Am. J. Trop. Med. Hyg., 87(5), 2012, pp. 796–805
Copyright © 2012 by The American Society of Tropical Medicine and Hygiene
Use of Multiple Data Sources to Estimate the Economic Cost of Dengue Illness in Malaysia
Donald S. Shepard,* Eduardo A. Undurraga, Rosemary Susan Lees, Yara Halasa, Lucy Chai See Lum, and Chiu Wan Ng
Schneider Institutes for Health Policy, Heller School, Brandeis University, Waltham, Massachusetts; Centre for Research in Biotechnology
for Agriculture, and Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
estimated the economic burden of dengue illness in Malaysia. Information about economic burden is needed for setting
health policy priorities, but accurate estimation is difficult because of incomplete data. We overcame this limitation by
merging multiple data sources to refine our estimates, including an extensive literature review, discussion with experts,
review of data from health and surveillance systems, and implementation of a Delphi process. Because Malaysia has a
passive surveillance system, the number of dengue cases is under-reported. Using an adjusted estimate of total dengue
cases, we estimated an economic burden of dengue illness of US$56 million (Malaysian Ringgit MYR196 million) per
year, which is approximately US$2.03 (Malaysian Ringgit 7.14) per capita. The overall economic burden of dengue would
be even higher if we included costs associated with dengue prevention and control, dengue surveillance, and long-term
sequelae of dengue.
Dengue represents a substantial burden in many tropical and sub-tropical regions of the world. We
Dengue virus infection is the most common arthropod-
borne disease of humans and, unlike most infectious diseases,
its infection rates are expanding. There are 50–100 million
dengue infections each year and approximately 24,000 result
in death, mostly in children.1–3Dengue represents a substantial
economic burden to communities and health services in many
tropical and sub-tropical regions, and has shown a four-fold
increase in the number of cases in the past 30 years.1–7South-
east Asia has the highest dengue incidence of all regions of the
world; cycles of epidemics have affected the region since the
1950s, and their magnitude is increasing.8
Dengue infection is among the most pervasive public
health problems in Malaysia.9,10The first reported dengue
episode in Malaysia was in 1901, and it has spread through-
out the whole country.10Dengue episodes have been tabu-
lated in Malaysia since 1974, and reported dengue cases have
increased over past decade (Figure 1). Since the 1980s, the
peak age incidence of dengue has shifted from children to
young adults. A prospective study in Malaysia showed that
dengue has a considerable adverse impact on social function,
vitality, and wellbeing, and represents a 60% lost in quality
of life (QoL) at its worst point.11
To set health policy priorities and to inform decision-
makers about the implementation of existing and new technol-
ogies to control dengue illness, policy makers and international
donors require information about the economic burden
of dengue.12,13We adjusted previous estimates of economic
burden of dengue to 2009 U.S. dollars by using the U.S. gross
domestic product (GDP) deflator.14Using the average of
reported dengue episodes during 2001–2005, Suaya and others7
estimated an annual economic burden of dengue (±SE) of US
$3.1 (±0.2), US$41.9 (±4.28), and US$52.5 (±11.4) million in
Cambodia, Malaysia, and Thailand, respectively. Results from
a comprehensive study of dengue costs in Malaysia (2000–
2005) and Thailand (2002–2007), including costs of dengue
illness, vector control, and government research and develop-
ment related to dengue, gave annual costs of dengue illness of
US$134 million in Malaysia and US$136 million in Thailand.15
Results from a study based on Khon Kaen Hospital, Thailand,
suggested that the national economic burden of dengue in
Thailand was US$173 million.6
However, accuracy of past estimates is limited because of
incomplete data. The total incidence of symptomatic dengue
illness is not fully captured by passive surveillance systems,
which usually under-report the total number of cases.16–21
Malaysia, like most countries, uses a passive surveillance sys-
tem to capture the incidence of dengue illness.22Although this
approach is helpful to identify outbreaks and to examine time
trends, passive surveillance has several limitations. Diagnoses
are difficult because of the variety of manifestations of dengue
infections. Disease notification relies on health care profes-
sionals, and there are differences in reporting from public and
private healthcare facilities and between epidemic and non-
epidemic periods. Also, the definition of a reported dengue
case varies.22–24We overcame these limitations by merging
multiple data sources to refine our estimates of the total num-
ber and the unit costs of dengue cases in Malaysia. We used
World Health Organization (WHO) case definitions of dengue
fever and dengue hemorrhagic fever.25
MATERIALS AND METHODS
Overview. We calculated the economic burden of dengue
from a societal perspective as the number of cases per year
multiplied by the direct and indirect costs per case. Because
dengue is a reportable illness, the number of cases officially
reported to Malaysia Ministry of Health (MoH) gives a rough
approximation of the true number. In 2009, that number was
41,454 cases (Ministry of Health Malaysia, unpublished data).
We used 2009 as the reference year because it was the year for
which we obtained the most comprehensive surveillance and
cost data, and it is a good representation of recent reported
dengue cases. Although using the average of a series of years
could have been useful if detailed data were available, the num-
ber of cases reported in 2009 was close tothe averages for 2006–
2010 (44,879 cases) and 2001–2010 (37,866 cases) (Figure 1).
To address possible under-reporting of cases, we first
obtained estimates of expansion factors from previous studies
and the Malaysia Foreign Worker Medical Examination Mon-
itoring Agency (FOMEMA). An expansion factor (EF) is the
*Address correspondence to Donald S. Shepard, Schneider Institutes
for Health Policy, Heller School, Brandeis University, Waltham, MA
02454-9110. E-mail: email@example.com
number by which reported cases need to be multiplied to
obtain the true number of cases. Using this information, we
then implemented a two-stage Delphi process to estimate spe-
cific EFs for Malaysia. The first stage was part of a one-day
workshop on the burden of dengue in Malaysia, facilitated by
the MoH, and the second was a follow-up by phone and e-mail
a few weeks later.26To estimate the unit costs of dengue cases,
we combined a publication from the University of Malaya, data
from special studies, national health accounts, and inflation
adjustments. Because we knew that each of the datasets would
have some limitations, we tried to design methods at the outset
that would compensate for them. We used Crystal Ball soft-
ware for a probabilistic analysis of the total costs of dengue
illness in the sensitivity analysis.27We report our results in U.S.
dollars by using the exchange rate of July 1, 2009 (1 Malaysian
Ringgit = US$0.284).28
Estimating the number of and treatment sites of dengue
cases. We classified cases of dengue officially reported by the
MoH in 2009 into ambulatory and hospitalized cases, and
then subdivided them between public and the private sectors
according to the location from which the case was reported.
Although dengue is a reportable disease in Malaysia, under-
reporting inevitably leads to a lower estimation of the real
number of cases. To estimate the total number of cases, we
had to first estimate the corresponding EFs.
Estimates of EF in Southeast Asia. An EF can be derived by
dividing the best estimate of the total number of dengue ill-
ness cases in a specified population in one year by the number
of reported cases considered dengue illness (whether they
actually were laboratory confirmed or real dengue illness) in
that population in that year. Some authors have used EFs
obtained from studies in the Western Hemisphere to estimate
the burden of disease in countries in Asia,29,30Given the
differences in epidemiology and surveillance systems, we
believe that it is preferable to rely on studies from the same
region. We have previously conducted a systematic literature
review (1995–2011) and identified nine published papers
reporting original, empirically derived EFs or the necessary
data to estimate the total incidence of dengue illness in four
countries in Southeast Asia.21We reviewed some of these
papers during the workshop. We used and commended the
cohort study from Kamphaeng Phet, Thailand, to illustrate the
use of EFs to estimate the total cases of dengue.31,32
FOMEMA system. Another source of evidence about
under-reporting of dengue in Malaysia came from an exami-
nation of the FOMEMA system. FOMEMA aims to reduce
the spread of communicable diseases by routinely testing
foreign workers in Malaysia for six communicable diseases
upon arrival and annually thereafter.33,34
The numbers of cases of communicable diseases identified
that were notified in 2009 to the MoH from this testing are
considerably fewer than the actual positive cases screened for
all of these conditions.26To illustrate this finding, we can con-
sider the case of malaria. Like dengue, malaria is a common
arthropod-borne disease of humans. Symptomatic malaria pro-
duces high fever and body pain, considerably reducing the
ability of the patient to work. On the basis of analysis of
FOMEMA data, the estimated EF for malaria is approxi-
mately 8. If malaria is assumed to be the most relevant disease
to compare with the dengue situation, we would expect to see
only approximately 10–12% of dengue cases being reported
to the MoH. The EF for all six communicable diseases in
FOMEMA is 52 cases of infection for each reported case,
indicating substantial under-reporting by private physicians.
These numbers were not used directly to estimate the EF for
dengue. Rather, they were presented as evidence for under-
reporting of communicable diseases in Malaysia, and consid-
ered by the experts attending the workshop as they estimated
EFs for dengue.
Laboratory tests in the private sector. Another way of esti-
mating the total dengue cases is by analyzing the number of
laboratory tests for dengue requested from the private sector.
Following economic growth and expansion in Malaysia in
recent decades35the country’s private health care sector has
grown substantially since the early 1980s.36
Pantai Holdings, which owns seven large hospitals, is a
major stakeholder in the private health sector in Malaysia.
Laboratories in Pantai hospitals implemented and analyzed
approximately 20% of the total private sector tests of sus-
pected dengue patients according to information that Pantai
laboratory staff received from the country’s main supplier
of dengue test kits. We projected the total numbers of tests in
the private sector by multiplying the number from Pantai
hospitals by 5 to account for their 20% market share.
The total number of true dengue cases receiving a labora-
tory test is probably somewhere between the total number of
positive laboratory test results and the number of tests per-
formed on patients who showed probable symptoms of dengue
because a negative test result does not necessarily rule out
dengue infection.37,38An illness can be reported as dengue
based on a clinical diagnosis. Laboratory tests are not required
and are not always performed because of cost or time
required. Public hospitals are more likely than private hospi-
tals to report a case as probable dengue, rather than obtaining
a laboratory confirmation.
Using the proportion of persons with dengue seen in the
private sector relative to the public sector, we could make an
estimate from the Pantai Holdings data of the total number of
suspected and actual dengue cases in the country. Most (60%)
hospitals in Malaysia are private, but only 24% of beds, 26%
of admissions, and 13% of hospital ambulatory visits are in
the private sector.26,39,40Based on the shares of acute care
1988–2010. Reported cases were obtained from the Ministry of
Health Malaysia18and the World Health Organization Regional
Office for the Western Pacific.19EF = adjusted dengue cases based
on workshop estimates.
Reported and projected cases of dengue in Malaysia,
DENGUE COSTS IN MALAYSIA
hospital beds in 2005 that were private (30% of urban beds
and 10% of rural beds) and that dengue is over-represented in
urban areas, we estimated that 26% of hospitalized dengue
patients are probably in private hospitals.26
Delphi process. A Delphi process uses expert knowledge
systematically to solve complex issues when there are insuffi-
cient data. It aims to achieve consensus on a specific matter
through several rounds of consultation.41Our Delphi process
had two rounds followed by an adjustment process for internal
consistency. The first round was carried out during a one-day
workshop facilitated by the Malaysian MoH on December 6,
2010, in Putrajaya, Malaysia. Experts from the academic, pub-
lic, and private sectors were asked to fill in a survey giving
their best estimates for the EFs for dengue cases in Malaysia.
We requested EFs for hospitalized and ambulatory cases in
the public sector, and hospitalized and ambulatory cases in
the private sector. Participants were asked to reach this esti-
mate based on the information discussed above, which was
presented and discussed at the workshop, bearing in mind
which evidence, data, or specific values they believed were
more reliable indicators. The second round of the Delphi
process was carried out by e-mail and telephone a few weeks
after the workshop, and enabled us to further refine our esti-
mates of total dengue cases. We sent participants a report and
a narrated presentation with improved results from the work-
shop, together with a request to re-estimate under-reporting
for public and private health sectors, and also the share of
dengue cases that are ambulatory. Participants are listed in
Understanding that private ambulatory facilities generally
refer patients to private hospitals, and public facilities refer
patients to public hospitals, we assumed that the ratio of pub-
lic to private hospitalized cases is the same as the ratio of
public to private ambulatory cases. We calculated multiplica-
tive adjustment factors for mean Delphi EFs for ambulatory
cases in the public and private sectors. These factors made the
distribution of dengue illness between hospitalized and ambu-
latory modalities consistent with the estimated share of ambu-
latory cases from the second round of the Delphi process.
Estimating the unit cost of dengue cases. Complete and
reliable information on the unit costs of providing inpatient
and outpatient medical care in the public and private sectors
was not readily available. We combined a variety of sources of
cost information in our estimates of dengue burden in Malaysia,
mostly focusing on costs of service provision in public health-
Costs of service provision in public hospitals. We used three
sources of data for estimating costs in the public sector. First,
we estimated the unit costs of inpatient and outpatient hospital
services in the University of Malaya Medical Center (UMMC),
a national referral hospital that was used in an eight-country
comparative study of dengue costs in2005.7We used these data
on the operating expenditures and workload of UMMC in 2005
and updated them using 2009 data. The 2009 unit costestimates
included salaries for academic clinicians not captured in the
2005 study. These clinicians were paid directly by the university
and information concerning their salaries was not kept by the
hospital. Information on academic staffing and their salaries
were obtained directly from the university. We then assumed
that 60% of the time of UMMC clinicians was spent on patient
care, and the rest was spent on research and teaching duties
Second, we used a study that examined the financial per-
formance of six public district hospitals, including estimates
of inpatient and outpatient unit costs (year 2001).43We used
data from the World Health Organization CHOosing Inter-
ventions Which Are Cost-Effective (WHO-CHOICE) pro-
ject.44Presumably because of the lack of suitable data,
Malaysia was not included among the 49 countries in the
WHO-Choice database of unit hospital costs. Using econo-
metric modeling, the project predicted the unit hospital costs
for countries in which data were not available. The WHO-
CHOICE project has made available online predicted esti-
mates for costs per bed day and costs per outpatient visit in
public hospitals in Malaysia for 2005.44These estimates
included capital costs but excluded costs of provision of drugs,
diagnostic tests, and procedures in the hospitals.
We combined data from the 2009 UMMC estimates (Table 1)
and the WHO-CHOICE to derive unit cost estimates associ-
ated to dengue illness.7,44We chose these studies because both
were available from international peer-reviewed publications
that used more recent data than the six district hospitals study.
We used the latter study only in the sensitivity analysis.43The
estimation process of the average unit cost per bed day and
average cost per outpatient visit is shown in Table 2. As part
of the expert workshop in Malaysia, we first agreed on the
Estimation of unit costs at the University of Malaya Medical Center, 2005 and 2009*
RowItem Source UMMC, 2005 UMMC, 2009
No. registered beds (official)
Annual bed days
Ambulatory clinic visits
Total ambulatory visits
Relative cost: visit/inpatient day
Ambulatory bed-day equivalents
Total bed day equivalents
Operating expenditure, US$ million
Cost per bed day equivalent, US$
Cost per ambulatory visit, US$
6 + 7
Shepard et al.
5 + 10
*Adapted from background tables from Suaya and others7and Shepard and others.42UMMC = University of Malaya Medical Center.
†Data from Hospital Annual Reports.
‡Includes salary of academic clinicians estimated to be US$7.7 million.
SHEPARD AND OTHERS
distribution of facility types by their total hospital beds in the
country (Table 2). We assumed that the share of hospitalized
and ambulatory dengue patients who seek care in each hospi-
tal type is proportional to the total number of beds the facility
has. The tertiary-level beds made up 50% of all hospital beds
in the country. Secondary and primary level hospital beds
made up 30% and 20% of the total, respectively. We used
the WHO-CHOICE estimates to derive the ratio of WHO-
CHOICE unit cost for secondary-level and primary-level hos-
pitals to the cost for tertiary-level hospitals (Table 2). The unit
cost per bed day in the primary-level hospital was 56% of the
costs in the tertiary-level hospital. We then applied these cost-
ratios to the 2009 UMMC unit cost estimates and combined
these figures with the distribution of hospital beds by facility
type (Table 2) to derive the weighted average of the unit cost
per bed day and per outpatient visit in a public hospital in
Malaysia in 2009 of US$219.16 and US$41.96, respectively.7,44
As a sensitivity analysis, we combined two new sets of esti-
mates with our best estimate by using a triangular distribution,
the approach in previously published estimates.7,16One set of
estimates used the WHO-CHOICE costs with the six district
hospital costs.43,44This set generated unit cost estimates for
hospital services of US$209.19 per bed day and US$55.59 per
outpatient visit (2009 US$), which are approximately 5% lower
for inpatient services and 32% higher per outpatient visit than
the unit costs obtained from UMMC estimates. The other set
of estimates used a weighted average between the UMMC
based estimates and the six district hospital based estimates.
Because we considered the UMMC estimate more accurate
than that from the six public hospitals, we gave them relative
weights of 0.67 and 0.33, respectively, to generate our last esti-
mate (US$212.51 perbed day and US$51.05 per outpatient visit).
Costs of service provision in public clinics. We derived the
unit costs of visits to public clinics from an unpublished cost
study on the provision of outpatient services in 11 public clinics
in Kedah by Lim.45The unit cost per visit (US$2.94 when
inflated to 2009 values)14was derived mainly from the operat-
ing budget of the clinics involved. This figure was confirmed by
participants in the workshop who had prior experience manag-
ing district health care. Although WHO-CHOICE also pro-
vided estimates for unit costs of public clinic visits, workshop
participants did not believe that they were applicable to den-
gue treatment in the public sector. The WHO-CHOICE esti-
mates applied to 20-minute visits, which are considerably
longer than typical public sector visits in Malaysia, and esti-
mates were mostly extrapolated from private sector static
health facilities data.
Costs of service provision in private hospitals. We used data
from the Malaysia National Health and Morbidity Survey II
to estimate private hospital costs.46This survey included more
than 13,600 households and approximately 60,000 persons. The
survey contained dataonreported inpatientandoutpatient out-
of-pocket expenditures (OOP) for public and private facilities.
We examined a subset of persons who reported OOP only for
private care, with no recourse for reimbursement or payment
from any third party payer (e.g., employer, health insurance).
Assuming that the OOP payment approximates the costs (and
profit) at these private hospitals, we estimated the costs per
bed-day in a private hospital in Malaysia and the unit cost of
an outpatient visit.
Using the mean values to estimate the costs, we found that
the data suggested that the estimate of OOP per bed-day in a
private hospital (US$249.24) was approximately 14% higher
than the weighted estimate we obtained for a bed-day in a
public hospital (US$219.16) (Table 2). However, it was within
the range of bed-day costs for different public hospital types
(US$147.84 in a primary level hospital and US$263.45 in a
tertiary level hospital) (Table 2). One possible interpretation
of this difference in costs is that 90% of the capacity of private
hospitals in Malaysia is in urban rather than rural areas.
To obtain an estimate of the cost per outpatient visit, we
took a weighted average between OOP per hospital outpatient
visit and OOP per general practitioner outpatient visit. The
cost for OOP per outpatient hospital visit is US$23.76, which
is approximately 43% lower than our estimate for outpatient
visits to a public hospital (US$41.96) (Table 2).
Indirect costs. The main source of indirect cost is work-time
lost (i.e., productivity loss) caused by illness. The indirect
costs include those incurred by the patient and also include
relatives’ time spent at home caring for the patient and in trips
to the hospital.47By dividing total indirect cost by the number
of days affected in the data for Malaysia of Suaya and others,7
we found that the average indirect cost per day was approxi-
mately 1.5 times the minimum wage. This estimate includes
the productivity loss of both the patient and relatives, and the
corresponding days of school and work days based on the age
distribution of cases. In 2011, Malaysia required a minimum
wage of US$199 per month for private security guards.48
However, policy debates examining wages in all sectors and
have mentioned a benchmark wage of US$199 per month.49
We used this benchmark as a reasonable economic value
each day lost to dengue and based our calculations of indirect
costs on it. We also used the study by Suaya and others to
estimate the total number of days lost to dengue fever in
Estimation of average unit costs per bed-day and per outpatient visit in a public hospital in Malaysia, 2009
Item and type of hospital
Estimated % of beds
by hospital type*
Ratio: cost of hospital
Unit cost per bed-day
Average cost per bed-day
Per outpatient visit
Average cost per outpatient visit
*Estimates of the distribution of beds by facility type were derived during the workshop. Estimations were made by using data from Suaya and others7World Health Organization–CHOICE.44
DENGUE COSTS IN MALAYSIA
Malaysia (averaging 11.2 days lost per ambulatory episode
and 16.2 days lost per hospitalized episode, including patient
and household impact).7
To include economic costs associated with deaths, we used
the human capital approach based on productivity lost,50and
estimated the years of premature life lost based on life expec-
tancy using WHO life tables for Malaysia.51We used the age
distribution of the 92 reported deaths related to dengue in the
year 2009 (Ministry of Health Malaysia, unpublished data,
2010). Although not discussed in the workshop, a paper from
Indonesia showed under-reporting in the region for dengue-
related deaths, even from major hospitals.18Recent studies
have also shown associations between dengue illness and
other severe health complications,52–58and we would expect
some of the resulting deaths not to be reported as dengue.
Because of the paucity of data, we assumed that the overall
under-reporting of dengue-related deaths is equal to the
under-reporting of hospitalized cases from the public and pri-
vate sectors, and that the deaths in the public and private
sectors are proportional to their shares of dengue cases. To be
conservative, we assumed that all workers earn minimum wage
and that students stay in school until they are 17 years of age.
We calculated the costs of school days lost using the estimates
of Suaya and others (2009 US$5.05 per day),7and adjusted for
time preferences using an annual discount rate of 3%.
Overall cost per case. We made several assumptions to esti-
mate the total costs per dengue case. First, because regional
breakdowns were not available, and dengue is primarily
urban, we assumed that the duration of dengue cases from
the UMMC data is on average representative of the national
data.7Second, we assumed that the national impact on
patients and households per dengue case is the same as that
estimated from UMMC data. This seems plausible because
the ratio of dengue cases in adults to those in children was
estimated at 2.3 in UMMC is similar to the ratio of 1.93 we
derived from using the incidence data of dengue fever and
dengue hemorrhagic fever data during 2003–2005.12Third,
we assumed that all dengue cases affect productive persons
and valued their time lost at the minimum wage if they are
adults or the cost per day of school if they are children.
Fourth, to update the indirect daily costs of dengue for per-
sons with hospitalized and ambulatory cases from the study by
Suaya and others,7we assumed that the relationship between
the minimum wage and the indirect costs in both settings has
not changed since 2005. Fifth, we assumed that the indirect
daily costs of dengue cases are the same for the private and
public sectors. This is a conservative assumption because we
would expect wealthier persons to receive treatment predom-
inantly at private facilities, which is likely to raise the average
indirect costs in this setting. We do not have the required data
to test this hypothesis and make a more accurate estimate.
Finally, we assume that the impact of dengue on total days lost
is caused by the average duration of an acute dengue case.
However, we did not include any costs associated with long-
term consequences of dengue, a possible chronic sequela of
illness referred to as post-infectious fatigue syndrome.59–63
Sensitivity analysis. The probabilistic sensitivity analysis
of total costs includes the simultaneous variation of five cate-
gories of parameters summarized in Table 3. We computed
20,000 Monte Carlo simulations for each parameter. Iterations
drew random values from the distribution of each input.7,27,43,44
Total number of cases. In the second round of the Delphi
process, 10 of 14 participants gave estimates for EFs and the
percentage of ambulatory cases. The respondents came from
varied settings (four from academia, two from the Ministry of
Health, and four from the private sector). Their EFs averaged
1.30 (range = 1.0–2.0) for public sector hospitalized cases and
2.45 (range = 1.3–5.0) for private hospitalized cases.
In the second round of the Delphi process, we addressed
ambulatory EFs indirectly through the share of cases treated
in the ambulatory sector. In that round, respondents’ estimated
percentage of dengue cases treated entirely in the ambulatory
sector averaged 58% (range = 30–95%). Thus, the triangular
distribution in the sensitivity analysis on the proportion of den-
gue cases that are ambulatory used these results as most likely,
minimal, and maximal values, respectively. Corresponding
adjusted EFs for each of the four categories of dengue patients
using the mean values of the Delphi process and the resulting
dengue cases adjusted using EF is shown in Table 4. The EF-
adjusted dengue cases satisfy our consistency checks in that the
ambulatory shares for public patients (50,186 of 86,527) and
private patients (40,955 of 70,613) both equal 58%. The upper
line in Figure 1 shows the EF-adjusted dengue cases by year.
Total costs of ambulatory and hospitalized dengue cases.
Estimated total costs of ambulatory and hospitalized dengue
cases in the public and private sectors using EF adjusted
dengue cases (considering a 58% share of ambulatory cases;
Table 4) is shown in Table 5. The annual burden of dengue in
Of this burden, 45.1% was from the private sector and 54.9%
was from the public sector; 51.2% corresponded to non-fatal
Summary of parameters varied simultaneously in sensitivity analysis, Malaysia*
Sensitivity analysis on parameter
Source or referenceDistribution MinimumCentralMaximum
EF of hospitalized cases (public sector)
EF of hospitalized cases (private sector)
Proportion of ambulatory treatment of dengue
Bed-day cost hospitalized dengue (public sector)
Visit cost ambulatory dengue (public sector)
Days lost per episode (hospitalized)
Days lost per episode (ambulatory)
No. of deaths from dengue
Delphi, round 2
Delphi, round 2
Delphi, round 2
7, 43, 44
7, 43, 44
Ministry of Health†
*EF = expansion factor; NA = not applicable; SE = standard error. Our cost estimates were derived using only the best estimate for these parameters.
†No. deaths correspond to reported deaths in 2009 (Ministry of Health Malaysia, unpublished data).
SHEPARD AND OTHERS
hospitalized cases of dengue, 34.3% to ambulatory cases, and
14.5% corresponded to indirect costs associated to deaths;
33.0% of the total costs were direct costs (e.g., hospital ser-
vices) and 67.0% were indirect costs (e.g., productivity loss).
Malaysia has a population of approximately 27.5 million
imately US$2.03 (MYR7.14). Because the GDP per capita of
Malaysia was approximately US$6,830 in 2009 US dollars
(MYR24,032), the cost of dengue illness represented 0.03%
of the per capita GDP.64Based on trends in the most recent
published National Health Accounts for Malaysia, the country
spent approximately US$363.80 (MYR1,280.09) per person
per year on health care.65Thus, the direct cost of dengue
represented0.18% ofthe country’s total healthexpenditures.
The 13 states and 3 federal territories in Malaysia vary con-
siderably in size and population, ranging from the federal terri-
tory of Putrajaya with approximately 68,000 persons to the
federal state of Selangor with approximately almost 5.5 million
persons.66The biggest urban area is the metropolitan area of
Kuala Lumpur. Using data of the reported cases of dengue
from the Department of Statistics, Malaysia,66and assuming
that the costs are proportional to the number of dengue cases,
we found that Kuala Lumpur represented approximately 9%
of the total costs of dengue fever in Malaysia.
Sensitivity analysis. In our sensitivity analysis, the factor
with the largest impact on the variation of total cost of dengue
in Malaysia was the share of ambulatory cases (75.0% of the
total variance), followed by the EFs for dengue reported in
private hospitals (17.8% of total variance), the EFs for den-
gue reported in public hospitals (6.6%), the total number of
deaths (0.2%), the total days lost because of ambulatory treat-
ment of dengue infection (0.2%), and the total days lost
because of hospitalization (0.1%). Because the estimates of
direct costs derived from UMMC and the six district hospital
study were similar, their contribution to the total variation of
costs was negligible. To illustrate this point, when we estimated
public sector unit costs data from the six district hospitals
instead of UMMC 2009 data, we found that the total annual
economic burden of dengue illness in Malaysia is US$54.45
million, which is only approximately a 2% change from our
original value. If we varied all these parameters simulta-
neously, the 95% certainty level (centered on the median) for
the total economic burden of dengue was US$44.30–$153.20
million, and the interquartile range was US$56.05–$80.60 mil-
lion. Although the mean values of costs we report are based on
our best assumptions and data, the width of the certainty levels
and interquartile range illustrates the uncertainty of estimates
of economic burden when using incomplete data.
Our findings suggest that there is substantial under-reporting
of symptomatic dengue fever in Malaysia, and that the eco-
nomic burden of dengue fever in Malaysia is considerable.
Combining multiple sources of data is critical to achieve reli-
able estimates of the total cases and economic burden of den-
gue fever. The EFs we used to adjust for under-reporting were
estimated on the basis of several data sources, including
existing literature, expert knowledge, and laboratory evidence.
The Delphi process led to an overall EF of 3.79, adjusted using
the mean value of 58% share of ambulatory cases. This EF is
well in the range of those of published studies from Southeast
Asia (EF = 3.1–9.1), and lower than the average from these
studies (7.8 times as many cases of dengue as those officially
reported).21However, our estimates are probably conserva-
tive, understating the overall burden of dengue in Malaysia,
for several reasons.
Cost of dengue illness by sector, setting, and component, Malaysia*
Indirect deathsTotal IndirectDirectTotal IndirectDirectTotal
Estimated costs per case (2009 US$)
Estimated aggregate costs from EF-adjusted dengue cases (58% ambulatory; 2009 US$1,000s)
Private 7,223 973 8,196
Total 16,073 3,07919,152
(23,900–42,600) (6,470– 9,790)(44,300–153,200)
*EF = expansion factor. The unit cost of death reported is the average cost; the actual values were estimated on the basis of the age distribution of reported deaths caused by dengue in 2009
(Ministry of Health Malaysia, unpublished data).
†The range corresponds to the 95% certainty levels (centered on the median) in our projections, and is given by the simultaneous variation of parameters as indicated in Table 3.27
Derivation of dengue cases in Malaysia using expansion factors (2009),
by sector and setting*
SectorHospitalized casesAmbulatory cases Overall
Adjusted EFs (combining mean factors, 58% share ambulatory)
No. reported dengue cases
Adjusted dengue cases using EFs (58% share ambulatory)
Row % 42.00
*EF = expansion factor.
†EFs were estimated by comparing the EF adjusted cases and reported cases, assuming
that the total ratio of public/private ambulatory cases is the same as this ratio for hospitalized
cases. The EFs for ambulatory cases were indirectly derived in two steps. First, we obtained
EFs for hospitalized cases in the private and public sectors (second column) and the share
of the total cases that were ambulatory (58%) from the Delphi process. Second, we derived
EFs for ambulatory cases by dividing the estimate of total ambulatory cases for each sector
by the officially reported cases.
‡The both row and overall column are derived by comparing the projected and
DENGUE COSTS IN MALAYSIA
First, a thorough study of EFs in Thailand and Cambodia
with EFs of 8.7 and 9.1, respectively,19was not available at
the time of the workshop. If that study had been available,
the EF estimates from the Delphi process might have been
Second, our estimates include only the acute phase of den-
gue illness. Recent evidence indicates that dengue causes a
substantial reduction in QoL of patients during the course of
their infection. A previous study in Malaysia found that the
reduction in QoL for dengue patients lasted longer than the
duration of the fever.11For some patients, symptoms are
more persistent but we do not know for how long these symp-
toms might last, and how they affect the QoL of a patient.
Dengue chronic fatigue refers to the long-term consequences
of dengue fever,62but only a few published studies have
examined this phenomenon. A study of adults in Cuba sug-
gests that 57% of the symptomatic dengue patients reported
having persistent symptoms for two years after their infec-
tion.59Other studies suggest that 25% of discharged patients
had symptoms after two months (Singapore),6347% of
patients had symptoms after 6 months (Cuba),60and 8.5% of
patients reported having difficulty in their daily activities after
2 months and 5.1% after 6 months (Brazil).63If dengue
chronic fatigue is as common as these studies suggest, then
our calculations of the total economic burden of dengue in
Malaysia would be considerably underestimated.
Third, our indirect cost estimates related to productivity loss,
resulting from illness and premature death, were based on
minimum wage instead of the more commonly used GDP or
gross national income per capita (approximately US$580
monthly output per capita in 2009).14We chose to base our
cost estimates on the minimum wage to best fit the types of
time lost. Among patients, some of the time loss from dengue is
from leisureand from children who are not inthe labor market.
Among family care givers, families can generally choose which
member takes care of a sick household member, or does not
enter the labor market so as to be available when needed. An
economically rational family would ask the lowest paid house-
hold member to take that task. We thought minimum wage
might better reflect the productivity of homemakers who
choose not to enter the labor market, or who are caregivers of
sick children. Using minimum wage also enabled us to make
our cost estimates comparable to previous studies in Malaysia.7
Fourth, our cost estimates do not include the costs of pre-
vention, surveillance, and dengue vector control activities.
These activities measurably increase the total economic bur-
den of dengue, as suggested by other studies. For example
they added 39% to the estimated economic burden of dengue
in Thailand,643% in Panama,67and 20% in Puerto Rico.68
There are vector control units administered by the Malaysian
MoH and by local councils (city hall, city councils, and munic-
ipal councils). There are yet other costs not considered, such
as the impact of dengue illness on tourism or the effects of the
seasonal clustering of dengue on health systems.69Despite
this limitation, our cost estimates improve previous estimates.
Our best estimate of the annual costs of dengue illness per
year (US$55.83 million) is between previous estimates by
Suaya and others7(US$41.92 million) and Lim and others15
(US$134.40 million). The estimate of Suaya and others is
based on the average of yearly reported dengue cases in
2001–2005 without adjustment for under-reporting. Because
our conservative estimate (the lower bound of our certainty
range was US$44.3 million) is based on lower ranges of expan-
sion factors and other parameters, it is similar to that of Suaya
and others.7Also, the unit costs in the study by Suaya and
others are based only on data from a national referral hospital
(UMMC). We overcame the limitations in their study by using
EFs and adjusting UMMC cost data by facility type using
WHO-CHOICE estimates.44These adjustments decreased
the cost per bed-day by 17% and per outpatient visit by 20%
compared with using only UMMC data.
The estimate of Lim and others15(US$134.40 million) is
considerably higher than our best estimate because, in addi-
tion to the cost of illness, those authors include costs of vector
control activities and research and development. Lim and
others15estimated an overall EF of 3.23, which was similar to
our estimated EF of 3.79, but their estimated overall unit cost
was higher (US$449.95 versus US$344.43 in our study). Lim
and others15used the average of reported dengue for 2002–
2007 (37,793 cases versus 41,454 in our study). From the values
reported by Lim and others,15we derived the component of
dengue costs in their study caused by illness. This result (US
$54.93 million) is virtually identical to our estimate (US$55.83
million). When we adjusted for GDP deflators in Malaysia and
each year’s exchange rate,26,66we found that our total cost
estimate was equivalent to US$71.05 million in 2012 US dollars
(2012 MYR218.27 million).
More generally, limited data required to extrapolate EFs
and costs across calendar years, age groups, locations, and
countries create substantial uncertainty in our estimates. This
variation is reflected in our wide sensitivity analysis. Some
limitations and areas of uncertainty deserve special attention.
First, the accuracy of the Delphi estimates depends on the
quality of the evidence examined and the combination of
knowledge from the group of experts. We examined evidence
of under-reporting from a wide variety of sources, including
published literature reporting serologic and capture–recapture
original studies in the region, and unpublished data from the
public and the private sectors. We included several experts
from academic, public, and private sectors, and achieved con-
sensus through two rounds of consultation and adjustments for
internal consistency. We believe that our estimates for EF and
the share of ambulatory cases are as accurate as available
evidence allowed at the time the workshop took place. We do
not recommend a Delphi panel as a substitute for gathering
more original data through well-designed capture–recapture
or cohort studies. However, such studies have time and
resource demands that are at least an order of magnitude
greater than organizing a Delphi panel and, to our knowledge,
none existed in Malaysia. A rigorous Delphi process is a good
alternative when facing complex health issues with budget and
The EF for dengue episodes in the private sector is proba-
bly most critical because there is a paucity of data. To check
our estimates, we examined data from private laboratories. A
total of 22,725 patients were tested for dengue infection in
Pantai hospitals in 2009, and 4,531 were positive for dengue.
Projecting to the overall private sector overall of Malaysia, we
estimated that 113,625 suspected dengue cases occurred in
2009, of which 22,655 were laboratory confirmed. The reported
number of dengue cases in the private sector was approxi-
mately 12,000 (Table 4), which was half the figure we projected
from the data of Pantai Holdings. Even the value of approxi-
mately 25,000 might hide a level of under-representation
SHEPARD AND OTHERS
because single-sample serologic tests might show false-negative
results (e.g., the test was conducted early in the illness, the
sample was not kept sufficiently cold or processed promptly,
or the infection was a secondary one).62Workshop participants
believed that the rate of false-negative results might be approx-
imately 50%. We expect that some cases are not tested, partic-
ularly those who are adults; thus, our estimates are consistent.
Correcting for this rate of false-negative results would increase
the projected number of dengue cases in the private sector to
approximately 50,000 (approximately 25,000+2). Assuming
that reporting of hospitalized cases is accurate (EF = 1), then
the remaining cases of dengue in the private sector, approxi-
mately 38,000 (approximately 50,000–12,000) cases should be
ambulatory, resulting in a high EF for hospitalized cases of
approximately 166 (EF ambulatory private sector is 38,000 of
229 or 166). Interestingly, this number is close to the estimate
of an EF of approximately 179 for ambulatory cases in the
private sector we calculated using the estimates from the Delphi
process (Table 4).
There is another confounding factor in estimating the num-
ber of cases: in the private sector a number of laboratory-
confirmed dengue patients are referred to the public sector
after a positive result. This factor might lead to the above
estimation of EF being inflated relative to the actual situation.
Although such cases will be reported twice, this approximately
25,000 might still be an over-expansion. The EFs for the private
sector, and particularly for ambulatory cases, remain an area
of considerable uncertainty. A comprehensive literature review
of EFs inSoutheast Asia showed no results for private sector or
ambulatory EFs for dengue.21
Second, our estimates of unit costs of inpatient and out-
patient could be further refined by distinguishing not only by
facility type (based on the number of beds), but also by region,
GDP, population, number of specialist physicians, and health-
care. Unfortunately, these data are not readily available.
In conclusion, information about economic burden is needed
for setting health policy priorities and deciding about the imple-
mentation of existing and new technologies, but accurate esti-
mation is difficult because of incomplete data. We overcame
this limitation by drawing on multiple data sources. Although
hospital-based reporting appeared relatively complete, the data
indicate that in the ambulatory and private sectors there is con-
siderable under-reporting of dengue cases. After adjusting for
under-reporting we found that dengue imposes a considerable
economic burden in Malaysia. Our results and previous studies
suggest that health policies aimed at controlling dengue effi-
ciently would most likely be economically valuable.
Received January 9, 2012. Accepted for publication June 29, 2012.
Published online October 1, 2012.
Acknowledgments: We thank the Ministry of Health, Malaysia, for
sponsoring the Burden of Dengue Workshop on December 6, 2010, in
Putrajaya, and also those who participated: Dr. Satwant al Singh,
Dr. Jeremy Brett, Ms. Sharon Chiang, Dr. Chee Kheong Chong,
Dr. Laurent Coudeville, Dr. Ahmad Faudzi bin Yusoff, Dr. B. K.
Ho, Dr. Shree Jacob, Dr. Ahamad Jusoh, Dr. P. Ravi Raviwharmman,
Ms. Puan Shanaliza Sulaiman, Dr. Jameela Zainuddin, Dr. Rosemary
Susan Lees, Dr. Lucy Lum, Dr. Chiu Wan Ng, and Dr. Donald Shepard;
Dr. Abdul Hasan, Dr. Lokman Hakim B. Sulaiman, Dr. Noor
Azimah bt. Hassan, and Stanley Ho for advice; Clare L. Hurley
for editorial assistance; and three anonymous reviewers for con-
Financial support: This study was supported by an agreement from
Sanofi Pasteur to Brandeis University.
Disclosure: Three participants in the workshop and Delphi process
(Jeremy Brett, Laurent Coudeville, and Shree Jacob) were employees
of Sanofi-Pasteur, a company currently developing a dengue vaccine.
Authors’ addresses: Donald S. Shepard, Eduardo A. Undurraga, and
Yara Halasa, Schneider Institutes for Health Policy, Heller School,
Brandeis University, Waltham, MA, E-mails: firstname.lastname@example.org,
email@example.com, and firstname.lastname@example.org. Rosemary Susan Lees,
Centre for Research in Biotechnology for Agriculture, University of
Malaya, Kuala Lumpur, Malaysia, E-mail: email@example.com.
Lucy Chai See Lum and Chiu Wan Ng, Faculty of Medicine, University
of Malaya, Kuala Lumpur, Malaysia, E-mails: firstname.lastname@example.org
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