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An international comparison of deceased and living organ donation/transplant rates in opt-in and opt-out systems: A panel study

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An international comparison of deceased and living organ donation/transplant rates in opt-in and opt-out systems: A panel study

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Background Policy decisions about opt-in and opt-out consent for organ donation are based on limited evidence. To fill this gap we investigated the difference between deceased and living organ donation rates in opt-in and opt-out consent systems across a 13 year period. We controlled for extensive covariates and estimated the causal effect of consent with instrumental variables analysis. Method This panel study used secondary data analysis to compare organ donor and transplant rates in 48 countries that had either opt-in or opt-out consent. Organ donation data were obtained over a 13-year period between 2000 and 2012. The main outcome measures were the number of donors, number of transplants per organ and total number (deceased plus living) of kidneys and livers transplanted. The role of consent on donor and transplant rates was assessed using multilevel modeling and the causal effect estimated with instrumental variables analysis. Results Deceased donor rates (per-million population) were higher in opt-out (M = 14.24) than opt-in consent countries (M = 9.98; Β = −4.27, 95% confidence interval (CI) = −8.08, −0.45, P = .029). However, the number of living donors was higher in opt-in (M = 9.36) than opt-out countries (M = 5.49; B = 3.86, 95% CI = 1.16, 6.56, P = .006). Importantly, the total number of kidneys transplanted (deceased plus living) was higher in opt-out (M = 28.32) than opt-in countries (M = 22.43; B = −5.89, 95% CI = −11.60, −0.17, P = .044). Similarly, the total number of livers transplanted was higher in opt-out (M = 11.26) than opt-in countries (M = 7.53; B = −3.73, 95% CI = −7.47, 0.01, P = .051). Instrumental variables analysis suggested that the effect of opt-in versus opt-out consent on the difference between deceased and living donor rates is causal. Conclusions While the number of deceased donors is higher than the number of living donors, opt-out consent leads to a relative increase in the total number of livers and kidneys transplanted.
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R E S E A R C H A R T I C L E Open Access
An international comparison of deceased and
living organ donation/transplant rates in opt-in
and opt-out systems: a panel study
Lee Shepherd
1,2
, Ronan E OCarroll
2
and Eamonn Ferguson
3*
Abstract
Background: Policy decisions about opt-in and opt-out consent for organ donation are based on limited evidence.
To fill this gap we investigated the difference between deceased and living organ donation rates in opt-in and
opt-out consent systems across a 13 year period. We controlled for extensive covariates and estimated the causal
effect of consent with instrumental variables analysis.
Method: This panel study used secondary data analysis to compare organ donor and transplant rates in 48
countries that had either opt-in or opt-out consent. Organ donation data were obtained over a 13-year period
between 2000 and 2012. The main outcome measures were the number of donors, number of transplants per organ
and total number (deceased plus living) of kidneys and livers transplanted. The role of consent on donor and transplant
rates was assessed using multilevel modeling and the causal effect estimated with instrumental variables analysis.
Results: Deceased donor rates (per-million population) were higher in opt-out (M=14.24)thanopt-inconsent
countries (M=9.98;Β=4.27, 95% confidence interval (CI) = 8.08, 0.45, P= .029). However, the number of living
donors was higher in opt-in (M= 9.36) than opt-out countries (M=5.49;B= 3.86, 95% CI = 1.16, 6.56, P=.006).
Importantly, the total number of kidneys transplanted (deceased plus living) was higher in opt-out (M=28.32) than
opt-in countries (M= 22.43; B=5.89, 95% CI = 11.60, 0.17, P= .044). Similarly, the total number of livers transplanted
was higher in opt-out (M= 11.26) than opt-in countries (M=7.53; B=3.73, 95% CI = 7.47, 0.01, P= .051). Instrumental
variables analysis suggested that the effect of opt-in versus opt-out consent on the difference between deceased and
living donor rates is causal.
Conclusions: While the number of deceased donors is higher than the number of living donors, opt-out consent leads
to a relative increase in the total number of livers and kidneys transplanted.
Keywords: Opt-in consent, Opt-out consent, Deceased organ donation, Living organ donation
Background
With the aim to increase the number of organs for
transplantation, national health authorities face the con-
undrum of whether they should change from an opt-in
to an opt-out consent system or visa-versa, or stick with
their current system. This is a key health policy question
facing all health services worldwide. Indeed, within the
UK, Wales has recently decided to change from opt-in to
opt-out consent. This is an area where opinions are strong
and evidence is weak, and there is little well-controlled
scientific evidence on which to base policy decisions. The
aim of this research is to address three key gaps in know-
ledge by examining the effect of opt-in versus opt-out le-
gislation (1) on both the number of deceased and living
donations, (2) on transplantation rates for different types
of organs and (3) as a causal factor.
There are sound reasons to believe that deceased
organ donation rates will be lower in opt-in than opt-out
consent systems. First, opt-out consent systems are likely
to bridge the gap between peoples intentions and their
behavior by removing the need to undertake any actions
in order to become an organ donor [1]. Second, people
may believe that defaults are policy-makersrecommended
* Correspondence: eamonn.ferguson@nottingham.ac.uk
3
School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK
Full list of author information is available at the end of the article
© 2014 Shepherd et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Shepherd et al. BMC Medicine 2014, 12:131
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course of action and act in accordance with this [1,2].
As a result, people should be more inclined to donate
their organs when the default is to be a donor (such as
in opt-out countries) than when the default is not to
donate ones organs (such as in opt-in countries). Finally,
people are likely to view failing to donate onesorgansas
more significant in opt-out than opt-in countries [3]. In line
with these arguments, research has found that donation
rates for heart beating donors diagnosed as brain stem dead
in intensive care (that is, donation after brainstem death or
DBD donors) are higher in opt-out than opt-in consent
countries [4-8] and that organ donor rates increase after
the introduction of opt-out consent [9].
The above evidence suggests that the introduction of
opt-out consent is likely to increase the number of organ
donors. However, there are three major problems with
this research. First, the majority of the studies focused
specifically on DBD donor rates. Although an important
index of organ donation, the effect of opt-out consent
becomes less clear-cut once other forms of organ dona-
tion are considered, such as living organ donation. There
are good reasons why the majority of existing research
has focused on the effect of consent on the deceased ra-
ther than the living donor rate; mainly that deceased do-
nors produce a greater number and variety of organs.
However, given that the majority of people on transplant
waiting lists require a kidney and that more than a third
of the total kidneys donated in the UK between 2012
and 2013 came from living donors [10], it seems reason-
able to suggest that research should assess the effect of
consent on both types of donation. This issue is espe-
cially important given that living kidney transplants are
greater in opt-in than opt-out countries [11]. Also while
the focus of consent type policy is specifically targeted at
deceased donations, it is not clear how, or even if, opt-in
or opt-out policies influence the living donations rate
epiphenomenally. That is, an intervention targeted at one
behavior influences a second potentially related behavior
that it is not the target for. Second, previous research has
focused on the number of deceased donors regardless of
the type of organ. It is unclear whether opt-out consent
increases the number of transplants regardless of organ
type. It is important to acknowledge that the number of
transplants will be influenced by the number of donors, as
well as other factors, such as quality of health care pro-
vided and availability of trained surgeons. Given that the
majority of transplants are for kidneys [10], it is possible
that the higher levels of donation in opt-out consent sys-
tems may be predominantly due to this specific organ and
that there is little difference for other organs, such as
hearts, lungs and livers. Although there is some research
assessing the role of opt-out consent on specific types of
organ transplants [6,11], to our knowledge no previous
study has compared transplant rates for a variety of organs
in a large number of opt-in and opt-out consent countries,
over an extended period of time, while attempting to con-
trol for as many potential covariates as possible.
A third key problem with research in this area is that
it is inevitably observational rather than experimental.
As a result, causality cannot be inferred. Fabre and col-
leagues [12] argue that because Spainsriseinorgan
donation rates occurred 10 years after the introduction
of opt-out consent, such legislation is unlikely to play
an immediate causal role. Spains rise in donation rates
occurred after the introduction of what is now known
as the Spanish Model. This involved creating a trans-
plant coordination network that operated at different
levels (hospital, regional and national level), placing
transplant coordinators at each procurement hospital,
and improving the quality of information received by
the general public [13]. Researchers have argued that
the positive impact of opt-out consent on deceased
donor rates may be due to the introduction of this
model rather than opt-out consent alone [12]. However,
because an effect takes time to emerge does not mean it is
not a causal factor that produced the intervening changes
that led to the increase. It should be seen as part of a
causal change rather than a single casual factor. Therefore,
consent type may still play a causal role. In such situa-
tions where it is impractical to conduct experimental
research, instrumental variable (IV) regression models
areonemethodthatmaybeusedtoestimateacausal
relationship [14].
Theaimofthepresentstudywastoaddressthese
limitations and extend previous research by assessing
the effects of opt-out versus opt-in consent legislation
on (1) the number of deceased and living donors per
million people in the population (or pmp), (2) the
number of deceased (kidneys, livers, hearts and lungs)
and living (kidneys and livers) transplants that occur
for each type of organ (pmp), and (3) whether a causal
relationship could be estimated using IV regression. In
line with previous research [1,5,11], we tested trans-
plant rates and donor rates relative to the population
size (that is, pmp) to avoid the number of people in the
population biasing the estimates. The panel study reported
in this paper investigated organ donation and transplant
rates in 48 countries (23 opt-in and 25 opt-out) between
the years 2000 to 2012. Moreover, we also obtained data
on the following covariates to ensure that any effects of
opt-out versus opt-in consent system on organ donation
was not explained by the following variables: road traffic
accident mortality rate, gross domestic product (GDP; per
capita, US $), the number of hospital beds (per 10,000
population) and the percentage of the population that
self-identified as Catholic. In the IV regression analyses
the instruments used were the legal system (whether the
country is more likely to use civil or common law) and the
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percentage of people in each country involved in non-health
based philanthropy (for example, volunteering time to an
organization, helping a stranger and donating money to a
charity; for a justification of these instruments, see below).
Methods
To be included in the study a country must have pub-
lished its organ donation and transplantation statistics on
the Transplant Procurement Managements International
Registry of Organ Donation and Transplantation
(IRODaT). This is an open and free database that is
easily accessible for researchers. The data are provided
by officials in each country, who are likely to be part
of health ministries or members of national transplant
organizations. At the time when the data were collected
there were data available for 88 countries. A total of 48 of
these countries met our inclusion criteria (23 opt-in and
25 opt-out) and were included in the sample (for details,
see Figure 1 and Table 1). Complex longitudinal models
usually require at least three data points [15]. Therefore,
we only included countries with three or more years of
deceased and living organ donor data between 2000 and
2012 to ensure that a reliable estimate was obtained.
Countries were also excluded if they had a population
below two million in 2000 because the reported statistics
are based on donation per million population and countries
with small populations would bias these data [5]. This is
likely to occur through the creation of outliers and by
inflating the mean donor and donation rate of the con-
sent system under which these countries operate.
Countries were also excluded if they had inconsistent
organ donation legislation across the nation, had chan-
ged their consent system in the 13 year period under
investigation, had paid organ donor programs, or high
levels of organ transplants occurring abroad (that is, a
high number of residents going abroad to receive a
transplant [11]). Moreover, we also excluded countries
that were reported to have high levels of organ trafficking
Excluded
1) Inactive deceased or living organ donation
program: 10
2) < 3 years deceased or living transplantation
data: 12
3) Population less than 2 million: 5
4) Inconsistent legislation across the nation: 3
5) Altered legislation between 2000 and 2012: 2
6) Legal paid organ donation program: 1
6) Large number of transplantations occurring
abroad: 1
7) High levels of organ trafficking: 2
9) Mixed common and civil law system: 4
Figure 1 Study flow diagram.
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and countries that had a mixture of civil and common law
(see Table 2).
Data sources
The number of deceased and living donors, as well as
the number of transplants per organ, were obtained
from the IRODaT database. The deceased organ donor data
consisted of both DBD and donation after cardiac death
(DCD) donors (if applicable). In line with IRODaT, any
donor or transplant score with a value of zero was regarded
as missing data. These data did not differentiate between
adult and child donors. Each countrys organ donation con-
sent legislation (scored 1 for opt-in and +1 for opt-out)
was obtained from previous research [4,5,11,17,18,21]. There
were some countries that were either not included in
this research or were categorized as having opt-in con-
sent in some studies and opt-out consent in other studies.
For these countries, legislation data was obtained from
websites belonging to the government or professional
organizations (see Table 1). In line with previous research
[1,4,5,7,11], GDP, whether the legal system was more in-
fluenced by common or civil laws (scored 1 for common
law and +1 for civil law), the percentage of self-identified
Catholics (scored 1for25%, 0 for >25% to 75%, and 1
for >75%), number of hospital beds (per 10,000 population),
and road traffic accident (RTA) fatality rate pmp were en-
tered into the analysis as covariates. GDP was entered into
the analysis because this variable is positively associated
with deceased organ donation rates [11]. Previous research
Table 1 Countries included into the analyses
Country Consent
system
Source of consent information
Argentina Opt-out 4,11,17
Australia Opt-in 4,5,11,16,17
Austria Opt-out 4,5,11,16,17
Belarus Opt-out 16
Belgium Opt-out 4,5,11,16,17
Brazil Opt-in
a
4,16
Bulgaria Opt-out 4,5
Canada Opt-in 4,5,11,16,17
Columbia Opt-out 11,16,17
Costa Rica Opt-out 4,16
Croatia Opt-out 4,5,11,16,17
Cuba Opt-in 11,16
Czech Republic Opt-out 4,5,11,16,17
Denmark Opt-in 4,5,11,16,17
Ecuador Opt-out 11,16
Finland Opt-out 4,5,11,16
France Opt-out 4,5,11,16,17
Germany Opt-in 4,5,11,16
Greece Opt-out 4,5,11
Guatemala Opt-in 11
Hong Kong Opt-in
b
www.organdonation.gov.hk/eng/
knowmore.html
Hungary Opt-out 4,5,11,17
Republic of Ireland Opt-in 4,5,11,16
Israel Opt-in
c
http://www.health.gov.il/English/Topics/
organ_transplant/Pages/organs_
donors.aspx
Italy Opt-out 4,5,11,16,17
Japan Opt-in 5,11,16
Latvia Opt-out 4,5
Lebanon Opt-in 17
Lithuania Opt-in
d
http://www.transplantacija.lt/content/
apiemus.en.html
Malaysia Opt-in 11,16,17
Mexico Opt-in 11,16
The Netherlands Opt-in 4,5,11,16,17
New Zealand Opt-in 4,5,11,16,17
Panama Opt-out 4,11
Poland Opt-out 4,5,11,16,17
Portugal Opt-out 4,5,11,17
Puerto Rico Opt-in 18
Romania Opt-in 4,5,16
Russia Opt-out 16
Singapore Opt-out 4,11,16
Slovak Republic Opt-out 4,5,11,16,17
Table 1 Countries included into the analyses (Continued)
Spain Opt-out 4,5,11,16
Sweden Opt-out 4,5,11,16,17
Taiwan Opt-in
b
http://www.nhi.gov.tw/English/webdata/
webdata.aspx?menu=11&menu_id=594&
WD_ID=594&webdata_id=3172
Tunisia Opt-out 11,16
UK Opt-in 4,5,11,16,17
USA Opt-in 4,5,11,16,17
Venezuela Opt-in 4,11,16
a
Brazils opt-out law was abolished in 1998 [16]. Therefore, this was regarded
as an opt-in country despite the fact that the opt-in legislation was not in in
place until 2001 [4].
b
According to the source people are required to join a
register if they wished to donate their organs. Therefore, this was regarded as
an opt-in country.
c
Israels consent legislation has been categorized as opt-in
by some studies [17,18] and opt-out by others [4,5]. According to the source
people are required to sign a donor card to testify that they wish to donate
their organs after their death. Therefore, we regarded Israel as an opt-in country.
d
The majority of research [4,5,11,17] regards Lithuania as an opt-in country.
However, one study [18] regarded this country as opt-out. In this country people
can register their consent and dissent for organ donation. If people have not
registered this, then consent is requested from next-of-kin. If the next-of-kin
cannot be contacted, donation can only occur if it is an emergency. In all other
cases consent is required from either the deceased or their next-of-kin. Therefore,
we concurred with the vast majority of previous research in categorizing
Lithuania as an opt-in country.
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has found that opt-out countries are likely to be predomin-
antly Catholic [5]. Moreover, deceased donor rates are
greater in countries with a high proportion of Catholics [8].
Therefore, in line with previous research [5,11], it was im-
portant to control for this variable. The number of hospital
beds was included in the model as an estimate of the qual-
ity of the healthcare infrastructure in each country. This en-
sured that any effect of consent was not due to opt-out
countries having a high-quality healthcare infrastructure.
Finally, countries with higher levels of RTA mortalities may
be more likely to have a large supply of donor organs
[5,7,11]. The inclusion of these covariates ensured that any
effect of consent legislation on organ donation was not in
fact due to these factors. We obtained population data from
the US Census Bureau in order to calculate the pmp esti-
mates. Finally, the type of legal system was entered into the
analysis because this variable is associated with the consent
system and, as such, was also examined as an IV [5]. The
sources of all the data are presented in Table 3.
Statistical analysis
Organ donation and transplant rates across the 13-year
period (2000 to 2012) were nested within countries. As
such, multi-level modeling (MLM) is the appropriate
statistical technique to assess the effect of country level
variables (for example, consent) on the within country
variation in donation rates. If this effect of nesting is not
accounted for in the statistical model the standard errors
(and, hence, significance) will be distorted by conflating
variation at one level (donation rate over time) with an-
other (country). Thus, the use of MLM provides a more
accurate overall assessment of the effect of consent
(which varies across country) on donation rate (which
varies within country). In each analysis we excluded
countries that had not transplanted the organ in question
over the 13-year period because this indicated an inability
or reluctance to transplant this organ. Consent system
(opt-in versus opt-out) was entered into the model as a
factor. Legal system, GDP, RTA pmp, hospital beds, and
the percentage of Catholics were entered as covariates as
between countries (Level 2 variables). These covariates
were all time invariant. The mean GDP over the 13-year
period was used in the analysis.
1
Years (2000 to 2012)
were coded 1 to 13 and were a repeated measure (Level 1)
factor. Organ donation/transplantation rates per year were
the outcome variables. The continuous Level 2 variables
(GDP, RTA and hospital beds) were grand mean centered.
The intercept was based on the mean level GDP, RTA and
hospital beds, and the proportion of countries in each of
the legal and Catholicism categories. The initial models
were random intercept models with year specified as a
random slope. These analyses were repeated for both de-
ceased and living donor rates, and for the transplantation
rates for each organ. These MLM analyses were con-
ducted in SPSS (Version 21). The multi-level path model
was specified in Mplus 7 [22].
The IV regression approach attempts to untangle
problems such as reverse causation (that is, whether
consent affects donation rates or visa-versa) and missing
variables in the model. IV regression estimates the causal
relationship between the endogenous predictor (con-
sent), by identifying IVs (correlated with the predictor,
unrelated to the outcome and orthogonal to the errors).
As the instrumental variable is associated with the pre-
dictor (consent) and not the outcome (or the error term)
it breaks the predictor into the part associated with error
and the part that is not. By isolating the part of the pre-
dictor that is not associated with error it is possible to
Table 2 Countries excluded from the analyses
Reason Countries excluded for this reason
Inactive deceased or living donor program
a
Armenia, Azerbaijan, Bangladesh, Egypt, El Salvador, Georgia, India, Libya, Luxembourg, Macedonia
Less than three years of living or deceased data
b
Bahrain, Bolivia, Brunei, Jordan, Kuwait, Morocco, Nicaragua, Pakistan, Paraguay, Peru, Slovenia,
Trinidad and Tobago
Population less than two million
c
Cyprus, Estonia, Iceland, Malta, Qatar
Inconsistent consent legislation
d
Dominican Republic, Switzerland, Turkey
Changed legislation in 13 year period
e
Chile, Uruguay
Legal paid system
d
Iran
Large number of transplants occurring abroad
d
Saudi Arabia
Reports of high levels of organ trafficking
f
Moldova, Ukraine
Mixed civil and common laws
g
Norway, Philippines, South Africa, South Korea
a
Data from the IRODaT database indicated that the country had not performed the transplant in the 13 year period under investigation.
b
Based on data from the
IRODaT database.
c
Based on population statistics from the U.S. Census Bureau. This was based on the population for 2000 because this was the first year under
investigation.
d
Based on previous research [11].
e
Based on previous research [11,17].
f
This was based on news reports [19,20].
g
Based on data from the World
Factbook. Although parts of the USA (Louisiana) and Canada (Quebec) use civil law, these were regarded as common law countries because this is the dominant
legal system. Similarly, although Spain has regional variations in the legal system it predominantly uses civil law and was, therefore, categorized as such. Although
Japans legal system is influenced by Anglo-American law, it is based on the German model of civil law and was, therefore, regarded as a civil law country.
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Table 3 Sources of the data for the study
Variable Source Website Permission needed
Organ donation
data
International Registry of Organ Donation and Transplantation* http://www.irodat.org/ (accessed 19/08/2013) No: http://www.irodat.org/?p=about
GDP per
capita (US$)
International Monetary Fund (2013), World Economic
Outlook database, Data and Statistics IMF*
http://www.imf.org/external/pubs/ft/weo/2013/01/weodata/
download.aspx (accessed 12/08/2013)
Yes: Permission Sought and Attained
The World Bank: GDP per capita: World Development
Indicators
http://data.worldbank.org (accessed 12/08/2013) No:http://web.worldbank.org/WBSITE/EXTERNAL/0,,content
MDK:22547097~pagePK:50016803~piPK:50016805~the
SitePK:13,00.html
Legal system Central Intelligence Agency World Factbook* https://www.cia.gov/library/publications/the-world-factbook/
fields/2100.html#133 (accessed 19/08/2013)
No:https://www.cia.gov/library/publications/the-world-
factbook/docs/contributor_copyright.html
Catholicism Central Intelligence Agency World Factbook* https://www.cia.gov/library/publications/the-world-factbook/
fields/2122.html#195 (accessed 12/08/2013)
No: see above link for Legal System
U.S. Department of State http://www.state.gov/j/drl/rls/irf/2010/index.htm
(accessed 12/08/2013)
No:http://www.state.gov/misc/87529.htm#copyright
RTA mortality
rate
Global status report on road safety: time for action. Geneva,
World Health Organization, 2009*
http://www.who.int/violence_injury_prevention/road_
safety_status/2009 (accessed 12/08/2013)
Yes: Permission Sought and Attained
Based on data from OECD (2010), IRTAD Road Safety
Annual Report 2009, OECD Publishing
http://dx.doi.org/10.1787/9789282102824-en
(accessed 12/08/2013)
Yes: Permission Sought and Attained
Hong Kong Road Safety Council http://www.roadsafety.gov.hk/annual_report/2006/eng/
foreword.html (accessed 12/08/2013)
Yes: Permission Sought and Attained
Taiwans Ministry of Transportations and Communications http://www.motc.gov.tw/en/home.jsp?id=255&parentpath=
0,150,250 (accessed 12/08/2013)
Yes: Permission Sought and Attained
Population U.S. Census Bureau* www.census.gov/population/international/data/idb/
informationGateway.php (accessed 13/08/2013)
Yes: Permission Sought and Attained
Helping a
stranger
Charities Aid Foundation (2010, 2011, 2012),
World Giving Index*
https://www.cafonline.org/pdf/WorldGivingIndex
28092010Print.pdf (accessed 13/08/2013)
Yes: Permission Sought and Attained
This is based on data from Gallups
WorldView World Poll
https://www.cafonline.org/pdf/World_Giving_Index_
2011_191211.pdf (accessed 13/08/2013)
Yes: see above information for World Giving Index 2010
https://www.cafonline.org/PDF/WorldGivingIndex
2012WEB.pdf (accessed 13/08/2013)
Yes: see above information for World Giving Index 2010
Hospital beds World Health Report: health systems: essential health
technologies. Geneva, World Health Organization, 2013*
http://apps.who.int/gho/data/node.main.70?lang=en
(accessed 30/09/2013)
Yes: Permission Sought and Attained
National Statistics Republic of China (Taiwan) http://eng.stat.gov.tw/lp.asp?ctNode=2267&CtUnit=1072
&BaseDSD=36&MP=5 (accessed 30/09/2013)
Yes: Permission Sought and Attained
Information Services Department, Hong Kong
Special Administrative Region Government
http://www.gov.hk/en/about/abouthk/factsheets/docs/
public_health.pdf (accessed 30/09/2013)
No:http://www.gov.hk/en/about/abouthk/factsheets/
docs/public_health.pdf
The World Bank: Hospital beds (per 1,000 people):
World Health Organization
http://data.worldbank.org (accessed 30/09/2013) No: see above link for GDP per capita for The World Bank
* = Primary source. For Cuba the number of self-identified Catholics was prior to Castro assuming power. The data for 1996 was used to estimate Puerto Ricos hospital beds because this was the most reliable information that
we were able to obtain. GDP, gross domestic product; RTA, road traffic accidents.
Shepherd et al. BMC Medicine 2014, 12:131 Page 6 of 14
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infer causal links between the predictor and outcome
[14]. IV regression requires large sample sizes [14,23]. In
this area of research large sample sizes based on between
country comparisons alone are unlikely to be achieved.
One way around this problem is to take advantage of the
panel data structure and apply Baltagis [24] error com-
ponent two-stage least squares (EC2SLS) approach to es-
timate IV regression in panel data. This approach was
implemented in Stata 13.
Two classes of IV were identified: legal system (com-
mon or civil law) and levels of non-health related philan-
thropy in each country. Civil law systems, compared to
common law, are generally more prescriptive. Legislation
for public goods is, therefore, more likely, and as such
they should be more likely to adopt an opt-out consent
system [5]. However, the variation in legal systems
should not directly affect the supply of organs (living or
deceased), only via consent.
Countries that have higher norms for non-health re-
lated philanthropy may also prefer an opt-in system of
consent. Higher levels of non-health related philan-
thropy are likely to be associated with a more active
attitude towards helping and giving. Indeed, people in
opt-in countries are more likely to see the act of organ
donation as a meaningful and active process, perhaps
reflecting a general norm that giving is an active process
[3]. Thus, we expect that countries with an opt-in policy
will exhibit higher levels of non-health related philan-
thropy (helping strangers, volunteering and donating
money). That is, where the countries attitude towards
non-health related philanthropy is positive, this will re-
flect giving as an active process, and in such countries
the more active opt-in consent process will be favored.
This higher non-health philanthropy in opt-in countries
should influence donation via the consent process only.
In support of this contention there is evidence to suggest
that health based philanthropy (for example, blood and
potentially organ donation) is not related to non-health
based philanthropy [25-27]. However, while both de-
ceased and living donation rates may be viewed as
altruistic, living donation is a more definitive altruistic
act it is at a cost to the donor, voluntary, and a benefit
to the recipient (there is no cost to the donor for de-
ceased donations) [28]. To avoid this potential problem
for applying IV regression we examine the potential
causal role of consent on the difference between living
and deceased donation rates in each country by year.
This also allows us to control in the models for any as-
sociation between living and deceased donation rates
that may be related in a compensatory manner (high de-
ceased donation rates linked to lower living rates and
visa-versa) within countries. Thus non-health based phil-
anthropy should be associated with the consent system
but not the difference in living versus deceased organ
donor rates. Non-health related philanthropy was esti-
mated by the percentage of people in each country who
were willing to help a stranger, volunteer or donate
money. These data were obtained from the World Giv-
ing Index (WGI) for the years 2010, 2011 and 2012 and
the average entered in the model for all 13 years (see
Table 3).
Ethics
All the data used in this reported panel study are publi-
cally available data (all sources and links to the original
data are provided) and the study was approved by the
Faculty of Health and Life Sciences Ethics Committee of
Northumbria University (reference RE-HLS-12-130704-
51d53de10a88b) on the 8 July 2013. Where required we
requested and obtained permission to use the data sources
reported in this paper (see Table 3, final column).
Results
National data
There were 48 countries in the final data set. For the
total deceased donors the number of years of data ran
from 3 to 13 years with a mean of 10.85 years (SD =
2.94). For the living donors the total number of years
ran from 3 to 13 years with a mean of 9.56 years (SD =
2.98). The number of countries did not systematically
vary as a function of self-identified Catholic bands
(25%, >25% to 75%, and >75%: χ
2
(2) = 3.88, P= .144).
There were significantly more civil (N = 38, 79%) than
common law countries (N = 10, 21%; χ
2
(1) = 16.33,
P< .001). The association of consent with the national
variables is presented in Table 4. The only significant effect
was an association between the consent system and the
legal system, with common law being more likely in opt-in
than opt-out consent countries.
Organ donor and transplant rates
The intra-class correlations were .89 for deceased dona-
tion and .85 for living donation. This indicates that 89%
of the variation in deceased donation rates is attributable
to variation at the country level, as is 85% of the vari-
ation in living donation rates. This indicates that MLM
is the appropriate analytic strategy for these data. As
such, we initially ran two separate random intercepts
MLMs with year specified as a random slope, comparing
the effect of (opt-in (N = 23) versus opt-out (N = 25)
consent) and the covariates on the number of either de-
ceased or living donors. The estimated effect of opt-in
versus opt-out consent was based at the average GDP,
RTA, hospital beds and with Catholicism and legal
system relative to the proportion in each category. The
results show that across the dataset, there were signifi-
cantly more deceased donors in opt-out than opt-in con-
sent systems (Table 5). However, there were significantly
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more living donors in opt-in than opt-out consent
systems. This effect remains after controlling for the co-
variates, indicating that consent had a unique effect on
both deceased and living donation rates. Importantly,
the number of living and deceased donors increases over
the years. We reanalyzed these data with Spain removed
from the analyses. We removed Spain because it is a
well-known system with a strong opt-out policy that
may influence the results. Thus, to test that the effects
were not due to unique factors associated with the Spanish
system we re-ran the models excluding Spain [5]. The pat-
tern of results was the same once Spain was removed
(Table 5), indicating that the findings were not due to any-
thing unique about the Spanish model. Moreover, we also
reanalyzed the data to examine the cross-level interaction
of consent with years on both deceased and living donor
rates. This interaction was not significant for either de-
ceased (P= .28) or living donors (P= .46). Thus, the effect
of consent was constant across the years.
We also ran a multi-level path model to explore the
interplay between the main study variables in more de-
tail. In this model, we specified a random slope between
deceased and living donation to examine if the deceased
donation rate predicts the living donation rate. We also
specified random slopes between years and donation
rates (living and deceased). Deceased and living donation
rates were treated as random intercepts predicted by the
Level 2 covariates (GDP, RTA and hospital beds were
grand mean centered). While this is a multi-level path
model and not an IV regression model, we include the
instruments (legal system and a latent factor represent-
ing non-health related philanthropy). This model is
shown in Figure 2. There are two things of note. First,
there is no significant association between deceased and
living donation rates, and both are independently pre-
dicted by consent. Living donation rates are higher
under opt-in and deceased under opt-out. The second
point of note is that the potential instruments operate in
the predicted manner. Greater non-health related phil-
anthropy is associated with opt-in consent systems and
civil law associated with opt-out consent.
When comparing the transplant rates for each type of
organ we found that deceased kidney and liver trans-
plants were higher in opt-out than opt-in consent sys-
tems (Table 6). There was a trend for deceased heart
transplants to be higher in opt-out than opt-in countries,
Table 5 The impact of opt-out consent on organ donation rates (pmp), 20002012
Type of
donation
Overall grand mean Opt-in
consent
Opt-out
consent
(No. countries,
total observations)
M(SE)M(SE) Consent Year
(B)
GDP
(B)
RTA
(B)
Catholic
(B)
Legal
(B)
Hospital
beds (B)
Deceased donors 12.11 (48, 521) 9.98 (1.30) 14.24 (1.28) t(41.24) = 2.26, P=.029 0.29*** 0.0004*** 0.01 3.19** 0.28 0.01
Living donors 7.42 (48, 459) 9.36 (0.95) 5.49 (0.94) t(41.11) = 2.89, P=.006 0.26** 0.0001* 0.002 2.05* 1.11 0.09**
Without Spain
Deceased donors 11.64 (47, 508) 9.87 (1.19) 13.41 (1.19) t(39.99) = 2.06, P= .046 0.29*** 0.0004*** 0.01 2.59* 0.07 0.02
Living donors 7.47 (47, 448) 9.33 (0.95) 5.60 (0.97) t(40.14) = 2.75, P=.009 0.26** 0.0001* 0.002 1.97* 1.13 0.09**
Table contains estimated means and standard errors. *P< .05, **P< .01, and ***P< .001. Consent scored 1 = opt-in, 1 = opt-out, Legal scored 1 = comm on law,
1 = civil law, Catholic scored 1=25%, 0 = > 25% to75%, 1 = >75%, continuous level-two variables (GDP, RTA and hospital beds) were grand mean centered.
Coefficients are all unstandardized. GDP, gross domestic product; RTA, road traffic accidents.
Table 4 Association of opt-out legislation to national variables
Predictors Opt-in M(SD) Opt-out M(SD) Statistic
GDP 23,313.06 (15,712.19) 17,229.90 (13,534.59) F(1, 46) = 2.08, P= .157, η
p
2
= .04
RTA 108.60 (62.45) 120.16 (41.95) F(1, 46) = 0.58, P= .452, η
p
2
= .01
Hospital beds 44.56 (27.77) 50.98 (26.57) F(1, 46) = 0.67, P= .417, η
p
2
= .01
Opt-in Opt-out
NN
Catholic 25% 11 10 χ
2
(2) = 1.84, P= .399
>25% to 75% 6 4
>75% 6 11
Law Civil 14 24 χ
2
(1) = 8.96, P= .003*
Common 9 1
*This estimate is based on Pearson Chi-Squared. Given the low number of opt-out and common law countries the Yates continuity correction (χ
2
(1) = 6.96, P=.008)or
Fishers Exact Test (P=.004, two-tailed) were applied. Both were significant. GDP, gross domestic product; RTA, road traffic accidents pmp.
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but this difference was not significant. The total number
of deceased lung transplants did not differ between opt-
out and opt-in consent systems. By contrast, there were
significantly more living kidney transplants in opt-in
than opt-out consent systems (P= .049). There was not
a significant difference between the number of living
liver transplants between opt-in and opt-out countries
(P= .590). Importantly, the total number of kidney
transplants (deceased plus living) was higher in opt-out
than opt-in countries (P= .044). Similarly, the total
number of liver transplants was higher in opt-out than
opt-in countries (P= .051). There are also effects show-
ing that organ donation rates are increasing over the
years (both deceased and living) except for heart and
lungs from deceased transplant and liver from living
transplant.
Instrumental variables regression: predicting the
difference in deceased and living donation rates
Separate random effects panel regressions with robust
standard errors showed that the averaged donation of
money in a county was not related to the difference in
deceased and living donations (B = 0.04, P= .52), nor
was volunteering (B = 0.03, P= .80), helping a stranger
(B = 0.10, P= .32) nor the type of legal system (B = 2.2,
P= .09). The first stage statistics from the panel IV re-
gressions showed that of the four instruments, volun-
teering was not significantly associated with consent
type (P= .12), the other three were (all Ps < .001). Thus,
volunteering was removed as an instrument. The first
stage statistics for the final model with three instruments
(legal system, the averaged donation of money and help-
ing a stranger) showed that the type of legal system was
Consent
(-1 = Opt in, 1 = Opt out)
Road Traffic Accidents (RTA)
(PPM)
Deceased
Donations
(PPM)
Years
Percentage Catholic
(– 1<25%, 0 =- 26-74%, 1
= >75%)
Hospital Bed s per 10,000
Gross Domestic Product
(GDP) per capita (US$),
Living
Donations
(PPM)
-0.07
0.30**
0.32***
Legal System
(-1 = common
law, 1 = civil law)
Helping
Helping a
stranger
Volunteering
Donating Money
-0.02*
1.0
0.45***
0.55***
2.6**
2.9**
0.0001***a
0.02
-0.01
-1.6*
-1.4
0.0001***a
-0.003
-0.07**
0.31^
Figure 2 Multi-level path model for prediction of deceased and living donation rates. Legend. ^P= .089, *P< .05, **P< .01, ***P< .001. RTA,
GDP and Hospital Beds are Grand Mean Centered. Slope between years and deceased and living donation rates and deceased and living donation
rates are random. N = 450, with 47 clusters (countries). There was no helping data from Cuba. Therefore, this country was not included in the analysis,
reducing the sample to 47 countries. Coefficients are unstandardized and the estimator is Maximum Likelihood with Robust Standard Errors. Blue paths
represent the effect of covariates and consent on living donation rate, yellow paths represent the effect of covariates and consent on deceased
donation rate, red paths represent the effect of years on both living and deceased donation rate, the green path is the effect of deceased donation
rate on living donation rate. The purple path is the effect of the helping latent factor on consent and the brown path the effect of the legal system on
consent. The black paths are the unstandardized factor loadings.
a
Rounded as MPlus only reports to three decimal places. GDP, gross domestic
product; RTA, road traffic accidents.
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significantly and positively associated with the type of
consent (B = 0.03, Z = 3.4, P= .001), such that countries
with a civil legal system were more likely to have an opt-
out system. Also, donating money (B = 0.002, Z = 2.99,
P= .003) and helping a stranger (B = 0.003, Z = 4.24,
P< .0001) were significantly negatively associated with
consent, such that levels of these types of non-health
related philanthropy were higher opt-in countries.
Table 7 shows the results of the IV panel regression
model. The first column is a Generalized Least Squares
(GLS) analysis, with robust standard errors that repli-
cates the majority of the main findings in Table 5 but for
the difference score. The IV regression model shows that
consent type predicts the relative prevalence of deceased
versus living donations, such that opt-out consent re-
sults in proportionally higher levels of deceased dona-
tions. The Sargan-Hansen test was calculated using the
xtoverid command of Schaffer and Stillman [29]. The
Sargan-Hansen test indicates that the orthogonality con-
straint was met.
Discussion and Conclusions
In terms of the policy dilemma posed at the start of this
paper, the results show that opt-out consent may lead to
an increase in deceased donation but a reduction in liv-
ing donation rates. Opt-out consent is also associated
with an increase in the total number of livers and kid-
neys transplanted.
Importantly, the relationship between deceased and
living donor rates was non-significant, implying that on
average, one is not compensating for the other. Indeed,
this would be unlikely as the range of organs available
from deceased donation is greater than that from living
Table 6 The impact of opt-out consent on organ transplant rates (pmp), 20002012
Grand mean
(N
T
, observed)
Opt-in
consent
Opt-out
consent
Organs & Source M(SE)N
I
M(SE)N
o
Consent Years
(B)
GDP
(B)
RTA
(B)
Catholic
(B)
Legal
(B)
Hospital
beds (B)
Deceased Kidney 18.67 (48, 522) 14.27 (1.84) 23 23.07 (1.80) 25 t(40.88) = 3.29,
P= .002
0.44*** 0.0006*** 0.02 4.08* 0.60 0.02
Liver 7.19 (47, 470) 5.51 (0.96) 22 8.88 (0.95) 25 t(40.23) = 2.37,
P=.022
0.23*** 0.0003*** 0.004 1.88* 0.19 0.02
Heart 2.86 (45, 473) 2.40 (0.39) 21 3.32 (0.37) 24 t(38.18) = 1.63,
P=.111
0.04 0.0001*** 0.007 0.50 0.21 0.001
Lungs 1.01 (31, 233) 0.79 (0.28) 17 1.22 (0.31) 14 t(23.37) = 0.97,
P=.343
0.01 < 0.0001 0.005 0.37 0.05 0.01
Living Kidney 6.57 (48, 507) 8.01 (0.99) 23 5.13 (0.98) 25 t(40.80) = 2.03,
P=.049
0.23** 0.0001* 0.01 1.93* 0.93 0.10***
Liver 1.14 (39, 320) 1.27 (0.37) 20 1.02 (0.39) 19 t(34.66) = 0.54,
P=.590
0.03 < 0.0001 0.01
0.22 0.37 0.01
Total Kidney 25.37 (48, 499) 22.43 (1.96) 23 28.32 (1.93) 25 t(40.91) = 2.08,
P= .044
0.68*** 0.0008*** 0.02 2.29 1.06 0.08
Liver 9.39 (37, 309) 7.53 (1.20) 19 11.26 (1.28) 18 t(30.81) = 2.03,
P= .051
0.32*** 0.0003** 0.003 1.60 0.05 0.02
a
The Slovak Republic had data for deceased and living liver transplants. However, there were no deceased and living liver data for the same year. Therefore, we
were unable to calculate total liver transplants for this country and it was removed from this analysis.
P= .085, *P< .05, **P< .01, and ***P< .001. N
T
= total
number of countries in analysis, observed = number of observations, N
I
= number of opt-in countries in the analysis, N
o
= number of opt-out countries in the
analysis. Consent scored 1 = opt-in, 1 = opt-out, Legal scored 1 = comm on law, 1 = civil law, Catholic scor ed 1=25%, 0 = > 25% to75%, 1 = >75%, continuous
level-two variables (GDP, RTA and hospital beds) were grand mean centered. Table contains estimated means and standard errors. GDP, gross domestic product;
RTA, road traffic accidents.
Table 7 Results of instrumental variable regression analysis
(EC2SLS) predicting the difference in deceased versus living
donation rates
Predictor GLS EC2SLS
Consent (1 = opt-in, 1 = opt-out) 4.03** 4.09*
Years 0.11 0.11*
% Catholic 5.7*** 5.3***
RTA 0.02 0.03
GDP 0.0003*** 0.0003**
Hospital beds 0.10^^ 0.09^
Overall R
2
0.44 0.45
Sargan-Hansen test 3.82 (4) P= .43
Number of groups 48 47
Number of observations 457 450
Average cluster size (range) 9.5 (3, 13) 9.6 (3, 13)
^^P=.076, ^P=.052,*P<.05,**P<.01,***P= .001. There are 47 countries in
these analyses as there were no data on helping for Cuba. GDP, RTA and Hospital
beds are Grand Mean centered. The outcome is the difference between deceased
and living (deceased living) donation rates, such that positive scores favor
deceased donation and negative scores living conation. Unstandardized
coefficients. EC2SLS, Error Component Two-Stage Least Squares; GDP, gross
domestic product; GLS, Generalized Least Squares; RTA, road traffic accidents.
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donation (only kidneys and liver lobes). We also found
that the number of deceased and living donors, and
the number of deceased kidney and liver transplants,
has increased over the years from 2000 to 2012. This
increase in donation and transplantation rates is likely
to be due to a variety of factors, including not only an
increase in the number of people willing to donate,
but also improved criteria for identifying and selecting
donors, improved transplantation procedures and an
increase in transplantation capacity (that is, greater
availability of surgeons, more transplant centers).
Unlike living kidney transplantation, living liver trans-
plantations were not significantly greater in opt-in than
opt-out countries. The mortality rate is significantly
higher for living liver donation than for living kidney do-
nation [30,31], which may make people more reluctant
to use this alternative to deceased liver donation. If the
number of donors is reduced so, as a consequence, is
the number of potential transplants. Indeed, research
suggests that patients may be reluctant to ask loved
ones to donate part of their liver because of the poten-
tial guilt that they would feel if their living donor fam-
ily member were to die during the procedure [32].
Moreover, there may also be a lack of trained surgeons
to undertake this procedure, further reducing living
liver transplant rates. There was also no significant dif-
ference between the number of lung transplants be-
tween opt-in and opt-out systems. While null results
are hard to interpret, the lack of any systematic effect
may reflect the low base rate in the availability of lungs
for donation. There is a high eligibility criteria for lung
transplants [33], which may reduce the likelihood of
obtaining a deceased lung donor. Moreover, there is a
higher mortality rate for lung transplants than kidneys
and livers [34], reducing the likelihood of this proced-
ure being undertaken. As a result, the number of de-
ceased lung transplants in opt-in and opt-out consent
countries is likely to be low.
The use of IV analysis enhanced previous research
in this area by estimating the causal effect of consent
on the difference in donation rates between deceased
and living donors. This analysis revealed that consent
was likely to influence the difference in donation rates
between deceased and living donors organ donation,
such that opt-out consent results in relatively greater
deceased than living donations. These analyses, in
combination with previous experimental research, fur-
ther support a causal interpretation. For example, ex-
perimental vignette-based research has found that
people were more willing to donate their organs when
opt-out rather than opt-in legislation was used [1].
This experimental research demonstrates the causal
effect of consent type on peoples support for organ
donation.
Factors influencing organ donation and transplantation
Although we support previous research in demonstrat-
ing greater deceased donor rates in opt-out than opt-in
countries, it may be too simplistic to state that the intro-
duction of opt-out consent will increase deceased dona-
tion rates. Indeed, there are examples where opt-out
consent has not improved donor rates. For example, in
France and Brazil the introduction of opt-out consent
had a detrimental effect on donation, which was partly
attributed to increased levels of mistrust towards med-
ical professionals [16,35]. This possibility was one con-
cern that led the Organ Donation Taskforce to conclude
that opt-out consent should not be introduced to the
UK in 2008. Although these case studies are informative,
they do not constitute rigorous and scientific evaluation
of the effect of consent on medical mistrust. Therefore,
further empirical evidence is needed to determine
whether levels of medical mistrust vary between opt-in
and opt-out countries and to establish the effect of this
on donation rates.
From the point of view of the results in this study,
there are numerous reasons why it is unlikely that fac-
tors associated with the Spanish Modelcan explain the
results of the present study (indeed, our results remain
the same when Spain is removed from the analyses).
First, the factors in the Spanish Model (for example,
multi-level transplant coordination network, hospital co-
ordinators) cannot explain why living donation was
lower in opt-out than opt-in countries. Second, the
number of intensive care beds is often regarded as influ-
encing the availability of organs [36]. We included the
number of hospital beds in our model, as a general index
of the quality of the healthcare infrastructure, and the ef-
fect of consent remained significant. However, it should
also be noted that although the number of intensive care
beds (per 100,000 people in the population) is higher in
Spain than the UK, it is substantially lower than a num-
ber of opt-in countries, such as Germany, US, and
Canada [37,38]. Indeed, the number of intensive care
beds in Germany is more than twice as many as in Spain
[37,38]. Moreover, there is not a significant difference
between the number of critical care beds in opt-in and
opt-out countries.
2
In addition, the fact that aspects of
the Spanish model have been introduced to both opt-in
(for example, UK) and opt-out countries (for example,
Italy) suggests that the differences found in the present
study are unlikely to be due to the Spanish model.
While the Spanish model itself may not be able to ex-
plain the effect of consent, aspects of the Spanish
Model are likely to be highly beneficial to deceased
donor rates [39]. Indeed, one recommendation of the
UK Organ Donation Taskforce [40] was to apply some
aspects of the Spanish Model to the UK organ donation
system. For example, in line with the Spanish Model
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clinical leads for organ donation have been appointed
in each Hospital Trust and aim to liaise with the trans-
plant team and the Hospital Trust to promote organ
donation. Set against a general background of the num-
ber of transplants and donations increasing from 2000
to 2012, there has been a 50% increase in deceased do-
nors since the publication of this report, which has
been partly attributed to the implementation of such
recommendations [41]. Importantly, this rise occurred
without any change to the UKs consent legislation.
This clearly demonstrates the success of applying some
aspects of the Spanish Model.
Future research and implications
A limitation of this research is that it cannot account for
the variability in the application of opt-out legislation.
Some countries apply either softor hardopt-out con-
sent legislation. In soft opt-out consent countries dona-
tion cannot take place without the permission of family
members. By contrast, in hard opt-out consent countries
organs can be transplanted from anyone who has not
registered their opposition to donation, regardless of
whether or not their family members have been con-
sulted. In the majority of opt-out consent countries the
permission of next-of-kin is required when the de-
ceaseds wishes are not known and next-of-kin can veto
donation [17]. Therefore, the majority of these countries
use soft opt-out consent. However, our results demon-
strate a difference between opt-in versus opt-out consent
countries despite this variability in the implementation
of this legislation across different nations. Therefore, we
found that overall opt-out consent is associated with
greater deceased donor rates. Given the lack of data on
the type of opt-out consent used in each country and
the limited number of countries available for the ana-
lyses, it was not feasible to test for these differences
using the current methodology. It remains for further re-
search when enough data are available to make mean-
ingful distinctions between opt-in versus both soft and
hard opt-out systems.
It is also important to assess other factors that are
likely to influence the organ donation system. For ex-
ample, organ donation and transplantations are likely to
be influenced by the use of the Spanish Model, the role
of the organ procurement organizations and the capacity
of the transplant system (for example, number of trained
surgeons and transplant centers). Again, data availability
may prevent researchers from assessing this using the
current methodology. Thus, it is imperative for trans-
plant organizations to routinely collect data on import-
ant organ donation indices (for example, consent type,
procurement procedure, number of intensive care beds
and trained surgeons) and make this publicly available to
develop future research and policy recommendations in
this area. Although such country-level data are inform-
ative, there are some limitations. For example, due to
data availability it is not possible to infer the role of atti-
tudes on consent type and donation rates. Therefore, fu-
ture research should apply other methodologies to further
test the effect of consent type. For example, researchers
could use vignette-based experimental studies [1], other
experimental laboratory-based work, such as economic
games [42], or pre-post time series designs. By combining
the findings from these different research methods, re-
searchers could have a greater understanding of the fac-
tors that promote organ donation and transplantation.
It should also be noted that opt-out consent countries
still have significant transplant waiting lists and suffer
from an organ donor shortage. The introduction of opt-
out consent legislation is, therefore, unlikely to totally
solve a countrys organ shortage. Indeed, organ donation
rates are multi-causal and a variety of factors need to be
considered to improve the availability of donor organs.
Consent legislation is one strategy, among many, for im-
proving donor rates. Other strategies need to be consid-
ered in order to alleviate the organ donor shortage. For
example, donor rates may be improved by introducing
aspects of the Spanish Model, increasing the transplant
capacity (for example, more trained surgeons and trans-
plant centers, and improving the ability to identify po-
tential donors. Also, given the relatively low levels of
living donation in opt-out systems, it may be possible to
reduce the number of people on waiting lists by develop-
ing these countriesliving organ donation infrastructure
and by presenting this option to relatives. Although liv-
ing organ donation is only used to supplement deceased
donation, presenting this option may save the lives of
patients who are unlikely to receive an organ from a de-
ceased donor. Indeed, research in Spain has found that
the introduction of living organ donation information
programs can increase uptake of this type of donation
[43]. The use of such programs has the potential to in-
crease living donation, which is likely to help alleviate
the shortage of donor organs [44].
Endnotes
1
We used a time invariant measure of GDP to make
sure that variation between countriesGDP was not re-
sponsible for the effect of consent legislation, allowing
us to compare the relative wealth of countries on aver-
age rather than financial growth (change in GDP) that is
influenced by many internal and external factors.
2
Using data from previous research [38], we compared
whether the number of critical care beds varies between
opt-in and opt-out countries. This previous research
only had critical care bed data for 23 (7 opt-in and 16
opt-out) of the countries in our sample. We did not in-
clude this data in the analysis because the sample size
Shepherd et al. BMC Medicine 2014, 12:131 Page 12 of 14
http://www.biomedcentral.com/1741-7015/12/131
was too small. However, we analyzed the data from these
23 countries in order to assess whether there were any
differences between opt-in and opt-out countries. Be-
cause there was only one year of data (2010) an analysis
of variance (ANOVA) was used to assess the results ra-
ther than MLM. The initial ANOVA simply assessed the
effect of consent on the number of critical care beds per
100,000 people in the population, without the inclusion
of any covariates. This analysis found that although there
were more critical care beds in opt-in (M= 12.90, SD =
9.09) than opt-out countries (M= 10.91, SD = 4.67), this
difference was not significant (P= .49). Next, we re-
peated the analysis controlling for GDP, RTA, number of
hospital beds, Catholicism, and legal system. The value
of these Level 2 variables was the same as those used in
the above models. Importantly, consent remained a non-
significant predictor of critical care beds after controlling
for these covariates (P= .13). We also found that the
number of critical care beds was strongly correlated with
the number of hospital beds (r= .66, P= .001). This sug-
gests that by including the number of hospital beds into
the MLMs in the main analyses we are partly accounting
for the number of critical care beds.
Abbreviations
CI: Confidence intervals; DBD: donation after brain stem death;
DCD: donation after cardiac death; EC2SLS: error component two-stage least
squares; GDP: gross domestic product; GLS: generalized least squares;
IRODaT: International Registry of Organ Donation; IV: instrumental variable;
pmp: per million population; RTA: road traffic accident; SD: standard
deviation; WGI: World Giving Index.
Competing interests
The authors declare that they have no competing interests. The authors
have liaised with NHS Blood and Transplant on a previous project, but this
organization did not influence this research in any way.
Authorscontributions
LS collated the data. LS and EF analyzed the data. LS, EF and REO drafted the
manuscript. REO and EF critically examined this paper. All authors read and
approved the final manuscript.
Acknowledgements
LS was supported by a grant from the Chief Scientist Office (CSO) of the
Scottish Government (CZH/4/686) to REO and EF while conducting this
research, which was not part of the work directly funded by the CSO. We would
like to thank Transplant Procurement Management for their help with this
project and for allowing us to use the data that they have collated. The IRODaT
database has been an important resource for this research. We would also like
to thank John Forsythe for his comments on earlier versions of this manuscript.
Author details
1
Department of Psychology, Northumbria University, Newcastle-upon-Tyne
NE1 8ST, UK.
2
Department of Psychology, University of Stirling, Stirling FK9
4LA, UK.
3
School of Psychology, University of Nottingham, Nottingham NG7
2RD, UK.
Received: 10 March 2014 Accepted: 16 July 2014
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The variability in deceased organ donation registries worldwide has received little attention. We considered all operating registries, where individual wishes about organ donation were recorded in a computerized database. We included registries which recorded an individual's decision to be a donor (donor registry), and registries which only recorded an individual's objection (non-donor registry). We collected information on 15 characteristics including history, design, use and number of registrants for 27 registries (68%). Most registries are nationally operated and government-owned. Registrations in five nations expire and require renewal. Some registries provide the option to make specific organ selections in the donation decision. Just over half of donor registries provide legally binding authorization to donation. In all national donor registries, except one, the proportion of adults (15+) registered is modest (<40%). These proportions can be even lower when only affirmative decisions are considered. One nation provides priority status on the transplant waiting list as an incentive to affirmative registration, while another nation makes registering a donation decision mandatory to obtain a driver's license. Registered objections in non-donor registries are rare (<0.5%). The variation in organ donor registries worldwide necessitates public discourse and quality improvement initiatives, to identify and support leading practices in registry use.
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Organ donations from deceased donors provide the majority of transplanted organs in the United States, and one deceased donor can save numerous lives by providing multiple organs. Nevertheless, most Americans are not registered organ donors despite the relative ease of becoming one. We study in the laboratory an experimental game modeled on the decision to register as an organ donor and investigate how changes in the management of organ waiting lists might impact donations. We find that an organ allocation policy giving priority on waiting lists to those who previously registered as donors has a significant positive impact on registration.
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The key question addressed in this paper is whether geographical differences in blood donation and philanthropy reflect differences in social capital. We do find considerable spatial variation in blood donation and philanthropy between municipalities in the Netherlands. But we do not find that blood donation and philanthropy have strong or even moderately positive relations with each other or with indicators of prosocial norms and engagement in voluntary associations. However, voter turnout is strongly related to both blood donation and philanthropy. We conclude that the spatial variation in blood donation and philanthropy is not due to differences in social capital.
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BACKGROUND: The benevolence hypothesis (both donor and recipient gain) suggests that blood donors, compared to non–blood donors have a general altruistic motivational preference based on warm glow (i.e., “I donate because it makes me feel good”). With objective behavioral economics tests of altruism and warm-glow giving, this paper offers the first direct experimental test of this hypothesis. The prediction that blood donors will be motivated in general by warm glow was compared to predictions from other theoretical models: strong reciprocity and empathy. STUDY DESIGN AND METHODS: Four experiments and one prospective study examined blood donors' and nondonors' motivations for general charitable giving and blood donation. Variants of the dictator game (DG; a charity DG [CDG] and a warm-glow version of a CDG) were used to provide objective measures of altruism. RESULTS: Blood donors gave less than nondonors on the CDG, but gave more on the warm-glow version. Blood donors' actual donations (in the CDGs and blood donation) were associated with feelings of warm glow. There was no evidence that blood donors were motivated by strong reciprocity or empathic concerns. CONCLUSIONS: This paper offers objective behavioral evidence that blood donors' charitable giving and blood donation, compared to non–blood donors, is more strongly motivated by warm glow. This provides additional support for the benevolence hypothesis of blood donation.