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Geographicvariationinthedeliveryofhigh-valueinpatientcare.pdf
RESEARCH ARTICLE
Geographic variation in the delivery of high-
value inpatient care
John RomleyID 1,2☯*, Erin Trish2☯, Dana Goldman1,2☯, Melinda Beeuwkes Buntin3☯,
Yulei He4☯, Paul Ginsburg1,5☯
1 Price School of Public Policy, University of Southern California, Los Angeles, California, United States of
America, 2 School of Pharmacy, University of Southern California, Los Angeles, California, United States of
America, 3 Vanderbilt University, Nashville, Tennessee, United States of America, 4 University of Maryland
University College, Adelphi, Maryland, United States of America, 5 Brookings Institution, Washington D.C.,
United States of America
☯ These authors contributed equally to this work.
Abstract
Objectives
To measure value in the delivery of inpatient care and to quantify its variation across U.S.
regions.
Data sources / Study setting
A random (20%) sample of 33,713 elderly fee-for-service Medicare beneficiaries treated in
2,232 hospitals for a heart attack in 2013.
Study design
We estimate a production function for inpatient care, defining output as stays with favorable
patient outcomes in terms of survival and readmission. The regression model includes hos-
pital inputs measured by treatment costs, as well as patient characteristics. Region-level
effects in the production function are used to estimate the productivity and value of the care
delivered by hospitals within regions.
Data collection / Extraction methods
Medicare claims and enrollment files, linked to the Dartmouth Atlas of Health Care and Inpa-
tient Prospective Payment System Impact Files.
Principal findings
Hospitals in the hospital referral region at the 90th percentile of the value distribution deliv-
ered 54% more high-quality stays than hospitals at the 10th percentile could have delivered,
after adjusting for treatment costs and patient severity.
PLOS ONE | https://doi.org/10.1371/journal.pone.0213647 March 25, 2019 1 / 11
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OPEN ACCESS
Citation: Romley J, Trish E, Goldman D, Beeuwkes
Buntin M, He Y, Ginsburg P (2019) Geographic
variation in the delivery of high-value inpatient care.
PLoS ONE 14(3): e0213647. https://doi.org/
10.1371/journal.pone.0213647
Editor: Ravishankar Jayadevappa, University of
Pennsylvania, UNITED STATES
Received: July 4, 2018
Accepted: February 26, 2019
Published: March 25, 2019
Copyright: © 2019 Romley et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The primary data
source for the project is CMS Medicare claims
data. The CMS data used in this project cannot be
shared with other researchers under the terms of
our Data Use Agreement (DUA). A researcher can
request access to the same data and obtain their
own DUA through the CMS Data Request Center
(https://urldefense.proofpoint.com/v2/url?u=https-
3A__www.resdac.org_cms-2Ddata_request_cms-
2Ddata-2Drequest-2Dcenter&d=DwIGaQ&c=
clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7
CSfnc_gI&r=Dq3XCqc5W3cxuMIbAN95iFjFR
gaCqQatH6Y8Kxmh84s&m=tzfyZNw6S7D9HV2tb
Conclusions
Variation in the delivery of high-value inpatient care points to opportunities for better quality
and lower costs.
Introduction
The Institute of Medicine has taken the position that “the only sensible way to restrain costs is
to enhance the value of the health care system.”[1] Value is an elusive term in health care, but
good value tends to mean high quality in relation to cost [2], and an array of initiatives in the
private and public sectors seek to improve quality while containing costs. For example, the
Centers for Medicare and Medicaid Services implemented its Hospital-Value Based Purchas-
ing and Hospital Readmissions Reduction Programs in 2013, and has recently been rolling out
Advanced Alternative Payment Models.[3]
This growing emphasis on value has outpaced the development of practical metrics of value
performance.[4] For therapeutic drugs, cost-effectiveness has long been the standard to evalu-
ate treatments. Nevertheless, a very lively dialogue about the appropriate framework for assess-
ing value in pharmaceuticals has re-emerged. The measurement of value is still more unsettled
in other settings, such as hospital care, even as reimbursement has been tied to indicators of
quality and cost.
There are many reasons to suspect important variation in the value of care that is delivered.
As scholars at the Dartmouth Institute first discovered and others have since confirmed, health
care utilization and spending vary markedly throughout the U.S. Quality of care is also highly
variable [5–7]; for example, among Medicare beneficiaries undergoing surgery in hospitals in
2009–2010, the 30-day risk-adjusted readmission rate was more than seventy percent higher at
the 75th percentile of its distribution than at the 25th percentile.[8]
Such variability in both quality and cost—the core elements of value—is strongly suggestive
of similar variation in value. Yet information about quality and cost is not directly informative
about value in care delivery. If hospitals in one region have better quality but higher cost than
those in another region, the formers’ care can be higher or lower-value than the latters’. If qual-
ity were higher but costs were the same, one could reach the qualitative conclusion that value
is higher, but not the quantitative conclusion as to how much higher.
This study uses a production function framework to develop a value metric for inpatient
care. Focusing on a high-prevalence medical condition - - heart attacks - - we assessed the
value of the care delivered to Medicare beneficiaries hospitalized in 2013, and examined how
value varies across regions.
Methods
Providers deliver high-value care by producing good quality in relation to their costs.[2]
Accordingly, we specify and analyze a production function for inpatient care; the output and
inputs of our production function are detailed below. This analytical framework, and the
closely related framework for cost functions, have been applied extensively to hospitals.[9–23]
The primary data source for our analysis was the Medicare Inpatient File from 2013. The
medical claims in this file report patient diagnoses and procedures, demographic characteris-
tics, charges and payments, dates of service, and the identity of the short-stay hospital. The
Variation in high-value hospital care
PLOS ONE | https://doi.org/10.1371/journal.pone.0213647 March 25, 2019 2 / 11
G9wdjzW-eWfRS094roJc6vSnYA&s=
dfhmalHLUmHDB0mYWEArnnK-
up1DQr682tZB3VZvOIA&e=). The researcher
should request Research Identifiable Files. See
https://urldefense.proofpoint.com/v2/urlu=https-
3A__www.resdac.org_cms-2Ddata_request_
research-2Didentifiable-2Dfiles&d=DwIGaQ&c=
clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p
7CSfnc_gI&r=Dq3XCqc5W3cxuMIbAN95iFjFR
gaCqQatH6Y8Kxmh84s&m=tzfyZNw6S7D9H
V2tbG9wdjzW-eWfRS094roJc6vSnYA&s=
ZPDgim_PD2mCQw6Cm3gIBm38f482K89
AVhrtLz8BuD0&e=. Assistance for accessing and
using these data is made available by the Research
Data Assistance Center (ResDAC). ResDAC is a
consortium of faculty and staff from the University
of Minnesota, Boston University, Dartmouth
Medical School, and the Morehouse School of
Medicine. ResDAC provides free assistance to
academic and non-profit researchers interested in
using Medicare, Medicaid, SCHIP, and Medicare
Current Beneficiary Survey (MCBS) data for
research. We will make available the code that is
used to generate our analytic data files and
conduct the analyses, and anyone will be able to
download the code from the repository hosted
here: https://healthpolicy.box.com/s/
emfiwrf4c6nc11zotyocig9hnvzorwly. Also included
will be a “readme” file that explains how a
researcher can get access to the data and a
description of the files that will guide a researcher
through use of the code.
Funding: This research was supported by the
Commonwealth Fund and the National Institute on
Aging. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
research-identifiable 20 percent sample file that we used also reports patient ZIP codes. Where
necessary, multiple claims were “rolled up” into a hospital stay.
We identified heart attack patients according to ICD-9 codes used in the Inpatient Quality
Indicator (IQI) for risk-adjusted mortality from the Agency for Healthcare Research and Qual-
ity (AHRQ).[24] We then applied a number of additional criteria to create our final heart
attack cohort. For example, patients who were transferred to other hospitals were excluded;
complete criteria are shown in an appendix. We further limited the cohort in this study to
elderly fee-for-service beneficiaries.[23]
To analyze the delivery of hospital care, we must define the output produced and the inputs
used to produce it. We defined output to include not only quantity—as is common in studies
of production—but also quality.[25] Specifically, following prior work [23], we measured the
total number of “high-quality” stays in which the patient survived at least 30 days beyond the
admission, and avoided an unplanned readmission within 30 days of discharge. Death dates
were available from the Medicare Beneficiary Summary File; unplanned readmissions were
identified based on the algorithm used by CMS for reporting and payment purposes.[26]
These favorable outcomes are publicly reported and incorporated into current Medicare reim-
bursement; for example, mortality has been included in CMS’s Hospital Value-Based Purchas-
ing Program since its introduction in fiscal year 2013.[27] Under our approach, only high-
quality stays count toward the output that hospitals produce.
In a supplemental analysis, we also accounted for patient experience, multiplying the num-
ber of survivors without a readmission by the percentage of survey respondents who would
have definitely recommended a hospital to friends and family from the Hospital Consumer
Assessment of Healthcare Providers and Systems (HCAHPS).[28]
Our output measure makes an assumption about the tradeoff between the quantity and
quality of hospital stays. In particular, output is unchanged if quality increases by one percent
while the quantity of stays decreases by one percent. To assess the robustness of our findings to
this assumption, we performed a sensitivity analysis that used the number of stays (regardless
of outcomes) as the dependent variable, and included mortality, readmission and satisfaction
rates as explanatory variables in the production model. In addition to health care, hospitals
produce graduate medical education, and so all models included variables for residents-per-
bed thresholds used in the literature and reported in the Inpatient Prospective Payment System
(PPS) Impact File.[29–32] To address the provision of tertiary care, all analyses also included
indicator variables for delivery of advanced cardiac and neurological procedures, as defined in
the Dartmouth Atlas of Health Care.[33]
To characterize hospital inputs—the key explanatory variable in the production model - -
we followed the literature on inpatient care in using an aggregate measure.[5, 6, 20, 21, 34]
Specifically, we measured the total cost to each hospital of treating patients in the heart attack
cohort (including patients with unfavorable outcomes.) To do so, we first converted total hos-
pital charges covered by Medicare to costs based on the cost-to-charge ratios submitted by hos-
pitals to CMS as part of their cost accounting reports, which are reported in the CMS Impact
File. We then adjusted for geographic differences in labor prices using the hospital wage index,
also from the Impact File; this adjustment was applied to the labor-related portion of the base
PPS payment rate. We measured costs in 2014 US dollars, based on the medical component of
the consumer price index. In a sensitivity analysis, we did not adjust for area wages; this analy-
sis assessed the impact of wage adjustment, as there have been concerns about mismeasure-
ment of wages.[35]
We followed prior work in addressing patient severity.[5, 6, 23, 36] For each hospital, we
included variables for the proportions of patients with heart attacks in specific locations based
on diagnosis codes (for example, 410.2 for acute myocardial infarction of the inferolateral
Variation in high-value hospital care
PLOS ONE | https://doi.org/10.1371/journal.pone.0213647 March 25, 2019 3 / 11
wall.)[21] We also included the proportions of a hospital’s patients with different numbers of
Charlson co-morbidities in the medical claims for heart attack stays [37], as well as the average
socio-demographic characteristics of patients’ zip codes from the 2009–2013 American Com-
munity Survey (for example, the poverty rate and the percentage of elderly residents with dis-
abilities.)[38] To further address patient severity, we adjusted for the likelihood of death
during the hospital stay, using the risk adjustment model developed by clinical experts as an
input into AHRQ’s heart attack mortality IQI.[24] The AHRQ risk model predicts the proba-
bility that a patient dies based on her age and sex, transfer from another hospital, and All Payer
Refined-Diagnosis Related Group (APR-DRG); each APR-DRG includes its own mortality-
risk scale.[39] We included covariates for average age and proportion female, which could be
related to treatment costs as well as patient severity. We also adjusted for race and ethnicity. In
a sensitivity analysis, we excluded all diagnosis-based covariates while adding the proportion
of patients admitted from the emergency room or transferred from another hospital, because
there is some evidence of regional differences in how conditions are diagnosed.[40, 41]
In studying inpatient treatment of heart attack in 2013, we focus on the value of care deliv-
ered within areas defined by hospital referral regions (HRRs) from the Dartmouth Atlas of
Health Care.[33] Thus, a high-value HRR is one whose hospitals tended to produce more
stays - - or a better rate of high-quality stays than expected - - given its levels of treatment costs
and patient severity. We implemented our model by assuming that HRR-level value was nor-
mally distributed and applying the method of maximum likelihood.[42, 43] For representative-
ness, each hospital-level observation was weighted by the number of patients treated. Our
approach produced an estimate of the proportion of (unmeasured) variation in output result-
ing from differences between HRRs in the average performance of their hospitals, compared
to the differences around the average among the hospitals within the HRRs (this latter varia-
tion reflects hospital-level value as well as randomness.) This approach did, however, make the
assumption that value was systematically unrelated to other factors, such as patient severity
across areas. In a sensitivity analysis, we relaxed this assumption using fixed-effects regression
to assess HRRs.
These analyses produced estimates of value for each HRR, adjusted for the reliability of the
value performance signal based on the size of the area. We transformed these HRR-specific
estimates into a value index with a national mean of 100.
We explored the relationship between quality, cost and value. While our production frame-
work analyzed total costs in relation to the total number of high-quality stays, it is natural and
commonplace to assess provider cost and quality based on average performance. We therefore
compared our value index to cost per stay and the rate of high-quality stays, adjusting each for
the patient and hospital characteristics noted above in independent regressions. The appendix
provides further information on the data and analyses, including additional robustness checks.
Results
In our 2013 sample, 33,713 elderly fee-for-service beneficiaries were admitted with a heart
attack to 2,232 hospitals in 304 hospital referral regions (HRRs) with at least 11 heart-attack
stays in our database of Medicare claims. Fifty-one percent of these patients were female, and
the average age was 80 years. The cost of these hospital stays averaged $14,900 in 2014 dollars.
In terms of outcomes, 87% of patients survived at least 30 days beyond the admission, while
86% of these survivors avoided an unplanned readmission within 30 days of discharge. The
overall rate of high-quality hospital stays (survival without readmission) was 74%.
Based on quality of care, treatment cost, patient severity and hospital characteristics
(including teaching status), our analytic framework quantifies value in inpatient heart attack
Variation in high-value hospital care
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care across the U.S. The national map in Fig 1 shows the value of care delivered in each HRR,
with dark green indicating the highest quintile of value. Compared to the U.S. average of 100,
Miami’s score on our value index was 87. Thus, hospitals in Miami produced 13% fewer high-
quality hospital stays (87%—100% = -13%) than hospitals in the average U.S. region would
have been expected to produce if their costs and patients had been the same. As another exam-
ple, Everett, Washington performed better than the national average, with a value index score
of 122. Both of these scores were statistically distinguishable (with 95% confidence) from the
national average; among all HRRs, 71% were significantly different from 100.
The range of value index scores is shown in the histogram in Fig 2. About one in 8 U.S.
regions had a value index in excess of 120, thus delivering at least 20% more value than the
national average, that is, 20% more high-quality heart attack stays than the average region after
adjusting for treatment cost and patient severity. The value index for the HRR at the 90th per-
centile of the distribution, compared to the score at the 10th percentile, exhibited a ratio of
1.54:1. That is, value in care delivery was 54% higher for the region whose performance
exceeded 9 out of 10 of all regions, compared to the region whose performance exceeded only
1 out of 10 regions. For the components of value, adjusted costs and quality of care, the corre-
sponding 90–10 ratios were 1.42:1 and 1.36:1, respectively. In terms of value in care delivery,
hospitals in the median HRR would have to increase their performance by 22% to reach the
top decile (i.e., the 90–50 ratio was 1.22.) These differences between HRRs accounted for 32%
of the unmeasured variation in hospital output.
Fig 3 shows quality, cost and value in the delivery of inpatient care for heart attack. Specifi-
cally, HRRs are characterized as above- or below-average in value, and are located within
quadrants defined by average cost and quality. In the upper left quadrant, adjusted cost is
below the national average, while adjusted quality is above average. Within this quadrant, 78%
of HRRs were above-average in value, with value index scores exceeding 100. In the bottom
right quadrant, cost is above average and quality below average. Here only 13% of HRRs are
above average in value. When costs and quality are above average—the upper right quadrant—
31% of regions deliver above average value, with higher quality than would have been expected
given the high costs. When both costs and quality are below average - - the bottom left quad-
rant—some HRRs (specifically, 63%) are also above average. Among all regions with above-
average value, 55% were below average in terms of adjusted quality or above average in cost.
The regression results (reported in the appendix) imply that a region with 10% higher cost
Fig 1. Value index for inpatient heart attack care in 2013, by hospital referral region grouped into quintiles. Note:
Darker green indicates higher value.
https://doi.org/10.1371/journal.pone.0213647.g001
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than another region lies on the same production function - - and thus delivers equivalent
value - - if the higher-cost region also delivers 8% more quality (in terms of the rate of high-
quality stays).
In a sensitivity analysis, we relaxed the assumption that HRR-level value was independent
of factors such as patient severity. The resulting (“fixed effects”) value index scores for HRRs
were quite similar to the scores from the primary analysis (ρ = +0.789, p< 0.001.) In another
sensitivity analysis, we redefined the dependent variable of hospital output as the number of
heart attack stays and included the rates of 30-day survival and unplanned readmission as
regression covariates, and again found similar value index scores for HRRs (ρ = +0.848,
p< 0.001.) The value index scores were also similar when we incorporated the patient experi-
ence into hospital output (ρ = +0.942, p< 0.001.) Finally, we found that the value scores were
not highly sensitive to the adjustment of costs for area wages or to the measurement of patient
severity based on recorded diagnoses; for both of these sensitivity analysis, the correlation coef-
ficient with the results of our primary analysis exceeded +0.90. The scores were also insensitive
to a number of other robustness tests described in the appendix.
Discussion
This study has used a framework for the production of high-quality health care to develop and
implement a measure of the value of inpatient care among Medicare beneficiaries with heart
attacks in 2013. Defining high-quality hospital stays by survival at least thirty days beyond the
Fig 2. Distribution of inpatient heart attack care value index across hospital referral regions.
https://doi.org/10.1371/journal.pone.0213647.g002
Variation in high-value hospital care
PLOS ONE | https://doi.org/10.1371/journal.pone.0213647 March 25, 2019 6 / 11
admission and avoidance of an unplanned readmission within thirty days of discharge, we
documented substantial variation in the value of the care that was delivered. In a key finding,
hospitals located in the hospital referral region (HRR) at the 90th percentile of the value distri-
bution delivered 54% more high-quality stays than hospitals located in the HRR at the 10th per-
centile would have been expected to produce if their treatment costs and patient severity had
been the same. Our findings were robust to a number of alternative approaches to value
measurement.
This variability in value is of a similar magnitude to, but somewhat larger than, the variabil-
ity in regional per capita Medicare spending found in the recent study by the National Acad-
emy of Medicine (NAM) of geographic variation in U.S. health care (there the adjusted 90:10
ratio for HRRs was 1.42.)[44] The NAM study also found substantial variation in Medicare
spending within a region. In our analysis, differences between HRRs accounted for only a
third of unobserved variation in the production of high-quality inpatient care for heart attacks.
While some of the remaining variation resulted from randomness in the production process,
Fig 3. Quality, cost and value in the delivery of inpatient heart attack care among hospital referral regions. Notes: Red lines indicate national averages. High-
quality stays is defined by 30-day survival without an unplanned readmission and willing to recommend hospital. Quality and cost are adjusted as explained in
manuscript.
https://doi.org/10.1371/journal.pone.0213647.g003
Variation in high-value hospital care
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the scale of the sub-regional variation suggests that individual hospitals within HRRs differed
in the value of the care that was delivered.
This variability in the value of care points to potential opportunities for higher quality,
lower costs, or both within the health care system. The prospect of such performance improve-
ments has spurred efforts by system participants and policy makers to experiment with new
models of delivery and financing. For example, in the context of inpatient care, the Centers for
Medicare and Medicaid Services (CMS) implemented a Hospital Value-Based Purchasing Pro-
gram in fiscal year 2013, in an effort to incentivize value in care delivery.[27, 45] Initially, the
program operationalized value solely on the basis of quality-of-care metrics. CMS later pro-
posed that a cost measure—namely, Medicare Spending per Beneficiary (MSPB)—be incorpo-
rated into value-based purchasing, starting in fiscal year 2017. The proposed 2017 rule for the
inpatient prospective payment system places a weight of twenty-five percent on the “Efficiency
and Cost Reduction” domain assessed according to the MSPB measure, with the remaining
weight distributed among a number of quality-oriented domains.[46]
This kind of policy choice effectively takes a stance on the nature of the relationship
between cost and quality in the delivery of high-value care. Our own framework for value
assessment provides empirical evidence about the tradeoff between cost and quality. In partic-
ular, for inpatient heart attack care, a ten-percent decrease in treatment costs would be consis-
tent with greater value if the rate of “high-quality” stays—achieving 30 day survival without an
unplanned readmission - - did not decrease by more than 8 percent. While other consider-
ations are certainly relevant (for example, basing payment partly on improvement so as to
incentivize and reward poor performers), value-oriented policy should be informed by the
real-world relationship among quality, cost and value.
In our findings, it is noteworthy that hospitals in some areas with high risk-adjusted costs
nevertheless tended to deliver good value, because their adjusted quality of care was also high.
Conversely, some areas with relatively low quality were high-value, due to sufficiently low
costs. The Medicare Payment Advisory Commission (MedPAC) deems hospitals to be efficient
if their risk-adjusted quality of care exceeds a uniform threshold, while their costs fall below
some threshold.[47] This kind of approach can reliably identify high-value providers, achiev-
ing good specificity—that is, turning up few false positives—in the language of applied statis-
tics. In our study, roughly nine out of ten HRRs with above-average quality and below-average
cost delivered above-average value. Yet this approach can miss instances of favorable perfor-
mance (thus lacking sensitivity, resulting in false negatives.) In our study, half of regions with
above-average value were below-average with respect to quality, or above-average with respect
to cost. To be sure, our value measure includes statistical noise—even if our production model
does not introduce any systematic biases—yet it contrasts with the MedPAC approach in rec-
ognizing the tradeoff between quality and cost.
This study does have a number of limitations. To begin with, our conclusions may not be
generalizable beyond the condition studied. Nevertheless, heart attack is an important contrib-
utor to hospital admissions in the United States, accounting for 3.7 million stays in 2010.[48]
Diseases of the heart accounted for almost 600,000 deaths in that year.[49]
Another limitation is that in operationalizing hospital output, we measured quality of care
based on mortality, unplanned readmissions and patient satisfaction. While these outcomes
are of considerable salience to patients and policy makers, other outcomes (such as functional
status and quality of life after hospitalization) may also merit attention in analyses of the value
of inpatient treatment. In addition, patient outcomes reflect not only the care received while in
the hospital, but also a range of other factors (for example, post-acute care and personal behav-
ior). In measuring patient satisfaction, the HCAHPS survey was not specific to heart attack
Variation in high-value hospital care
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stays nor to Medicare patients, though reported measures are adjusted for factors including
age and service line, and CMS uses HCAHPS to evaluate cardiac care.[50]
Finally, our measure of hospital inputs did not capture the cost of services provided by phy-
sicians who were not hospital employees. Incorporating other kinds of health care costs repre-
sents a fruitful direction for future research.
Despite these limitations, this study strongly suggests that there is wide variation across the
United States in the value of the inpatient care delivered to Medicare beneficiaries, just as there
are substantial differences in quality and cost of care.[1] Variation in value points to the exis-
tence of important opportunities for achieving uniformly high-value care, with better quality
and / or lower costs for Medicare beneficiaries who suffer heart attacks. There are likely lessons
to be learned from the practices and cultures of high-performing hospitals, to then be imple-
mented by providers whose value in care delivery lags behind. In the context of quality of care,
researchers and practitioners have explored the factors that distinguish hospitals with superior
patient outcomes.[51, 52] A necessary first step in any such undertaking is measurement, in
our case, of value in care delivery. Moreover, to enhance provider performance and effective
policymaking, it is critically important to understand value in broader settings, outside of
heart attack, at sites of care other than hospitals, and indeed in the delivery of both episodes of
care and population health across multiple providers and within health systems.
Supporting information
S1 File. Appendix.
(DOCX)
Author Contributions
Conceptualization: John Romley, Dana Goldman, Paul Ginsburg.
Formal analysis: John Romley, Erin Trish, Dana Goldman, Melinda Beeuwkes Buntin, Yulei
He, Paul Ginsburg.
Funding acquisition: John Romley.
Methodology: John Romley, Erin Trish, Dana Goldman, Melinda Beeuwkes Buntin, Yulei He,
Paul Ginsburg.
Project administration: John Romley.
Supervision: John Romley.
Writing – original draft: John Romley.
Writing – review & editing: John Romley, Erin Trish, Dana Goldman, Melinda Beeuwkes
Buntin, Yulei He, Paul Ginsburg.
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