Cash incentives versus defaults for HIV testing: A randomized clinical trial

Abstract

Background
Tools from behavioral economics have been shown to improve health-related behaviors, but the relative efficacy and additive effects of different types of interventions are not well established. We tested the influence of small cash incentives, defaults, and both in combination on increasing patient HIV test acceptance.

Methods and findings
We conducted a randomized clinical trial among patients aged 13–64 receiving care in an urban emergency department. Patients were cross-randomized to 0, 1, 5,and5, and 10 incentives, and to opt-in, active-choice, and opt-out test defaults. The primary outcome was the proportion of patients who accepted an HIV test. 4,831 of 8,715 patients accepted an HIV test (55.4%). Those offered no monetary incentive accepted 51.6% of test offers. The 1treatmentdidnotincreasetestacceptance(increase11 treatment did not increase test acceptance (increase 1%; 95% confidence interval [CI] -2.0 to 3.9); the 5 and $10 treatments increased test acceptance rates by 10.5 and 15 percentage points, respectively (95% CI 7.5 to 13.4 and 11.8 to 18.1). Compared to opt-in testing, active-choice testing increased test acceptance by 11.5% (95% CI 9.0 to 14.0), and opt-out testing increased acceptance by 23.9 percentage points (95% CI 21.4 to 26.4).

Conclusions
Small incentives and defaults can both increase patient HIV test acceptance, though when used in combination their effects were less than additive. These tools from behavioral economics should be considered by clinicians and policymakers. How patient groups respond to monetary incentives and/or defaults deserves further investigation for this and other health behaviors.

Full text

RESEARCH ARTICLE
Cash incentives versus defaults for HIV
testing: A randomized clinical trial
Juan Carlos C. Montoy
1
*, William H. Dow
2
, Beth C. Kaplan
1
1Department of Emergency Medicine, University of California, San Francisco, San Francisco, California,
United States of America, 2Division of Health Policy and Management, School of Public Health University of
California, Berkeley, Berkeley, California, United States of America
*juancarlos.montoy@ucsf.edu
Abstract
Background
Tools from behavioral economics have been shown to improve health-related behaviors,
but the relative efficacy and additive effects of different types of interventions are not well
established. We tested the influence of small cash incentives, defaults, and both in combina-
tion on increasing patient HIV test acceptance.
Methods and findings
We conducted a randomized clinical trial among patients aged 13–64 receiving care in an
urban emergency department. Patients were cross-randomized to $0, $1, $5, and $10
incentives, and to opt-in, active-choice, and opt-out test defaults. The primary outcome was
the proportion of patients who accepted an HIV test. 4,831 of 8,715 patients accepted an
HIV test (55.4%). Those offered no monetary incentive accepted 51.6% of test offers. The
$1 treatment did not increase test acceptance (increase 1%; 95% confidence interval [CI]
-2.0 to 3.9); the $5 and $10 treatments increased test acceptance rates by 10.5 and 15 per-
centage points, respectively (95% CI 7.5 to 13.4 and 11.8 to 18.1). Compared to opt-in test-
ing, active-choice testing increased test acceptance by 11.5% (95% CI 9.0 to 14.0), and opt-
out testing increased acceptance by 23.9 percentage points (95% CI 21.4 to 26.4).
Conclusions
Small incentives and defaults can both increase patient HIV test acceptance, though when
used in combination their effects were less than additive. These tools from behavioral eco-
nomics should be considered by clinicians and policymakers. How patient groups respond
to monetary incentives and/or defaults deserves further investigation for this and other
health behaviors.
PLOS ONE | https://doi.org/10.1371/journal.pone.0199833 July 6, 2018 1 / 10
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OPEN ACCESS
Citation: Montoy JCC, Dow WH, Kaplan BC (2018)
Cash incentives versus defaults for HIV testing: A
randomized clinical trial. PLoS ONE 13(7):
e0199833. https://doi.org/10.1371/journal.
pone.0199833
Editor: Marcel Yotebieng, The Ohio State
University, UNITED STATES
Received: December 27, 2017
Accepted: June 14, 2018
Published: July 6, 2018
Copyright: ©2018 Montoy 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: All anonymized data
underlying the study are within the paper and its
Supporting Information files.
Funding: The project described was supported by
Award Number RC4AG039078 from the National
Institute On Aging to WHD. The funder had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
manuscript. The content is solely the responsibility
of the authors and does not necessarily represent
the official views of the National Institute On Aging
or the National Institutes of Health.

Registration
Clinical Trials NCT01377857.
Introduction
Behavioral economics approaches such as defaults and incentives for changing patient behav-
ior have been implemented across a wide range of clinical settings. Monetary incentives have
been employed as a means to modify health-related behaviors in substance abuse treatment
[1], smoking cessation [2], weight loss [3], risky sexual behavior [4,5], and some one-time or
infrequent behaviors such as immunization [6] and HIV screening [7]. Defaults have likewise
been shown to be effective at influencing behaviors; for example, the prescribing of generics
over brand-name medications [8,9], end-of-life decisions in advance directives [10], and par-
ticipation in diabetes care [11]. Because both incentives and defaults have proven effective, fur-
ther research is needed to develop our understanding regarding which types of interventions
are more effective at changing specific types of behaviors–a question best answered through
at-scale head-to-head experimentation [12].
This paper analyzes a head-head randomized trial of approaches to increase HIV testing
among emergency department patients. Identifying HIV infections remains a top priority in
addressing the ongoing HIV epidemic [13–15], but despite widespread agreement that univer-
sal opt-out screening should be adopted [16–19], failure to screen is the norm across all hospi-
tal types [20,21]. A previous publication using a subset of data from this trial (arms with no
monetary incentives) found that changing defaults for HIV testing yielded clinically significant
differences in HIV testing [22]. Here we estimate the extent to which various cash incentives
increase HIV test acceptance, compare this effect head-to-head with the effect of defaults, and
analyze whether incentives and defaults can be used together to optimize test acceptance.
Methods
We conducted a randomized clinical trial in the emergency department of an urban teaching hos-
pital and regional trauma center. Between June 18, 2011, and June 30, 2013, non-clinical staff
approached patients in the emergency department: once to offer a rapid HIV test and once for a
questionnaire. Patients were identified and approached by study staff during times not interfering
with their clinical care. Accepted tests were completed as part of their care in the department. The
ten-minute self-administered questionnaires were described generically as improving emergency
department care. After both the test and questionnaire responses were recorded, patients were fully
debriefed and written consent was obtained. Per state and federal law, and with the approval of the
institutional review board (IRB), minors were able to consent to the study. The study received IRB
approval from the University of California, San Francisco, was conducted and reported in accor-
dance with CONSORT guidelines, and was registered as clinicaltrials.gov study NCT01377857.
The protocol has been described previously and presented in greater detail in S1 Text [22].
Monetary incentives were assigned at the zone-day level: all patients in each of the four ED
zones on a given day received the same treatment assignment. Incentives were assigned to
each zone using a random-number generator, independent from the other zone assignments.
A random number generator was used to create default wording (opt-in, active-choice, and
opt-out) treatment assignments, randomized at the patient level, each with equal probability.
Patients were also randomly assigned to be offered the questionnaire either before or after the
HIV test offer. No incentive was offered for questionnaire completion. The incentive, default,
and questionnaire timing treatment assignments were cross-randomized in a factorial design.
Study staff began each shift in one of four emergency department zones and approached all
eligible patients in that zone prior to moving to the next zone. The starting zone was determined
HIV testing behavioral economics
PLOS ONE | https://doi.org/10.1371/journal.pone.0199833 July 6, 2018 2 / 10
Competing interests: The authors have declared
that no competing interests exist.

at the day level using a random-number generator, in which each zone had a 25% chance of
being the starting zone any given day. Staff were not blinded to treatment assignments.
Participants
Study inclusion criteria were: age 13–64, able to consent to HIV testing and study inclusion,
and English- or Spanish-speaking. Patients were excluded if known HIV-positive, had tested
for HIV in past three months, pregnant, in police custody, or had participated in this study in
the previous three months.
Protocol
Using a standardized script, study staff informed patients that the emergency department was
offering rapid screening HIV tests. Patients were told that the testing was non-targeted and
routine, and used a rapid assay with results available during their ED visit, approximately 1–2
hours. The test offer followed: opt-in “You can let me, your nurse, or your doctor know if
you’d like a test today,” active-choice “Would you like a test today?” or opt-out “You will be
tested unless you decline.” Finally, if the patient was assigned to a positive monetary incentive,
they were informed, “To encourage testing today we are offering a $1 cash incentive” (substi-
tuting $5 or $10 as relevant). No mention of monetary incentives was made to patients who
were assigned to the $0 treatment.
Study staff notified clinicians of patients accepting HIV tests. No pre-test counseling was
performed. Patients were informed of negative test results by their nurse or clinician. Positive
test results were disclosed by the patient’s clinician in accordance with the protocol established
by the hospital’s HIV Rapid Testing and Referral Program.
Statistics
The primary outcome was test acceptance percentage. Treatment effects were estimated with
univariate and multivariable ordinary least squares regression. Tables report raw linear regres-
sion coefficients, which are directly interpretable as the difference in the proportion of subjects
who accept an HIV test; interaction effects are similarly straightforward to interpret [23].
We also examined effects across HIV risk subgroups, per approximated Denver HIV Risk
Score (S1 Table) [24,25]. Scores depend on demographics (age, gender, race/ethnicity), risk
behaviors (sex with a male, vaginal intercourse, receptive anal intercourse, IV drug use), and
past HIV testing. We classified patients as low risk (score under 20), intermediate risk (scores
20–39), and high risk (scores 40 or higher). For patients who did not complete the questionnaire,
the risk score was estimated using available data only. While analysis by risk level was a planned
analysis, the Denver HIV Risk Score was published and validated during our data collection, so
these risk definitions were not pre-specified. Because patient responses within the same zone
and on the same day could be correlated, we clustered standard errors by day and emergency
department zone (zone-day level). Sensitivity analyses, including different model specifications
using ordinary least squares and multivariable logistic regression, are presented in the Support-
ing Information. Randomization and all analyses were performed using STATA 13.1.
Planned sample size was sufficient to detect a 5 percentage point difference in test accep-
tance between treatment arms with 80% power at a 5% significance level between the no incen-
tive treatment assignment and one of the positively-valued incentive assignments within one
of the default assignments. This 5 percentage point effect size was the minimum difference we
deemed to be clinically important. We assumed a baseline test acceptance percentage of 50%.
This predicted a sample size of 2,349 for the no incentive group and 1,175 for each of the
incentive groups (no incentive was designed to have a greater quantity than each of the
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positively-valued incentive arms). These sample sizes yield a total of 5,874 patients within each
default group, for a total of 17,622 patients in the study. Our actual enrolled sample size was
smaller than originally planned due to enrollment difficulties.
Results
Participation and randomization
Research assistants approached 10,463 patients to offer HIV tests and questionnaires. 8,715
(82.3%) of patients consented to inclusion in the study. Randomization yielded no significant
differences in demographic groups across monetary incentive treatment assignments
(Table 1); demographics according to default assignment are presented in S2 Table. The distri-
butions of demographics and chief complaints did not vary by assignment to monetary incen-
tive. Fig 1 shows the flow of patients through treatment assignments, with consent rates for
each incentive-default combination.
Treatment effects
HIV tests were accepted by 4,831 patients (55.4%). Those offered no monetary incentive accepted
51.6% of test offers; those offered $1, $5, and $10 accepted 52.6%, 62.1%, and 66.6% of tests,
respectively. These unadjusted differences showin in Table 2, Column 1 and Fig 2 reflect an abso-
lute difference between the $1 treatment and no incentive treatment of 1% (95% confidence
interval -2.0 to 3.9); the $5 and $10 treatments increased test acceptance rates by 10.5 and 15 per-
centage points, respectively (95% CI 7.5 to 13.4 and 11.8 to 18.1). Patients in the opt-in scheme
accepted 43.8% of test offers, unadjusted for incentives. Patients in the active-choice scheme
were 11.5 percentage points more likely to accept test offers (95% CI 9.0 to 14.0); those in the
opt-out scheme were 23.9 percentage points more likely to accept testing (95% CI 21.4 to 26.4).
Incentives and defaults are considered jointly under a model without interaction terms and
a model with them (Table 2, Columns 3 and 4, respectively). The estimates of the effects of
monetary incentives and of defaults are similar in the multivariable model without interactions
(Table 2, Column 3) to the estimates from each of the univariate models. When the effects of
incentives are measured separately for each default (Table 2, Column 4), each of the cash
incentives have the largest effect within the opt-in group. The $1 incentive was associated with
a 6.2 percentage point increase in test acceptance (95% CI 1.4 to 11.0); it did not increase test
acceptance among the active-choice or the opt-in group. The effects of the $5 and $10 incen-
tives were attenuated in the opt-out group.
Risk of infection
The sample of patients enrolled in the study was comprised of 40.3% low-risk, 50.4% interme-
diate-risk, and 9.3% high-risk patients. Univariate analysis shows that intermediate-risk
patients were 7.1, and high risk were 9.1 percentage points more likely to test than low-risk
patients (95% CI 5.0 to 9.3 and 5.3 to 12.8, respectively).
When the effect of incentives is calculated separately for each group, the estimates show a
similar pattern to the results from the univariate model: the $1 incentive has no effect on testing,
and the $5 and $10 each increase test acceptance. None of the interaction terms is significantly
different from 0, suggesting that the monetary incentives affected behavior equally across risk
groups. Sensitivity analyses are presented in the supplementary material: risk-specific interac-
tion terms (S3 Table), estimation with a logistic regression (S4 Table), and a back-of-the-enve-
lope calculations to account for differential study participation rates (S5 and S6 Tables).
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Fig 3 presents results from a model that estimates the effects of incentives on test uptake sep-
arately for each default within patients from each risk category: coefficients were estimated for
incentives, defaults, and risk level, and each two-way and three-way interaction between them.
Discussion
This study tested two types of behavioral economics interventions–monetary incentives and
defaults–and found evidence that each can be effective in increasing HIV test uptake. This is to
Table 1. Demographics.
VARIABLES
(1)
All subjects
(2)
No incentive
(3)
$1
(4)
$5
(5)
$10
Male 5192 (59.6) 2887 (60.1) 811 (59.5) 798 (58.4) 696 (58.6)
Age 40 (30–52) 32 (40–53) 41 (30–53) 41 (29–51) 42 (29–52)
American Indian / Alaska Native 105 (1.2) 59 (1.2) 14 (1.0) 15 (1.1) 17 (1.4)
Asian 817 (9.4) 451 (9.4) 132 (9.7) 130 (9.5) 104 (8.8)
Black 2256 (25.9) 1249 (26.0) 346 (25.4) 341 (25.0) 320 (27.0)
Native Hawaiian / Pacific Islander 259 (3.0) 140 (2.9) 33 (2.4) 41 (3.0) 45 (3.8)
White 4894 (56.2) 2676 (55.7) 785 (57.6) 777 (56.9) 656 (55.3)
Unreported 585 (6.7) 330 (6.9) 84 (6.2) 90 (6.6) 81 (6.8)
Latino
a
2152 (24.7) 1163 (24.2) 338 (24.9) 355 (26.0) 295 (24.9)
Spanish 1048 (12.0) 572 (11.9) 176 (12.9) 170 (12.4) 130 (11.0)
High school completion 5256 (60.3) 2844 (59.3) 846 (62.1) 827 (60.5) 739 (62.3)
LGBT 1028 (11.8) 589 (12.3) 164 (12.0) 149 (10.9) 126 (10.6)
Chief complaint Abdominal 1775 (20.4) 979 (20.4) 296 (21.7) 265 (19.4) 235 (19.8)
Cardiovascular 1020 (11.7) 544 (11.3) 176 (12.9) 167 (12.2) 133 (11.2)
Endocrine 107 (1.2) 61 (1.3) 16 (1.2) 12 (0.9) 18 (1.5)
General / other 572 (6.6) 288 (6.0) 88 (6.5) 106 (7.8) 87 (7.3)
GU / renal 509 (5.8) 302 (6.3) 69 (5.1) 71 (5.2) 67 (5.6)
Musculoskeletal 1388 (15.9) 763 (15.9) 210 (15.4) 212 (15.5) 203 (17.1)
Stroke 30 (0.3) 18 (0.4) 2 (0.1) 5 (0.4) 5 (0.4)
Neurologic non-stroke 523 (6.0) 296 (6.2) 64 (4.7) 82 (6.0) 81 (6.8)
Oral / dental 129 (1.5) 69 (1.4) 21 (1.5) 17 (1.2) 22 (1.9)
Psychiatric 87 (1.0) 52 (1.1) 13 (1.0) 12 (0.9) 10 (0.8)
Respiratory 660 (7.6) 372 (7.8) 111 (8.1) 94 (6.9) 83 (7.0)
Skin 651 (7.5) 386 (8.0) 76 (5.6) 106 (7.8) 83 (7.0)
Substance use 196 (2.2) 92 (1.9) 46 (3.4) 33 (2.4) 25 (2.1)
Trauma 799 (9.2) 422 (8.8) 132 (9.7) 140 (10.2) 105 (8.8)
Did not complete questionnaire 1689 (19.4) 940 (19.6) 268 (19.7) 238 (17.4) 243 (20.5)
Risk Category
Low
3510 (40.3) 1943 (40.5) 537 (39.4) 554 (40.6) 576 (48.5)
Intermediate 4394 (50.4) 2388 (49.8) 695 (51.0) 697 (51.0) 614 (51.7)
High 811 (9.3) 469 (9.8) 130 (9.5) 115 (8.4) 97 (8.2)
Previously tested for HIV 7049 (80.9) 3880 (80.8) 1105 (81.1) 1114 (81.6) 950 (80.0)
Observations 8,715 4,800 1,362 1,366 1,187
Each cell contains number (percentage); age is median and (25–75% interquartile range)
LGBT = self-identified lesbian, gay, bisexual, or transgender
Race adds to greater than 100% because each respondent could report multiple races.
a
Latino is categorized as an ethnicity, separate from race.
https://doi.org/10.1371/journal.pone.0199833.t001
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our knowledge the first study to directly compare two types of behavioral economics interven-
tions in any health behavior context. Recent research has evaluated how to target a single type
of intervention, but has not yet compared different types of interventions [26]. In large part
this literature has explored repeated behaviors or behavior over time, such as medication
adherence and weight loss [27,28].
Fig 1. Flow diagram. Of 10,463 patients approached for inclusion in study, 8,715 consented. Because patientswere retrospectively consented, no
patients were excluded after being consented for inclusion.
https://doi.org/10.1371/journal.pone.0199833.g001
Table 2. OLS raw differences.
Variables
Incentives
(1)
Defaults
(2)
Incentives and defaults
(3)
Incentives, defaults, and interactions
(4)
Incentives
$1 0.01 (0.016) 0.012 (0.016) 0.062 (0.025)
$5 0.105 (0.017) 0.106 (0.016) 0.142 (0.029)
$10 0.150 (0.016) 0.147 (0.016) 0.182 (0.027)
Defaults
Active-choice 0.115 (0.013) 0.117 (0.013) 0.133 (0.018)
Opt-out 0.239 (0.013) 0.239 (0.013) 0.279 (0.017)
Incentives x Defaults
$1 x Active-choice -0.071 (0.035)
$1 x Opt-out -0.083 (0.036)
$5 x Active-choice -0.023 (0.037)
$5 x Opt-out -0.086 (0.040)
$10 x Active-choice -0.006 (0.038)
$10 x Opt-out -0.095 (0.036)
Constant 0.516 (0.008) 0.437 (0.010) 0.399 (0.011) 0.380 (0.013)
Observations 8,715 8,715 8,715 8,715
Dependent variable = acceptance of HIV test. Each column shows percentage point difference in HIV test acceptance estimated from an ordinary least squares
regression (standard error). Omitted categories for incentives, defaults, and risk groups: no incentive, opt-in, and low risk, respectively.
Standard errors are clustered at zone-day level.
 p<0.01,
 p<0.05,
p<0.1
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The interventions were tested both separately and in combination with each other with a
rigorous design that included random assignment to small monetary incentives and patient-
level randomization to a one-sentence variation in test offer, with all else held constant. The
effects were persistent across all model specifications and levels of patient risk of infection,
though the effects were somewhat attenuated when defaults and incentives were used together:
the $1 incentive increased test acceptance in the opt-in but not the other default settings, and
the $5 and $10 incentives were less effective under the opt-out default than the other default
settings. In general, higher-risk patients tested at higher rates than lower-risk patients and had
smaller responses to treatments. Among all treatment assignments, opt-out had the largest
effect, followed by the $10 incentive.
Compared to previously published work from this study, which demonstrated that defaults
significantly affect patient behavior, this study places two classes of behavioral economics nudges
in direct comparison with nearly double the sample size. We again confirmed that active-choice
is a category distinct from opt-in, both providing policymakers with clearer guidance on how to
implement policies and also bringing this field in closer alignment with the existing literature in
psychology and economics [29,30]. Despite being universally present in health care, defaults
have been understudied in medicine and this topic deserves further attention.
The proportion of patients accepting testing may vary in other settings and with other pop-
ulations as compared to those within this single-center study. However, patients with a wide
Fig 2. HIV consent by treatment assignment. Proportion of patients accepting an HIV test according to treatment assignment: 2a monetary incentives, 2b
defaults, and 2c incentive x default combinations.
https://doi.org/10.1371/journal.pone.0199833.g002
Fig 3. HIV consent by incentive-default treatment assignment and risk of infection. Proportion of patients accepting an HIV test according to incentive-
default treatment assignment, stratified by risk group. Risk of infection was estimated by the Denver HIV Risk Score: <20 low risk, 20–39 intermediate risk,
40 high risk.
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range of demographics, chief complaints, and risk factors for HIV were included in the study.
Although the particular percentages may be quite different, the patterned responses to small
monetary incentives and also to op-in, active-choice, and opt-out test offers may be expected
for HIV testing in other settings as well as for decisions about other medical tests.
By blinding patients to the study itself and also to its components, the retrospective
informed consent design has the advantage of minimizing or even eliminating many potential
sources of bias but introduces the risk of bias from post-randomization withdrawal. We see
evidence of this: the proportion of approached patients who participated in the study increases
monotonically with monetary incentives. However, the difference in participation rates is
small and did not drive the results here; sensitivity analysis did not change the primary results.
The three monetary incentive values used in this study are somewhat arbitrary but are on a
scale that might reasonably be chosen by a hospital or health system. The $1 serves to test if, as
previously found [7], whether a monetary incentive is offered is more important than the value
of the incentive–a finding we did not replicate. We chose immediate cash incentives in order to
maximize the response under the prediction from behavioral economics that equivalent pay-
ments such as a check given immediately or cash given later would likely yield smaller increases
in test acceptance rates, as would a deduction of the same dollar amount from one’s hospital bill.
Our ED population had few barriers to testing: there was no travel time, scheduling, written
consent, or, in most cases, additional blood draws. But, even under the $10, opt-out treatment
assignment, test uptake did not approach 100%. This result is cause for pessimism about the
potential for small incentives, defaults, or both to achieve the target of universal screening.
This suggests that some patients truly believe the test is not worthwhile, and for others the psy-
chological costs of learning one’s HIV status are too high. This poses a challenging question of
how to achieve universal testing and identify all existing cases of HIV infection. Nevertheless,
among high-risk patients the combination of incentives and defaults raised test acceptance
from 48% in the $0 opt-in arm to 80% in the $10 opt-out arm.
This study directly compares two behavioral economics interventions and adds to the exist-
ing evidence that small interventions can have significant effects in directing patients toward
more optimal health-related behaviors. Our results have the potential to help inform how to
structure HIV test offers for other emergency departments as well as other health care settings.
The finding that, on average, moving from opt-in to opt-out testing influenced behavior more
than even the largest incentive reinforces the notions that the medicine is not just a transac-
tion, and what we say to patients matters. This field is still relatively new, and much remains to
be learned about how and in what settings to use behavioral economics approaches to improv-
ing health-related behavior. How patients respond to monetary incentives and defaults
deserves further investigation for this and other health problems.
Supporting information
S1 Text. Protocol.
(DOCX)
S1 Table. Denver risk score components.
(DOCX)
S2 Table. Demographics.
(DOCX)
S3 Table. Risk-specific results.
(DOCX)
HIV testing behavioral economics
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S4 Table. Alternate models.
(DOCX)
S5 Table. Sensitivity analysis.
(DOCX)
S6 Table. Sensitivity analysis.
(DOCX)
Author Contributions
Conceptualization: Juan Carlos C. Montoy, William H. Dow, Beth C. Kaplan.
Data curation: Juan Carlos C. Montoy.
Formal analysis: Juan Carlos C. Montoy, William H. Dow.
Funding acquisition: Juan Carlos C. Montoy, William H. Dow, Beth C. Kaplan.
Investigation: Juan Carlos C. Montoy.
Methodology: Juan Carlos C. Montoy.
Project administration: Juan Carlos C. Montoy, Beth C. Kaplan.
Resources: Beth C. Kaplan.
Software: Juan Carlos C. Montoy.
Supervision: William H. Dow.
Writing – original draft: Juan Carlos C. Montoy.
Writing – review & editing: Juan Carlos C. Montoy, William H. Dow.
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