and Instrumental Activities of 
Daily Living (IADL); EQ-5D-5L 
18 
Participation 
Life Space 
Mobility 
9+ 
Employment 
Health and Retirement Study’s 
Job Status Form 
1 
Financial Toxicity 
NHLBI COVID-19 Financial 
Toxicity scale 
10 
Mental Health 
PHQ-9 Depression Symptoms 
scale 
9 
Unmet Need 
PROMISE instrumental support 
4 
Non-VA Healthcare 
VIREC Utilization Measures  
2 
Fatigue + Symptoms 
PROMIS Fatigue Short Form 7a; 
open-ended questions 
9 
 
Aim 2 Study Population: The population of Veterans eligible for 
random sampling into the survey will be the same as those eligible 
for inclusion in Aim 1. Therefore, all data from Aim 1 will be linkable 
to survey responses via a cross walk.  
Cases: Our primary case definition are Veterans infected with 
SARS-CoV-2, as defined by the standard locally available test at 
the time of their infection, and identified within the COVID-NST. We 
will restrict to Veterans seeking primary care in VA. Viral infection 
index date will be defined as the date of sample collection for each 

patient’s first positive test. We will conduct stratified random sampling with differential survey weights so that cases 
are equally divided based on maximal severity of their acute COVID course in VA to include members of the 
following mutually exclusive groups based on patterns of health care utilization within the first 30 days after initial 
infection with SARS-CoV-2: not hospitalized in VA; hospitalized in VA but not mechanically ventilated; mechanically 
ventilated. Stratified random sampling with variable sampling weights offers two advantages: (1) sampling weights 
assure that the aggregated population results are still representative of the study population as a whole; while (2) 
ensuring adequate representation to support comparisons across these subgroups of interest. Stratum- and wave-
specific sampling fractions will be chosen based on data on survival to the relevant time-point in. 
In general, we will organize the sampling of cases (and later of comparators) around the waves of the epidemic, as 
defined by their nadirs between national death rates—each approximately 3-4 months long, as shown in Figure 2 
generated based on data from the COVID tracking project. As shown in Table 3, we will initiate surveys of Veterans 
who have survived their initial SARS-CoV-2 infection. The viral infection “index date” will be defined as date of each 
patient’s first positive test. This will be a longitudinal survey such that once we start surveying a Veteran, that same 
Veteran will be surveyed in later waves as well—we will not “resample” between, for example, the 12- and 24-month 
surveys. 
Comparators: To support causal inference, we will also sample Veterans without COVID who are matched to 
participants with COVID-19. In general, cases will be compared to comparators matched on risk of developing 
COVID infection during a given wave, incorporating geography at the VAMC and VISN level to take into account 
spatiotemporal heterogeneity and small area variations. There are certain nuanced issues around precise 
definitions of pseudo-index dates for comparators, and particularly avoiding immortal time bias, to which we will 
closely attend during implementation with the input of the Methods Core. In general, we will use coarsened exact 
matching or propensity scores to ensure balance on key measurable covariates, both of which our group has 
substantial experience with. 
Table 3: Example of a Timeline of Cohorts for Survey Operations. Dates on the left indicate when the COVID-19 
infection occurred. Dates on the top indicate when the survey would be done. Blocks of similar surveys are shaded. 
The numbers 12, 18, 24, etc. represent how many months after infection each survey is, to evaluate longitudinal 
changes over time. 
 
Sample Size: We will enroll up to 3,000 Veterans over the course of the survey, interviewing them each 2-4 times. 
This sample size is driven by power calculations needed to examine key subgroups of interest, including looking for 
differences across waves of COVID-19, and in racial and gender minority groups of keen interest to the VA’s 
mission. Taking this sample size into consideration, our selection of measures emphasized continuous or multi-
valued outcomes over binary outcomes to increase statistical power. 
Survey operations: Our primary survey administration mode, as in past work, will be computer-assisted telephone 
administration by highly trained surveyors using highly validated instruments. When telephone administration is not 
feasible, we will offer REDCap-administered mail surveys.  

We have used this approach—primarily phone-based administration of instruments by highly trained VA employees 
with substantial support, understanding of Veterans’ issues, and of VA safety protocols—successfully in VA Ann 
Arbor studies including ResCU 1, ResCU 2 (IIR’s 13-079 and 17-045, Protocols 1597244 and 1597246). 
We will minimize total survey error by random sampling from a group of Veterans known from the VA’s CDW to 
ensure very high recruitment and retention rates—our group routinely exceeds recruitment rates of 80% in studies 
of recently critically ill patients or Veterans, with whom we have conducted over 4,000 interviews, including among 
individuals with substantial disability and cognitive impairment.46 These high recruitment, retention, and survey 
completion rates have been achieved through careful attention to detail, stable diverse staff (some of whom are 
Veterans) in whom we invest in ongoing training, and with whom we engage in constant refinement of methods. 
These methods (including survey practices, personnel training, and customized database organization built in VA 
REDCap and integrated into staff calendaring and email) have been codified as Standard Operating Procedures 
upon which we will build.  
REDCap: VA REDCap (Research Electronic Data Capture) is a web-based application hosted and maintained by 
the VA Informatics and Computing Infrastructure (VINCI) and VA Information Resource Center (VIReC). It is housed 
on a VINCI server physically located at the VA Austin Information Technology Center (AITC) in Austin, Texas and is 
available only on the VA network. REDCap data collection and management projects rely on a thorough study-
specific data dictionary defined in an iterative self-documenting process. This includes a study-specific user rights 
management system administered by the Project Owner or Principal Investigator. REDCap (www.project-
redcap.org) was developed specifically around HIPAA-Security guidelines. REDCap has been disseminated for use 
locally at other institutions and currently supports 1,700+ academic/non-profit consortium partners on six continents 
and over 305,000 research end-users.  
Survey Measures: Our survey measure selection is driven by our conceptual agenda, ease of use, comparability 
with key other studies on COVID-19 and other relevant populations, and a pragmatic respect for our respondents’ 
time and therefore the need to balance multiple competing potential measures in a sufficiently short survey. As 
such, we favor general purpose, brief instruments, even as we recognize the imperfections of such instruments for 
some specialist needs.  
These survey measures are nearly all highly standard measures that have been used thousands of times in 
Veterans and other populations to ensure that they can be asked and answered reliably and without undue 
respondent burden. 
Other Issues: 
Performance of individual survey items will be closely monitored over time. Items will be considered for replacement 
if found to have significant ceiling/floor effects, disproportionate respondent burden, or diminished scientific interest 
as knowledge of COVID-19 evolves. Yet it is also recognized that comparability within and between cohorts is a key 
scientific advantage, and so core items that function well will be preserved to the extent possible.  
Linkage to Aim 1 data: The scale of surveys needed to be completed, the importance of avoiding systematic non-
response error, and the continuing disruption of mail service mean that written informed consent and written HIPAA 
authorizations are not practical. Instead, verbal informed consent and permission to link patients’ survey responses 
with their EHR for research purposes will be obtained and recorded, allowing for full integration of survey and EHR 
and other sources of data. Note that this also means that we will be able to use linkages to reduce respondent 
burden in reporting early healthcare use. 
Data Dissemination Strategy: Every six-months, beginning six months after the first survey is collected, updated 
releases of all structured survey items and basic summary scores will be released via VINCI for all VA investigators 
with appropriate permissions—but specific HIPAA-protected identifiers will not be included. The research 
information sheet and recruitment letter include the VA’s recommended explicit language about this and the option 
to opt out. 

Analytic Methods: Descriptive: We will summarize each of the outcomes listed in Table 2 for the patient cohort at 
each 6-month after the index illness. Summary statistics (mean, median, mode, range; frequency; missing data) will 
be reported in tabular and graphical format. Survey weights will be incorporated throughout to allow generalizability 
of estimates to the source Veteran population receiving VA primary care. 
Analytic Methods: Variation in Survey Outcomes: A similar approach as in Aim 1 will be used for defining variation 
in key survey-reported outcomes, albeit with greater constraints on the number of covariates that can be included 
due to the more limited size of the survey sample. Multi-level models will be estimated after controlling for patient-
level factors such as age, race, and summary Elixhauser comorbidity score48, and nested within hospitals. 
Longitudinal growth curve models within respondents over time will be used, as we have before, to examine 
evolution of outcomes over time. 
In general, we expect quite little missing data due to our prior experience with very high item-level completion in 
administering these specific instruments to Veterans after hospitalization. However, should this be a concern, we 
will use case-wise deletion or multiple imputation, depending on the frequency of missingness. 
Analytic Methods: Causal Effects of COVID on Clinical Outcomes: Our primary causal scientific question is: “What 
is the effect of a COVID-19 infection compared to not having gotten a COVID-19 infection at that time on patient-
reported financial and mental health functioning?” 
Patients who experienced (and survived) COVID-19 will be matched to a comparator patient cohort. Causal 
inference is strengthened by the following strategies: (a) matching patients with and without COVID-19 on key 
patient characteristics, and ensuring that both groups had equal ability to be selected into the study (e.g., due to 
survival), (b) administering surveys on the same timeframe between COVID-19 patients and comparators, relative 
to a selected index date/time zero, (c) collecting sufficient data on key covariates that are unmeasured in the EHR 
and guided by directed acyclic graphs (DAGs) and other causal graphs generated in collaboration with the CORC’s 
Methods Core, (d) accounting for those covariates through inverse probability of treatment weights or similar 
approaches.  
 
5.2 Recruitment Methods  
Aim 1 
This is a data analysis study of VINCI-CDW data. We will access the health records of all enrolled Veterans at the 
VA since February 2020.  
Aim 2 
Sample size: We anticipate 600-700 surveys per COVID-19 wave, in each of 3 waves, for a total of up to 3,000 
Veterans surveyed.  
Subject identification/recruitment: Our specific approach to recruitment here (after eligible Veterans are identified) is 
an exact replication of what we have done successfully in VA Ann Arbor studies including ResCU 1, ResCU 2 (IIR’s 
13-079 and 17-045, Protocols 1597244 and 1597246). This approach has been repeatedly reviewed and approved 
by VA Ann Arbor’s IRB, most recently May 2021. It is also being used in parallel studies outside VA that have been 
reviewed in detail by the University of Michigan and Vanderbilt University’s IRBs, acting as the central IRB for the 
National Institutes of Health’s COVID-19 follow-up studies. 
This approach emphasizes balancing two key competing needs: (a) giving Veterans the opportunity to contribute to 
research and be heard; with (b) not pestering or coercing Veterans. As evidence that this approach strikes the 
correct balance, we note that in ResCU 1, 96% of all follow-up calls were completed. We interpret this specific data 
point—as well as the numerous positive comments from participants—to mean that they appreciated the 
opportunity to contribute. We heard over and over again that they were eager to participate in follow-up calls 

because they enjoyed the first call and were glad that VA and NIH were interested in hearing their perspective. Our 
use of highly trained surveyors, including both Veterans and COVID-19 survivors, is crucial to providing the 
respectful environment that makes this possible. 
Thus, we will utilize existing data from Aim 1 to identify subjects for recruitment, as outlined above. Aim 1 has been 
approved with a HIPAA waiver and full waiver of informed consent. 
Those selected will be mailed a recruitment letter (with an option to opt out) and research information sheet 
describing the study, 1-2 months before the first planned survey. We will then follow-up with a phone call to discuss 
the study with those who do not opt out from further contact. Before each contact attempt, we will check CDW to 
ensure the Veteran is still alive and is not hospitalized. We will also use VHA’s national electronic medical record 
(EMR), by way of VistaWeb and JLV. 
In light of past practices and experiences, we have learned that it is important to Veterans and other patients to try a 
range of call times, and give them multiple opportunities to participate. In a recent study of patients recovering from 
respiratory failure by our group—reviewed and approved by Vanderbilt’s IRB and an NIH Data Safety and 
Monitoring Board—it required a median and [interquartile range (IQR)] were 6 [3-13], 4 [2-10], 5 [2-11] calls to find a 
time when it was convenient for participants to complete the survey at 3-months, 6-months and 12-months 
respectively. Other studies conducted by our long-term outcomes survey team have shown similar results. In a 
study of patients recovering from infection, it required a median and [interquartile range (IQR)] of 2 [1-3], 3 [1-4], 2 
[2-4] calls to find a time that was convenient for participants to complete the survey at 1-month, 3-months, and 6-
months post-hospital discharge. In a study of patients with sepsis-induced hypotension, a median and [interquartile 
range (IQR)] were 4 [2-8] and 3 [2-6] calls to find a time that was convenient for participants to complete the survey 
at 6-months and 12-months. Again, the 80-90% repeated participation by these cohort members (as well as their 
comments to us) suggest this call intensity was viewed as providing flexible opportunities to participate, rather than 
being unwelcome. We need to be very explicit—the higher number in the interquartile range means that 1 in 4 
participants who completed a survey required more than that number of calls to participate. Our analyses suggest 
that survivors with less education require more calls, in part because of their more variable work schedules. Setting 
a limit on calls too low will systematically exclude participants, particularly less educated participants, from the 
ability to contribute to their fellow Veterans’ recovery.  
Our approach to contacting Veterans who do not opt out is informed by these data, and informed by our study team 
members who include a COVID-19 survivor and a Veteran who has a long experience doing peer support to other 
Veterans. We will attempt to contact Veterans who do not opt out up to 12 times to obtain informed consent, only 
leaving messages 3 of those times. We will always cease contact at the Veterans’ request, at any time; if they do 
not provide informed consent within the 12 contact attempts, we will also cease. If a Veteran does not respond after 
7 call attempts, we will send a Consent Contact Letter to the Veteran, which will count as 1 of our 12 contact 
attempts. We will then make 4 more call attempts if needed. After answering all of their questions about the study, 
we will obtain verbal informed consent from those who agree to participate in the study and then proceed with the 
survey. Veterans will be reminded that they can stop the survey at any time with no adverse consequences for 
them. When telephone administration is not feasible, we will offer REDCap-administered web-based surveys or mail 
surveys. 
When Veterans cannot complete the survey themselves, we will contact their proxy as indicated by the Veteran. We 
will follow similar recruitment procedures for the proxy (i.e. mail a recruitment letter and information sheet then 
follow-up via phone). Please see the Proxy Protocol for more detailed information. Any identifiable information 
collected for Proxy’s will be stored in Access.  
Participant Incentives: Our specific approach to recruitment here is an exact replication of what we have done 
successfully in VA Ann Arbor studies including ResCU 1, ResCU 2 (IIR’s 13-079 and 17-045, Protocols 1597244 
and 1597246). Veterans will be mailed a $10 gift card for each survey to which they answer any questions at 12, 18, 

24, 30, and 36 months, totaling up to $40 (Note: not all COVID-19 waves will be eligible for all surveys waves, as 
per Table 3 above).  
 
5.3 Informed Consent Procedures  
Aim 1 
This study is a secondary analysis of data of approximately 8 million electronic VA health records. It would not be 
feasible to contact and subjects to obtain informed consent.  
Aim 2 
All local site study personnel will be trained regarding human subjects protections requirements and how to obtain 
and document informed consent for the aims outlined below.  
• 
We are requesting a waiver of documentation of informed consent.  
• 
Those selected will be mailed an information sheet describing the study, and a recruitment letter with 
an invitation to participate, including an opt-out option, 6 months after index illness. We will then 
follow-up with a phone call to discuss the study with those who do not opt out from further contact. 
Approved study team members will obtain verbal consent from those who agree to participate in the 
study and then proceed with the survey. Agreement to participant in this study will be documented in 
REDCap. Our specific approach to recruitment here is an exact replication of what we have done 
successfully in VA Ann Arbor studies including ResCU 1, ResCU 2 (IIR’s 13-079 and 17-045, 
Protocols 1597244 and 1597246). 
• 
All hard copy records will be maintained in a locked cabinet in the research team’s locked office.  
Computerized data will be stored in an electronic folder maintained on the VA Server or behind the 
VA Firewall, here: I:\Iwashyna CORC LTO. Only approved and trained study team members will 
collect and have access to study data. Only aggregate study data will be shared.   
We will ask participants if they agree to the storage, sharing, and use of their research data for future 
use in research studies related to COVID-19 infection and other health conditions. Researchers that 
access this data for future research use may be national or international and affiliated or unaffiliated 
with the VA, and may only use the data for research purposes. Future studies will have appropriate 
data use agreements in place as well as receive approval by an IRB and other oversight committees 
prior to starting any research. This specific approach, including the language used to inform the 
Veteran and of their option to opt-out, is taken directly from the VA’ Cooperative Studies large 
“EPIC3” study of biosamples in COVID-19. We note that we will not have linked biosamples, yet still 
use their stringent approach to requesting permissions. Please see the Data Repository section on 
page 25 for additional details.   
 
5.4 Inclusion/Exclusion Criteria 
Inclusion: 
• 
All VA enrollees since February 2020, which includes all patients who were tested for 
SARS-CoV-2 and are included in the VA COVID-19 Shared Data Resource in CDW. 
Exclusion: 
• 
VA employees 
 

5.5 Risk/Benefit Assessment  
Aim 2 Risks: This is a minimal risk study. Federal regulations at 45 CFR 46.111(a)(1) require that risks to subjects 
are minimized by using procedures which are consistent with sound research design. There are no study 
procedures, and there are no consent forms that pose a potential risk to privacy. Potential risks for this study 
include breach of confidentiality. All data will be collected and stored securely: 
• 
Protected Health Information: we are requesting a HIPAA authorization waiver for the 
screening portion of this study. We will collect Veteran contact information to contact them for 
surveys and interviews, as well as social security numbers and medical record numbers. This 
PHI will only be kept on Microsoft Access behind the VA firewall in a protected folder, here 
I:\Iwashyna CORC LTO, or on VA REDCap. 
• 
Survey Data: Survey data will not contain identifiable information. We will use REDCap to 
collect and temporarily store survey-based data, and other non-identifiable administrative 
data, such as consent date, survey date, and information related to 
refusal/exclusion/withdrawal. This data will be collected via the VA REDCap service by 
approved study team members at AAVA. We will use arbitrary study IDs to link a Veteran’s 
identifiable information with the information we collect via REDCap. Data will be downloaded 
on a weekly basis to a protected folder, here I:\Iwashyna CORC LTO. 
Performing this study under waiver of documentation of informed consent for Veterans will minimize risk to privacy, 
as there will be fewer paper records that have potential to cause a security breach.   
Our efforts to protect privacy and respond to survey respondent distress are based on years of experience using 
specific approaches that are an exact replication of what we have done successfully in VA Ann Arbor studies 
including ResCU 1, ResCU 2 (IIR’s 13-079 and 17-045, Protocols 1597244 and 1597246). 
To minimize the risk of a breach of confidentiality, we will perform the following steps.  First, as soon as the cohort is 
defined by the data manager, each patient in the cohort will be assigned a unique study ID.  We will then create a 
password protected electronic tracking file that maps the study participant’s identifying information to the study ID.  
No identifying information will be placed on data collection forms (e.g. surveys).  Identifiers of potential recruits and 
study participants will be required to allow for follow-up contacts and to link data from different sources during the 
data collection phase.  These will be kept in password protected files for the duration of the project and will then be 
destroyed according to VHA Records Control Schedule 10-1 (RCS 10-1) once direction for destruction of research 
records is published by VHA.  Electronic data will be maintained behind a secure firewall maintained by the Ann 
Arbor coordinating center, here I:\Iwashyna CORC LTO. All identifiable data will be kept behind the firewall, with 
access only available to key study team members.  The data manager will be responsible for creating analytic 
datasets for statisticians and investigators; these datasets will be de-identified per HIPAA guidelines.  All resulting 
research data will be presented in aggregate only.  Furthermore, study staff sign a pledge of confidentiality and 
understand that breach of confidentiality is grounds for dismissal.  Study staff are required to complete annual 
training on privacy and HIPAA, as well as triennial training on human subjects protection.  Access to participant 
data, both paper and electronic, will be given to VA project staff only and on an “as-needed” basis.  There are no 
treatments, procedures, or interventions as part of this study.  Any complaints/concerns expressed to the study staff 
by participants, providers, or anyone else affected by this study will be immediately reported to the PI and IRB, as 
will any unexpected events. 
If a participant becomes distressed, the research staff will intervene as appropriate (i.e., comfort the participant 
and/or seek assistance from the PIs).  All contacts and interviews will be conducted in a sensitive manner that 
protects the dignity of respondents.  Research staff working with participants will be trained for situations in which a 
respondent expresses the need for help with a personal or emotional problem.  If a respondent verbalizes this, the 
research team member will ask if he or she would like the contact information for a Veteran’s help line.  If the 
respondent wants such information, the staff member will provide it. In the event that a participant expresses 

suicidal or homicidal ideation, or other serious psychiatric or medical symptoms, the PI will be notified immediately 
and advise the research staff member as to the most appropriate course of action (i.e. contacting appropriate 
authorities such as a mental health professional or police).  Staff will follow the protocol for PHQ-9 suicidality 
assessment should a patient indicate suicidal thoughts while completing the PHQ-9 (see Protocol for PHQ-9 for 
more information). All project staff will have been trained on VA’s suicide risk assessment and response guidelines, 
including the capability of directly connecting suicidal participants to the 24-hour VA suicide hotline.   
We wish to draw attention to this approach to participant distress or suicidality because we believe it represents a 
particular strength of our group, both our personnel and standard operating procedures. Our team of interviewers 
are long-term career professionals with substantial training and experience; they include Veterans and COVID-19 
survivors to insure those perspectives are baked into everything our team does. We have published our approach to 
safely handling disclosure of suicidal ideation in the peer reviewed literature47, and pride ourselves on being able to 
ensure Veteran safety is always the highest priority. 
Aim 2 Benefits: there are no direct benefits from participating in this study. Contributing to this science may build a 
better understanding of the long-term impacts of COVID-19 on Veterans. 
5.6 Withdrawal of Subjects  
For Aim 2, there are no consequences for withdrawing from this study. Subjects who wish to withdraw may 
do so at any time. We will keep any data collected up to the time of withdrawal. Withdrawal will be 
documented in REDCap. 
 
5.7 Study Evaluations 
For Aim 1, using national VA electronic health record data from VINCI-CDW, we will obtain the results of all 
nasopharyngeal or oropharyngeal swabs performed for SARS-CoV-2 PCR at the time of data abstraction 
and identify positive (detected) versus negative results.  
Potential predictor variables will be extracted as of the time of testing for SARS-CoV-2 (for risk 
factors/prediction of hospitalization) and also as of the time of hospitalization (for risk factors/prediction of 
ICU admission, mechanical ventilation or death).  
Using VINCI-CDW data, we will identify the following potential predictors/risk factors which are suspected to 
be associated with adverse outcomes in COVID-19: 
Demographic factors: age, race, ethnicity, sex, socioeconomic status (including data from USVETS),  
VA consolidated community care datasets (that track use of non-VA care paid for by the VA through 
the FEE basis file and the Patient Integrity Tool - PIT) 
Case management/care coordination (VA Consult Toolbox)   
Vital signs: temperature, blood pressure, heart rate and oxygen saturation, CAN score  
Geographic factors: state, county, rural/urban, geocoded home address and associated ambient air 
pollution, distance to VA primary care and hospital 
Comorbidities: diabetes, hypertension, COPD, asthma, history of solid organ transplantation, chronic 
kidney disease, cirrhosis, obesity (BMI), congestive heart failure, stroke, cardiovascular disease, 
HIV.  
Severity of illness: Measured using variables used to calculate the VA case severity index.14 
Co-infection with other viral or bacterial pathogens: influenza A or B, respiratory syncytial virus, 
bacterial co-infection. 
Laboratory tests: CBC with differential, low lymphocyte count and high neutrophil to lymphocyte ratio 
(implicated in early studies as predictor), comprehensive metabolic panel [electrolytes and liver 
function tests], albumin (low albumin implicated as adverse predictor), brain natriuretic peptide, 

PT/INR, CRP, pro-calcitonin, magnesium, troponin and IL-6 (if available, implicated as marker of 
cytokine storm). 
Medications that may worsen outcomes: immunosuppressives, immunomodulators, corticosteroids, 
angiotensin converting enzyme inhibitors (ACE-I), angiotensin receptor blockers (ARB), non-steroidal 
anti-inflammatory drugs (NSAIDS) and thiazolidinediones which may increase the ability of the virus 
to enter the cells by increasing the expression of ACE2 receptors.  
Supportive medications: vasopressors, inotropes, antibiotics. 
Medications potentially used to treat COVID-19: remdesivir (antiviral), tocilizumab (anti-IL-6), 
lopinavir/ritonavir, chloroquine/hydroxychloroquine, azithromycin, ribavirin, interferon-alpha.  
We will also identify receipt of imaging studies (e.g., chest X-rays and CT and MRI scans), 
echocardiograms, EKGs and pulmonary function tests and their finding can be extracted from the 
associated text integration utility (TIUs) notes in CDW 
Potential long-term health outcomes we propose to extract from CDW: 
Pulmonary: home O2 prescription, abnormalities in pulmonary function tests, abnormal radiographic 
findings on chest imaging, persistent symptoms (dyspnea, cough), development of fibrotic lung 
disease, bronchiectasis, and pulmonary vascular disease. 
Renal: hospitalized acute kidney injury, development/worsening of chronic kidney disease, initiation 
of acute or long-term dialysis. 
Cardiovascular: new onset or worsening hypertension, heart failure, cardiac arrest, sudden cardiac 
death, myocardial infarction, acute coronary syndrome, abnormal EKG or echocardiography findings, 
atrial fibrillation, myocarditis, hypercoagulability, DVT, pulmonary embolism and requirement for 
cardiac interventions (e.g., CPR, LVAD, cardioversion, coronary revascularization). 
Neurological: hemorrhagic or ischemic stroke, acute neuropathy or encephalopathy, new onset 
seizure, rapidly progressive muscle weakness, meningoencephalitis, and acute myelitis. 
Mental Health: newly diagnosed or worsening depressive disorders, depression screening, PTSD 
and other anxiety and mood disorders, serious mental illness (schizophrenia, bipolar, and/or 
psychosis), suicidal ideation, suicidal attempts, deaths due to suicide, and homelessness. 
Substance Use: Presence of or treatments for unhealthy alcohol use, alcohol use disorder, tobacco 
use, stimulant use disorder (amphetamine and/or cocaine), opioid use disorders, and other drug use 
disorders (cannabis, hallucinogens and/or sedatives). 
Functional Status and Pain: development of frailty or functional impairment (e.g., injurious falls, 
wheelchair use), cognitive impairment (dementia, delirium, memory clinic visits, prescriptions for anti-
cholinergic agents) and reduced independence (e.g., inpatient rehabilitation, skilled nursing facility 
care, skilled home care and home-based primary care referrals) or new diagnoses of headache, joint 
pain, fibromyalgia, and chronic fatigue, as well as increase in number of comorbidities. 
Text Integration utilities (TIU) notes: to support more details qualitative and quantitative analysis of 
the electronic health record, we will search text and develop natural language processing (NLP) 
algorithms using the VISA search tool within VINCI to study the content of documentation in the VA-
wide electronic health records of cohort members. This will allow us to provide a rich description of 
the content of notes and create variables not available in structured data sources including 
information documentation related to care processes and interactions with the health care system. In 
order to understand the clinical context of the content of TIU notes, we will need to have access to 
the specialty/discipline and clinical role of the authors of clinical notes.  While we will present extracts 
from the electronic health record in presentations and manuscripts, the names (e.g., patients, family 
members, clinicians, medical centers) and dates that appear in TIU notes will be removed from any 
materials presented or published outside the study team so that individual quotations cannot be 
traced to patients, providers or other individuals.  In some instances, we will indicate the season 
(spring, summer, winter and fall) of the year that particular quotations appeared in patients’ electronic 
health records so that quotations can be situated in the context of the evolving pandemic but will not 
record actual dates. 
 

Medicare, Medicaid and USRDS linked Medicare data 
We will obtain Medicare, Medicaid and USRDS linked Medicare enrollment, claims and survey data for 2018 
through the most current year available through VIReC for all Veterans included in our SARS-CoV-2 
treatment and comparison groups.  
 
5.8 Data Analysis  
For Aim 1, the data analysis team will consist of Dr. Berry, Dr. Green, Dr. Ioannou, Dr. Boyko and Dr. 
O’Hare. Dr. Green will abstract the analytic variable from VINCI-CDW. Dr. Berry, the biostatistician will work 
closely with Dr. Ioannou and Dr. Boyko to create the analytic models 
Approach: We will use a combination of traditional epidemiologic studies and machine learning algorithms to 
determine the long-term, multi-systemic and functional adverse outcomes and/or syndromes related to SARS-CoV-
2.  
Comparison Groups: We will compare the incidence of long-term outcomes in infected and uninfected Veterans, 
before vs. after the pandemic and in other potentially informative control groups (e.g. untested Veterans, flu-infected 
patients.15)  
Unsupervised Machine Learning (ML) Algorithms: We will use ML algorithms to reveal unexpected clusters or 
patterns of long-term outcomes, which can then be used to generated hypothesis to be tested in more focused 
statistical analyses. Unsupervised ML analyses will be executed at 6-monthly sequential time points because we 
anticipate that patterns of long-term complications may vary depending on time since infection. 
Regression Analyses: We will build multivariable models (e.g., logit, generalized linear and time to event models) to 
evaluate associations between SARS-CoV-2 infection and the development of specific long-term outcomes that are 
either suspected a priori (see list below) or suggested by the results of unsupervised analyses. As appropriate, we 
will also consider approaches such as propensity score matching and instrumental variable analysis to account for 
measured and unmeasured confounding, respectively.  
Adverse Outcomes: We will evaluate outcomes of mortality, hospitalization, readmission, healthcare utilization and 
costs, days in the community as well as the organ-specific outcomes listed above. 
Aim 2 data analyses are described in the study methods.  
6. Reporting 
As Aim 1 is a secondary analysis of data from VINCI-CDW. The only risk is a potential breach of confidentiality of 
privacy. This is minimized by keeping the data on password protected VINCI servers at all times. In the unlikely 
event of a data security incident, we will follow current facility reporting requirements: report (within 1 hour) to the 
study PI, VA Puget Sound Information Security Officer, Privacy Officer, R&D Associate Chief of Staff, and IRB via 
the director. Protocol deviations or other violations and non-compliance will be reported according to current 
regulatory requirements: reported to the IRB within 5 business days of becoming aware of the event. 
Amy Bohnert, study Principal Investigator, will report any unanticipated problems, serious adverse events, and 
protocol deviations to the Ann Arbor VA IRB following local and institutional policies. 
7. Privacy and Confidentiality 
Aim 1 
PHI will be utilized in this data analysis study. This study involves the use of VINCI-CDW data only and no direct 
contact with participants at any time during the study. The potential risks for harm from this research include those 
that may result from invasion of privacy or breach of confidentiality.   

1. Data will always be maintained on VINCI-CDW. Only aggregate data without PII/PHI may be transferred 
from VINCI. Data will not be shared to anyone outside of the VA. 
2. Analytic datasets will identify patients only by a study ID number 
3. Only IRB approved study members will have access to the study data. 
4. All investigators with access to data will sign the VINCI confidentiality and data use agreement forms and 
have many years’ experience working with these data 
5. Participants’ protected health information will not be disclosed to anyone outside of the approved research 
team.   
6. Standard VA data security practices will be in effect at all times, and any breach of VA data security protocol 
will be reported following VA Puget Sound reporting requirements (see section 6.0 Reporting). 
Aim 2 
Protected Health Information: we are requesting a HIPAA authorization waiver for the screening portion of this 
study. We will collect Veteran contact information to contact them for surveys and interviews, as well as social 
security numbers and medical record numbers. This PHI will only be kept on Microsoft Access behind the VA 
firewall in a protected folder, here I:\Iwashyna CORC LTO, or on VA REDCap. 
Survey Data: Survey data will not contain PHI. This data will be collected via the VA REDCap service by approved 
study team members. Data will be downloaded on a weekly basis to a protected folder, here I:\Iwashyna CORC 
LTO.  
Interview Data: Interview data may inadvertently contain PHI. This data will be collected by phone and saved on 
Microsoft Access behind the VA firewall in a protected folder, here I:\Iwashyna CORC LTO, or on VA REDCap. 
Veteran PHI will be linked with survey and interview data via a cross walk that links their unique study ID.  
We will not disclose PHI to anyone outside of this study, and then only access necessary PHI for the purposes of 
this study. 
Standard VA data security practices will be in effect at all times, and any breach of VA data security protocol will be 
reported following Ann Arbor VA reporting requirements. 
8. Communication Plan 
The research team consisting of IRB approved study sites at VA Puget Sound (coordinating center), VA 
Palo Alto, VA Portland, VA Ann Arbor, and Durham VA will hold regularly scheduled meetings using 
Microsoft Teams. A study specific team will be set up on Microsoft Teams for the purposes of 
communication and secure document sharing.    
9. Information Security and Data Storage/Movement 
VA Informatics and Computing Infrastructure (VINCI) 
The VA Informatics and Computing Infrastructure (VINCI) is a Department of Veterans Affairs (VA) Health Services 
Research & Development (HSR&D) resource center that provides a secure, central analytic platform for performing 
research and supporting clinical operations activities. It is a partnership between the VA Office of Information 
Technology (OI&T) and the Veterans Health Administration Office of Research and Development (VHA ORD). 
VINCI includes a cluster of servers for securely hosting suites of databases integrated from select national VA data 

sources. VINCI servers for data, applications and virtual sessions are physically located at the VA Austin 
Information Technology Center (AITC), located in Austin, Texas. This secure enclave with 105 high-performance 
servers and 1.5 petabytes of high-speed data storage has multiple layers of security and disaster recovery to 
prevent data loss. 
To ensure the protection of Veteran data, VINCI maintains compliance with the guidelines set forth by Veterans 
Health Administration (VHA) Handbook 1200.12, Use of Data and Data Repositories in VHA Research, and all other 
applicable VA and VHA policies and regulations. In addition, VINCI has undergone all security certification activities 
in support of obtaining an Authorization to Operate (ATO). Access to VINCI resources are approved in accordance 
with the requirements of National Data Systems (NDS), VHA Handbook 1200.12, Use of Data and Data 
Repositories in VHA Research, and all other applicable VA and VHA policies and regulations. All data transferred 
from VINCI is subject to audit for compliance. 
VA-credentialed research staffs are granted access to study-specific data along with tools for analysis and reporting 
in the secure, virtual working environment through a certified VHA network computer within the VA.  If not working 
within a VA or VHA hosted office environment containing VA network access, researchers may apply for and then 
access VINCI through an approved Virtual Private Network (VPN) and Remote Desktop application. The remote 
computing environment enables data analysis to be performed directly on VINCI servers, offering a number of 
advantages: uniform security standards for access; a common point of entry for all investigators who use the data; 
tools for analysis and reporting; tighter and more consistent control of data quality; and the ability to standardize and 
update terminology and format as technology and methodology improve.  
Data collection 
VA provides care to Veterans at over 1,400 points of care. At the core of virtually all care processes is a broadly 
scoped and extensively used electronic health record system known as the Veterans Information System 
Technology Architecture (VistA). VistA provides a longitudinal view for patients receiving care nationwide including 
diagnoses, procedures, medications, labs, physiologic measurements, and text notes and reports. VA uses 130 
VistA implementations to provide electronic health record services nationwide for just over 20 million Veterans 
historically. The aggregate content of these 130 VistA systems includes 2.8 billion documents (e.g., Progress Notes, 
Discharge Summaries, Reports) accumulating at a rate of 865,000 each day; 7.2 billion lab values (+1.6 million 
each day), 4.3 billion orders (+949,000 each day), and 4.3 billion medication administrations and prescription fills 
(+809,000 each day). 
Data are aggregated from individual VistA systems to the VA Corporate Data Warehouse where it is modeled and 
prepared for use. Data published by the VHA Decision Support System (DSS), Inpatient and Outpatient Medical 
SAS (MedSAS), VA Health Economics Resource Center (HERC) cost data, Vital Status and VA-CMS linked data 
files maintained by VA Information Resource Center (VIReC), CDC National Death Index VA-linked data, and 
several other specialty data sets can be requested through VINCI. VA National Data Services (NDS) and other data 
stewards regulate the right to use the data, but VINCI facilitates the process. When study requests are approved, 
project-specific data are extracted from source databases and placed in SQL tables accessible only to the research 
team and VINCI data managers. 
Data storage 
The study team will keep all sensitive patient data on VINCI project servers maintained by VINCI OI&T personnel 
and only summarized data without PHI will be downloaded from VINCI to local storage media without data steward 
permission. Research staff will use an audited VINCI download utility to move summarized data for reports, 
presentations and publications from VINCI servers to local storage media. The VINCI download utility provides an 
audit path including a copy of the downloaded material. All study team personnel with access to sensitive patient 
data will stay current on their VA approved information security training and VA approved privacy policy training. No 
sensitive patient data will be shared with anyone who does not have a VA appointment. 

Data Access  
We will use VHA’s national electronic medical record (EMR), by way of VistaWeb and JLV. 
The only persons having access to the data will be the PIs, the study coordinator and other persons as authorized 
by the PI. The PI acknowledges that he/she has the ultimate responsibility for security of research study data. OI&T 
personnel will extract and move study data from VA Corporate Data Warehouse (CDW) source files to the secure 
VINCI project space allocated to this study by VINCI. OI&T and VINCI personnel not under the purview of the PI will 
control the servers, network, processors, firewall and software behind the VINCI firewall, including access rights 
granted to study personnel.  
Study data will be kept in accordance with the Department of Veterans Affairs record control schedule 10-1 (RCS 
10-1). Upon completion of the research project, the PI in conjunction with the VA Information Security Officer (ISO), 
and in accordance with VA policy, will ensure that, study data containing sensitive, confidential information will be 
returned to the VA, sanitized and removed from all servers, desktops, removable storage devices, etc. When any 
study personnel are no longer a part of the research team, the PI will remove that person’s access to all study data 
and notify the VA Information Security Officer of such action. VINCI personnel will be responsible for maintaining 
VINCI servers where study data will be kept. It will be VINCI personnel who will move, backup and remove study 
data from VINCI servers and who will control access to data stored on VINCI servers. The PI will request 
termination of data access rights for study personnel who are no longer part of the study team.  
All study team personnel with access to sensitive patient data will stay current on their VA approved information 
security training and VA approved privacy policy training. Sensitive patient data will not be shared with anyone who 
does not have a VA appointment.  
Data Storage Location  
Study data containing sensitive patient information will be stored on VINCI servers at the Austin Information 
Technology Center, 1615 Woodward St., Austin, TX 78772-0001. The specific server where the data will be stored 
will be chosen by VINCI personnel. The server name and location within the Austin Information Technology Center 
may be changed at any time at the discretion of VINCI personnel.  
Specialized Software  
All software used to access sensitive patient data, whether provided by VINCI, or developed by the study team, will 
run in virtual desktop sessions on VINCI servers within the Austin Information Technology Center and behind the 
VINCI firewall.  
Sensitive Patient Data on VINCI Servers 
Sensitive patient data will not leave the VINCI environment except by explicit permission of the data steward. The 
data, applications and virtual computing sessions used by the study team reside behind the VINCI firewall in the 
Austin Information Technology Center. Researchers will use Microsoft Remote Desktop Connection software to 
access virtual computing sessions from their local workstation. User interface screens are generated on a local 
workstation but all data processing is done in a virtual computing session on a remote VINCI server. At no time in 
the data analysis process is data downloaded to local servers or workstations. Sensitive patient data may appear on 
the local screen but it is not stored on the local workstation. When the user logs out of the virtual session, no trace 
of the sensitive data will be available at the local workstation. Microsoft Remote Desktop Connection software uses 
a VA-approved secure communications protocol between the local workstation and the remote VINCI server. There 
is no World Wide Web access from servers behind the VINCI firewall.  
All study data containing PHI or other sensitive patient data in electronic format or in paper format whether stored 
on VA or non-VA equipment will be maintained on password protected and encrypted computers or on VA 
REDCap, behind locked doors, in a secured building. No sensitive patient information will be included in any 

unencrypted, character-based communication between study team members or with other VA or non-VA personnel. 
Study team members with access to sensitive patient data on VINCI will all have the same data access rights and 
restrictions. 
 
 
Data Repository  
VA Puget Sound (coordinating center) has received approval for the creation of a data repository based on VA 
electronic health records and CORC telephone survey data for investigators that will enable them to examine the 
short-term and long-term outcomes in Veterans infected and uninfected with SARS-CoV-2 or other appropriate 
controls. There are no research hypotheses associated with this repository, given its purpose is establishment of a 
data repository. New hypothesis will be developed by investigators, who will then be able to make use of the data 
placed in the repository after obtaining all necessary approvals. Survey data of those who agreed to the release of 
their data will be included in the repository. For full details on the repository, please see the protocol entitled 
“COVID-19 Observational Research Collaboratory (CORC) Research Data Repository (RDR),” created and 
approved by VA Puget Sound.  
10. References  
1. 
Fraser E. Long term respiratory complications of covid-19. BMJ. 2020;370:m3001. 
2. 
Daher A, Balfanz P, Cornelissen C, et al. Follow up of patients with severe coronavirus disease 2019 
(COVID-19): Pulmonary and extrapulmonary disease sequelae. Respir Med. 2020;174:106197. 
3. 
Del Rio C, Collins LF, Malani P. Long-term Health Consequences of COVID-19. JAMA. 2020. 
4. 
Carfi A, Bernabei R, Landi F, Gemelli Against C-P-ACSG. Persistent Symptoms in Patients After Acute 
COVID-19. JAMA. 2020;324(6):603-605. 
5. 
Garrigues E, Janvier P, Kherabi Y, et al. Post-discharge persistent symptoms and health-related quality of 
life after hospitalization for COVID-19. J Infect. 2020. 
6. 
Halpin SJ, McIvor C, Whyatt G, et al. Postdischarge symptoms and rehabilitation needs in survivors of 
COVID-19 infection: A cross-sectional evaluation. J Med Virol. 2020. 
7. 
Puntmann VO, Carerj ML, Wieters I, et al. Outcomes of Cardiovascular Magnetic Resonance Imaging in 
Patients Recently Recovered From Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 
2020;5(11):1265-1273. 
8. 
Drew DA, Nguyen LH, Steves CJ, et al. Rapid implementation of mobile technology for real-time 
epidemiology of COVID-19. Science. 2020;368(6497):1362-1367. 
9. 
Lambert, N. J. & Survivor Corps. COVID-19 ‘Long Hauler’ Symptoms Survey 
Report.https://dig.abclocal.go.com/wls/documents/2020/072720-wls-covid-symptom-study-doc.pdf (2020). 
10. 
Siegelman JN. Reflections of a COVID-19 Long Hauler. JAMA. 2020;324(20):2031-2032. 
11. 
Dr. Fauci Warns This COVID Syndrome is Striking Even Healthy People. Available at:  
https://www.eatthis.com/fauci-covid-symptoms-fatigue/  Last accessed 11/30/2020. 
12. 
Prescott HC, Girard TD. Recovery From Severe COVID-19: Leveraging the Lessons of Survival From 
Sepsis. JAMA. 2020;324(8):739-740. 
13. 
Fan E, Dowdy DW, Colantuoni E, et al. Physical complications in acute lung injury survivors: a two-year 
longitudinal prospective study. Crit Care Med. 2014;42(4):849-859. 
14. 
Render ML, Deddens J, Freyberg R, et al. Veterans Affairs intensive care unit risk adjustment model: 
validation, updating, recalibration. Critical care medicine. 2008;36(4):1031-1042. 
15. 
Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in 
observational studies. Epidemiology. 2010;21(3):383-388. 
16.0 
Prescott HC, Angus DC. Enhancing Recovery From Sepsis: A Review. JAMA. 2018;319(1):62-75. 
17.0 
Prescott HC, Iwashyna TJ, Blackwood B, et al. Understanding and Enhancing Sepsis Survivorship. 
Priorities for Research and Practice. Am J Respir Crit Care Med. 2019;200(8):972-981. 
18.0 
Iwashyna TJ, Mikkelsen ME. 17.12. Medical problems in ICU survivors. In: Firth J, Colon C, Cox T, eds. 
Oxford Textbook of Medicine.2020. 
19.0 
Mikkelsen ME, Netzer G, Iwashyna TJ. Post-intensive care syndrome (PICS). In: Finlay G, ed. 

UpToDate2020. 
20.0 
Quartin AA, Schein RM, Kett DH, Peduzzi PN. Magnitude and duration of the effect of sepsis on 
survival. Department of Veterans Affairs Systemic Sepsis Cooperative Studies Group. JAMA. 
1997;277(13):1058-1063. 
21.0 
Yong E. Long-Haulers Are Redefining COVID-19. In. The Atlantic2020. 
22.0 
News N. 'Post intensive-care syndrome': Why some COVID-19 patients may face problems even after 
recovery. https://www.nbcnews.com/health/health-news/post-intensive-care-syndrome-why- some- 
covid-19-patients-may-n1166611. Published 2020. Accessed. 
23.0 
Gulliver Caspersen S. When the Doctor Is a Covid ‘Long Hauler’. In. New York Times2020. 
24.0 
Collins F. Trying to Make Sense of Long COVID Syndrome. 
https://directorsblog.nih.gov/2021/01/19/trying-to-make-sense-of-long-covid-syndrome/. Published 
2021. Accessed. 
25.0 
Del Rio C, Collins LF, Malani P. Long-term Health Consequences of COVID-19. JAMA. 2020. 
26.0 
Gorna R, MacDermott N, Rayner C, et al. Long COVID guidelines need to reflect lived experience. 
Lancet. 2021;397(10273):455-457. 
27.0 
Prescott HC, Girard TD. Recovery From Severe COVID-19: Leveraging the Lessons of Survival From 
Sepsis. JAMA. 2020;324(8):739-740. 
28.0 
Huang C, Huang L, Wang Y, et al. 6-month consequences of COVID-19 in patients discharged from 
hospital: a cohort study. Lancet. 2021;397(10270):220-232. 
29.0 
Bellan M, Soddu D, Balbo PE, et al. Respiratory and Psychophysical Sequelae Among Patients With 
COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4(1):e2036142. 
30.0 
Robillard R, Daros AR, Phillips JL, et al. Emerging New Psychiatric Symptoms and the Worsening of 
Pre-existing Mental Disorders during the COVID-19 Pandemic: A Canadian Multisite Study: Nouveaux 
symptomes psychiatriques emergents et deterioration des troubles mentaux preexistants pendanturant la 
pandemie de la COVID-19: une etude canadienne multisite. Can J Psychiatry. 2021:706743720986786. 
31.0 
Tenforde MW, Kim SS, Lindsell CJ, et al. Symptom Duration and Risk Factors for Delayed Return to 
Usual Health Among Outpatients with COVID-19 in a Multistate Health Care Systems Network - United 
States, March-June 2020. MMWR Morb Mortal Wkly Rep. 2020;69(30):993-998. 
32.0 
Bowles KH, McDonald M, Barron Y, Kennedy E, O'Connor M, Mikkelsen M. Surviving COVID-19 After 
Hospital Discharge: Symptom, Functional, and Adverse Outcomes of Home Health Recipients. Ann 
Intern Med. 2020. 
33.0 
Chopra V, Flanders SA, O'Malley M, Malani AN, Prescott HC. Sixty-Day Outcomes Among Patients 
Hospitalized With COVID-19. Ann Intern Med. 2020. 
34.0 
Donnelly JP, Wang XQ, Iwashyna TJ, Prescott HC. Readmission and death after hospitalization for 
COVID-19 in a large multi-hospital system. JAMA. 2020;in press. 
35.0 
Wang HE, Kabeto MM, Marquita G, et al. Trajectory of Cognitive Decline After Sepsis. Crit Care Med. 
2021;Online first. 
36.0 
Ioannou GN, Locke E, Green P, et al. Risk Factors for Hospitalization, Mechanical Ventilation, or Death 
Among 10131 US Veterans With SARS-CoV-2 Infection. JAMA Netw Open. 2020;3(9):e2022310. 
37.0 
Eastment MC, Berry K, Locke E, et al. Body mass index (BMI) and outcomes of SARS-CoV-2 among 
US veterans. Obesity (Silver Spring). 2020. 
38.0 
Fan VS, Dominitz JA, Eastment MC, et al. Risk Factors for testing positive for SARS-CoV-2 in a 
national US healthcare system. Clin Infect Dis. 2020. 
39.0 
Lopez L, 3rd, Hart LH, 3rd, Katz MH. Racial and Ethnic Health Disparities Related to COVID-19. JAMA. 
2021. 
40.0 
Prescott HC, Levy MM. Survival From Severe Coronavirus Disease 2019: Is It Changing? Crit Care 
Med. 2020. 
 
41.0 
Hynes D, Edwards S, Hickok A, et al. Veterans’ Use of VA Primary Care in an Era of Expanding 
Choice. Med Care. 2021;in press. 
42.0 
King G, Nielsen R, Coberley C, Pope J, Wells A. Comparative effectiveness of matching methods for 
causal inference. unpublished manuscript. 2011. 
43.0 
Iacus SM, King G, Porro G. A theory of statistical inference for matching methods in causal research. 
Political Analysis. 2019;27(1):46-68. 
44.0 
James PB, Wardle J, Steel A, Adams J. Post-Ebola psychosocial experiences and coping mechanisms 
among Ebola survivors: a systematic review. Trop Med Int Health. 2019;24(6):671-691. 
45.0 
Seminog OO, Goldacre MJ. Risk of pneumonia and pneumococcal disease in people with severe 

mental illness: English record linkage studies. Thorax. 2013;68(2):171-176. 
46.0 
Yao H, Chen JH, Xu YF. Patients with mental health disorders in the COVID-19 epidemic. Lancet 
Psychiatry. 2020;7(4):e21. 
47.0 
Phillips KA, Morrison KR, Andersen R, Aday LA. Understanding the context of healthcare utilization: 
assessing environmental and provider-related variables in the behavioral model of utilization. Health 
Serv Res. 1998;33(3 Pt 1):571-596. 
48.0 
Gelberg L, Andersen RM, Leake BD. The Behavioral Model for Vulnerable Populations: application to 
medical care use and outcomes for homeless people. Health Serv Res. 2000;34(6):1273-1302. 
49.0 
Donabedian A. Evaluating the quality of medical care. 1966. Milbank Q. 2005;83(4):691-729. 
50.0 
Moore BJ, White S, Washington R, Coenen N, Elixhauser A. Identifying Increased Risk of Readmission 
and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index. 
Med Care. 2017;55(7):698-705. 
51.0 
Wang L, Porter B, Maynard C, et al. Predicting risk of hospitalization or death among patients receiving 
primary care in the Veterans Health Administration. Med Care. 2013;51(4):368-373. 
52.0 
Wagner TH, Upadhyay A, Cowgill E, et al. Risk Adjustment Tools for Learning Health Systems: A 
Comparison of DxCG and CMS-HCC V21. Health Serv Res. 2016;51(5):2002-2019. 
53.0 
You JH, Lee SA, Chun SY, et al. Clinical Outcomes of COVID-19 Patients with Type 2 Diabetes: A 
Population-Based Study in Korea. Endocrinol Metab (Seoul). 2020;35(4):901-908. 
54.0 
Hernan MA, Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not 
Available. Am J Epidemiol. 2016;183(8):758-764. 
55.0 
Hernan MA, Sauer BC, Hernandez-Diaz S, Platt R, Shrier I. Specifying a target trial prevents immortal 
time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70-75. 
56.0 
Hernan MA. Does water kill? A call for less casual causal inferences. Ann Epidemiol. 2016;26(10):674- 
680. 
57.0 
Schwartz S, Gatto NM, Campbell UB. Causal identification: a charge of epidemiology in danger of 
marginalization. Ann Epidemiol. 2016;26(10):669-673. 
58.0 
Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability 
among survivors of severe sepsis. JAMA. 2010;304(16):1787-1794. 
59.0 
Taylor SP, Chou SH, Sierra MF, et al. Association between Adherence to Recommended Care and 
Outcomes for Adult Survivors of Sepsis. Ann Am Thorac Soc. 2020;17(1):89-97. 
60.0 
Watkins-Taylor C. Remaking a Life: How Women Living with HIV/AIDS Confront Inequality. Los 
Angeles: U California Press; 2019. 
61.0 
NHLBI PETAL Clinical Trials Network. Early Neuromuscular Blockade in the Acute Respiratory Distress 
Syndrome. N Engl J Med. 2019;380(21):1997-2008. 
62.0 
Bucy RA, Hanisko KA, Kamphuis LA, Nallamothu BK, Iwashyna TJ, Pfeiffer PN. Suicide Risk 
Management Protocol in Post-Cardiac Arrest Survivors: Development, Feasibility, and Outcomes. Ann 
Am Thorac Soc. 2017;14(3):363-367. 
63.0 
van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser 
comorbidity measures into a point system for hospital death using administrative data. Med Care. 
2009;47(6):626-633. 
64.0 
Hynes DM, Stroupe KT, Fischer MJ, et al. Comparing VA and private sector healthcare costs for end- 
stage renal disease. Med Care. 2012;50(2):161-170. 
65.0 
Humensky J, Carretta H, de Groot K, Brown MM, Tarlov E, Hynes DM. Service utilization of veterans 
dually eligible for VA and Medicare fee-for-service: 1999-2004. Medicare Medicaid Res Rev. 2012;2(3). 
66.0 
Hynes DM, Maciejewski ML, Atkins D. HSR Commentary: Linking VA and Non-VA Data to Address 
Important US Veteran Health Services Research Issues. Health Serv Res. 2018;53 Suppl 3:5133-5139. 
67.0 
Hynes DM, Koelling K, Stroupe K, et al. Veterans' access to and use of Medicare and Veterans 
Affairs health care. Med Care. 2007;45(3):214-223. 
68.0 
Kaul B, Hynes DM, Hickok A, et al. Does Community Outsourcing Improve Timeliness of Care 
for Veterans With Obstructive Sleep Apnea? Med Care. 2021;59(2):111-117. 
69.0 
Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 
2001;20(4):461-494. 
70.0 
O'Neill S, Kreif N, Grieve R, Sutton M, Sekhon JS. Estimating causal effects: considering three 
alternatives to difference-in-differences estimation. Health Serv Outcomes Res Methodol. 2016;16