Introduction
Dengue virus (DENV) is the most important arbovirus among humans. With around half the world population at risk and recent estimates of about 60–100 million symptomatic infections per year,1,2 DENV imposes a substantial burden to communities and health systems in most tropical and subtropical countries.3–6 Dengue can be caused by any of four viral serotypes (DENV 1–4); symptoms range from asymptomatic or mild febrile illness to severe dengue and, in some cases, death.7,8
Dengue is a major public health problem in the Philippines and is endemic in all regions of the country.9,10 The country's outbreaks are largely seasonal, with most episodes occurring during the wet season (June–February).11 The Philippines has made dengue a notifiable disease since 1958, has all four DENV serotypes circulating9 and ranks among the countries with the highest number of dengue episodes in southeast Asia.12–14 On average, 170,503 symptomatic DENV infections and 750 deaths were officially reported to the Philippines Department of Health (DoH) annually from 2010 to 2014, i.e., an incidence of about 178 symptomatic dengue episodes per 100,000 population and a reported case fatality rate of approximately 0.44% (Philippines DoH, unpublished communication, September 2015).15 A recent review of the epidemiology of dengue in the Philippines showed that the incidence rate of dengue was highest among children of 5–14 years of age, with over 80% of dengue-related deaths occurring among individuals of less than 20 years of age.9
Dengue surveillance in the Philippines depends mostly on disease reporting units (DRUs), which include sentinel hospitals, private clinics, rural health units (RHUs), municipal or city health offices, and human quarantine stations, to report all suspected, probable, and confirmed dengue episodes since 2007 to the Philippines Integrated Disease Surveillance and Response System.9,16,17 The surveillance system largely focuses on hospitalized cases, particularly those with severe symptoms.10,15 About 93% of all dengue episodes reported in 2010–2014 were hospitalized patients and, of these, half were reported from private facilities.18 However, a substantial share of dengue episodes may not be reported, thus hindering estimates of the true burden of dengue in the Philippines.
The complexity of dengue illness limits the accuracy of reporting. Reporting rates vary with severity of symptoms and treatment setting, with more severe, hospitalized, and episodes treated in the public sector more likely to be reported than those less severe, ambulatory, or privately treated.4,12,19–22 The severity of DENV infections has been associated with younger age,23–25 newly introduced serotype,26,27 secondary infection,28–30 greater time interval between infections,23 and host genotype,31,32 among other factors that indirectly impact the rate of reporting. Misdiagnosis, particularly in countries with high incidence of other febrile illnesses,33–36 and underdiagnosis due to limited sensitivity and cost constraints of diagnostics tests may also contribute to underreporting.37,38 Additional sources of uncertainty in estimates of dengue incidence have been discussed elsewhere,39 and several studies have estimated average reporting rates of dengue episodes.3,40,41 Most studies have been limited to cohorts of children and/or adolescents.40 Evidence from Puerto Rico and Brazil, both of which have a well-funded surveillance system, suggests that even fatal DENV infections may be underreported.42,43 These findings, together with the variability in reporting rates shown in previous studies,21,44,45 underscore the need to improve understanding of the relation between passive surveillance and accurate reporting of dengue cases.
Having an accurate estimate of disease incidence and burden of dengue is important to inform decisions about health policy, research, and program impact, based on reliable and comparable measures in time.39,46 Dengue surveillance systems are essential to estimate disease incidence; however, the sensitivity of surveillance systems is limited. Surveillance systems in most dengue-endemic countries, including the Philippines, are passive, depending on the patient presenting to the professional health sector for treatment and the provider reporting the case to public health authorities. Design and implementation limitations of dengue surveillance systems may hinder accurate estimates of disease burden and challenge evidence-based decision-making, and the need for more effective surveillance systems has long been acknowleged.39,46–50
Here we estimated the average reporting rate and expansion factor (EF) of dengue episodes in the Philippines comparing active surveillance data of symptomatic DENV infections with cases reported to the surveillance system. Specifically, we compared active surveillance data of symptomatic DENV infections in a prospective community-based seroepidemiological cohort, including children (6 months to 15 years) and adults, in Punta Princesa, Cebu City, Philippines from March 2012 to March 2013 with reported dengue episodes based on passive surveillance data from the Cebu City Health Department (CCHD). Punta Princesa is an urban barangay (smallest government unit) located in the South District of the city (Figure 1), with a population of about 22,400.53 Using our adjusted estimate of total dengue episodes, we estimated the disease burden of dengue using disability-adjusted life years (DALYs).
Location of Punta Princesa in Cebu City (shaded), Region VII, Philippines (inset).
Citation: The American Society of Tropical Medicine and Hygiene 96, 4; 10.4269/ajtmh.16-0488
Materials and Methods
Passive dengue surveillance.
We obtained the number of reported dengue episodes (April 2012 to March 2013) in Punta Princesa, Cebu City, from the CCHD.54,55 The case definition used by the CCHD is based on the Manual of Procedures for the Philippine Integrated Disease Surveillance and Response,56 which follows the World Health Organization (WHO) 1997 classification of dengue illness (undifferentiated fever, dengue fever, dengue hemorrhagic fever, and dengue shock syndrome).57 The Philippines DoH officially updated its dengue classification by levels of severity (dengue without and with warning signs, severe dengue), as recommended by current WHO guidelines,7 in its Revised Dengue Clinical Case Management Guidelines.58 However, dengue is still reported based on the WHO 1997 classification system because not all hospitals have adopted the new classification scheme. Most reported dengue cases are based on clinical diagnosis and are not laboratory confirmed; a laboratory diagnostic test usually requires out-of-pocket payment by the patient. We obtained census data from the Philippine Statistics Authority59 and estimated monthly reported incidence rates of dengue (per 1,000) by dividing the monthly reported dengue cases by the population of Punta Princesa (N = 27,303).
Active dengue surveillance by cohort study.
The Armed Forces Research Institute of Medical Sciences (AFRIMS) and the Philippines AFRIMS Virology Research Unit (PAVRU) conducted a prospective community-based seroepidemiological cohort study in Punta Princesa, Cebu City. The cohort included 1,008 enrolled volunteers. Inclusion criteria for the cohort included the following: 1) male or female ≥ 6 months of age, 2) resident of Punta Princesa, and 3) understood, approved, and signed the written informed consent and/or assent (if children > 12 years of age). The study excluded participants who had suspected active tuberculosis, or lived in the same household as a person with suspected active tuberculosis, to reduce risk to the research staff. Blood samples were collected at baseline and at 12 months. We estimated monthly incidence rates of dengue (per 1,000) in the cohort.
The health status of enrolled cohort participants was monitored weekly through short message service, phone call, and/or home visit by the PAVRU research team, Cebu City. Acute illness in a cohort participant with fever in the previous 7 days or with measured fever (≥ 38°C) was investigated. Participants were clinically assessed at acute, 2-, 5-, and 8-day visits, and a convalescent visit at the third week. Blood samples were collected at the acute and third-week visits from all participants who reported fever in the past 7 days or whose measured fever was ≥ 38°C and were transported to the PAVRU laboratory. Serum aliquots of these blood samples were frozen at ultralow temperatures (−70°C) until ready for further testing. Further details about the cohort and active surveillance have been reported elsewhere.60,61
Detection of DENV.
Aliquots of the blood samples of participants with suspected DENV infection were sent for laboratory analysis to AFRIMS. Detection of DENV RNA in the acute blood samples was done by reverse transcription polymerase chain reaction (RT-PCR) following Lanciotti and others62 with modifications (see Supplemental Material for further details). Serological testing for evidence of DENV infection was done in the acute phase and third-week blood samples by DENV IgM/IgG ELISA.60
Estimation of EFs.
EFs are used to obtain a more accurate estimate of number of illness episodes and can be estimated as the number of dengue episodes in a specified population and setting divided by the number of episodes reported to the surveillance system (EF = total episodes of dengue/reported episodes). To estimate EFs of dengue episodes in Punta Princesa, we divided monthly incidence rates of laboratory-confirmed dengue episodes from active surveillance (our best estimate of the true incidence of dengue) by the incidence rate of reported dengue episodes based on passive dengue surveillance for Punta Princesa from the CCHD. We estimated the reporting rate (proportion of episodes reported) as the inverse of EF.
Estimates of the disease burden of dengue.
Despite documented variation of reporting rates in time and location,21,44,45 we used our results to improve estimates of dengue burden in the entire country. We based our burden of disease estimates on average reported nonfatal and fatal dengue cases in the Philippines in 2010–2014, the most recent 5 years of surveillance data available, to provide a more stable estimate of the burden of dengue, considering the substantial annual variation of disease incidence.
We estimated the disease burden of dengue in DALYs, a summary measure of population health that combines morbidity and mortality outcomes.63 A DALY is the sum of a measure equivalent to the years of life lost due to disability and a measure of the years lost due to premature death (YLL). DALYs were developed in the early 1990s to compare population health across countries and in time, and the original 1990 Global Burden of Disease (GBD) project used age-weights and time-discounting.51,64 The definition of DALYs was updated for the GBD 2010 study by Murray and others63,65,66 at the Institute of Health Metrics and Evaluation (IHME), dropping age-weights and time-discounting, which is also the DALY definition currently used by WHO.67 To enhance comparability with other studies, we have reported DALYs using both age-weights and time-discounting (hereafter original GBD), and without age-weights and time-discounting (hereafter IHME-GBD).
We obtained duration of illness in ambulatory and hospitalized dengue episodes from a previous study10 and the age distribution of fatal (2003–2005) and nonfatal (2000–2009) dengue episodes from the Philippines DoH. We did not use data on duration of illness or age distribution from CCHD, because our objective was to estimate DALYs at the national level. We estimated the years of life lost based on GBD-2010 standard abridged life table for computing years of premature life lost.65 We allocated dengue episodes to treatment settings based on the results from a Delphi panel workshop including 34 national and international dengue experts in Cebu City, Philippines, in 2013.10 To estimate DALYs using original GBD methodology, we used the same parameters as previous dengue studies,52,68,69 namely, a disability weight of 0.81 (range: 0.6–0.92), age constant of 0.16243, age weight of 0.04, and an annual discount rate of 3%.
Sensitivity analysis and uncertainty.
Because substantial uncertainty still remained around many of the main parameters in our model, we conducted a probabilistic sensitivity analysis of our estimates based on Monte Carlo simulations. A Monte Carlo simulation consists of running repeated trials, based on random sampling from the probabilistic distribution of the parameters in the model, to obtain the frequency distribution of numbers of dengue episodes and other results of interest. We computed 10,000 Monte Carlo simulations for each parameter, simultaneously varying the following parameters based on ranges and probability distributions in the dengue literature: EF for nonfatal and fatal dengue episodes, proportion of cases hospitalized, average length of stay at the hospital, average number of ambulatory visits prior to hospitalization, average number of visits for ambulatory patients, and disability weights for dengue. To estimate uncertainty for nonfatal EF, we obtained the standard deviation from the sample of monthly estimates of reporting rates and assumed a truncated normal distribution (censored at 5%). For fatal EF, we used a beta-PERT distribution with minimum, mode, and maximum values based on the literature.42,43 We showed the sensitivity of our estimates to our main model parameters using a tornado diagram.
Ethics.
The prospective cohort study was approved by the Institutional Review Boards of Vicente Sotto Memorial Medical Center, Cebu City, Philippines, the Walter Reed Army Institute of Research, and the overall dengue burden analysis was approved by the Committee for the Protection of Human Studies in Research at Brandeis University. All participants in the study or their parents (for children under age 18) gave written informed consent and written assent was obtained from children older than age 12.
Results
Prospective cohort.
The cohort included 1,008 enrolled volunteers from Punta Princesa, with about 200 per age category at entry (6 months to 5 years, 6–15 years, 16–30 years, 31–50 years, and > 50 years) and a balanced distribution of female and male participants. Table 1 shows the main characteristics of the Punta Princesa, Cebu City, prospective cohort. Of 1,008 participants enrolled, 854 followed all activities during the year of the study following the study protocol. Reasons for not completing all activities included relocation out of the study area, consent withdrawal, lost to follow-up, and developing other health conditions.58 No individuals were excluded from enrollment because of their active pulmonary tuberculosis or that of a household member.
Characteristics of the prospective cohort in Punta Princesa, Cebu City, Philippines, March 2012 to March 2013
| Characteristic | N (%) |
|---|---|
| Enrolled participants | 1,008 (100.0) |
| Participants who completed study* | 854 (84.7) |
| Females at enrollment | 508 (50.4) |
| Participants by age group: (enrolled/completed) | |
| 6 months to 5 years | 203 (20.2)/148 (17.4) |
| 6–15 years | 201 (20.0)/184 (21.6) |
| 16–30 years | 200 (19.9)/168 (19.7) |
| 31–50 years | 204 (20.2)/172 (20.1) |
| > 50 years | 200 (19.8)/182 (21.3) |
| Participant's household size at enrollment | |
| 1 | 16 (1.6) |
| 2–3 | 207 (20.5) |
| 4–6 | 526 (52.2) |
| 7–10 | 237 (23.5) |
| > 10 | 22 (2.2) |
| Number of children < 16 years in household at enrollment | |
| 0 | 199 (19.7) |
| 1 | 231 (22.9) |
| 2 | 229 (22.7) |
| 3 | 180 (17.9) |
| > 3 | 169 (16.8) |
Participants who completed all study activities considered in the study protocol at 12 months including enrollment and 12-month blood collections.
Disease surveillance.
Table 2 compares the incidence rates of symptomatic DENV infections per 1,000 population in Punta Princesa based on active surveillance from the prospective cohort study and from passive surveillance as reported by CCHD. The estimated EFs showed more variation when using monthly incidence compared with cumulative incidence, because with cumulative incidence the sample size increases, providing a more stable estimate, and smooths seasonal differences in reporting rates.45
Symptomatic dengue infection incidence rates per 1,000 population in Punta Princesa from active surveillance in the prospective cohort and from passive surveillance as reported by the CCHD
| Month | Punta Princesa cohort (n) | Incidence rate per 1,000 pop. | Expansion factors as a function of: | ||
|---|---|---|---|---|---|
| Pta. Princesa cohort | CCHD* | Monthly incidence (per 1,000 pop.) | Cumulative incidence† (per 1,000 pop.) | ||
| April 2012 | 581 | 1.72 | 0.22 | 7.8 | 7.8 |
| May 2012 | 922 | 3.25 | 0.18 | 17.8 | 12.3 |
| June 2012 | 922 | 0.00 | 0.33 | 0.0 | 6.8 |
| July 2012 | 932 | 2.15 | 0.22 | 9.8 | 7.5 |
| August 2012 | 932 | 3.22 | 0.37 | 8.8 | 7.8 |
| September 2012 | 988 | 1.01 | 0.44 | 2.3 | 6.5 |
| October 2012 | 968 | 0.00 | 0.26 | 0.0 | 5.6 |
| November 2012 | 948 | 2.11 | 0.40 | 5.2 | 5.6 |
| December 2012 | 941 | 0.00 | 0.33 | 0.0 | 4.9 |
| January 2013 | 931 | 1.07 | 0.29 | 3.7 | 4.8 |
| February 2013 | 923 | 1.08 | 0.26 | 4.2 | 4.7 |
| March 2013 | 908 | 1.10 | 0.29 | 3.8 | 4.7 |
CCCH = Cebu City Health Department; pop. = population; Pta. Princesa = Punta Princesa cohort study.
CCHD rate shows the incidence rate per 1,000 population of symptomatic dengue infections in Pta. Princesa as reported through passive surveillance.
Cumulative reflects average since April 2012.
We next examined whether the reported monthly (April 2012 to March 2013) dengue cases in Punta Princesa (barangay level) followed a pattern similar to those reported at the regional administrative level, Central Visayas (Region VII) in the Philippines. Figure 2A shows these distributions as a proportion of annual reported dengue episodes. Figure 2B shows the correlation between the monthly distribution of cases in Punta Princesa and Central Visayas (r = 0.67; P = 0.02), which suggests that passive surveillance at both administrative levels was significantly correlated. Figure 2C shows the correlation between the distribution of dengue episodes and the EF based on comparing bimonthly dengue incidence from active and passive surveillance systems in Punta Princesa (r = −0.58; P = 0.22). Bimonthly EFs were more stable than monthly EFs. We obtained higher EFs during the months when there was higher relative number of dengue episodes (i.e., high season).
Distribution of reported nonfatal dengue episodes in Punta Princesa and Region VII, Philippines, and expansion factor (EF)-based comparison of monthly incidence of dengue from active and passive surveillance systems in Punta Princesa. (A) The distribution of reported dengue episodes by month in Punta Princesa and Region VII, Philippines (April 2012 to March 2013), as a proportion of annual reported dengue episodes. (B) The correlation between the monthly distribution of cases in Punta Princesa and Region VII. (C) The correlation between the distribution of dengue episodes and the EF based on comparing monthly incidence of dengue from active and passive surveillance systems in Punta Princesa. Pta. denotes Punta.
Citation: The American Society of Tropical Medicine and Hygiene 96, 4; 10.4269/ajtmh.16-0488
Estimates of the disease burden of dengue.
Dengue incidence varies substantially across years. Our best estimate to adjust for underreporting of dengue episodes in the Philippines based on comparing the cumulative incidence of dengue from active and passive surveillance systems in Punta Princesa is to use an EF = 4.7, that is, for each nonfatal dengue episode reported 4.7 symptomatic nonfatal dengue episodes occur. Even though these data corresponded to a single dengue season, we considered the reporting of symptomatic dengue episodes in 2012 as representative of the latest 5-year available data for two reasons. First, after refining and expanding the surveillance system, the Philippines Integrated Disease Surveillance and Response System remained largely unchanged during 2010–2014.9 Second, the number of reported dengue episodes increased in 2010 and subsequently remained consistently high,9,10 probably largely due to improved reporting, as suggested by a larger increase in dengue cases than in deaths.9 Figure 3 shows 2010–2014 reported cases averaging 170,503 nonfatal and 750 fatal. Because the mortality rate for dengue is low, our cohort was not large enough to estimate underreporting of fatal dengue episodes. However, at least two studies performed in Puerto Rico42 and Brazil43 provide evidence of underreporting of dengue with a range of 2–5 fatal dengue episodes per fatal case reported. We used an EF of two to be conservative (range for sensitivity analysis: 1–5).
Reported and adjusted dengue episodes in the Philippines, 2010–2014. Adjustment based on expansion factor of 4.7 comparison between active and passive surveillance systems in Punta Princesa.
Citation: The American Society of Tropical Medicine and Hygiene 96, 4; 10.4269/ajtmh.16-0488
Table 3 shows the parameter values, distributions, and data sources used to address uncertainty in our data and to estimate the 95% certainty levels (CL) of our main results. We modeled the variation in reporting rates of nonfatal dengue episodes based on the comparison of monthly incidence rates between passive and active surveillance systems. We estimated an EF of 4.7 (95% CL: 2.2–15.1). We estimated a total of 794,255 annual episodes of dengue (95% CL: 382,161–2,581,385) in the Philippines in 2010–2014. Of these, we estimated a total of 516,266 (95% CL: 228,830–1,630,468) dengue patients were hospitalized annually, based on the treatment setting allocation from a Delphi panel. Last, we estimated a total of 1,500 annual fatal episodes of dengue (95% CL: 907–2,904).
Parameters values, probabilistic distributions, and sources of data used in the probabilistic sensitivity analysis
| Item | Best | Parameters | Values | Distribution | Source |
|---|---|---|---|---|---|
| Reporting rate for nonfatal dengue (%) | 21 | (μ, σ) | (21, 12) | Normal* | Pta. Princesa active and passive surveillance |
| Expansion factor for fatal dengue | 2.0 | (min, mode, max) | (1.0, 2.0, 5.0) | Beta-PERT† | Tomashek and others42; Pamplona and others43 |
| Percentage of cases hospitalized (%) | 65 | (min, mode, max) 40 | (40, 65, 80) | Beta-PERT | Delphi panel‡10 |
| Length of stay in hospital (days) | 4.21 | (min, max) | (4.02, 4.38) | Uniform | Edillo and others10 |
| Ambulatory visits before hosp.(n) | 4.6 | (min, mode, max) | (2.3, 4.6, 6.9) | Beta-PERT | Edillo and others10 |
| Visits ambulatory treatment | 4.2 | (min, mode, max) | (2.1, 4.2, 6.3) | Beta-PERT | Edillo and others10 |
| Disability weights DALYs | 0.81 | (min, mode, max) | (0.60, 0.81, 0.92) | Beta-PERT | Meltzer and others52; Murray 199451 |
DALY = disability-adjusted life year; hosp. = hospital; max. = maximum; min. = minimum; n = number; Pta. Princesa = Punta Princesa cohort study.
The standard deviation was obtained from the sample of monthly estimates of reporting rates.
The Beta-PERT is a specific form of the beta distribution in which the mean and standard deviation are estimated as a function of expert's assessment of minimum, maximum, and mode values (PERT approximation). We used a scale parameter λ = 4 for the distribution.
The allocation of dengue episodes to treatment settings was based on the results from a Delphi panel workshop conducted in 2013 in Cebu City, the Philippines, which included 34 national and international experts.10
Table 4 shows the main results for disease burden estimates by treatment setting adjusted for underreporting of nonfatal and fatal dengue episodes. We found a substantial disease burden, with 535 (95% CL: 353–988) DALYs per million population using age-weights and time-discounting (original GBD method), and 997 (95% CL: 644–1,838) DALYs per million population without age and time adjustments (IHME-GBD method). The main difference in results between the two methods was driven by YLL, which highlighted the relevance of age-weights and time-discounting for comparability purposes. Dropping time-discounting and age-weights from DALY estimates implied an important shift toward deaths at younger ages and away from valuing more a year of healthy life for those at a more productive age (20–50 years). Most of disease burden of dengue came from YLL (75% original GBD; 87% IHME-GBD).
Annual disease burden of nonfatal and fatal dengue in the Philippines (2010–2014)
| Indicator (per million population) | Original GBD* | IHME-GBD† |
|---|---|---|
| YLD—ambulatory | 27.0 | 27.0 |
| 95% CL | 10–94 | 10–94 |
| YLD—hospitalized | 105.4 | 105.1 |
| 95% CL | 44–337 | 42–330 |
| YLL‡ | 402.3 | 865.2 |
| 95% CL | 247–773 | 530–1,663 |
| DALYs | 534.8 | 997.3 |
| 95% CL | 353–988 | 644–1,838 |
CL = certainty level; DALYs = disability-adjusted life years; YLD = years lost due to disability; YLL = years of life lost due to premature death.
Original Global Burden of Disease (GBD) refers to the original definition of DALYs proposed by Murray in 1994,51 and subsequently used by Global Burden of Disease studies conducted by the World Health Organization. We used the same parameters as in previous studies52,68,69 for comparability.
IHME-GBD refers to an updated definition of DALYs adopted by Murray and others at the Institute of Health Metrics and Evaluation (IHME) for the GBD 2010 study,65 where age-weighs and time-discounts were dropped from disease burden estimates. Without age or time discounts, the estimates are YLD = incidence × duration × disability weight; and YLL = incidence × year of life lost due to premature death. The full equation and rationale for original GBD are described elsewhere.54
We estimated the years of premature life lost based on GBD-2010 standard abridged life table for computing years of premature life lost.65
Figure 4 shows the main sources of variability in our estimated burden of dengue in DALYs per million population (based on original GBD method, with age-weights and time-discounting). The vertical line at 535 DALYs per million population shows the point estimate for the burden of dengue. The “tornado diagram” shows the 95% CL obtained through the 10,000 Monte Carlo simulations varying each parameter alone and varying all parameters simultaneously (top bar in the diagram). The main source of variation for our estimates came from the estimated EFs, because they determine the estimated incidence of the disease.
Variability of disease burden estimates in disability-adjusted life years (DALYs) per million population (using the original Global Burden of Disease method), based on the variation of the main parameters in the sensitivity analysis. The point estimate for the disease burden of dengue is shown by the vertical line in the figure at 535 DALYs per million population. All together denotes the simultaneous variation of all the parameters in the model, as shown in Table 3. EF denotes expansion factors, Amb. denotes dengue episodes treated in an ambulatory setting, Prop. hospitalized (%) denotes the proportion of dengue patients that are hospitalized on average, as determined by a Delphi panel,10 disability weight refers to the disability weights used for dengue and the corresponding variation.51,52
Citation: The American Society of Tropical Medicine and Hygiene 96, 4; 10.4269/ajtmh.16-0488
Discussion
Our results confirmed that dengue has been under reported in the Philippines, as previous studies have suggested.10,12 We found a cumulative reporting rate of 21% of symptomatic DENV infections, equivalent to an EF of 4.7 (95% CL: 2.2–15.1). Because EFs were estimated by comparing dengue incidence rates per person under follow-up in active and passive surveillance, participant attrition should not have affected our main results. Based on surveillance data in the Philippines for 2010–2014, we estimated 794,255 annual dengue episodes (95% CL: 382,161–2,581,385) and a disease burden of 535 (95% CL: 353–988) DALYs per million population using age-weights and time-discounts (original GBD) and 997 (95% CL: 644–1,838) DALYs per million population without age and time adjustments (IHME-GBD).
Our estimated EF was comparable to previous estimates of EFs in the Philippines and also in Central Visayas. Borja and others70 found that about 81% of dengue episodes were not reported in Manila, Muntinlupa, Baguio, Iloilo, Cebu, and Davao, which resulted in an overall EF of about 5.3 for these cities. Undurraga and others12 estimated that only about 13% of symptomatic dengue episodes in southeast Asia are reported. Using a regression model based on empirical studies from other countries in the region and an index of health quality, that study estimated a reporting rate of 14.3% of all symptomatic DENV infections in the Philippines, or EF of 7.0 (EF = 1/reporting rate). Comparing active surveillance based on preliminary results from this cohort in Punta Princesa, Cebu City, with the CCHD's passive surveillance data from March to October 2012, Edillo and others10 derived an empirical reporting rate of 13.3% for the Philippines (EF = 7.2). This preliminary rate is within the 95% CL of the present study. Using data from a dengue vaccine prospective cohort of children (2–14 years of age) in two study centers, Nealon and others71 compared incidence densities from active surveillance with incidence rates from the national passive surveillance system and obtained an EF of 11.5 (95% CL: 9.1–14.3). Toan and others40 estimated EFs for the Philippines as 15 and 14 episodes of dengue for each reported episode in 2007 and 2010, respectively, by comparing incidence rates from prospective community-based studies with estimated incidence at the country level. Their estimates were based on a follow-up study of young children (aged 2–15 months) in San Pablo, Laguna, in 2007–2008,72 and on a community-based enhanced surveillance program of children (2–14 years or age) in various cities in 2010–201173. If these cohorts were done in areas with higher than national average of incidence rates of dengue, these annual EF estimates may be overestimates, but were still within the range we obtained from Punta Princesa. The wide 95% CL for our EF estimates in Punta Princesa reflect the variance in monthly estimates of underreporting, mostly due to the relatively small sample size of our cohort, which had only 15 symptomatic dengue cases.
The results support previous evidence that reporting rates of dengue episodes may vary substantially over time21,44,45 and among locations. These variations may be explained by differential access to health care and health-care quality, providers' attention to dengue, variation in DENV serotypes, patients' health-seeking behavior, and mosquito population densities, among other factors. In the Philippines, dengue surveillance is largely conducted by DRUs; their size, infrastructure, quality of care, and connectivity vary substantially across the country and thus may result in variations in reporting rates by locality. As the DoH in Cebu City has collaborated with local and international partners on dengue research since 2005,74 dengue reporting might be better there than in the Philippines overall. It is important to bear in mind that EFs are used to improve estimates of dengue burden. The importance of having exact EF estimates for specific times and locations depends on their application. For example, more refined estimates may be needed to target control strategies most efficiently. Public health officials may need only approximate estimates of disease incidence, however, to support resource allocation between dengue and other conditions.
Our estimate of the annual disease burden of dengue was higher than a previous estimate for 2001–2010 (433 original GBD DALYs per million population),5 possibly due to higher incidence of dengue, and comparable to an estimate for 2013 (1,350 IHME-GBD DALYs per million population),2 but both estimates fell within our 95% CL. The results suggested that the annual burden of dengue was higher than estimates for other infectious diseases, including rabies (110 and 49 DALYs per million population based on IHME-GBD and original GBD methods, respectively)75 and intestinal fluke infections (590 IHME-GBD DALYs per million population in Philippines and Thailand together),76 and about 10% the disease burden estimated for tuberculosis (5,350 original GBD DALYs per million population).77
Last, even though reporting rates vary by year and geographic area, if we applied the estimated EF to reported episodes of dengue and deaths in the Philippines in 2013, we would obtain a total of 1,264,000 estimated cases of apparent dengue and 1,312 deaths for 2013 (Figure 3). These results are near the lower bound of the total number of dengue episodes estimated for the Philippines in 2013 by Stanaway and others2 (3.9 million 95% CL: 1.4–8.6) and are comparable to their estimated dengue deaths (1,210 95% CL: 450–1,612). The nearly 820,000 estimated number of hospitalized patients for 2013 based on a Delphi panel in the Philippines10 was about twice the 386,000 inpatient episodes estimated in Shepard and others6 for 2013, based on extrapolations from other studies.
The relatively limited study length and geographic area of the study restricted our ability to extrapolate results to other years and regions. Dengue cases in Punta Princesa, Cebu City, represented 0.06% of the total dengue cases reported in the Philippines by DoH 2012, or 0.09% relative to the mean number of cases (2008–2012) in the entire country, which has about 40,000 barangays. As discussed above, reporting rates of dengue vary temporally and geographically due to variation in dengue epidemiology, surveillance practices, demographics, health-care infrastructure, and access, all of which may affect the accuracy of our estimates. We would encourage initiating additional sites with active surveillance, particularly in locations that have not participated in previous research. Comparisons between active and passive surveillance in such sites should result in more nationally representative estimates of EFs. Such studies could rely on community health workers for active surveillance of febrile illness followed by diagnostic testing, particularly as dual (NS1 and IgG/IgM) rapid diagnostic tests become more accurate, easier to use, and less expensive. Such studies would benefit participants through improved access to dengue diagnosis and treatment and policy makers through better epidemiological data.
However, the fact that previous studies have shown comparable results underscores the validity of our main conclusions. Despite active surveillance, some dengue illnesses may still have gone undetected, particularly milder episodes. Because reporting of dengue varies by severity and treatment setting, it would have been helpful to distinguish underreporting of inpatient and outpatient episodes separately to obtain a more accurate estimate of disease burden. Unfortunately, estimating an EF by treatment setting requires a much larger study cohort. To strengthen evidence about underreporting, it would have been ideal to compare whether specific patients detected in the active surveillance were also reported in the CCHD passive surveillance; unfortunately, we lacked the data to do so due to privacy protections within each data source. Another limitation includes the reliance on expert opinion to allocate dengue cases by treatment setting.10 Finally, our estimates of disease burden did not include persistent symptoms, such as fatigue, asthenia, depression, and weight loss, that have been associated with DENV infection,8,78 as acknowledged by the WHO since 1997.57 Persistent symptoms may represent about a 40% increase in disease burden estimates over those from acute impacts.78
Conclusions
Our results provided evidence that a substantial number of symptomatic DENV infections have not been accounted for in routine reporting in the Philippines, as has been empirically found elsewhere. There are several ongoing efforts to control DENV transmission, including vaccines,79–81 antiviral drugs,82–84 and various strategies of vector control.85–88 The Philippines has a high dengue incidence and has already initiated a school-based dengue vaccination program in Manila.89 These estimates of the disease burden of dengue should help inform and refine policy decisions and increase understanding of dengue among the public.
ACKNOWLEDGMENTS
We thank I. Tac-an, S. Ygonia, and D. Macasocol of CCHD, Cebu City, Philippines, for the dengue data in the study site, N.B. Amoin for assistance with additional data acquisition, Clare L. Hurley for editorial assistance, and participants in the National Workshop on the Disease and Economic Burden of Dengue, Cebu City, Philippines, February 23, 2013, for their comments.
- 1.↑
Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh O, Myers MF, George DB, Jaenisch T, Wint GRW, Simmons CP, Scott TW, Farrar JJ, Hay SI, 2013. The global distribution and burden of dengue. Nature 496: 504–507.
- 2.↑
Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, Hay SI, Bedi N, Bensenor IM, Castañeda-Orjuela CA, Chuang T-W, Gibney KB, Memish ZA, Rafay A, Ukwaja KN, Yonemoto N, Murray CJL, 2016. The global burden of dengue: a systematic analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis 16: 712–723.
- 3.↑
Shepard DS, Halasa YA, Undurraga EA, 2014. Economic and disease burden of dengue. Gubler DJ, Ooi EE, Vasudevan SG, Farrar J, eds. Dengue and Dengue Hemorrhagic Fever. Wallingford, UK: CAB International, 50–77.
- 4.↑
Shepard DS, Coudeville L, Halasa YA, Zambrano B, Dayan GH, 2011. Economic impact of dengue illness in the Americas. Am J Trop Med Hyg 84: 200–207.
- 5.↑
Shepard DS, Undurraga EA, Halasa YA, 2013. Economic and disease burden of dengue in Southeast Asia. PLoS Negl Trop Dis 7: e2055.
- 6.↑
Shepard DS, Undurraga EA, Halasa YA, Stanaway JD, 2016. The global economic burden of dengue: a systematic analysis. Lancet Infect Dis 16: 935–941.
- 7.↑
World Health Organization, 2009. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. New edition. Available at: http://apps.who.int/iris/bitstream/10665/44188/1/9789241547871_eng.pdf. Accessed September 8, 2016.
- 8.↑
Simmons CP, Farrar JJ, Nguyen VVC, Wills B, 2012. Current concepts: dengue. N Engl J Med 366: 1423–1432.
- 9.↑
Bravo L, Roque VG, Brett J, Dizon R, L'Azou M, 2014. Epidemiology of dengue disease in the Philippines (2000–2011): a systematic literature review. PLoS Negl Trop Dis 8: e3027.
- 10.↑
Edillo FE, Halasa YA, Largo FM, Erasmo JNV, Amoin NB, Alera MTP, Yoon I-K, Alcantara AC, Shepard DS, 2015. Economic cost and burden of dengue in the Philippines. Am J Trop Med Hyg 92: 360–366.
- 11.↑
Edillo F, Madarieta S, 2012. Trends of dengue infections (1997–2008) in Cebu Province, Philippines. Dengue Bull 36: 37–49.
- 12.↑
Undurraga EA, Halasa YA, Shepard DS, 2013. Use of expansion factors to estimate the burden of dengue in Southeast Asia: a systematic analysis. PLoS Negl Trop Dis 7: e2056.
- 13.
World Health Organization, Western Pacific Region, 2013. Emerging Disease Surveillance and Response: Dengue Situation Updates. WPRO 2014. Available at: http://www.wpro.who.int/emerging_diseases/DengueSituationUpdates/en/. Accessed January 7, 2014.
- 14.↑
Dominguez NN, 1997. Current DF/DHF prevention and control programme in the Philippines. Dengue Bull 21: 41–47.
- 15.↑
Department of Health, Republic of the Philippines, 2012. Disease Surveillance, Dengue Morbidity. Available at: http://dev1.doh.gov.ph/disease-surveillance. Accessed January 7, 2014.
- 16.↑
Capeding MR, 2012. Dengue Surveillance and Diagnostics in the Philippines. First Asian Dengue Vaccination Advocacy (ADVA) Group Regional Workshop, Bangkok, Thailand, September 21–23, 2012.
- 17.↑
National Epidemiology Center, 2008. Manual of Procedures for the Philippine Integrated Disease Surveillance and Response. Manila, Philippines: Department of Health Philippines.
- 18.↑
Edillo FE, Erasmo JN, Halasa YA, Amoin NB, Largo FM, Shepard DS, 2013. Dengue surveillance system in action in the Philippines. Am J Trop Med Hyg 89: 178.
- 19.↑
Duarte HHP, Franca EB, 2006. Data quality of dengue epidemiological surveillance in Belo Horizonte, southeastern Brazil. Rev Saude Publica 40: 134–142.
- 20.
Tien NTK, Luxemburger C, Toan NT, Pollissard-Gadroy L, Houng VTQ, Be PV, Rang NN, Wartel TA, Lang J, 2010. A prospective cohort study of dengue infection in schoolchildren in Long Xuyen, Viet Nam. Trans R Soc Trop Med Hyg 104: 592–600.
- 21.↑
Wichmann O, Yoon IK, Vong S, Limkittikul K, Gibbons RV, Mammen MP, Ly S, Buchy P, Sirivichayakul C, Buathong R, Huy R, Letson GW, Sabchareon A, 2011. Dengue in Thailand and Cambodia: an assessment of the degree of underrecognized disease burden based on reported cases. PLoS Negl Trop Dis 5: e996.
- 22.↑
Shepard DS, Undurraga EA, Lees RS, Halasa YA, Lum L, Ng CW, 2012. Use of multiple data sources to estimate the economic cost of dengue illness in Malaysia. Am J Trop Med Hyg 87: 796–805.
- 23.↑
Montoya M, Gresh L, Mercado JC, Williams KL, Vargas MJ, Gutierrez G, Kuan G, Gordon A, Balmaseda A, Harris E, 2013. Symptomatic versus inapparent outcome in repeat dengue virus infections is influenced by the time interval between infections and study year. PLoS Negl Trop Dis 7: e2357.
- 24.
Torres JR, Torres CG, 2002. Dengue in Latin America. A unique situation. Dengue Bull 26: 62–69.
- 25.↑
Halstead SB, 2006. Dengue in the Americas and southeast Asia: do they differ? Rev Panam Salud Publica 20: 407–415.
- 26.↑
Vaughn DW, Green S, Kalayanarooj S, Innis BL, Nimmannitya S, Suntayakorn S, Endy TP, Raengsakulrach B, Rothman AL, Ennis FA, 2000. Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity. J Infect Dis 181: 2–9.
- 27.↑
Fried JR, Gibbons RV, Kalayanarooj S, Thomas SJ, Srikiatkhachorn A, Yoon IK, Jarman RG, Green S, Rothman AL, Cummings DAT, 2010. Serotype-specific differences in the risk of dengue hemorrhagic fever: an analysis of data collected in Bangkok, Thailand from 1994 to 2006. PLoS Negl Trop Dis 4: e617.
- 28.↑
Gibbons RV, Kalanarooj S, Jarman RG, Nisalak A, Vaughn DW, Endy TP, Mammen MP, Srikiatkhachorn A, 2007. Analysis of repeat hospital admissions for dengue to estimate the frequency of third or fourth dengue infections resulting in admissions and dengue hemorrhagic fever, and serotype sequences. Am J Trop Med Hyg 77: 910–913.
- 29.
Olkowski S, Forshey BM, Morrison AC, Rocha C, Vilcarromero S, Halsey ES, Kochel TJ, Scott TW, Stoddard ST, 2013. Reduced risk of disease during postsecondary dengue virus infections. J Infect Dis 208: 1026–1033.
- 30.↑
Guzman MG, Alvarez M, Halstead SB, 2013. Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement of infection. Arch Virol 158: 1445–1459.
- 31.↑
Sierra BDL, Kouri G, Guzman MG, 2007. Race: a risk factor for dengue hemorrhagic fever. Arch Virol 152: 533–542.
- 32.↑
Halstead SB, Streit TG, Lafontant JG, Putvatana R, Russell K, Sun W, Kanesa-Thasan N, Hayes CG, Watts DM, 2001. Haiti: absence of dengue hemorrhagic fever despite hyperendemic dengue virus transmission. Am J Trop Med Hyg 65: 180–183.
- 33.↑
Sharp TM, Moreira R, Soares MJ, da Costa LM, Mann J, DeLorey M, Hunsperger E, Muñoz-Jordán JL, Colón C, Margolis HS, 2015. Underrecognition of dengue during 2013 epidemic in Luanda, Angola. Emerg Infect Dis 21: 1311.
- 34.
Senn N, Luang-Suarkia D, Manong D, Max Siba P, Hannan McBride WJ, 2011. Contribution of dengue fever to the burden of acute febrile illnesses in Papua New Guinea: an age-specific prospective study. Am J Trop Med Hyg 85: 132.
- 35.
Ellis EM, Neatherlin JC, Delorey M, Ochieng M, Mohamed AH, Mogeni DO, Hunsperger E, Patta S, Gikunju S, Waiboic L, Fields B, Ofula V, Konongoi SL, Torres-Velasquez B, Marano N, Sang R, Margolis HS, Montgomery JM, Tomashek KM, 2015. A household serosurvey to estimate the magnitude of a dengue outbreak in Mombasa, Kenya, 2013. PLoS Negl Trop Dis 9: e0003733.
- 36.↑
Stoler J, Delimini RK, Bonney JK, Oduro AR, Owusu-Agyei S, Fobil JN, Awandare GA, 2015. Evidence of recent dengue exposure among malaria parasite-positive children in three urban centers in Ghana. Am J Trop Med Hyg 92: 497–500.
- 37.↑
Guzman MG, Jaenisch T, Gaczkowski R, Vo TTH, Sekaran SD, Kroeger A, Vazquez S, Ruiz D, Martinez E, Mercado JC, Balmaseda A, Harris E, Dimano E, Leano PSA, Yoksan S, Villegas E, Benduzu H, Villalobos I, Farrar J, Simmons CP, 2010. Multi-country evaluation of the sensitivity and specificity of two commercially-available NS1 ELISA assays for dengue diagnosis. PLoS Negl Trop Dis 4: e811.
- 38.↑
Guzmán MG, Kouri G, 2004. Dengue diagnosis, advances and challenges. Int J Infect Dis 8: 69–80.
- 39.↑
Shepard DS, Undurraga EA, Betancourt-Cravioto M, Guzmán MG, Halstead SB, Harris E, Mudin RN, Murray KO, Tapia-Conyer R, Gubler DJ, 2014. Approaches to refining estimates of global burden and economics of dengue. PLoS Negl Trop Dis 8: e3306.
- 40.↑
Toan NT, Rossi S, Prisco G, Nante N, Viviani S, 2015. Dengue epidemiology in selected endemic countries: factors influencing expansion factors as estimates of underreporting. Trop Med Int Health 20: 840–863.
- 41.↑
Sarti E, L'Azou M, Mercado M, Kuri P, Siqueira JB Jr, Solis E, Noriega F, Ochiai RL, 2016. A comparative study on active and passive epidemiological surveillance for dengue in five countries of Latin America. Int J Infect Dis 44: 44–49.
- 42.↑
Tomashek KM, Gregory CJ, Rivera Sánchez A, Bartek MA, Garcia Rivera EJ, Hunsperger E, Muñoz-Jordán JL, Sun W, 2012. Dengue deaths in Puerto Rico: lessons learned from the 2007 epidemic. PLoS Negl Trop Dis 6: e1614.
- 43.↑
Pamplona LG, de Melo Braga DN, da Silva LMA, Aguiar MG, Castiglioni M, Silva-Junior JU, de Carvalho Araújo FM, da Costa Pereira RA, Malta DL, de Lima Pompeu MM, 2016. Postmortem diagnosis of dengue as an epidemiological surveillance tool. Am J Trop Med Hyg 94: 187–192.
- 44.↑
Vong S, Goyet S, Ly S, Ngan C, Huy R, Duong V, Wichmann O, Letson GW, Margolis HS, Buchy P, 2012. Under-recognition and reporting of dengue in Cambodia: a capture-recapture analysis of the National Dengue Surveillance System. Epidemiol Infect 140: 491–499.
- 45.↑
Olkowski S, Stoddard ST, Halsey ES, Morrison AC, Barker CM, Scott TW, 2016. Sentinel versus Passive Surveillance for Measuring Changes in Dengue Incidence: Evidence from Three Concurrent Surveillance Systems in Iquitos, Peru. Available at: http://biorxiv.org/content/biorxiv/early/2016/02/18/040220.full.pdf. Accessed March 5, 2016.
- 46.↑
DeRoeck D, Deen J, Clemens JD, 2003. Policymakers' views on dengue fever/dengue haemorrhagic fever and the need for dengue vaccines in four southeast Asian countries. Vaccine 22: 121–129.
- 47.
Beatty ME, Stone A, Fitzsimons DW, Hanna JN, Lam SK, Vong S, Guzman MG, Mendez-Galvan JF, Halstead SB, Letson GW, Kuritsky J, Mahoney R, Margolis HS, for The Asia-Pacific and Americas Dengue Prevention Boards Surveillance Working Group, 2010. Best practices in dengue surveillance: a report from the Asia-Pacific and Americas Dengue Prevention Boards. PLoS Negl Trop Dis 4: e890.
- 48.
Racloz V, Ramsey R, Tong SL, Hu WB, 2012. Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Negl Trop Dis 6: e1648.
- 49.
Badurdeen S, Valladares DB, Farrar J, Gozzer E, Kroeger A, Kuswara N, Ranzinger SR, Tinh HT, Leite P, Mahendradhata Y, Skewes R, Verrall A; WHO/TDR Working Group, 2013. Sharing experiences: towards an evidence based model of dengue surveillance and outbreak response in Latin America and Asia. BMC Public Health 13: 607.
- 50.↑
Ooi EE, Gubler D, Nam V, 2007. Dengue Research Needs Related to Surveillance and Emergency Response. Report to the Scientific Working Group Meeting on Dengue, Geneva, October 1–5, 2006. Geneva, Switzerland: World Health Organization (WHO), 124–133.
- 51.↑
Murray CJL, 1994. Quantifying the burden of disease: the technical basis for disability-adjusted life years. Bull World Health Organ 72: 429–445.
- 52.↑
Meltzer MI, Rigau-Perez JG, Clark GG, Reiter P, Gubler DJ, 1998. Using disability-adjusted life years to assess the economic impact of dengue in Puerto Rico: 1984–1994. Am J Trop Med Hyg 59: 265–271.
- 53.↑
Philippines Statistics Authority, 2015. Philippine Standard Geographic Code. Municipality/City: Cebu City. Available at: http://www.nscb.gov.ph/activestats/psgc/municipality.asp?muncode=072217000®code=07&provcode=22. Accessed May 2016.
- 54.↑
Cebu City Health Department (CCHD), 2012. Dengue Surveillance Report. Cebu City, Philippines: CCHD.
- 55.↑
Cebu City Health Department (CCHD), 2013. Dengue Surveillance Report. Cebu City, Philippines: CCHD.
- 56.↑
Catinding NT, Niñal MO, Roque VGJ, 2008. Manual of Procedures for the Philippines Integrated and Surveillance and Response. Manila, Philippines: Department of Health, 228.
- 57.↑
World Health Organization, 1997. Dengue Haemorrhagic Fever: Diagnosis, Treatment, Prevention and Control. Available at: http://www.who.int/csr/resources/publications/dengue/Denguepublication/en/. Accessed September 2016.
- 58.↑
Ona E, 2012. Revised Dengue Clinical Case Management Guidelines: Administrative Order Number 2012-0006. Manila, Philippines: Department of Health.
- 59.↑
Philippines Statistics Authority - National Statistics Office, 2012. Population and Housing Census. Available at: http://www.web0.psa.gov.ph/statistics/census/population-and-housing. Accessed December 11, 2013.
- 60.↑
Alera MT, Srikiatkhachorn A, Velasco JM, Tac-An IA, Lago CB, Clapham HE, Fernandez S, Levy JW, Thaisomboonsuk B, Klungthong C, Macareo LR, Nisalak A, Hermann L, Villa D, Yoon I-K, 2016. Incidence of dengue virus infection in adults and children in a prospective longitudinal cohort in the Philippines. PLoS Negl Trop Dis 10: e0004337.
- 61.↑
Yoon I-K, Alera MT, Lago CB, Tac-An IA, Villa D, Fernandez S, Thaisomboonsuk B, Klungthong C, Levy JW, Velasco JM, 2015. High rate of subclinical chikungunya virus infection and association of neutralizing antibody with protection in a prospective cohort in the Philippines. PLoS Negl Trop Dis 9: e0003764.
- 62.↑
Lanciotti RS, Calisher CH, Gubler DJ, Chang G-J, Vorndam AV, 1992. Rapid detection and typing of dengue viruses from clinical samples by using reverse transcriptase-polymerase chain reaction. J Clin Microbiol 30: 545–551.
- 63.↑
Murray CJL, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, Aboyans V, Abraham J, Ackerman I, Aggarwal R, Ahn SY, Ali MK, Alvarado M, Anderson HR, Anderson LM, Andrews KG, Atkinson C, Baddour LM, Bahalim AN, Barker-Collo S, Barrero LH, Bartels DH, Basáñez M-G, Baxter A, Bell ML, Benjamin EJ, Bennett D, Bernabé E, Bhalla K, Bhandari B, Bikbov B, Abdulhak AB, Birbeck G, Black JA, Blencowe H, Blore JD, Blyth F, Bolliger I, Bonaventure A, Boufous S, Bourne R, Boussinesq M, Braithwaite T, Brayne C, Bridgett L, Brooker S, Brooks P, Brugha TS, Bryan-Hancock C, Bucello C, Buchbinder R, Buckle G, Budke CM, Burch M, Burney P, Burstein R, Calabria B, Campbell B, Canter CE, Carabin H, Carapetis J, Carmona L, Cella C, Charlson F, Chen H, Cheng AT-A, Chou D, Chugh SS, Coffeng LE, Colan SD, Colquhoun S, Colson KE, Condon J, Connor MD, Cooper LT, Corriere M, Cortinovis M, de Vaccaro KC, Couser W, Cowie BC, Criqui MH, Cross M, Dabhadkar KC, Dahiya M, Dahodwala N, Damsere-Derry J, Danaei G, Davis A, Leo DD, Degenhardt L, Dellavalle R, Delossantos A, Denenberg J, Derrett S, Des Jarlais DC, Dharmaratne SD, Dherani M, Diaz-Torne C, Dolk H, Dorsey ER, Driscoll T, Duber H, Ebel B, Edmond K, Elbaz A, Ali SE, Erskine H, Erwin PJ, Espindola P, Ewoigbokhan SE, Farzadfar F, Feigin V, Felson DT, Ferrari A, Ferri CP, Fèvre EM, Finucane MM, Flaxman S, Flood L, Foreman K, Forouzanfar MH, Fowkes FG, Fransen M, Freeman MK, Gabbe BJ, Gabriel SE, Gakidou E, Ganatra HA, Garcia B, Gaspari F, Gillum RF, Gmel G, Gonzalez-Medina D, Gosselin R, Grainger R, Grant B, Groeger J, Guillemin F, Gunnell D, Gupta R, Haagsma J, Hagan H, Halasa YA, Hall W, Haring D, Haro JM, Harrison JE, Havmoeller R, Hay RJ, Higashi H, Hill C, Hoen B, Hoffman H, Hotez PJ, Hoy D, Huang JJ, Ibeanusi SE, Jacobsen KH, James SL, Jarvis D, Jasrasaria R, Jayaraman S, Johns N, Jonas JB, Karthikeyan G, Kassebaum N, Kawakami N, Keren A, Khoo JP, King CH, Knowlton LM, Kobusingye O, Koranteng A, Krishnamurthi R, Laden F, Lalloo R, Laslett LL, Lathlean T, Leasher JL, Lee YY, Leigh J, Levinson D, Lim SS, Limb E, Lin JK, Lipnick M, Lipshultz SE, Liu W, Loane M, Ohno SL, Lyons R, Mabweijano J, MacIntyre MF, Malekzadeh R, Mallinger L, Manivannan S, Marcenes W, March L, Margolis DJ, Marks GB, Marks R, Matsumori A, Matzopoulos R, Mayosi BM, McAnulty JH, McDermott MM, McGill N, McGrath J, Medina-Mora ME, Meltzer M, Mensah GA, Merriman TR, Meyer AC, Miglioli V, Miller M, Miller TR, Mitchell PB, Mock C, Mocumbi AO, Moffitt TE, Mokdad AA, Monasta L, Montico M, Moradi-Lakeh M, Moran A, Morawska L, Mori R, Murdoch ME, Mwaniki MK, Naidoo K, Nair MN, Naldi L, Narayan KM, Nelson PK, Nelson RG, Nevitt MC, Newton CR, Nolte S, Norman P, Norman R, O'Donnell M, O'Hanlon S, Olives C, Omer SB, Ortblad K, Osborne R, Ozgediz D, Page A, Pahari B, Pandian JD, Rivero AP, Patten SB, Pearce N, Padilla RP, Perez-Ruiz F, Perico N, Pesudovs K, Phillips D, Phillips MR, Pierce K, Pion S, Polanczyk GV, Polinder S, Pope CA 3rd, Popova S, Porrini E, Pourmalek F, Prince M, Pullan RL, Ramaiah KD, Ranganathan D, Razavi H, Regan M, Rehm JT, Rein DB, Remuzzi G, Richardson K, Rivara FP, Roberts T, Robinson C, De Leòn FR, Ronfani L, Room R, Rosenfeld LC, Rushton L, Sacco RL, Saha S, Sampson U, Sanchez-Riera L, Sanman E, Schwebel DC, Scott JG, Segui-Gomez M, Shahraz S, Shepard DS, Shin H, Shivakoti R, Singh D, Singh GM, Singh JA, Singleton J, Sleet DA, Sliwa K, Smith E, Smith JL, Stapelberg NJ, Steer A, Steiner T, Stolk WA, Stovner LJ, Sudfeld C, Syed S, Tamburlini G, Tavakkoli M, Taylor HR, Taylor JA, Taylor WJ, Thomas B, Thomson WM, Thurston GD, Tleyjeh IM, Tonelli M, Towbin JA, Truelsen T, Tsilimbaris MK, Ubeda C, Undurraga EA, van der Werf MJ, van Os J, Vavilala MS, Venketasubramanian N, Wang M, Wang W, Watt K, Weatherall DJ, Weinstock MA, Weintraub R, Weisskopf MG, Weissman MM, White RA, Whiteford H, Wiebe N, Wiersma ST, Wilkinson JD, Williams HC, Williams SR, Witt E, Wolfe F, Woolf AD, Wulf S, Yeh PH, Zaidi AK, Zheng ZJ, Zonies D, Lopez AD, AlMazroa MA, Memish ZA, 2012. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380: 2197–2223.
- 64.↑
World Health Organization, 2008. The Global Burden of Disease: 2004 Update. Available at: http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/. Accessed April 4, 2016.
- 65.↑
Murray CJ, Ezzati M, Flaxman AD, Lim S, Lozano R, Michaud C, Naghavi M, Salomon JA, Shibuya K, Vos T, 2012. GBD 2010: design, definitions, and metrics. Lancet 380: 2063–2066.
- 66.↑
Murray CJL, Barber RM, Foreman KJ, Ozgoren AA, Abd-Allah F, Abera SF, Aboyans V, Abraham JP, Abubakar I, Abu-Raddad LJ, Abu-Rmeileh NM, Achoki T, Ackerman IN, Ademi Z, Adou AK, Adsuar JC, Afshin A, Agardh EE, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Alla F, Allebeck P, Almazroa MA, Alsharif U, Alvarez E, Alvis-Guzman N, Amare AT, Ameh EA, Amini H, Ammar W, Anderson HR, Anderson BO, Antonio CAT, Anwari P, Arnlöv J, Arsenijevic VSA, Artaman A, Asghar RJ, Assadi R, Atkins LS, Avila MA, Awuah B, Bachman VF, Badawi A, Bahit MC, Balakrishnan K, Banerjee A, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Basu A, Basu S, Basulaiman MO, Beardsley J, Bedi N, Beghi E, Bekele T, Bell ML, Benjet C, Bennett DA, Bensenor IM, Benzian H, Bernabé E, Bertozzi-Villa A, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bienhoff K, Bikbov B, Biryukov S, Blore JD, Blosser CD, Blyth FM, Bohensky MA, Bolliger IW, Başara BB, Bornstein NM, Bose D, Boufous S, Bourne RRA, Boyers LN, Brainin M, Brayne CE, Brazinova A, Breitborde NJK, Brenner H, Briggs AD, Brooks PM, Brown JC, Brugha TS, Buchbinder R, Buckle GC, Budke CM, Bulchis A, Bulloch AG, Campos-Nonato IR, Carabin H, Carapetis JR, Cárdenas R, Carpenter DO, Caso V, Castañeda-Orjuela CA, Castro RE, Catalá-López F, Cavalleri F, Çavlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chiang PP, Chimed-Ochir O, Chowdhury R, Christensen H, Christophi CA, Cirillo M, Coates MM, Coffeng LE, Coggeshall MS, Colistro V, Colquhoun SM, Cooke GS, Cooper C, Cooper LT, Coppola LM, Cortinovis M, Criqui MH, Crump JA, Cuevas-Nasu L, Danawi H, Dandona L, Dandona R, Dansereau E, Dargan PI, Davey G, Davis A, Davitoiu DV, Dayama A, De Leo D, Degenhardt L, Del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, Dharmaratne SD, Dherani MK, Diaz-Torné C, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Duber HC, Ebel BE, Edmond KM, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Estep K, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Felson DT, Fereshtehnejad SM, Fernandes JG, Ferrari AJ, Fitzmaurice C, Flaxman AD, Fleming TD, Foigt N, Forouzanfar MH, Fowkes FG, Paleo UF, Franklin RC, Fürst T, Gabbe B, Gaffikin L, Gankpé FG, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Giroud M, Giussani G, Gomez Dantes H, Gona P, González-Medina D, Gosselin RA, Gotay CC, Goto A, Gouda HN, Graetz N, Gugnani HC, Gupta R, Gupta R, Gutiérrez RA, Haagsma J, Hafezi-Nejad N, Hagan H, Halasa YA, Hamadeh RR, Hamavid H, Hammami M, Hancock J, Hankey GJ, Hansen GM, Hao Y, Harb HL, Haro JM, Havmoeller R, Hay SI, Hay RJ, Heredia-Pi IB, Heuton KR, Heydarpour P, Higashi H, Hijar M, Hoek HW, Hoffman HJ, Hosgood HD, Hossain M, Hotez PJ, Hoy DG, Hsairi M, Hu G, Huang C, Huang JJ, Husseini A, Huynh C, Iannarone ML, Iburg KM, Innos K, Inoue M, Islami F, Jacobsen KH, Jarvis DL, Jassal SK, Jee SH, Jeemon P, Jensen PN, Jha V, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Karch A, Karema CK, Karimkhani C, Karthikeyan G, Kassebaum NJ, Kaul A, Kawakami N, Kazanjan K, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khan G, Khang YH, Kieling C, Kim D, Kim S, Kim Y, Kinfu Y, Kinge JM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kosen S, Krishnaswami S, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kyu HH, Lai T, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larsson A, Lawrynowicz AE, Leasher JL, Leigh J, Leung R, Levitz CE, Li B, Li Y, Li Y, Lim SS, Lind M, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Lofgren KT, Logroscino G, Looker KJ, Lortet-Tieulent J, Lotufo PA, Lozano R, Lucas RM, Lunevicius R, Lyons RA, Ma S, Macintyre MF, Mackay MT, Majdan M, Malekzadeh R, Marcenes W, Margolis DJ, Margono C, Marzan MB, Masci JR, Mashal MT, Matzopoulos R, Mayosi BM, Mazorodze TT, Mcgill NW, Mcgrath JJ, Mckee M, Mclain A, Meaney PA, Medina C, Mehndiratta MM, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Mitchell PB, Mock CN, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Montañez Hernandez JC, Montico M, Montine TJ, Mooney MD, Moore AR, Moradi-Lakeh M, Moran AE, Mori R, Moschandreas J, Moturi WN, Moyer ML, Mozaffarian D, Msemburi WT, Mueller UO, Mukaigawara M, Mullany EC, Murdoch ME, Murray J, Murthy KS, Naghavi M, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nejjari C, Neupane SP, Newton CR, Ng M, Ngalesoni FN, Nguyen G, Nisar MI, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Ohno SL, Olusanya BO, Opio JN, Ortblad K, Ortiz A, Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Park JH, Patten SB, Patton GC, Paul VK, Pavlin BI, Pearce N, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phillips BK, Phillips DE, Piel FB, Plass D, Poenaru D, Polinder S, Pope D, Popova S, Poulton RG, Pourmalek F, Prabhakaran D, Prasad NM, Pullan RL, Qato DM, Quistberg DA, Rafay A, Rahimi K, Rahman SU, Raju M, Rana SM, Razavi H, Reddy KS, Refaat A, Remuzzi G, Resnikoff S, Ribeiro AL, Richardson L, Richardus JH, Roberts DA, Rojas-Rueda D, Ronfani L, Roth GA, Rothenbacher D, Rothstein DH, Rowley JT, Roy N, Ruhago GM, Saeedi MY, Saha S, Sahraian MA, Sampson UK, Sanabria JR, Sandar L, Santos IS, Satpathy M, Sawhney M, Scarborough P, Schneider IJ, Schöttker B, Schumacher AE, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serina PT, Servan-Mori EE, Shackelford KA, Shaheen A, Shahraz S, Shamah Levy T, Shangguan S, She J, Sheikhbahaei S, Shi P, Shibuya K, Shinohara Y, Shiri R, Shishani K, Shiue I, Shrime MG, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh JA, Singh L, Skirbekk V, Slepak EL, Sliwa K, Soneji S, Søreide K, Soshnikov S, Sposato LA, Sreeramareddy CT, Stanaway JD, Stathopoulou V, Stein DJ, Stein MB, Steiner C, Steiner TJ, Stevens A, Stewart A, Stovner LJ, Stroumpoulis K, Sunguya BF, Swaminathan S, Swaroop M, Sykes BL, Tabb KM, Takahashi K, Tandon N, Tanne D, Tanner M, Tavakkoli M, Taylor HR, Te Ao BJ, Tediosi F, Temesgen AM, Templin T, Ten Have M, Tenkorang EY, Terkawi AS, Thomson B, Thorne-Lyman AL, Thrift AG, Thurston GD, Tillmann T, Tonelli M, Topouzis F, Toyoshima H, Traebert J, Tran BX, Trillini M, Truelsen T, Tsilimbaris M, Tuzcu EM, Uchendu US, Ukwaja KN, Undurraga EA, Uzun SB, Van Brakel WH, Van De Vijver S, van Gool CH, Van Os J, Vasankari TJ, Venketasubramanian N, Violante FS, Vlassov VV, Vollset SE, Wagner GR, Wagner J, Waller SG, Wan X, Wang H, Wang J, Wang L, Warouw TS, Weichenthal S, Weiderpass E, Weintraub RG, Wenzhi W, Werdecker A, Westerman R, Whiteford HA, Wilkinson JD, Williams TN, Wolfe CD, Wolock TM, Woolf AD, Wulf S, Wurtz B, Xu G, Yan LL, Yano Y, Ye P, Yentür GK, Yip P, Yonemoto N, Yoon SJ, Younis MZ, Yu C, Zaki ME, Zhao Y, Zheng Y, Zonies D, Zou X, Salomon JA, Lopez AD, Vos T, 2015. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet 386: 2145–2191.
- 67.↑
World Health Organization, 2016. Disability Weights, Discounting and Age Weighting of DALYs. Available at: http://www.who.int/healthinfo/global_burden_disease/daly_disability_weight/en/. Accessed September 26, 2016.
- 68.↑
Undurraga EA, Betancourt-Cravioto M, Ramos-Castañeda J, Martínez-Vega R, Méndez-Galván J, Gubler DJ, Guzmán MG, Halstead SB, Harris E, Kuri-Morales P, Tapia-Conyer R, Shepard DS, 2015. Economic and disease burden of dengue in Mexico. PLoS Negl Trop Dis 9: e3547.
- 69.↑
Luz PM, Grinsztejn B, Galvani AP, 2009. Disability adjusted life years lost to dengue in Brazil. Trop Med Int Health 14: 237–246.
- 70.↑
Borja MP, Lorenzo FME, Tallo VL, Rivera PT, Tan AG, 2007. Final Report of the Burden of Dengue in the Philippines. Manila, Philippines: Department of Health Central Office.
- 71.↑
Nealon J, Taurel A-F, Capeding MR, Tran NH, Hadinegoro SR, Chotpitayasunondh T, Chong CK, Wartel TA, Beucher S, Frago C, 2016. Symptomatic dengue disease in five southeast Asian countries: epidemiological evidence from a dengue vaccine trial. PLoS Negl Trop Dis 10: e0004918.
- 72.↑
Capeding RZ, Brion JD, Caponpon MM, Gibbons RV, Jarman RG, Yoon I-K, Libraty DH, 2010. The incidence, characteristics, and presentation of dengue virus infections during infancy. Am J Trop Med Hyg 82: 330–336.
- 73.↑
Capeding MR, Chua MN, Hadinegoro SR, Hussain IIHM, Nallusamy R, Pitisuttithum P, Rusmil K, Thisyakorn U, Thomas SJ, Huu Tran N, Wirawan DN, Yoon I-K, Bouckenooghe A, Hutagalung Y, Laot T, Wartel TA, 2013. Dengue and other common causes of acute febrile illness in Asia: an active surveillance study in children. PLoS Negl Trop Dis 7: e2331.
- 74.↑
Philippines AFRIMS Virology Research Unit (PAVRU), 2015. PAVRU Research Laboratory. Available at: http://pavru.afrims.org/. Accessed October 29, 2016.
- 75.↑
Hampson K, Coudeville L, Lembo T, Sambo M, Kieffer A, Attlan M, Barrat J, Blanton JD, Briggs DJ, Cleaveland S, 2015. Estimating the global burden of endemic canine rabies. PLoS Negl Trop Dis 9: e0003709.
- 76.↑
Fürst T, Keiser J, Utzinger J, 2012. Global burden of human food-borne trematodiasis: a systematic review and meta-analysis. Lancet Infect Dis 12: 210–221.
- 77.↑
Peabody JW, Shimkhada R, Tan C, Luck J, 2005. The burden of disease, economic costs and clinical consequences of tuberculosis in the Philippines. Health Policy Plan 20: 347–353.
- 78.↑
Tiga DC, Undurraga EA, Ramos-Castañeda J, Martínez-Vega R, Tschampl CA, Shepard D, 2016. Persistent symptoms of dengue: estimates of the incremental disease and economic burden in Mexico. Am J Trop Med Hyg 94: 1085–1089.
- 80.
Capeding MR, Tran NH, Hadinegoro SRS, Ismail HIHJM, Chotpitayasunondh T, Chua MN, Luong CQ, Rusmil K, Wirawan DN, Nallusamy R, Pitisuttithum P, Thisyakorn U, Yoon I-K, van der Vliet D, Langevin E, Laot T, Hutagalung Y, Frago C, Boaz M, Wartel TA, Tornieporth NG, Saville M, Bouckenooghe A, 2014. Clinical efficacy and safety of a novel tetravalent dengue vaccine in healthy children in Asia: a phase 3, randomised, observer-masked, placebo-controlled trial. Lancet 384: 1358–1365.
- 81.↑
Villar L, Dayan GH, Arredondo-García JL, Rivera DM, Cunha R, Deseda C, Reynales H, Costa MS, Morales-Ramírez JO, Carrasquilla G, Rey LC, Dietze R, Luz K, Rivas E, Montoya MCM, Supelano MC, Zambrano B, Langevin E, Boaz M, Tornieporth N, Saville M, Noriega F, 2015. Efficacy of a tetravalent dengue vaccine in children in Latin America. N Engl J Med 372: 113–123.
- 82.↑
Barrett AD, 2012. Short-course oral corticosteroid therapy is not effective in early dengue infection. Clin Infect Dis 55: 1225–1226.
- 83.
Halstead SB, 2013. Dengue vascular permeability syndrome: what, no T cells? Clin Infect Dis 56: 900–901.
- 84.↑
Dow G, Mora E, 2012. The maximum potential market for dengue drugs V 1.0. Antiviral Res 96: 203–212.
- 85.↑
Erlanger TE, Keiser J, Utzinger J, 2008. Effect of dengue vector control interventions on entomological parameters in developing countries: a systematic review and meta-analysis. Med Vet Entomol 22: 203–221.
- 86.
Luz PM, Vanni T, Medlock J, Paltiel AD, Galvani AP, 2011. Dengue vector control strategies in an urban setting: an economic modelling assessment. Lancet 377: 1673–1680.
- 87.
Hoffmann AA, Montgomery BL, Popovici J, Iturbe-Ormaetxe I, Johnson PH, Muzzi F, Greenfield M, Durkan M, Leong YS, Dong Y, Cook H, Axford J, Callahan AG, Kenny N, Omodei C, McGraw EA, Ryan PA, Ritchie SA, Turelli M, O'Neill SL, 2011. Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature 476: 454–457.
- 88.↑
Atkinson MP, Su Z, Alphey N, Alphey LS, Coleman PG, Wein LM, 2007. Analyzing the control of mosquito-borne diseases by a dominant lethal genetic system. Proc Natl Acad Sci USA 104: 9540–9545.
- 89.↑
HDT/Sunnex, 2016. Three Regions Selected to Get Free Dengue Vaccines. Manila, Philippines: Sun Star.